Abdelaal 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. 2021 May 19;6(21):13807-13816. doi: 10.1021/acsomega.1c01340. eCollection 2021 Jun 1.
Real-time prediction of the formation pressure gradient is critical mainly for drilling operations. It can enhance the quality of decisions taken and the economics of drilling operations. The pressure while drilling tool can be used to provide pressure data while drilling, but the tool cost and its availability limit its usage in many wells. The available models in the literature for pressure gradient prediction are based on well logging or a combination of some drilling parameters and well logging. The well-logging data are not available for all wells in all sections in most wells. The objective of this paper is to use support vector machines, functional networks, and random forest (RF) to develop three models for real-time pore pressure gradient prediction using both mechanical and hydraulic drilling parameters. The used parameters are mud flow rate (), standpipe pressure, rate of penetration, and rotary speed (RS). A data set of 3239 field data points was used to develop the predictive models. A different data set unseen by the model was utilized for the validation of the proposed models. The three models predicted the pore pressure gradient with a correlation coefficient () of 0.99 and 0.97 for training and testing, respectively. The root-mean-squared error (RMSE) ranged from 0.008 to 0.021 psi/ft for training and testing, respectively, between the predicted and the actual pore pressure data. Moreover, the average absolute percentage error (AAPE) ranged from 0.97% to 3.07% for training and testing, respectively. The RF model outperformed the other models by an of 0.99 and RMSE of 0.01. The developed models were validated using another data set. The models predicted the pore pressure gradient for the validation data set with high accuracy ( of 0.99, RMSE around 0.01, and AAPE around 1.8%). This work shows the reliability of the developed models to predict the pressure gradient from both mechanical and hydraulic drilling parameters while drilling.
地层压力梯度的实时预测主要对钻井作业至关重要。它可以提高决策质量和钻井作业的经济性。随钻压力工具可用于在钻井时提供压力数据，但工具成本及其可用性限制了其在许多井中的使用。文献中用于压力梯度预测的现有模型基于测井或一些钻井参数与测井的组合。在大多数井中，并非所有井段的所有井都有测井数据。本文的目的是使用支持向量机、功能网络和随机森林（RF）开发三个模型，利用机械和水力钻井参数实时预测孔隙压力梯度。所使用的参数是泥浆流量（）、立管压力、钻速和转速（RS）。使用3239个现场数据点的数据集来开发预测模型。模型未见过的不同数据集用于验证所提出的模型。这三个模型预测孔隙压力梯度的训练和测试相关系数（）分别为0.99和0.97。预测的孔隙压力数据与实际孔隙压力数据之间的训练和测试均方根误差（RMSE）分别为0.008至0.021 psi/ft。此外，训练和测试的平均绝对百分比误差（AAPE）分别为0.97%至3.07%。RF模型的相关系数为0.99，RMSE为0.01，优于其他模型。使用另一个数据集对开发的模型进行了验证。这些模型以高精度预测了验证数据集的孔隙压力梯度（相关系数为0.99，RMSE约为0.01，AAPE约为1.8%）。这项工作表明了所开发模型在钻井时根据机械和水力钻井参数预测压力梯度的可靠性。
