Ahdeema, Jamal

Project Title

Robust and hybrid optimization for Optimal Design of Well Completion in Multilateral Wells with Multiple Types of Flow Control Devices

Project Abstract

Finding the optimal well completion design for multilateral wells with multiple types of flow control devices (FCDs) poses a challenging task when it comes to optimization. This challenge stems from the large number of highly correlated control variables, uncertainties associated with reservoir properties, and the intricate dynamics of fluid flow within the wellbore system. These factors result in computationally demanding and uncertain objective functions, making it difficult for standard optimization workflows to find the optimal design.

The lack of a reliable optimization workflow has led the industry to adopt a simplified, static approach to optimize intelligent completion design, disregarding the long-term dynamic performance of the reservoir. To address this issue, this project aims to establish a robust optimization framework that incorporates multiple optimization algorithms, global sensitivity analysis techniques and proxy modelling using supervised machine learning techniques. The goal is to tackle the complex optimization problem that arises from combining various flow control devices in multilateral wells and achieve a more comprehensive and effective design within limited simulation budget. A fast hybrid optimization framework is then developed for robust, optimal intelligent completion design in multilateral wells that simplifies the problem and prioritizes the optimization process based on the expected impact of the control variables. 

Supervisors

Dr. Morteza Haghighat Sefat, Dr. Khafiz Muradov

Contact

University Email: jm514@hw.ac.uk
Personal web address: linkedin.com/in/jamal-ahdeema-4186151b7