Lithology prediction method of coal-bearing reservoir based on stochastic seismic inversion and Bayesian classification: a case study on Ordos Basin

Abstract

Abstract The Upper Palaeozoic coal measures in the Ordos Basin are rich in unconventional natural gas resources; however, the reservoir heterogeneity is relatively strong, which majorly restricts the accuracy of lithology prediction. Stochastic seismic inversion can synthesize the geological–geophysical information and establish high-resolution reservoir models based on geostatistical theory to obtain good inversion results; moreover, it can characterize reservoir lithology and fluid-bearing properties. The present study aimed to propose a high-precision lithology classification method for coal-bearing strata in the Ordos Basin, using Bayesian classification and stochastic seismic inversion. Initially, the reservoir geological model was established on the basis of the sequential Gaussian simulation algorithm, and stochastic seismic inversion was performed in combination with rock physics analysis, thereby obtaining a high-resolution elastic parameter data volume. Thereafter, the probability density function (PDF) and the probability density confusion matrix (PDF confusion matrix) were introduced to quantitatively analyse the ability of sensitive elastic parameters to distinguish lithology. Eventually, a logging lithology-fluid classification template was established based on the Bayesian classification technology; furthermore, a 3D lithology-fluid prediction was completed in combination with the inversion results. The prediction results are in good accordance with the logging data, which verifies the feasibility and effectiveness of the method.