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The work focuses on the modelling and imputation of oil and gas reservoirsparameters, specifically, the problem of predicting the oil recovery factor(RF) using Bayesian networks (BNs). Recovery forecasting is critical for theoil and gas industry as it directly affects a company's profit. However,current approaches to forecasting the RF are complex and computationallyexpensive. In addition, they require vast amount of data and are difficult toconstrain in the early stages of reservoir development. To address thisproblem, we propose a BN approach and describe ways to improve parameterpredictions' accuracy. Various training hyperparameters for BNs wereconsidered, and the best ones were used. The approaches of structure andparameter learning, data discretization and normalization, subsampling onanalogues of the target reservoir, clustering of networks and data filteringwere considered. Finally, a physical model of a synthetic oil reservoir wasused to validate BNs' predictions of the RF. All approaches to modelling basedon BNs provide full coverage of the confidence interval for the RF predicted bythe physical model, but at the same time require less time and data formodelling, which demonstrates the possibility of using in the early stages ofreservoirs development. The main result of the work can be considered thedevelopment of a methodology for studying the parameters of reservoirs based onBayesian networks built on small amounts of data and with minimal involvementof expert knowledge. The methodology was tested on the example of the problemof the recovery factor imputation.

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering