Alsaihati Ahmed, Elkatatny Salaheldin, Abdulraheem Abdulazeez
Department of Petroleum Engineering, College of Petroleum & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
ACS Omega. 2020 Dec 31;6(1):934-942. doi: 10.1021/acsomega.0c05570. eCollection 2021 Jan 12.
Equivalent circulation density (ECD) is an important part of drilling fluid calculations. Analytical equations based on the conservation of mass and momentum are used to determine the ECD at various depths in the wellbore. However, these equations do not incorporate important factors that have a direct impact on the ECD, such as bottom-hole temperature, pipe rotation and eccentricity, and wellbore roughness. This work introduced different intelligent machines that could provide a real-time accurate estimation of the ECD for horizontal wells, namely, the support vector machine (SVM), random forests (RF), and a functional network (FN). Also, this study sheds light on how principal component analysis (PCA) can be used to reduce the dimensionality of a data set without loss of any important information. Actual field data of Well-1, including drilling surface parameters and ECD measurements, were collected from a 5-7/8 in. horizontal section to develop the models. The performance of the models was assessed in terms of root-mean-square error (RMSE) and coefficient of determination ( ). Then, the best model was validated using unseen data points of 1152 collected from Well-2. The results showed that the RF model outperformed the FN and SVM in predicting the ECD with an RMSE of 0.23 and of 0.99 in the training set and with an RMSE of 0.42 and of 0.99 in the testing set. Furthermore, the RF predicted the ECD in Well-2 with an RMSE of 0.35 and of 0.95. The developed models will help the drilling crew to have a comprehensive view of the ECD while drilling high-pressure high-temperature wells and detect downhole operational issues such as poor hole cleaning, kicks, and formation losses in a timely manner. Furthermore, it will promote safer operation and improve the crew response time limit to prevent undesired events.
等效循环密度（ECD）是钻井液计算的重要组成部分。基于质量和动量守恒的解析方程用于确定井筒不同深度处的ECD。然而，这些方程没有考虑对ECD有直接影响的重要因素，如井底温度、钻杆旋转和偏心度以及井筒粗糙度。这项工作引入了不同的智能机器，即支持向量机（SVM）、随机森林（RF）和函数网络（FN），它们可以对水平井的ECD进行实时准确估计。此外，本研究还阐明了主成分分析（PCA）如何用于降低数据集的维度而不丢失任何重要信息。从一口5-7/8英寸水平段的1号井收集了包括钻井地面参数和ECD测量值在内的实际现场数据，以建立模型。根据均方根误差（RMSE）和决定系数（ ）对模型性能进行评估。然后，使用从2号井收集的1152个未见数据点对最佳模型进行验证。结果表明，在预测ECD方面，RF模型优于FN和SVM模型，训练集的RMSE为0.23， 值为0.99，测试集的RMSE为0.42， 值为0.99。此外，RF对2号井ECD的预测RMSE为0.35， 值为0.95。所开发的模型将有助于钻井人员在钻高压高温井时全面了解ECD，并及时检测井下作业问题，如井眼清洁不良、井涌和地层漏失。此外，它将促进更安全的作业并缩短人员响应时间限制，以防止意外事件发生。
