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  Compositional Characterisation of Reservoir Fluids using Data Analytics and Machine


   School of Energy and Electronic Engineering

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  Dr Jebraeel Gholinezhad  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Energy and Electronic Engineering and will be supervised by Dr Jebraeel Gholinezhad.

The work on this project could involve:

  • Literature review on reservoir fluid properties and machine learning algorithms
  • Data collection (partially available)
  • Coding in Python (or a similar software)
  • Building AI and machine learning models with different realisations based on a varying set of functional groups
  • Preparing manuscripts for dissemination of the results

Project description

Characterisation of reservoir fluids and obtaining their original composition is of utmost importance in reservoir engineering since it is one of the key inputs in reservoir performance evaluation techniques such as material balance, well test analysis, reserve estimates, and compositional reservoir simulation. Ideally, the compositions and other fluid properties should be obtained from actual measurements in the form of a fluid PVT study conducted on real samples collected from either wellbore or at the surface. Quite often, however, these measurements are either not available, or very costly to obtain. In such cases, empirically derived correlations are used to predict the required properties. However, the success of such correlations in prediction depends mainly on the range of data and geographical area at which they were originally developed.

Machine learning (ML) techniques such as artificial neural network (ANN), genetic algorithm (GA), support vector machine (SVM), etc. are useful information processing tools that have recently attracted increasing attention in oil and gas industry. In particular, it has been employed to estimate hydrocarbon fluid properties including bubble point pressure, formation volume factor and viscosity to name a few. However, the number of studies that have been carried out towards using these techniques in predicting the fluid compositions are very limited. 

Preliminary studies of using machine learning tools in estimating fluid PVT properties conducted as a short-term funded project showed promising results which have been compiled in a manuscript to be submitted for publication. This research will be expanded in a more systematic study by a PhD candidate. The data science skills acquired by doing this multidisciplinary project in a gold-rated university will put the candidate in an excellent position for the future career considering that data analytics and artificial intelligence are undoubtedly one of the most demanding skillsets in today’s job market.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

You’ll be expected to have:

- Strong mathematic background

- Knowledge of fluid PVT properties or reservoir engineering

- Knowledge of data science and machine learning 

- Coding experience with Python or similar programming language

How to Apply

We encourage you to contact Dr Jebraeel Gholinezhad ([Email Address Removed]) to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: SENE5651023.


Computer Science (8)

Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).