Application of Machine Learning in Hydraulic Fracturing: A Review
Yulin Ma, Man Ye
Abstract
Hydraulic fracturing is a widely used technology to increase oil and gas production, and accurate prediction of the postpressure production capacity of hydraulic fracturing is the key to the efficient development of oil and gas fields. However, the multiplicity and asymmetry of reservoir parameters, as well as the high degree of nonlinearity of fluid flow, often make semianalytical modeling and numerical simulation to predict the production behavior a challenge. Based on the research on the application of machine learning (ML) methods in hydraulic fracturing, this paper analyzes the limitations and applicability of classical ML algorithms as well as combinatorial models, summarizes the practical applications of ML in hydraulic fracturing operations, and discusses the ML algorithms to assist hydraulic fracturing analysis and improve hydraulic fracturing production rates. Finally, the development of interpretable modeling methods based on knowledge embedding and knowledge discovery is a challenge and a future direction for fracking research.