Machine learning for process improvement and resource management in the oil and gas sector

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Canada is the fourth largest producer of oil and gas combined and the sector represents 5.6% of Canada's GDP with British Columbia being the second largest producer. Oil and gas operators apply to the provincial regulator authorities (the BC Oil and Gas Commission - BCOGC - in British Columbia) for approvals before beginning work (e.g. environmental permits or consultation with Indigenous Nations among others). Efficacy of the Commission is critical for the public, the Indigenous communities affected, and the economic dynamism of the sector. This research partnership proposes to explore the use of data science and machine learning techniques to look at two challenges in regards to improving services for all BCOGC stakeholders groups: (1) benchmarking the application processing timelines, including improving the estimated time for consultation with the impacted indigenous communities, and (2) forecasting application volumes to support resource management at the BCOGC. This research responds to a need to evaluate a new application process introduced by the commission, which allows operators to combine multiple activities (e.g. wells, road...) in one application rendering predictions and their interpretability challenging. Other challenges lie in the amount of data available and the absence of deterministic relationships, the knowledge base being mostly made up of rules and regulations that are not necessarily comparable in mainstream machine learning tools. In this research, we will build a unified framework that can incorporate the non-commensurable information for improved predictions. The factors affecting the timeline can be found in similar processes in other provinces, and with operators' internal processes. Standardizing expectations around application timelines across jurisdictions will allow operators to better plan their investments in Canada. It also provides an opportunity for regulators to compare service levels and performances, and collaborate on finding opportunities to improve. Forecasting the number of applications received by BCOGC will have a direct impact on BCOGC resource allocations, thus supporting higher quality responses, and ensuring the identification of environmental risks.