2026-09-20T02:43:34Z rgu-repository.worktribe.com

oai:rgu-repository.worktribe.com:348759 2026-09-04T15:00:36Z 084104101115105115 openaccess

Thesis Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence. This work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from an industry partnership with ODS-Petrodata, a business intelligence company in Aberdeen, Scotland. This research proposes statistical models that serve as a foundation for building a novel operational tool for forecasting the performance of rig drilling operations. A prototype for a tool able to forecast the future performance of a drilling operation is created using the obtained data, the statistical model and the experts' domain knowledge. This work makes the following contributions: applying K2GA and Bayesian networks to a real-world industry problem; developing a well-performing and adaptive solution to forecast oil drilling rig performance; using the knowledge of industry experts to guide the creation of competitive models; creating models able to forecast oil drilling rig performance consistently with nearly 80% forecast accuracy, using either logistic regression or Bayesian network learning using genetic algorithms; introducing the node juxtaposition analysis graph, which allows the visualisation of the frequency of nodes links appearing in a set of orderings, thereby providing new insights when analysing node ordering landscapes; exploring the correlation factors between model score and model predictive accuracy, and showing that the model score does not correlate with the predictive accuracy of the model; exploring a method for feature selection using multiple algorithms and drastically reducing the modelling time by multiple factors; proposing new fixed structure Bayesian network learning algorithms for node ordering search-space exploration. Finally, this work proposes real-world applications for the models based on current industry needs, such as recommender systems, an oil drilling rig selection tool, a user-ready rig performance forecasting software and rig scheduling tools. Fournier, François A. PhD Doctoral 2013-03-31 John McCall, Andrei Petrovski and Peter Barclay oai:rgu-repository.worktribe.com:348759 https://rgu-repository.worktribe.com/348759/1/FOURNIER%202013%20Probabilistic%20modelling%20of%20oil%20rig%20drilling Innovate UK Business intelligence Oil and gas industry Bayesian networks Metaheuristics https://rgu-repository.worktribe.com/output/348759 2013 en openAccess https://creativecommons.org/licenses/by-nc/4.0/ Public