Assessment of Fracture Density Distribution from Image Logs for
Sensitivity Analysis in the Asmari Fractured Reservoir
1ZOHREH MOVAHED, 2MEISAM ASHRAF, 3ALI ASGHAR MOVAHED
1Schlumberger, Kuala Lumpur, MALAYSIA
2Ahawz Oil and Gas Research Department, IRAN
3University of Bergen, NORWAY
Abstract: Characterizing fracture properties in naturally fractured reservoirs poses a significant challenge. While well-
testing remains valuable, it often fails to provide precise descriptions of these properties. Bridging this gap requires the
integration of geological expertise to enhance fracture assessment. This study addresses the limitations of well-test
analysis and explores the application of Conventional Image Logs in structural, fracture, and geomechanical analysis.
However, effectively combining these applications with well-test analysis on a field scale reveals a substantial
knowledge gap. A critical challenge in this context is the absence of a defined procedure for calculating the variable
"σ," a crucial parameter for simulating fractured carbonate reservoirs using image log fracture density. Integrating
geological knowledge is essential to reduce uncertainties associated with well-test analysis and provide more accurate
characterizations of fracture properties. Image log data processing emerges as a valuable avenue for gaining insights
into the static attributes of naturally fractured reservoirs. This study focuses on Characterizing fractures using data from
ten image logs and Developing a more accurate simulation model through the interpretation of images, with a particular
emphasis on OBM imaging. The main goals of this fracture study revolve around establishing correlations between
fracture densities well by well within the simulation and enhancing the accuracy of the simulation model by
incorporating fracture data from image logs. Borehole imaging tools such as FMI/FMS and OBMI-UBI play a pivotal
role in identifying significant structural features, including faults, fractures, and bedding. Fine-tuning fracture
parameters during the history matching process, while potentially time-consuming, significantly impacts other historical
match parameters. Consequently, the reliability of reservoir simulation results, predictions, and recovery enhancement
strategies hinges on the precision of fracture properties and their distribution within the model. Recent advances in
interpretation techniques have expanded the horizons of image interpretation, enabling the creation of more accurate
simulation models for fractured reservoirs using fracture data obtained from image logs. The overarching goal of this
project is to comprehensively evaluate a fractured reservoir field by integrating data from ten individual wells.
Keywords: Well-testing, fracture evaluation, Image log data, fracture density, simulation sensitivity analysis.
Received: July 23, 2022. Revised: October 12, 2023. Accepted: November 11, 2023. Published: December 14, 2023.
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1 Introduction
The Zagros Mountains folded belt is distinguished by
its concentrically folded geological formations [1]. The
intricate character of the geological structures within
the Zagros Mountain belt underscores the necessity for
obtaining precise data on structural dip and subsurface
fault patterns. This information is essential for the
successful planning of development and infill wells. In
certain wells, the observed thickness of formations
exceeds what was originally anticipated. This can be
attributed to varying factors, such as steeper bedding
dips or the presence of reverse faults. In some instances,
pinpointing the exact cause of these unpredictably
greater thicknesses can be a challenging endeavor [2].
In this complex geological setting, borehole imaging
logs played a critical role in detecting the structural and
reservoir geometry[3]. Accurate reservoir description
through the use of image logs, particularly in thinly
laminated reservoirs, emerged as a key factor in
facilitating effective field development [4]. Structural
and reservoir geologists can readily identify fracture
features and classify different types of fractures along
the wellbore by directly utilizing the FMI (Formation
MicroScanner) log, moreover, in situations where
seismic data is unavailable, the FMI log serves as a
valuable tool for these geologists, enabling them to
provide essential information that can be used to
develop dependable solutions for significant geological
challenges[5][6]. Understanding the origins of fracture
systems is a complex endeavor, often involving factors
such as fracture angle, shape, orientation, abundance,
and the relationships between different fracture sets.
These factors are typically derived from sources like
core samples, borehole imaging logs, and various
logging tools. However, these sources may not always
provide precise orientation data. To shed light on the
genetic history of fracture systems, various fracture
models, including tectonic and diagenetic origins, are
employed [7] [8].
Interpreting the origins of fracture systems necessitates
a multidisciplinary approach that combines geological
insights with rock mechanics principles. It operates on
the assumption that natural fracture patterns reflect the
stress conditions that existed during the fracturing
process, akin to laboratory tests conducted under
similar conditions. Natural fracture patterns are then
compared to laboratory-derived patterns and the
inferred stress and strain fields at the time of fracturing.
Essentially, any model capable of describing stress or
strain fields can be utilized to elucidate fracture
distribution [9] [10] [11].
A genetic classification system for natural fractures
proves invaluable in breaking down complex fracture
systems into distinct origin components. This approach
plays a pivotal role in defining the structure and
predicting reservoir quality enhancements based on
fracture data, rendering it a more manageable task [12]
[7].
The Gachsaran field, with its dimensions of 44 km in
length and 5 km in width, exhibits an asymmetric
structure. Located in the Dezful embayment to the south
of Gachsaran city, a thrust along the southern flank
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resulted in the northern part being thrust over the
southern part (Fig 1). The primary reservoirs in this field
are the Asmari and Sarvak formations. The Asmari
formation, situated above the Pabdeh, comprises
shallow-water Oligocene-Miocene limestone and was
the first reservoir to produce substantial quantities of oil
in Iran. Notably, the Asmari formation boasts an
exceptional production capacity due to its extensive
fracturing [13].
Fig 1: Geographic position of the Gachsaran field on the map
of Iranian oilfields.
The dominant lithology within the Asmari formation is
gray limestone, characterized by a well-developed
network of fractures (Fig 2). The non-productive
interval consists of rock with less than 5% porosity and
less than 1md permeability. In contrast, the productive
intervals typically exhibit porosities ranging from 5% to
25%, with an average of around 12%. Matrix
permeability is generally low, but fractures within the
pay intervals significantly enhance it, often exceeding 5
Darcies, resulting in remarkable flow rates.
Fig 2: Illustrates the detailed lithology of the Asmari
reservoir. This visualization provides a visual understanding
of the reservoir's characteristics[14].
In naturally fractured reservoirs like Asmari, the
identification and evaluation of fractures are of
paramount importance for exploration, drilling, and
well completion, as they have a profound impact on
flow rates. Characterizing these fracture systems
involves a multitude of methods that integrate
geoscientific and engineering data.
By consolidating data from image logs, petrophysical
data, well tests, production logs, formation tests, and
core analyses, a comprehensive approach is established
to estimate reliable fracture properties and their
distribution. This work presents a workflow that
focuses on precise sensitivity analysis by integrating
diverse data types, ultimately enhancing well
productivity, which addresses significant challenges
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encountered in naturally fractured reservoirs. In
essence, the detection and characterization of fractures
are pivotal in the ongoing quest to improve
productivity.
2. Problem Formulation
This study leverages a comprehensive dataset of
fracture properties, including fracture density, aperture,
orientation, porosity, and permeability, derived from
Formation Micro-Imager (FMI) and oriented Borehole
Micro-Imager Ultrasonic Borehole Imager (OBMI-
UBI) logs. Ten wells were extensively equipped with
borehole imaging tools, such as FMI/FMS, OBMI-UBI,
and conventional logs, to detect and map significant
structural elements, including faults, fractures, and
beddings(Fig 3).
Fig 3: In this project, essential structural features, including
faults, fractures, and beddings, are pinpointed using borehole
imaging tools across the ten wells, encompassing FMI/FMS,
OBMI-UBI, and conventional logging techniques.
These datasets serve as the foundational inputs for a
dual porosity modeling approach, which is instrumental
in enhancing the precision of subsurface fluid flow
simulations.
The workflow unfolds as follows:
2.1.Characterization of Fractures Using Image
Logs:
Fracture data are derived from the interpretation of
image logs, encompassing attributes such as fracture
density, aperture, orientation, porosity, and
permeability. These insights are instrumental in
understanding the fracture network within the reservoir.
2.2.Construction of a Static Model of the
Fractured Reservoir:
The image interpretation results, which include fracture
attributes, are integrated into a static reservoir model
using COUGAR software. This model provides a visual
representation of the fractured reservoir, offering a
foundation for subsequent simulations.
2.3.Sensitivity Analysis in the Simulation Model
with Emphasis on Fracture Density Data (σ):
The static model of the fractured reservoir serves as the
basis for simulation experiments. The focus of these
simulations is on sensitivity analysis, with a particular
emphasis on fracture density data (σ). By varying
fracture density parameters and examining their impact
on fluid flow behavior, this analysis contributes to a
deeper understanding of the reservoir's dynamics.
In summary, this workflow leverages fracture data
extracted from image logs to construct a static reservoir
model and subsequently conducts sensitivity analysis
within the simulation model, with the primary objective
of explaining the influence of fracture density on fluid
flow characteristics in the subsurface reservoir(Fig 4).
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Fig 4: This workflow utilizes fracture data obtained from
image logs to build a static reservoir model. It then proceeds
to perform sensitivity analysis within the simulation model,
primarily focusing on elucidating how fracture density
impacts fluid flow dynamics in the subsurface reservoir.
3 Problem Solution
3.1. Characterizing Fractures through Borehole
Imaging
Fractures play a pivotal role in reservoir
characterization, profoundly impacting assessments of
reservoir potential, production management, injection
planning, and well design, regardless of well
orientation—be it vertical, inclined, or horizontal. The
role of fractures is multifaceted, contingent on several
factors, including reservoir goals, quality, and their
proximity to fluid interfaces (such as gas-oil, gas-water,
or oil-water).
Accurately identifying and comprehending fractures is
of paramount practical significance for the following
reasons:
Orientation: Different fracture types appear in
various orientations in response to the
geological stresses during fracturing. Precise
identification of these types is crucial for
predicting the orientations of entire fracture
populations, enabling optimal drilling
directions and reservoir model development.
Fluid Flow: Each fracture type possesses
unique fluid-flow properties, directly
influencing reservoir performance.
Rock and Geological Context: Specific
fracture types are associated with distinct rock
types and geological settings.
Shape and Density: Individual fractures
exhibit characteristic shape and size
distributions and adhere to specific density
patterns, which are essential for constructing
comprehensive 3D reservoir models.
Fractures are essentially planar features with no
noticeable displacement of blocks along their planes.
Their dip can vary, featuring steeper dips in tensional
and wrench regimes and high to low-angle dips in
compressional regimes.
Fractures may present either open or tight (closed)
apertures, or they could be filled with minerals like
clays, calcite, anhydrite, pyrite, and others. In image
logs, fractures typically manifest as linear features with
dips steeper than the structural dip.
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Appreciating fracture information is paramount due to
the enhanced permeability these features offer in
fractured reservoirs, significantly influencing reservoir
productivity.
Key questions arise: Is the reservoir fractured or
unfractured? If fractured, what are the fracture types
(open or closed), and how extensive are they? Do
fractures occur as a single set or multiple sets, and what
is their dominant strike orientation? Answers to these
inquiries are invaluable for geologists and reservoir
engineers in assessing reservoir size, identifying
optimal well locations, and formulating well
completion plans. Production engineers also rely on this
data to optimize production and well-completion
strategies.
While reservoir cores provide the most precise
information, coring highly fractured zones or
unconsolidated sandstone formations is not always
practical.
Furthermore, coring the entire reservoir length in each
well is both cost-intensive and time-consuming.
Borehole images offer a valuable alternative to cores,
particularly when in-depth information on geological,
structural, and sedimentological features is necessary.
Schlumberger provides high-quality borehole images
for wells drilled with diverse drilling mud types,
including water-based, oil-based, or synthetic-based
mud. These images are obtained in wells with various
geometries, spanning from vertical wells to highly
deviated ones.
The primary goal of image log surveys is to analyze
fractures. Interpretation of images is conducted in
conjunction with open-hole logs, enabling correlations
between observed fractures in the images and responses
recorded in logs such as sonic, nuclear, and resistivity.
The subsequent discussion delves into numerous
fracture attributes.
3.1.1 Evaluating Fracture Aperture
Assessing fracture aperture is of utmost significance
due to its direct impact on fracture network
permeability. This influence escalates exponentially
with the cubic size of the aperture.
Moreover, fracture aperture determines the available
space within the fractures, thereby governing the
storage volume, often referred to as fracture porosity,
within the fracture system.
When aperture plays a role in fluid flow and storage, it
becomes essential to consider not only the width of the
fracture but also to estimate the volume of cement fill,
quantify the percentage of contact area between fracture
walls—commonly termed "asperities," and measure the
surface roughness of these walls. The quantification of
surface roughness is often accomplished using the Joint
Roughness Coefficient (JRC) method [15].
It's important to note that measuring aperture is
inapplicable for closed fractures lacking an open
aperture. Instead, measurements are focused on open
fractures, categorized in various ways, with a priority
on areas where data confidence is higher. Typically,
these results are presented in centimeters within the
combined display.
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Calculating fracture aperture is based on a well-
established equation developed through modeling
conducted at the Schlumberger Research Center in
France. According to this equation, fracture aperture is
determined as a function of two primary factors: the
resistivity of the drilling mud (Rm) and the resistivity
of the invaded zone within the formation (Rxo)(Fig. 5).
bb RxoRmAcW
1
...
(Where 'W' represents fracture aperture, 'A' indicates an
excess flow of current through the FMI/FMS electrode,
'Rm' denotes the resistivity of the drilling mud, 'Rxo'
stands for the resistivity of the invaded zone, while 'c'
and 'b' are constants derived from the model).
3.1.2 Quantifying Fracture Density
To quantify fracture density, we conducted a
comprehensive analysis that involved counting both
open and closed fractures per meter. Through this
examination, we pinpointed specific areas or intervals
characterized by heightened densities of both open and
closed fractures.
The identification of open-fracture zones within these
intervals was based on various factors, including
fracture density and their spatial arrangement, often
forming clusters.
Our analysis revealed varying fracture densities among
the wells, with GS-119 and the Thrust zone showing the
highest number of fractures, while GS-318 exhibited
the lowest fracture density (Fig. 5, Fig. 6, Fig. 7, Fig. 8,
and Fig. 9). Notably, we observed a strong correlation
between fracture density and cumulative production
data.
Furthermore, it has become increasingly apparent that
Production Logging Tools (PLT) possess substantial
potential for evaluating flow characteristics in both
fractured and non-fractured sections of the reservoirs.
These findings, particularly the maps illustrating
fracture strike patterns, have empowered NISOC to
make well-informed decisions regarding placement for
development, ultimately leading to increased
productivity and the achievement of production targets
for future wells.
The enthusiastic reception and endorsement of this data
and methodology by geologists and reservoir engineers
have been a significant highlight of this project (Fig. 5).
Significantly, our findings underscore the substantial
potential of Production Logging Tools (PLT) in
assessing flow characteristics within both fractured and
non-fractured segments of the reservoir. Leveraging
these insights, particularly through the application of
Fracture Strike maps, has empowered NISOC to make
well-informed decisions concerning well-placement for
development.
As a result, this advancement has notably boosted
productivity, facilitating substantial progress toward
meeting future production goals. The enthusiastic
reception and endorsement of this data and
methodology by both geologists and reservoir engineers
stand out as key highlights of this project's success.
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A
B
Fig. 5: A) Header details and B) Summary Log showcasing
crucial data on Resistivity, Density, Porosity, Dips, Fracture
Density, and Apertures within the Asmari, Pabdeh, and Gurpi
Formations. This comprehensive summary provides a
consolidated view of key parameters across these formations,
aiding in a thorough analysis and understanding of their
geological characteristics.
3.1.3 Determining Fracture Orientation
Determining the orientation of fractures involved
reading the azimuth by observing the sinusoidal troughs
visible on the directional scale located at the top of the
image. These fractures often presented a more
pronounced contrast anomaly compared to other
porosity features.
This heightened contrast arose from fractures being
saturated with conductive borehole fluid, with the
exaggeration of the anomaly attributed to wellbore
breakout along the fracture. In certain cases, these
fractures could be obscured by highly conductive vugs.
Consequently, we conducted a meticulous examination
of both grayscale images and electrical wiggle-trace
data to identify fractures.
The micro-scanner boasts an impressive resolution of
approximately 10 mm, enabling the detection of even
finer features, some as small as a few microns. Micro-
scanner images offered an excellent visual correlation
with core samples, aiding in the interpretation of
sedimentary characteristics at various scales within the
rock formations. This approach facilitated the precise
identification of fractures and their orientation,
distinguishing them from high-angle bedding features.
Upon analyzing these fractures about the dip of the
bedding data, it becomes evident that open fractures
tend to exhibit oblique, longitudinal, or transverse
orientations concerning the bedding strike, categorizing
them into these specific types.
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An examination of statistical plots concerning the dip
angles of open fractures in the Asmari formation
fracture zones revealed a broad range of dip angles and
azimuths. This variability suggests the potential
development of faults within this geological area. This
visual depiction provides an extensive overview, outlining
the distribution and orientation of natural fractures alongside
the bedding dip characteristics within this specific geological
formation. Such detailed mapping serves as a valuable
resource for understanding the structural complexities and
orientations critical for reservoir characterization and
exploration endeavors in the Asmari Formation (Fig. 6).
Fig. 6: Shows a comprehensive map detailing the natural
fractures and bedding dip within the Asmari Formation.
The detailed azimuth map of natural fractures and bedding in
the Asmari Formation provides extensive insight into the
directional orientations of natural fractures and bedding
characteristics within this geological layer. This intricate map
stands as a crucial resource, aiding in the comprehension of
structural complexities, directional patterns, and geological
intricacies essential for reservoir evaluation and strategic
exploration endeavors within the Asmari Formation(Fig. 7).
Fig. 7: presents a detailed cartographic representation
delineating the azimuthal orientation of natural fractures
alongside bedding within the Asmari Formation.
The fracture orientation and fracture density map in the
Gachsaran field provides a thorough overview,
illustrating the directional alignments of fractures as
well as variations in their density across this specific
field. Such a meticulous mapping representation serves
as a fundamental resource, facilitating a deeper
understanding of fracture orientations and densities
critical for optimizing reservoir performance and
guiding extraction strategies within the Gachsaran field.
The use of fracture strike maps proved instrumental for
NISOC in optimizing well placement for development
wells, leading to increased productivity and setting
them on the path to achieving production targets for the
future (Fig. 8).
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Fig. 8: Provides an extensive overview detailing the
spatial distribution of fracture orientation and
fracture density specifically within the Gachsaran
Field. This map offers a comprehensive
visualization, illustrating the directional alignment
and density variations of fractures within this
specific field.
Tectonic fractures and fracture density map
offers an extensive overview, showcasing the
spatial arrangement of tectonic fractures and
variations in fracture density specific to this
field. The intricate mapping serves as a
fundamental resource, providing insights into
the structural intricacies and density patterns of
tectonic fractures essential for reservoir
assessment and strategic planning within the
Gachsaran field. Most fractures identified in
this study are associated with folding and
display oblique orientations, with thrust faults
influencing the strike of fractures in GS-245
(resulting in transverse fractures) as depicted in
Fig. 9.
Fig. 9: presents an elaborate map delineating the
distribution of tectonic fractures and fracture density
within the Gachsaran Field.
The correlation between fracture density and
production logs shows a compelling and
intricate link between fracture density and the
data extracted from production logs. This
representation vividly portrays a noteworthy
harmony between the density of fractures
within a reservoir and the detailed information
logged during the production phase. This
correlation serves as a tangible testament to the
profound connection between these two critical
parameters.
The coherence observed between fracture
density and production logs signifies a deep
interdependence, underscoring their mutual
reliance. This revelation strongly implies that
regions displaying heightened fracture density
are intricately linked to augmented levels of
production. The alignment between these facets
not only substantiates their correlation but also
accentuates the pivotal role they play in
reservoir management.
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This robust correlation becomes an invaluable
indicator for making informed strategic
decisions in managing reservoirs. It illuminates
a pathway toward optimizing production
efficiency by pinpointing areas characterized
by higher fracture density for refined extraction
outcomes. Such strategic decisions based on
this alignment can greatly influence the
effectiveness of extraction methodologies and
resource allocation.
Ultimately, this visual representation
offers a clear and tangible demonstration of the
substantial correlation between fracture density
and production logs. It underscores their
intrinsic relationship, establishing a foundation
for informed decision-making in reservoir
management to elevate production yields by
strategically targeting areas rich in fracture
density. The strong correlation between fracture
density and cumulative production data
underscores the success of the project. This
study is well on its way to becoming a standard
workflow for the assessment of other fractured
reservoirs, solidifying its enduring influence on
reservoir analysis and development. The
enthusiastic reception and endorsement of this
data and methodology by geologists and
reservoir engineers are significant highlights of
this project(Fig. 10).
This enhanced understanding has
contributed to the optimization of well
placement for development, resulting in
increased productivity and progress toward
achieving future production targets.
Furthermore, this project has shifted NISOC's
focus toward understanding reservoir
performance within fractured reservoirs,
highlighting the importance of formation
pressure data and revealing the substantial
potential of Production Logging Tools (PLT)
for comprehensive flow analysis. Notably, our
findings highlight the substantial potential of
Production Logging Tools (PLT) for evaluating
flow characteristics in both fractured and non-
fractured segments of the reservoir. The
application of these insights, particularly the
use of Fracture Strike maps, has empowered
NISOC to make well-informed decisions
regarding well placement for development,
ultimately leading to increased productivity and
the achievement of production targets for future
wells.
Fig. 10: Demonstrates a compelling correlation
between fracture density and production logs.
3.2. Simulation Sensitivity Analysis
3.2.1.Sigma Computation
In the past two decades, reservoir simulation
has emerged as a pivotal tool for guiding
decision-making in reservoir development.
This significance is particularly pronounced
when dealing with fractured carbonate
reservoirs, which account for a significant
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portion of the world's proven oil reserves.
Reservoir simulation encompasses a variety of
techniques, which can be broadly classified into
four groups: sequential simulation, p-field
simulation, object simulation, and
optimization-based techniques. In our study, we
specifically concentrate on the category of
sequential simulation algorithms.
This group stands out for its versatility and
applicability to a wide range of problems.
Moreover, specifically, we employ the
sequential Gaussian simulation algorithm,
which assumes that the variable follows a
Gaussian distribution.
Even in cases where this assumption doesn't
hold, it is possible to transform the variable's
marginal distribution into a Gaussian form.
This transformation allows us to work with the
variable, even when it doesn't meet the
Gaussian distribution assumption.
A diagram depicting the theory of sequential
simulation offers a thorough depiction of the
sequential simulation theory, elucidating the
sequential modeling process and its application
within the specified context.
The diagram provides a valuable visual aid,
outlining the sequential simulation methodology and
its implications, serving as a critical reference for
understanding the sequential simulation process as
discussed in the cited source [16](Fig. 11).
Fig. 11: Shows a comprehensive diagram detailing
the theory of sequential simulation, as referenced in
[16].
Oda (1985) introduced an analytical equation
for computing the fracture-permeability tensor,
while Lough et al. (1997) proposed an approach
using the boundary-element method. Oda's
method involves numerical integration based
on statistical parameters of fracture sets, where
equivalent permeabilities are assumed to be
linearly dependent on density variations.
Notably, Oda's solution allows for efficient
calculations without the need for flow
simulation. However, it is most suitable for
well-connected, high-density Discrete Fracture
Networks (DFNs) [10] [17].
In contrast, the second approach is numerically
driven. It necessitates solving a steady-state
flow problem within the discrete fractured
network under specified boundary conditions,
utilizing Poiseuille's formula for fracture flows.
This method takes into account the entire
system geometry but requires more
computational time, making it typically
applicable to scenarios with lower fracture
density. Moreover, the Dual-Porosity (DP)
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formulation assumes perpendicular fractures
oriented along grid axes.
When it comes to simulating fluid flow in
naturally fractured carbonate reservoirs while
preserving their inherent heterogeneity and
computational efficiency, specific challenges
arise. The range of fracture scales spans from
small-scale diffuse fractures to intermediate-
scale sub-seismic faults and large-scale seismic
faults. Due to computational limitations,
incorporating the Discrete Fracture Network
(DFN) model into field-scale models is not
feasible, given the potential presence of billions
of fractures within each cubic kilometer of
reservoir rock. As a result, upscaling becomes
necessary for flow simulation [18].
In these simulations, dual-porosity reservoir
simulation tools are commonly employed.
These tools implicitly represent the geological
model of fault and fracture networks, as well as
matrix media, using larger grid blocks. The
transition from DFN models or implicit fracture
models to dual-porosity reservoir simulation
parameters is achieved through upscaling
procedures.
These parameters include equivalent fracture
permeability and equivalent matrix-block
dimensions or shape factors. The upscaling of
fracture permeability can be performed using
either analytical or numerical techniques.
To effectively model fractured reservoirs,
comprehensive fracture network information is
essential. Fig. 12 offers a comprehensive
exploration of the distinct categories of
fractures resulting from folding processes. The
diagram provides detailed insights into the
diverse fracture types arising from folding
phenomena, elucidating their characteristics
and implications within the context of the
referenced source [16].
This illustration serves as a valuable reference,
aiding in the understanding of fracture
formations attributed to folding processes as
described in the cited literature. Once this data
is available, simulations can be conducted with
the dual-porosity option incorporated into
conventional simulators.
Fig. 13 suggests an inclusive view of the
methodology employed in dual porosity
modeling, elucidating the process, steps, and
parameters involved in this modeling approach.
The diagram serves as a valuable reference,
detailing the intricacies of the dual porosity
modeling methodology discussed in the cited
source [16].
Its comprehensive portrayal aids in understanding
the complexities and applications of dual porosity
modeling within the specified context.
This approach requires the preparation of
engineering parameters for both the matrix and
the flow through them, which can be sourced
from well-testing, core analysis, log data, and
geophysical studies.
Well-log data, in particular, plays a pivotal role
in improving fluid flow modeling in the media.
Parameters such as fracture density, aperture,
orientation, porosity, and permeability,
obtained through logging, are invaluable for
dual-porosity modeling.
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Fracture density assists in estimating the
dimensions of matrix blocks within the model,
which is crucial for estimating the sigma
parameter representing transmissibility
between the matrix and fractures. The inclusion
of aperture information enables the estimation
of fracture permeability and porosity.
Meanwhile, fracture orientation is essential for
aligning grid coordinates with the flow
direction [16].
In this section of the study, we emphasize the
significant enhancement in reservoir modeling,
especially in estimating the sigma parameter
based on well log fracture density data obtained
from various wells.
To begin the calculation of essential
engineering parameters for simulation, we start
with log data as the foundation.
The primary parameters of focus include
fracture density, fracture aperture, and fracture
orientation. Fracture density (FD) is determined
by taking the inverse of the block dimension,
with a representing the block dimension and 'b'
representing the fracture aperture.
The parameter 'Sigma' plays a crucial role as a
transfer function that bridges the matrix and
fractures within the model. Its calculation is
based on the formula Sigma = 12 * (FD^2). In
this study, we utilize the average values of 'b'
and 'a' to estimate the permeability and porosity
of fractures within the model, as depicted in Fig.
14.
This figure offers a comprehensive view,
showcasing the utilization of image log-derived
data as input for the simulation model, along
with the representation of specific parameters,
denoted as "a" and "b," within the reservoir
model.
The visual depiction aids in understanding the
integration of image log data into simulation
models and the representation of crucial
parameters within the reservoir model for
accurate and detailed analysis.
Fig. 12: Defines various types of fractures induced
by folding, as discussed in [16].
Fig. 13: Presents an in-depth depiction of the dual
porosity modeling methodology as outlined in [16].
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A
B
Fig. 14: A) Input Data derived from Image Logs for
the Simulation Model. B) Illustration and Depiction
of Parameters "a" and "b" within the Reservoir
Model.
The computation of Sigma involves the
incorporation of all fracture density data
derived from the logs, as illustrated. The
primary focus of this study is to assess the
simulation's sensitivity to Sigma.
To accomplish this, a comparative analysis is
conducted, contrasting scenarios in which
Sigma is determined from log-derived fracture
parameters with cases in which Sigma is
assumed as a fixed value across the entire
reservoir.
COUGAR software is employed for this
analysis, providing valuable insights into the
impact of Sigma on the simulation's results. The
fracture density and orientation data are
integrated into the simulation model, enabling
the development of a more precise simulation
model for the fractured reservoir by utilizing
fracture data from the image logs.
Fig. 15 focuses on illustrating a key factor
employed in the computation or determination
of population fracture density. The figure
serves as a pivotal reference point, elucidating
the essentiality and significance of this
particular parameter within the context of
calculating fracture density within a given
population or dataset.
Fig. 16 provides a comprehensive view of the
spatial arrangement and variation in fracture
density across the field. The figure provides a
detailed depiction, presenting the distribution
pattern of fracture density, enabling a deeper
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understanding of the density variations and
their significance within the Gachsaran Field.
This project combines data from ten individual
wells to create a comprehensive model for
evaluating fractured reservoir fields. The results
of the simulated fracture density distribution are
validated in GS-341(Fig. 16).
In circumstances where 3D seismic
data is either lacking or of subpar quality, our
study emphasizes the critical importance of
borehole image data. It has become evident that
a heightened focus on acquiring more image
logs is essential for comprehensive fracture
characterization and enhanced fracture
modeling. In conclusion, we stress the need for
additional image logs to facilitate
comprehensive fracture characterization and
robust modeling.
Fig. 15: Represents a specific parameter crucial in
calculating population fracture density.
Fig. 16: Showcases the resulting distribution of
fracture density within the Gachsaran Field.
3.2.2.Sensitivity Analysis and Pressure
Profile Assessment
Following a sensitivity analysis involving a
constant production rate of 2000 barrels per day
across ten wells, our study delved into the
assessment of pressure changes within the field
under three distinct scenarios:
Scenario 1: In this case, we assigned a
fixed value of 0.001 (1/ft²) to Sigma
throughout the entire reservoir.
Scenario 2: Sigma was maintained at a
constant value of 100 (1/ft²) across the
entire reservoir.
Scenario 3: Sigma was determined
from the fracture density distribution
within the reservoir, utilizing fracture
density logs, as illustrated in (Fig. 17).
The examination of pressure profiles yielded
compelling insights. In Scenario 1, where the
distribution of fracture density in the reservoir
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was considered, the pressure remained
relatively stable due to the heterogeneity in
Sigma distribution.
Conversely, in both Scenario 2 and Scenario 3,
where Sigma was evenly distributed, the
pressure exhibited a more significant decline
within the reservoir. In conclusion, our study
has emphasized the pivotal role of fracture
density, volume, and conductivity in driving
field recovery rates.
Accurate representation of the fracture network
across the field, with a particular focus on
fracture density, proves crucial in
approximating the dimensions of matrix blocks
within our modeling framework.
The spatial distribution of fractures exerts a
direct and substantial influence on production
from distinct regions, underlining the need for
meticulous consideration when modeling both
individual wells and the entire field.
It offers a comprehensive representation,
illustrating both the simulated performance of
fractured reservoirs and the outcomes of
sensitivity analysis. These components serve as
fundamental aspects for understanding the
behavior, performance, and variability of
fractured reservoirs under varying conditions,
aiding in informed decision-making and
strategic planning within reservoir management
The integration of data from image logs and
other fracture information collection methods
has proven invaluable for achieving precise
reservoir modeling and enhancing recovery
outcomes.
Our project has significantly advanced
our understanding of the utilization of
Formation MicroScanner (FMS), Formation
MicroImager (FMI), Oil-Based MicroImager
(OBMI), and Ultrasonic Borehole Imager
(UBI) data, both at the individual well level and
on a field scale.
Furthermore, this project has
highlighted persistent challenges within the oil
field, including issues related to the
conventional application of image logs on a
field-wide scale, potential pitfalls associated
with drilling wells in high fracture density
areas, and the absence of established
procedures for calculating sigma within
simulation models for fractured reservoirs.
Our study has revealed the pivotal role
played by fracture density, volume, and
conductivity in driving field recovery rates.
Accurate representation of the fracture network
across the field, with a specific emphasis on
fracture density, is imperative for accurately
approximating the dimensions of matrix blocks
within our modeling framework. The spatial
distribution of fractures exerts a direct and
substantial impact on production from distinct
regions, necessitating meticulous consideration
when modeling both individual wells and the
entire field.
Moreover, this endeavor has enriched
our understanding of the reservoir's fracture
system and its direct impact on production. We
have achieved precise verification of fracture
properties on a field scale and have introduced
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a novel approach to compute Sigma in the
simulation model based on image logs.
Additionally, this project has redirected
NISOC's focus toward understanding reservoir
performance within fractured reservoirs,
underlining the importance of enhanced
formation pressure assessment and the need for
additional wireline formation testing.
A
B
Fig. 17: A) Simulation depicting Fractured
Reservoir Performance. B) Presentation of
Sensitivity Analysis Results. This figure provides an
intricate view, showcasing the simulation of
fractured reservoir performance alongside the
results derived from sensitivity analysis.
4. Conclusion
The study underscores the vital role of
borehole image data, especially when 3D
seismic data is insufficient. It emphasizes the
necessity for more image logs to understand
fractures thoroughly and create robust models.
The research significantly advances
comprehension of borehole imaging
technologies and addresses challenges in oil
fields such as effectively using image logs,
drilling in high-fracture-density zones, and
lacking standardized procedures for fractured
reservoir simulation models.
Establishing a strong link between
fracture density and cumulative production
marks a major success, potentially becoming a
standard assessment method for similar
fractured reservoirs, influencing reservoir
analysis and development. Positive feedback
from experts adds weight to its significance.
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The findings emphasize fracture
properties' critical role in field recovery rates.
Accurate representation of fractures,
particularly density, is crucial for the modeling
framework, impacting production in various
areas. This study deepens understanding of
fractures' impact on production, validating
fracture properties at a field scale and
introducing a new approach for sigma
computation based on image logs.
Additionally, the project highlights the
importance of understanding fractured
reservoir performance, advocating for
improved formation pressure assessment and
wireline formation testing. This deeper insight
optimizes well placement, enhancing
productivity to meet future production targets.
By focusing on fractured reservoir
performance, the study stresses the significance
of formation pressure data and Production
Logging Tools (PLT) for comprehensive flow
analysis. Utilizing Fracture Strike maps aids
informed decisions on well placement, thereby
boosting productivity and achieving production
targets for future wells.
Acknowledgement:
We wish to extend our sincere gratitude
to all the individuals who played a pivotal role
in the success of this project. Without their
unwavering support and contributions, our
achievement would not have been possible. We
would like to express our special thanks to the
late Professor Ahmad Shemirani and Professor
Abbas Sadeghi for their invaluable guidance
and expertise. Furthermore, our deep
appreciation goes to NIOC South for their
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Contribution of Individual Authors to the
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Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
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(Attribution 4.0 International, CC BY 4.0)
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Creative Commons Attribution License 4.0
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