Optimization of Well Placement and Production for Large - scale Mature Oil Fields

Optimal oil field development strategies, especially well locations and production strategies for mature oil fields, should be determined to sustain yields. For a large-scale oil field, these problems are nonlinear, nonconvex, and computationally expensive. In this study, an efficient and robust derivative-free computational framework was developed to determine the optimal number, locations, and injection/production rates of infill wells for mature oil fields. The characteristics of mature fields were briefly described; optimization formulation and computational framework were presented. For this problem, the robust and parallelizable PSwarm, a hybrid of a pattern search algorithm and a particle swarm optimization, was investigated. The approach was applied to a large-scale real oil field that currently includes approximately 200 wells. Our optimized results were compared with those of the current plan provided by the oil industry. In particular, a higher oil production with the same amount of water injection and a higher net present value were obtained by our optimized approach than by the current plan. Therefore, the new derivative-free computational framework can efficiently solve well placement and production optimization problems for large-scale mature oil fields.

[1]  Albert C. Reynolds,et al.  An Adaptive Hierarchical Multiscale Algorithm for Estimation of Optimal Well Controls , 2014 .

[2]  A. Auger,et al.  Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models , 2011, Computational Geosciences.

[3]  Luís N. Vicente,et al.  PSwarm: a hybrid solver for linearly constrained global derivative-free optimization , 2009, Optim. Methods Softw..

[4]  Liming Zhang,et al.  Smart Well Pattern Optimization Using Gradient Algorithm , 2016 .

[5]  Louis J. Durlofsky,et al.  Generalized Field-Development Optimization With Derivative-Free Procedures , 2014 .

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

[7]  Nikolaos V. Sahinidis,et al.  Derivative-free optimization: a review of algorithms and comparison of software implementations , 2013, J. Glob. Optim..

[8]  Jan Dirk Jansen,et al.  Adjoint-based optimization of multi-phase flow through porous media – A review , 2011 .

[9]  Eduardo Camponogara,et al.  Derivative-free methods applied to daily production optimization of gas-lifted oil fields , 2015, Comput. Chem. Eng..

[10]  Xiao Zhang,et al.  A hybrid multi-objective PSO-EDA algorithm for reservoir flood control operation , 2015, Appl. Soft Comput..

[11]  Tarek Ahmed,et al.  Reservoir Engineering Handbook , 2002 .

[12]  Iftekhar A. Karimi,et al.  Optimal producer well placement and production planning in an oil reservoir , 2013, Comput. Chem. Eng..

[13]  Ghazi AlQahtani,et al.  A Computational Comparison between Optimization Techniques for Wells Placement Problem: Mathematical Formulations, Genetic Algorithms and Very Fast Simulated Annealing , 2014 .

[14]  Ronald D. Haynes,et al.  Simultaneous and sequential approaches to joint optimization of well placement and control , 2014, Computational Geosciences.

[15]  Yinyu Ye,et al.  Waterflood management using two-stage optimization with streamline simulation , 2014, Computational Geosciences.

[16]  Luís N. Vicente,et al.  A particle swarm pattern search method for bound constrained global optimization , 2007, J. Glob. Optim..

[17]  Stein Krogstad,et al.  Adjoint-based surrogate optimization of oil reservoir water flooding , 2015 .

[18]  Akhil Datta-Gupta,et al.  Well Placement Optimization in a Mature Carbonate Waterflood using Streamline-based Quality Maps , 2012 .