آمار

تعداد نشریات21
تعداد شماره‌ها341
تعداد مقالات3,608
تعداد مشاهده مقاله4,602,673
تعداد دریافت فایل اصل مقاله3,066,337
Karimi Rad, Mohammad Saeed, Alimoradi, Andisheh, Fathi, Mahdi, Kalhor Mohammadi, Mojtaba, Tahmasbi Nowtarki, Kourosh. (1402). Optimizing Drilling Fluid Properties Using Deep Learning Algorithms to Reduce Drilling Problems in a Middle Eastern Oil Field. , 2(1), 1-12. doi: 10.22077/jgm.2024.7709.1028
Mohammad Saeed Karimi Rad; Andisheh Alimoradi; Mahdi Fathi; Mojtaba Kalhor Mohammadi; Kourosh Tahmasbi Nowtarki. "Optimizing Drilling Fluid Properties Using Deep Learning Algorithms to Reduce Drilling Problems in a Middle Eastern Oil Field". , 2, 1, 1402, 1-12. doi: 10.22077/jgm.2024.7709.1028
Karimi Rad, Mohammad Saeed, Alimoradi, Andisheh, Fathi, Mahdi, Kalhor Mohammadi, Mojtaba, Tahmasbi Nowtarki, Kourosh. (1402). 'Optimizing Drilling Fluid Properties Using Deep Learning Algorithms to Reduce Drilling Problems in a Middle Eastern Oil Field', , 2(1), pp. 1-12. doi: 10.22077/jgm.2024.7709.1028
Karimi Rad, Mohammad Saeed, Alimoradi, Andisheh, Fathi, Mahdi, Kalhor Mohammadi, Mojtaba, Tahmasbi Nowtarki, Kourosh. Optimizing Drilling Fluid Properties Using Deep Learning Algorithms to Reduce Drilling Problems in a Middle Eastern Oil Field. , 1402; 2(1): 1-12. doi: 10.22077/jgm.2024.7709.1028

Optimizing Drilling Fluid Properties Using Deep Learning Algorithms to Reduce Drilling Problems in a Middle Eastern Oil Field

Journal of Geomine
دوره 2، شماره 1، خرداد 2024، صفحه 1-12    اصل مقاله (935.19 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22077/jgm.2024.7709.1028
نویسندگان
Mohammad Saeed Karimi Rad1؛ Andisheh Alimoradi* 2؛ Mahdi Fathi3؛ Mojtaba Kalhor Mohammadi1؛ Kourosh Tahmasbi Nowtarki1
1Pars Drilling Fluids Co.
2Department of Mining and Petroleum Engineering, Imam Khomeini International University
3Kavoshgaran Consulting Engineers
چکیده
Drilling fluid is among the most important requirements for drilling oil, gas, and geothermal wells. Many drilling challenges are directly or indirectly related to the drilling fluids; therefore, optimizing the drilling fluid has a significant effect on the quality of drilled wellbore, and the risk of drilling operations. In this paper, it is tried to develop deep learning algorithms to optimize drilling fluid properties to minimize the possibility of occurrence of possible problems in one target field in the Middle East. This paper deals with the method of artificial intelligence for the first time to investigate the possibility of estimating optimum drilling fluid parameters using drilling and geological parameters to minimize problems- without considering the location of the target wells. Two artificial intelligence algorithms ‘’LSSVM’’ and ‘’MLP-FFBP’’ were used to train the machine to optimize drilling fluids (such as mud density, yield point, plastic viscosity, etc.) to minimize drilling fluid challenges such as stuck pipe, tight hole, formation influx, and even loss circulation. Results showed that for optimizing drilling fluid parameters in a newly drilled well, the developed AI networks have good capability to estimate parameters for some drilling fluid parameters such as mud density, plastic viscosity, water percentage, and API filtration properties with the accuracy of more than 95% for train and more than 85% for test data. Moreover, results showed that drilling fluids have a direct effect on the tight holes and stuck incidents.
کلیدواژه‌ها
Drilling Fluid Design؛ Drilling Optimization؛ Artificial Intelligence؛ Deep Learning Algorithms؛ Drilling Problems
آمار
تعداد مشاهده مقاله: 460
تعداد دریافت فایل اصل مقاله: 183


نماد الکترونیک 

 

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب