Model predictive control of artificial gas-lift process

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Artificial gas-lift (AGL) technique is one of the most widely used methods in the oil production to maintain acceptable oil flow to the processing equipment when the reservoir pressure is not high enough. In spite of its popularity, the AGL process is prone to casing-heading instability phenomenon, which is revealed as significant flow oscillations. This is undesirable as it results in production losses and unstable behavior that has negative impact on the downstream equipment. Controller design for such a process is very challenging as it exhibits highly nonlinear dynamics. In this thesis, we investigate the application of different types of model predictive control (MPC) techniques to the AGL. First, a new design of subspace predictive controller (SPC) is developed for gas-lift process. The SPC is a data driven algorithm, which is based on linear predictor to predict future output based on process input and output data. The linear prediction model is derived offline. Thereby, the key feature of the proposed approach is that precise knowledge of the model and on-line optimization are not required to derive the control law. Then, another type of MPC, the predictive generalized minimum variance control (PGMVC) is employed to derive a robust controller based on the state estimation to stabilize the AGL process when casing-heading phenomenon occurs. A closed-form optimal control law is obtained based on the Taylor series approximation. Further, a nonlinear state observer is produced and combined with the controller to ensure closed-loop control through variables that are most beneficial to the system performance, which are unmeasurable and can be obtained only via estimation. Through simulation studies, the effectiveness of the proposed controller is demonstrated. Finally, the stability analysis of AGL process is discussed by operating both openings of gas injection choke and oil production choke. Furthermore, a trajectory optimization algorithm is developed to minimize the process transition time, where the operation point moves from unstable region to stable region.

روابط وملفات

التعريف والنوع

رقم الوثيقة

07fde203-464d-4259-b74d-c36286ba8491

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2283570_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Ahmed Al Durra","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Jing Shi","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Model predictive control of artificial gas-lift process

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Applied sciences Gas-lift Mpc Optimization Stabilization Electrical engineering 0544:Electrical engineering

إشراف وإعداد

الإشراف

Ahmed Al Durra

الإعداد

Jing Shi

الاقتباسات الببليوغرافية