Wireline logging plays a critical role in coalbed methane exploration. However, the lack of crucial log data, such as neutron and sonic logs, makes coalbed methane exploration difficult. In this paper, we propose a principal component regression model incorporating a multiscale wavelet analysis, a histogram calibration, a principal component analysis, and a multivariate regression to reconstruct essential neutron and sonic logs from conventional logs (i.e., density, resistivity, gamma ray, spontaneous potential, and caliper logs). Our proposed model does not need core-related correlation, and there is no local optimization. We have applied the model to evaluate coalbed methane content in a real case. Firstly, we use the multiscale wavelet analysis and histogram calibration to improve logs’ reliability and lateral comparability. Then, we apply principal component analysis to transform the well-correlated wireline logs into linearly independent components and regress reconstruction functions for neutron and sonic logs with multivariate regression. The reconstructed logs are like the measured logs in trend, mean, and scale. Finally, we apply the reconstructed neutron logs to predict the coalbed methane-content distribution. The predicted distribution is not only following the regional distribution characteristics of coalbed methane enrichment zones but also validated by the coalbed methane production data. In summary, the successful applications of wireline-log reconstruction and regional coalbed methane-content prediction have demonstrated the reliability of the proposed principal component regression model.

Coal-hosted gallium-rich ores are mainly explored with geochemical analyses, and their elasticities lack research. This paper incorporated core testing, rock-physics modeling, and Monte Carlo simulations to characterize the elastic parameters of gallium-rich cores and discuss whether coal-hosted gallium-rich ores are elastically detectable. The measured cores from No. 6 coal in the Heidaigou mine showed that the gallium contents strongly correlate to the boehmite contents with a 0.96 correlation coefficient. The rock-physics modeling results showed that mineral compositions and contents are critical factors influencing elastic parameters, and elastic parameters in No. 6 coal showed profound heterogeneities as mineral compositions and contents. The preferred parameters for classifying and grouping different mineral-rich cores are the bulk modulus and moduli ratio. Cross-plotting bulk modulus vs. moduli ratio can qualitatively group measured cores and Monte-Carlo simulated realizations into different mineral-rich and saturation states properly. Concerning the factors of boehmite content, porosity, and saturation state, an interpretation template for boehmite-rich coal was proposed and used. As the template interpreted readings close to the measured contents, the built templates can quantitatively interpret boehmite and gallium contents in coal-hosted ores with high precision. In summary, the coal-hosted gallium-rich ores are elastically detectable.

Tectonically deformed coal (TDC) is closely related to gas outbursts. Since TDC exploration is an essential objective for coalfield exploration, it is of great significance to study the petrophysical properties of TDCs and explore their differences. This study collected 17 TDCs and undeformed coal samples from the Huaibei coalfield and ultrasonically tested their petrophysical parameters, including densities, P- and S-wave velocities, and their derived petrophysical parameters (VP/VS ratio, P- and S-wave impedances). Undeformed coal and TDCs with different deformation types (brittle, shear, and plastic deformations) show significant differences in their petrophysical parameters, and cross-plot analysis can directly differentiate them. As with traditional geological methods, acoustically measured petrophysical parameters are good indicators to determine the type of coal deformation. However, the TDCs with the same deformation type have similar petrophysical parameters; it is not easy to distinguish them directly. Instead, the proposed method incorporating principal component analysis and clustering can accurately distinguish up to five classes of TDCs. Different types of tectonic deformation environments and their intensities are highly correlated with the clustering results. This paper also provides essential petrophysical parameters for undeformed coal and TDCs in the Huaibei coalfield, and these parameters can help interpret undeformed coal and TDCs using wireline logs and seismic data.