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To predict the lithofacies distributions for modelling hydrocarbon reservoir heterogeneities, geostatistical tools like the indicator variogram-based methods provide an efficient solution. However, for the sparse datasets, they potentially encompass drawbacks like the ambiguity of variograms and generating unrealistic patterns. Nevertheless, Transition probabilities (TPs)-based techniques using Markov chains (MCs) have a notable potential to overcome these problems. Hence, the facies distributions were predicted in an Iranian hydrocarbon reservoir using three methods including a novel algorithm proposed here (CGCMC). Using generalized coupled Markov Chain (GCMC), sequential indicator simulation (SIS), and a new combination of GCMC with the transition probability Markov Chain (TP/MC) framework, 50 realizations were generated through each method. Then, the models were evaluated regarding the reproduction of their: facies proportions, variograms/TPs and geological soundness. By ensemble probability distributions (EPDs) the spatial uncertainties were represented. Accordingly, CGMC produced more acceptable results quickly and more conveniently with a higher data integration capacity.

Facies modelling for hydrocarbon reservoirs using a combined Markov chain method