Impact of geological modeling processes on spatial coalbed methane resource estimation

Abstract

Spatial coalbed methane (CBM) resource estimation is based on spatial distributions of coal, coal adsorbed gas content and coal density. However, the spatial distribution of gas content can be generated via two different geological modeling processes: (1) The gas content distribution is generated by geological modeling based on the interpreted gas content at boreholes; (2) distributions of gas content related logs or coal properties are generated firstly, then the gas content distribution is calculated based on the spatial distributions of logs or coal properties by the relationship between the gas content and logs or coal properties. This paper presents a study to compare the impact of these two processes on CBM resource estimation for coal seam no. 3 (CS-3) in southeast Qinshui Basin, China. Well logs from 22 wells, laboratory data from five wells and well tops from 131 wells for CS-3 are used in log interpretation and geological modeling. The simple kriging (SK) is used to build the structural model and the coal distribution. Weighted and unweighted omni-directional variograms for structural residual and coal thickness are calculated using an in-house program. Logs of gamma-ray (GR) and density (DEN or RHOB) are distributed in 3D by using sequential Gaussian simulation (SGS) with SK algorithm. Artificial neural network (ANN) is used to build the relationship of the measured raw gas content (RGC; gas content in raw coal basis) with the logs of GR, DEN and measured depth (MD). Then the RGC is distributed in 3D by the two geological modeling processes. CBM resources are calculated in 3D based on the cells' volume, coal density and RGC. Results show that RGC increases with an increase in burial depth. Total CBM resources for the study area calculated by these two processes are similar for CS-3 but the distribution probability of high gas content is highly different which is important for locating wells.