Neutron capture logging calibration and data analysis for environmental contaminant assessment

Koizumi, C.J.

doi:10.1016/j.jappgeo.2006.06.001

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…With the advancement of coalbed methane (CBM) exploration and development in China, wireline-logging technologies played a crucial role in CBM reservoir evaluation and development (Chen et al., 2018; Hatherly et al., 2016; Huang et al., 2019; Ren et al., 2018; Teng et al., 2015; Wang et al., 2017; Zhou and Esterle, 2008; Zhou and O’Brien, 2016). However, several factors, such as instrument inconsistency and logging environments, affect log data quality and cause systematic or non-systematic errors (Kang et al., 2018; Koizumi, 2007; Niebuhr et al., 2001). The lack of enough wireline-log data is a common phenomenon in the early stage of CBM exploration.…”

Section: Introductionmentioning

confidence: 99%

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

Wan

Chen

Song

et al. 2020

Energy Exploration & Exploitation

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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.

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Section: Introductionmentioning

confidence: 99%

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

Wan

Chen

Song

et al. 2020

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

“…Neutron-based interrogation is a powerful method of elemental characterization that has been applied to threat and contraband detection [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], material analysis [17][18][19], and minerals and oil exploration [20,21]. Neutron-based elemental characterization systems typically operate using the technique of neutron-induced gamma-ray spectroscopy.…”

Section: Introductionmentioning

confidence: 99%