Self-learning controllers in the oil and gas industry
العناوين الأخرى
متحكمات التعلم الذاتي في صناعة النفط و الغاز
المؤلفون المشاركون
al-Dabouni, Sayyar
al-Shihab, Husayn Ali Muhammad
المصدر
Journal of Petroleum Research and Studies
العدد
المجلد 2021، العدد 30 (28 فبراير/شباط 2021)، ص ص. 18-35، 18ص.
الناشر
وزارة النفط مركز البحث و التطوير النفطي
تاريخ النشر
2021-02-28
دولة النشر
العراق
عدد الصفحات
18
التخصصات الرئيسية
الموضوعات
- الذكاء الاصطناعي
- التعليم الذاتي
- الصناعة النفطية
- أنظمة التحكم
- الفن التفاعلي
- الشبكات العصبية(الحاسبات الإلكترونية)
- النماذج الخطية(إحصائيات)
- البرمجة الديناميكية
الملخص EN
Recently, solving the optimization-control problems by using artificial intelligence has widely appeared in the petroleum fields in exploration and production.
This paper presents the state-of-the-art reinforcement-learning algorithm applying in the petroleum optimization-control problems, which is called a direct heuristic dynamic programming (DHDP).
DHDP has two interactive artificial neural networks, which are the critic network (provider a critique/evaluated signal) and the actor network (provider a control signal).
This paper focuses on a generic on-line learning control system in Markov decision process principles.
Furthermore, DHDP is a model-free learning design that does not require prior knowledge about a dynamic model; therefore, DHDP can be appllied with any petroleum equipment or devise directly without needed to drive a mathematical model.
Moreover, DHDP learns by itself (self-learning) without human intervention via repeating the interaction between an equipment and environment/process.
The equipment receives the states of the environment/process via sensors, and the algorithm maximizes the reward by selecting the correct optimal action (control signal).
A quadruple tank system (QTS) is taken as a benchmark test problem, that the nonlinear model responses close to the real model, for three reasons: First, QTS is widely used in the most petroleum exploration/production fields (entire system or parts), which consists of four tanks and two electrical-pumps with two pressure control valves.
Second, QTS is a difficult model to control, which has a limited zone of operating parameters to be stable; therefore, if DHDP controls on QTS by itself, DHDP can control on other equipment in a fast and optimal manner.
Third, QTS is designed with a multi-input-multi-output (MIMO) model for analysis in the real-time nonlinear dynamic system; therefore, the QTS model has a similar model with most MIMO devises in oil and gas field.
The overall learning control system performance is tested and compared with a proportional integral derivative (PID) via MATLAB programming.
DHDP provides enhanced performance comparing with the PID approach with 99.2466% improvement.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Dabouni, Sayyar& al-Shihab, Husayn Ali Muhammad. 2021. Self-learning controllers in the oil and gas industry. Journal of Petroleum Research and Studies،Vol. 2021, no. 30, pp.18-35.
https://search.emarefa.net/detail/BIM-1271047
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Dabouni, Sayyar& al-Shihab, Husayn Ali Muhammad. Self-learning controllers in the oil and gas industry. Journal of Petroleum Research and Studies No. 30 (2021), pp.18-35.
https://search.emarefa.net/detail/BIM-1271047
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Dabouni, Sayyar& al-Shihab, Husayn Ali Muhammad. Self-learning controllers in the oil and gas industry. Journal of Petroleum Research and Studies. 2021. Vol. 2021, no. 30, pp.18-35.
https://search.emarefa.net/detail/BIM-1271047
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
-
رقم السجل
BIM-1271047
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر