Ekechukwu Gerald, Adejumo Abayomi
Louisiana State University, Baton Rouge, USA.
Oriental Energy Resources Limited, Lagos, Nigeria.
Sci Rep. 2024 Aug 1;14(1):17780. doi: 10.1038/s41598-024-66702-w.
When drilling wells for energy explorations, it is important to regulate the formation pressures appropriately to prevent kicks, which can lead to unimaginable loss of lives and properties. This is usually done by controlling the equivalent circulating density (ECD), which responds to the dynamic conditions that occur during drilling. The conventional approach to determine ECD is via mathematical modeling or downhole measurements. However, the downhole measurement tools can be very expensive, and the mathematical models do not provide a high degree of accuracy. Some previous authors have proposed using machine learning (ML) techniques to improve the degree of accuracy of the ECD predictions. In this work, we employed an extreme gradient-boosting (XGBoost) methodology to predict ECD values. The model's accuracy was determined using correlation coefficients (R) and root mean square errors (RMSE) as their performance metrics. The results showed a strong prediction capability with an R and RMSE of 1.00 and 0.0005 for the training data and an R and RMSE of 0.989 and 0.023 for the testing/blind data set, respectively. The developed model outperformed those obtained using other popular machine learning techniques. Lastly, an interpretation of the model results showed that mud weight, weight on hook, and standpipe pressure contributed the most to the ECD prediction values.
在为能源勘探钻井时，适当地调节地层压力以防止井涌非常重要，因为井涌可能导致难以想象的生命和财产损失。这通常通过控制当量循环密度（ECD）来实现，ECD会对钻井过程中出现的动态情况做出响应。确定ECD的传统方法是通过数学建模或井下测量。然而，井下测量工具可能非常昂贵，而且数学模型的准确性不高。一些先前的作者提出使用机器学习（ML）技术来提高ECD预测的准确性。在这项工作中，我们采用了极端梯度提升（XGBoost）方法来预测ECD值。使用相关系数（R）和均方根误差（RMSE）作为性能指标来确定模型的准确性。结果表明，该模型具有很强的预测能力，训练数据的R和RMSE分别为1.00和0.0005，测试/盲数据集的R和RMSE分别为0.989和0.023。所开发的模型优于使用其他流行机器学习技术获得的模型。最后，对模型结果的解释表明，泥浆比重、大钩悬重和立管压力对ECD预测值的贡献最大。
