Abstract

Well log analysis and production testing in coal are the initial requirements to judge the prospectivity of a coalbed methane (CBM) reservoir. The process of prospect identification through laboratory studies is accurate although it is time-consuming and expensive. Therefore, we developed a methodology to identify prospective coal seam by establishing multiple regression models of geophysical well log parameters vs. organic/inorganic contents from laboratory-tested core samples for one seam. The Langmuir’s equation and methane adsorption isotherm were used to estimation of gas and saturation content by developing a regression model from organic content. Gas and coal contents (ash, moisture, fixed carbon, and volatile matter) were obtained from the subsequent propagation of the established equations to other wells. Gas saturation increased with depth from 60 to 69%. Mapped seam thickness and gas content were in the ranges of 10.0–54.0 m and 6.1–28.2 cc/g, respectively. Overlaying of seam thickness and gas content identified the sweet spots in releasing potential future well locations. Errors within the permissible limit between the predicted and observed values indicate the gas estimation to be reliable. Another application for electro-facies classification was demonstrated by applying multi-resolution graph-based clustering architecture to capture texture parameters from histogram and auto-covariance function in resistivity image log. Determination of lithology by correlation of resistivity image and geophysical well log corroborated with the depositional environment having fining upward formational sequence. Thus, this study helps in estimating proximate components, gas content, and saturation with depth in coal seam for production optimization to better understand its implications on the dewatering and gas production periods in the Bokaro CBM reservoir situated in India.

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