SUMMER 2017 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

ABSTRACT

Premature breakthrough and low sweep efficiency during wa-terfloods result from heterogeneity in the permeability field in a reservoir. Production wells with inflow control valves/in-flow control devices (ICVs/ICDs) have the ability to reduce the water-oil ratio by shutting in flow ports with high water cut. Subsequently, this technology has been limited to providing a response only after the detection of water breakthrough at pro-duction wells. Deep reading technologies have been reported as successful in detecting an approaching waterfront before water breakthrough. The objective of this study is to investigate if the technology for early front detection combined with ICVs/ICDs can improve sweep efficiency in horizontal waterfloods.

In this study, a synthetic horizontal well pair model was built, using a black oil reservoir simulator, to study the per-formance of waterflooding in a single layer heterogeneous oil reservoir and to understand the value of the information ob-tained from deep reading technology when it is used to control the ICVs/ICDs with the goal of optimizing the waterflood. The simulation results show that using ICVs/ICDs in the horizon-tal production well significantly improves sweep effi-ciency and reduces water production. Early detection of the waterfront with deep reading technologies pro-vides incremental oil recovery. An optimum location for waterfront detection occupies a specific range be-tween the injector and the producer, able to improve oil production within specified injection and produc-tion constraints. Deep reading technologies may also provide valuable information about the mobility field between the well pair that can be used to reduce un-certainty in heterogeneity and to update the geologi-cal model for better history matching and forecasting production.

INTRODUCTION

In recent years, the technology to detect water injec-tion fronts1-3 and conformance control with inflow control valves/inflow control devices (ICVs/ICDs) has seen tremendous development in the industry. A cross-well electromagnetic field survey was recently

deployed in Saudi Arabia between two horizontal wells, spaced at more than 1.3 km apart, with the goal of imaging fluid dis-tribution between a water injector and an oil producer in a carbonate reservoir characterized by a relatively tight matrix with highly conductive fracture corridors4. The results from this survey clearly show that the injected water was confined in the fracture corridors, leaving unswept volumes in the tighter and less permeable layers of the interwell volumes of the reser-voir, Fig. 1. Field data and the results of simulation studies5-12 indicate that using ICVs/ICDs to solve conformance control is-sues, such as gas-water coning, is beneficial.

In all these studies, ICVs/ICDs can react only after the breakthrough of water-gas at the production wells. Subse-quently, the value of controlling ICVs/ICDs based on early front detection technology is not been fully understood, and as a result, there is reluctance on the part of operating companies to invest in them. In this study, the objective is to investigate if the technology for early front detection combined with ICVs/ICDs can help to improve sweep efficiency and reduce the wa-ter-to-oil ratio in horizontal waterfloods.

Deep Reading Technology Integrated with Inflow Control Devices to Improve Sweep Efficiency in Horizontal Waterfloods

Dr. H. Onur Balan, Dr. Anuj Gupta, Dr. Daniel T. Georgi, Dr. Ali M. Alkhatib and Dr. Alberto F. Marsala

Fig. 1. 3D resistivity inversion cube from a cross-well electromagnetic survey between a water injector and a watered out oil producer4. Dark blue zones represent reservoir volumes swept by the injected water. The water injection front is clearly not uniform. A newly drilled slanted well in the interwell area (white line) targeted an unswept zone of the reservoir.

Fig. 2. A single layer simulation model for horizontal waterflooding.

Injector

Producer

42,000 ft (140 grids × 300 ft)

20,000 ft (80 grids × 250 ft)

Fig. 1. 3D resistivity inversion cube from a cross-well electromagnetic survey between a water injector and a watered out oil producer4. Dark blue zones represent reservoir volumes swept by the injected water. The water injection front is clearly not uniform. A newly drilled slanted well in the interwell area (white line) targeted an unswept zone of the reservoir.

SAUDI ARAMCO JOURNAL OF TECHNOLOGY SUMMER 2017

METHODOLOGY

In this study, a single layer simulation model (140 × 80 grids) with two horizontal wells, Fig. 2, was built to simulate water-flooding using a black oil reservoir simulator (CMG IMEX). The length of both wells and the well spacing are 30,000 ft and 10,000 ft, respectively. Since this is a single layer model, the gravity segregation is ignored, and areal sweep efficiency is used as an indicator of increased recovery.

Initial reservoir pressure in the model is 4,400 psi, Fig 3. Connate water saturation, which is equal to irreducible water saturation, is 0.2. Water is injected at a constant rate of 5,500 barrels per day (b/d) and at a maximum bottom-hole pressure (BHP) of 5,500 psi. Oil is produced at a constant rate of 5,000 b/d (oil formation volume factor = 1.1 reservoir barrel/stock tank barrel), which ensures void replacement. Bubble point pressure is 2,000 psi. Minimum BHP assigned for the producer is 2,200 psi to prevent free gas generation around the producer. Independently acting ICVs/ICDs control each perforation on the horizontal producer. If the waterfront is detected at the pro-duction well, the ICV/ICD is shut-in when the corresponding perforation is larger than 10%. If the waterfront is detected at a distance from the production well, then the ICV/ICD is shut-in when water saturation in the assigned distant grid is larger than 0.2. Although these constraints seem to be very conservative, the simulation results will provide an idea about the maximum benefit obtained from using deep reading technology and ICVs/ICDs together.

Figure 3 also shows the alternative locations for waterfront detection assigned in the model to find the optimum location between the wells where detection can be used to maximize oil production with ICVs/ICDs. Moreover, different synthetic geomodels were built to investigate the effect of heterogene-ity in the permeability field on oil recovery, water production and breakthrough, Fig. 4. All simulations were run for 50,000 days.

RESULTS AND DISCUSSIONS

The Effect of Heterogeneity in a Permeability Field

Water displaces oil more uniformly in a homogeneous case than it does in a heterogeneous case, Fig. 5. Water phase pre-

fers to flow through the least resistant pathway from injector

to producer. Therefore, water breakthrough for a heteroge-

neous case occurs much earlier than it does for a homogeneous

case, Fig. 6. This results in lower areal sweep efficiency and

40% lower oil recovery in the heterogeneous case.

The effect of heterogeneity is expected, and it can be more

pronounced in actual cases, given the uncertainty in reservoir

characterization. Applying such a workflow to a real reservoir

model would require considering multiple history matched

permeability realizations to produce representative results

with respect to the effect of uncertainty in the characterized

heterogeneity.

The Effect of Using ICVs/ICDs

Using ICVs/ICDs at the production well significantly improves

areal sweep efficiency, Fig. 7. For this case, if the waterfront

is detected at the production well, the ICV/ICD responds with

a shut-in if the flowing stream exceeds 10% water cut. This

helps to divert water from high permeability regions to low

permeability regions. Improved sweep efficiency results in

higher oil production and lower water production with de-

layed breakthrough.

Fig. 2

Fig. 3

Alternative Locations for Water-Front

Detection

Production Constraints = 1. qomax = 5000 stb/day (Bo = 1.1 rb/stb)2. BHPmin = 2200 psi (Pbp=2000 psi)

Ld = 0

Ld = 1.0

Injection Constraints =1. qwmax = 5500 stb/day2. BHPmin = 5500 psi

ICV/ICDsIf (Sw > 0.21), Then Shut-inthe corresponding ICV/ICD

(Swi = 0.2)

Pi = 4400 psi

42,000 ft (140 grids × 300 ft)

20,000 ft (80 grids × 250 ft)

30,000 ft

Injector

Producer

10,000 ft (40 grids × 250 ft)

Alternative Locations

for Waterfront Detection

If (Sw > 0.21), then shut-in the corresponding ICV/ICD (Swi = 0.2)

Fig. 2. A single layer simulation model for horizontal waterflooding.

Fig. 2

Fig. 3

Alternative Locations for Water-Front

Detection

Production Constraints = 1. qomax = 5000 stb/day (Bo = 1.1 rb/stb)2. BHPmin = 2200 psi (Pbp=2000 psi)

Ld = 0

Ld = 1.0

Injection Constraints =1. qwmax = 5500 stb/day2. BHPmin = 5500 psi

ICV/ICDsIf (Sw > 0.21), Then Shut-inthe corresponding ICV/ICD

(Swi = 0.2)

Pi = 4400 psi

42,000 ft (140 grids × 300 ft)

20,000 ft (80 grids × 250 ft)

30,000 ft

Injector

Producer

10,000 ft (40 grids × 250 ft)

Alternative Locations

for Waterfront Detection

If (Sw > 0.21), then shut-in the corresponding ICV/ICD (Swi = 0.2)

Fig. 3. A schematic showing alternative locations for waterfront detection between the horizontal wells.

Fig. 3. A schematic showing alternative locations for waterfront detection between the horizontal wells.

Fig. 4. Synthetic geomodels, each with a different heterogeneity in the permeability field.

Alternative Locations for Water-Front

Detection

Production Constraints = 1. qomax = 5000 stb/day (Bo = 1.1 rb/stb)2. BHPmin = 2200 psi (Pbp=2000 psi)

Ld = 0

Ld = 1.0

Injection Constraints =1. qwmax = 5500 stb/day2. BHPmin = 5500 psi

ICV/ICDsIf (Sw > 0.21), Then Shut-inthe corresponding ICV/ICD

(Swi = 0.2)

Pi = 4400 psi

Homogeneous

GeoModel #2 GeoModel #3

GeoModel #4 GeoModel #5

GeoModel #1Unit = [mD]

Producer

Injector

Fig. 4. Synthetic geomodels, each with a different heterogeneity in the permeability field.

SUMMER 2017 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Fig. 6. Heterogeneity (red line) in a permeability field leads to lower oil recovery (left) and higher water production with early breakthrough (right).

Fig. 7

Fig. 8

GeoModel #1 No ICV/ICD

@ 50,000 days

GeoModel #1 with ICV/ICD (Water-Front Detection at Prod. Well)

@ 50,000 days

No ICV/ICDICV/ICD (Water Front Detection @ Prod. Well, Ld = 0)ICV/ICD (Water Front Detection @ Ld = 1/3)

Cum

ulat

ive

Oil

SC

(bbl

)

No ICV/ICD

ICV/ICD (Waterfront Detection at Prod. Well, Ld = 0)

ICV/ICD (Waterfront Detection at Ld = 1/3)

Time (day)

Fig. 7. Water saturation maps after 50,000 days of waterflooding with (left) and without (right) ICV/ICDs in the Geomodel #1 case.

Fig. 5

Fig. 6

Homogeneous@ 50,000 days

Heterogeneous (GeoModel #1)@ 50,000 days

Cumulative Oil Prod. Cumulative Water Prod.

Water Saturation at 50,000 days Water Saturation at 50,000 days

C

umul

ativ

e W

ater

SC

(bbl

)

C

umul

ativ

e O

il SC

(bbl

)

Cum

ulat

ive

Wat

er S

C (b

bl)

Time (day) Time (day) Homo. Case (50 md)

Hetero. Case (5-500 md) Homo. Case (50 md)

Hetero. Case (5-500 md)

Fig. 5. Water saturation maps for heterogeneous (left) and homogeneous (right) cases after 50,000 days of waterflooding. Sweep efficiency is significantly reduced by heterogeneity in a permeability field.

Fig. 5

Fig. 6

Homogeneous@ 50,000 days

Heterogeneous (GeoModel #1)@ 50,000 days

Cumulative Oil Prod. Cumulative Water Prod.

Water Saturation at 50,000 days Water Saturation at 50,000 days

C

umul

ativ

e W

ater

SC

(bbl

)

C

umul

ativ

e O

il SC

(bbl

)

Cum

ulat

ive

Wat

er S

C (b

bl)

Time (day) Time (day) Homo. Case (50 md)

Hetero. Case (5-500 md) Homo. Case (50 md)

Hetero. Case (5-500 md)

SAUDI ARAMCO JOURNAL OF TECHNOLOGY SUMMER 2017

Using ICVs/ICDs with Deep Reading Technology

In the previous section,

water is detected after its

breakthrough. In this sec-

tion, however, two different

scenarios were simulated to

show the effect of pre-break-

through detection of the

waterfront on oil and water

production. Ld is defined as

the dimensionless distance

between the injector and the

producer, previously seen in

Fig. 3, and it ranges from

zero at the producer to 1 at

the injector. If Ld = 0, then

the waterfront is detected at

the production well. If Ld =

1/3, then it is detected at a distance of one-third of the total well spacing from the producer.

The results show that oil recovery increases if the ICVs/ICDs are closed in response to the earlier waterfront detec-tion, Fig. 8. That increased oil production is accompanied by a slight increase in water production, Fig. 9, which may result from the heterogeneity of the permeability field and the for-mulation of the problem. As ICVs/ICDs are closed in response to early detection of the waterfront, injected water is diverted from high to low permeability regions, Fig. 10. At some ICVs/ICDs, however, water production continues even after break-through, since the diversion means no waterfront is detected by those ICVs/ICDs so they are not triggered to perform a shut-in.

Optimum Location for Early Waterfront Detection

This section describes the search for an optimum location for early waterfront detection, (Ld)opt, the point between the hor-izontal injector and producer that allows ICVs/ICDs to maxi-mize oil production. Five different synthetic geomodels — pre-viously seen in Fig. 4 — were used in the simulations to show the effect of a different heterogeneity in the permeability field on the optimum location. It was found that (Ld)opt ranges from 0.25 to 0.35 regardless of the geomodels used in this study, Fig. 11.

Future work will focus on incorporating the effects of grav-ity and uncertainty in heterogeneity, determined by using multiple realizations of real geological models, into the per-formance of this workflow so as to recommend more robust reservoir management policies. This will include studying the effects of reservoir parameters controlling mobility and will consider a number of uncertainty quantification methods, such as the probabilistic collocation method13, 14 and the multilevel Monte Carlo method15.

Fig. 7

Fig. 8

GeoModel #1 No ICV/ICD

@ 50,000 days

GeoModel #1 with ICV/ICD (Water-Front Detection at Prod. Well)

@ 50,000 days

No ICV/ICDICV/ICD (Water Front Detection @ Prod. Well, Ld = 0)ICV/ICD (Water Front Detection @ Ld = 1/3)

Cum

ulat

ive

Oil

SC

(bbl

)

No ICV/ICD

ICV/ICD (Waterfront Detection at Prod. Well, Ld = 0)

ICV/ICD (Waterfront Detection at Ld = 1/3)

Time (day)

Fig. 8. Detection of the waterfront at a point one-third of the distance to the production well (green line) results in an incremental oil recovery.

Fig. 9

Fig. 10

No ICV/ICDICV/ICD (Sw Detection @ Prod. Well, Ld = 0)ICV/ICD (Sw Detection @ Ld = 1/3)

Injector 1

Producer

0 10,000 20,000 30,000 40,000

0 10,000 20,000 30,000 40,000

-20

,00

0-1

0,0

00

0

-20

,00

0-1

0,0

00

0

0.00 0.50 1.00 miles0.00 1.50 3.00 km

File: horz_max_rate_inj_max_rate_min_bhp_prod_hetero_independent_icv_early_shut_in_set_j49.irfUser: balanoxDate: 7/11/2014Scale: 1:79059Y/X: 1.00:1Axis Units: ft

0.19

0.25

0.31

0.38

0.44

0.50

0.56

0.62

0.69

0.75

0.81

Water Saturation 2091-11-01 K layer: 1

Injector 1

Producer

0 10,000 20,000 30,000 40,000

0 10,000 20,000 30,000 40,000

-20

,00

0-1

0,0

00

0

-20

,00

0-1

0,0

00

0

0.00 0.50 1.00 miles0.00 1.50 3.00 km

File: horz_max_rate_inj_max_rate_min_bhp_prod_hetero_independent_icv_early_shut_in_set_j49.irfUser: balanoxDate: 7/11/2014Scale: 1:79059Y/X: 1.00:1Axis Units: ft

0.19

0.25

0.31

0.38

0.44

0.50

0.56

0.62

0.69

0.75

0.81

Water Saturation 2091-11-01 K layer: 1

No waterdetection

Water isstill produced

Water is diverted fromhigh to low perm

Cum

ulat

ive

Wat

er S

C (b

bl)

No ICV/ICD

ICV/ICD (Sw Detection at Prod. Well, Ld = 0)

ICV/ICD (Sw Detection at Ld = 1/3)

Time (day)

Fig. 9. Detecting the waterfront earlier (green line) has only a small effect on water production; however, cumulative water production is still much lower than in the no ICV/ICD case.

Fig. 10

Fig. 10. Diversion of water from high to low permeability regions means that no waterfront is detected at certain ICVs/ICDs, so water production continues even after breakthrough.

SUMMER 2017 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

CONCLUSIONS

In recent years, the oil industry has seen tremendous develop-ment in deep reading technologies and in the availability of ICVs/ICDs. In this simulation study, the benefits of proactively controlling ICVs/ICDs based on early waterfront detection technology to improve sweep efficiency and reduce water pro-duction in horizontal waterfloods have been investigated.

The major conclusions drawn are:

• Using ICVs/ICDs at the horizontal production well sig-nificantly improves sweep efficiency and reduces water production.

• Early detection of the waterfront with deep reading technology provides incremental oil recovery.

• An optimum location for early waterfront detection occupies a specific range between the injector and the producer, able to improve oil production within the specified injection and production constraints.

• Deep reading technology may also provide valuable information about the mobility of the field, which can be used to reduce uncertainty in geological models for better history matching and production forecasting.

NOMENCLATURE

(qo)max maximum oil production rate, stb/d

BHPmin minimum bottom-hole pressure, psia

(qw)max maximum water production rate, stb/d

Pi initial reservoir pressure, psi

Sw water saturation, fraction

Swi initial water saturation, fraction

Ld dimensionless distance between the injector and producer

(Ld)opt dimensionless optimum location between the injector and producer

ACKNOWLEDGMENTS

The authors would like to thank the management of Aramco Services Company and Saudi Aramco for their support and permission to publish this article.

This article was presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, November 7-10, 2016.

REFERENCES

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2. Marsala, A.F., Lyngra, S., Widjaja, D.R., Laota, A.S., et al.: “Fluid Distribution Inter-Well Mapping in

Fig. 11. Optimum location range for early waterfront detection.

Fig. 11

1.80E+08

1.85E+08

1.90E+08

1.95E+08

2.00E+08

2.05E+08

0 0.2 0.4 0.6 0.8 1

Cum

Oil

Prod

(bb

l)

Dimensionless Distance From Prod Well where Sw Detected

@ 126 Years

GeoModel1

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GeoModel4

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ProductionWell

InjectionWellDimensionless Distance where Water-front is detected, Ld

Opt

imum

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duct

ion

(bbl

)

Dimensionless Distance where the Waterfront is Detected (Ld)

GeoModel1 GeoModel2 GeoModel3 GeoModel4 GeoModel5

Production Well

Injection Well

1

SAUDI ARAMCO JOURNAL OF TECHNOLOGY SUMMER 2017

Multiple Reservoirs by Innovative Borehole to Surface Electromagnetic: Survey Design and Field Acquisition,” IPTC paper 17045, presented at the International Petroleum Technology Conference, Beijing, China, March 26-28, 2013.

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BIOGRAPHIES

Dr. H. Onur Balan is a Petroleum Engineer working with the Reservoir Engineering Technology Team at the Aramco Service Company’s Aramco Research Center – Houston. His areas of expertise include numerical reser-voir simulation, well performance

analysis and production forecasting for unconventional res-ervoirs, and carbon dioxide enhanced oil recovery.

Onur has authored or coauthored more than 10 papers and one patent. He is a member of the Society of Petroleum Engineers (SPE) and the Society of Petrophysicists and Well Log Analysts (SPWLA).

Onur received both his B.S. and M.S. degrees in Petroleum and Natural Gas Engineering, and a minor degree in Remote Sensing and Geographical Information Systems, from Middle East Technical University, Ankara, Turkey. He received his Ph.D. degree in Petroleum Engineering from the University of Texas at Austin, Austin, Texas.

Dr. Anuj Gupta is a Petroleum Engineering Consultant working with the Reservoir Engineering Technology Team at the Aramco Service Company’s Aramco Research Center – Houston, where he is responsible for integrated modeling and simulation of

tight source rock reservoirs. Prior to joining Aramco, Anuj was on the petroleum engineering faculty for 21 years at various institutions, including the University of Oklahoma, Missouri University of Science & Technology (formerly University of Missouri-Rolla), Louisiana State University, ADNOC Petroleum Institute and Texas A&M University in Qatar. In his academic role, he taught more than 20 different courses covering various aspects of petroleum engineering and supervised the Ph.D. dissertation and M.S. thesis research of many students. Anuj’s areas of interest include unconventional reservoirs, reservoir charac-terization, petrophysics, enhanced oil/gas recovery, special core analysis and drilling.

He has published more than 100 journal articles and conference papers on different aspects of petroleum engineering and authored several patent applications since joining the Aramco Research Center.

Anuj is a 25-year active member of the Society of Petroleum Engineers (SPE), where he has served on and chaired several committees. Anuj helped SPE in launching the pilot phase of the “SPE Certified Engineer” Program. He has also taught numerous training courses in industry locations around the world.

In 1983, Anuj received his B.E. degree from Delhi University, New Delhi, India, and in 1987 and 1991, he received his M.S. and Ph.D. degrees, respectively, in Petroleum Engineering from the University of Texas at Austin, Austin, Texas. Anuj is also a registered Professional Engineer.

Dr. Daniel T. Georgi is the Team Leader for the Reservoir Technology Team at the Aramco Service Company’s Global Research Center – Houston.

Previously, Dan worked for Baker Hughes for more than 22 years in many positions. In 2007, he became

the first Baker Hughes Technology Fellow. Dan also served as the Vice President of Baker Hughes’ Regional Technology Centers, and he started the Rio de Janeiro and Dhahran Research and Technology Centers. Prior to that, he was the Director of STAR (Strategic Technology and Advanced Research), which developed formation evaluation technology for INTEQ and Baker Atlas. As Director of STAR, Dan was instrumental in founding the Baker Hughes Novosibirsk Technology Center. He was also the Director of Research at Core Laboratories, then part of Western Atlas International. Prior to holding that position, Dan worked for Exxon Production Research Company and Esso Resources Canada Ltd. for 10 years in various positions in research and formation evaluation.

During his extensive career, he has been involved with tool development, interpretation development, core log integration and reservoir engineering, as well as the evaluation of conventional, fractured, heavy oil and unconventional shale reservoirs.

Dan has published extensively on many formation evaluation topics, but most recently on permeability and permeability anisotropy, as well as multiphase flow in horizontal wells.

Dr. Ali M. Alkhatib is a Reservoir Engineer currently working with the Reservoir Engineering Technology group of Saudi Aramco’s Exploration and Petroleum Engineering Center – Advanced Research Center (EXPEC ARC). His research interests include

enhanced oil recovery, uncertainty quantification and stochastic optimization. Ali is a member of the Society of Petroleum Engineers (SPE) and has served on the steering committees of a number of SPE workshops. He is also a technical reviewer for the Journal of Computational Geosciences and the Journal of Petroleum Science and Engineering.

Ali received his B.S. degree in Chemical Engineering with Management from the University of Edinburgh, Edinburgh, U.K., and his M.S. and Ph.D. degrees in Petroleum Engineering from Imperial College London, London, U.K.

He has authored and/or coauthored more than 15 conference and peer-reviewed technical papers.

SAUDI ARAMCO JOURNAL OF TECHNOLOGY SUMMER 2017

Dr. Alberto F. Marsala has more than 25 years of oil industry experience. For the last 11 years, he has been working in Saudi Aramco’s Exploration and Petroleum Engineering Center – Advanced Research Center (EXPEC ARC).

Alberto started his career with Eni and Agip, where he developed expertise in several upstream disciplines, including 4D seismic, reservoir characterization, petrophysics, geomechanics, drilling and construction in environmentally sensitive areas. Alberto worked on the Technology Planning and R&D committee of Eni. He was Head of Performance Improvement for the KCO joint venture (Shell, ExxonMobil, Total, and others) in charge of the development of giant fields in the northern Caspian Sea.

Alberto is currently the Focus Area Champion for Deep Diagnostic on the Reservoir Engineering Technology Team of EXPEC ARC, where he is pioneering innovative technologies for formation evaluation, including advanced mud logging, wireline logging, logging while drilling, and deep reading methods for reservoir mapping and monitoring.

In 1991, Alberto received his Ph.D. degree in Nuclear Physics from the University of Milan, Milan, Italy, and in 1996, he received an M.B.A. in Quality Management from the University of Pisa, Pisa, Italy. He also holds a Specialization in Innovation Management, received in 2001.