Deep Shale Gas-Bearing Parameters Evaluation Method Based on Carbon Isotope Fractionation and Logging Data

How to accurately and quickly obtain shale gas-bearing (specifically including gas-in-place content and adsorbed gas ratio) has been a challenge that has attracted extensive attention from both academia and industry. Conventional logging interpretation means are often subject to large errors when applied to the interpretation of the base parameters in tight shale formations. The accuracy of logging evaluation models established based on theoretically flawed and inaccurate traditional gas-bearing evaluation methods is also questionable. In this study, a gas-bearing evaluation method based on the carbon isotope fractionation (CIF) model and logging data is proposed, which can accurately and conveniently assess gas-bearing without evaluating shale base parameters. First, the base parameters, geochemical data, and field canister degassing data of 867 core samples from 17 wells in the Luzhou area, Sichuan Basin were collected. Then, the CIF model was used to obtain accurate gas-bearing parameters and to discuss highly relevant shale base parameters affecting free and adsorbed gas content. Finally, based on the CIF model, Ideal Gas Law, Langmuir model, and logging data, a new gas content calculation method was established. The results show that the increase in φ will provide more storage space for free gas, while the increase in TOC would be more favorable for adsorbed gas adsorption. Moreover, the effect of higher Sw on adsorbed gas tends to stabilize, but occupy more space for free gas. The calculated values of the model fit well with the measured values. The microlayers from Long-111 to Long-113 are the best in terms of gas content, with the total gas content ranging from 3.91 to 12.72 cm3/g, averaging 6.92 cm3/g, and the adsorbed gas ratio ranging from 13.6 to 38.42%, averaging 25.68%. Establishing an accurate link between gas-bearing parameters and logging data based on CIF is of great significance to breaking the limitations of traditional logging interpretation methods.