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

Kuwait Journal of Science

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