History matching of naturally fractured reservoirs based on the recovery curve method

Abstract The discrete fracture network (DFN) and Multiple-Continua concept are among the most widely used methods to model naturally fractured reservoirs. Each faces specific limitations. The recently introduced recovery curve method (RCM) is believed to be a compromise between these two current methods. In this method the recovery curves are used to determine the amount of mass exchanges between the matrix and fracture mediums. Two recovery curves are assigned for each simulation cell, one curve for gas displacement in the presence of the gravity drainage mechanism, and another for water displacement in the case of the occurrence of the imbibition mechanism. These curves describe matrix–fracture mass transfer more realistically and therefore can be of great use when obtaining historical production data. This paper presents the potential of the RCM within the framework of history matching of naturally fractured reservoirs to determine the appropriate recovery curves. In particular, the phase contact positions of a sector model, extracted from a real fractured reservoir, are matched throughout the production history using the RCM. Therefore, the main contribution of this work is using the historical data of contact positions to obtain a better description of the matrix–fracture communication. The distribution of an ensemble of the history matched models was illustrated by Boxplot, and the Genetic Algorithm (GA) and the Neighborhood-Bayes technique (NAB) were utilized for optimization and uncertainty quantification, respectively. The calculated recovery curves results are in good agreement with the production history of the model and the Bayesian credible interval (P10–P90) of the proposed method is satisfactory. This work also confirmed the potential applicability of the combined GA and NAB method for optimization and uncertainty quantification purposes.

[1]  D. Oliver,et al.  Recent progress on reservoir history matching: a review , 2011 .

[2]  M. Sambridge Geophysical inversion with a neighbourhood algorithm—II. Appraising the ensemble , 1999 .

[3]  Khalid Aziz,et al.  New Transfer Functions for Simulation of Naturally Fractured Reservoirs with Dual Porosity Models , 2006 .

[4]  M. T. Amiry Importance and Applicability of a Generalized Shape Factor by Modeling Dual Porosity Reservoirs , 2012 .

[5]  C. C. Mattax,et al.  Imbibition Oil Recovery from Fractured, Water-Drive Reservoir , 1962 .

[6]  K. Aziz,et al.  Matrix-fracture transfer shape factors for dual-porosity simulators , 1995 .

[7]  L. Durlofsky,et al.  Efficient real-time reservoir management using adjoint-based optimal control and model updating , 2006 .

[8]  Sebastian Geiger,et al.  Static and Dynamic Assessment of DFN Permeability Upscaling , 2012 .

[9]  H. Kazemi,et al.  NUMERICAL SIMULATION OF WATER-OIL FLOW IN NATURALLY FRACTURED RESERVOIRS , 1976 .

[10]  Hossein Kazemi,et al.  Improvements in Simulation of Naturally Fractured Reservoirs , 1983 .

[11]  G. I. Barenblatt,et al.  Basic concepts in the theory of seepage of homogeneous liquids in fissured rocks [strata] , 1960 .

[12]  Georg M. Mittermeir,et al.  Derivation of the Kazemi–Gilman–Elsharkawy Generalized Dual Porosity Shape Factor , 2011, Transport in Porous Media.

[13]  M. Sambridge Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space , 1999 .

[14]  Michael Andrew Christie,et al.  Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification , 2010 .

[15]  Sarim Jamal,et al.  Effect of DFN Upscaling on History Matching and Prediction of Naturally Fractured Reservoirs , 2013 .

[16]  David C. Hoaglin,et al.  Applications, basics, and computing of exploratory data analysis , 1983 .

[17]  Erdal Ozkan,et al.  A Critical Review for Proper Use of Water/Oil/Gas Transfer Functions in Dual-Porosity Naturally Fractured Reservoirs: Part II , 2009 .

[18]  Yasin Hajizadeh,et al.  Ant colony optimization for history matching and uncertainty quantification of reservoir models , 2011 .

[19]  Abbas Firoozabadi,et al.  Water Injection in Water-Wet Fractured Porous Media: Experiments and a New Model with Modified Buckley-Leverett Theory , 1999 .

[20]  C. W. Harper,et al.  A FORTRAN IV program for comparing ranking algorithms in quantitative biostratigraphy , 1984 .

[21]  Roberto Aguilera,et al.  Naturally Fractured Reservoirs , 1980 .

[22]  Zoltan E. Heinemann,et al.  Reserve Estimation for Naturally Fractured Reservoirs Using Numerically Derived Recovery Curves , 2008 .

[23]  Zoltan E. Heinemann,et al.  Method to Preliminary Estimation of the Reserves and Production Forecast for Dual Porosity Fractured Reservoirs , 2008 .

[24]  Jonathan Carter,et al.  Errors in History Matching , 2004 .

[25]  L. Y. Hu,et al.  History Matching of Object-Based Stochastic Reservoir Models , 2005 .

[26]  Martin J. Blunt,et al.  Multirate-Transfer Dual-Porosity Modeling of Gravity Drainage and Imbibition , 2007 .

[27]  E. Jaynes Probability theory : the logic of science , 2003 .

[28]  Arne Graue,et al.  Oil Production by Spontaneous Imbibition from Sandstone and Chalk Cylindrical Cores with Two Ends Open , 2010 .

[29]  J. R. Gilman An Efficient Finite-Difference Method for Simulating Phase Segregation in the Matrix Blocks in Double-Porosity Reservoirs , 1986 .

[30]  L. K. Thomas,et al.  Fractured Reservoir Simulation , 1980 .

[31]  Karsten Pruess,et al.  A Physically Based Approach for Modeling Multiphase Fracture-Matrix Interaction in Fractured Porous Media , 2004 .

[32]  W. B. Whalley,et al.  The use of fractals and pseudofractals in the analysis of two-dimensional outlines: Review and further exploration , 1989 .

[33]  Bernard Bourbiaux,et al.  Simulation of Naturally Fractured Reservoirs. State of the Art - Part 2 – Matrix-Fracture Transfers and Typical Features of Numerical Studies , 2010 .

[34]  Satomi Suzuki,et al.  History Matching of Naturally Fractured Reservoirs Using Elastic Stress Simulation and Probability Perturbation Method , 2007 .

[35]  Yong S. Yang Collapse Pressure of Coiled Tubing Under Axial Tension , 1997 .

[36]  J. E. Warren,et al.  The Behavior of Naturally Fractured Reservoirs , 1963 .

[37]  Michael Andrew Christie,et al.  Prediction under uncertainty in reservoir modeling , 2002 .

[38]  T. B. Tan,et al.  Reservoir Characterization of a Fractured Reservoir Using Automatic History Matching , 1993 .

[39]  Zillur Rahim,et al.  An Analytical Model for History Matching Naturally Fractured Reservoir Production Data , 1990 .

[40]  Mohan Kelkar,et al.  Automatic History Matching of Naturally Fractured Reservoirs and a Case Study , 2005 .

[41]  Sohrab Zendehboudi,et al.  Empirical Modeling of Gravity Drainage in Fractured Porous Media , 2011 .

[42]  Mohan Kelkar,et al.  Efficient History Matching in Naturally Fractured Reservoirs , 2006 .

[43]  David Sirda Shanks Oil Rim Tool Measures Reservoir Fluids Continuously in Real Time, Below Pump , 2013 .