| dc.description.abstract | Periods of sub-optimal production rates, or complete shut-downs, add negative numbers to the revenuegraph for oil companies. Oil and gas are produced from several reservoirs and through many wells withvarying gas/oil proportion, making it a complex process that is difficult to control. As a part of a threestepprocess for utilizing data in the oil production domain, this thesis derive methods for combiningevent patterns, called restricted association rules, in time series in order to warn about future anomalies inoil production processes. Two problems have been considered: Network learning and network reasoning.The suggested solution consists of building an Association Rules Network (ARN) from the rule set givenas input. After transforming the hypergraph-based ARN to a directed acyclic graph, correlations betweennodes are found by applying the shortest-path principle. Motivated by the shortcomings of this simplesolution, it is shown how a method for learning Bayesian networks with support for representation oftemporal dependencies can be derived from the initial ARN. The concept, named Temporal BayesianNetwork of Events (TBNE), is a powerful, but yet complex solution that enjoys the properties of Bayesiannetwork reasoning while at the same time representing temporal information. This thesis has shown thatit is theoretically feasible to combine restricted association rules in order to create a network structurefor reasoning. It is concluded that the final choice of solution must be based on a carefully considerationof the trade-off between complexity and expressiveness, and that a natural continuation is testing thesuggested concepts with real data. | nb_NO |
