About the Project
The big bets of the oil and gas industry increasingly involve uncertainty. These decisions in exploration, development, and production of hydrocarbons, along with more recent challenges of CO2 storage and sequestration or hybrid energy solutions, are uncertain because of lack of information. We can buy information and partially resolve uncertainty, but this comes at a cost. While added information could come from disparate sources, including sensors, surveys, or expert opinion, and could partially resolve uncertainty, the central question in any information acquisition activity is “is the information worthwhile”? in other words, do the benefits of information outweigh the costs?
This PhD projects aims to answer this question using the methods of Bayesian networks combined with economics of information. Considering the multi-disciplinary nature of value of information, this work addresses framing, analysis, and decision insights resulting from information acquisition. The information comes from a variety of sources, and in different frequencies. Furthermore, valuations involve technical and economic factors with distinct characteristics. The aim is to construct useful models that excel in multi-dimensional valuations and supply decision insight for the major challenges of the oil and gas industry.
Developing useful decision models is not an easy task. In practice, projects have complex contract structures, include a series of technical and commercial decisions, and involve uncertainty. For most real-world applications, decision support models have achieved little. The traditional economical models of information valuation focus on few value drivers, usually without considering a proper frame of decisions. Even when uncertainties are identified and described, the valuation model becomes too complex and effortful that the solutions lack the decision insight. The traditional off-the-shelf packages are also inadequate to tackle such valuation models.
With this project, we aim to develop a useful, material, and economical model of decisions. The model is aimed at application of “value of information” and uses stochastic methods to describe uncertainty. Bayesian networks draw probabilistic conclusions on multiple sources of information. Finally, the contribution of the model is not in its comprehensiveness, it is in its usefulness to generate decision insight.
Bayesian networks illustrate the relationship between decisions, sources of uncertainty, and value functions. As such, they rely on are rigorous depictions of the decision problem. Yet such analysis is not complete without the insights from information economics. The optimal decision making is a function of technical factor as well as economic dynamics.
The ideal candidate for this research is an individual with good grasp of probability, statistics, and economics, who is also familiar with the operations in the oil and gas and energy industries. The research works involves applications of computer software and coding, experience of developing decision support systems is also beneficial.
This project is available to ALL students, whether UK, EU or overseas. The successful candidate should possess at minimum a Masters and undergraduate degree in engineering or sciences with a specialisation or interests in mathematics, economics, and computer science. Experience in petroleum and energy industries is beneficial. Background on decision analysis and economics is desirable but not a necessity.
To apply you must complete our online application form. Please select PhD programme Petroleum Engineering and include the full project title, reference number and supervisor on your application form. Ensure that all fields marked as ‘required’ are complete.
You must complete the section marked project proposal; upload a supporting statement documenting your reasons for applying to this particular PhD project, and why you are an ideal candidate for the position. You will also need to provide a CV, a copy of your degree certificate and relevant transcripts. You will be asked to enter details of an academic referee. Until your nominated referee has uploaded their statement, your application will not be marked as complete and will not be considered by the review panel. You must also provide 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 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 in September 2020.
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