Title: Predicting Oil Recovery Coefficient With Machine Learning Methods on Data Generated From the Buckley-Leverett Model
Abstract: The pursuit of enhancing oil extraction processes is now embracing technological advancements, particularly in machine learning (ML), to address the inefficiencies and environmental challenges inherent in conventional methods. This research explores the use of ML algorithms for accurately predicting the Oil Recovery Coefficient (COR), an essential metric for evaluating the effectiveness of oil extraction strategies. Leveraging synthetic data generated through the Buckley-Leverett model, which incorporates variables such as viscosity, porosity, time, and production rate, this study rigorously assesses five ML models: Linear Regressors, Random Forest Regressors, Extreme Gradient Boosting Regressors, Support Vector Machines, and Artificial Neural Networks. Our findings demonstrate that Artificial Neural Networks and Random Forest Regressors notably excel, with R-squared values reaching 0.99981 and 0.99821, respectively, on test datasets. These achievements highlight the superior predictive precision and dependability of these models in estimating COR, marking a significant advancement over existing prediction techniques. In conclusion, this investigation not only evidences the transformative potential of machine learning in refining oil recovery forecasts.
Keywords: machine learning;EOR;Buckley-Leverett;oil recovery coefficient;predictive analytics;artificial neural network
Review score: Moderate
Review comment: This manuscript makes a contribution to the field of petroleum engineering by addressing the critical challenge of predicting the Oil Recovery Coefficient (COR) with a good precision. The employment of various machine learning models, including Linear Regressors, Random Forest Regressors, Extreme Gradient Boosting Regressors, Support Vector Machines, and Artificial Neural Networks, represents a comprehensive approach to evaluating the efficacy of different predictive tools in the context of COR estimation.

The use of the Buckley-Leverett model for generating synthetic data incorporating key geological attributes and operational parameters adds a layer of innovation to the study, enhancing its relevance and applicability. The detailed performance analysis based on R-squared, MAE, MSE, and MBE metrics provides clear evidence of the superiority of Artificial Neural Networks and Random Forest Regressors in COR prediction. This insight is particularly valuable, as it points to specific machine learning models that can significantly improve the accuracy of oil recovery predictions, thereby supporting the development of more efficient and sustainable oil extraction techniques.

Moreover, the study's acknowledgment of the potential benefits of integrating real-world data and applying these models under field conditions suggests a promising direction for future research. Such endeavors could further refine the predictive accuracy and practical utility of the models, aligning closely with the industry's sustainability objectives by enabling more efficient resource management and minimizing environmental impacts.

The manuscript is well-structured, articulating the research problem, methodology, findings, and implications with clarity and precision.