Multi-Objective Optimization of Sucker Rod Pump Operating Parameters for Efficiency and Pump Life Improvement Based on Random Forest and CMA-ES

The design parameters of the sucker rod pumping unit (SRPU) are influenced by multiple factors. Traditional methods based on oil production engineering theories involve numerous simplifications, making it difficult to effectively address the complex realities of oilfields, thereby requiring improvement in the reliability of pumping system design solutions. This paper, based on the massive design schemes and corresponding operational performance data accumulated during the long-term development of oilfields, innovatively proposes an intelligent optimization model combining Random Forest and Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES). This model overcomes the shortcomings of insufficient data and incomplete design indicators in the establishment of lifting design models. By standardizing and processing the data from 5000 historical lifting scheme sets, a sample database of SRPU lifting system designs was created, covering dimensions such as well geology, fluid, and production. Based on this, aiming at system efficiency and pump life expectancy, geological development characteristic parameters and lifting design parameters were taken as variables to establish a predictive model for the operation effect of the lifting system. The dataset was divided into 8:1:1 subsets for training, hyperparameter tuning and performance testing. Subsequently, an optimization model was established to jointly optimize the lifting system design parameters. Case studies show that the intelligent optimization method can simultaneously optimize parameters such as pump setting depth, pump diameter, stroke, and frequency, with expected improvements in system efficiency of 6.75% and pump life expectancy of 29%.