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Optimising Energy Well Plug and Abandonment through Data Analytics and Modelling

Project Description

Over the past century the petroleum industry has met the vast majority of global energy demand and continues to do so despite a recent and continuing drive towards de-carbonization (Rockström et al., 2017). At the end of their economic life all petroleum wells must be properly decommissioned, the cost of which is colossal. Almost 50% of the total decommissioning cost is associated with well plug and abandonment (P&A) whereby an energy well is sealed in perpetuity. A key aim of decommissioning is to prevent leakage of petroleum fluids into the environment, particularly greenhouse gas emissions to atmosphere, which contribute to climate change and global warming (Cahill et al. 2019). As industry prepares to undertake the decommissioning of hundreds of thousands of wells in the coming decades (Vrålstad et al. 2019), significant knowledge gaps persist with respect to what constitutes an optimal and cost-effective P&A configuration, as well as how to predict performance over time. For example, key questions remain unanswered such as; what are the typical construction configurations employed and how have they varied with time? What are the associated costs? How and where has performance been assessed and by what methods? How have varying P&A configurations performed with time (e.g. do they leak and if so at what rates and how often)? What risk factors contribute to poor performance? And, can we predict performance with time?

This Ph.D. project aims to address these key outstanding questions and contribute towards optimizing the decommissioning process. This will be achieved through the following 3 main tasks:

1. Systematic literature review on global well decommissioning and P&A practices
The literature review includes an overview of global historic and current well P&A practices, information on performance, cost evaluation and identification/assessment of risk factors for sub-optimal performance.

2. Data Analytics of existing P&A well data towards understanding performance and risk factors
Relevant regulatory and industry data for on- and off-shore wells from the UK, U.S., Canada and other regions will be assessed (e.g. the UK OGA’s newly updated NDR for all continental shelf wells). Advanced statistical data analysis methods (e.g. multivariate analyses, multilevel logistic regression etc.), as well as machine learning techniques, will be employed to gain insights on the performance of different P&A configurations from data held on the tens of thousands of existing wells abandoned over the past century. From these analyses, risk factors for sub-optimal performance will be identified and a risk tool developed. This component will be directly linked to ongoing projects with the UK Environment Agency and the University of British Columbia, Vancouver, Canada (for which there may be opportunity to visit on a research stay as part of the Ph.D.).

3. Well P&A modelling for performance assessment and optimal design
Data attained from task 1 and 2 will be used to further develop well P&A modelling frameworks. Numerical models have been developed to support decision-making in a risk-based approach to well P&A design, as part of an ongoing project funded by key UKCS oil and gas operators. The reliable well P&A modelling framework, will form the basis of a prediction tool capable of evaluating effectiveness of P&A designs over relevant timescales. This will further advance the paradigm shift towards risk-based well P&A design, which is of particular interest to all stakeholders.

The Ph.D. project would suit an enthusiastic, professional and proactive candidate with a M.Sc. in Geoscience, Earth Sciences, Petroleum Geology, Engineering Geology or similar. It is well aligned and connected to facilitate the successful candidate into a career in academia, government/regulatory agency, a research centre or industry.

To make an application please visit the website.

Funding Notes

The scholarship will cover tuition fees and provide an annual stipend of approximately £15,009 for the 36 month duration of the project and is available to applicants from the UK, EU and overseas.

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