PhD AI-Assisted History Matching and Optimization for Subsurface Reservoirs

Details

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

Characterising subsurface reservoirs is critical for energy production, groundwater management, and carbon storage, yet it remains a formidable challenge due to the high complexity and uncertainty of these formations. History matching is the standard procedure for quantifying these uncertainties, specifically regarding formation porosity, permeability, and fracture distributions. While this process has evolved from manual adjustments to Assisted History Matching (AHM), it remains an ill-posed inverse problem that is computationally intensive. Because diverse geological scenarios can yield identical data, uncertainty remains high.

Artificial Intelligence (AI) has emerged as a transformative solution, shifting the paradigm toward physics-informed architectures. By leveraging machine learning and generative neural networks, AI can preserve geological realism while significantly accelerating model calibration. Unlike traditional methods, which are often too slow to handle thousands of iterations, AI-driven models can integrate real-time data streams. This shift enables rapid, data-rich simulations that improve the accuracy of reservoir behaviour predictions. The primary aim of this project is to develop and apply physics-informed AI frameworks to enhance history matching and optimisation processes.

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Engineering.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr Y Zhou at yingfang.zhou@abdn.ac.uk. If you require any additional assistance in submitting your application or have any queries about the application process, please don’t hesitate to contact us at researchadmissions@abdn.ac.uk