Sand Production Prediction with Machine Learning using Input Variables from Geological and Operational Conditions in the Karazhanbas Oilfield, Kazakhstan
Shabdirova A. Kozhagulova A. Samenov Y. Minh N. Zhao Y.
December 2024Springer
Natural Resources Research
2024#33Issue 62789 - 2805 pp.
This paper describes a comprehensive approach to predict sand production in the Karazhanbas oilfield using machine learning (ML) techniques. By analyzing data from 2000 wells, the research uncovered the complex dynamics of sand production and emphasized the critical need for accurately predicting the peak sand mass and its occurrence time. ML techniques can have a significant impact on prediction of sand production and on the optimization of oilfield operation, which can be improved with the combined use of enriched training data and domain-specific knowledge. The research underscored the influence of geological factors, especially fault proximity, on prediction accuracy. Domain and field knowledge is needed to formulate different production scenarios for prediction purposes such that the relevant data can be selected for the training of ML models. Moreover, new metrics are needed to evaluate model performance as the applied method is tailored for different operational strategies. As the peak sand mass is considered a pivotal event in field operation, new metrics in terms of peak prediction accuracy and peak time prediction accuracy were introduced to evaluate the performance of ML models. A suite of ML algorithms was employed in the study, which demonstrated notable accuracy in the classification of sand-producing wells.
machine learning , prediction accuracy , Sand production
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School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Oil and Gas Faculty, Atyrau University of Oil and Gas, Atyrau, Kazakhstan
Fulbright University Vietnam, Ho Chi Minh City, Viet Nam
School of Engineering and Digital Sciences
Oil and Gas Faculty
Fulbright University Vietnam
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