A Method for Optimizing Production Layer Regrouping Based on a Genetic Algorithm

• Published in: Processes Vol. 12; no. 9; p. 1881
• Main Authors: Cui, Lining, Zhang, Jiqun, He, Dehai, Pu, Longchuan, Peng, Boyang, Ping, Xiaolin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Algorithms; Analysis; Comparative analysis; Computer simulation; Efficiency; Engineering; Enhanced oil recovery; Fitness; Genetic algorithms; Genetic research; Mathematical models; Methods; Multilayers; Mutation; Numerical analysis; Optimization; Permeability; Petroleum mining; Porosity; Production methods; Reservoir engineering; Simulation methods; Thickness; Water flooding; China
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr12091881

As waterflooding multi-layer reservoirs reach the high-water-cut stage, inter-layer conflicts become increasingly serious, leading to a worsening development effect over time. Production layer regrouping is an effective approach for resolving inter-layer conflicts and improving waterflooding efficiency. At the current stage, there are limitations to most of the methods of production layer regrouping. This article proposes a smart method for optimizing the layer regroup plan based on a genetic algorithm. Comprehensively considering various factors that affect the regroup of layers, such as layer thickness, porosity, permeability, remaining oil saturation, remaining reserves, recovery ratio, water cut, etc., based on the combination principle of “smaller intra-group variance and larger inter-group variance of each influencing factor are expected”, a genetic algorithm is used to calculate the fitness value of the initial combination schemes, and the advantageous schemes with higher fitness values are selected as the basis of the next generation. Then, crossover and mutation operations are performed on those advantageous schemes to generate new schemes. Through continuous selection and evolution, until the global optimal solution with the highest fitness value is found, the optimal combination scheme is determined. Comparative analysis with numerical simulation results demonstrates the reliability of this intelligent method, with an increased oil recovery of 4.34% for the sample reservoir. Unlike selecting a preferable plan from a limited number of predefined combination schemes, this method is an automatic optimization to solve the optimal solution of the problem. It improves both efficiency and accuracy as compared to conventional reservoir engineering methods, numerical simulation methods, and most mathematical methods, thus providing effective guidance for EOR strategies of waterflooding reservoirs in the high-water-cut stage.