Support vector regression for prediction of gas reservoirs permeability

  • سال انتشار: 1390
  • محل انتشار: مجله معدن و محیط زیست، دوره: 2، شماره: 1
  • کد COI اختصاصی: JR_JMAE-2-1_004
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 499
دانلود فایل این مقاله

نویسندگان

R Gholami

PhD student, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

A Moradzadeh

Prof. of Geophysical Exploration, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

چکیده

Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of permeability because they are usually available for all of the wells. Hence, attempts have been made to utilize well log data to predict permeability. However, because of complicate and non-linear relationship of well log and core permeability data, usual statistical and artificial methods are not completely able to provide meaningful results. In this regard, recent works on artificial intelligence have led to the introduction of a robust method generally called support vector machine (SVM). The term SVM is divided into two subcategories: support vector classifier (SVC) and support vector regression (SVR). The aim of this paper is to use SVR for predicting the permeability of three gas wells in South Pars filed, Iran. The results show that the overall correlation coefficient (R) between predicted and measured permeability of SVR is 0.97 compared to 0.71 of a developed general regression neural network. In addition, the strength and efficiency of SVR was proved by less time-consuming and better root mean square error in training and testing dataset.

کلیدواژه ها

Permeability; hydrocarbon reservoir; well logs; support vector machine; neural network

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.