Modeling with Markov Chains Methodology, Case Study: Lithofacies in a Hydrocarbon Reservoir, SW Iran

Identifying and interpreting subsurface heterogeneities, especially Litofacies, plays definitely an important role in assessing and managing hydrocarbon resources. Variety of methods have been developed in order to model discrete features of hydrocarbon reservoirs, as Litofacies, which the majority of them have focused on intra-well modeling, and are not applicable for 2D or 3D modeling between oil wells. Furthermore, developing a novel methodology to bring a more factual reservoir facies has always been a matter of attraction, and is effective in lowering risk of decision making in different exploratory stages. These days, Markov Chains is used as a powerful tool for facies modeling. This method is based on conditional probabilistic and providing transitional matrix of states. This study is carried out on an oil field, South-West Iran; where the Asmari Formation is its main reservoir. Here, interval of the Asmari Formation and its cap rock in a 12 kilometers long section, 110 meters width, is classified into three main parts, by the means of Markov Chains modeling. The best result of modeling was obtained with nine wells and four seismic horizons that brought 87% accuracy in average.