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.

本文介绍了一种利用机器学习 (ML) 技术预测卡拉赞巴斯油田出砂量的综合方法。通过分析 2000 口井的数据，该研究揭示了出砂的复杂动态，并强调了准确预测峰值出砂量及其出现时间的迫切需要。机器学习技术可以对出砂预测和油田操作优化产生重大影响，可以通过结合使用丰富的训练数据和特定领域知识来改进。该研究强调了地质因素，特别是断层邻近性对预测精度的影响。需要领域和领域知识来制定不同的生产场景以进行预测，以便可以选择相关数据来训练机器学习模型。此外，需要新的指标来评估模型性能，因为所应用的方法是针对不同的运营策略量身定制的。由于峰值砂量被认为是现场作业中的关键事件，因此引入了峰值预测精度和峰值时间预测精度方面的新指标来评估机器学习模型的性能。研究中采用了一套机器学习算法，在产砂井分类方面表现出显着的准确性。