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Seismic history matching and machine learning, Edinburgh Time-Lapse Project


Project Description

The current oil and gas fields are now challenging to produce and the time for easy extraction is gone. More complex and accurate representations of the field with models are required to take any decision concerning field development. A common technique is to update and more frequently constrain the model with acquired multi-disciplinary data. These data are integrated through history matching, a mathematical framework to correct the existing model to match to the observed data. One of the most challenging tasks in the history matching procedure occurs when time-lapse seismic is used. Indeed, seismic data needs to be processed through different and complicated steps which carries uncertainty, which is a difficult qualitative and quantitative mix of bias, errors, misinterpretations and cumulative numerical errors. For decades this uncertainty has been challenging to quantify, and it is still an unsolved problem up to now.

We propose in this project to replace the tedious multiple steps in our conventional workflow by an artificial intelligent framework (machine learning techniques). These methods, coupled with advances in computing power, are mature enough to be carried over into this challenging topic of seismic history matching. This new type of model update will deliver a more rapid and reliable answer and then impact directly the decision making. For instance, a successful output would be to propose locations for new infill drilling based on the artificial Intelligence-driven history matched models. We anticipate the student will familiarise themselves with the data, then investigate the artificial intelligence framework for history matching. Finally, they will apply the workflow to a selected dataset in order to achieve the model update procedure. Data for the project is donated by oil companies, and will consist of 3D and 4D seismic data, wireline logs, production data, a field simulation model and possibly a geological model.

You will join the ETLP research team which has twenty years of experience in quantitative 4D seismic interpretation and is funded by a number of oil, energy and service companies. For more information on our activities please visit our website: https://etlp.hw.ac.uk. You will work on this PhD project supervised by a multi-disciplinary team of Prof Colin MacBeth and Dr Romain Chassagne.

The successful candidate should have a strong interest in applied research and possess at minimum a masters AND undergraduate degree in geophysics, physics, reservoir engineering or a similar field. Formally four years of university study including a minimum of one year at an advanced level are required. Programming skills are an essential requirement of this project, whilst some experience of fluid flow simulation and seismic processing is also necessary. Several years of additional experience working in industry on reservoir development is desirable but not a necessity.

Please complete our online application form at https://www.hw.ac.uk/study/apply/uk/postgraduate.htm and apply for PhD Petroleum Engineering. You must quote the full project title and reference number on your application form. You will also need to provide a CV, a copy of your degree certificate and relevant transcripts; detailed copies of your degree marks and a technical reference. We also require proof of your ability in the English language (if English is not your mother tongue or if you have not already studied for a degree that was taught in English within the last 2 years). We will require an IELTS certificate showing an overall score of at least 6.5 with no component scoring less than 6.0 or a TOEFL certificate with a minimum score of 90 points.

Applicants must be available to start the PhD by January 2020. Please contact Prof Colin MacBeth () for informal information.

Funding Notes

This scholarship is available to ALL students, whether home, EU or overseas. The scholarship will cover full tuition fees and provide an annual stipend of £15,009 for the 42 month duration of the project.

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