A rapid waterflooding optimization method based on INSIM-FPT data-driven model and its application to three-dimensional reservoirs

Abstract Conventional production optimization for waterflooding reservoirs relies on full-scale reservoir simulators and repeated simulation runs to calulate gradient information. These factors lead to expensive computational cost. Different from the conventional optimization, in this paper, a novel waterflooding optimization method is developed by running a data-driven interwell numerical simulation model with flow-path tracking (INSIM-FPT) in three dimensions. With the application of flow-path tracking strategy, the more-accurate calculations of dynamic allocation factors and control pore volumes can be obtained from INSIM-FT-3D model. Moreover, based on the INSIM model, we define producer-centered production efficiency of wells with multiple perforations and propose a rapid waterflooding optimization method. The optimized well production rates and injection rates are obtained with the help of the oil cut and the existing injector-centered allocation factors derived from INSIM model. The new optimization method requires only one time forward run without repeated iterative calculations. And the optimization is completely based on reservoir-engineering points of view, making it easy to explain oil increment mechanisms. Finally, a synthetic case and the other large-scale field case is established to test its optimization performance.

[1]  Albert C. Reynolds,et al.  INSIM-FT-3D: A Three-Dimensional Data-Driven Model for History Matching and Waterflooding Optimization , 2019 .

[2]  L. Lake,et al.  Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model , 2015 .

[3]  R. Okuno,et al.  Robust Isenthalpic Flash for Multiphase Water/Hydrocarbon Mixtures , 2015 .

[4]  Hui Zhao,et al.  Maximization of a Dynamic Quadratic Interpolation Model for Production Optimization , 2013 .

[5]  Jan Dirk Jansen,et al.  Dynamic Optimization of Waterflooding With Smart Wells Using Optimal Control Theory , 2004 .

[6]  F. E. Jansen,et al.  Non-Stationary Estimation of Reservoir Properties Using Production Data , 1997 .

[7]  Turgay Ertekin,et al.  Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies , 2020 .

[8]  Belkis Teresa Refunjol,et al.  Reservoir Characterization of North Buck Draw Field Based on Tracer Response and Production/Injection Analysis , 1996 .

[9]  Liehui Zhang,et al.  Numerical simulation of water flooding in natural fractured reservoirs based on control volume finite element method , 2016 .

[10]  Xuecheng Tai,et al.  An efficient method for smart well production optimisation , 2009 .

[11]  Qi Zhang,et al.  A new and fast waterflooding optimization workflow based on INSIM-derived injection efficiency with a field application , 2019, Journal of Petroleum Science and Engineering.

[12]  Pablo Hugo Gentil.  The Use of Multilinear Regression Models in Patterned Waterfloods: Physical Meaning of the Regression Coefficients , 2005 .

[13]  Morteza Sayarpour,et al.  Development and application of capacitance-resistive models to water/CO₂ floods , 2008 .

[14]  Yuetian Liu,et al.  Manufacturing Method of Large-Scale Fractured Porous Media for Experimental Reservoir Simulation , 2013 .

[15]  M. A. Cardoso,et al.  Use of Reduced-Order Modeling Procedures for Production Optimization , 2010 .

[16]  Lin Cao,et al.  A Physics-Based Data-Driven Numerical Model for Reservoir History Matching and Prediction With a Field Application , 2016 .

[17]  Albert C. Reynolds,et al.  Life-cycle production optimization of an oil field with an adjoint-based gradient approach , 2013 .

[18]  Denis José Schiozer,et al.  A methodology to evaluate and reduce reservoir uncertainties using multivariate distribution , 2015 .

[19]  Jun Yao,et al.  Nonlinear constrained production optimization based on augmented Lagrangian function and stochastic gradient , 2016 .

[20]  S. E. Buckley,et al.  Mechanism of Fluid Displacement in Sands , 1942 .

[21]  Didier Yu Ding,et al.  Efficient simulation of hydraulic fractured wells in unconventional reservoirs , 2014 .

[22]  J. G. Hillestad,et al.  Reservoir Simulation Using Bottomhole Pressure Boundary Conditions , 1980 .

[23]  Geir Nævdal,et al.  Production Optimization Using Derivative Free Methods Applied to Brugge Field Case , 2014 .

[24]  Denis José Schiozer,et al.  A new methodology to reduce uncertainties in reservoir simulation models using observed data and sampling techniques , 2010 .

[25]  Hui Zhao,et al.  An interwell connectivity inversion model for waterflooded multilayer reservoirs , 2016 .

[26]  K. Heffer,et al.  Novel techniques show links between reservoir flow directionality, earth stress, fault structure and geomechanical changes in mature waterfloods , 1997 .

[27]  Albert C. Reynolds,et al.  Minimizing the Risk in the robust life-cycle production optimization using stochastic simplex approximate gradient , 2017 .

[28]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[29]  R. Okuno,et al.  Multiphase isenthalpic flash integrated with stability analysis , 2016 .

[30]  Bin Yuan,et al.  Special Focus on Produced Water in Oil and Gas Fields , 2018 .

[31]  Bjarne A. Foss,et al.  Performance Analysis for Closed-Loop Reservoir Management , 2011 .

[32]  Kai Zhang,et al.  Theoretical research on reservoir closed-loop production management , 2011 .

[33]  A. Reynolds,et al.  Waterflooding optimization with the INSIM-FT data-driven model , 2018, Computational Geosciences.

[34]  Mahmoud Reza Pishvaie,et al.  Application of an improved harmony search algorithm in well placement optimization using streamline simulation , 2011 .

[35]  Abbas Naderifar,et al.  Development of a flow based dynamic gridding approach for fluid flow modeling in heterogeneous reservoirs , 2016 .

[36]  R. Okuno,et al.  A robust algorithm for isenthalpic flash of narrow-boiling fluids , 2014 .

[37]  Hao Liu,et al.  Evaluation of CO2 sequestration capacity in complex-boundary-shape shale gas reservoirs using projection-based embedded discrete fracture model (pEDFM) , 2020 .

[38]  Albert C. Reynolds,et al.  A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Waterflooding Performance , 2017 .

[39]  Shimon Even,et al.  Graph Algorithms , 1979 .

[40]  Hui Zhao,et al.  INSIM: A Data-Driven Model for History Matching and Prediction for Waterflooding Monitoring and Management with a Field Application , 2015, ANSS 2015.

[41]  Zhenyu Guo,et al.  INSIM-FT in three-dimensions with gravity , 2019, J. Comput. Phys..

[42]  Chunhong Wang,et al.  Production Optimization in Closed-Loop Reservoir Management , 2009 .

[43]  Dongxiao Zhang,et al.  Efficient Ensemble-Based Closed-Loop Production Optimization , 2009 .

[44]  Hui Zhao,et al.  History matching and production optimization of water flooding based on a data-driven interwell numerical simulation model , 2016 .

[45]  Larry W. Lake,et al.  A Capacitance Model To Infer Interwell Connectivity From Production and Injection Rate Fluctuations , 2006 .

[46]  J. Sheng,et al.  Dynamic Permeability Models in Dual-Porosity System for Unconventional Reservoirs: Case Studies and Sensitivity Analysis , 2017 .

[47]  Kamy Sepehrnoori,et al.  Development of an Efficient Embedded Discrete Fracture Model for 3D Compositional Reservoir Simulation in Fractured Reservoirs , 2014 .

[48]  Daniel Brent Weber,et al.  The use of capacitance-resistance models to optimize injection allocation and well location in water floods , 2009 .

[49]  Albert C. Reynolds,et al.  A Stochastic Optimization Algorithm for Automatic History Matching , 2007 .

[50]  Dean S. Oliver,et al.  Ensemble-Based Closed-Loop Optimization Applied to Brugge Field , 2010 .

[51]  Zhimin Du,et al.  Practical simulation of multi-porosity reservoirs through existing reservoir simulator , 2017 .

[52]  L. Lake,et al.  Inferring Interwell Connectivity Only From Well-Rate Fluctuations in Waterfloods , 2003 .

[53]  Wei Liu,et al.  Flow-Path Tracking Strategy in a Data-Driven Interwell Numerical Simulation Model for Waterflooding History Matching and Performance Prediction with Infill Wells , 2020 .

[54]  Hui Zhao,et al.  Artificial-neural-network (ANN) based proxy model for performances forecast and inverse project design of water huff-n-puff technology , 2020 .

[55]  C. Shekhar,et al.  A Unified Algorithm for Phase-Stability/Split Calculation for Multiphase Isobaric-Isothermal Flash , 2017 .

[56]  Mabkhout M. Al-Dousari,et al.  An artificial neural network model for predicting the recovery performance of surfactant polymer floods , 2013 .