Application of Machine Learning Techniques in the Optimization of Produced Water Treatment: A Literature Review

Abstract

The increasing complexity and variability of operational and physicochemical conditions in produced water treatment within the oil and gas industry require advanced computational solutions capable of managing multivariate data and modeling nonlinear behaviors. In this context, machine learning (ML) techniques have emerged as powerful tools for predicting critical parameters, optimizing treatment processes, and supporting decision-making. This study presents a critical review of publications from 2015 to 2025, focusing on the application of ML in the optimization of produced water treatment. The literature search was conducted in the Scopus database using specific descriptors and Boolean operators, and resulted in 81 selected studies. The number of publications has grown significantly since 2020, with the United States, China, Canada, and Brazil leading the research effort. The predominant techniques include artificial neural networks (ANNs), support vector machines (SVM), and ensemble models such as Random Forest and XGBoost, which showed high predictive accuracy (R² > 0.90 in most studies). Key input variables included operational parameters, ionic composition, and historical production data. Reported outcomes include improved removal efficiency, scale control, and operational cost reduction of up to 30%. Despite advances, limitations remain regarding overfitting risks, lack of long-term validation, and model interpretability. Overall, the findings suggest that ML techniques represent a promising approach to enhance produced water treatment, enabling more efficient, adaptable, and economically viable industrial applications.