چکیده مقاله
Analysis of well testing signals is a widely used technique for characterizing the hydrocarbon reservoirs This technique can simply reveal wellbore, reservoir, and boundary types through identification of characteristic shapes on the pressure derivative PD graphs Since the traditional matching processes often fail to correctly detect characteristic shape of the PD graphs, in this study multi output least squares support vector machines MLS SVM is proposed for identification two different reservoirs and four boundary types Indeed, homogenous and dual porosity reservoirs with different external boundaries including closed, constant pressure, infinite acting, and single sealing fault are considered Parameters of the MLS SVM is firstly adjusted by 784 synthetic PD graphs obtained from PanSystem software Performance of the designed MLS SVM is then evaluated using an actual field and 196 new synthetic well testing signals Classification accuracy is used for evaluation performance of the proposed smart model Results indicates that the proposed smart approach is able to identify different reservoir and boundary types with 100% classification accuracy
کلیدواژهها
نویسندگان
Mehrafarin Moghimihanjani
Chemical & Petroleum Engineering Department Sharif University of Technology Tehran, Iran
Nima Sharifi Rayeni
Chemical & Petroleum Engineering Department Science and Research Branch, Islamic Azad University, Tehran, Iran
شیوه ارجاع
Moghimihanjani, Mehrafarin and Sharifi Rayeni, Nima,1399,Application of multi-output least-squares support vector machines (MLS-SVM) for classification of oil reservoirs from well testing signals,8th International Conference on Innovation and Research in Engineering Sciences
ارائهشده در
مجموعه مقالات هشتمین کنفرانس بین المللی نوآوری و تحقیق در علوم مهندسی2 اسفند 1399