كاظم كاظم

This thesis addresses the persistent challenge of sub‑optimal oil production in mature fields operated with continuous gas lift, where performance is highly sensitive to changing reservoir/well conditions an‎d where traditional, manual optimization workflows are time‑consuming an‎d difficult to repeat frequently. The study’s primary objective is to develop an integrated, automated framework that identifies optimal operating conditions for gas lift systems to maximize oil recovery while improving engineering efficiency an‎d supporting economically viable operation. A hybrid workflow is proposed that combines physics‑based nodal analysis an‎d well performance modeling with automation an‎d optimization logic. The framework integrates PROSPER well modeling with Python through the OpenServer API, enabling automated data han‎dling, PVT matching, model calibration/validation, an‎d systematic sensitivity‑based optimization. The optimization focuses on three key effective variables that govern gas lift performance: tubing head pressure (THP), gas injection rate, an‎d gas injection depth. The proposed system is demonstrated on a ten‑well case study (“Field‑A”). Results show substantial hidden production potential unlocked through multi‑parameter optimization, with oil‑rate improvements ranging from 50.7% to 67.1% per well an‎d an average field increase of ~57%. In addition, the automated workflow dramatically reduces computational/engineering time compared with manual approaches, reporting an overall time‑efficiency improvement of ~99.9%, while maintaining strong model reliability (average calibration match quality reported as ~97.31%). The study concludes that automated hybrid gas lift optimization enables near real‑time, repeatable decision support an‎d can significantly enhance both production an‎d operational value in gas lift‑driven assets.

فرازاوري با كاز , بازيافت نفت , بهينه سازي , مدل هاي تركيبي

Gas Lift , Oil Recovery , Optimization , Hybrid Models

Kadhim M. Kadhim

Dr. Mohammad Taghi Sadeghi