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Big data of multiphase flow measurement for optimising oil and gas production

  • Full or part time
  • Application Deadline
    Friday, May 31, 2019
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Oil and gas occupies a significant sector in the world’s energy structure. The trend to construct digital oil fields based on internet of things has raised increasing demand for accurately monitor the health and production of the entire oil field down to individual wells in a real-time manner. Revolutionary flow measurement techniques have been developed to fulfil such requirements and the vision is to optimise reservoir management and production process. However, the explosive volumes of flow sensing/imaging data produced by these techniques has led to a pressing challenge. Given the significance of extracting information to the maximum, this project is to explore how can these data be effectively analysed and interpreted for optimising oil and gas production. This project is an emerging area of research with many uncertainties to be resolved. By making use of data science, effective connections between flow sensing/imaging data and process control, analysis and fault diagnosis will be identified. This will give guidance for the energy industry to improve efficiency, reduce cost and meet production goals.

Objectives of this PhD project include:

a) Explore artificial intelligence based data analysis techniques, e.g. deep neural networks, to significantly improve the measurement accuracy of dynamic flow parameters for oil/gas/water flows.
b) Develop a multi-scale, multi-dimensional and online oil and gas production monitoring, analysing and diagnosing data platform to aid decision making and production control.
c) Perform pilot-scale and oil-field validations of the developed methods.

Successful applicant will work closely with the researchers from the Agile Tomography Group at the School of Engineering, the Bayes Centre and the School of Informatics at the University of Edinburgh, and Tsinghua University in China, to deliver high-quality interdisciplinary research and make impact on the oil and gas industry.

Funding Notes

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Enthusiastic and self-motivated candidates are sought with a background in sensing and signal processing.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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