Reservoir Production Performance Optimization Algorithm Based on Numerical Simulation

doi:10.7657/XJPG20220515

Abstract

Abstract:

When the conventional optimization algorithms are applied to optimized development of large scale reservoirs, the problems such as slow convergence speed, low optimization efficiency and difficult integration with field applications occur. To solve these problems, a well production performance control model was established. A global optimal solution of the model was found by using the simulated annealing genetic (SAG) algorithm and Latin hypercube sampling (LHS) algorithm. Furthermore, the convergence speed of the local solution of the model was accelerated by using the synchronous perturbation stochastic approximation (SPSA) algorithm, and a well production performance control software was developed and applied to the H block in Daqing oilfield. Compared with conventional well production systems, the best scheme of the optimized well production performance control model increases the cumulative oil production of H block by 5.68×10⁴ m³ within 5 years, which ensures the well production performance control and optimization, and provides a new method for efficient development of large-scale oilfields.

Key words: numerical simulation, reservoir production performance, optimization algorithm, performance control, simulated annealing genetic algorithm, synchronous perturbation stochastic approximation algorithm, Latin hypercube sampling algorithm

CLC Number:

TE319

LEI Zexuan, XIN Xiankang, YU Gaoming, WANG Li. Reservoir Production Performance Optimization Algorithm Based on Numerical Simulation[J]. Xinjiang Petroleum Geology, 2022, 43(5): 612-616.

Figures/Tables 4

References 23

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