Alsabaa Ahmed, Gamal Hany, Elkatatny Salaheldin, Abdulraheem Abdulazeez
College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Sensors (Basel). 2020 Mar 17;20(6):1669. doi: 10.3390/s20061669.
Tracking the rheological properties of the drilling fluid is a key factor for the success of the drilling operation. The main objective of this paper is to relate the most frequent mud measurements (every 15 to 20 min) as mud weight (MWT) and Marsh funnel viscosity (MFV) to the less frequent mud rheological measurements (twice a day) as plastic viscosity (PV), yield point (YP), behavior index (n), and apparent viscosity (AV) for fully automating the process of retrieving rheological properties. The adaptive neuro-fuzzy inference system (ANFIS) was used to develop new models to determine the mud rheological properties using real field measurements of 741 data points. The data were collected from 99 different wells during drilling operations of 12 ¼ inches section. The ANFIS clustering technique was optimized by using training to a testing ratio of 80% to 20% as 591 data points for training and 150 points, cluster radius value of 0.1, and 200 epochs. The results of the prediction models showed a correlation coefficient (R) that exceeded 0.9 between the actual and predicted values with an average absolute percentage error (AAPE) below 5.7% for the training and testing data sets. ANFIS models will help to track in real-time the rheological properties for invert emulsion mud that allows better control for the drilling operation problems.
追踪钻井液的流变特性是钻井作业成功的关键因素。本文的主要目的是将最频繁的泥浆测量值（每15至20分钟一次），如泥浆重量（MWT）和马氏漏斗粘度（MFV），与较不频繁的泥浆流变测量值（每天两次），如塑性粘度（PV）、屈服点（YP）、流性指数（n）和表观粘度（AV）相关联，以实现流变特性获取过程的完全自动化。采用自适应神经模糊推理系统（ANFIS）开发新模型，利用741个数据点的现场实测数据来确定泥浆的流变特性。这些数据是在12 ¼英寸井段的钻井作业期间从99口不同的井中收集的。通过使用80%至20%的训练与测试比例（591个数据点用于训练，150个数据点）、0.1的聚类半径值和200个训练轮次对ANFIS聚类技术进行了优化。预测模型的结果表明，训练数据集和测试数据集的实际值与预测值之间的相关系数（R）超过0.9，平均绝对百分比误差（AAPE）低于5.7%。ANFIS模型将有助于实时追踪反相乳化泥浆的流变特性，从而更好地控制钻井作业问题。
