Efficient Optimization: Unveiling the Application of Ensemble Learning Combined with the CMA-ES Algorithm in Hydraulic Fracturing Design

Abstract

Optimizing fracturing parameters is crucial for enhancing production and reducing costs in oil and gas exploration and development. Effectively integrating geological and engineering parameters for the automated optimization of fracturing design continues to pose challenges. This study utilizes the cluster-based local outlier factor method for anomaly detection and removal from the dataset, significantly enhancing data quality. By integrating diverse models, including tree-based models and neural networks, an ensemble model for production prediction was developed. This approach successfully addresses the limitations of relying on a single model and achieves high-precision production forecasting. Furthermore, a Covariance Matrix Adaptation Evolution Strategy (CMA-ES)-based framework was established to comprehensively optimize the design parameters of fracturing projects. Optimization practices for two selected wells resulted in a 168.54% increase in production and identified the optimal design parameter configuration for all cases studied. The results of this study demonstrate the feasibility and effectiveness of the proposed ensemble prediction model and optimization framework in practical applications. Data-driven optimization strategies are expected to play a larger role in future oil and gas development, driving technological innovation and advancement in the field.