Hydraulic Optimization Predication Using Acritical Neural Networks (ANNs) In an Iraq Oil Field

سال انتشار: 1401
محل انتشار: اولین همایش مهندسی عمران و منابع زمین
کد COI اختصاصی: CEREENG01_116
زبان مقاله: انگلیسی
تعداد مشاهده: 326

نویسندگان

Islamic Azad University, Science and Research Branch, Department of Petroleum and Chemical Engineering, MSc’ StudentPetroleum Engineer, Iraqi Ministry of Oil, Basra oil company, Basra, Iraq

Armin Hosseinian

Assistant Professor, Faculty of Civil Engineering and Earth Resources, Islamic Azad University, Central Tehran Branch, Tehran, Iran Post Doctorate Fellowship, Department of Earth Science and Ocean, University of British Columbia, Vancouver, Canada

چکیده

Independent factors in this study include RPM, torque, differential pressure, hook load, and mud characteristics,whereas the dependent parameters Q, TFA, SPM, and formation variables like depth. An accurate projection of pumppressure alerts the driller to potential issues like circulation issues, washouts, subterranean blowouts, and kicks,allowing for the safe implementation of corrective measures. Presents an in-depth summary of drilling optimizationmethods. The MATLAB fitting tool was used to create an ANN model for hydraulics prediction. This modelsuccessfully predicts pump pressure vs depth in similar formations. Therefore, five input variables and a single outputvariable (pump pressure) were included in a three-layer feed-forward neural network that was trained using theLevenberg-Marquardt back-propagation technique. The ANN technique's simulated results showed a high degree ofagreement between the observed pump pressure and projected values for both the training and test data sets. Based onthe findings, the variables with the greatest influence on this model were: depth, effective diameter, and total flowarea, followed by flow rate, differential pressure, mud characteristics, etc. However, this model should be utilizedwith care owing to the small sample size. Since the information about inclination angle and dog leg severity wasdisregarded, this model may be used for vertical wells.

کلیدواژه ها

Pump Pressures, artificial neural network, backpropagation neural network, radial basis functional networks , Hydraulic Fracturing,

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.