Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore - Ilius Mondal (KeAi Communications Co., Ltd., 2022)
Rock types, pore structures and permeability are essential petrophysical outputs, and they contribute considerably to the highest degree of uncertainty in reservoir characterisation. These factors have a strong influence on exploration and field development decisions. Core analysis is the best approach for estimating permeability, assigning rock types and characterising pore networks. Wireline logs are the most often employed method for estimating the parameters at each data point of reservoirs since there are more un-cored wells than cored wells. Artificial intelligence, on the other hand, is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface data. This is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous basins. Artificial Neural Networks and Self-Organizing Maps are examples of machine learning approaches that can be used in both supervised and unsupervised modes for modelling and prediction. Eocene carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore structures. This paper outlines a novel method of rock fabric classification, pore structure characterization, flow unit classification and robust reservoir permeability modelling based on an integrated approach that incorporates core measurements, log data and machine learning techniques. The pore structure has been characterised by the combination of conventional core, capillary pressure and nuclear magnetic resonance data. Artificial neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units.