Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors
المؤلفون المشاركون
Ahmed, Abdulmalek
Elkatatny, Salaheldin
Ali, Abdulwahab
Abughaban, Mahmoud
Abdulraheem, Abdulazeez
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-22
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges.
One of the greatest difficulties is the loss of circulation.
Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost.
There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones.
Most of these approaches are difficult to apply due to some constraints in the field.
The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones.
Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors.
Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them.
High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R).
All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09.
ANN is the most accurate model with R=0.99 and RMSE=0.05.
An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ahmed, Abdulmalek& Elkatatny, Salaheldin& Ali, Abdulwahab& Abughaban, Mahmoud& Abdulraheem, Abdulazeez. 2020. Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors. Journal of Sensors،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1190648
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ahmed, Abdulmalek…[et al.]. Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors. Journal of Sensors No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1190648
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ahmed, Abdulmalek& Elkatatny, Salaheldin& Ali, Abdulwahab& Abughaban, Mahmoud& Abdulraheem, Abdulazeez. Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1190648
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1190648
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر