Petroleum Engineering Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Box: 5049, Dhahran 31261, Saudi Arabia.
Sensors (Basel). 2020 Jun 21;20(12):3506. doi: 10.3390/s20123506.
Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.
钻进速度（ROP）定义为单位时间内单位面积去除的岩石量。它受到几个不可分割的因素的影响。目前确定 ROP 的模型包括基本的数学和物理方程，以及经验相关性的使用。考虑到钻井过程的复杂性，人工智能（AI）的使用是一个游戏规则改变者，因为现在可以在建模过程中完全考虑到大多数未知参数。本文的目的是首次评估优化的自适应神经模糊推理系统（ANFIS）、功能神经网络（FN）、随机森林（RF）和支持向量机（SVM）模型从 S 形井眼剖面的钻进参数实时预测 ROP 的能力，基于钻进参数钻压（WOB）、钻柱旋转（DSR）、扭矩（T）、泵冲（GPM）和立管压力（SPP）。使用两口井的数据进行训练和测试（井 A 和井 B 分别有 4012 和 1717 个数据点），并使用一口井进行验证（井 C，有 2500 个数据点）。井 A 和井 B 的数据在训练-测试阶段结合在一起，并随机分为 70:30 的训练-测试比例。结果表明，ANFIS、FN 和 RF 模型可以有效地从 S 形井眼剖面的钻进参数预测 ROP，而 SVM 模型的精度非常低。ANFIS、FN 和 RF 模型对训练数据的 ROP 预测平均绝对百分比误差（AAPE）分别为 9.50%、13.44%和 3.25%。对于测试数据，ANFIS、FN 和 RF 模型对 ROP 的预测平均绝对百分比误差（AAPE）分别为 9.57%、11.20%和 8.37%。ANFIS、FN 和 RF 模型在 ROP 预测方面优于现有的经验相关性。ANFIS 模型对验证数据的 ROP 预测平均绝对百分比误差（AAPE）为 9.06%，FN 模型预测的 ROP 平均绝对百分比误差（AAPE）为 10.48%，RF 模型预测的 ROP 平均绝对百分比误差（AAPE）为 10.43%。SVM 模型对验证数据的 ROP 预测平均绝对百分比误差（AAPE）非常高，为 30.05%，所有经验相关性的 AAPE 均大于 25%。
