English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Reconstruction of critical coalbed methane logs with principal component regression model: A case study