Seismic facies analysis, modeling and geobody extraction by machine learning in an oilfield in Iran

  • سال انتشار: 1401
  • محل انتشار: اولین همایش بین المللی هوش مصنوعی، علم داده و تحول دیجیتال در صنعت نفت و گاز
  • کد COI اختصاصی: OILANDGAS01_042
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 316
دانلود فایل این مقاله

نویسندگان

Mohammadreza Vanaki

of Geoscience, Mapna OGDC, Tehran

Seyed Mohammad Hossein Hashemi

Senior Reservoir Engineer, Mapna OGDC, Tehran

Behrooz Abbaspour

Vice President of Subsurface Operations, Mapna OGDC, Tehran

چکیده

In this paper, we have used the supervised learning analysis, which is one of the machine learning methods, so that it is possible to determine the facies and build their model more accurately, and then proceed to the extraction of different geobodies.The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs.In geology, facies are a body of rock with specified characteristics which can be any observable attribute of rocks (such as their overall appearance, composition, or condition of formation), and the changes that may occur in those attributes over a geographic area. Facies encompasses all of the characteristics of a rock including its chemical, physical, and biological features that distinguish it from adjacent rock. Seismic facies analysis based on the Bayesian classification has been implemented for this oilfield. In this regard, different lithofacies (with distinct characteristics) have been proposed based on the rock physics concepts and petrophysical evaluations. Moreover, these lithofacies were suitable for differentiating by elastic properties.

کلیدواژه ها

Seismic facies analysis, machine learning, geobody, Bayesian classification, supervised learning algorithms

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.