Production Reallocation of the Pazflor Oil Field
Correction of the Produced Volumes of Oil, Water and Gas per Well using the Integrated Field Management (IFM) by Petex
Yolanda da Purificação Mambo Gaspar Tati
Dissertation for obtention of the Master Degree in
Petroleum Engineering
Supervisors: Prof. Dr. Amílcar de Oliveira Soares (IST),
Eng. Sebastien Pérrier (TOTAL)
Júri
President: Prof. Dr. Maria João Correia Colunas Pereira
Supervisor: Prof. Dr. Amílcar de Oliveira Soares
Vogais: Prof. Dr. Leonardo Azevedo Guerra Raposo Pereira
September 2015
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Thanks
To all of those who participated in my education. First, to my father, who taught me not to fail. Second,
to my mother, who taught me to believe. To Camila Gonçalves and Mr. Fialho, who taught me there
was also a place for me. To all the teachers that gave me the best part of them and encouraged me to
be the best part of me. To all the colleagues I’ve always wanted to be around of. To Rafa, that was
always there. And, finnally, to Carlos who was heaven sent.
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Abstract
The present study aims to allocate the volumes of oil, water and gas to the different wells and
producing lines of the Pazflor oil field.
The production allocation was held through the measurement and evaluation of the deviations first
obtained by the readings of the multiphase flow rates.
In the Pazflor producing field, the measurement of the produced volumes of oil, water and gas is first
held at the well level by multiphase flow meters. These measurements are associated to different
ranges of uncertainties due to technical limitations of this equipment.
The correction proposed for the Multiphase Flow Meters readings is obtained through the Integrated
Field Manager. The corrections on the production curves are done by modifying the Watercut and the
Gas to Oil Ratio on the periods in which the Bottom Hole Pressure estimated deviates in more than 3
bar from the real Bottom Hole Pressure (directly measured through the Down Hole Gauge). These are
the only two parameters that are altered in order to obtain new oil, water and gas rates that are
consistent with the directly measured Bottom Hole Pressure.
Here, density and friction assumptions are associated to the volumes recorded by the Multiphase Flow
Meters and are used to compute a Bottom Hole Pressure and a Tubing Head Pressure that has to
match the directly measured pressure obtained by the Down Hole Gauge in order to be considered as
valid. When out of the admissible range of pressure, the correspondent Watercut and GOR curves
were corrected in order to match the behavior suggested by the Bottom Hole Pressure. The
corrections and adjustments follow the fundamental physical constraints that affect the reservoir and
are always done with maximum parsimony.
The Multiphase Flow Meters measurements are based on density functions and on the different
sensors readings that can automatically be switched to either an oil continuous mode- recording the
permittivity- or a water continuous mode- recording the conductivity. It switches to the water
continuous mode if the watercut is higher than 45%. It often occurs that the conductivity of reference
which the water continuous mode uses to calculate does no longer matches the conductivity of the
produced water in the medium. The reliability on the Multiphase Flow Meter measurements decrease
on the group of wells that are linked to a higher production of water, where water can be misread as
hydrocarbons or vice versa.
For the cases where the Watercut is higher than 45%, two scenarios were created in order to
determine a range of uncertainty on admissible values given by the Integrated Field Manager.
The well physical parameters evolution in time was also followed, mainly in cases where strong
corrections had to be applied in order to obtain matching pressure values. The density of the flow (also
related to the permittivity and conductivity) is used to confirm the abrupt changes in the different
phases components. The Well Head Pressure, the Well Head Temperature and the Bottom Hole
Pressure are also used to infer about the realiability of the corrected production curves.
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These Multiphase Flow Meters corrections lead to a new Multiphase Flow Meters production scenario.
In order to infer about the global admissibility of this process and of the IFM tool, a process of data
reconciliation for validation took place. The total of the produced volumes of oil, water and gas was
determined at the levels of the Subsea Separation Units, Pumps and Topside through the
measurements held by the flow transmitters installed in the producing lines and Floating Production
Storage Offloading unit.
Through a closed loop process, it is possible to take global conclusions about the precisions of the
measurements at different production stages and to infer about the reliability of the different sources of
data acquisition used nowadays in this field. The flow rate value registered by the oil tanks flow
transmitters (before selling the oil) is considered to have no uncertainty associated to it. By comparing
all the oil production curves with the last one (oil tanks) it is possible to go back on the process and
improve the corrections applied. The periods of flow rate mismatch in between the wells production
curves and the topside (oil tanks) would directly allow to identify the producing line showing more
discrepancies for the identic production period as the degrees of freedom would be reduced at this
stage. After that, it would be possible to more accurately spot the wells in which the flow rates of oil,
water and gas had to be corrected in order to match the real flow rate measured before selling the oil.
The empirical method that was applied showed no biased results. This robust empirical method
allowed to estimate the produced volumes of oil, water and gas with an estimation error that is not
higher than approximately 5% during more than 80% of the studied period. It was also possible to
discover that the Multiphase Flow Meter was underestimating in more than 50% the produced water
and gas during 90% of the wells producing time. For oil, the Multiphase Flow Meter shows a maximum
error on estimation of approximately 25% during 70% of the producing period.
This deviations openned a path to question the reliability on the models that are currently on use. For
some wells, the building consistent production scenarios implied allocating Gas to Oil Ratio values
bellow the Rs or a little displacement on time of the water breakthrough for other wells.
This consisted in a considerable adding value conclusions and acknowledgements since it brought
attention to where should resources be applied in order to achieve the main goal: producing oil.
This correction process was applied to all the Pazflor wells since the beginning of production (1st
August 2011) until 25th February 2015 so the main evolution trend of the production was captured.
Key-words: Production Reallocation; Multiphase Flow Metering; Integrated Field Management;
Pazflor; TOTAL; Angola; Deep Offshore.
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Table of Contents
THANKS ............................................................................................................................... 3
ABSTRACT .......................................................................................................................... 5
TABLE OF CONTENTS ........................................................................................................ 7
TABLE OF FIGURES............................................................................................................ 9
LIST OF ACRONYMS ......................................................................................................... 15
CHAPTER 1. INTRODUCTION .......................................................................................... 16
CHAPTER 2. TOTAL'S BLOCK 17 .................................................................................... 18
2.1 BLOCK 17 – A WORLD SCALE BLOCK ....................................................................... 18
CHAPTER 3. PAZFLOR ..................................................................................................... 19
3.1 A TOP OF THE ART PROJECT .................................................................................... 19
3.2 STRUCTURE AND EQUIPMENT ................................................................................... 19
3.3 MEASUREMENT OF PRODUCED VOLUMES .................................................................. 21
3.3.1 The Multiphase Flow Meters (MPFM) ............................................................... 21
CHAPTER 4. PRODUCTION ALLOCATION ...................................................................... 24
4.1 THE NEED AND THE GOAL ......................................................................................... 24
4.2 THE SOFTWARE - INTEGRATED PRODUCTION MODELLING (IPM) BY PETROLEUM EXPERTS
24
4.3 IFM PROCESS - CORRECTION METHODOLOGY ........................................................... 26
4.4 IFM PROCESS - THE DARCY'S LAW AS THE RELATION IN BETWEEN GOR AND WATERCUT
WITH PRESSURE ................................................................................................................ 28
4.5 CORRECTION CRITERIA FOR THE REALLOCATION ....................................................... 30
4.5.1 The Gas-Oil Ratio minimum value .................................................................... 30
4.5.2 Maximum Parsimony Criteria ........................................................................... 31
4.5.3 Reservoir Depletion in time .............................................................................. 32
4.6 THE CORRECTION: MPFM RAW VS MPFM CORRECTED ............................................. 33
4.6.1 Production line: P10 ......................................................................................... 33
4.6.2 Production line - P20 ........................................................................................ 50
4.6.3 Production line - P30 ........................................................................................ 56
4.6.4 Production line - P40 ........................................................................................ 58
CHAPTER 5. METHOD ROBUSTNESS ............................................................................. 60
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CHAPTER 6. DATA RECONCILIATION RESULTS ........................................................... 61
CHAPTER 7. UNCERTAINTIES AND ADMISSIBILITY ..................................................... 63
CHAPTER 8. CONCLUSIONS ........................................................................................... 77
CHAPTER 9. WAY FORWARD .......................................................................................... 78
CHAPTER 10. REFERENCES ............................................................................................ 79
ANNEXES ........................................................................................................................... 80
ANNEX I. CUMULATIVE FREQUENCY GRAPHS ....................................................................... 80
ANNEX II. DEVIATION GRAPHS .......................................................................................... 104
ANNEX III. CURVE REPAIR + MOVING AVERAGES ALGORITHM ............................................. 125
ANNEX IV. RS TABLE FOR THE PAZFLOR WELLS ................................................................. 132
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Table of Figures
FIGURE 1 PRODUCTION REALLOCATION WORKFLOW SCHEME................................................. 17
FIGURE 2 PAZFLOR PRODUCING LINES SCHEME. ........................................................................ 20
FIGURE 3 EXAMPLE OF MPFM'S AND ILLUSTRATION OF ITS LOCATION SUBSEA. ................... 21
FIGURE 4 SIMPLIFIED ILLUSTRATION OF THE VENTURI TUBE. ................................................... 22
FIGURE 5 THE MPFM MEASURING PRINCIPLES. ............................................................................ 22
FIGURE 6 IFM AND MODELS INTERACTION. ................................................................................... 25
FIGURE 7 CALIBRATION IN THE ALLOCATION WORKFLOW. ........................................................ 26
FIGURE 8 REBUILDING PRODUCTION HISTORY FOR ZNA-ED WELL. ......................................... 27
FIGURE 9 GRAVITY AND FRICTION IN A FLOWING WELL.FOR THE SAME QTOTAL, THE
PRESSURE DROP INCREASES WHEN THERE IS MORE WATER PRESENT IN THE FLOW. 29
FIGURE 10 EXAMPLE OF TWO POSSIBLE CORRECTED SCENARIOS FOR PRP-FB-A. .............. 31
FIGURE 11 GRAPHIC ILLUSTRATION OF THE MINIMUM CHANGE CRITERIA APPLIED. ............ 32
FIGURE 12 GRAPHIC ILLUSTRATION OF THE CRITERIA FOR THE LEVEL OF CERTAINTY
ABOUT CORRECTIONS AT A CERTAIN STAGE OF THE PRODUCING TIME. ........................ 33
FIGURE 13 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE LINE P10. .................................................................................. 34
FIGURE 14 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE LINE P10. .............................................................................................. 34
FIGURE 15 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE WELL ZNA-E0A ......................................................................... 35
FIGURE 16 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE WELL ZNA-E0A..................................................................................... 36
FIGURE 17 WELL HEAD TEMPERATURE EVOLUTION IN TIME FOR THE WELL ZNA-E0A ......... 36
FIGURE 18 WELL HEAD PRESSURE EVOLUTION IN TIME FOR THE WELL ZNA-E0A ................. 37
FIGURE 19 BOTTOM-HOLE PRESSURE EVOLUTION IN TIME FOR THE WELL ZNA-E0A ........... 37
FIGURE 20 EVOLUTION OF THE PRESSURE AT THE WELL HEAD. P1 – MEASURED
PRESSURE. P2 – ESTIMATED PRESSURE. .............................................................................. 38
FIGURE 21 P1 VS P2 – LINEAR REGRESSION. P1 – MEASURED PRESSURE. P2 – ESTIMATED
PRESSURE. ................................................................................................................................... 38
FIGURE 22 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE WELL ZNA-E0E. ........................................................................ 39
FIGURE 23 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE WELL ZNA-E0E..................................................................................... 40
FIGURE 24 WELL HEAD TEMPERATURE EVOLUTION IN TIME FOR THE WELL ZNA-E0E ......... 40
FIGURE 25 WELL HEAD PRESSURE EVOLUTION IN TIME FOR THE WELL ZNA-E0E ................. 41
FIGURE 26 BOTTOM-HOLE PRESSURE EVOLUTION IN TIME FOR THE WELL ZNA-E0E ........... 41
FIGURE 27 EVOLUTION OF THE PRESSURE AT THE WELL HEAD. P1 – MEASURED
PRESSURE. P2 – ESTIMATED PRESSURE. .............................................................................. 42
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FIGURE 28 P1 VS P2 – LINEAR REGRESSION. P1 – MEASURED PRESSURE. P2 – ESTIMATED
PRESSURE. ................................................................................................................................... 42
FIGURE 29 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE WELL ZNA-E0D. ........................................................................ 43
FIGURE 30 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE WELL ZNA-E0D .................................................................................... 44
FIGURE 31 WELL HEAD PRESSURE EVOLUTION IN TIME FOR THE WELL ZNA-E0D ................. 44
FIGURE 32 WELL HEAD TEMPERATURE EVOLUTION IN TIME FOR THE WELL ZNA-E0D ......... 45
FIGURE 33 BOTTOM-HOLE PRESSURE EVOLUTION IN TIME FOR THE WELL ZNA-E0D ........... 45
FIGURE 34 EVOLUTION OF THE PRESSURE AT THE WELL HEAD. P1 – MEASURED
PRESSURE. P2 – ESTIMATED PRESSURE. .............................................................................. 46
FIGURE 35 P1 VS P2 – LINEAR REGRESSION. P1 – MEASURED PRESSURE. P2 – ESTIMATED
PRESSURE. ................................................................................................................................... 46
FIGURE 36 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE WELL PRP-F0BA ...................................................................... 47
FIGURE 37 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE WELL PRP-F0BA .................................................................................. 48
FIGURE 38 WELL HEAD TEMPERATURE EVOLUTION IN TIME FOR THE WELL PRP-F0BA ....... 48
FIGURE 39 WELL HEAD PRESSURE EVOLUTION IN TIME FOR THE WELL PRP-F0BA .............. 49
FIGURE 40 BOTTOM-HOLE PRESSURE IN TIME FOR THE WELL PRP-F0BA ............................... 49
FIGURE 41 BOTTOM-HOLE PRESSURE EVOLUTION IN TIME FOR THE WELL PRP-F0BA ......... 50
FIGURE 42 P1 VS P2 – LINEAR REGRESSION. P1 – MEASURED PRESSURE. P2 – ESTIMATED
PRESSURE. ................................................................................................................................... 50
FIGURE 43 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE LINE P20. .................................................................................. 51
FIGURE 44 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE LINE P20. .............................................................................................. 52
FIGURE 45 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE WELL PRP-FA0. ........................................................................ 53
FIGURE 46 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR WELL PRP-FA0. ............................................................................................ 53
FIGURE 47 WELL HEAD TEMPERATURE EVOLUTION IN TIME FOR THE WELL PRP-FA0 ......... 54
FIGURE 48 WELL HEAD PRESSURE EVOLUTION IN TIME FOR THE WELL PRP-FA0 ................. 54
FIGURE 49 WELL HEAD PRESSURE EVOLUTION IN TIME FOR THE WELL PRP-FA0 ................. 55
FIGURE 50 P1 VS P2 ........................................................................................................................... 55
FIGURE 51 EVOLUTION OF THE PRESSURE AT THE WELL HEAD. .............................................. 56
FIGURE 52 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE LINE P30. .................................................................................. 57
FIGURE 53 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE LINE P30. .............................................................................................. 57
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FIGURE 54 COMPARISON OF THE WATER AND OIL FLUX FOR THE MPFM RAW AND THE
MPFM CORRECTED FOR THE LINE P40. .................................................................................. 59
FIGURE 55 COMPARISON OF THE GAS FLUX FOR THE MPFM RAW AND THE MPFM
CORRECTED FOR THE LINE P40. .............................................................................................. 59
FIGURE 56 ABOVE AND BELOW, RESPECTIVELY, THE RESUME OF THE PRODUCTION
SUBSEA NETWORK AND OF THE TOPSIDE STORAGE AND OFFLOADING NETWORK. ..... 62
FIGURE 57 COMPARISON OF OIL PRODUCTION IN ORDER OF A MEASURED TIME. ................ 63
FIGURE 58 COMPARISON OF WATER PRODUCTION IN ORDER OF A MEASURED TIME. ........ 64
FIGURE 59 COMPARISON OF GAS PRODUCTION IN ORDER OF A MEASURED TIME. .............. 64
FIGURE 60 CUMULATIVE VIEW FOR OIL. ......................................................................................... 65
FIGURE 61 CUMULATIVE VIEW FOR WATER. .................................................................................. 65
FIGURE 62 CUMULATIVE VIEW FOR GAS. ....................................................................................... 66
FIGURE 63 CUMULATIVE VIEW FOR OIL. ......................................................................................... 66
FIGURE 64 CUMULATIVE VIEW FOR WATER. .................................................................................. 67
FIGURE 65 CUMULATIVE VIEW FOR GAS. ....................................................................................... 67
FIGURE 66 DAILY ERROR MPFM AFTER CORRECTION OIL PRODUCTION. ............................... 68
FIGURE 67 MPFM AFTER CORRECTION OF WATER PRODUCTION. ............................................ 68
FIGURE 68 MPFM AFTER CORRECTION OF GAS PRODUCTION. ................................................. 69
FIGURE 69 FPSO OIL PRODUCTION. ................................................................................................ 69
FIGURE 70 FPSO WATER PRODUCTION. ......................................................................................... 70
FIGURE 71 FPSO GAS PRODUCTION. .............................................................................................. 70
FIGURE 72 DAILY ESTIMATION ERROR OF THE OIL PRODUCTION. ............................................ 71
FIGURE 73 DAILY ESTIMATION ERROR OF THE WATER PRODUCTION. ..................................... 72
FIGURE 74 DAILY ESTIMATION ERROR OF THE GAS PRODUCTION. .......................................... 73
FIGURE 75 DAILY ESTIMATION ERROR OF THE RAW OIL PRODUCTION. .................................. 74
FIGURE 76 DAILY ESTIMATION ERROR OF THE RAW OIL PRODUCTION. .................................. 74
FIGURE 77 DAILY ESTIMATION ERROR OF THE RAW OIL PRODUCTION. .................................. 75
FIGURE 78 IFM PERCENTUAL ERROR FOR OIL PRODUCTION. ................................................... 75
FIGURE 79 IFM PERCENTUAL ERROR FOR WATER PRODUCTION. ............................................ 76
FIGURE 80 IFM PERCENTUAL ERROR FOR GAS PRODUCTION. .................................................. 76
FIGURE 81 CUMULATIVE FRECUENCY OF P1 FOR THE WELL ZNA-E0A ..................................... 80
FIGURE 82 CUMULATIVE FRECUENCY OF P2 FOR THE WELL ZNA-E0A ..................................... 80
FIGURE 83 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL ZNA-E0A....................... 81
FIGURE 84 CUMULATIVE FRECUENCY OF P1 FOR THE WELL ZNA-E0D .................................... 81
FIGURE 85 CUMULATIVE FRECUENCY OF P2 FOR THE WELL ZNA-E0D .................................... 82
FIGURE 86 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL ZNA-E0D ...................... 82
FIGURE 87 CUMULATIVE FRECUENCY OF P1 FOR THE WELL ZNA-E0E ..................................... 83
FIGURE 88 CUMULATIVE FRECUENCY OF P2 FOR THE WELL ZNA-E0E ..................................... 83
FIGURE 89 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL ZNA-E0E....................... 84
FIGURE 90 CUMULATIVE FRECUENCY OF P1 FOR THE WELL ZNA-E0H .................................... 84
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FIGURE 91 CUMULATIVE FRECUENCY OF P2 FOR THE WELL ZNA-E0H .................................... 85
FIGURE 92 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL ZNA-E0H ..................... 85
FIGURE 93 CUMULATIVE FRECUENCY OF P1 FOR THE WELL ZNA-EA0 ..................................... 86
FIGURE 94 CUMULATIVE FRECUENCY OF P2 FOR THE WELL ZNA-EA0 ..................................... 86
FIGURE 95 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL ZNA-EA0....................... 87
FIGURE 96 CUMULATIVE FRECUENCY OF P1 FOR THE WELL ZNA-EAA .................................... 87
FIGURE 97 CUMULATIVE FRECUENCY OF P2 FOR THE WELL ZNA-EAA .................................... 88
FIGURE 98 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL ZNA-EAA ...................... 88
FIGURE 99 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-F0BA .................................. 89
FIGURE 100 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-F0BA ................................ 89
FIGURE 101 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-F0BA .................. 90
FIGURE 102 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-F0G .................................. 90
FIGURE 103 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-F0BA ................................ 91
FIGURE 104 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-F0BA .................. 91
FIGURE 105 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-FA0 ................................... 92
FIGURE 106 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-FA0 ................................... 92
FIGURE 107 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-FA0 .................... 93
FIGURE 108 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-FAB .................................. 93
FIGURE 109 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-FAB .................................. 94
FIGURE 110 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-FAB .................... 94
FIGURE 111 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-F1C .................................. 95
FIGURE 112 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-F1C .................................. 95
FIGURE 113 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-F1C .................... 96
FIGURE 114 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-FAF .................................. 96
FIGURE 115 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-FAF .................................. 97
FIGURE 116 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-FAF .................... 97
FIGURE 117 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-FAI .................................... 98
FIGURE 118 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-FAI .................................... 98
FIGURE 119 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-FAI...................... 99
FIGURE 120 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-F0E ................................... 99
FIGURE 121 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-F0E ................................. 100
FIGURE 122 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-F0E .................. 100
FIGURE 123 CUMULATIVE FRECUENCY OF P1 FOR THE WELL PRP-F0F ................................. 101
FIGURE 124 CUMULATIVE FRECUENCY OF P2 FOR THE WELL PRP-F0F ................................. 101
FIGURE 125 CUMULATIVE FRECUENCY OF P1 AND P2 FOR THE WELL PRP-F0F ................... 102
FIGURE 126 DEVIATION (P2-P1)/P1 AT WELL ZNA-EA .................................................................. 104
FIGURE 127 DEVIATION (P2-P1)/P2 AT WELL ZNA-EA .................................................................. 104
FIGURE 128 DEVIATION (P2-P1)/WATERCUT1 AT WELL ZNA-EA ................................................ 105
FIGURE 129 DEVIATION (P2-P1)/GOR1 AT WELL ZNA-EA ............................................................ 105
FIGURE 130 DEVIATION (P2-P1)/P1 AT WELL ZNA-ED .................................................................. 105
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FIGURE 131 DEVIATION (P2-P1)/P2 AT WELL ZNA-ED .................................................................. 106
FIGURE 132 DEVIATION (P2-P1)/WATERCUT1 AT WELL ZNA-ED................................................ 106
FIGURE 133 DEVIATION (P2-P1)/GOR1 AT WELL ZNA-ED ............................................................ 106
FIGURE 134 DEVIATION (P2-P1)/P1 AT WELL ZNA-EEB ............................................................... 107
FIGURE 135 DEVIATION (P2-P1)/P2 AT WELL ZNA-EEB ............................................................... 107
FIGURE 136 DEVIATION (P2-P1)/WATERCUT1 AT WELL ZNA-EEB ............................................. 107
FIGURE 137 DEVIATION (P2-P1)/GOR1 AT WELL ZNA-EEB .......................................................... 108
FIGURE 138 DEVIATION (P2-P1)/P1 AT WELL ZNA-EH .................................................................. 108
FIGURE 139 DEVIATION (P2-P1)/P2 AT WELL ZNA-EH .................................................................. 108
FIGURE 140 DEVIATION (P2-P1)/WATERCUT1 AT WELL ZNA-EH................................................ 109
FIGURE 141 DEVIATION (P2-P1)/GOR1 AT WELL ZNA-EH ............................................................ 109
FIGURE 142 DEVIATION (P2-P1)/P1 AT WELL ZNA-EA0 ................................................................ 109
FIGURE 143 DEVIATION (P2-P1)/P2 AT WELL ZNA-EA0 ................................................................ 110
FIGURE 144 DEVIATION (P2-P1)/WATERCUT1 AT WELL ZNA-EA0 .............................................. 110
FIGURE 145 DEVIATION (P2-P1)/GOR1 AT WELL ZNA-EA0 .......................................................... 110
FIGURE 146 DEVIATION (P2-P1)/P1 AT WELL ZNA-EAA ............................................................... 111
FIGURE 147 DEVIATION (P2-P1)/P2 AT WELL ZNA-EAA ............................................................... 111
FIGURE 148 DEVIATION (P2-P1)/WATERCUT1 AT WELL ZNA-EAA ............................................. 111
FIGURE 149 DEVIATION (P2-P1)/GOR1 AT WELL ZNA-EAA .......................................................... 112
FIGURE 150 DEVIATION (P2-P1)/P1 AT WELL PRP-F0BA ............................................................. 112
FIGURE 151 DEVIATION (P2-P1)/P2 AT WELL PRP-F0BA ............................................................. 112
FIGURE 152 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-F0BA ........................................... 113
FIGURE 153 DEVIATION (P2-P1)/GOR1 AT WELL PRP-F0BA ........................................................ 113
FIGURE 154 DEVIATION (P2-P1)/P1 AT WELL PRP-F0G ............................................................... 113
FIGURE 155 DEVIATION (P2-P1)/P2 AT WELL PRP-F0G ............................................................... 114
FIGURE 156 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-F0G ............................................. 114
FIGURE 157 DEVIATION (P2-P1)/GOR1 AT WELL PRP-F0G .......................................................... 114
FIGURE 158 DEVIATION (P2-P1)/P1 AT WELL PRP-FA0 ................................................................ 115
FIGURE 159 DEVIATION (P2-P1)/P2 AT WELL PRP-FA0 ................................................................ 115
FIGURE 160 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-FA0 .............................................. 115
FIGURE 161 DEVIATION (P2-P1)/GOR1 AT WELL PRP-FA0 .......................................................... 116
FIGURE 162 DEVIATION (P2-P1)/P1 AT WELL PRP-FAB ............................................................... 116
FIGURE 163 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-FAB ............................................. 116
FIGURE 164 DEVIATION (P2-P1)/GOR1 AT WELL PRP-FAB .......................................................... 117
FIGURE 165 DEVIATION (P2-P1)/P1 AT WELL PRP-FAC ............................................................... 117
FIGURE 166 DEVIATION (P2-P1)/P2 AT WELL PRP-FAC ............................................................... 117
FIGURE 167 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-FAC ............................................. 118
FIGURE 168 DEVIATION (P2-P1)/GOR1 AT WELL PRP-FAC ......................................................... 118
FIGURE 169 DEVIATION (P2-P1)/P1 AT WELL PRP-FAF ................................................................ 118
FIGURE 170 DEVIATION (P2-P1)/P2 AT WELL PRP-FAF ................................................................ 119
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FIGURE 171 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-FAF ............................................. 119
FIGURE 172 DEVIATION (P2-P1)/GOR1 AT WELL PRP-FAF .......................................................... 119
FIGURE 173 DEVIATION (P2-P1)/P1 AT WELL PRP-FAI ................................................................. 120
FIGURE 174 DEVIATION (P2-P1)/P2 AT WELL PRP-FAI ................................................................. 120
FIGURE 175 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-FAI ............................................... 120
FIGURE 176 DEVIATION (P2-P1)/GOR1 AT WELL PRP-FAI ........................................................... 121
FIGURE 177 DEVIATION (P2-P1)/P1 AT WELL PRP-F0E ................................................................ 121
FIGURE 178 DEVIATION (P2-P1)/P2 AT WELL PRP-F0E ................................................................ 121
FIGURE 179 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-F0E .............................................. 122
FIGURE 180 DEVIATION (P2-P1)/GOR1 AT WELL PRP-F0E .......................................................... 122
FIGURE 181 DEVIATION (P2-P1)/P1 AT WELL PRP-F0F ................................................................ 123
FIGURE 182 DEVIATION (P2-P1)/P2 AT WELL PRP-F0F ................................................................ 123
FIGURE 183 DEVIATION (P2-P1)/WATERCUT1 AT WELL PRP-F0F .............................................. 123
FIGURE 184 DEVIATION (P2-P1)/GOR1 AT WELL PRP-F0F .......................................................... 124
FIGURE 185 NOISE AND ERRORS IN THE RAW PRODUCTION DATA. ....................................... 125
FIGURE 186 NOISE CLEANING ALLOWS THE GLOBAL BEHAVIOR OF THE CURVE TO BE
IDENTIFIED. ................................................................................................................................ 127
FIGURE 187 PRODUCTION DATA AFTER NOISE CLEANING AND GROUP COMPATIBILITY IN
BETWEEN POINTS. .................................................................................................................... 127
FIGURE 188 FITTED LINE CHOOSEN FOR EACH GROUP OF POINTS. ...................................... 129
FIGURE 189 ORIGINAL POINTS IN THE FINAL GROUPS. RESULT OF A DERIVATIVE
TOLERANCE DTOL=100 AND A GAP TOLERANCE = 10%. .................................................... 129
FIGURE 190 BAR CHART FOR GROUP IDENTIFICATION. ............................................................ 129
FIGURE 191 ORIGINAL PRODUCTION RAW DATA ........................................................................ 130
FIGURE 192 PRODUCTION CURVE SMOOTHED BY THE MOVING AVERAGE ALGORITHM. ... 130
FIGURE 193 PRODUCTION CURVE SMOOTHED BY A STRONGER AVERAGING AND MORE
FLEXIBLE LIMITS FOR CONNECTING GROUPS. .................................................................... 131
FIGURE 194 PRODUCTION CURVE SMOOTHED BY A STRONGER AVERAGING AND MORE
FLEXIBLE LIMITS FOR CONNECTING GROUPS. .................................................................... 131
15
List of Acronyms
ACA- Acácia (wells)
BHP- Bottom Hole Pressure
BHT- Bottom Hole Temperature
IPM- Integrated Production Management
IFM- Integrated Field Management
FPSO- Floating Production Storage Offloading
GOR- Gas to Oil Ratio
MPFM- Multiphase Flow Meter
P10- Production line 10
P20- Production line 20
P30- Production line 30
P40- Production line 40
P1- Directly measured pressure
P2- Estimated Pressure
PRP- Perpétua (wells)
Q- Total Discharge
Qgas- Gas Discharge
Qoil- Oil Discharge
Qwater- Water Discharge
Rso- Solution Gas-oil Ratio
THP – Tubing Hole Pressure
WHP - Well Head Pressure
WHT- Well Head Temperature
ZNA- Zínia (wells)
16
Chapter 1. Introduction
The automatic determination of the produced volumes in a subsea level is associated to high ranges
on uncertainty and sereval technical limitations on measurements. Multiphase flow meters are
currently used in the most-advanced facilities as a solution for the problem of the inflow-rate metering
in subsea wells in real time, which is critical for reservoir characterization and well optimization.
Nevertheless, testing, calibrating and post-installation tuning of MPFMs is expensive and can be
problematic. Regular maintenance is often needed and malfunctioning can be constant on these
devices performance. Because of these, drifts, frozen values, data sources failure, out-of-range values
and signal anomalies are often observed on measurements.
It is key that Engineers assume an active role on this systematic measurement process. Attention and
the criticism are needed not only to spot but also to infer on what would be a reasonable solution in a
presence of an existing error.
In the present domain, the Production Allocation Problem regarding the subsea multiphase metering,
some work was previously done in order to minimize the uncertainty from the readings. New
equipment (software) was developed in order to overcome many of the physical metering limitations
and bringing consistency to the data. Nevertheless, these equipments also need maintenance as the
whole system works in a non-stationary process, changing its characteristics over time and as the
reservoir depletes.
Using an investigative strategy, the production data was analyzed in order to build trends that would
be consistent with the physical parameters measured during production. This was done through the
combination of two main different tools: Integrated Field Management (IFM) for the reallocation of
produced volumes and Microsoft Excel for finding production patterns, crosschecking and validating
corrected values. The limitation of IFM relies in the fact that it depends on the accuracy of the inserted
models (PVT, pipelines, etc.). Mainly because of PVT imprecisions it can often provide results based
on biased inputs. Also, if the MPFM values are wrong, that will mean that models are also being built
based on incorrect production volumes. That is why there is the need to change the produced volumes
in the software itself. The IFM works based on two direct measurements: the bottom-hole pressure
and the tubing hole pressure. With these two values it is possible to obtain the pressure drop
associated to the flux and, after, it is possible to run calculations on the total fluids discharge occurring
with the flux – using the Darcy’s law. In the IFM, by knowing the real pressure drop occurring when the
well flows, it is possible to correct the produced volumes that were first measured by the MPFM as the
IFM is also based on density and friction, using the pressure loss on elevation equation.
The challenge was to correct the produced volumes in time so that the estimated pressure drop would
equal the directly measured pressure drop (for which no uncertainties apply). The directly measured
BHP is the key for the solution of this inverse problem: the good calibration of the production
parameters (volumes of oil, volumes of water, volumes of gas, well head pressure and temperature)
17
relies in the good adjustment in between the directly measured BHP and the estimated BHP (resulting
from the new corrected volumes). This will determine either if the correction is good or not.
The solution for each well was obtained through and iterative process of readjustment in which the
sum of the total produced volumes for oil, water and gas had to match the equivalent amounts
measured with no uncertainty at the FPSO level where it comprises the sum of all the wells of the
field.
In a final stage, it was possible to verify that the correction process apllied improved the volumes
allocation for all the wells. It was also possible to assess the level on uncertainty for each MPFM in
time, in order to forecast the deviated results that are constantly being provided by these devices. The
conclusions obtained during this study also made it possible to spot which PVT models had to be
reviewed and corrected in order to fully assess the reservoirs of the Pazflor field.
This thesis consists in a Monitoring work that pursuits a better forecasting, management and
optimization of the oil field and the resources used on the oil production.
Inflow-rate
metering
Figure 1 Production Reallocation workflow scheme.
18
Chapter 2. TOTAL'S BLOCK 17
2.1 BLOCK 17 – A World Scale Block
The Block 17 is a world-class oil fields block that covers nearly 4,000 square kilometers (km2) located
from 150 to 270 kilometers off the coast of Angola. This acreage has become the stage for a unique
industrial adventure, with 15 discoveries – developments that have set global benchmarks for the
industry – and a spectacular production outlook. By May 25, 2010, less than a decade after first oil
from the block, its cumulative production had reached 1 billion barrels.
The Block 17 is a large world scale block that represents 1/3 of the total production of the Angolan Oil.
In the following work, although it might seem a system bearing simple characteristics, it is full of
complexities that could only be overcame by sequential synthesis that are made up in order to
approach and understand the processes in a more immediate way.
It began in 1996 with the discovery of Girassol, where a bold field development plan paved the way for
record-setting production of 200,000 barrels per day in 2001. By 2007, the successive tie-backs of the
Jasmim and Rosa fields to the Girassol FPSO, and the start-up of Dalia (one of the largest deepwater
fields ever developed), had boosted the block’s total output to around 500,000 barrels per day. The
Pazflor field came on stream in August 2011 and would quickly ramp up to plateau production. The
development added a further 220,000 barrels per day to cumulative production from Block 17 thanks
to a new world first: the full deployment of a complete subsea artificial lift system with gas/liquids
separation on the seafloor. Growth continued with the 2014 start-up of CLOV, the block’s fourth
production hub, which is contributing with an additional 160,000 barrels per day of oil.
19
Chapter 3. PAZFLOR
3.1 A Top of the Art Project
The Pazflor project marks the first-ever use of subsea separation technology offshore West Africa, and
it is also a world-first use of subsea separation technology in a development of such scale.
The Pazflor project offshore Angola began producing oil and gas using gas/liquid subsea separation
equipment supplied by FMC Technologies, in September 2011.
Located in the prolific Block 17, some 93 miles (150 km) offshore of Angola, Pazflor produces oil and
gas from 26 subsea wells, supported by 20 water injection and two gas injection wells, drilled in four
separate reservoirs in water depths of 2,000 to 4,000 feet (600 to 1,200 m).
The field covers an area of 238 square miles (600 km2).
A system of flowlines and risers transport the produced fluids to the Floating Production Storage and
Offloading vessel (FPSO) with the processing capacity of 220,000 bopd.
Subsea separation is the key enabling technology making efficient production of this oil possible, as
three of the four Pazflor reservoirs contain very heavy, viscous oil at relatively low reservoir pressures.
The heavy oil will comprise about two-thirds of the liquids produced at Pazflor. Low reservoir pressure
of 2,900 psi (200 bar) meant artificial lift would be needed. Gas lift and multiphase pumping were
assessed but found less efficient than liquid/gas separation with liquid boosting. Subsea separation
allows use of a gas-tolerant hybrid pump to lift the oil and water to the surface, while the gas flows
under its own pressure. Separation results in a more stable flow in the risers.
Separation also simplifies Pazflor’s hydrate prevention strategy. Since the separators operate at a
pressure of 333 psi (23 bar), fluids downstream of each SSU remain outside the hydrate formation
window. Upstream of the SSUs, simple depressurization prevents hydrate formation in the event of an
extended shutdown, without any chemical injection from the surface. The subsea separation system
was instrumental in the development of such an enormous field and available from the first day of
production.
Ultimately, The Pazflor project would not have been feasible without the introduction of Subsea
Separation technology.
3.2 Structure and Equipment
The Pazflor structure was specially conceived with the purpose of meeting all the features needed in
its complex domain. The FPSO has a topside weight of 35,494t.. It is designed with a processing
capacity of 200,000bpd of oil, 150mmcf/d of gas, and a storage capacity of about 1.9 million barrels of
crude.
20
Facilities are planned for a 20-year design life, and quarters are provided for 220 operation and
maintenance personnel.
Technip's Deep Blue and Deep Pioneer were responsible for engineering, procurement, fabrication
and installation of more than 80km (50 miles) of production and water injection rigid flowlines, flexible
risers and integrated production bundle risers, plus engineering, procurement and fabrication of 60km
(37 miles) of umbilicals.
Acergy was responsible for engineering, procurement, fabrication, and installation of 55km (34 miles)
of water injection, gas injection, and gas export lines, umbilicals, and 20 rigid jumpers, plus installation
of all manifolds, three subsea separation units with umbilicals and FPSO mooring lines.
In the Figure 2, below, a scheme of the subsea producing lines of the Pazflor is presented.
Figure 2 Pazflor producing lines Scheme.
21
3.3 Measurement of Produced Volumes
3.3.1 The Multiphase Flow Meters (MPFM)
The measurement of the produced volumes is held by Multiphase Flow Meters (MPFM) which are
equipments able to deduce the proportion and flow rate of each fluid phase (oil, water and gas) per
well.
MPFM’s are chosen according to the type of measurement, performance, medium to be applied on
and accuracy requirements. The velocity, pressure, temperature and needs in terms of calibration and
monitoring are also took into account when picking the most adequate type. Another crucial aspect
affecting the performance of these devices regards the ability to overcome the presence of substances
that can be seen as contaminants of the flow, such as injected chemicals, and to be able to read when
in salty and acidic environments. In the Figure 3, below, two examples of MPFM's are present (on the
right) and its location relatively to the FPSO and the umbilicals is also shown (on the left).
The MPFMs are installed in the wellhead of each oil producer well of Pazflor.
For the application of custody transfer measurements of fluid hydrocarbons, positive displacement
meters and turbine meters have been preferred. For gas metering, gas orifice meters and ultrasonic
flow meters are most common. Coriolis meters are in use for liquid measurements, but can also take
gas measurement applications.
For the application of allocation measurements, multiphase flow meters have been adopted, especially
for subsea production systems - as for Pazflor.
A number of factors have instigated the recent rapid uptake of multiphase measurement technology.
In Pazflor, all the oil producer wells are equipped with MPFMs.
3.3.1.1 MPFM Measuring Principles
The MPFM is equipped with a nucleonic sensor that emits gamma-rays through the produced mixture
- from an emissor to a receptor-, the energy that arrives at the receptor allows it to calculate a loss of
energy to me medium that is after associated to a permitivitty value that is possible to link to a certain
amount of hydrocarbons present in the flow.
Figure 3 Example of MPFM's and illustration of its location subsea.
22
The MPFM works in two different modes. It automatically switches from the Oil Continous mode to the
Water Continuous Mode (or vice-versa). The Water Continuous Mode is activated when the Watercut
is greater than 45% and the measurements are possible measuring the conductivity of the fluid
present in the flow. When working in the Oil Continous Mode, the MPFM estimates de fractions of the
differente phases present in the flow through the measurement of the permitivitty of the fluid.
The Venturi tube is also a fundamental part of the MPFM. According to the Venturi effect, a fluid
passing through smoothly varying constrictions experience changes in velocity and pressure. These
changes can be used to measure the flowrate of the fluid.
As a liquid flows through an orifice, the square of the fluid velocity is directly proportional to the
pressure differential across the orifice and inversely proportional to the specific gravity of the fluid. The
greater the pressure differential, the higher the velocity; the greater the density, the lower the velocity.
The volume flow rate for liquids can be calculated by multiplying the fluid velocity times the flow area.
By taking into account units of measurement, the proportionality relationship previously mentioned,
energy losses due to friction and turbulence, and varying discharge coefficients for various types of
pipelines configurations, the flow rate equation can be written as follows:
(Eq. 1)
In the scheme bellow, Figure 5, it is possible to see a resume of the MPFM Measuring Principles.
Figure 4 Simplified illustration of the Venturi tube.
Figure 5 The MPFM Measuring Principles.
23
The readings of the MPFMs are stored and daily updated on the PI (Processor that includes all the
data of the FPSO, well by well, second by second) after that it is automatically inserted in the
Production Data Management System (PDMS) and it can be imported either in Flores (in-house
reservoir database currently out of use), Excel or T-More (software database being currently
developed).
Once in Excel, it is possible to plot the curves representing the produced volumes of Oil, Water and
Gas.
These MPFMs offer substantial economic and operating advantages over their phase separating
predecessor. Nevertheless, it is still widely recognized that no single MPFM on the market can meet
all multiphase metering requirements.
In the industry, the way forward and development trends on MPFM can be resumed as the following:
Accuracy improvement for fiscal allocation applications
Low cost systems
Non gamma-ray systems
New technology: Tomography/Imaging
Downhole measurements
24
Chapter 4. Production Allocation
4.1 The Need and the Goal
The Allocation process is defined by breaking down measures of quantities of extracted hydrocarbons
across the different wells of the field, from one or more reservoirs. In some cases, such as Pazflor, the
well extracts hydrocarbons from more than one geologic formation or reservoir; hence it is useful to
divide the oil field and its well streams by formations or layers. Allocation is commercially rooted in the
need to distribute the costs, revenues and taxes among multiple players collaborating on field
development and production of oil and gas.
There are various incentives for collaborations in the oil industry: one is risk and cost, sharing the
practice by issuing licenses for exploration and production to a partnership of oil companies; another is
the aim of improving production efficiency, producing from multiple layers or multiple oil fields following
the most optimized strategy.
The final goal of this work is to define the most correct production value per well (and per phase of oil,
water and gas), from an extensive set of sensors with inherent uncertainties and showing biased
results.
The understanding of the main trends will lead to better decisions on which wells to either open or
close and where to invest the operating resources.
The objective is to elaborate principles fitting trends that are supported by physical assumptions
regarding the multiphase flow in pipelines, letting the human intervention overcome the effect of drifts
by proposing expressions that eliminate discrepancies and bring consistency to the data.
4.2 The Software - Integrated Production Modelling (IPM) by Petroleum
Experts
'Petroleum Experts’ is a french software company that were one of the pioneers in the direction of
integrating reservoir, production, process and facilities studies. They brought the Integrated Production
Modeling software (IPM) to the market, an outstanding tool that comprises their industry reference
softwares Prosper, GAP, MBAL and IFM.
PROSPER is a well performance, design and optimization program for modelling most types
of well configurations found in the worldwide oil and gas industry today.
GAP is a multiphase oil and gas optimizer tool that models the surface gathering network of
field production systems.
MBAL helps the engineer better define reservoir drive mechanisms and hydrocarbon volumes.
25
IFM integrates models, data and workflows.
IPM models the complete oil and gas production system including reservoir, wells and the
surface network.
The unique global optimization approach permits the engineer to determine the optimum setting to
maximum production or revenue, taking account of all constraints that are set in the system. These
results can then be used to implement adjustments at the field level to achieve the optimization goals.
4.2.1.1 Integrated Fiel Management (IFM)
The correction of the produced flow rates were held on IFM. This plataform integrates models, data
and workflows, allowing field management tasks to be organized and expedited with maximum
efficiency, from real-time field surveillance tasks to probabilistic forecasting using computer clusters.
The IFM allows engineers to monitor field performance at any time, with continuous real-time
monitoring and surveillance workflows; it automatizes repetitive tasks, allowing engineers to focus on
added-value tasks and it includes the creation and management of multiple scenarios, ensuring the
field optimization and forecasting.
IFM's design is rooted in the belief that sound production system models (i.e. reservoir, well, network
and downstream models) can be used to gain an invaluable understanding of the field's behavior.
Models are a solid basis for field management tasks, such as: field production optimization; well rate
allocation (and consequently a field-wide production allocation); production forecasts; and real-time
production monitoring and surveillance.
Models provide the basic physics that relate the behavior of process variables in reservoirs, wells,
production networks and process facilities. However, a producing field is a dynamic process, and
therefore is constantly changing. Well tests, field measurements, and operational events are field
readings that require proper validation before field management tasks can be undertaken. Figure 6
resumes the Models interation on IFM.
Figure 6 IFM and Models interaction.
26
ModelCatalogue interacts with IFM, providing it with accurate models at any time. This interaction is
done automatically, and ModelCatalogue acts as the 'access door' to any models IFM may need.
4.2.1.2 Well Rate Allocation Workflow
Traditional methods allocate production according to the available well test data. By continuously
assessing well rates through comparison with calibrated well models, production allocation can be
significantly improved.
4.3 IFM Process - Correction Methodology
The image Figure 8 shows the Well Historical Analysis Workflow (WHAW) used to rebuild the
production history of ZNA-ED well. The top and middle plots represent the liquid rate and the Watercut
respectively as given by the MPFM (in Purple) and by the corrected scenario (in blue), the bottom plot
represents the difference between the directly measured BHP and the estimated using the Prosper
Model and the MPFM flow rates.
The green box represents the maximum admissible difference (+/- 3 bar) in order to take the MPFM
rates estimation as acceptable. When the pressure data is out of the green range it means that the
Watercut and the GOR have to be corrected because the flowing pressure is not coincident with the
real directly measured BHP.
The corrections on the production curves are done by modifying the Watercut and the GOR on the
periods in which the BHP estimated deviates in more than 3 bar from the real BHP (directly measured
through the Down Hole Gauge). These are the only two parameters that are altered in order to obtain
new oil, water and gas rates that are consistent with the directly measured BHP.
Figure 7 Calibration in the Allocation workflow.
27
The changes are inserted in GOR and Watercut. The impact of these modifications is after possible to
see by selecting the views of the menu on the right.
After running the calculations with the recently modified parameters, it is possible to observe in the
bottom chart of the pannel view (third chart of Figure 8) if the inserted parameters lead us to a
scenario that is consistent with the real measured BHP. If so, the point will be moved to the green
area.
When producing, the well head pressure will decrease as the produced volumes increase and will rise
as they decrease (when the Wells are choked). For the well head temperature the behavior will be the
opposite one: as it will follow the produced volumes and show higher values when it increases and
Figure 8 Rebuilding production history for ZNA-ED well.
28
lower ones when it decreases. The temperature on the top will be proportional to the producing
volumes and it will only decline if the well is either choked or closed.
Regarding the bottom hole, its pressure is expected to decrease as the produced volumes rise while
its temperature will remain stable around a constant value.
According to these considerations the potential errors are spotted and sent to the Material Balance
team for further revision, analysis and correction of the identified errors before they become “official”.
4.4 IFM Process - The Darcy's Law as the relation in between GOR and
Watercut with Pressure
In fluid dynamics the Darcy's law is a phenomenologically derived constitutive equation that describes
the flow of a fluid through a porous medium. The law was formulated based on the results of
experimnts on the flow of water through beds of sand. Darcy's law is used to describe oil, water and
gas flows through petroleum reservoirs and, in the present study, it is also the expression in which the
IFM calculations are based. When altering the values for the gas flow (GOR) and for the water flow
(Watercut) this expression provides a correspondent result for the pressure drop during
production(from the wellbore to the wellhead).
The Darcy's law presents a simple proportional relationship between the instantaneous discharge rate
through a porous medium, the viscosity of the fluid and the pressure drop over a given distance.
(Eq. 2)
The total discharge, Q (units of volume per time) is equal to the product of the permeability (k, units of
area) of the medium, the cross-sectional area (A) to flow, and the pressure drop (Pb - Pa), all divided
by the dynamic viscosity (in SI units, e.g. Kg/(m.s) or Pa.s), and the length L the pressure drop is
taking place over. The negative sign is needed because the fluids flow from high pressure to low
pressure. Dividing both sides of the equation by the area and using more general notation leads to
(Eq. 3)
where q is the filtration velocity or Darcy flux (discharge per unit area, with units of lenght per time,
m/s) and P is the pressure gradient vector.
For a multiphase flow, an approximation is to use Darcy's law for each phase with permeability
replaced by phase permeability, which is the permeability of the medium multiplied with relative
permeability. This approximation is only valid if the interfaces between the fluids remain static, which is
not true in general, but it is still a reasonable model under steady-stade conditions.
Assuming that the flow of a phase in the presence of another phase can be viewed as single phase
flow through a reduced pore network, it is possible to add the subscript i for each phase to Darcy's law
above written for Darcy flux, and obtain for each phase in the multiphase flow the expression below,
29
(Eq. 4)
where Ki is the phase permeability for phase i. From this, we can also define relative permeability Kri
for phase i as
(Eq. 5)
where K is the permeability for the porous medium, as in Darcy's law.
The Eq. 4 can be written for , , and . The GOR can be written as the ratio in between
and . It is possible to write the GOR as
(Eq. 5)
And the watercut as
(Eq. 6)
While producing, for each well, the values of the bottom-hole pressure and the tubing-hole pressure
are known. These directly measured values will allow the IFM to calculate the pressure drop (Pa - Pb)
and the pressure gradients . For each well, the permeability of the flowing medium (k), the cross-
sectional area to flow (A), the dynamic viscosity of the fluid ( ) and the length (L) are known and
constant values. That allows the software to calculate possible solutions for the total discharge (Q), for
each Darcy flux per phase and, consequently, the values for the Watercut and the GOR.
Through analysing how the pressure drop (Pb – Pa) varies with the total discharge (Q), it is possible to
infer on how the components of the total discharge, Qoil, Qwater and Qgas, are relatively present in
the flow. If, in a case a), a small variation on the total discharge corresponds to a high variation on the
pressure drop and, in a case b), the same variation on the total discharge corresponds to a smaller
variation on the pressure drop, that means that in the case a) the total discharge has a higher water
component (Qwater) and, in case b), there is a higher gas component (Qgas). Therefore, in case a)
there is more water present in the flow (higher watercut) and, in case b), there is relatively more gas
present in the flow (higher GOR).
THP
BHP
FLUX
Figure 9 Gravity and friction in a flowing well.For the same Qtotal, the pressure drop increases
when there is more water present in the flow.
30
This is explained by the pressure drop by gravity or vertical elevation equation
(Eq. 7)
where is the pressure drop, is the density of the flowing fluid, g is the acceleration of gravity and
is the vertical elevation.
It is based on the Darcy’s law and on the pressure drop equation that the IFM validates a certain GOR
and Watercut as occurring for each directly measured BHP and THP (pressure drop).
4.5 Correction Criteria for the Reallocation
When applying the corrections in the production flow rates there were different combinations of
Watercut and GOR for a certain period that would be solution to the problem of matching the
estimated pressure with the real pressure. The number of degrees of freedom at this stage would
reflect the level of uncertainty of this Reallocation Problem. For that reason, some correction criteria
had to be set, in order to ensure that the new corrected scenarios would be foundamented and
consistent with the characteristics and constraints of the whole complex system.
The correction was applied according to a conservative approach.
It was only at the last stage, when the data was confronted with the data measured at the FPSO level,
that the new corrected scenarios built under this criteria were sliglty alterred to follow the irrefutable
production curves given by the FPSO for each well.
4.5.1 The Gas-Oil Ratio minimum value
The GOR is the ratio of the volume of gas that is present in the solution to the volume of oil at
standard conditions.
The GOR increases approximately linearly with pressure and is a function of the composition of the oil
and the gas. Heavy oil, as the oil being produced at Pazflor, contains less dissolved gas when
compared to a lighter oil. The GOR increases with pressure, until the bubble point pressure is
reached, after that it remains constant and the oil is said to be undersaturated.
In the IFM, when appliying corrections to the GOR of the produced volumes, it was set that the GOR
corrected values (at the surface) should always be higher tran the value of GOR measured for the
same well at higher depths. As we are producing, it is expected that with the loss of pressure the
31
volume of gas dissolved in the liquid will increase as a fraction of the oil also changes from the liquid
to the gas phase.
4.5.2 Maximum Parsimony Criteria
The total discharge will be the sum of the oil, water and gas component. The IFM number of solutions
will correspond to the number of combinations of Qoil, Qwater and Qgas that lead to the total
discharge, Q, corresponding to the directly measured pressure drop. For this reason a maximum
parsimony criteria was adopted, stablishing that the correction applied in the GOR and the Watercut
for each well would always be the most conservative one, maintaining the new corrected values of
GOR and Watercut as closest as possible from the original ones.
(Eq. 8) in standard conditions
(Eq. 9) in standard conditions
According to the expressions above, it was a priori expected that the plot of Watercut and the GOR
correction would would present the shape of an exponential function, because in the Watercut
expression the volumes of oil and gas come in the denominator. In Figure 10, an example of how two
different solutions can be obtained for the same well and how they are relatively positioned in terms of
percentual Watercut and GOR corrections.
For every well, the corrected scenario closer to the origin was the scenario validated on IFM.
(Eq. 10)
(Eq. 11)
This was an interative process, that would only stop when no closer solution to the origin would be
possible to find.
Figure 10 Example of two possible corrected scenarios for PRP-FB-A.
%
%
32
4.5.3 Reservoir Depletion in time
An investigating on the production history of each producing line and well lead us to state a general
trend about the level of certainty of the correction to apply at either a early or late stage of life of an oil
producer well.
For the present stage of production reallocation, it was established that the moment were the Water
Breakthrough was located for each well would be considered as certain. According to the past
acquired experience on production metering and on the MPFM performance, it was known that the
equipment does not show errors in estimation of the flow rates when the Watercut is low. At the
begginning of production, when the MPFM detects a fluid matrix where the hydrocarbons are present
in a higher proportion than water, the MPFM automatically works on the Oil Continuous Mode where it
does the measurements based of the permetivitty of the fluids present in the mixture. Here, the
nucleonic sensor of the MPFM emits gamma-rays through the produced mixture - from an emissor to
a receptor-, the energy that arrives at the receptor allows it to calculate a loss of energy to me medium
that is after associated to a permitivitty value that is possible to link to a certain amount of
hydrocarbons present in the flow. On the other hand, when the Watercut is greater than 45%, the
MPFM automatically switches to the Water Continuous Mode where it does the measurements based
on the conductivity of the fluids present in the mixture. Here, the precision of the MPFM is highly
affected. This problem is related to the fact that it is needed a conductivity of reference to use as input
parameter in order to well calibrate the MPFM. The conductivity is a function of the salinity of the
present water, which has been difficult to assess at the different stages of production. In an advanced
stage of production, after the water breakthrough, as the watercut increases the MPFM estimations
become more deviated: the volumes of water are likely to be considerably underestimated.
For these, a qualitatite strategy was adopted, in order to set in which stage of production would it be
more likely to correct either the GOR, the Watercut or both.
At a early stage of production, before the water breakthrough, if any correction is needed, it should to
be applied on the GOR. After the water breakthrough, when some water starts to show, it corrections
Figure 11 Graphic Illustration of the minimum change criteria applied.
33
should be logically applied on both. When the Watercut is great than 45%, the corrections are likely to
be as much applied on the GOR as on the Watercut.
4.6 The Correction: MPFM Raw vs MPFM Corrected
4.6.1 Production line: P10
In the Figures 13 and 14 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas). The first figure represents the evolution
corresponding to the liquid phase and the second represents the same evolution in time
corresponding to the gas phase. The objective is to see how different were the allocated flows
(Validated Scenario) from the originally measured flows (Raw MPFM). The exact same approach is
used when analysing wells individually.
According to IFM, the corrections on the flow rates for the P10 line show the following main aspects:
Although the water breakthrough is well allocated, the corrected water rates from October
2012 are approximately 30% higher than registered.
From July 2013 it is possible to see an improvement on metering as the deviation in between
Raw and Corrected values for water rates significantly decreased.
The oil rates deviation are impacted by the water breakthrough. The corrected values are
approximately 7% below the measured ones. Also from July 2013 the values start to converge
again. From November 2014 the deviations increase to again to a maximum value of
approximately 25% for water and approximately 23% for oil in late February 2015.
Figure 12 Graphic Illustration of the criteria for the level of certainty about corrections at a certain stage of the producing time.
34
The corrected trend for the gas rate shows two different evolution stages. In the first, the
corrected rate for the gas generally matches the measured rates. Here, there is a deviation
around 25% from May 2012 up to October 2012. In the second evolution stage the results
show that the MPFM is generally overestimating the gas rates. From August up to November
2013 it was overestimated in approximately 28%, then it overestimates to a value which is the
double of the corrected rate until May 2014 and from November up to December 2014 it
shows a deviation around 35%. From January 2015 the gas rate values start to converge
again.
0
2000
4000
6000
8000
10000
12000
[Sm3/day]
[Measured time]
MPFM Raw vs MPFM Corrected
Raw MPFMQoil
Raw MPFMQwaater
ValidatedScenario Qoil
ValidatedScenarioQwater
Figure 13 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the line P10.
0
200
400
600
800
1000
1200
1400
[Sm3/day]
[Measured time]
MPFM Raw vs MPFM Corrected
Raw MPFMQgas
ValidatedScenario Qgas
Figure 14 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the line P10.
35
4.6.1.1 Trend Corrections per Well - Consistency Check
For the wells of the previous producing line (P10) in which strong deviations had to be applied in order
to obtain the corrected rates validated by the IFM, the resulting physical production parameters (WHT,
WHP and BHP) plots are also shown at the end of each section. This way, it is possible to confirm that
the new corrected scenario was consistently built.
4.6.1.1.1 ZNA-E0A
NOTE: For this well, it was not possible to find an adjustable trend from October 2012 until July 2013.
During this period there are no input parameters on IFM that provide a validation of the production
values. Therefore, it is not possible to interpret the results during this period.
Nevertheless, it is possible to confirm that measurements of the MPFM in ZNA-EA are providing good
results for at least approximately 70% of its production period.
Regarding the non-corrected period, it is still possible to observe that the well head temperature
evolution shows an increase from July 2012 until late June 2013- moment when the well was closed-,
while in the BHP pressure trend it is possible to see an increase also from July 2012 which indicates
that less oil is being produced and more water is present on the flow.
In the Figures 15 and 16 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas).
Below, in the Figures 17, 18 and 19 the resulting physical production parameters WHT, WHP and
BHP plots are respectively shown. Confirming a consistent evolution of the 3 parameters for this well
in the new corrected scenario.
Figure 15 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the well ZNA-E0A
36
Figure 16 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the well ZNA-E0A
Figure 17 Well head temperature evolution in time for the well ZNA-E0A
(ºC)
37
Figure 18 well head pressure evolution in time for the well ZNA-E0A
Figure 19 Bottom-hole pressure evolution in time for the well ZNA-E0A
(Bar)
(Bar)
38
Bellow, in the Fugures 20 and 21 it is possible tho see how the changes in the production parameters
lead to an estimated pressure that well captures the trend of the real pressure.
4.6.1.1.2 ZNA-E0E
The measurements of the MPFM in ZNA-EEB are providing good results for oil and water rates in
approximately 85% of its production period – the misreading occurs from middle of November 2014
until late February 2015 and does not overcome the value of 5% in deviation neither for oil nor water.
This occurs short after the water rates overcome the oil rates. During that period, the oil is
overestimated while the water is underestimated by the MPFM.
0
50
100
150
200
250
300
Pressure (Bar)
[Measured time]
Evolution of the pressure at well head
P1
P2
Figure 20 Evolution of the pressure at the well head. P1 – measured pressure. P2 – estimated pressure.
120
130
140
150
160
170
180
190
200
120 140 160 180 200
P2
P1
P1 vs P2
Figure 21 P1 vs P2 – linear regression. P1 – measured pressure. P2 – estimated pressure.
(Bar)
(Bar)
39
Here, the core problems occur for the gas rates measurements where it misread the gas volumes
during the whole studied period. It is possible to identify three main periods. In the first two the gas
rates are being underestimated by the MPFM where the deviation in between gas measured and
corrected rates decreases in March 2014 from a value of approximately 38% to a deviation value of
approximately 20%; In the final period, the rates are overestimated in approximately 30%. This last
period is coincident with the misreading period on oil and water, also when the watercut reaches the
value of approximately 50%.
The corrected gas rates evolution is confirmed by the well head temperature that shows two different
increasing slopes that are coincident with the two trends followed by the gas rates in the two first
different stages– also the two stages when the proportion of water in the flow increases, first, with a
higher and, after, with the same slope as the oil rates. It is also possible to identify a decreasing trend
in the plots which is coincident with the third evolution stage.
The both WHP and BHP pressure plots have an inflexion point around early February 2014. This
confirms the fact that it is from this date that the proportion of oil present in the flow decreases
significantly while the water production rate increases – as a confirmation of the corrected scenario.
In the Figures 22 and 23 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas). In the Figures 24, 25 and 26 the resulting
physical production parameters WHT, WHP and BHP plots are respectively shown. Confirming a
consistent evolution of the 3 parameters for this well in the new corrected scenario.
0
600
1200
1800
2400
3000
3600
[Sm3/day]
[Measured time]
MPFM Raw vs MPFM Corrected
Raw MPFMQoil
Raw MPFMQwater
ValidatedScenario Qoil
ValidatedScenarioQwater
Figure 22 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the well ZNA-E0E.
40
Figure 23 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the well ZNA-E0E
Figure 24 Well head temperature evolution in time for the well ZNA-E0E
(ºC)
41
Figure 25 Well head pressure evolution in time for the well ZNA-E0E
In the Figures 27 and 28 it is possible tho see how the changes in the production parameters lead to
an estimated pressure that well captures the trend of the real pressure.
Figure 26 Bottom-hole pressure evolution in time for the well ZNA-E0E
(Bar)
(Bar)
42
Figure 27 Evolution of the pressure at the well head. P1 – measured pressure. P2 – estimated pressure.
Figure 28 P1 vs P2 – linear regression. P1 – measured pressure. P2 – estimated pressure.
4.6.1.1.3 ZNA-E0D
According to the IFM, the measurements of the MPFM in ZNA-ED start to misread when the water
breakthrough takes place.
The WBT is well allocated but the water rate that shows after starts to be approximately 80% higher
than the registered one and, on middle of August 2013, it reaches the value of approximately 27% of
deviation. From September 2013, the registered and corrected measurements start matching again,
showing the maximum deviation of approximately 5% for the oil rates and 13% for the water rates.
This improvement on measurements was verified until late October 2014 when the deviations highly
increased again. On December 2014 it reaches values of approximately 85% and 75%– for the water
rates and the oil rates, respectively.
(Bar)
(Bar)
43
The gas rates are well allocated until October 2014 when the presence of gas in the flow starts to be
overestimated, reading a value that should be in an average approximately 70% lower than the one
registered by the MPFM.
According to the correction applied, the flow has a lower proportion of oil compared to water ever
since the water breakthrough took place. At that moment the watercut overcame a value of
approximately 40% it is when the higher deviations show for both rates.
The well head temperature trend shows an increase from the moment the change in the rates
evolution starts (increasing volumes of water and lower volumes of oil in the flow). In the WHT, WHP
and BHP plots it is possible to see a point of inflexion in the moment when the corrected water rate
reaches its maximum value after a trend of continuous increase- approximately 3550 Sm3/day in
August 2013. The physical parameters are also consistent with the gas rate corrected trend. It is
possible to see on the BHP plot the impact due to the increase on the gas rate from June 2014.
In the Figures 29 and 30 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas).
Figure 29 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the well ZNA-
E0D.
44
Figure 30 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the well ZNA-E0D
In the Figures 31, 32 and 33 the resulting physical production parameters WHT, WHP and BHP plots
are respectively shown. Confirming a consistent evolution of the 3 parameters for this well in the new
corrected scenario.
Figure 31 Well head pressure evolution in time for the well ZNA-E0D
(Bar)
45
Figure 32 Well head temperature evolution in time for the well ZNA-E0D
Figure 33 Bottom-hole pressure evolution in time for the well ZNA-E0D
The biplot on Figure 35 presents a considerable dispersion in the first part of the graph due to fact
that, for this was, the DP Venturi was, during some time, out of range. Because of that, during that
period, there is a lack of data regarding the production parameters for this well and it is impossible on
IFM to estimate a better value for the pressure observed while producing.
(Bar)
(ºC)
46
0
20
40
60
80
100
120
140
160
180
200
Pressure (Bar)
[Measured time]
Evolution of the pressure at well head
P1
P2
Figure 34 Evolution of the pressure at the well head. P1 – measured pressure. P2 – estimated pressure.
120
130
140
150
160
170
180
190
200
120 140 160 180 200
P2
P1
P1 vs P2
Figure 35 P1 vs P2 – linear regression. P1 – measured pressure. P2 – estimated pressure.
(Bar)
(Bar)
47
4.6.1.1.4 PRP-F0BA
According to the IFM correction, the measurements of the MPFM in PRP-F0BA for the oil and water
rates were providing values with less than 5% of deviation during approximately 76% of the studied
period– until middle April 2014. The gas rates show higher deviations and during a period that
represents more than 80% of the total production period of the well.
For the Liquid phase, the deviations started on September 2013 when the watercut reached the value
of approximately 37%. In April 2014, when the watercut had the average value of approximately 47%,
the previous 5% of deviation in oil and water rates increased to approximately 10% and 8% for water
and oil rates, respectively.
For the gas rates, the most significant deviation occurred from April 2012 until January 2013 when the
gas was underestimated in approximately 40%. From September 2013 it starts to be overestimated in
approximately 55% of the corrected value- this stage is coincident with the deviations on the Liquid
phase readings as well.
The well head temperature shows an increase by gradually higher slopes which confirm the increase
of the water rates from the beginning of the production. The higher slope and the following stabilization
are coherent with the validated evolution trend for both- oil and water- in the flow. The validated trends
on the IFM are also consistent with the WHP and BHP evolution trends. For the WHP and the BHP,
the moment the water and oil corrected rates intersect is coincident with an inflexion point on both
plots. In the Figures 36 and 37 it is presented the evolution in time of the oil produced flow (Qoil),
water produced flow (Qwater) and the gas produced flow (Qgas).
Figure 36 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the well PRP-
F0BA
48
Figure 37 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the well PRP-F0BA
In the Figures 38, 39 and 40 the resulting physical production parameters WHT, WHP and BHP plots
are respectively shown. Confirming a consistent evolution of the 3 parameters for this well in the new
corrected scenario.
Figure 38 Well head temperature evolution in time for the well PRP-F0BA
(ºC)
49
Figure 39 Well head pressure evolution in time for the well PRP-F0BA
Figure 40 Bottom-hole pressure in time for the well PRP-F0BA
(Bar)
(Bar)
50
Bellow, in Figures 41 and 42 it is possible tho see how the changes in the production parameters lead
to an estimated pressure that well captures the trend of the real pressure.
Figure 41 Bottom-hole pressure evolution in time for the well PRP-F0BA
Figure 42 P1 vs P2 – linear regression. P1 – measured pressure. P2 – estimated pressure.
4.6.2 Production line - P20
In the Figures 30 and 31 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas). For better understanding the figures, the
first figure represents the evolution corresponding to the liquid phase and the second the same
evolution in time corresponding to the gas phase. The objective is to see how different were the
allocated flows (Validated Scenario) from the originally measured flows (Raw MPFM). The exact same
approach is used when analysing wells individually.
According to IFM, the corrections on the flow rates for the P20 line present the following main aspects:
The water breakthrough is not well estimated by the MPFM. According to the correction
applied, it happened approximately 2 months after, around late January 2012.
(Bar)
(Bar)
51
Generally, the deviations mainly occur in two main stages from middle August 2012 until late
August 2013 and, after, from September 2013 until early February 2014. Where the
proportion of oil compared to water in the flow is, respectively, lower and higher than
registered by the MPFM.
Both reach this maximum value after September 2014 when the MPFM measurements start to
overestimate on the oil rates and to underestimate on the water rates.
The gas rates face a correction that is more extended in time. Until September 2013 the
results show that the MPFM was under estimating the gas rates. The trend of gas production
was well captured by the MPFM. Nevertheless, the measured rates would be approximately
10% below the correct gas rate and, from late October 2012 this deviation would significantly
increase to a maximum value of approximately 15% in late August 2013. After that, from
October 2013 the MPFM started to continuously overestimate the gas rate with a deviation
that would reach the value of approximately 25% in February 2015.
In the Figures 43 and 44 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas) for all the wells of the P20 line.
0
2000
4000
6000
8000
10000
12000
14000
16000
[Sm3/day]
[Measured time]
MPFM Raw vs MPFM Corrected
Raw MPFMQoil
Raw MPFMQwater
ValidatedScenario Qoil
ValidatedScenarioQwater
Figure 43 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the line P20.
52
Figure 44 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the line P20.
4.6.2.1 Trend Corrections per Well - Consistency Check
For the wells of the previous producing line (P20) in which strong deviations had to be applied in order
to obtain the corrected rates validated by the IFM, the resulting plots are shown below.
4.6.2.1.1 PRP-FA0
According to the IFM correction, the measurements of the MPFM in PRP-FA0 for the oil and water
rates were providing values with less than 5% of deviation only during approximately 10% of its
production period. The corrections applied show that the MPFM experienced different measurement
stages that lead the readings to significant deviations either for the oil, water or the gas rates.
The water breakthrough registered on late November 2011 is now allocated on late January 2012.
After this date, the raw and corrected measurements for oil and water rates are coincident for a period
of 5 months. Like in PRP-F0G, it is also from August 2012 when the higher deviations occur, but, in
the case of this well, the oil rates are underestimated while the water and gas rates are overestimated.
For the liquid rates the deviations show values that reach approximately 50%, 20%, respectively for oil
and water rates until July 2013. The validated scenario shows that the water rates overcame the oil
rates around late August 2012 and the proportion of water compared to oil in the flow is significantly
higher than registered. According to the MPFM, this rates intersection happened in December 2012-
approximately 4 months after. When reopened, from September 2014 until the end of the studied
period, the deviations in between raw and corrected values decreased.
The WHT, WHP and BHP for this well are consistent with the high variations pattern followed during
the production. It is possible to observe the significant increase of the water in the flow by analyzing
these physical parameters.
53
In the Figures 45 and 46 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas) for all the wells of the P20 line.
Figure 45 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the well PRP-
FA0.
Figure 46 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for well PRP-FA0.
In the Figures 47, 48 and 49 the resulting physical production parameters WHT, WHP and BHP plots
are respectively shown. Confirming a consistent evolution of the 3 parameters for this well in the new
correctedscenario.
54
Figure 47 Well head temperature evolution in time for the well PRP-FA0
Figure 48 Well head pressure evolution in time for the well PRP-FA0
(Bar)
(ºC)
55
In Figures 50 and 51 it is possible tho see how the changes in the production parameters lead to an
estimated pressure that well captures the trend of the real pressure.
120
130
140
150
160
170
180
190
200
120 140 160 180 200
P2
P1
P1 vs P2
Figure 49 Well head pressure evolution in time for the well PRP-FA0
Figure 50 P1 vs P2
(Bar)
(Bar)
(Bar)
56
4.6.3 Production line - P30
In the Figures 38 and 39 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas). For better understanding the figures, the
first figure represents the evolution corresponding to the liquid phase and the second the same
evolution in time corresponding to the gas phase. The objective is to see how different were the
allocated flows (Validated Scenario) from the originally measured flows (Raw MPFM). The exact same
approach is used when analysing wells individually.
According to IFM, the corrections on the flow rates for the P30 line present the following main aspects:
The water breakthrough is well estimated by the MPFM.
Generally, the deviations for the oil rates show two different stages. From the beginning of the
production to August 2012 the oil rates are overestimated in approximately 60%; after that
date, the deviation values would only overcome the 10% of deviation from April until
September 2013, from November 2013 until April 2014 and from September 2014 until the
end of the studied period, reaching the maximum values of 30%, 35% and 25% during this
referred periods, respectively.
Regarding the water rates, the maximum deviation occur: first from June 2013 until September
2013, where the presence of water is underestimated in approximately 35%; and after from
September 2013 until September 2014 where it is overestimated in approximately 28% of the
corrected value.
Regarding the gas rates, the IFM shows that the MPFM was not able to capture the gas main
trend. It is possible to observe two different deviation stages, where it is significantly
overestimated and underestimated, being corrected by values that are respectively the half
and the double of the registered ones.
120
130
140
150
160
170
180
190
200
210
Pressure (Bar)
[Measured time]
Evolution of the pressure at well head
P1
P2
Figure 51 Evolution of the pressure at the well head.
57
The IFM also shows that for this line the corrected water rates overcame the corrected oil
rates around September 2014. According to the MPFM, this intersection happened around
April 2014, 5 months earlier.
In the Figures 52 and 53 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas) for all the wells of the P30 line.
Figure 53 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the line P30.
0
2000
4000
6000
8000
10000
12000
[Sm3/day]
[Measured time]
MPFM Raw vs MPFM Corrected
Raw MPFMQoil
Raw MPFMQwater
ValidatedScenario Qoil
ValidatedScenarioQwater
Figure 52 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the line P30.
58
4.6.4 Production line - P40
In the Figures 40 and 41 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas). For better understanding the figures, the
first figure represents the evolution corresponding to the liquid phase and the second the same
evolution in time corresponding to the gas phase. The objective is to see how different were the
allocated flows (Validated Scenario) from the originally measured flows (Raw MPFM). The exact same
approach is used when analysing wells individually.
For the Oligocene, the wells ACA805, ACA813 and ACA814 are not possible to correct on the IFM
due to the lack of down hole gauge. Modifications cannot be applied on any of the segments of the
studied period, independently on when the gauge was lost.
For this reason, the validated scenarios for these 3 wells are coincident with the original MPFM raw
scenarios.
Due to the fact that the contribution on oil, water and gas rates of these 3 wells was not possible to be
allocated, building a consistent trend evolution scenario for the P40 line lead to significant deviation
values either for oil, water or gas rates. These deviations possibly show the gaps left by the volume
rates that were not possible to account for. For the gas rates it is possible to observe a constant
underestimation, where the measured rates are lower than the corrected rates in an average value of
approximately 60%. For the liquid rates, the deviation in between raw and corrected values follows the
pattern shown in the image below.
In the Figures 52 and 53 it is presented the evolution in time of the oil produced flow (Qoil), water
produced flow (Qwater) and the gas produced flow (Qgas) for all the wells of the P40 line.
59
Figure 55 Comparison of the Gas flux for the MPFM raw and the MPFM corrected for the line P40.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
[Sm3/day]
[Measured time]
MPFM Raw vs MPFM Corrected
Raw MPFMQoil
Raw MPFMQwater
ValidatedScenario
ValidatedScenarioQwater
Figure 54 Comparison of the Water and Oil flux for the MPFM raw and the MPFM corrected for the line P40.
60
Chapter 5. Method Robustness
In order to infer about the validation of the empirical method applied, it was done multivariate
approaches for simultaneously observing the effect of the different variables.
As presented in the previous section, the analysis of the linear regression resulting of the biplot of the
real pressure versus the estimated pressure allowed to infer about how centered the estimation was
on the variable of control (P1).
A univariate analysis was also performed. Histograms were built in order to see in which classes of
values were the values of P1 and P2 located and evaluate if the distribution of both values had a
similar shape. Throught calculating the plots of the cumulative frecuencies for both variables P1 and
P2 it was possible to see that both cumulative curves would be practically coincident for all the studied
wells.
The Figures from 81 up to 125, in Annex I. show the best P1 and P2 matching for every well of the
Pazflor oil field.
As in a multivariate experiment, varying parameters simultaneously, rather than one at a time, can be
more efficient and can allow the effects between parameters to be observed. A series of plots were
calculated in order to infer on how the deviations in between P1 and P2 would vary with P1 (real
pressure), P2 (estimated pressure), Watercut1 and the GOR1. It was possible to observe in this plots
that the deviation values were independent of the values either of P1, P2, Watercut1 or GOR1. This
prooves the robustness of the method.
The analitycal results of the empirical method applied show it is not biased.
The plots can be consulted in the Annex II, Figures from 126 up to 184 as well as the deviation plots
linked to the Bivariate Analysis and the histograms for the real Pressure (P1) and the estimated
Pressure through IFM (P2).
61
Chapter 6. Data Reconciliation Results
The FPSO unit comprises de TOPSIDE production level which includes the treatment facilities
responsible for the final stage of the production process, before offloading the oil. The volumes and
rates measurements that take place in the TOPSIDE are more accurate than the measurements held
in the previous levels. This accuracy is well established for the oil volumes measurements but the
confidence level decreases when it is due to either volumes of water or volumes of produced gas. At
the end of each one of the following sections, the measured rates obtained for the different levels –
Subsea Separation Units and Pumps- will be compared to the rates measured on the FPSO. At a final
stage, the MPFM corrected results are superposed to the Topside curves in order to justify its
consistency.
This way the accuracy was assessed and it was possible to infer about the level of calibration not only
on the MPFM’s but also in the SSU’s and Pumps.
The major reference is the Oil curve provided by the measurements on the FPSO oil tanks.
It is important to emphasize that the measurements provided by the Oil thanks are considered to be
100% certain, as it is registered by the fiscal meters before offloading the oil for being sold. The
volumes of water and gas measured at the FPSO level do not have the same level of certainty
associated. As shown in the schemes below, the water and the gas are split in different parts and
porposes in the producing and treatment chain. The accuracy when metering this fractions at the
MPSO is not as high as when metering the main producting product - oil.
For that reason, matching the FPSO Oil production curve with the IFM Oil Corrected Estimation curve
was the main goal during the data reconciliation process that would also take us back to IFM for
readjustments on the corrections previouly applied. Having this curves coincident would also
atomatically mean that the other production curves obtained through the correction on IFM - for water
and gas- would be correctely allocated.
62
. Figure 56 Above and below, respectively, the resume of the production subsea network and of the topside storage and offloading network.
63
Chapter 7. Uncertainties and Admissibility
Comparing the oil, water and gas production estimated by the corrected IFM with the FPSO measures
will be fundamental to understand the level of uncertainty of the production allocation strategy and
methodology.
All the devices that register the production rates provide noisy and sometimes intermitent readings. In
the following sections, in order to better capture the production behavior of the combination of wells
there was the need to create a "Curve Repair and Moving Average Algorithm" to clean the noise and
eleminate the errors. The explanation of how this Algorithm was created can be found in Annex III.
Below, we can see for oil, water and gas, respectively, the FPSO production curve, the IFM corrected
estimation, the MPFM Raw estimation curve, and the difference between the FPSO and the IFM
corrected estimation. Respectively in Figures 57, 58 and 59.
-10000
0
10000
20000
30000
40000
50000
[Sm3/day]
[Measured time]
OIL PRODUCTION
OIL-FPSO
OIL-IFM
OIL-RAW MPFM
OIL-FPSO vsOIL-IFM
Figure 57 Comparison of oil production in order of a measured time.
64
Figure 58 Comparison of water production in order of a measured time.
Figure 59 Comparison of gas production in order of a measured time.
It is on the difference that we should focus, in order to establish sound conclusions about the data and
the study. Because these production curves were full of noise or special events, and we do want to
understand the global behavior without losing the numerical details available, it is interesting to see
the cumulative behavior of both curves (FPSO Production curve and IFM Corrected Estimation curve),
and the difference between them. With the cumulative view, noise will be automatically reduced, and a
65
global cumulative behavior will be captured if we do accept that random noise will be cumulatively
neglectable with respect to the final results or global trends.
Below, in Figures 60, 61 and 62 we can see the behavior of data in a cumulative view- for oil, water
and gas, respectively.
Figure 61 Cumulative view for water.
-200000
-100000
0
100000
200000
300000
400000
500000
-10 000
0
10 000
20 000
30 000
40 000
50 000
11/01/2011 19/09/2011 27/05/2012 02/02/2013 11/10/2013 19/06/2014 25/02/2015
[Sm3/day]x103 [Sm3/day]
[Measured time]
Cumulative view for oil
OIL-FPSO
OIL-IFM
OIL-FPSO vsOIL-IFM
Figure 60 Cumulative view for oil.
66
Figure 62 Cumulative view for gas.
Focusing on the differences of the cumulative oil, water and gas production from both curves is
important, but we must acknowledge that this information is not fully consistent, as its meaning will
change along the period of study. As the curve progresses, the meaning of its value gets more
relevant in terms of the total average error achieved in the end, and we cannot see properly the
magnitude of the daily error that we need to observe. Obviously we could observe in the first instance
the initial difference curve from both daily production lines, but unfortunately this curve, because of the
noise, lacks of clarity to be properly evaluated. To overcome this problem the following strategy was
implemented:
First, it is needed to clean the noise of this cumulative difference, so that we can picture it as a smooth
curve. Below we can see the "clean" curve inside the original curve for oil, water and gas -
respectively- Figures 63, 64 and 65.
-200000
-100000
0
100000
200000
300000
400000
[Sm3/day]
[Measured time]
Oil cumulative diference
OIL CUM DIF NONSMOOTH
OIL CUM DIF SMOOTH
Figure 63 Cumulative view for oil.
67
Figure 64 Cumulative view for water.
Figure 65 Cumulative view for gas.
68
In order to observe now the “cleaned” differences between The FPSO and the IFM Corrected
Estimation production lines, we can simply calculate a new curve that is in fact the derivative of this
new smoothed cumulative difference curve.
In Figure 66, 67 and 68 it is possible to observe the global evolution of the daily Error, respectively for
oil, water and gas.
-2000
-1000
0
1000
2000
3000
Error [%]
[Measured time]
MPFM after Correction Oil Production Daily Errors
Daily Error (%)
-1000
0
1000
2000
3000
4000
[Sm3/day]
[Measured time]
MPFM AFTER CORRECTION WATER PRODUCTION
Water Absolute Error
Water Absolute Error
Figure 66 Daily error MPFM after correction oil production.
Figure 67 MPFM after correction of water production.
69
After the average absolute daily error is estimated, it is also needed to understand that the importance
of this error is still dependent of the total daily production values. For that reason, we do want to
understand how much this error represent in terms of percentual deviation for the total oil production.
To perform this we will consider the FPSO production values, but again we will follow the same
strategy, by cleaning the noise of the cumulative FPSO production curve, and in the end calculating
the derivative curve, accepting it as a smooth representation of the occurred production.
The absolute oil, water and gas production measured in the FPSO is shown on Figures 69,70 and 71.
Figure 69 FPSO OIL production.
-400
-200
0
200
400
600
[Sm3/day]
[Measured time]
MPFM AFTER CORRECTION GAS PRODUCTION
Gas Absolute Error
Figure 68 MPFM after correction of gas production.
70
-1000
4000
9000
14000
19000
24000
29000
34000
[Sm3/day]
[Measured time]
FPSO water production
FPSO Water
0
1000
2000
3000
4000
5000
6000
[Sm3/day]
[Measured time]
FPSO gas production
FPSO Gas
Figure 71 FPSO gas production.
Figure 70 FPSO water production.
71
Now, it is possible to divide the absolute error by the absolute production and to infer about the
percentual error in the time, for oil, water and gas, respectively, in Figures 72, 73 and 74.
As we can see, there seem to exist 4 relevant periods for the Oil % Daily Estimation Error:
1 – First non-zero production days : Some errors higher than 15 %
2 - Start to 11-02-2012 : Approximately maximum errors of 12 % occurred
3 - 11-02-2012 to 29-04-2104 : Approximately maximum errors of 5 % occurred
4 - 29-04-2104 to End : Approximately maximum errors of 3 % occurred
-10
-5
0
5
10
15
20
Error [%]
[Measured time]
Daily estimation error oil production
Daily Error (%)
Figure 72 Daily estimation error of the oil production.
72
For the water production, 6 relevant periods for the % Daily Estimation Error can be identified:
1 – First non-zero production days : Some errors lower than 5 %
2 - 10-01-2012 to 06-06-2102 : Errors in between 5% and 12 % occurred
3 - 06-06-2102 to 01-08-2102 : Approximately maximum errors of 5 % occurred
4 - 01-08-2102 to 19-02-2013 : Approximately maximum errors of 9 % occurred
5- 19-02-2013 to 02-07-2013 : Approximately maximum errors of 5 % occurred
6- 02-07-2013 to End : Approximately maximum errors of 14 % occurred
-10
-5
0
5
10
15
20
Error [%]
[Measured time]
Daily estimation error water production
Daily Error (%)
Figure 73 Daily estimation error of the water production.
73
For the gas production, 5 relevant periods for the % Daily Estimation Error can be identified:
1 – First non-zero production days : Some errors higher than 15 %
2 - 20-08-2011 to 28-08-2013 : Approximately maximum errors of 8 % occurred
3 - 28-08-2013 to 08-06-2014 : Errors in between 8% and 16 % occurred
4 - 08-06-2014 to 22-09-2014 : Approximately maximum errors of 8 % occurred
5- 22-09-2014 to End : Errors lower than 5 %
There are several relevant conclusions:
1 – It seems that the errors have been mitigated along the production period, as the global maximums
have been decreasing consistently;
2 – In global behavior we can say that most of the production estimation has a global error inferior to 5
%;
3 – The oscillating nature of the error in terms of over and under estimating, contributed to the
stabilization of the global accumulated error. However it is not clear the reasons why this behavior is
oscillating, and for that reason, expected global error must be estimated in a conservative manner,
assuming a general percentual error that represents a constant over or under estimation in larger
period of the analysis.
-10
-5
0
5
10
15
20
Error [%]
[Measured time]
Daily estimation error gas production
Daily Error (%)
Figure 74 Daily estimation error of the gas production.
74
It is also interesting to compare the obtained data with the final non corrected results, using the Raw
MPFM Estimated values, instead the ones updated with IFM.
As before, we can also picture the percentual daily oil, water and gas errors on estimation,
respectively on Figures 75, 76 and 77.
-40
-20
0
20
40
60
80
100
120
Error [%]
[Measured time]
Daily error raw oil production
Daily Error (%)
Figure 75 Daily estimation error of the raw oil production.
0
20
40
60
80
100
120
Error [%]
[Measured time]
Daily Error raw MPFM water production
Daily Error (%)
Figure 76 Daily estimation error of the raw oil production.
75
In the next plots we will compare the percentual Raw Error in Estimation previously obtained with the
Corrected IFM percentual Error in Estimation. For oil, water and gas, respectively, Figures 78, 79 and
80.
IFM error for Oil
Figure 78 IFM percentual error for oil production.
-60
-40
-20
0
20
40
60
80
100
120
11-18-2010 9-14-2011 7-10-2012 5-6-2013 3-2-2014 12-27-2014 10-23-2015
IFM Corrected % Error
Raw % Error
40
60
80
100
120
140
160
Error [%]
[Measured time]
Daily Error raw MPFM gas production
Daily Error (%)
Figure 77 Daily estimation error of the raw oil production.
76
The IFM corrections conducted the results to a much acceptable level of error.
-20
0
20
40
60
80
100
120
140
Error (%)
[Measured time]
IFM error for water
IFM CORRECT
RAW
-20
0
20
40
60
80
100
120
140
Error (%)
[Measured time]
IFM error for gas
IFM CORRECT
RAW
Figure 79 IFM percentual error for water production.
Figure 80 IFM percentual error for gas production.
77
Chapter 8. Conclusions
Several correction cycles and iterations were performed as a consequence of the adopted procedure
of comparing the corrected MPFM production scenarios values with the ones obtained by the Flow
Transmitters available in the FPSO.
The corrected scenarios from the IFM were gradually affected by the difference between each one
and the production curve from the FPSO. At this level, the oil production rate measured at the FPSO
tanks is assumed as a fundamental reference zero uncertainty. As a consequence, it was this final
difference that determined which IFM readjustments were needed at the end of each correction cycle.
This way it was possible to conclude through the calculation of errors in estimation that the produced
volumes of oil, water and gas were being significantly either under or overestimated during the studied
period.
It is important to mention that for matching the corrected MPFM and FPSO production curves it was
necessary to assign GOR values that were below the Rs threshold during several production periods
and for several wells. This can be understood by acknowledging that when facing the need to increase
the total oil rate for a given production volume, it is necessary to decrease simultaneously in the IFM
the amount of water and gas (GOR), obtaining at a next stage the pressure validation by considering
the density and friction in the IFM.
It allows us to conclude that the PVT models need to be reviewed and corrected. The solution gas-oil
ratio is often the most significant component of the PVT correlations. It directly impacts on the oil
formation volume factor (Bo), the oil viscosity and the oil compressibility.
In the Annex IV, Table I, it is possible to consult the Rso table for the Pazflor wells. In green the wells
where the GOR is always above the value of Rso, in red the others.
The IFM corrections conducted the results to a much acceptable level of error in estimation, however it
is important to note the corrected data error in estimation still mimics the oscillating nature of the raw
MPFM error. Even taking in account that IFM corrections are made on individual wells, and that the
final results we are seeing here are the result of the superposition of several wells and lines, this
scaled behavior of the error curve from raw to corrected raises a question: shouldn’t the corrections
drive the results to a more homogenous and constant percentual error? It is not absolutely clear the
answer to this question, but probably the reason for this is the fact that IFM corrections are made over
time segments rather than over individual days. Because of this the convergence of the corrections
may be less precise on individual days, and leading the results to time periods of drag in the direction
of smaller errors, still maintaining the original curve behavior. The relevance of this may be important
because it is likely that a more refined IFM correction will lead to an overall smaller average error.
It is also important to note that the errors of the Raw MPFM data version are relatively high.
78
In the Integrated Field Management tool, the Models Auditing settings may be not efficiently set up, as
one of its features is to real time estimate production. I would suggest further investigation on how to
optimize the IFM integration in our fields and at the same time, select and define protocols and
procedures to further reduce the production allocation errors.
Chapter 9. Way Forward
In terms of correction strategy and best value estimation, building an artificial neuronal network would
represent a significant adding value approach of the studied problem. Using an artificial neuronal
network to estimate or approximate functions that would simoultaneously depend on the large number
of inputs inherent to this case of study would certainly provide better results in terms of performance,
accuracy and time.
79
Chapter 10. References
Exploration and Production Internal Files, TOTAL E&P Angola
L.P. Dake, Fundamentals of Reservoir Engineering - Developments in Petroleum Science 8,
Elsevier, Amsterdam (1978)
Prof. Dr. Antonio Costa e Silva, Notes on IST MEP subject of "Oil & Gas"
Hewitt, G.F. (1982) Chapter 2, Handbook of Multiphase Systems (Ed. G. Hetsroni),
Hemisphere Publishing Corporation, New York
Roxar Multiphase Flow Meters, ROXAR
F. Viana, P. Mehdizadeh, Challenges on Multiphase Flow Metering i Heavy Oil Applications,
Society of Petroleum Engineers, Canada (2013)
F. Khan et al, Well-performance Monitoring (WPM): Creating Added VAlue From Raw Data
and Application to the Girassol Deepwater-field Case, Society of Petroleum Engineers, SPE
Economics and Management (2012)
80
Annexes
Annex I. Cumulative Frequency Graphs
P10
ZNA-E0A
Figure 81 Cumulative frecuency of P1 for the well ZNA-E0A
Figure 82 Cumulative frecuency of P2 for the well ZNA-E0A
Number
of Days
(Bar)
81
Figure 83 Cumulative frecuency of P1 and P2 for the well ZNA-E0A
ZNA-E0D
Figure 84 Cumulative frecuency of P1 for the well ZNA-E0D
0
200
400
600
800
1000
1200
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
82
Figure 85 Cumulative frecuency of P2 for the well ZNA-E0D
Figure 86 Cumulative frecuency of P1 and P2 for the well ZNA-E0D
ZNA-E0E
0
200
400
600
800
1000
1200
1400
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
83
Figure 87 Cumulative frecuency of P1 for the well ZNA-E0E
Figure 88 Cumulative frecuency of P2 for the well ZNA-E0E
Number
of Days
Number
of Days
(Bar)
(Bar)
84
Figure 89 Cumulative frecuency of P1 and P2 for the well ZNA-E0E
ZNA-E0H
Figure 90 Cumulative frecuency of P1 for the well ZNA-E0H
0
100
200
300
400
500
600
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
85
Figure 91 Cumulative frecuency of P2 for the well ZNA-E0H
Figure 92 Cumulative frecuency of P1 and P2 for the well ZNA-E0H
ZNA-EA0
0
50
100
150
200
250
300
350
400
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency ofP1Cumulative frequency ofP2
Number
of Days
Number
of Days
(Bar)
(Bar)
86
Figure 93 Cumulative frecuency of P1 for the well ZNA-EA0
Figure 94 Cumulative frecuency of P2 for the well ZNA-EA0
Number
of Days
Number
of Days
(Bar)
(Bar)
87
Figure 95 Cumulative frecuency of P1 and P2 for the well ZNA-EA0
ZNA-EAA
Figure 96 Cumulative frecuency of P1 for the well ZNA-EAA
0
50
100
150
200
250
300
350
400
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
88
Figure 97 Cumulative frecuency of P2 for the well ZNA-EAA
Figure 98 Cumulative frecuency of P1 and P2 for the well ZNA-EAA
PRP-F0BA
0
10
20
30
40
50
60
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
89
Figure 99 Cumulative frecuency of P1 for the well PRP-F0BA
Figure 100 Cumulative frecuency of P2 for the well PRP-F0BA
Number
of Days
Number
of Days
(Bar)
(Bar)
90
Figure 101 Cumulative frecuency of P1 and P2 for the well PRP-F0BA
P20
PRP-F0G
Figure 102 Cumulative frecuency of P1 for the well PRP-F0G
0
10
20
30
40
50
60
90 100 110 120 130 140 150 160 170
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
91
Figure 103 Cumulative frecuency of P2 for the well PRP-F0BA
Figure 104 Cumulative frecuency of P1 and P2 for the well PRP-F0BA
PRP-FA0
0
200
400
600
800
1000
1200
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
92
Figure 105 Cumulative frecuency of P1 for the well PRP-FA0
Figure 106 Cumulative frecuency of P2 for the well PRP-FA0
Number
of Days
Number
of Days
(Bar)
(Bar)
93
Figure 107 Cumulative frecuency of P1 and P2 for the well PRP-FA0
PRP-FAB
Figure 108 Cumulative frecuency of P1 for the well PRP-FAB
0
200
400
600
800
1000
1200
1400
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
94
Figure 109 Cumulative frecuency of P2 for the well PRP-FAB
Figure 110 Cumulative frecuency of P1 and P2 for the well PRP-FAB
PRP-F1C
0
100
200
300
400
500
600
700
800
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
95
Figure 111 Cumulative frecuency of P1 for the well PRP-F1C
Figure 112 Cumulative frecuency of P2 for the well PRP-F1C
Number
of Days
Number
of Days
(Bar)
(Bar)
96
Figure 113 Cumulative frecuency of P1 and P2 for the well PRP-F1C
PRP-FAF
Figure 114 Cumulative frecuency of P1 for the well PRP-FAF
0
200
400
600
800
1000
1200
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
97
Figure 115 Cumulative frecuency of P2 for the well PRP-FAF
Figure 116 Cumulative frecuency of P1 and P2 for the well PRP-FAF
PRP-FAI
0
100
200
300
400
500
600
700
800
900
1000
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
98
Figure 117 Cumulative frecuency of P1 for the well PRP-FAI
Figure 118 Cumulative frecuency of P2 for the well PRP-FAI
Number
of Days
Number
of Days
(Bar)
(Bar)
99
Figure 119 Cumulative frecuency of P1 and P2 for the well PRP-FAI
P30
PRP-F0E
Figure 120 Cumulative frecuency of P1 for the well PRP-F0E
0
100
200
300
400
500
600
700
800
900
1000
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
100
Figure 121 Cumulative frecuency of P2 for the well PRP-F0E
Figure 122 Cumulative frecuency of P1 and P2 for the well PRP-F0E
PRP-F0F
0
200
400
600
800
1000
1200
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
Number
of Days
(Bar)
(Bar)
101
Figure 123 Cumulative frecuency of P1 for the well PRP-F0F
Figure 124 Cumulative frecuency of P2 for the well PRP-F0F
Number
of Days
Number
of Days
(Bar)
(Bar)
102
Figure 125 Cumulative frecuency of P1 and P2 for the well PRP-F0F
0
200
400
600
800
1000
1200
1400
90 110 130 150 170 190
Cumulative frequency P1 and P2
Cumulative frequency of P1
Cumulative frequency of P2
Number
of Days
(Bar)
103
104
Annex II. Deviation Graphs
P10
ZNA-EA
Figure 126 Deviation (P2-P1)/P1 at well ZNA-EA
Figure 127 Deviation (P2-P1)/P2 at well ZNA-EA
105
Figure 128 Deviation (P2-P1)/Watercut1 at well ZNA-EA
Figure 129 Deviation (P2-P1)/GOR1 at well ZNA-EA
ZNA-ED
Figure 130 Deviation (P2-P1)/P1 at well ZNA-ED
106
Figure 131 Deviation (P2-P1)/P2 at well ZNA-ED
Figure 132 Deviation (P2-P1)/Watercut1 at well ZNA-ED
Figure 133 Deviation (P2-P1)/GOR1 at well ZNA-ED
107
ZNA-EEB
Figure 134 Deviation (P2-P1)/P1 at well ZNA-EEB
Figure 135 Deviation (P2-P1)/P2 at well ZNA-EEB
Figure 136 Deviation (P2-P1)/Watercut1 at well ZNA-EEB
108
Figure 137 Deviation (P2-P1)/GOR1 at well ZNA-EEB
ZNA-EH
Figure 138 Deviation (P2-P1)/P1 at well ZNA-EH
Figure 139 Deviation (P2-P1)/P2 at well ZNA-EH
109
Figure 140 Deviation (P2-P1)/Watercut1 at well ZNA-EH
Figure 141 Deviation (P2-P1)/GOR1 at well ZNA-EH
ZNA-EA0
Figure 142 Deviation (P2-P1)/P1 at well ZNA-EA0
110
Figure 143 Deviation (P2-P1)/P2 at well ZNA-EA0
Figure 144 Deviation (P2-P1)/Watercut1 at well ZNA-EA0
Figure 145 Deviation (P2-P1)/GOR1 at well ZNA-EA0
111
ZNA-EAA
Figure 146 Deviation (P2-P1)/P1 at well ZNA-EAA
Figure 147 Deviation (P2-P1)/P2 at well ZNA-EAA
Figure 148 Deviation (P2-P1)/Watercut1 at well ZNA-EAA
112
Figure 149 Deviation (P2-P1)/GOR1 at well ZNA-EAA
PRP-F0BA
Figure 150 Deviation (P2-P1)/P1 at well PRP-F0BA
Figure 151 Deviation (P2-P1)/P2 at well PRP-F0BA
113
Figure 152 Deviation (P2-P1)/Watercut1 at well PRP-F0BA
Figure 153 Deviation (P2-P1)/GOR1 at well PRP-F0BA
P20
PRP-F0G
Figure 154 Deviation (P2-P1)/P1 at well PRP-F0G
114
Figure 155 Deviation (P2-P1)/P2 at well PRP-F0G
Figure 156 Deviation (P2-P1)/Watercut1 at well PRP-F0G
Figure 157 Deviation (P2-P1)/GOR1 at well PRP-F0G
115
PRP-FA0
Figure 158 Deviation (P2-P1)/P1 at well PRP-FA0
Figure 159 Deviation (P2-P1)/P2 at well PRP-FA0
Figure 160 Deviation (P2-P1)/Watercut1 at well PRP-FA0
116
Figure 161 Deviation (P2-P1)/GOR1 at well PRP-FA0
PRP-FAB
Figure 162 Deviation (P2-P1)/P1 at well PRP-FAB
Figure 163 Deviation (P2-P1)/Watercut1 at well PRP-FAB
117
Figure 164 Deviation (P2-P1)/GOR1 at well PRP-FAB
PRP-FAC
Figure 165 Deviation (P2-P1)/P1 at well PRP-FAC
Figure 166 Deviation (P2-P1)/P2 at well PRP-FAC
118
Figure 167 Deviation (P2-P1)/Watercut1 at well PRP-FAC
Figure 168 Deviation (P2-P1)/GOR1 at well PRP-FAC
PRP-FAF
Figure 169 Deviation (P2-P1)/P1 at well PRP-FAF
119
Figure 170 Deviation (P2-P1)/P2 at well PRP-FAF
Figure 171 Deviation (P2-P1)/Watercut1 at well PRP-FAF
Figure 172 Deviation (P2-P1)/GOR1 at well PRP-FAF
120
PRP-FAI
Figure 173 Deviation (P2-P1)/P1 at well PRP-FAI
Figure 174 Deviation (P2-P1)/P2 at well PRP-FAI
Figure 175 Deviation (P2-P1)/Watercut1 at well PRP-FAI
121
Figure 176 Deviation (P2-P1)/GOR1 at well PRP-FAI
P30
PRP-F0E
Figure 177 Deviation (P2-P1)/P1 at well PRP-F0E
Figure 178 Deviation (P2-P1)/P2 at well PRP-F0E
122
Figure 179 Deviation (P2-P1)/Watercut1 at well PRP-F0E
Figure 180 Deviation (P2-P1)/GOR1 at well PRP-F0E
123
PRP-F0F
Figure 181 Deviation (P2-P1)/P1 at well PRP-F0F
Figure 182 Deviation (P2-P1)/P2 at well PRP-F0F
Figure 183 Deviation (P2-P1)/Watercut1 at well PRP-F0F
124
Figure 184 Deviation (P2-P1)/GOR1 at well PRP-F0F
125
Annex III. Curve Repair + Moving Averages Algorithm
Summary
For certain quantity measurements, discrete values are obtained in a time ordered fashion. These
series of values, may represent non-random behaviors, and may contain one or more continuous sets
of points, where it is expected a continuous curve and derivative, that can be grouped or separated by
specific characteristics in the beginning or end, as abrupt average or derivative changes.
However, for certain measurement processes, several aspects can decrease the quality of the
obtained points, introducing random errors or noise in the curves, bringing difficulties to read the
information in a clear graphic representation. It will also create a high amount of obstacles to
automatically process the data. These errors can be a result of failures or bad calibration of the
reading entities, but there is an important assumption that most of the time, the behavior is well caught
by the obtained points, and that there may only be necessary to capture clearly the major trends of the
data.
To overcome the problems referenced as noise, wrong readings or simply unwanted data fluctuation,
a simple flexible algorithm was developed to perform data repair and trend identification.
This algorithm has 3 fundamental steps. Point cleaning, group compatibility, and curve smoothing.
The Algorithm
The developed method is based on the assumption that there is no unique correct solution to the
problem, and for that reason, there are a set of parameters that highly influence the behavior of the
algorithm and that can be used to make trials with the data, until the cleaning and classification is
acceptable in the user’s perspective.
To better understand the steps of the method, a simple example is referenced.
Figure 185 Noise and errors in the raw production data.
0
5000
10000
15000
20000
25000
1
45
8
9
13
3
17
7
22
1
26
5
30
9
35
3
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In the previous picture, there is clearly one or more well identified trends, and a continuous set of
points introducing noise and errors. One can see easily the global behavior of the curve, but that
behavior is not immediately visible when processing the raw available data.
Step1 – Noise cleaning
The first step has the goal to eliminate points that represent high magnitude changes. High magnitude
changes can be the result of 2 factors:
a) Errors in the measurements;
b) Abrupt changes in the behavior.
There are 2 problems associated with the elimination of these points, but we will see later that it will
not affect negatively the goal of the algorithm:
a) Erratic points may not occur isolated. There may be several “wrong points” in a sequence.
Of course, if this sequence is too long, it will be considered a trend;
b) When the curve changes realistically, eliminating these points may represent a reduction
of the boundary points of a given well identified trend.
The fundamental decisions in this process of elimination are:
a) How many points do we eliminate?
b) What conditions do we use to decide the elimination?
Clearly there is no unique way to perform this step, and the actual implemented algorithm in VBA and
Excel is not meant to solve robustly all types of problem that may follow inside this category of
situations. However a decision was made to use a 2 stage elimination process.
The 2 stage noise cleaning is performed in the following manner:
a) Choose a Derivative Limit D1 as an input parameter. This parameter must be tough in
such a way that it will be smaller than the derivatives near the vicinity of the points where
we can clearly see abrupt changes, but will be higher than the derivatives that occur
naturally around along the small fluctuations of the curve;
b) The algorithm will now calculate for each point a local derivative magnitude. This is simply
done by measuring the absolute left and right derivatives for each point and averaging
both;
c) All points that will have a derivative magnitude superior to D1 will be eliminated. The
elimination does not create any implicit point in the eliminated position. It simply assumes
that the given discrete position has no point value. Points that will appear isolated and
surrounded by eliminated points will also be eliminated;
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d) Choose a second Derivative Limit D2 as an input parameter. We will repeat the process,
especially because normally, for the given real situations being solved, the erratic points
usually appear not completely isolated;
e) Recalculate derivatives as in b);
f) Eliminate again points, this time, where the magnitude of the derivative is greater than D2.
Eliminate again points that appear isolated and surrounded by eliminated points.
In this example, using a magnitude of 900 for both D1 and D2 we obtained the following points:
Figure 186 Noise cleaning allows the global behavior of the curve to be identified.
From these points we already have an implicit set of groups. We can see each group as a continuous
sequence of points without interruption. An interruption occurs when one or more point is missing
because of elimination. A clear picture of the groups and interruptions can be seen in the zoom below
in the X axis zone between 500 and 600.
Figure 187 Production data after noise cleaning and group compatibility in between points.
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Step 2 – Group compatibility
In this stage, we want to reorganize the existing groups in bigger ones, based on the fact that these
isolated groups may in fact represent the same trend. To achieve this regrouping we could opt for
several simple or complex strategies. The actual implementation opted for a very simple solution
described below:
a) For each group calculate the Line Equation parameters A and B (where y(x)=A.x+B) that
best fits the points contained in the group. This best fit is achieved by calculating A and B
to minimize the sum of the squares of the distances between each point and the line;
b) Define a derivative tolerance DTol;
c) Define a gap tolerance GTol as a percentage of the maximum occurred magnitude in the
whole data;
d) For each possible connection between 2 adjacent groups calculate the derivative
difference of the fitted lines between the line of group n and line of group n+1. Define a
condition 1 that is true if this difference is smaller than the defined DTol parameter;
e) For each possible connection between 2 adjacent groups calculate the vertical distance
between 2 points projected in a vertical line, located in the x coordinate that corresponds
to the average between the last x coordinate of the group n and the first x coordinate of
the group n+1. This projection is made by extending the fitted line of each group onto the
referred vertical line. Define a condition 2 that is true is this distance is smaller than the
defined GTol parameter;
f) If condition 1 and condition 2 are met simultaneously, then the 2 groups being tested are
connected and the process continues until no connections can be made.
Below we can see globally and in more detail the points represented not on the original position, but
over each fitted line for each group.
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Figure 188 Fitted line choosen for each group of points.
Figure 189 Original points in the final groups. Result of a Derivative tolerance DTol=100 and a Gap tolerance =
10%.
Based on the final connections made, we can represent the original points in the final groups. In this
case it was used a Derivative tolerance DTol=100 and a Gap tolerance = 10%:
Figure 190 Bar chart for group identification.
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To better understand the start and end of each group, a bar chart is pictured below to identify the
groups from 1 to 7.
Figure 191 Original Production Raw Data
Step 3 – Moving average
In this stage, we want to smooth the data, but only inside each identified already cleaned trend group.
To achieve this goal we will define the number of points with the parameter NP to be used for the
moving average calculation. This number must be odd. In this case we used the number of 21.
The algorithm introduces a special restraint to this number of points. In the case that the count of
points in the group is smaller than the double of the NP, than NP must be smaller than half of the
points of the group. With this, we will avoid unnecessary flattening of points in small groups.
Below we can picture the original curve and the curve points, already smoothed by the moving
average algorithm inside each group in 2 versions. The second version used a stronger averaging and
more flexible limits for connecting groups.
Original:
Figure 192 Production curve smoothed by the moving average algorithm.
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Final result version 1:
Figure 193 Production curve smoothed by a stronger averaging and more flexible limits for connecting groups.
Final result version 2:
Figure 194 Production curve smoothed by a stronger averaging and more flexible limits for connecting groups.
Many other solutions could be achieved by changing the algorithm parameters. There is no
assumption that one is better than the other. Only human interpretation can decide if the obtained
solution fits the desired purpose.
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Annex IV. Rs Table for the Pazflor wells
Table I. Rs table for the Pazflor wells. In green the wells where the GOR is always above the value of
Rs, in red the others