The global offshore oil and gas industry is constantly challenged with complex operational activities, increasing uncertainties, strict regulations and delicate health, safety and environmental issues. That has made offshore deepwater drilling operation the most time sensitive activity in the upstream oil and gas industry with high probabilities of cost and time overrun. Unfortunately, the current cost estimation models are not robust enough to deal with the multi-variables associated with cost overrun in the offshore deepwater drilling industry in the Sub-Sahara Africa. This study therefore developed a mathematical model that can give accurate estimations with limited data, precisely capture risk elements and factor probability results of all the possible cost variables in the offshore deep-water drilling operations. The study combined Bayesian approach with Activity-based costing (ABC) model to address the limitations of most existing models using primary data collected and secondary data extrapolated from past literatures, published official drilling data and companies’ financial and operational reports. The integrated model showed promising results when tested against three offshore fields’ data across three different countries (Erha-Nigeria, Jubilee-Ghana and Luanda-Angola). Findings from the analysis of the three fields showed cost estimates to be 10% more accurate than the estimates from existing cost estimation models in Sub-Sahara Africa. Further analysis also demonstrated the ability of the model to reduce the regional cost overrun from about 40% to 20%, thereby underlining the efficacy of the model in estimating offshore drilling cost. The strengths, weaknesses as well as the implications of using the model were also discussed. Additionally, the study developed an improved elicitation framework and guidelines to help facilitate cost estimation in the offshore deep-water drilling operations based on the Bayesian approach. The developed elicitation process was used to collect the primary data for this work and generated probabilistic response on the known unknowns and unknown unknowns’ variables in the oil and gas industry Finally, the research analysed and produced findings on cost reduction techniques for the offshore drilling industry.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:731442 |
| Date | January 2017 |
| Creators | Gyasi, Evans Akwasi |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/97927/ |
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