Reservoir Simulation ...What Is It
Authors
A.S. Odeh (Mobil Research and Development Corp.)
DOI
http://dx.doi.org/10.2118/2790-PA
Document ID
SPE-2790-PA
Publisher
Society of Petroleum Engineers
Source
Journal of Petroleum Technology
Volume
21
Issue
11
Publication Date
Ноябрь 1969
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Introduction
Reservoir simulation is based on well known reservoir engineering equations and
techniques - the same equations and techniques the reservoir engineer has been
using for years. In general, simulation refers to the representation of some process
by either a theoretical or a physical model. Here, we limit ourselves to the
simulation of petroleum reservoirs. Our concern is the development petroleum
reservoirs. Our concern is the development and use of models that describe the
reservoir performance under various operating conditions. performance under
various operating conditions. Reservoir simulation itself is not really new.
Engineers have long used mathematical models in performing reservoir
engineering calculations. Before the performing reservoir engineering calculations.
Before the development of modem digital computers, however, the models were
relatively simple. For example, when calculating the oil in place volumetrically,
the engineer simulated the reservoir by a simple model in which average values for
the porosity, saturation, and thickness were used. Although simulation in the
petroleum industry is not new, the new aspects are that more detailed reservoir
features, and thus more accurate simulations, have become practical because of the
capability afforded by the computers now available. The more detailed description,
however, requires complex mathematical expressions that are difficult to
understand, and this difficulty has caused some engineers to shy away from using
simulators, and others to misuse them. We in the petroleum industry are in the
reservoir simulation revolution. As time goes on, simulators will be used more and
more, so a basic understanding of reservoir modeling is essential. The engineer,
especially, must become competent in setting up simulation problems, in deciding
on appropriate input data, and in evaluating the results.
Basic Analysis
If a reservoir is fairly homogeneous, average values of the reservoir properties,
such as porosity, are adequate to describe it. The average pressure, time, and
production behavior of such a reservoir under a production behavior of such a
reservoir under a solution gas drive, for example, are normally calculated by the
familiar methods of Tamer, Muskat, or Tracy. All of these methods use the
material balance equation normally referred to as the MBE. A simple expression
for the oil MBE is the following:
(cumulative net withdrawal in STB) = (original oil in place in STB) - (oil
remaining in place in STB)
The cumulative net withdrawal is the difference between the oil that leaves the
reservoir and the oil that enters it. In this basic analysis, there is no oil entering the
reservoir since the boundaries are considered impermeable to flow. Thus, the MBE
reduces to its simplest form. Such a reservoir model is called the tank model (Fig.
1). It is zero dimensional because rock, fluid properties, and pressure values do not
vary from point to point. Instead, they are calculated as average values for the
whole reservoir. This tank model is the basic building block of reservoir
simulators. Now let us consider a reservoir represented by a sandbar. Let the two
halves of the sandbar vary in lithology. The sandbar as a whole cannot be
represented by average properties, but each half can. Thus, the sandbar consists of
two tank units, or cells, as they are normally called. The MBE describes the fluid
behavior in each cell as in the previous tank model.
http://apos-project.eu/Articles/reservoir-simulation-in-the-oil-industry.html
Reservoir simulation has become an integral part of the oil and gas business over the last 50 years. A
large proportion of reservoir engineers now specialise in the use of reservoir simulations to help make
large capital decisions, to estimate reserves, and to diagnose and improve the performance of producing
reservoirs. This penetration into the industry has been enabled by advances in computing hardware,
software design, and improved numerical algorithms and formulations.
In order to manage and optimise hydrocarbon production, oil companies need to model oil and gas
recovery under different production scenarios. These simulators numerically model the subsurface flow of
oil, water and gas.A model of the porous reservoir rock is constructed on a three-dimensional mesh using
a finite-volume discretisation. Within the model, each individual mesh cell models the physics of the flow
behaviour. Production elements such as producer or injector wells are treated as boundary conditions.
Despite the significant advances in computing power in recent years, the average cell size - for large
reservoirs - in the horizontal direction has remained in the order of hundreds of meters.This implies that
petrophysical properties, pressures, and saturations are assumed to be constant over significant rock
volume (of the order 100m x 100m x 10m). To fully utilize high-resolution seismic data and drilling well
measurements at the desired one-metre scale, one would need the capacity to run reservoir simulations
on meshes containing billion of cells.
The current generation of parallel
reservoir simulations are only effective on low numbers of processing units: typically, speed-up tails off at
as few as 16 parallel processes. An objective of the APOS project is to improve both the numerical
techniques and the overall scalability of reservoir simulation software, to support the use of more complex
physical models (such as enhanced oil recovery, polymer injection, thermal process, and so on) and to
allow finer-mesh resolutions to be employed.
Pierre Samier, (APOS project team and Total), 22nd July 2011.
Static, dynamic data integration
improves reservoir modeling,
characterization
09/01/2014
Shouxiang M. Ma
Saudi Aramco
Dhahran
Murat M. Zeybek Fikri J. Kuchuk
Schlumberger
Al-Khobar
This article introduces a methodology for reservoir characterization, geological modelling, and well-performance prediction. The
methodology integrates petrophysical and pressure-transient data to build a detailed geological reservoir model (RM) with
anisotropy.
Detailed petrophysical reservoir characterization, which is critical to reservoir management, consists of data acquisition, data
processing, and data distribution in space, or modeling. Data typically include lithology, porosity (φ), water saturation (Sw), zone
thickness (h), and permeability (k).
Permeability is the most difficult to characterize. This is especially true in carbonates, such as Saudi Arabia's Arab-D reservoir,
due to the heterogeneous pore structure caused by depositional environments and diagenesis, such as dolomitization, compaction,
cementation, and fracturing. Fig. 1 shows lithography and porosity typically seen in Arab-D cores and logs.
Pictured are (a) volumetrics from a typical Arab-D carbonate reservoir (blue indicates limestone, green,
dolomite, and pink anhydrite); (b) a comparison between Arab-D log and core porosities; and (c) WFT
and VIT setup schematics showing packer-probe configuration in a four-layer section.
The most common techniques for in situ reservoir-permeability characterization are based on pressure transient analysis (PTA).
This can be analysis of wireline formation testing (WFT), with measurements typically 10-50 ft from the well depending on
formation properties, duration of production, and buildup periods. Or, PTA can be performed on date from conventional well
testing, with investigation depths from hundreds to thousands of feet.1 2
Generally, a measurement with a deeper depth of investigation has poorer vertical resolution.
Typically, two WFT modes are used to estimate reservoir permeability: a pretest with probes and a vertical-interference test
(VIT) that includes packers and probes (Fig. 1).
A pretest requires a drawdown volume of less than 20 cu cm of fluid, generally mud filtrate. As a result, estimates from a WFT
pretest are near-wellbore mobility indicators.
During a VIT, hundreds of liters of reservoir fluid are pumped out at 1-30 b/d for as long as 1 hr. This allows reservoir
permeability estimation up to 50 ft into the reservoir, providing a much more comprehensive picture.
Unlike other reservoir petrophysical properties, permeability is directional. Currently, the only techniques that are used routinely
for directional reservoir permeability characterization are based on PTA, such as a VIT. 3
The RM was validated by comparisons with dynamic data from production logs, downhole pressure tests, and injection-falloff
tests. Later it was used in a single-well reservoir simulation to predict well performance, infer in situ reservoir scale reservoir
conditions, relative permeability, and capillary pressure.
The methodology was developed in a joint-research project with Saudi Aramco and Schlumberger. Results reported in this article
are part of a larger study, some of the details of which have been published previously. 4 5
New methodology
In the new methodology, petrophysical properties derived from openhole logs and wireline formation testing (WFT) are
calibrated with core-analysis data before being distributed in space to build a geological model.
The established model can be verified from borehole fluid-flow profiles measured by a production log even though layers with no
flow or low flow due to skin, low permeability, or low pressure may not be detectable by a production log. See accompanying
equations box.
The following summarizes details of the methodology for single-well data integration, reservoir characterization, reservoir
modeling, and well-performance prediction (Fig. 2).
Data preparation, integration
Core data are reviewed and quality controlled for geological features (such as depositional environments and layering), lithology,
pore types, porosity, permeability, and grain density.
Openhole logs are then reviewed, quality controlled, processed, and interpreted for lithology, porosity, grain density, water
saturation, zoning, and zone thickness.
WFT pretest data are reviewed, quality controlled and processed for estimating mobility and then for qualitatively determining
permeability.
Together with other geological information, the core data, openhole logs, and WFT pretests are integrated for a foot-by-foot
formation evaluation and reservoir characterization.
Geological model
Next, a layered, single-well geological model is generated from the detailed formation evaluation and reservoir characterization.
WFT and VIT data are analyzed to quantify vertical and horizontal permeabilities of the layers selected for the VIT.
The geological model is updated with the vertical and horizontal permeabilities determined from analyses of all VITs.
This layered-anisotropic-geological model is fine-tuned by integrating geological features and the range of permeabilities
obtained from performing a single-well numerical PTA-with the pressure and pressure derivatives as the history-matching
parameters-for each VIT.
Reservoir model
An RM is established by iteratively validating the fine-tuned geological model with Σkh from a production log and the total
KavgH from downhole pressure buildup and falloff tests, as shown in our two equations.
History matching downhole pressure and flow rate allows the RM to be used in a single-well reservoir simulation for wellperformance prediction or in any reservoir-characterization and management studies.
Test well application
To test the efficacy of this new methodology, Saudi Aramco drilled a research well, Well-A, in 2001 across the Arab-D carbonate
reservoir, acquiring petrophysical data.
Conventional cores were taken from the top 250 ft of the target reservoir. Core description, petrographics, and routine and special
core analyses were performed on selected core samples.
Openhole logs acquired included caliper, spectral gamma ray, bulk density, thermal-neutron porosity, sonic, array-induction
resistivity, micro-resistivity, resistivity imaging, mineralogy, and nuclear magnetic resonance. Twenty-five WFT pretests and
eight VITs were conducted.
Following completion, the well was allowed to produce oil for 1 day to clean out mud invasion. Then, the baseline-flow profile
was established from the production log.
The well was shut in to perform a buildup test for total KavgH at connate water saturation (Swc) by using the downhole
permanent-pressure gauge just above the top of the tested zone.
After the well was produced, another pressure buildup test was performed immediately before water injection to confirm the
determined KavgH at Swc.
During water injection, a stepwise rate change was applied. Each injection rate lasted 3-5 hr, depending on the time required for
the electrode-resistivity-array measurement and production-log measurements.
The initial injection rate was 1,000 b/d. At an incremental increase of about 1,000 b/d, the final rate reached 8,200 b/d at the end
of the eighth test. A production log was run to obtain the injection profile during each test.
The well was then shut in for a falloff test to determine the total KavgH at remaining oil saturation and skin.
All of the injected water and some oil were produced back to the surface with a nitrogen lift for 14 days. During this period, a
production log was frequently run to monitor the fluids produced.
After the well stopped producing water, it was shut in for a final pressure-buildup test to estimate KavgH at a reduced water
saturation (similar to, but usually larger than, the original connate water saturation).
Processing, interpretation
Core description and petrographic analysis were conducted to extract information on the reservoir's depositional environment and
rock typing and to identify layers.
Conventional core analysis under stress was performed on selected core samples to provide data for log calibration and reservoir
layering. On a subset of cores, adjacent twin plugs were taken-one horizontally and another vertically-for permeability
measurements. The following, illustrated in Fig. 3, was observed.
1. Correlations between permeability and porosity strongly depend on rock type.
2. The difference between vertical and horizontal permeability is not obvious at the core plug scale. This may be attributed to the
following:
• Laboratory permeability measurements can contain uncertainties and the difference between horizontal and vertical
permeability at core-plug scale can be chalked up to these.
• To ensure a plug's mechanical integrity, samples are taken in more homogeneous sections where rock anisotropy is less.
• Even though small-scale rock anisotropy can be observed in thin sections, it is probably true that the larger the scale, the more
obvious the rock anisotropy.
Logs, WFT presets, VITs
As previously mentioned, a complete suite of openhole logs was run once again. These logs were quality controlled, processed,
and interpreted for lithology porosity, and water saturation. Correlations were also used qualitatively to predict reservoir
permeability. Use of the processed logs and core data in geological modeling has been previously described and published. 5
As summarized in Fig. 2, which describes a model built with geological and petrophysical data, 25 pretests were performed with
Probe 1 for formation-pressure profiling.
Eight VITs were conducted with a configuration of a dual packer and two observation probes, Probes 1 and 2 (Fig. 1c). Thirteen
additional pretests were performed with both probes during the VITs. The use of VITs has been demonstrated to be a powerful
tool for characterizing reservoir heterogeneity.1 6-8
A pump-out module was used for fluid withdrawal to create pressure transients in the formation. These were monitored by
crystal-quartz pressure gauges and strain gauges at the dual packer and observation probes.
Fig. 4 shows the acquired downhole data, including:
• Track 1-Reservoir pressure (with an oil gradient of 0.32 psi/ft) from the probes, the packer, and during the interference test.
• Track 2-Pretest drawdown mobilities.
• Track 3-Image log and the positions of the VITs.
• Track 4-Reservoir porosity.
• Track 5-Formation resistivity
Reservoir porosity and formation resistivity data provide quantitative information for reservoir layering, while the image log is
used to check the reservoir layering.
As shown in Fig. 4, pretest data can be processed for formation pressure and fluid mobilities. Formation pressure derived from
the probe pretest is as accurate as that obtained from a packer test or a well test (Track 1). Therefore, it is routinely used for
reservoir-fluid typing, fluid-contacts identification, and free-water-level determination.
On the other hand, the probe pretest drawdown mobility is rather qualitative, due to its small volume drawdown (typically 5-20
cu cm). It has a shallow depth of investigation, and it is affected by formation damage in the invaded zone and by near-wellbore,
small-scale heterogeneity.
Because of the small volume drawdown, the mobility determined typically does not include anisotropy. Consequently, pretest
drawdown mobility can only be qualitatively used for reservoir rock and fluid characterization.
As described in Fig. 2, VIT data can be processed for reservoir rock anisotropy assessment. This VIT data processing workflow
is expanded in Fig. 5. Processing the VIT data requires a robust geological model to match the packer's and probe's pressures and
pressure derivatives, with predicted vertical and horizontal permeability. Because this matching is for all VITs, an iterative
process is necessary.
In a heterogeneous reservoir, pressure changes at the observation probes-especially the one with the farthest spacing-may be very
small. A successful VIT in this situation, requires verry-high-precision pressure gauges (Fig. 6).
In addition to determining vertical and horizontal permeability, as described in Fig. 5, VITs also can be useful in reservoir-fluidflow-regime identification and detailed reservoir heterogeneity characterization.
Examining the pressure and its derivative vs. buildup time, as we have in Figs. 6 and 7, reveals the following:
• Probe pressure changes of 0.1 psi are observed repeatedly with the high-resolution crystal quartz gauges.
• Measured packer and probe pressures are matched or reproduced satisfactorily with the geological model.
• Fluid-flow regimes are identified from features of buildup-pressure derivatives. The identified flow regime is consistent with
the geological model.
Final reservoir model
Integration of geological information with data derived from core description and core analysis, openhole logs, WFT pretests and
VITs established the RM in Test Well A, following the methodology described in Fig. 2 and shown in Table 1. This model is
considered accurate because it integrates all relevant data and, more importantly, because it is internally consistent with VIT
pressure and pressure derivatives.
The established RM is further validated in terms of its production behaviors by comparing its data with the fluid-flow profile
derived from production logs, and the total KavgH derived from numerical analyses of well-test pressure and pressure derivative.
Results show that the model matches the well's dynamic behavior very closely (Fig. 8).
As shown in Fig. 9, the RM can be used in well-performance studies by matching and predicting the bottomhole pressure and
flow rate. It has also been used in Test Well A for:
• Identifying and characterizing reservoir heterogeneity.
• Inverting reservoir scale and reservoir condition relative to permeability and capillary pressure.
• Assessing oil recovery by waterflooding.
• Monitoring water movement in situ in connection with measurements of a specially designed electrode-resistivity array and
permanent downhole pressure gauges.4 5
Acknowledgements
The authors would like to thank the management of Saudi Aramco and Schlumberger for their permission to publish this article.
References
1. Ayan, C., Hafez, H., Hurst, S., Kuchuk, F.J., O'Callaghan, A., Peffer, J., et al., "Characterizing Permeability with Formation
Testers," Oilfield Review, Vol. 13, No. 3, October 2001, pp. 2-23.
2. Kuchuk, F.J., "Radius of Investigation for Reserve Estimation from Pressure Transient Well Tests," presented to the SPE
Middle East Oil and Gas Show and Conference, Manama, Bahrain, Mar. 15-18, 2009.
3. Onur, M., and Kuchuk, F.J., "Nonlinear Regression Analysis of Well Test Pressure Data with Uncertain Variance," presented
to the SPE Annual Technical Conference and Exhibition, Dallas, Oct. 1-4, 2000.
4. Kuchuk, F.J., Zhan, L., Ma, S.M., Al-Shahri, A.M., Ramakrishnan, T.S., Altundas, B., et al., "Determination of In-Situ TwoPhase Flow Properties through Downhole Fluid Movement Monitoring," SPE Reservoir Evauation and Engineering, Vol 13, No.
1, 2010, pp. 575-587.
5. Zhan, L., Kuchuk, F.J., Al-Shahri, A.S., Ma, S.M., Ramakrishnan, T.S., Altundas, B., et al., "Characterization of Reservoir
Heterogeneity through Fluid Movement Monitoring with Deep Electromagnetic and Pressure Measurements," SPE Reservoir
Evaluation & Engineering, Vol. 13, No. 3, June 2010, pp. 509-522.
6. Kuchuk, F.J., "Pressure Behavior of the MDT Packer Module and DST in Crossflow-Multilayer Reservoirs," Journal of
Petroleum Science and Engineering, Vol. 11, No. 2, June 1994, pp. 123-135.
7. Kuchuk, F.J., Halford, F., Hafez, H., and Zeybek, M., "The Use of Vertical Interference Testing to Improve Reservoir
Characterization," presented to the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, Oct. 13-15, 2000.
8. Zeybek, M., Kuchuk, F.J., and Haez, H., "Fault and Fracture Characterization Using 3D Interval Pressure Transient Tests,"
presented to the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, Oct. 13-16, 2002.
Based on a paper presented to the SPE Annual Technical Conference and Exhibition, New Orleans, Sept. 30 - Oct. 2, 2013.
The authors
Shouxiang M. Ma (shouxiang.ma@aramco.com) is a senior petrophysical consultant in Saudi Aramco's reservoir description
division. He holds a BS from the China University of Petroleum, Shandong, and an MS and PhD from the New Mexico Institute
of Mining and Technology, Socorro, all in petroleum engineering. Ma is a member of the Society of Core Analysts and the
Society of Petroleum Engineers (SPE).
Murat M. Zeybek (zeybek@slb.com) is a reservoir engineering advisor and reservoir and production domain champion for
Schlumberger's Middle East region. He holds a BS from the Technical University of Istanbul and an MS and PhD from the
University of Southern California, Los Angeles, all in petroleum engineering. Zeybek is a member of SPE.
Fikri J. Kuchuk (kuchuk1@slb.com), a Schlumberger fellow, is currently chief reservoir engineer for the company's testing
services. He holds a BS from the Technical University of Istanbul, and an MS and PhD from Stanford University, Palo Alto,
Calif., all in petroleum engineering. Kuchuk is a member of SPE, the Society for Industrial and Applied Mathematics, the
Russian Academy of Natural Sciences, and the American National Academy of Engineering.
Reservoir modeling for simulation purposes
From AAPG Wiki
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Development Geology Reference Manual
Series
Part
Methods in Exploration
Reservoir engineering methods
Chapter
Reservoir modeling for simulation purposes
Author
Koen Weber
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Fluid flow in a reservoir is controlled by bed continuity, the presence of baffles to flow, and
the permeability distribution (see Fluid flow fundamentals). Reservoir heterogeneities influencing fluid flow
range from large scale faults and discontinuities down to thin shale intercalations, sedimentary structures, and
even pore scale features (Figure 1).
Figure 1 Classification of reservoir heterogeneity types.
Simulation studies (see Conducting a reservoir simulation study: an overview) performed at early stages of field
development are done to estimate parameters such as optimal well spacing, while at later stages the objectives
may be infill drilling or secondary recovery. The role of the geologist is to provide reservoir models that give a
sufficient description of those parameters that control the fluid flow relevant to the planned simulation
study.[1] The key to effective and economic field development planning lies in early recognition of those
reservoir characteristics that control drive mechanisms, sweep efficiency, and consequently, well spacing
requirements and possible need for pressure support. The significance of the various heterogeneity types for
oil recovery is summarized in Table 1, where • denotes a strong effect, and × a moderate effect.
Table 1 Significance of reservoir heterogeneity type for oil recovery [2]
Sweep efficiency
Reservoir
continuity
Reservoir heterogeneity type
ROS in swept
zones
Rock-fluid
interactions
Horizontal Vertical
Sealing fault
•
•
Semi-sealing fault
×
•
•
Nonsealing fault
×
•
•
Boundaries as genetic units
•
•
•
Permeability zonation within genetic
units
×
•
×
Baffles within genetic units
×
•
×
Laminations, cross-bedding
×
×
•
Microscopic heterogeneity
•
×
Textural types
•
•
Mineralogy
•
Tight fracturing
×
Open fracturing
•
•
•
•
The steps in the modeling process are as follows:

Determining the facies of the reservoir rock through data gathering from cores and logs

Rock typing for each environment of deposition to estimate permeability from log derived porosity and
estimation of vertical permeability

Correlation of all wells to provide a framework for the delineation of a simulation model

Determination of an optimal grid block pattern using the flow unit principle

Mapping the reservoir properties in each grid block layer
Contents
[hide]

1 Data gathering

2 Rock typing and permeability estimation

3 Well correlation

4 Grid block patterns

5 Mapping of reservoir properties

6 Modeling carbonate reservoirs

7 See also

8 References

9 External links
Data gathering
The realism and reliability of the resulting model is a function of the reservoir heterogeneity but also of the
planning that has gone into the data gathering. Defining suitable data gathering schemes for a specific field
requires multidisciplinary cooperation and a sound understanding of the significance of the data.[3] Analog
cases and sensitivity studies on prototype models can guide this process which is crucial to the reliability of the
results. In Table 2, an overview is given of the value of the different types of data for the identification and
quantification of reservoir heterogeneities. Only a part of the data are of geological nature. Reliable information
on reservoir connectivity is often derived from production and pressure tests (see Production
testing and Pressure transient testing). In particular, it is very difficult to determine large scale vertical
permeability from core and log data.
Table 2 Value of data for identification and quantification of reservoir heterogeneities [4]
Reservoir
pressure
Reservoir
heterogeneity type
Production data/tests
Well logging
Rock
sample
Outcr
Deta
opor
iled
analo
seis Horizo Vertica
g
Pul Tra Produ
Produ
Produ
R
SWS
mic
reserv
ntal
l
se cer ction
Stan Spe
Cor
ction
ction
O
cutti
oir
distrib distrib
tes test histor
dard cial
es
tests
logs
S
ngs
ution ution
ts s
y
Sealing fault
•
•
×
×
•
×
•
•
Semi-sealing fault
•
•
×
×
×
×
×
Nonsealing fault
•
×
×
Boundaries as genetic
×
units
•
•
×
×
Permeability
zonation within
genetic units
×
×
×
Baffles within genetic
units
×
×
×
•
×
×
•
×
×
•
×
×
•
×
•
×
•
•
•
•
×
•
×
•
×
•
•
Laminations, crossbedding
×
•
•
Microscopic heteroge
neity, textural
types, mineralogy
×
Open fracturing
×
×
•
×
•
×
•
×
×
×
Tight fracturing
×
×
•
•
×
•
×
•
×
•
•
×
•
×
×
×
×
The detail to which a reservoir can be modeled is a function of both the degree of heterogeneity and the data
density. At an early stage of development, one may be restricted to seismic data, sparse well data, and
production tests only. In these circumstances, we reap maximum benefit from knowledge of the typical flow
characteristics associated with the prevailing facies and diagenetic overprint and of a data base to predict
reservoir body continuity and architecture. Regional experience in analog fields can often be used to guide the
modeling process.[5]
The data that form the basis for reservoir modeling ideally comprise regional data, seismic data, cores, logs,
pressure measurements, wireline formation tests, pulse tests, and well-planned production tests (Table 2).
Modern three-dimensional seismic data can be used for a range of modeling purposes from structural analysis
to reservoir properties such as thickness, lithology, porosity, and pore fill [6] (also see Geophysical methods).
The effect of large scale features such as faults can be estimated on the basis of geological experience and
modeling[7] or it must be evaluated by pressure measurements or fluid level differences.
Rock typing and permeability estimation

Figure 2 Analysis of core data for facies identification and rock quality assessment.

Figure 3 Log-facies calibration and determination of facies-related rock characteristics.
The first priority in describing the reservoir rock is the determination of the environment of deposition and the
range of lithofacies that occur within the reservoir (see Lithofacies and environmental analysis of clastic
depositional systems and Carbonate reservoir models: facies, diagenesis, and flow characterization). Regional
stratigraphic information, cores, and sidewall samples are used for this purpose. Of particular interest is the
rock typing through a study of porosity, permeability, petrography, and capillary properties (Figure 2)
(seeLaboratory methods). For simulation purposes, permeability is a major parameter, and estimating the
permeability profile in noncored wells is of prime importance.[8] The basis for these techniques is multivariate
analysis of the combined logging data. Discriminant analysis of log response using a core calibrated system
usually leads to the best results. In general, one has to combine several rock types into an electrofacies class
mainly because of the poor vertical resolution of the nuclear logs if run in standard fashion. If the porosity and
permeability relationships of combined rock classes differ little, this is an acceptable simplification (Figure 3).
Well correlation
Figure 4 Correlation of reservoir units and subdivision of reservoir In flow units.
The next step in reservoir modeling is correlating the reservoir intervals from well to well (Figure 4). This
procedure is strongly dependent on facies and well spacing. A requirement is a sound database of genetic unit
geometry, for example, width to thickness ratio of a specific sand body type. When no deterministic correlation
can be made of reservoir units, it may be necessary to use probabilistic modeling techniques, but in such cases
only prototype simulations are usually carried out.[9]
The framework for constructing simulation models is controlled by facies distribution, major permeability
contrasts, and impermeable layers.[10] Again, maximum use should be made of reservoir engineering data with
emphasis on wireline formation pressures. Geological predictions of sealing surfaces are often unreliable
because of the presence of small scale faults or local erosion.
Grid block patterns
The reservoir has to be subdivided in such a way that all major baffles are represented and that the individual
layers and bodies can be described using average petrophysical properties that lead to realistic flow
simulations. For this purpose, the term flow unit has been introduced, which is defined as a volume of rock
characterized by a specific combination of geological and petrophysical properties that influence fluid flow
(see Flow units for reservoir characterization). Flow units can be composed of a combination of stratigraphic
elements if they do not essentially differ in fluid flow criteria.[5]
The subdivision of the reservoir into flow units is typically a multidisdplinary activity because geological and
reservoir engineering information has to be used together. Economic considerations with respect to numbers of
grid blocks to be used also play a role.
Reservoir simulation operates on the principle of simultaneously solving the flow equations between adjacent
blocks of rock in response to offtake from wells. The larger the number of grid blocks used, the closer the
model resembles the geological prototype. However, even with the fastest computers, the simulation models
are restricted to a maximum of some 60,000 grid blocks. For economic reasons, most simulations make use of
less than 10,000 grid blocks. Consequently, grid block sizes normally range from 100 to 300 m in diameter and
from 5 to 15 m in thickness (see Conducting a reservoir simulation study: an overview).
Practical grid block sizes usually imply the amalgamation of a few geological features. Thus, an averaging
procedure is required to obtain realistic overall flow characteristics for the blocks as a whole. The influence of
discontinuous shale breaks on vertical permeability, for example, can be estimated using a statistic
approach.[11] Averaging horizontal and vertical permeability over grid block size units is a difficult task. In
practice, the effective horizontal permeability usually ranges from the arithmetic to the geometric average of the
permeability profile of the block. The more continuous the sublayers of the flow unit, the closer the average is
to the arithmetic average. The more random the permeability, the closer it gets to the geometric average.
Geostatistical methods have become popular to tackle these problems.[12] Vertical permeabilities are difficult to
measure, and the values used are often based either on experience for a given facies or on vertical pulse tests
or other pressure data evaluation.
Mapping of reservoir properties
Figure 5 Mapping of reservoir properties per grid block layer to provide input for the reservoir simulation.
The geological input to three-dimensional reservoir simulation must consist of structural maps and property
maps. Typically, maps must be prepared for every simulation model layer, specifying the distribution of
net/gross, isochores, porosity, horizontal and vertical permeability, capillary pressure curve characteristics, and
water saturation (Figure 5). Also, the geologist and the reservoir engineer have to cooperate to define pseudorelative permeability curves for different internal grid block heterogeneity types. The matching phase of the
simulation study requires similar cooperation to arrive at a final model with realistic properties.
Modeling carbonate reservoirs
Modeling of carbonate reservoirs is generally more difficult than modeling clastic reservoirs. The reason is that
carbonate rocks usually undergo a much more complex pattern of diagenetic processes. As a result, the
permeability distribution can be complex and poorly related to original facies distribution. A further complication
can be the occurrence of open fractures.
Carbonate reservoirs without fractures can in principle be treated similarly to clastic reservoirs. Reservoir
simulation of so-called dual porosity reservoirs is difficult because of the problem of quantifying the degree of
capillary contact across fractures. Recently, methods have been proposed to tackle the problem of block-toblock interaction.[13] In all cases, carbonate reservoirs require a considerable amount of core studies and
frequent use of the scanning electron microscope and its auxiliary equipment.