Field scale history matching and assisted history matching using streamline simulation

Field Scale History Matching and Assisted History Matching Using Streamline Simulation. (August 2003) Arun Kharghoria, B.S., Indian School of Mines, Dhanbad, India; M.S., University of Tulsa Chair of Advisory Committee: Dr. Akhil Datta-Gupta In this study, we apply the streamline-based production data integration method to condition a multimillion cell geologic model to historical production response for a giant Saudi Arabian reservoir. The field has been under peripheral water injection with 16 injectors and 70 producers. There is also a strong aquifer influx into the field. A total of 30 years of production history with detailed rate, infill well and re-perforation schedule were incorporated via multiple pressure updates during streamline simulation. Also, gravity and compressibility effects were included to account for water slumping and aquifer support. To our knowledge, this is the first and the largest such application of production data integration to geologic models accounting for realistic field conditions. We have developed novel techniques to analytically compute the sensitivities of the production response in the presence of gravity and changing field conditions. This makes our method computationally extremely efficient. The field application takes less than 6 hours to run on a PC. The geologic model derived after conditioning to production response was validated using field surveillance data. In particular, the flood front movement, the aquifer encroachment and bypassed oil locations obtained from the geologic model was found to be consistent with field observations. Finally, an examination of the permeability changes during production data integration revealed that most of these changes were aligned along the facies distribution, particularly the ‘good’ facies distribution with no resulting loss in geologic realism. We also propose a novel assisted history matching procedure for finite difference simulators using streamline derived sensitivity calculations. Unlike existing assisted history matching techniques where the user is required to manually adjust the parameters, this procedure combines

[1]  Akhil Datta-Gupta,et al.  Estimating Relative Permeability From Production Data: A Streamline Approach , 2000 .

[2]  Akhil Datta-Gupta,et al.  Integrating field production history in stochastic reservoir characterization , 1997 .

[3]  Akhil Datta-Gupta,et al.  Swept Volume Calculations and Ranking of Geostatistical Reservoir Models Using Streamline Simulation , 2000 .

[4]  Michael A. Saunders,et al.  LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares , 1982, TOMS.

[5]  D. McLaughlin,et al.  A Reassessment of the Groundwater Inverse Problem , 1996 .

[6]  Rapid Simulation of Multiphase Flow Through Fine-Scale Geostatistical Realizations Using a New, 3-D, Streamline Model: A Field Example , 1996 .

[7]  P. B. Crawford,et al.  Low Areal Sweep Efficiencies In Flooding Heterogeneous Rock , 1970 .

[8]  Akhil Datta-Gupta,et al.  Timestep Selection During Streamline Simulation Through Transverse Flux Correction , 2004 .

[9]  J. Scales.  Tomographic inversion via the conjugate gradient method , 1987 .

[10]  Akhil Datta-Gupta,et al.  A Multiscale Approach to Production Data Integration Using Streamline Models , 1999 .

[11]  Per Christian Hansen,et al.  Solution of Ill-Posed Problems by Means of Truncated SVD , 1988 .

[12]  Akhil Datta-Gupta,et al.  In-Situ Characterization of Residual NAPL Distribution Using Streamline-Based Inversion of Partitioning Tracer Tests , 1999 .

[13]  R. Snieder,et al.  Solving large linear inverse problems by projection , 1990 .

[14]  Akhil Datta-Gupta,et al.  Structure Preserving Inversion: An Efficient Approach to Conditioning Stochastic Reservoir Models to Dynamic Data , 1997 .

[15]  R. V. Higgins,et al.  A Computer Method to Calculate Two-Phase Flow in Any Irregularly Bounded Porous Medium , 1962 .

[16]  Martin J. Blunt,et al.  A 3D field scale streamline simulator with gravity and changing well conditions , 1996 .

[17]  John C. Martin,et al.  Numerical Solution of Multiphase, Two-Dimensional Incompressible Flow Using Stream-Tube Relationships , 1979 .

[18]  Akhil Datta-Gupta,et al.  Integrating dynamic data into high-resolution reservoir models using streamline-based analytic sensitivity coefficients , 1999 .

[19]  Akhil Datta-Gupta,et al.  Timestep Selection During Streamline Simulation via Transverse Flux Correction , 2003 .

[20]  Akhil Datta-Gupta,et al.  Streamline-based Production Data Integration Under Changing Field Conditions , 2001 .

[21]  H. Fay Prats,et al.  The Application of Numerical Methods to Cycling and Flooding Problems , 1951 .

[22]  D. W. Peaceman.  Representation Of A Horizontal Well In Numerical Reservoir Simulation , 1993 .

[23]  Roel Snieder,et al.  Obtaining smooth solutions to large, linear, inverse problems , 1994 .

[24]  A. D. Gupta,et al.  Detailed Characterization of a Fractured Limestone Formation by Use of Stochastic Inverse Approaches , 1995 .

[25]  Akhil Datta-Gupta,et al.  A semianalytic approach to tracer flow modeling in heterogeneous permeable media , 1995 .

[26]  Akhil Datta-Gupta,et al.  Identifying Reservoir Compartmentalization and Flow Barriers Using Primary Production: A Streamline Approach , 2002 .