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Rooki, Reza, Rahimi, Mojtaba. (1403). Prediction of Rheological Properties of Drilling Fluids Using Two Artificial Intelligence Methods: General Regression Neural Network and Fuzzy Logic. , (), -. doi: 10.22059/jchpe.2024.380925.1551
Reza Rooki; Mojtaba Rahimi. "Prediction of Rheological Properties of Drilling Fluids Using Two Artificial Intelligence Methods: General Regression Neural Network and Fuzzy Logic". , , , 1403, -. doi: 10.22059/jchpe.2024.380925.1551
Rooki, Reza, Rahimi, Mojtaba. (1403). 'Prediction of Rheological Properties of Drilling Fluids Using Two Artificial Intelligence Methods: General Regression Neural Network and Fuzzy Logic', , (), pp. -. doi: 10.22059/jchpe.2024.380925.1551
Rooki, Reza, Rahimi, Mojtaba. Prediction of Rheological Properties of Drilling Fluids Using Two Artificial Intelligence Methods: General Regression Neural Network and Fuzzy Logic. , 1403; (): -. doi: 10.22059/jchpe.2024.380925.1551

Prediction of Rheological Properties of Drilling Fluids Using Two Artificial Intelligence Methods: General Regression Neural Network and Fuzzy Logic

Journal of Chemical and Petroleum Engineering
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 12 دی 1403    اصل مقاله (745.65 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jchpe.2024.380925.1551
نویسندگان
Reza Rooki1؛ Mojtaba Rahimi* 2
1Birjand University of Technology, Birjand, Iran
2Department of Petroleum Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran; Stone Research Center, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran
چکیده
The rheological properties of drilling fluids, including viscosity and yield point, are essential for the effectiveness of drilling operations. Inaccurate predictions of these parameters may lead to costly complications during the drilling operation. Among artificial intelligence (AI) methods, the general regression neural network (GRNN) approach and the fuzzy logic method possess high speed of estimation and also less adjustable parameters compared to other methods. Despite the great capability of these two methods, they have seldom been used to estimate the rheological properties of drilling fluids. Hence, through programming in MATLAB software, the capabilities of these methods in predicting the rheological properties of drilling fluids were investigated by comparison of their predictions against experimental results. The neural network contained one input layer with three inputs (clay mass, Na2Co3 concentration, and Gum Arabic concentration), one hidden layer with 38 neurons, and one output layer with three outputs (apparent viscosity (AV), plastic viscosity (PV), and yield point (YP)). In the fuzzy logic method, the optimal value of the clustering radius was considered 0.1 in this research. Based on the two methods designed, the value of R (about 0.99) and RMSE (about 0.5) between predicted values and the measured values of rheological properties in training and testing data were extremely good. Our findings indicate that both AI methods can be utilized to predict the rheological parameters of drilling fluids with different compositions.
کلیدواژه‌ها
Artificial Intelligence؛ Machine Learning؛ General Regression Neural Network؛ Fuzzy logic؛ Rheological Properties
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تعداد مشاهده مقاله: 43
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