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