Applying an optimized low risk model for fast history matching in giant oil reservoir
Other Title(s)
تطبيق نموذج أمثل منخفض المخاطر للمواءمة التاريخية السريعة في خزان نفط عملاق
Joint Authors
Karimi, Mujtaba
Murtazawi, Ali
Ahmadi, Muhammad
Source
Issue
Vol. 46, Issue 1 (31 Jan. 2019), pp.84-89, 6 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2019-01-31
Country of Publication
Kuwait
No. of Pages
6
Main Subjects
Earth Sciences, Water and Environment
Abstract AR
في هذا البحث، تم تطبيق أحدث طرق المواءمة التاريخية الآلية (AHM) على حقول جدباء حقيقية تحتوي على 14 بئر نشط مع استجابات متعددة (من حيث معدل الإنتاج، ضغط القاع و ضغط كتلة البئر) تقع في الجزء الجنوبي من إيران.
تم استخدام خوارزمية آلة متجة الدعم المعدلة لإنشاء نموذج بروكسي مدمج على أساس تصميم تجريبي.
و من ثم، تم ضبط كل معلمات النموذج لإعادة إنتاج التاريخ المرصود في نموذج بروكسي الذي تم إنشاؤه.
و بالتالي، تم بناء نموذج بروكسي المقترح بنجاح باستخدام 1086 عينة بناء على معامل R2 لحوالي 0.9 من مجموعة البيانات المستخدمة في التدريب و الاختبار.
و أخيرا، تم تحسين هذه العملية من خلال خوارزميتين رئيسيتين للوصول إلى أفضل الحلول و هي الخوارزمية الوراثية و خوارزمية استمثال عناصر السرب
Abstract EN
History matching is still one of the main challenging parts of reservoir study especially in giant brown fields with lots of wells.
In these cases, history matching with conventional manual technique needs many runs and takes months to get a match.
In this paper the latest approaches for automated history matching (AHM) were applied to a real brown field with 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in south part of Iran.
Modified support vector machine was employed to create proxy model in which 44 model parameters were incorporated based on design of experimental.
Thereafter, all model parameters were adjusted to reproduce the observed history within the created proxy model.
A robust framework for building the proxy model was programmed with data exchange ability between commercial reservoir simulator software and the proxy model routine.
Accordingly, the proposed proxy model was successfully constructed using 1086 samples based on R2 coefficient of about 0.9 for the trained and test dataset.
Finally, the process was optimized by two main algorithms for reaching best solutions which are genetic and particle swarm optimization.
American Psychological Association (APA)
Karimi, Mujtaba& Murtazawi, Ali& Ahmadi, Muhammad. 2019. Applying an optimized low risk model for fast history matching in giant oil reservoir. Kuwait Journal of Science،Vol. 46, no. 1, pp.84-89.
https://search.emarefa.net/detail/BIM-1500282
Modern Language Association (MLA)
Karimi, Mujtaba…[et al.]. Applying an optimized low risk model for fast history matching in giant oil reservoir. Kuwait Journal of Science Vol. 46, no. 1 (Jan. 2019), pp.84-89.
https://search.emarefa.net/detail/BIM-1500282
American Medical Association (AMA)
Karimi, Mujtaba& Murtazawi, Ali& Ahmadi, Muhammad. Applying an optimized low risk model for fast history matching in giant oil reservoir. Kuwait Journal of Science. 2019. Vol. 46, no. 1, pp.84-89.
https://search.emarefa.net/detail/BIM-1500282
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 88
Record ID
BIM-1500282