Copyright 2003, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the SPE International Improved Oil Recovery Conference in Asia Pacific held in Kuala Lumpur, Malaysia, 20–21 October 2003. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836 U.S.A., fax 01-972-952-9435.

Abstract This paper presents an innovative approach to integrate fracture, well test and production data into the static description of a reservoir model as an input to the flow simulation. The approach has been successfully implemented into a field study of a giant naturally fractured carbonate reservoir in the Middle East. This study was part of a full field integrated reservoir characterization and flow simulation project.

The main input available for this work includes matrix properties, fracture network, well test and production data. Stochastic models of matrix properties were generated using geostatistical methodology based on well logs, core, seismic data and geological interpretation. Fracture network was described in the reservoir as lineaments (fracture swarms) showing two major fracture trends. The network and its properties, i.e., fracture porosity and permeability, were generated by reconciling seismic, well logs, and dynamic data (well test and PLT).

The challenge of the study is to integrate all the input in an efficient and practical way to produce a consistent model between static and dynamic data. As a result, it is expected to reduce the history matching effort. This challenge was solved by an innovative iterative procedure between the static and dynamic models.

The static part consists of the calibration of model permeability to match the well test permeability. It is done by comparing their flow potentials, kh. In this analysis the dominant factor in controlling production at each well, either matrix or fracture, was determined. Based on the dominant factor, matrix or fracture permeability was modified accordingly. This way the changes in permeability are kept inline with the geological understanding of the field.

The dynamic part was carried out through a full field flow simulation to integrate production data. The flow simulation at this stage was used to match production capacity, i.e. to determine whether the given permeability (matrix and fracture) distribution is enough to produce the fluid at the specified pressure during the producing period of the well. The iteration is stopped once a reasonable production capacity match is obtained. In general, a good match was achieved within 3-4 iterations. The generated reservoir description is expected to substantially reduce the effort required to obtain a good history match.

Introduction This paper presents the approach, implementation and results of fracture integration process into a reservoir model. The study is part of a fully integrated reservoir characterization and flow simulation study of an oilfield in the Middle East. A comprehensive integrated reservoir characterization was conducted by considering all available data, namely well logs and cores, geological interpretation, seismic (structures and inversion derived porosity), fracture network, and pressure build up tests. The approach used in the study was a stochastic approach where multiple reservoir descriptions were generated to quantify the uncertainty in the future performance.1,2

Reservoir properties for each realization were generated using a geostatistical technique that produces properties, i.e., porosity, permeability and water saturation, consistent with the underlying rock type description. The description was based on core and log data. Additionally, porosity, which affects the permeability description, was also constrained to the seismic derived porosity. The permeability distribution generated by this method was referred to as the core-derived permeability in this paper. Since core-measurement commonly represents the matrix property of the rock, the core-derived permeability mentioned above was also referred to as matrix permeability.

It is commonly observed that the well test permeability values do not match the thickness-weighted core-permeability averages. This is partly due to the differences in the measurement scales of core samples, which cover a few inches, and well tests, which investigate several hundred feet around the well bore. In addition, the presence of fractures and/or high permeability channels will further enhance the difference between the two sources of data. The mismatch between these two permeabilities may be small or as high as three orders of magnitude. Therefore, reservoir descriptions

SPE 84876

An Innovative Approach To Integrate Fracture, Well Test and Production Data into Reservoir Models Asnul Bahar, SPE, and Harun Ates, SPE, Kelkar and Associates, Inc., Maged H. Al-Deeb, SPE, Salem E. Salem, SPE, Hussein Badaam, and Steef Linthorst, SPE, ADCO, and Mohan Kelkar, SPE, The University of Tulsa

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based on core measurements alone cannot honor the well test results and need to be properly modified.

The comparison between core-derived permeability with well-test derived permeability should be done carefully considering the following: 1. Volume of Interest: Model permeability based on core

measurements should represent the same volume as volume investigated by the well test.

2. Measurment Scale: Due to the scale differences, i.e., fine scale of core-derived permeability and coarse scale of the well test permeability, a proper upscaling technique is required to upscale the core-based model permeability.

3. Flow Behavior: It is also important to consider the flow behaviour around the wellbore. A radial upscaling technique is needed in order to reflect the effect of radial flow behaviour around well bores.

Considering these criterias, the ratios between core based and well test measured permeabilities are calculated at each well. It was observed that the ratio is close to one where there are no macro-fractures but increasing significantly around the fractured areas. These large ratios between core based and well test measured permeabilities were attributed to thepossible presence of a fracture network connected to the well bore.

In this study, multiple realizations of fracture maps werealso generated stochastically based on the seismic data, borehole image logs, core interpretation and dynamic data. Additionally, property of the fracture, i.e., fracture porosity and anisotropic distribution of fracture permeabilities, were also generated at the grid block level. However, the main chalange still remains as how to integrate this fracture networkmodel and well test information gathered by the afore mentioned analysis, into the static reservoir model (i.e., matrix model) in order to obtain a representative fluid flow simulation model.

The field being studied is a naturally fractured carbonate reservoir, which is commonly modeled by a dual porosity model, where the fractures and matrix are treated separately. However, it was decided to model this reservoir with conventional single medium model due to the relatively high matrix property. This is not unique to the reservoir under study, since it has also been shown in the literature that single porosity model can adequately model dual porosity reservoir in many instances.3,4

The reservoir was divided into three main reservoir units U1, U2 and U3 on the basis of petrophysical variations and characteristics. Subsequently, it was subdivided into 11 flow units based on sequence stratigraphy.

Data The approach discuss in the next section are based on the availability of four types of data/information, namely matrix and fracture models, interpreted Pressure Build Up (PBU) tests and production data. Matrix Model. The petrophysical properties of matrix system are commonly generated by integrating well-log, cores, and in some cases seismic derived-properties using geostatistical methodology. Furthermore, prediction at the uncored wells for rock type and permeability might be done to improve data

coverage, which in turn will improve the statistics of the result.

An important feature that needs to be satisfied is that the petrophysical properties have to be consistent with the underlying geological description of the field. That is, the generated properties, namely porosity and permeability, should be in agreement with the underlying facies/rock type description.5

In addition to constraining to the geological information, matrix model should also be constrained to all available data/information, such as seismic-derived porosity.6

Three realizations of matrix property were available for this study. They are referred as the Low, Medium and High cases. These three realizations were selected from 48 realizations previously generated.1 The selection was done based on the ranking of Sweep Efficiency (SE), STOIIP and Heterogeneity Index (HI).7 The sweep efficiency was calculated based on the time-of-flight principle of streamline simulation.8

Figure 1 (a), (b) and (c) show the examples of longitudinal cross-section of rock type, porosity, and permeability respectively. The consistency among these properties is shown clearly in this figure. For example, the areas of high porosity and permeability coincide with the good rock types (RRT 4 and 7), whereas the areas of poor porosity and permeability coincide with the bad rock types (RRT 1 and 2).

Fracture Model. The presence of fractures has always been an issue for the assestment of field productivity and reserves. Techniques to model fracture distribution are available in the industry.9,10 These are based on seismic data together with well-cores, borehole image log and well-dynamic performance to map fracture locations in the reservoir using the curvature analysis principle.

In practice, curvature corresponds to the second derivative of topography for a given orientation. For fracture mapping purposes, curvature at a certain point is assigned to be positive or negative. Based on the observation at the well location (core and borehole image log), the positive curvature value may indicate the possible fracture locations whereas negative curvature may indicate non-fracture locations. Furthermore, fracture locations determination may be enhanced by the seismic facies analysis that uses other seismic attributes such as Dips, Edge, Curvature and Trace Dissimilarity. The details of such techniques are beyond the scope of this paper and can be found elsewhere.9,10

The availability of information about the location of fractures across the entire reservoir indeed gives valuable information for the overall modeling process. It may serve as a guide for better modeling of permeability.11 However, this information alone may not be enough to quantitatively describe the fracture system. For this reason, the hydraulic characterization of the fracture system, based on the well test and production data is required in order to get fracture properties that can be used to derive fracture porosity and fracture permeability.

For this study, fractures were observed from cores, bore hole images, mud-losses, production log and pressure transient analysis. The fractures were found to be grouped as clusters that can be interpreted as fracture swarms or as sub-seismic

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faults, oriented in two main directions N40E and N70E (Figure 2). There was no diffuse fracturing determined from the available data. Based on the geological observations, the two fracture trends are likely to be open.

Three stochastic realizations of fracture network, namely 500m, 750m, and 1000m, were generated for this study. The names were based on the average fracture length used to generate them. Figure 3 shows the zoom view of thes realizations in the southern part of the reservoir. In general, the three maps are similar to each other, but in fact some differences exists in the detail.

The hydraulic characterization of the fracture, that includes productivity and water breakthrough analysis were also performed. It was concluded that the production in the upper part of the reservoir (Unit U1) was mainly enhanced by fracture. On the other hand, for the middle reservoir unit (Unit U2), the production was enhanced by both fracture and super-K rock (sucrosic dolomite). The sucrosic dolomite rock was represented by rock type number 7 in the model. The lower reservoir unit (Unit U3) was a non-productive unit, except for the upper section which shows similar characteristic with the lowest section of the middle reservoir unit U2.

Additionally, grid block fracture properties have also been calculated based on the PLT data. The well test data were not explicitly included in the calculations; instead, it was qualitatively checked against estimated fracture permeabilities. The procedure of this calculation is beyond the scope of this paper. The calculated fracture porosity was in the range between 0.000015% to 0.3797 % whereas fracture permeability was in the range between 215 to 5700 mD, 70 to 5700 mD, and 0.5 to 12000 mD, for x, y, and z directions, respectively. The permeability anisotropy ratio ranges between 0.05 to 6.4, 0.0001 to 16.9, and 0.001 to 61.4, for (ky/kx), (kz/kx), and (ky/kz), respectively.

Interpreted PBU Tests. The data required from the PBU test include flow potential, kh, and radius of investigation, Re. Additionally, the model used in the interpretation also provided a valuable information. Figure 4 shows the typical response of PBU test for a well that is located within the fracture network (fracture well), whereas Figure 5 shows the response of PBU test for a well that is located outside the fracture network (matrix well).

Approach, Implementation and Results The main objective of this study is to integrate matrix and fracture systems using the available information in a consistent manner. Figure 6 shows the workflow used to achieve this objective. The workflow consists of an interative procedure that provides the link between geological static model and fluid flow simulation (dynamic) model. The use of flow simulation in this case is limited to the production capacity match only. It is not intended for history match purposes. The advantage of this system is that changes that were made on the permeability were performed within the geological understanding of the field while honoring the dynamic data. Thus, consistency between the two models (static and dynamic) can be maintained. The iterative process is terminated once satisfactory production capacity match, as evaluated from flow simulation, is achieved.

Enhancement Factor. One of the main ideas of the methodology proposed for this study is that the radial upscaling around a wellbore within a given investigation radius should match the flow capacity, i.e., permeability-thickness (kh), obtained from well tests. Since the core-permeability is in general lower than the well test permeability, when a mismatch between the two permeabilities occurs, the core permeability will be enhanced by a factor. This factor is called as an Enhancement Factor, EF, defined as the ratio between the well test kh and the simulated kh, as shown in Eq. (1) below.

el

welltestkhkhEF

mod= (Eq. 1)

Pre-Calibration. The workflow is divided into two main tasks, namely Pre-Calibration and Calibration. The pre-calibration step consists of three tasks, namely Dominant Factor Determination, Upscaling and Flow Simulation.

Dominant Factor Determination. The objective of the Dominant Factor Determination is to determine whether a well is predominantly controlled by matrix or fracture system. This can be done by performing one of the following two analyses.

(1) Enhancement Factor (EF) Analysis. The EF analysis was done by calculating the EF (see Eq. 1) for two different systems. The first one is the system with matrix only, i.e., assuming there is no fracture in the reservoir. The second system is the one that includes both matrix and fracture (with permeability of fracture based on PLT data only).

Figure 7 and Figure 8 show the results of the EF Analysis. For the first system (matrix only), The EF for all well strings vary significantly, ranging from 1.02 to 1100 (Figure 7). However, it is important to note that even after including fracture permeability based on PLT data, more than 50% of the wells have EF greater than 10 (Figure 8). This is a good indication of the fracture permeability which may not be captured by PLT data, but were “seen” by PBU Tests.

For the second system (matrix + fracture), the EF distribution has changed. The changes vary from well to well. They are significant for some wells, e.g. Well 13V-U1, and are insignificant for other wells, e.g., Well 15V-U2. Also, the reduction of EF from most of the wells can be seen from Figure 8, where the histogram has shifted to the left. It is also interesting to note that the introduction of the fracture into the system has changed the EF to even lower than 0.1 that indicates the effect of “over-enhancement” by the fracture. This is probably due to fracture model creating fractures which should not be there, or fractures which are sealed.

The changes of EF can be crosschecked by evaluating the location of each well (Figure 9). The wells that show significant change of EF is classified as Fracture Well, whereas the wells that are not so sensitive are classified as the Matrix Well.

(2) Sensitivity Analysis. The objective of the sensitivity analysis is to determine the sensitivity of the EF with respect to the changes in matrix or fracture permeabilities. This analysis provides the confirmation of the results obtained from the previous analysis (i.e., EF Analysis) with regard to the dominant factor. The analysis was conducted by multiplying

4 SPE 84876

the original matrix or fracture permeability (globally) with several constants. The wells that are sensitive to the changes in fracture permeability are classified as the fracture wells and likewise the wells that are sensitive to the changes in matrix permeability are classified as the matrix well. The results of the sensitivity analysis for the two wells used as examples in the EF analysis, i.e., Well 13V-U1 and Well 15V-U2, are shown in Figure 10 and Figure 11. In Figure 10 it is shown that Well 13V-U1 is sensitive to the changes of the fracture system whereas Well 15V-U2 is not. On the other hand, in Figure 11, Well 15V-U2 is sensitive to the changes of the matrix system whereas Well 13V-U1 is not. Thus, these results confirm the result of the EF Analysis shown previously.

Figure 12 shows the results of the Dominant Factor Determination for all wells for the three stochastic fracture realizations. It can be seen that most of the realizations predicted the same dominant factor except at 6 wells. This shows the presence of uncertainty with respect to the determination of fracture location.

Upscaling. Upscaling serves as the link between the static and dynamic models due to differences on the model size. For this study, the upscaling was done to reduce from 4.2 million grid blocks down to 180,000 grid blocks. Size reduction for this model was determined based on the dynamic upscaling technique using the streamline simulation method.7 Additionally, an appropriate upscaling technique such as flux-based (numerical simulation) method is required to ensure proper upscaling of permeability.

Flow Simulation. During the pre-calibration analysis, full-field flow simulation was conducted for the entire production history. The purpose of flow simulation at this stage is for the evaluation of production capacity match only. It is not intended for full history matching purposes, such as matching water/gas production. Production capacity match is evaluated from the capability of wells in producing oil at the specified rate and at the observed flowing bottom hole pressure, for a given permeability distribution. The evaluation produces three possible conditions, namely under-capacity, matched-capacity, or over-capacity. Depending on the condition at each well, it either needs further enhancement (under-capacity), does not need further enhancement (matched-capacity), or needs reduction in permeability (over-capacity).

Figure 13 shows the flow simulation results for the well with EF ≈ 1. It can be seen that this well is capable of producing the oil at the specified flowing bottom hole pressure. Wells with this condition is classified as “matched capacity”. On the other hand, for wells that has EF >> 1 the flow simulation results clearly shows that those wells were not able to produce the oil at the specified condition (Figure 14). The wells with this condition were classified as “under capacity”. Lastly, for wells with EF << 1, even though the production rate is matched perfectly, but the simulated pressure is way above the observed data (Figure 15). These wells are classified as “over capacity”. These results provide valuable information in modifying the permeability distribution in order to match the production data. Additionally, these results also demonstrate that a good link exists between the concept of EF and production capacity.

Figure 16 shows the result of the production capacity match for all wells. The results show that most of the fractured wells have been identified to match the production capacity. This may indicate that the Fracture Network has successfully predicted the location of major fractures that can be captured by seismic data. On the other hand, the production capacity at most of the matrix wells were classified as “under capacity”. This may be due the presence of micro fracture (observed as chicken-wire fracture), which cannot be captured by seismic data.

Calibration. The calibration step also contains three tasks, namely Permeability Adjustment, Upscaling and Flow Simulation. The objective of the permeability adjustment is to adjust matrix and fracture permeabilities separately to match the well test and production capacity. The adjustment can be done in two steps. The first step is for matrix adjustment and the second step is for fracture adjustment. Matrix adjustment can be done globally using kriging of the EF and then applied to the original matrix distribution. The control points for the kriging should be from the matrix wells or from wells that are both influenced by matrix and fracture. Fracture enhancement can be done in several ways, either globally or locally. In this study, local adjustment was considered more appropriate considering possibility of variable fracture conductivity, which was assumed constant during the dynamic characterization of fracture study. However, it is important to note that in order to be consistent with the assumption of vertical extension of the fracture (from top to bottom of reservoir), fracture adjustment was done uniformly in the vertical direction.

Figure 17 shows the result of kriging process for the matrix system for the middle reservoir unit U2. The use of kriging provides smooth changes of EF within the reservoir. This, in turn, will create smooth changes in the original permeability distribution. That is, it avoids abrupt local changes of permeability that may occur when permeability multiplier within a “box” definition is used in the flow simulation model. Note also, that these changes were done within the static geological framework of the model. Thus, consistency between the static and dynamic models is maintained.

Figure 18 shows the comparison of permeability distribution before and after adjustments. This adjustment was done by multiplying the original permeability with the kriged EF Map shown in Figure 17. It can be seen that the adjustment process does not create any significant abrupt changes anywhere in the reservoir.

The results of the calibration process show significant improvement on the production capacity match (see last column of Figure 16). All of the “under capacity” wells have been transformed into the “matched capacity” category. However, some of the “over capacity” wells remain the same. For this case, it appears that the introduction of fractures has added too much permeability around those wells. There are several possible reasons as to why this occurs, such as the possibility of closed fractures around those wells and the possibility of incorrect fracture locations.

The results shown in Figure 16 were obtained using the assumption that all fractures are more likely to be opened. This assumption was made based on the “global” geological

SPE 84876 5

observation. However, subsequent dynamic sensitivity analysis indicates the possibility of closed fracture for the wells that were still classified as “over-capacity”, especially for the wells that were located in the Upper Section (U1) of Southern Part of the reservoir. This possibility was also supported by the fact that these areas were so tight that it could not sustain production of more than 500 bpd, which was considered low for this field.

The final results of the integration process were obtained by using the assumption that all fractures in the southern part of the field were closed. Figure 19 shows the good match at an “over-capacity” well, obtained after implementing this assumption. Note that due to the assumption of vertical extension, the closed fractures were also extended to the bottom of the reservoir. Due to the high content of sucrosic dolomite in the Middle Section of the reservoir, closing the fractures around these area do not affect the flow simulation match for the wells that were completed in the Middle Section (U2) of the reservoir.

The results presented here have been used as the input for comprehensive history matching. The details of the history matching will be presented in a later paper. The approach in this paper has significantly reduced the effort in getting good history match.

Summary The study presented in this paper can be summarized as follows: 1. An innovative workflow was designed and implemented

into a field study to model the behaviour of naturally fractured reservoir.

2. The workflow was based on the availability of four type of data/information, namely matrix and fracture models, interpreted PBU Tests and Production Data.

3. Matrix model was obtained using geostatistical methodology after integrating well logs, core and seismic data.

4. Fracture model was obtained based on the curvature analysis of 3D Seismic, Borehole Image, Core, and Dynamic Data.

5. PBU Tests Data was used as the reference of permeability around well location, i.e., within the volume of investigation of the well test.

6. The Production Data was used as the well history of flow simulation model.

7. The workflow contains an iterative process between the static and dynamic model to ensure consistency between the two models.

8. Two tasks were included in the workflow, namely Pre-Calibration and Calibration. The purpose of the Pre-Calibration is to obtain a better understanding about the system in order to have a good system of permeability adjustment. The purpose of Calibration is to adjust permeability within the geological understanding of the model while matching the dynamic performance of the reservoir.

9. Implementation of the technique has resulted in permeability distribution that matches the production capacity of all the wells while maintaining geological understanding of the reservoir.

10. The results of the integration process have been used as input for comprehensive history matching of the full field model. Substantiall reduction in the effort to history match the field is observed as the outcome of the integration process.

References 1. Al-Deeb, et al., “Fully Integrated 3D-Reservoir Characterization

and Flow Simulation Study: A Field Case Example”, SPE 78510 presented at the 10th Abu Dhabi International Petroleum and Exhibition Conference held in Abu Dhabi, U.A.E., 13-16 October 2002.

2. Bahar, A., et al., “Practical Approach in Modeling Naturally Fractured Reservoir: A Field Case Study”, SPE 84078 presented at the SPE Annual Technical Conference and Exhibition, held in Denver, Colorado, 5 - 8 October 2003.

3. Agarwal, B., Hermensen, H, Stylte, J.E. and Thomas, L.K.: "Reservoir Characterization of Ekofisk Field: A Giant Chalk Reservoir in Norwegian North Sea - History Match," SPEREE (December 2000) 534-543.

4. Van-Lingen, P., Sengul M., Daniel J-M., and Cosentino, L, “Single Medium Simulation of Reservoirs with Conductive Faults and Fractures”, SPE 68165, presented at the 2001 SPE Middle East Oil Show, held in Bahrain, 17-20 March 2001.

5. Bahar, A. and Kelkar, M. “Journey From Well Logs/Cores to Integrated Geological and Petrophysical Properties Simulation: A Methodology and Application”, SPE Reservoir Evaluation and Engineering 3(5), October 2000.

6. Doyen, M., Psaila, D.E., Den Boer, L.D., and Jans, D., “Reconciling Data at Seismic and Well Log Scales in 3D Earth Modeling”, SPE 38698, presented at the 1997 SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, 5 – 8 October 1997

7. Ates, H., et al., “Ranking and Upscaling of Geostatistical Reservoir Models Using Streamline Simulation: A Field Case Study”, SPE 81497 presented at the 2003 SPE Middle East Oil Show, held in Bahrain, 9-12 June 2003.

8. Idrobo, E. A., Choudhary, M. K., Datta-Gupta, A., “Swept Volume Calculations and Ranking of Geostatistical Reservoir Models Using Streamline Simulation”, SPE 62557 presented at the 2000 SPE/AAPG Western Regional Meeting held in Long Beach, California, 19 – 23 June 2000

9. Bourbiaux, et al. “An Integrated Workflow to Account for Multi-Scale Fractures in Reservoir Simulation Models: Implementation and Benefits”, SPE 78489, presented at the 10th Abu Dhabi International Petroleum and Exhibition Conference held in Abu Dhabi, U.A.E., 13-16 October 2002.

10. Bloch G., et al., “Seismic Facies Analysis for Fracture Detection: A New and Powerful Technique”, SPE 81526, presented at the 2003 SPE Middle East Oil Show, held in Bahrain, 9-12 June 2003.

11. Charfeddine, M., et al., “Reconciling Well Test and Core Derived Permeability using Fracture Network: A Field Case Example”, SPE 78499 presented at the 10th Abu Dhabi International Petroleum and Exhibition Conference held in Abu Dhabi, U.A.E., 13-16 October 2002.

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(a)

(b)

(c)

Figure 1 Longitudinal Cross-Section of Matrix Properties: (a) Rock Type, (b) Porosity, and (c) Permeability

N40E

N70E

Figure 2 Fracture Maps Shown the Two Main Directions (N40E and N70E)

500 m 750 m

1000 m

Figure 3 Three stochastic realizations of fracture network

Figure 4 Pressure & Pressure Derivative Response for Fracture Well

Figure 5 Pressure & Pressure Derivative Response for Matrix Well

SPE 84876 7

Pre-CalibrationDominant Factor

UpscalingFlow Simulation

Production CapacityMatch

Accepted ?

NO

FinishYES

CalibrationPermeability Adjustment

UpscalingFlow Simulation

Figure 6 Iterative Procedure of Fracture and Matrix Integration Involving Static and Dynamic Models

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Figure 7 Results of EF Analysis showing Variation of EF for Two Different Models; System with Matrix Only and System with Matrix + Fracture

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(a) Location of a Fracture Well (b) Location of a Matrix Well Figure 9 Well Locations Relative to Fracture Network; (a) Fracture Well, (b) Matrix Well

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Figure 10 Sensitivity Analysis of Wells 13V-U1 and 15V-U2 with Respect to the Fracture System

8 SPE 84876

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Index String 500 m 750 m 1000 m

1 1V-U1 M F M2 11V-U1 F F F3 12V-U1 M M M4 13V-U1 F F F5 15V-U1 M M M6 18V-U1 M M M7 19V-U1 M M M8 20V-U1 F F M9 12H-U1 F F F10 18H-U1 F F F11 19H-U1 F F F12 22H-U1 M F F13 24H-U1 F F F14 25H-U1 M M M15 39H-U1 F M F16 1V-U2 M F M17 4V-U2 F F F18 6V-U2 F F F19 11V-U2 F F F20 12V-U2 M M M21 13V-U2 F F F22 14V-U2 M M M23 15V-U2 M M M24 18V-U2 M M M25 19V-U2 M M M26 6H-U2 F F F27 11H-U2 F F F28 21H-U2 M M M29 25H-U2 M M F30 26H-U2 F F F31 27H-U2 F F F32 32H-U2 F F F33 34H-U2 F F F34 35H-U2 M M M35 37H-U2 M M M36 38H-U2 M M M37 4V-U3 F F F38 4V-COM F F F39 6V-U3 F F F

M = MatrixF = Fracture

Figure 12 Results of the EF Analysis that shows Dominant

Factor for All Wells for the Three Stochastic Realization of Fracture Network.

Figure 13 Flow Simulation Result for Well with EF ≈ 1.0

Figure 14 Flow Simulation Result for Well with EF >>1.0

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Figure 15 Flow Simulation Result for Well with EF << 1.0

M = MatrixF = Fracture

Index String Dominant Factor

Production Capacity Before

Adjustment

Production Capacity

After Adjustment

2 11V-U1 F MATCH MATCH4 13V-U1 F UNDER MATCH8 20V-U1 F UNDER MATCH9 12H-U1 F OVER OVER

10 18H-U1 F OVER OVER11 19H-U1 F MATCH MATCH13 24H-U1 F UNDER MATCH15 39H-U1 F MATCH MATCH17 4V-U2 F MATCH MATCH18 6V-U2 F MATCH MATCH19 11V-U2 F MATCH MATCH21 13V-U2 F UNDER MATCH26 6H-U2 F MATCH MATCH27 11H-U2 F MATCH MATCH30 26H-U2 F MATCH MATCH31 27H-U2 F MATCH MATCH32 32H-U2 F UNDER MATCH33 34H-U2 F MATCH MATCH37 4V-U3 F MATCH MATCH38 4V-COM F UNDER MATCH39 6H-U3 F MATCH MATCH1 1V-U1 M UNDER MATCH3 12V-U1 M UNDER MATCH5 15V-U1 M UNDER MATCH6 18V-U1 M UNDER MATCH7 19V-U1 M UNDER MATCH

12 22H-U1 M MATCH MATCH14 25H-U1 M UNDER MATCH16 1V-U2 M UNDER MATCH20 12V-U2 M UNDER MATCH22 14V-U2 M UNDER MATCH23 15V-U2 M UNDER MATCH24 18V-U2 M UNDER MATCH25 19V-U2 M UNDER MATCH28 21H-U2 M MATCH MATCH29 25H-U2 M UNDER MATCH34 35H-U2 M UNDER MATCH35 37H-U2 M UNDER MATCH36 38H-U2 M UNDER MATCH

Figure 16 Result of the Production Capacity Match Before and After Adjustment

Figure 17 Kriged Map of EF for Matrix System for the Middle Reservoir Unit U2.

10 SPE 84876

(a) Before Adjustment

(b) After Adjustment

Figure 18 Comparison of Permeability Distribution; (a) Before and (b) After Adjustment

Figure 19 Flow Simulation Result for an “Over-Capacity” Well Obtained After Implementing the Assumption of Closed Fractures for the Southern Part of the Field