  In this paper, a multipurpose Bayesian-based method for data analysis, causal
inference and prediction in the sphere of oil and gas reservoir development is
considered. This allows analysing parameters of a reservoir, discovery
dependencies among parameters (including cause and effects relations), checking
for anomalies, prediction of expected values of missing parameters, looking for
the closest analogues, and much more. The method is based on extended algorithm
MixLearn@BN for structural learning of Bayesian networks. Key ideas of
MixLearn@BN are following: (1) learning the network structure on homogeneous
data subsets, (2) assigning a part of the structure by an expert, and (3)
learning the distribution parameters on mixed data (discrete and continuous).
Homogeneous data subsets are identified as various groups of reservoirs with
similar features (analogues), where similarity measure may be based on several
types of distances. The aim of the described technique of Bayesian network
learning is to improve the quality of predictions and causal inference on such
networks. Experimental studies prove that the suggested method gives a
significant advantage in missing values prediction and anomalies detection
accuracy. Moreover, the method was applied to the database of more than a
thousand petroleum reservoirs across the globe and allowed to discover novel
insights in geological parameters relationships.
