The Use of Advanced Well Logging Tools and Techniques Towards Improved Reservoir Characterization...

7/30/2019 The Use of Advanced Well Logging Tools and Techniques Towards Improved Reservoir Characterization and Their V

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The Use of Advanced Well Logging Tools and Techniques Towards Improved Reservoir

Characterization And Their Value To Reservoir Engineering

A Critical Analysis

The oil and gas industry has been known for a long time to exhibit great prowessin creating innovative solutions towards problems encountered in the searching,

exploiting and developing of petroleum resources for the use of mankind. Within the

discipline of well logging, such innovations have been placed at the forefront for

improving reservoir characterization and generating data for reservoir engineers and

reserves evaluators.

A well log can be considered a record of one or more physical measurements as a

function of depth in a borehole. Since the advent of well logging in the 1920s, its

subsequent development into a sophisticated technology revolutionized the oil and gas

exploration and production industry. The ability to look and measure such things as

formation type, formation dip, porosity, fluid type and other important factors

transformed the drilling and completion of oil and gas wells from an ill-defined art into a

refined science. Well logging has come a very long way since with logging while

drilling[LWD] being the most significant step in the past 30 years.

New tools and evaluation techniques are constantly being developed and

evaluated for their use and this paper shall outline the use of three such innovations and

their current status.

1. Magnetic resonance, specifically using MR to distinguish oil, water, gas and oil basedmud-filtrate [MRF]

2. Simulating resistivity profiles of Mud-filtrate invasion to obtain suitable representativewaterflood data

3. Resistivity anisotropy - its effect on reserves and tools to resolve vertical and

horizontal components of resistivity


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1. Magnetic resonance, specifically using MR to distinguish oil, water, gas and oil based

mud-filtrate [MRF]

Introduction

In nature, atoms do not stay stationary but move about rotating in particular

orientations [hence possessing angular momenta]. Certain elements also seem to exhibit

properties as though they were magnets spinning about an axis. H1 and C13 nuclei both

exhibit this property. [Peter Atkins- Physical Chemistry] These magnets can now be

aligned, if placed in an induced magnetic field and will resonate at a particular

electromagnetic frequency, dependant on the properties of the molecules and the induced

field. This gives rise to the magnetic resonance of the nuclei of the molecules. The

concept of nuclear magnetic resonance [NMR] has long been a highly valuable technique

to chemists for elucidating molecular structures of molecules as the technique is

noninvasive and can be quite sensitive. Medical professionals have used Magnetic

Resonance Imaging [MRI] to image hydrogen protons in water molecules in cells to look

noninvasively at organs and their current medical state.

Well loggers and formation evaluation specialists have previously found

applications of magnetic resonance theory in determining hydrocarbon viscosity and total

porosity. The most recent innovations, however, have been in the field of Magnetic

Resonance Fluid [MRF] characterization. NMR becomes a very powerful technique as its

measurement is independent of current formation evaluation measures such as porosity

and density. This means that answers to reservoir characterization problems solved with

NMR information can be quite conclusive and can be combined with current formation

evaluation techniques to give great insight into interpretation problems. The following

chapter shall describe the technological innovations of Schlumbergers MR Scanner*,

the challenges faced by magnetic resonance techniques and the most current NMR fluid

characterization method. [Freedman et al SPE 71713]


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Schlumberger MR Scanner

Schlumbergers MR Scanner is currently one of the most advanced logging

tools within the field of magnetic resonance. It boast numerous features which will be

outlined further, inclusive of irreducible fluid storage volume determination based on

lithology-independent porosity, high vertical resolution, advanced fluid characterization

independent of lithology and multiple depths of investigation with measurements in

transverse [T2] and longitudinal [T1] relaxation time.

In NMR logging, a magnetic field is emitted and experienced by Hydrogen atoms

and they begin to align themselves in this magnetic field. Then, another magnetic is

applied perpendicular to the first field which causes the atoms to precess at an angle 90

0

to the first field. When the second field is removed, these atoms begin to realign

themselves into their original orientation and begin to emit electromagnetic radiation at

radio-wave frequency [RF]. At first, all molecules precess at the same speed but as time

goes on, the atoms begin to precess at different speeds because of the non-homogeneity

of the magnetic field and the signal RF decays. This is picked up by antennae and when


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further pulses of perpendicular magnetic fields are applied, the signal decays even

further. The signals are then received as echoes. The time for the process of precessing to

realign after the pulse gives rise to T2 or Transverse relaxation.

T2 relaxation can also happen because of molecular processes which are related to

petrophysical properties which cannot be compensated for and these correspond to

factors such as fluid porosity and pore size distribution. These processes are grain surface

relaxation, relaxation by molecular diffusion in magnetic field gradients and relaxation by

bulk fluid processes.

Grain surface relaxation is dependent upon the surface to volume ratio of the pore

spaces and hence when this factor is accounted for in the signal received an idea of pore

size distribution can be attained as it is proportional to the signal received by the

antennae. Because of the MR Scanners multiple antennae receive signals at different

frequencies, different depths of investigation [DOI] are possible thereby easily

identifying mud-filtrate characteristics and native formation fluids. The highest resolution

antenna corresponds to a shallowest depth of investigation of 1.25 inches. The DOI

possible occur from 1.5, 2.3, 2.7 and 4 inches dependant on the modes set on the MR

Scanner. This gives a view of the formation beyond filtrate invasion and formation

damage in many cases.

Molecular diffusion relaxation is dependent on amount of H 1 atoms that collide

with each other and lose energy thermally rather than by precessing. This affects the echo

train spacings, decay rates and amplitudes of the RF signals. This enables measurement

of molecular diffusion rates and this is dependant on the volume of fluid present in the

formation and hence the total fluid porosity.

Bulk fluid processes are important when the bulk fluid phase comes in contact

with another phase and is prevented from contacting the pore walls or when large vuggy

pores are present and the bulk fluid does not come in contact with the pore walls. These

effects affect the T2 relaxation and hence can be evaluated for the purposes of reservoir

characterization.

The length of time is takes for T2 signals to show decay that shows the H1 atoms

no longer come back in alignment with the initial magnetic field is called T1 or


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longitudinal relaxation time. All molecules give a particular T1 signature and this is used

to identify light hydrocarbons via a quick look T1 contrast interpretation that is also

possible with this tool.

With this information, the MR Scanner can perform fluid saturation depth logging

showing off which sections of the formation may contain movable water rather than just

simply filtrate invasion. Low oil saturation estimates from resistivity measurement can be

rechecked with depth logging to determine if the lower resistivity reading is due to

invasion of water based mud or low in-situ hydrocarbon saturation.

Advanced diffusion editing acquisition methods combined with multi-frequency

capability of the MR Scanner provide robust fluid saturation and hydrocarbon viscosity

answers. Guru, U et al 2008 mentions the latest methods in use with the MR Scanner

inclusive of the density magnetic resonance porosity [DMRP] technique [elaborated in

Freedman et al, 1998] for improved estimation of porosity and hydrocarbon saturation in

pay, the use of high resolution dual DOI with different polarization times on both

antennae to evaluate thin beds and assist in detecting very light hydrocarbons and finally

the measurement of diffusion rates and comparison to T1 and T2 relaxation times for

MRF.[elaborated in DePavia, et al SPE 84482]

[Source: Schlumberger MR Scanner Catalogue]

Nuclear Magnetic Fluid Characterization [MRF]

Several methods have been developed in the literature for in-situ fluid

characterization. Formerly, methods involving MRF involved the calibration of tool

response to various mixtures of hydrocarbon and water to evaluate comparative readings

from the well logs. This method shows many short comings in that the molecular

diffusion coefficients may not be single valued in reservoirs with multiple molecular

species present [Bloembergen et al 1948]. It has been shown that viscosity is inversely

proportional to proton relaxation times but this is applicable to each constituent species in

the crude oil. General information on rough classes of hydrocarbons relative to gas/oil

ratios and empirical NMR viscosity correlations for crude oils are available. Data of

mixtures of hydrocarbon and water [SPE 49010] and the constituency viscosity model


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[CVM] link to multi-fluid relaxation times has been discussed by Freedman, R et al 2001

[SPE 71713]

Now, multi-frequency capabilities of NMR devices allow a variety of new

measurement parameters with which to determine fluid types. Plots of molecular

diffusion coefficients and T2 relaxation times are now possible at any depth and inversion

of this data allows for accurate fluid typing giving relative volumes of bound water, free

water, oil and gas.[SPE 84482]


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Recent papers on 4D NMR [Heaton et al 2008] have made attempts to improve

the inversion of well log data for fluid typing by considering 4 signal dimensions of

molecular diffusion rate, T2, T1 and radial variation of the signal at different DOI. The

inclusion of radial variation to the data analysis shows the change in fluid properties and

concentrations through depth much in the same way multiple resistivity measurements

measure at water saturation at different DOI. This 4D inversion of data assumes that

bound fluid volume is constant at all DOI being observed.

Short Comings

As with all NMR logging tools, the time needed to recover a suitable signal

distribution for T2 relaxation times competes with the overall speed of logging the well.

The MR Scanner boasts of capability to log at speeds up to 900 ft/hr for T2 radial

profiling but up to 3, 600 ft/hr for bound fluid logging.

A minimum vertical resolution of 7.5 inches is achieved by the tools superior

technical design elements that incorporate the distance from the RF signal to antennae

with the strength of the magnetic field applied and the frequency of the pulsed magnetic

field.

Choosing data acquisition parameters for determining petrophysical cutoffs for

bound fluid and distributions for magnetic resonance fluid characterization are very

important and have been covered in other papers. [Flaum et al 1998] If chosen

inappropriately, it may be impossible to properly interpret the data. It is highly

recommended that a job planner be use before data acquisition to ensure the parameters

used are appropriate. This isnt so much an issue when comparing T 2 relaxation with

diffusion rates for MRF however, but the quality of data can still be compromised.

Things such as no. of echoes, no. of repeats, wait time and echo spacing acquisition

parameters that are not properly set could potentially miss signals from formation fluids

that maybe present.

The position in the T2distribution where a particular fluid appears is dependent on

the fluids location with respect to the rock surface within a pore space. If it is in large

pores (greater than 200 microns) then it will exhibit bulk properties, generally long T1and


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T2times. Examples of large pores are vugs and coarse grained sands. If the fluids cannot

come in contact with the rock surface they will also exhibit bulk properties. The other

extreme is when fluids are in close contact with the rock surface then the T1and T2times

will be very short, tens of milliseconds versus seconds. Examples of early time fluids are

irreducible water and clay water. Heavy oils can also appear at an early time in the T2

distribution. Interpretation models can accommodate any of these fluids. It is the

responsibility of the petrophysicist analyzing the data to decide which is most appropriate

for his project.

Early T2 relaxation times usually correspond to bound water and hence a T 2 cutoff

is usually applied to the data when collected to separate signals from irreducible water

from producible free water. NMR tools respond to the H 1 density, which is directly

proportional to porosity in fresh-water filled rocks. When the fluids are different from

this, the porosity needs to be adjusted for the differences in H1. Porosity is computed by

integrating the area under the T2 distribution. The area under the T2 distribution is a

function of the initial polarization, T1. If the hydrogen nuclei are not fully polarized (WT

not several times T1) then the T2 signal will under-represent the porosity. This is what

gives rise to the relatively slow logging speeds of MR logging tools as the tool must wait

for a complete response of T2 signal so that the entire distribution is captured.


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2. Simulating resistivity profiles of Mud-filtrate invasion to obtain suitable representativewaterflood data [Predicting waterflood performance data through numerical simulation of

mud-filtrate invasion.]

Figure 1. Resistivity Profiles of Mud-Filtrate Invaded Formation

Scientific papers on the simulation of mud-filtrate invasion have been a very

recent topic in the area of formation evaluation. The profiles of mud-filtrate invasion can

help identify a lot of different formation factors, notably porosity, water saturation and

permeability and this is usually evident in resistivity readings with different depths of

investigation. The flowing of mud-filtrate into a sand is, however, very similar to the

process of water flooding or even chemical flooding where one phase of fluid is pumped

into the formation at a particular pressure to displace or move the in situ fluid through the

sand. The following shall briefly outline the most recent advancements in modeling

filtrate invasion over time and how this modeling of invasion can be applied to the field

of enhanced recovery.


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Modeling Filtrate Invasion

Wu et al 2005 adequately describes a method for simulating the behaviour and

physics of mudcake growth and filtrate invasion. The model couples mudcake growth

and immiscible multiphase filtrate invasion. George, B et al 2004 also constructively

assesses in situ hydrocarbon saturation [S0] in the presence of deep invasion and highly

saline connate water by considering the mixing of fresh water mud and saline connate

water at the water concentration front of the invading mud-filtrate. Both papers rely on

the input of petrophysical parameters either measured or derived from mostly core data or

synthesized to resemble typical data variations.

The general approach considers a static filtration where by the mudcake growth

occurs at a constant rate and then reaches a limiting point of growth because of dynamic

parameters. The model used mostly is derived from Dewan and Chenevert, 2001 WBM

or Semmelbeck et al 1995. The flow rate function is usually described by Darcys law

while general assumptions are similar to those undergone in reservoir simulation.

Numerical simulation of the phenomena seems to be based largely on the physics of

water injection into a well. Initially, the filtrate invasion model assumed for interpretation

defines rough boundaries for extent of invasion with piston like displacement but the use

of numerical simulation techniques helps us eliminate this idealistic picture to obtain a

better picture of the invasion profile in a sand formation for better interpretation of

resistivity measurements.

Malik, M et al 2008 quantifies the effects of petrophysical properties on AIT

measurements acquired in the presence of Oil Based Mud [OBM] -Filtrate invasion.

Sensitivity analyzes from these papers helps quantify the importance of variation of

petrophysical parameters to the process of mudcake build up and mud-filtrate invasion

using OBM. Wu et al 2005 also covers the influence of WBM properties and

petrophysical parameters on filtrate invasion but only does a time lapse curve of

resistivity and does not attempt to reconstruct resistivity profiles from either induction or

laterolog measurements although such an extension is not far fetched as it is done in

Salazar, J et al 2005 and George et al 2004.


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All the above papers seem to take similar steps in performing the simulation and

reproduce resistivity measurements even given the use of induction and/or laterolog tools.

[The use of both tools and the effect on the measurement is discussed at the end of

George, B et al 2004 but for fresh water mud on a highly saline connate water formation]

Salazar, J et al 2006 and 2005 use similar methods for permeability elucidation

but extrapolates from core data in a key well for use in uncored wells. This method

shows great promise as parameters can now be derived from the logged wells that can be

influential in understanding the effectiveness of water flooding in uncored wells. The

method discussed below is taken from Salazar, J et al 2005 and is applicable to

multiphase, immiscible filtrate invasion.

Figure adapted From M. Malik et al 2008

Simulating Mud-filtrate invasion in uncored wells

The first stage of the study consists of a full petrophysical analysis using both

core and log data from the key well. Rock types are identified and porosity,

permeability, capillary pressure and water saturation are related to the geological

framework and lithofacies present. Basically this stage sets up physical properties that

exist in the near wellbore region.


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The ultimate aim of the second stage is to derive reservoir compartments and flow

units by integrating log and core data with rock-type models and storage and flow

capacity plots. Flow units are taken as horizontal layers to simulate the invasion process

in a two dimensional chemical flood simulator. [Previously, the simulator used was

developed at University of Texas and is commercially called UTCHEM. (Delshad et al

1996) but this has now been modified and is now referred to as INVADE. Results from

INVADE have been proven to be numerically comparable to other commercial

simulators (SPE 71739)] This stage sets up the complete physical framework in the

simulator that the mud-filtrate will invade inclusive of the macro and micro scale

properties as they vary in the actual physical environment.

The third stage of quantifies the influence of mud-filtrate invasion on spatial

distribution of fluids in the permeable rocks of the formation by doing sensitivity analysis

of the time evolution of the mud-filtrate. Basically the resistivity values are computed

from water saturations and salt concentrations and history matched with actual measured

values from well logs.

Lastly, based on the analysis for the key well, a petrophysical analysis is

performed in any additional wells without core data for the physical structure elucidation

and mud-filtrate invasion is carried out in the wells with the only free parameter beign the

average absolute permeability per flow unit. All other remaining petrophysical

parameters are either estimated from well logs or extrapolated from the key well core

data. In this method, a modified Winland permeability equation [Pittman, 1992] is used to

compute the initial value of absolute permeability and this value is progressively adjusted

until calculated shallow resistivity and measured shallow array induction log show a

reasonable match. Time lapsed resistivity log data between LWD and wireline logs can

be used also to calibrate this data. This new methodology estimates in-situ absolute

permeability and is consistent with radial length [depth] of investigation and vertical

resolution of induction logs.

Advantages of simulating mud-filtrate invasion

Because the process of filtrate invasion in overbalanced drilling basically consists

of the mud-filtrate sweeping away in-situ oil, data gained from simulation may be


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comparable to data from water flooded core data in terms of quantifying displacement

efficiency, profiles of in-situ hydrocarbon saturation, endpoint relative water

permeability, irreducible hydrocarbon saturation and near wellbore behaviour of injected

water. In theory, the time lapsed resistivity profiles are history matched given the

conditions of the filtrate invasion and the simulation generated gives an idea of the water

saturation profiles, the permeability of the formation to filtrate and the pressure drop

across the mudcake.

If the pore volume and the volume of filtrate lost known, then water saturation

profiles will give the displacement efficiency of the filtrate at a given distance away from

the wellbore. Although there is a maximum length of invasion of filtrate, the area up to

the end of the flushed zone corresponds to the irreducible hydrocarbon saturation. Total

pore volume up to this point minus volume of invaded fluid will give the movable

volume of hydrocarbon displaced and this gives an indication of displacement efficiency

which is essential information for the reservoir engineer planning a water flood.

Of course, relative permeability of the formation to water is important for the

calculating of fractional flow in the reservoir. But more importantly, the behavior of the

invaded fluid in the near well bore region is significant as we may be able to observe

several phenomena such as streaking from low permeability sands to higher permeability

sand units and channeling of filtrate past the hydrocarbon. Such behaviour is extremely

beneficial as core measurements in the laboratory can not see this behaviour in profile.

Saturation profiles seen on this scale may also give the true shape of the saturation profile

on the reservoir scale.

Also, with the advent of simulation of OBM filtrate invasion, an obvious

application can be the obtaining of data for a chemical flood. Although data from this

may not be as readily justifiable against actual core data, the fact exist that research into

the area may lead quite interesting results.

Value to Reservoir engineer

It is said that data only adds value if that data creates opportunities to improve

decisions. The reservoir engineer is usually concerned with how much hydrocarbon will

be recovered and at what rate the recovery will take place. As such, the uncertainty in


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acquiring information in both aspects should be quantified in order to have an

understanding of the risk you are being exposed to versus the potential reward of a

successful decision.

In the following diagram, the decision faced by the reservoir engineer is modeled

and we see that there is a chance that even if the correct decision is made, the information

gathered may still be insufficient. Hence, from the diagram one can see that it is very

important to understand and [to some extent] quantify the risk associated with the

decision options to obtain information. In many cases, the option of carrying out a

simulation and taking a core may be exposed to the same uncertainties such as

uncertainties on the reservoir scale involving thief zones, faults, baffles and other

heterogeneities. However, because of the differences in the relative cost of doing a core

operation and simulation, the engineer must evaluate the expected value of each decision,

take into account the risk profile of the company and make a decision as to which option

exposes the company to least risk with the most potential [expected value] profit.

However, given that simulation takes information that is commonly acquired from the

time a well is drilled, the cost of simulating filtrate invasion becomes quite low when

compared to the cost of doing even a wireline core operation. This may lead to deceiving

results when comparing expected values of both options. Hence it is also within the

prevue of engineer to reasonably asses the correctness of the expected value calculation.

It is, by no means, an easy decision between both options as the uncertainties of

all measured parameters must now be quantified and applied to mathematical evaluation

of the water flood and the range of possible answers be quantified in terms of their

probability for each decision.


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Figure 2. Modeling A Decision to Perform a Water Flood

Coreinformatio

n

Coreinformatio

n

Coreinformatio

n

Simulation

Coreinformatio

n

Neither

DECISION

OPTIONS

YES

NO

Simulation

Neither

Simulatio

nNeither

Simulation

Neither

[Chance ofobtaining sufficient

data]

SufficientData

SufficientData

InsufficientData

InsufficientData


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3. Resistivity anisotropy - its effect on reserves and tools to resolve vertical and

horizontal components of resistivity

The word anisotropy is derived from a Greek root word that means unequal of

turning. In the oil industry, it is used to describe the character of formations whose

physical properties are significantly different in tow or three orthogonal directions.

Anisotropy can occur at various scales that are not always resolvable via traditional well

logging tools. These scales are; at the pore scale [micro-scale], at the laminations or thin

clay layer scale [meso-scale] and at the log resolvable bedding scale [macro-scale]. In an

effort to measure meso-scale anisotropy with a logging tool, measurements made by that

tool must be made in multiple directions. This is the case with tri-axial induction tools

although they are not made for higher spatial resolution but rather to measure vertical

[Rv] and horizontal Rh resistivity and sense changes in anisotropy axially over length of

resolution.

The triaxial resistivity tool described here is an experimental prototype described by

Rosthal et al (2003) and now called the Rt Scanner. This prototype, using co-located

sensor technology, had a triaxial transmitter and two triaxial receiver arrays. The long

array had a main coil placed 39" from the transmitter and a bucking coil placed 27" from

the transmitter. The short array had spacings of 27" and 21". Thus the sensor array

located at 27" consisted of six collocated coils, the main coils for the 27" array as well as

bucking coils for the 39" array. The tool also included a conventional short 9" array. Rt

Scanner tool has six triaxial arrays for Rvand Rh to be calculated at

each of the six triaxial spacings. Three single-axis receivers are used to

fully characterize the borehole signal to remove it from the triaxial

measurements. The Rt Scanner tool delivers standard AIT* Array Induction Imager

Tool measurements for correlation with existing field logs along with formation dip and

azimuth calculation for structural interpretation.

This tool helps resolve readings in horizontal resistivity [Rh] measurements especially in

thin bedded pay or low resistivity pay. In this case, the resistivity anisotropy data is best

interpreted using the laminated sand-shale model introduced by Clavaud et al(2003 &


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2005). This model considers alternating layers of high resistivity isotropic sands,

separated by low resistivity anisotropic shales. To solve the underdetermined horizontal

and vertical resistivity equations, a volume fraction of shale must be entered as well as a

shale anisotropy ratio.

The Clavaud Sand-Shale model further constrains the shale anisotropy ratio according to

whether the sand fraction is more or less resistive than the shale fraction at a given level.

A saturation equation, in this case the dual water model, is applied to both the computed

resistivity of the sand and of the shale fractions. Total water saturation is then calculated

by taking a volumetric average of the volume of fluids in each component, sand and

shale. This value can then be compared to a similar total saturation estimate computed

from a conventional petrophysical model. [Calvert, S et al, 2006 SPWLA]

Laminated sand-shale models with anisotropic shales have been discussed

extensively in literature but the interpretation methods usually are written in often

elaborate mathematical equations which, often, make the interpretation of data difficult as

a particular solutions sensitivity to errors can be a tedious process analytically. Also, no

clear procedure to determine key parameters such as shale anisotropy or guide to the

choice of solution really exist.

A Graphical crossplot can give a better insight into petrophysical changes than a

set of equations by using the interactivity of instant visualization of solutions within the

analysis.

Objectives of graphical analysis are:

- To determine the shale anisotropy parameters and whether it is necessary to create

multiple zones

- To define the region where each analytical solution is applicable

- To illustrate the effect of data outliers on the results

- To quickly perform sensitivity analysis


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The precise methodology for the interpretation of anisotropic shales is covered in several

literature sources and the above graphical method is covered in World Oil, September

2007 in the article titled Graphical Analysis of Laminated Sand-Shale Formations In the

Presence of Anisotropic Shales by Cao Minh et al. Its use in formation evaluation is

quite obvious with the resolving of resistivity measurements given the horizontal

resistivity.


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Reference:

Magnetic Resonance Fluid Characterization

Freedman, R. et al; Field Applications of a New Nuclear Magnetic Resonance Fluid

Characterization Method; SPE 71713, 2001 SPE

Cannon, D.E., Cao Minh, C. et al; Quantitative NMR Interpretation; SPE 49010, 1998 SPE

Schlumberger MR Scanner catalogue , Produced by Schlumberger Marketing Communications,

November 2005, Schlumberger

Anand, V. et al; NMR Diffusional Coupling: Effects of Temperature and Clay Distribution;

PETROPHYSICS Vol. 49, No. 4, August 2008, Pgs 362-372; 2008 SPWLA

Guru, U. et al; Low-Resistivity Pay Evaluation using Multidimensional and High-Resolution

Magnetic Resonance Profiling; PETROPHYSICS Vol. 49, No. 4 August 2008, Pgs 342-350; 2008 SPWLA

Heaton, N., Bachman, H. N., Cao Minh, C. et al; 4D NMR- Applications of the Radial

Dimension in Magnetic Resonance Logging; SPWLA 48th Annual Logging Symposium, 2007,Paper P

Flaum, C., Kleinberg, R.L., Bedford, J.; Bound Water Volume, Permeability and Residual Oil

Saturation from Incomplete Magnetic Resonance Logging Data; SPWLA 39th Annual Logging

Symposium, 1998, Paper UU

Tri-axial Resistivity Measurements

Calvert, S. and Pritchard, T.; Triaxial Array Induction Tool Aids Field Development For The

North Coast Marine Area, Trinidad W.I.; SPWLA 47th Annual Logging Symposium, 2006,Paper N

Clavaud, J., Cao Minh, C.; Graphical Analysis of Laminated Sand-Shale Formations in the

Presence of Anisotropic Shales; September 2007 Pgs 37-44 World Oil, 2007

Schlumberger Rt Scanner catalogue, Produced by Schlumberger Marketing Communications,

January 2006, Schlumberger 2006

Clavaud, J.; Intrinsic Electrical Anisotropy of Shale: The Effect Of Compaction;

PETROPHYSICS Vol. 49 No. 3 June 2008, Pgs 243-260; 2008 SPWLA

Simulation of Mud-filtrate invasion

Salazar, J.M., Torres-Verdin, C.; Assessment of Permeability from Well Logs Based on Core

Calibration and Simulation of Mud-Filtrate Invasion; PETROPHYSICS Vol. 46, No. 6

December 2005, Pgs 434-451; 2006 SPWLA

George, B., Torres-Verdin, C.; Assessment of In-Situ Hydrocarbon Saturation in the Presence

of Deep Invasion and Highly Saline Connate Water; PETROPHYSICS Vol. 45, No. 2 March-

April 2004, Pgs 141-156; 2004 SPWLA

Civan, F.; A Multi-Phase Mud Filtrate Invasion and Wellbore Filter Cake Formation Model;

SPE 28709 1994 SPE


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Wu, J., Torres-Verdin, C.; Numerical Simulation of Mud Filtrate Invasion in Deviated Wells;

SPE 71739 2001 SPE

Wu, J., Torres-Verdin, C.; The Influence of Water-Base Mud Properties and Petrophysical

Parameters on Mudcake Growth, Filtrate Invasion and Formation Pressure;PETROPHYSICS Vol. 46, No. 1 February 2005, Pgs 14-32; 2006 SPWLA

Malik, M. et al; Effects of Petrophysical Properties on Array-Induction Measurements

Acquired in the Presence of Oil-Base Mud-Filtrate Invasion; SPWLA 48th Annual Logging

Symposium, 2007, Paper AAA


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