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Integration of static and dynamic reservoir data to optimize the generation of subsurface fracture map

  • Reda Abdel AzimEmail author
Open Access
Original Paper - Production Engineering
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Abstract

This paper presents a new three-dimensional fully coupled poroelastic numerical model to simulate pressure transient response of naturally fractured reservoirs. One of the main applications of the new approach is to improve the reservoir characterization by decreasing the uncertainties associated with subsurface fracture map and to understand the interaction between fracture and matrix. The model is based on a hybrid methodology to simulate fluid flow by combining a single continuum and discrete fracture network approaches. To decrease the uncertainty associated with subsurface fracture map, a different realizations of the discrete fracture systems are generated based on image logs, seismic, geological features and the tectonic history of the studied reservoir. An iterative loops have been used between the reservoir static model and dynamic well-test data to optimize the generation of subsurface fracture maps. At each loop, a new discrete fracture system is used and the simulated pressure transient data are compared with the available field data. The process is repeated till the matching between the simulated and the measured pressure transient data is achieved using the most appropriate fracture realization. The numerical model is validated against an analytical pressure transient solution for dual-porosity system, and then applied to a real case taken from fractured basement reservoir in offshore Southern Vietnam. The developed hybrid methodology used to simulate fluid flow and rock deformation to improve the characterization of the fractured basement by simulating the pressure transient testing. The results predicted by the presented model are in a good agreement with field data; also the model was able to predict the fractures network distribution and fractures inherent properties around the tested well.

Keywords

Dynamic well test Naturally fractured reservoirs Finite element modelling 

Introduction

Flow transport in fractured medium differs from that in porous medium, an intergranular porous medium. In naturally fractured reservoirs, the matrix provides the main storage for fluids, while flow transport takes place through fracture network system. The first step in fluid flow simulation is to characterize the fracture network, therefore the length, geometry and orientation of fractures. Characterization of fracture system has been carried out by integrating information from two different sources; static data (seismic, well logs, core description, borehole images, tectonic history, geological structures, etc.) and dynamic data (well-test data and well production history). Readers can be referred to the publication by “Gholizadeh Doonechaly and Rahman (2012)” for interpretation of static data and generation of subsurface fracture map using various techniques. The dynamic data, on the other hand, have been used to further improve subsurface fracture properties. To date the fractured medium, however, is represented by bulk macroscopic values of averaged point-to-point variations of relevant properties over a representative elementary volume (REV) instead of discrete fractures.

Currently three major approaches have been used to simulate fluid flow in a fractured reservoir which includes single continuum, dual-porosity and discrete fracture network.

In the single continuum approach, the reservoir is divided into a number of blocks and the fracture properties in each block are averaged by using a representative effective permeability tensor. Estimation of the effective permeability tensor for regular fracture pattern of simple geometries was first proposed by Lough (Lough et al. 1997). The approach was further improved by Teimoori et al. (2005) to simulate fluid flow in arbitrary oriented and intersected fracture system (Teimoori et al. 2005). Despite the improved computational efficiency of the single continuum approach, it does not adequately address the flow behaviour of fractures (Tarahhom et al. 2009).

In the dual continuum approach, the reservoir is divided into two major parts: fractures and matrix. According to Warren and Root (1963), a fractured reservoir comprises a series of sugar cubes with evenly spaced fractures, as shown in Fig. 1. In this approach, fractures provide the main flow paths, while the matrix acts as a source of fluid. The fluid transfer between the fractures and the matrix is defined based on the specific transfer functions. Lee (1977), Pruess (1985) and Kazemi (1969) introduced a range of different matrix/fracture transfer functions to simulate the fluid flow in large scales. Following these works, a significant number of studies have been carried out in both analytical and numerical frameworks (Pride and Berryman 2003, Gong et al. 2008).
Fig. 1

Dual-porosity model description of a naturally fractured reservoir: a actual reservoir and b sugar cube reservoir model

The limitations of dual-porosity/permeability approach are as follow: (1) the fluid distribution within the matrix blocks remains constant during the simulation period, (2) the model cannot be applied to disconnected and discrete fratured (oriented fractures) media and (3) a small number of large-scale fractures can be considered for flow simulation. Therefore, the developed numerical model presented in this paper overcomes all above mentiond limitations by considering fluid flows through matrix porous media and discrete fractures.

The discrete fracture approach, on the other hand, has been proposed as an explicit means of considering the fluid flow and transport inside individual fractures (Karimi-Fard et al. 2004).

The transmissivity of individual fractures and their effect on fluid flow have been studied by considering fracture properties, namely orientation, size and location into the flow calculation. This approach was first introduced for single phase flow. Among of the earliest authors that represent the fracture as 1D entity in 2D fractured porous system were Noorishad et al. (1984) and Baca et al. (1984). Further studies were done by Wei et al. (1998), Karimi-Fard et al. (2004), Rogers et al. (2007) and Watanabe et al. (2010). The main difficulty of discrete fracture approach is the need for extensive computation involved in reservoir scale flow simulation.

Wei et al. (1998) developed a 3D numerical model in order to simulate pressure transient through fracture/matrix system. The results indicated that the simulated pressure derivative showed a different behaviour for each fracture pattern configuration. Also authors showed that dual-porosity model failed to describe the behaviour of fluid flow through fractured system in many cases. Carlson (2003) was using specific transfer functions to simulate the flow transfer from fractures to matrix. It was assumed that the fractures provide the main flow conduit and matrix acts as a source/sink to the fractures.

Basquet et al. (2005) used a homogenization method to simulate pressure transient through fractured system. The idea is to simplify discrete fracture network approach by reducing number of generated fracture nodes. This approach keeps the actual fracture network geometry and also the hydraulic properties of the whole system. Casabianca et al. (2007) presented a discrete fracture network model by using an integrated interpretation methodology to improve the characterization of a fractured chalk reservoir.

Recently, there are many studies on using pressure transient data for naturally fractured reservoir modelling. Morton (2012) presented two new techniques used to calibrate numerical-based fracture model with well-test data by integrating a reservoir model inversion technique and boundary element method for determining the pressure transient behaviour of the reservoir with arbitrary distributed vertical fractures. Kuchuk and Biryukov (2012) presented semi-analytical solution in order to understand pressure behaviour of continuously and discretely fractured reservoirs. This solution used to interpret well-test data of formation containing network of discrete conductive fractures. The author showed that Warren and Root's (1963) dual-porosity model is not adequate for pressure transient well-test interpretation as it does not capture the behaviour of these reservoirs.

In this study, a hybrid methodology—combining the single continuum and the discrete fracture approaches—is utilized to increase the efficiency of the fluid flow simulation. The reservoir domain is discretized using tetrahedral elements, and fluid flow is then simulated in these elements by using the single continuum approach. In the proposed methodology, a threshold value for fracture radius is defined. Fractures, with the radius smaller than the threshold value, are used to generate the grid-based permeability tensor. Fractures, with radius longer than the threshold value, are explicitly discretized in the domain by using the triangular elements, and the fluid flow is modelled using the discrete fracture approach.

Model validation

To validate the presented numerical model, a sugar cube reservoir is created using in house 3D mesh generator code with two sets of orthogonal vertical fractures having the same dip angle (90°) and different azimuth angles (0°, 90°). An equal fracture spacing of 250 m is assumed with a vertical well at the centre of the model penetrating the whole reservoir thickness (see Fig. 2). Horizontal fractures are ignored as usually not observed below moderate depth, and only vertical fractures are considered (Tankersley et al. 2013). Properties of the fractures, matrix and stresses values are presented in Table 1. A single phase draw down test is performed for 3500 days at constant production rate of 4500 STB/day simulated numerically using a discrete fracture network approach. The pressure response at the wellbore was compared against analytical pressure transient solution introduced by Warren and Root (1963) for dual-porosity model.
Fig. 2

Stressed sugar cube model with two sets of orthogonal vertical fractures, a vertical well penetrating the model from top to the bottom

Table 1

Input data for a sugar cube model used in a numerical simulation validation process

Parameter

Value

Reservoir dimensions

7500 m × 7500 m × 1620 m

Fracture spacing

250 m

Matrix permeability

0.01 mD

Matrix porosity

0.01

Fracture permeability

1000 mD

Wellbore storage

0 bbl/psi−1

Initial reservoir pressure

10,000 psia

Fluid viscosity

0.2 cp

Fluid compressibility

22.4E−06 psi−1

Horizontal stresses

9000 psi

Vertical stress

12,000 psi

Figure 3 shows that the simulated response of discrete fracture model matched well with the analytical solution. The derivative behaviour looks like a bell between 20 and 1000 h. This behaviour indicates that within the transition flow period the matrix starts to feed the fractures network. During this flow period, the oil production at the wellbore is very low and pressure starts to drop slowly. The dip of this bell-shaped behaviour is controlled by the value of matrix storativity ratio (ω). As the (ω) gets smaller, the dip gets deeper and starts earlier.
Fig. 3

log–log plot of pressure drop and pressure derivative of a drawdown test for a dual-porosity model with a vertical well using Discrete Fracture Model

The horizontal portion of the pressure derivative curve from 1000 to 40,000 h indicates the ending of transition period and starting of the composite system flow. This flow period is controlled by the value of interporosity flow coefficient (λ).

The unit slope of pressure derivative curve between 10,000 and 100,000 h indicates pseudo-steady-state condition for the entire reservoir volume. By using this flow period, a reservoir volume and shape can be calculated.

At early-time response for idealized dual-porosity transient behaviour with a very low wellbore storage effects, a first radial flow regime is expected to appear before starting of transition flow period. This radial flow regime is governed by the flow only inside the fractures network. The simulation result does not show that, as the response of discrete fracture network model before 10 h is not clear and the presented simulation model ignored the wellbore storage effect.

Case study

The test case is taken from granitic oil-bearing formation in southern offshore Vietnam (Farag et al. 2010). The formation is highly fractured with fractures having short lengths identified from image logs and forming the storage capacity of the reservoir. Geological interpretation showed that the reservoir has very low matrix porosity and permeability. Pore space in the rock is formed through the fractures network and digenetic processes. A Drill Stem Test (DST) was conducted in this formation with controlled flow periods before shutting to understand the extent of the reservoir from the wellbore, prove the possibility of the hydrocarbon existence, and evaluate well deliverability and reservoir performance.

Farag et al. (2010) used a simple model of parallel vertical fractures with a vertical well to simulate main build-up period of DST test by ignoring the actual distribution of fracture network around the tested well.

The aim of this study is to generate a subsurface fracture map using available field data, and to use the presented model to simulate the main build-up period of the DST test to calibrate logs permeability values in the area under study.

Generation of discrete fracture map of a typical basement reservoir

The author used an innovative methodology to generate the 3D subsurface fracture map of the studied reservoir by integrating field data, such as wellbore images and conventional well logs. In this approach, an object-based conditional global optimization technique is used to generate the subsurface fracture map of the reservoir which combines the following: (1) statistical analysis of different sources of data (as mentioned above); (2) finite element-based modelling of tectonic history of the reservoir structure to generate probabilistic fracture attributes; (3) development of complex relationship between different sources of data (data sources mentioned in (1) and the data generated in (2)) using back propagation neural network; (4) sequential Gaussian stochastic simulation to generate object-based 3D subsurface fracture map and (5) simulated annealing optimization technique to generate an optimum subsurface fracture map. In object-based model, each single fracture is treated as a single object with its specific properties such as location (centre point), dip, azimuth and size (radius). Each object possesses a variety of rules for behaviour in space such as shift, rotate, grow, shrink, multiply or disappear. The optimization process involves a series of trial and errors utilizing the nominated rules to minimize the objective function which is the difference between each fracture system realization and the target. The procedure is detailed in Gholizadeh Doonechaly and Rahman (2012).

Figure 4 shows the generated subsurface fracture map around the tested well. Figure 5 shows the DST history data. Reservoir input data used for numerical simulation model are presented in Table 2.
Fig. 4

Three-dimensional generated fracture map (a), three-dimensional mesh (tetrahedral elements for matrix and triangle elements for fractures) used in the simulation process (b) distribution of applied stresses in the generated mesh

Fig. 5

DST history used to test the applicability of the current numerical simulation model in improving reservoir characterization process (Farag et al. 2010)

Table 2

Reservoir inputs parameters for numerical simulation model

Parameter

Value

Reservoir dimensions

600 m × 600 m × 90 m, Vertical well is partially penetrated the formation thickness

Matrix permeability

0.01 mD

Matrix porosity

0.002

Fracture aperture

0.04 mm

Fracture permeability

100 D

Fracture porosity

0.1

Fracture compressibility

3 × 10−4 Psi−1

Initial reservoir pressure

5063 psia

Fluid viscosity

1.38 cp

Fluid compressibility

1.0E−06 psi−1

Production time before shut in (t p)

72 h

Production flow rate before shut in

5571 bbl/d

Horizontal stresses

4800 psia

Vertical stress

6000 psia

The hybrid approach

Single phase fluid flow in a typical fractured basement reservoir is simulated by coupling 3D permeability tensors (heterogamous permeability, see Fig. 6) with flow through discrete fractures. Long fractures (l ≥ 40 m) along with their original properties (orientations and locations) are discretized explicitly within the reservoir domain.
Fig. 6

Description of how the reservoir domain is discretized for hybrid methodology: a the reservoir domain is divided into a number of grid blocks without considering of long fractures; b 3D block-based permeability tensor for short to medium fractures; c the block-based permeability tensors are distributed to the corresponding tetrahedral elements (matrix porous media) and long fractures are discretized explicitly

The well path has been divided into number of segments and each segment has a different flux which depends on the element size intersected by each one. Figure 7 shows how the wellbore intersected with blocks of lower fracture density.
Fig. 7

Close view of fracture intensity map around the wellbore. The producing well partially penetrates the formation thickness and intersects with blocks that have a low fracture intensity value. Fracture block intensities less than 0.165 m−1 are removed from the fracture intensity map

Firstly, different subsurface fracture maps have been generated using the available field data. The number of fractures that have been created is 4000 fractures. The fractures with length <40 m were used to estimate the block-based permeability tensor, and long fracture with length >40 m were discretized explicitly in the reservoir domain. The fracture aperture has been assumed of 0.04 mm which is equal to the maximum value shown in the image logs. Due to the lack of information about far field stresses, the horizontal stresses are estimated based on overburden stress (Zoback 2007). Results of simulated as well as measured data from field test are presented in Fig. 8.
Fig. 8

Shut in pressure plot of build-up test for measured and simulated data using two different values of fracture aperture 0.04 mm and 0.004 mm

As shown in Fig. 8, a poor matching occurred between build-up history and simulated data at first trial using fracture aperture = 0.04 mm using the best generated fracture realization. In addition, the figure (see Fig. 8) shows the simulated pressure curve is higher than measured one. This behaviour is obtained due to the use of a large fracture aperture that enables pressure to build up quickly around the wellbore.

Since the simulated results were moving in the direction as expected as it has almost the same trend of the history data, the decision is taken by keeping the fracture realization that has been predicted using the iteration process as described before, and further trials were continued by changing fracture aperture.

After several trials, the simulated data are matched with the history data by using fracture aperture of 0.004 mm. The early-time response is truncated from gauge data after 20 min from the beginning of the test by using Horner plot (Aguilera 1980); wellbore storage effect has been neglected. The numerical model was able to match the build-up test reasonably well for about 52 h.

Figure 9 presents a log–log plot between (P ws − P wf) versus ∆t e (equivalent time), where
$$\Delta t_{e} = \frac{\Delta t}{{\frac{{t_{p} + \Delta t}}{\Delta t}}}.$$
(1)
Fig. 9

Log–Log plot of build-up test for a vertical partially penetrated well in a highly fractured system

Figure 9 shows a good match between measured and simulated pressure change using fracture aperture = 0.004 mm. The simulated and measured data before 0.1 h does not show the effect of wellbore storage (slope of plotted data before 0.1 h is not a unit slope).

Figure 10 shows a log–log plot of pressure derivatives of measured and simulated data vs time (∆t).. By using this plot most of flow regimes can be defined. The first flow regime as expected is a spherical flow which occurs due to partially penetrated well used in the numerical model and is seen in the derivative plot as a negative slope trend. The second flow regime is a radial flow which appears in the derivative curve as a flat line with zero slope (m = 0). The third flow regime is a linear flow recognized as a positive half-slope trend caused by the fractures network.
Fig. 10

Log–Log plot for measured and simulated pressure derivative data

The build-up period was simulated successfully using the generated subsurface fracture map around the area under study. The new model was able to characterize the area around the tested well and also the fracture aperture value has been predicted.

Conclusion

A new numerical simulation model is developed using a hybrid technique of single continuum and Discrete Fracture Network approaches to simulate well pressure transient response for improving reservoir static model by decreasing uncertainties in reservoir characterization process. The new model used a permeability tensor concept to replace fractures with a short length by an equivalent tensor in a three-dimensional space using a finite element technique. A discrete fracture network approach used to simulate fluid flow inside long fractures to observe the effect of these fractures on the pressure response at the tested well.

A model was validated against analytical solution of Warren and Root using dual-porosity reservoir to test its robustness and accuracy, and then the applicability of the model on how to simulate actual build-up test is performed. The model is able to characterize the simulated area around the studied well by predicting the proper fractures network distribution and also the fractures network inherent parameters.

References

  1. Aguilera R (1980) Naturally fractured reservoirs. Petroleum Publishing Company. Tulsa, OK, pp 200–220Google Scholar
  2. Baca RG, Arnett RC, Langford DW (1984) Modelling fluid flow in fractured porous rock masses by finite-element techniques. Int J Numer Meth Fluids 4(4):337–348CrossRefGoogle Scholar
  3. Basquet R, Cohen CE, Bourbiaux B (2005). Fracture flow property identification: an optimized implementation of discrete fracture network models, paper SPE 93748 presented at the 14th SPE middle east oil and gas show and conference, 12–15 March, BahrainGoogle Scholar
  4. Carlson R (2003) Practical reservoir simulation: using, assessing, and developing results. PennWell, TulsaGoogle Scholar
  5. Casabianca D, Jolly RJH, Pollard R (2007) The machar oil field: water flooding a fractured chalk reservoir. Geol Soc Lond Spec Publ 270(1):171–191CrossRefGoogle Scholar
  6. Durlofsky LJ (1991) Numerical calculation of equivalent grid block permeability tensors for heterogeneous porous media. Water Resour Res 27(5):699–708CrossRefGoogle Scholar
  7. Farag SM, Mas C, Maizeret P-D, Li B, Le HV (2010) An integrated workflow for granitic basement reservoir evaluation. Soc Petroleum Eng. doi: 10.2118/123455-PA Google Scholar
  8. Gholizadeh Doonechaly N, Rahman SS (2012) 3D hybrid tectono-stochastic modeling of naturally fractured reservoir: application of finite element method and stochastic simulation technique. Tectonophysics 541–543:43–56CrossRefGoogle Scholar
  9. Gong B, Karimi-Fard M, Durlofsky LJ (2008) Upscaling discrete fracture characterizations to dual-porosity, dual-permeability models for efficient simulation of flow with strong gravitational effects. SPE J Richardson 13(1):58CrossRefGoogle Scholar
  10. Karimi-Fard M, Durlofsky LJ, Aziz K (2004) An efficient discrete fracture models applicable for general purpose reservoir simulators. SPE J 9(2):227–236CrossRefGoogle Scholar
  11. Kazemi H (1969) Pressure transient analysis of naturally fractured reservoirs with uniform fracture distribution. SPE J 9:451–462CrossRefGoogle Scholar
  12. Kuchuk FJ, Biryukov D (2012). Transient pressure test interpretation from continuously and discretely fractured reservoirs. In: SPE annual technical conference and exhibition. Society of Petroleum Engineers, San Antonio, TXGoogle Scholar
  13. Lee DA (1977) Flow in fractured porous media. Water Resour Res 26:351–356Google Scholar
  14. Lough MF, Lee SH, Kamath J (1997) A new method to calculate effective permeability of gridblocks used in the simulation of naturally fractured reservoirs. SPERE 12(3):219–224CrossRefGoogle Scholar
  15. Morton KL (2012) Integrated interpretation for pressure transient tests in discretely fractured reservoirs, paper SPE 154531 presented at the EAGE annual conference & exhibition incorporating SPE Europe, 4–7 June, Copenhagen, DenmarkGoogle Scholar
  16. Noorishad J, Tsang C, Witherspoon P (1984) Coupled thermal-hydraulic mechanical phenomena in saturated fractured porous rocks: numerical approach. J Geophys Res 89:10365–10373CrossRefGoogle Scholar
  17. Pride SR, Berryman JG (2003) Linear dynamics of double-porosity dual-permeability materials I Governing equations and acoustic attenuation. Phys Rev E 68(3):036603CrossRefGoogle Scholar
  18. Pruess K (1985) A practical method for modeling fluid and heat flow in fractured porous media. Soc Petrol Eng J 25(01):14–26CrossRefGoogle Scholar
  19. Rogers S, Enachescu C, Trice R, Buer K (2007) Integrating discrete fracture network models and pressure transient data for testing conceptual fracture models of the Valhall chalk reservoir, Norwegian North Sea. Geol Soc Lond, Spec Publ 270(1):193–204CrossRefGoogle Scholar
  20. Snow DT (1969) Anisotropie permeability of fractured media. Water Resour Res 5(6):1273–1289CrossRefGoogle Scholar
  21. Tankersley T, Pan Y, Narr W, Laidlaw CP, Flodin E, Hui M-H, Bateman P (2013) Integration of pressure transient data in modeling tengiz field, kazakhstan—a new way to characterize fractured reservoirs. Soc Petroleum Eng. doi: 10.2118/165322-MS
  22. Tarahhom F, Sepehrnoori K, Marcondes F (2009) A novel approach to integrate dual porosity model and full permeability tensor representation in fractures. Paper presented at the SPE reservoir simulation symposiumGoogle Scholar
  23. Teimoori A, Chen Z, Rahman SS, Tran T (2005) Effective permeability calculation using boundary element method in naturally fractured reservoirs. Pet Sci Technol 23(5–6):693–709CrossRefGoogle Scholar
  24. Warren JE, Root PJ (1963) The behaviour of naturally fractured reservoirs. SPE J 3:245–255CrossRefGoogle Scholar
  25. Watanabe N, Wang W, McDermott CI, Taniguchi T, Kolditz O (2010) Uncertainty analysis of thermo-hydro-mechanical coupled processes in heterogeneous porous media. Comput Mech 45(4):263–280CrossRefGoogle Scholar
  26. Watanabe N, Wang W, Taron J, Görke UJ, Kolditz O (2011) Lower-dimensional interface elements with local enrichment: application to coupled hydro-mechanical problems in discretely fractured porous media. Int J Numer Methods Eng 90(8):1010–1034. doi: 10.1002/nme.3353 Google Scholar
  27. Wei L, Hadwin J, Chaput E, Rawnsley K, Swaby P (1998) Discriminating fracture patterns in fractured reservoirs by pressure transient test, paper SPE 49233 presented at the SPE annual technical conference and exhibition, 27–30 September, New Orleans, LAGoogle Scholar
  28. Zienkiewicz OC, Taylor RL (2000) The finite element method: solid mechanics, vol 2. Butterworth-heinemannGoogle Scholar
  29. Zimmerman RW, Bodvarsson GS (1996) Hydraulic conductivity of rock fractures. Transp Porous Media 23(1):1–30CrossRefGoogle Scholar
  30. Zoback MD (2007) Reservoir geomechanics. Cambridge University Press, ChicagoGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Mining, Petroleum and Metallurgical Department, Faculty of EngineeringCairo UniversityGizaEgypt

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