Abdelaal Ahmed, Elkatatny Salaheldin, Abdulraheem Abdulazeez
College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
Sci Rep. 2022 Jul 5;12(1):11318. doi: 10.1038/s41598-022-15493-z.
Accurate real-time pore pressure prediction is crucial especially in drilling operations technically and economically. Its prediction will save costs, time and even the right decisions can be taken before problems occur. The available correlations for pore pressure prediction depend on logging data, formation characteristics, and combination of logging and drilling parameters. The objective of this work is to apply artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to introduce two models to estimate the formation pressure gradient in real-time through the available drilling data. The used parameters include rate of penetration (ROP), mud flow rate (Q), standpipe pressure (SPP), and rotary speed (RS). A data set obtained from some vertical wells was utilized to develop the predictive model. A different set of data was utilized for validating the proposed artificial intelligence (AI) models. Both models forecasted the output with a good correlation coefficient (R) for training and testing. Moreover, the average absolute percentage error (AAPE) did not exceed 2.1%. For validation stage, the developed models estimated the pressure gradient with a good accuracy. This study proves the reliability of the proposed models to estimate the pressure gradient while drilling using drilling data. Moreover, an ANN-based correlation is provided and can be directly used by introducing the optimized weights and biases, whenever the drilling parameters are available, instead of running the ANN model.
准确的实时孔隙压力预测至关重要，尤其是在钻井作业中，在技术和经济方面都意义重大。其预测能够节省成本、时间，甚至能在问题出现之前做出正确决策。现有的孔隙压力预测相关性取决于测井数据、地层特性以及测井与钻井参数的组合。本研究的目的是应用人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS），通过可用的钻井数据引入两种模型来实时估算地层压力梯度。所使用的参数包括机械钻速（ROP）、泥浆流速（Q）、立管压力（SPP）和转速（RS）。利用从一些垂直井获得的数据集来开发预测模型。使用另一组不同的数据来验证所提出的人工智能（AI）模型。两个模型在训练和测试时预测输出的相关系数（R）都很好。此外，平均绝对百分比误差（AAPE）不超过2.1%。在验证阶段，所开发的模型以良好的精度估算了压力梯度。本研究证明了所提出的模型在利用钻井数据进行钻井时估算压力梯度的可靠性。此外，还提供了基于人工神经网络的相关性，只要有钻井参数，通过引入优化的权重和偏差，就可以直接使用，而无需运行人工神经网络模型。
