A New Regression-Based Method for Accurate Measurement of Coal and Shale Gas Content

摘要

Gas content and storage capacity are the key parameters for determination of the gas resources and reserves in unconventional reservoirs. These parameters must be obtained from laboratory experiments in the core samples such as desorption canister tests and adsorption isotherm experiments. Desorption canister testing is performed to determine the total adsorbed gas content, gas composition and the total desorption time. Adsorption isotherm experiments are conducted to determine the gas storage capacity with pressure and for CO2 sequestration purposes. Other analyses of coals include proximate analysis and bulk density measurements of all samples. Shales are commonly analyzed for total organic carbon in lieu of proximate analysis. The gas content is estimated by placing selected freshly cut reservoir samples in air tight sealed canisters and measuring desorbed gas volume as a function of time at atmospheric conditions. Total gas content is the summation of three components: "lost gas", desorbed gas, and "residual gas". "Lost gas" is the volume of the gas that desorbs from the sample during the recovery process at wellsite, before the core sample can be sealed in a desorption canister. "Residual gas" is the gas that remains sorbed on the sample at the completion of the canister desorption test. A disadvantage of this procedure is the estimation of "lost gas". The volume of the "lost gas" is usually estimated by extrapolation of desorbed data to time zero using linear and/or polynomial curve-fit to the plot of cumulative desorbed gas versus square root of time. The differences between both methods can become more pronounced especially in high gas content reservoirs. In this paper a new method, which is based on nonlinear regression of measured gas content, is presented. This technique offers an accurate estimation of lost gas which coupled with sorption isotherm impacts the calculation of gas in place, the recoverable reserve and production profiles.