SPE-223391-MS
A. V. Gavrilov, S. E. Togaev, E. N. Limanskii, and K. A. Abidov, Surhan Gas Chemical Operating Company,
Tashkent, Uzbekistan
Copyright 2024, Society of Petroleum Engineers DOI 10.2118/223391-MS
This paper was prepared for presentation at the SPE Caspian Technical Conference and Exhibition scheduled to be held in Atyrau, Kazakhstan, 26 – 28 November 2024.
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Abstract
The paper presents the evolution of the simulation approach for a green gas field in the Republic of
Uzbekistan. The field is currently being appraised as part of the pilot development design process. The
reservoir is composed of intensely fractured carbonate rock.
Appraisal results that explicitly required an immediate transition from single porosity (SP) simulation
to the dual porosity (DP) approach are described along with the resulting impact on production forecasting
and reservoir development design.
During an appraisal campaign, a well interference test intended for a dual media study has been
conducted. The SP simulation model, which was used as a main RE tool for designing the field development,
failed to simulate it. This required the implementation of the DP approach.
The DP model predicts a shorter period of gas production plateau and earlier water breakthrough
compared to the SP model. The uncertainty most impacting on cumulative production is revealed to be the
aquifer volume. In the DP model case, the larger the aquifer, the shorter the production plateau and the
sooner water breakthrough occurs. Conversely, the SP model case establishes the positive impact of the
aquifer volume on cumulative production. Thus, the SP approach conceals significant RE risks, leading to
suboptimal field development decisions. The appraisal program and the pilot development project for the
field have been updated to ensure they remain optimal, considering the new simulation approach.
Therefore, for the fractured reservoir – where reservoir permeability is provided by fractures and porosity
is attributed to matrix – it is crucial to conduct fracture studies and implement the appropriate simulation
approach from the very first steps of field exploration.
Matching a DP model to an interference test data results in determining storage and flow capacity
parameters: porosity, permeability and compressibility for the matrix and fractures separately, whereas the
analytical model's interpretation provides only integrated parameters of dual media – interporosity flow
coefficient and storativity ratio.
Furthermore, at the early stages of a field development the fracture compressibility, which critically
impacts reservoir performance, can only be evaluated using the approach presented.
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Well Interference Test in Naturally Fractured Gas Reservoir: Numerical
Simulation and Impact on Reservoir Performance Forecasting
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Introduction
Figure 1—General overview of the field Jurassic carbonate and
carbonate/anhydrite sections in the geological model cross-section.
As the majority of known local anticlinal structures in the Amu-Darya and consequently Afghan-Tajik
basin, the considered field structure was formed during the Neogene time as a result of Alpine orogeny
related tectonic deformations. And again, as many of the regional structures, the considered field belongs
to the linear anticlinal zone along the fault. (Figs. 1 and 2). The tectonic deformations that resulted in the
formation of the structure also define the intense fracturing of formation rocks.
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The field is located in southern Uzbekistan, in the Baysun District of the Surkhandarya Region. Geologically,
the field lies in the northwestern part of the Afghan-Tajik Basin, which is a former part of the Amu-Darya
sedimentary basin. The Amu-Darya basin is a large Jurassic-Tertiary depression that overlies Paleozoic
basement and Triassic rift systems (Ulmishek, 2004). The uplift of the SW Hisar Range during the Alpine
Himalayan Orogeny separated the Afghan-Tajik Basin from the Amu-Darya basin (Brunet et al., 2017).
The sedimentary section of the Afghan-Tajik basin was deposited from Jurassic to Eocene. And the
Jurassic part of the section consists of Lower to Middle Jurassic coal bearing clastic sediments, the
likely source rocks for the oil and gas fields in the area, Callovian-Oxfordian carbonates overlain by
Kimmeridgian-Tithonian carbonates/anhydrites and evaporites of the Gaurdak Formation (Tillyabaev et al.,
2019).
One of the main productive reservoirs of the field belongs to the carbonate section of the CallovianOxfordian. And the paper is devoted to the study of this reservoir (XVa-XVI productive horizons in local
nomenclature). The reservoir is composed of layers: limestones, dolomitized limestones with some amount
of clay, thin layers of bituminous clay, oolitic bituminous limestones, and tight clayey limestones (Fig. 1).
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The studying carbonate reservoir is saturated with dry gas at abnormal reservoir pressure (gradient of
more than 20 kPa/m) underlain by formation water (Fig. 2).
The considered field is currently the largest green field of the Republic of Uzbekistan. So, the gas supply
of the country in forthcoming years depends significantly on the successful development of the field.
Currently, the field is under an appraisal program and being prepared for pilot production. Regarding the
upstream part, the preparation includes: 3D-seismic survey, drilling and detailed testing of vertical appraisal
wells, designing the pilot development project, and drilling/completion of deviated production wells.
The Problem Definition
The field is being appraised with drilling and testing of vertical wells all over the gas bearing zone (Fig.
2). The reservoir study in these wells involves:
• advanced logging set which includes: microimaging, cross-dipole acoustics, and nuclear magnetic
resonance (NMR) logging (at some wells);
• wireline formation tester (at some wells);
• selective stimulation and testing of several perforation intervals in a liner cemented in the reservoir
part (plug&perf technology).
Regarding the dynamic well testing, there are several types of tests that have been performed in appraisal
wells:
• wireline formation testing,
• testing tubing string with downhole shut-in valve,
• testing tubing string with only surface shut-in,
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Figure 2—Structural map of Jurassic carbonate section top with appraisal wells.
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• testing with simultaneous involvement of 2 wells – well interference test.
Well Interference Test Description
The well interference test at the studied reservoir was designed specifically for fracture properties
characterization (Gavrilov et al., 2023). According to the test design, 2 wells were selected as participants
of the test: well O1 and well E1 (Fig. 2 and 3a). The sequence of flows and shut-ins in the active well was
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Wireline formation tester was implemented in probe and dual packer configurations and showed limited
success as during the testing procedure it was challenging to get a hermetically sealed inflow interval in
highly productive – i.e. fractured – parts of the wellbore. Only 2 intervals out of tens of attempts were
successfully tested in the higher carbonate-anhydrite section of the field (Ramatullaev et al., 2019). The
radius of the reservoir investigation was rather small as the time of testing was limited by the need for
drilling mud circulation. So, the data obtained with the wireline tester characterizes a near-wellbore zone
and doesn't allow for studying the formation properties far from the wellbore, in interwell space.
At the moment the number of successful downhole shut-in tests is also rather limited due to technological
difficulties of overpressured reservoir testing. The reservoir pressure exceeds hydrostatic by more than
twice, which requires special and not readily available in the region heavy brines to get somewhere close
to pressure equilibrium above and below the testing packer/downhole tubing valve during the testing.
Otherwise, with common in the region 1200 kg/m3 brine there is a high differential pressure across the
downhole valve that could even move the testing packer upward and ruin the static conditions of the well
during pressure build-up. Several tries have been performed by now, and in almost every one of them, the
pressure build-up data is disturbed by not precisely constant volume of the studied wellbore during the shutin period.
So, the majority of single well tests performed in the field involve surface shut-in for down-hole
registration of pressure build-up. The behavior of pressure and Bourdet derivative on diagnostic plots of
these tests is usually highly affected by the effects of wellbore storage (WBS), high skin, and tubing phase
segregation, which dominate during early and middle times (Gavrilov et al., 2023).
Then the dual media reservoir characteristics cannot be practically determined with the described kinds
of tests. Effectively, the reliable results of these tests are permeability-thickness (kh), skin and reservoir
pressure (P). The basic kind of simulation model – with the single porosity approach – represents these well
tests results fairly well. So, until the SP simulation model was proven to be inapplicable by solid data this
model had been used as a reservoir engineering tool for designing the pilot development project.
Surely, it was well known that the reservoir strongly possesses the dual media affected behavior. And to
get the solid characteristics of dual media, a well interference test has been planned and conducted. Due
to the definitely static condition of the observation well during the disturbing operations in the active well,
the pressure response is free of obscuring effects, such as WBS, tubing phase segregation, and high skin
(Houze et al., 2024). Therefore, diagnostic plots of transient pressure in well interference tests definitely
and clearly represent the dual media behavior of the reservoir and can be accurately interpreted by matching
the analytical dual media model. (Gavrilov et al., 2023).
Simulation of the well interference test by the SP model resulted in a definite failure to get an acceptable
match. So, it was finally proven that the SP model was not applicable, and all project decisions based on
the simulations must be updated with another simulation tool that would be able to represent the fractured
reservoir nature. Then the DP model has been constructed on the basis of the available DFN static model
(Togaev et al., 2024). The DP model accurately matched the interference test data revealing important
parameters of dual media: porosity, permeability, and compressibility – for each medium (fractures, matrix)
separately, in contrast to the analytical model's interpretation, which provides only integrated parameters of
dual media. The project decisions have been revised by the newly approved simulation tool.
SPE-223391-MS
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sending pressure disturbance signals to the observation well. Firstly, the signals were sent from well E1 to
well O1 (Fig. 3b), and then vice versa – in the opposite direction (Fig. 3c). It is important to note that there
was no production from the reservoir at the time of testing; other activities like drilling and construction were
also paused in order to eliminate any possible extraneous noises in the pressure response of the observation
well.
Interpretation of the pressure response in observation wells by the analytical dual media model allowed
to reveal parameters: porosity-thickness (φh), permeability-thickness (kh), and dual media parameters –
interporosity flow coefficient (λ) and storativity ratio (ω) (Gavrilov et al., 2023):
(1)
where rw = radius of wellbore, km, φm = matrix permeability and porosity, kf, φf = fracture permeability and
porosity, ctf = total fracture compressibility, ctm = total matrix compressibility, σ = dual media shape factor
(sigma).
Simulation of the Interference Test by the SP Model
The SP simulation model is matched to dynamic data which includes single well tests results: data arrays
of permeability-thickness, skin, and reservoir pressure. Reservoir pressure data on the period of the well
testing campaign (several years) shows a slight but distinct depletion trend which allows for an estimate of
reservoir volume and confirms the correctness of petrophysical and geological models in the base of the
simulation models.
First run of the matched to single well tests SP model on the interference test data shows that the pressure
response of the observation well to the impulses of the disturbing well is somewhat "smooth" – red line in
Fig. 4 in comparison to red dots of actual data.
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Figure 3—Scheme of the well interference test (Gavrilov et al., 2023): (a) geological cross-section of testing zone, (b) sending
pressure disturbance signals from well E1 to well O1, (c) sending pressure disturbance signals from well O1 to well E1.
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SPE-223391-MS
Technical variations of porosity and permeability in the SP model – green and blue lines in Fig. 4 –
suggest that one may try to match the model at least as an exercise to get a better understanding of the
model's capability. The best achieved technical match is shown by the black line in Fig. 4 and it required
a dramatic porosity decrease – well outside the uncertainty range determined by the depletion trend of the
well testing campaign. And still this "best match" doesn't look like an appropriate representation of actual
pressure response.
So, the expected conclusion is that the SP model with practical cell sizes of at least several tens of meters
doesn't allow simulating the interference test within the adequate ranges of reservoir parameters. And even
setting the reservoir parameters to extremely high/low values doesn't really help to get the appropriate match.
Thus, from this moment of confirmed failure of the SP model to represent the actual field data, the SP
model cannot be considered an applicable tool for designing the reservoir development.
Simulation of the Interference Test by the DP Model
The interporosity flow coefficient obtained by the interpretation of the interference test with the analytical
dual media model shows that the permeability of fractures is several orders of magnitude higher than the
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Figure 4—Simulation of the well interference test by the SP model. Red line – the base model pressure response.
Green and blue lines – variations of porosity and permeability. Black line – the "best match" SP realization.
SPE-223391-MS
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(2)
The gas compressibility is accurately determined by gas composition-based equation of state (EOS)
modeling with matching to PVT-cell experiments, so the actual uncertainty is the fracture void
compressibility (cff).
Sensitivity analysis resulted in ranking of the input parameters according to their impacts on the objective
function (Fig. 5).
Figure 5—Tornado diagram of the most impacting the objective function reservoir parameters.
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permeability of the matrix (Gavrilov et al., 2023). Then the fluid flow in the reservoir practically takes
place only through fractures; the flow directly between matrix blocks can be neglected as well as the flow
between matrix blocks and wellbores. This means that the dual porosity (DP) simulation approach can be
selected instead of the computationally expensive dual porosity/dual permeability (DPDK) option. Based
on the upscaled DFN model, the DP model has been designed and matched to data arrays of single well tests.
To match the DFN model to the interference test data, the objective function has been constructed as a
measure of the model match quality. It uses the mean absolute error (MAE) between paired observations of
simulated and measured pressures in 2 observation wells.
The uncertain reservoir parameters used for matching the DP model with well interference test have been
selected: porosity of matrix (φm), fracture permeability (kf), matrix permeability (km), shape factor of dual
media – sigma (σ), total fracture compressibility (ctf) and fracture porosity (φf).
As the reservoir hydrocarbon fluid is dry gas with a density at reservoir conditions 3 times lower than
the density of reservoir water (brine), it is practical to assume that fractures are saturated only with gas.
Then the total fracture compressibility (ctf) is represented as a sum of gas compressibility (cg) and fracture
void compressibility (cff):
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SPE-223391-MS
1. Minimizing the objective function by multiple simulations, find the best estimate of the most
impacting uncertainty – fracture permeability (kf) property of the model.
2. Take sigma (σ) as a fixed property delivered by the DFN static model and then minimizing the
objective function – find the matrix permeability (km) property.
3. It is known that porosity of fractures (φf) is proportional to hydraulic aperture (A) of the fractures, and
the permeability of fractures (kf) is proportional to the third power of aperture (Ahr, 2008):
(3)
Then, based on the determination at the step #1 of fracture permeability, determine the fracture
hydraulic aperture (A) property and consequently the fracture porosity (φf) property.
4. Having determined the fracture porosity (φf) property, find the fracture void compressibility (cff) by
minimizing the objective function with multiple simulations.
The quality of the achieved DP model match in 2 directions of the interference test (signal from well O1
to well E1 and vice-versa) is shown in Figs. 6 and 7, from which it is clear that the quality of the DP match
far surpasses that of attempts with the SP model (Fig. 4). So, the DP model has been proven to be a reservoir
engineering tool that reliably represents the fractured reservoir behavior.
Figure 6—Simulation of the well interference test in direction "well E1 -> well O1" by the DP model.
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Most impacting parameter revealed to be the matrix porosity (φm). And as it was mentioned above, the
reservoir volumetric parameters have been determined in a narrow uncertainty range by previous studies,
so it has been reasonably decided not to use matrix porosity as a variable for matching the DP model.
It is worth noting that the impacts of sigma (σ) and matrix permeability (km) are equal as these parameters
appear in the interporosity flow coefficient (λ) formula (Eq. 1) as equally impacting multipliers, i.e., in terms
of impact on the objective function, it doesn't matter which one of them changes; important is their product.
An identical situation exists regarding the total fracture compressibility (ctf) and fracture porosity (φf):
they form a product within the dual media storativity ratio (ω) (Eq. 1), which is a parameter that really
impacts the objective function.
The algorithm of the DP model matching consists of the following set of steps.
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As it can be seen from the algorithm of the model matching, the parameters determined by matching
itself are: fracture permeability (kf), fracture porosity (φf), matrix permeability (km) and fracture void
compressibility (cff). And the reliability of their determination aside from other factors strongly depends
on the quality of the DFN model. Properties of the DFN model which have been set as constants during
the matching – fracture density, and dual media shape factor – define obtained by matching parameters.
As appraisal wells in the field have been covered with the advanced logging set including micro imaging
survey, and fracture density and orientation (dip, dip strike azimuth) are parameters most surely determined
by micro imaging survey, this allows us to consider the DFN model reasonably reliable.
Production Forecasting with the DP Simulation Model
As the main development scenario involves depletion drive of the reservoir, the compaction of fractures is
an important aspect of the reservoir performance that must be properly addressed in the DP model. As the
reservoir depletes, the effective pressure increases:
(4)
where Peff_n = effective pressure in the n direction, Sn = stress component in the n direction, α = Biot
parameter, P = reservoir pressure, v, h, H = indices of stress direction: vertical, minimum horizontal and
maximum horizontal correspondingly. As effective pressure is the function of current reservoir pressure,
then hereinafter, in the sake of simplicity, the discussion proceeds in terms of reservoir pressure.
To establish the model of fracture compaction, a fracture is considered as two parallel plates spaced apart
by a distance equal to the hydraulic aperture A of a real rough-walled fracture (Fig. 8). Two springs between
plates represent the "elasticities" of fracture void and of saturating the fracture gas.
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Figure 7—Simulation of the well interference test in direction "well O1 -> well E1" by the DP model.
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Then, as two springs are arranged in parallel, the total "elasticity" is the sum of two "elasticities". As
compressibility is inversely proportional to "elasticity", then for the compressibility of the hydraulic aperture
(cA) we have:
(5)
or, reformulating:
(6)
where:
(7)
and Z = the gas compressibility factor.
The definition of the aperture compressibility is:
(8)
from where we get the expression of aperture as a function of reservoir pressure:
(9)
where Pi = initial reservoir pressure; Ai = fracture hydraulic aperture at initial reservoir pressure.
Looking back on Eq. 3, we recall that the permeability of the fracture system is proportional to the cube
of the aperture, then for the fracture permeability we have:
(10)
where kfi = fracture permeability at initial reservoir pressure. According to Eqs. 7 and 8, we get tables
of relative decrease for fracture aperture and fracture permeability, which are then implemented in the
ROCKTAB keyword of the simulation model (Fig. 9).
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Figure 8—Parallel plate" representation of a fracture.
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So, fracture void compressibility determined by matching the DP model to the interference test defines
the fracture compaction rate and the DP model is enabled to represent the productivity decline with the
reservoir depletion.
Comparison of Production Forecasts by SP and DP Models
So, we have two models:
• the SP model, which is matched to all single well tests, and prior to interference test results had
been used as the main RE tool,
• the DP model which is matched to all single well tests and to the interference test.
The forecasts of production parameters by these models on the main development scenario are presented
in Table 1 and in Fig. 10. With the same plateau production rate, the plateau period shortens by 20%
according to the DP forecast in comparison with the SP forecast.
Table 1—Relative measures of base and optimized development scenarios calculated with SP and DP models.
Development scenario /
Simulation Model
Plateau gas
production
period
Plateau gas
production rate
Cumulative
gas production
Cumulative
water
production
Peak daily
water
production rate
Well count
Relative units
Base scenario / SP model
1.0
1.00
1.0
1.0
1.0
1.0
Base scenario / DP model
0.8
1.00
0.9
3.7
4.2
1.0
Optimized scenario / DP model
1.0
0.83
0.9
3.5
3.4
1.0
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Figure 9—Fracture aperture and fracture permeability compaction model implemented in the DP simulation model.
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Formation water production profiles are drastically different in DP and SP forecasts: the SP brine
production gently increases to a moderate peak while the DP water profile is characterized by a sharp
breakthrough to the levels that have to be considered while designing surface facilities. The DP forecast
shows water breakthrough with a maximum daily rate 4.2 times greater than the SP forecast, and the
cumulative water 3.7 times greater in the DP forecast than in the SP forecast.
Uncertainty Analysis Observations
The uncertainty most impacting on cumulative gas production is revealed to be the aquifer volume. The
reservoir gas is sufficiently overpressured, and this could be a confirmation of the geological estimate
according to which the overall reservoir volume is not much bigger than the gas saturated part of the
reservoir, i.e., the volume of the water-bearing part of the reservoir is rather limited. To investigate what
happens in cases other than the base geological estimate, several options of the aquifer volume have been
considered in DP and SP cases (Figs. 11 and 12).
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Figure 10—Production forecast for the base development scenario on the SP /
the DP models and for the optimized development scenario on the DP model.
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Figure 12—Impact of the aquifer volume uncertainty on the DP production forecast.
In the DP model case, the larger the aquifer, the shorter the production plateau and the sooner water
breakthrough occurs. Conversely, the SP model case establishes the positive impact of the aquifer volume
on cumulative production. Thus, the SP approach conceals significant RE risks, leading to suboptimal field
development decisions.
This means that the appraisal program must be revised to target the uncertainty of the aquifer volume
as a top priority.
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Figure 11—Impact of the aquifer volume uncertainty on the SP production forecast.
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Update of the Pilot Development Project
Figure 13—Scheme of production wells lower completion for the water inflow control.
The appraisal program has been updated, including drilling and testing of an appraisal well in the waterbearing zone of the reservoir to study the aquifer properties.
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The appraisal program and the pilot development project for the field have been updated to ensure they
remain optimal considering the next generation simulation approach.
It has been revealed, according to the economic model, that the plateau production rate previously
justified with the SP model is not optimal according to the DP forecast and has to be lowered in favor of
keeping the plateau production period the same. So, the development strategy has been revised: the stable
production rate has been set at 83% of the initially planned rate, and that results in keeping the stable
production period for the same time with the same production well stock. Parameters of the optimized
development scenario in comparison with the previous base development scenario calculated on the DP and
the SP model are presented in Table 1 and Fig. 10.
The expected rapid water breakthrough requires considering the options of water production control in
the lower completion of production wells. So, in the updated pilot development project the lower completion
includes a liner with several sliding sleeves separated by open hole packers, which divide the production
section into isolated intervals (Fig. 13). This completion allows for closing the interval subjected to rapid
water breakthrough and elongates the life of the well.
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Discussion
Interference Test Matching with the DP Simulation Model
Involvement of the simulation approach to interpret the interference test allows us to get not only integrated
parameters of dual media but also to determine the particular parameters such as porosity, permeability,
and compressibility for matrix and fractures media separately. This becomes possible due to involvement
of the information on geometrical parameters of the dual media – fracture density and dual media shape
factor (sigma).
The fracture void compressibility is a parameter of special interest as its uncertainty results in remarkable
uncertainty of the gas production profile. The effect of fracture void compressibility (cff) variation by an
order of magnitude in both directions from the value determined by the DP model matching is shown in
Fig. 14. It is important to note that the value of storativity ratio (ω) is being kept unchanged in all 3 cases
in Fig. 14. So, having reliably determined the storativity ratio (ω) by the analytical interpretation of the
interference test, we could have drastically different gas production forecasts due to the uncertainty of the
fracture void compressibility (cff) value.
Figure 14—Impact of the fracture void compressibility uncertainty on the DP production forecast.
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Interference Test Interpretation by the Analytical Model
Interpretation of an interference test by the analytical dual media model results in determining integrated
dual media parameters – interporosity flow coefficient (λ) and storativity ratio (ω), and integrated interwell
space parameters: kh and ϕh.
The interference test is free from obscuring effects compromising the single well tests – WBS, tubing
phase segregation, high skin. This allows for getting a clear picture of dual media behavior on a log-log
plot. And the key benefit of the interference test is a dynamic determination of the volumetric parameter:
porosity-thickness (ϕh) – which is impossible to determine by a single well test.
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Conclusion
1. Advance logging of appraisal wells and DFN modeling on the basis of micro imager surveys.
2. Performing a well interference test with the involvement of 2 (or more) appraisal wells.
3. A DP simulation model construction on the basis of the upscaled DFN model and matching to the
interference test results.
4. Updating of a fracture compaction model on the fracture void compressibility obtained by the DP
model matching.
5. Implementation of the DP model with fracture compaction model as a main RE tool for designing
a field development.
For this type of reservoir fracture properties can only be obtained from well tests and from whole core
analysis, whereas calculations from wireline log data do not provide accurate information for the evaluation
of fracture contributions to reservoir performance (Nelson, 2001). In the conditions of the studied field
fractures of most interest (conductive) do not allow retrieving a whole core as a solid piece as it breaks into
debris during drilling. Not to mention that even whole core samples and reservoir fractures are objects of
drastically different scales. Then the presented approach is the only way of estimating "fracture contributions
to reservoir performance" (Nelson, 2001) at the appraisal stage of a field development.
Nomenclature
= fracture hydraulic aperture
= fracture hydraulic aperture at initial reservoir pressure
= hydraulic aperture compressibility
= gas compressibility
= fracture void compressibility
= total fracture compressibility
= total matrix compressibility
= reservoir net pay
= total dual media permeability (fractures and matrix)
= fracture permeability
= fracture permeability at initial reservoir pressure
= matrix permeability
= reservoir pressure
= initial reservoir pressure
Peff_n = effective pressure in the n direction
rw = wellbore radius
Sn = stress component in the n direction
v, h, H = indices of stress direction: vertical, minimal horizontal and maximal horizontal
Z = gas compressibility factor
λ = interporosity flow coefficient
A
Ai
cA
cg
cff
ctf
ctm
h
k
kf
kfi
km
P
Pi
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The studied reservoir according to Nelson's classification (Nelson, 2001) belongs to Type II of fractured
reservoirs: "fractures provide essential permeability". The presented study confirms once again that for this
kind of reservoir, it is crucially important to conduct specialized fracture studies at the very first steps of a
field appraisal. The results of field, laboratory and cameral fracture studies must be properly implemented
in a field development project at the earliest possible phase in order to avoid suboptimal project decisions
and keep the project value at the designed level.
The presented approach of timely fractured reservoir study includes the following steps.
SPE-223391-MS
σ
φ
φm
φf
ω
α
= dual media shape factor (sigma)
= total dual media porosity (fractures and matrix)
= matrix porosity
= fracture porosity
= dual media storativity ratio
= Biot parameter
Ahr, Wayne M. 2008. Geology of Carbonate Reservoirs – The identification, Description and Characterization of
Hydrocarbon Reservoirs in Carbonate Rocks. A John Wiley & Sons, Inc., Hoboken, New Jersey, USA.
Brunet, M.-F., Ershov, A. V., Korotaev, M. V. et al 2017. Late palaeozoic and Mesozoic evolution of the Amu Darya Basin
(Turkmenistan, Uzbekistan) Geological Society, London, Special Publications 427: 89–144. https://doi.org/10.1144/
SP427.18
Gavrilov, A. V., Togaev, Sh. E., Abidov, Kh. A. et al 2023. Experience of interwell interference testing in a fractured
gas reservoir. Actual Problems of Oil and Gas 2(41): 124–140. https://doi.org/10.29222/ipng.2078-5712.2023-41.art8
(In Russ.)
Houze O., Viturat D., Fjaere O. S. et al 2024. Dynamic data analysis – v5.60.01. KAPPA. Paris, France.
Nelson, R. A. 2001. Geologic Analysis of Naturally Fractured Reservoirs, 2nd Edition. Gulf Publishing, Houston, USA
Ramatullayev, S., Blinov, V., Tukhtaev, R. et al 2019. The fracture characterization and assessment of gas potential with
advanced formation tester in low permeability fractured carbonate reservoir. Presented at the SPE Annual Caspian
Technical Conference, Baku, Azerbaijan, 16–18 October. SPE-198376-MS. https://doi.org/10.2118/198376-MS
Tillyabaev, M., Abidov, Kh., Klevitskiy, A. et al 2019. Study of the prospects for gas-containing terrigenous
deposits of the Middle and Lower Jurassic on one of the areas within the Afghan-Tajik Depression. Presented at
the SPE Russian Petroleum Technology Conference, Moscow, Russia, 22–24 October. SPE-196941-MS. https://
doi.org/10.2118/196941-MS
Togaev, S. E., Gavrilov, A. V., Abidov, K. A. and Frik V. L. 2024. Practical Aspects of Discrete Fracture Network
Modelling with Neural Network Assisted Integration of Available Data. Presented at the 2024 SPE Caspian Technical
Conference and Exhibition, Atyrau, Kazakhstan, 26-28 November. SPE-223399-MS. https://doi.org/10.2118/223399MS
Ulmishek, G. F. 2004. Petroleum Geology and Resources of the Amu-Darya Basin, Turkmenistan, Uzbekistan,
Afghanistan, and Iran. U.S. Geological Survey Bulletin 2201–H.
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