Deformation Degree Estimate for Coal Seam using Well Logs as Input: A Case Study

Abstract

The presence of tectonic deformed coal (TDC) is a prerequisite for coal-and-gas outburst. With a higher degree of TDC deformation, there is a greater possibility of coal-and-gas outbursts. The estimate of deformation degree for coal seam is critically important for mining safety. In this study, we focus on the No. 8 coal seam of Luling coalmine to identify and estimate its deformation degree using well logs, multiscale wavelet analysis, cluster analysis, and ternary diagrams. Since the original well logs contain noise, we first perform denoising with multi-scale wavelet analysis and produce their large-scale and medium-scale output components. Then, we classify the No. 8 coal seam into different sub lithological seams with cluster analysis using the large-scale and medium-scale components as inputs. The classified sub lithological seams include the undeformed coal, the cataclastic coal, the granulated coal, the mylonitized coal, and the gangue. Finally, we group the study area into four regions based on degree of deformation with ternary diagrams using classified sub seam thickness as input. The regions with III and IV deformation degrees are mostly composed of highly deformed TDCs and are prone to coal-and-gas outburst. [Figure: see text]

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

“…The signals in wireline logs include high-frequency, medium-frequency, and low-frequency components. The low-frequency and medium-frequency components are mostly useful signals (Chen et al., 2018; Honorio et al., 2012; Yu et al., 2010). In contrast, the high-frequency component is mostly noise.…”

“…MWA is a method to analyze the time–frequency characteristics of a signal (Chen et al., 2018). For a given time-domain signal f ( t ), it can be transformed into the time–frequency domain where a is the scale factor, b is the shift factor, t is time, and ψ(t) is a mother wavelet.…”

“…Normally, we use the orthogonal Daubechies wavelet (e.g., db8) to process log data (Chen et al., 2018). We can transfer the input data into small-scale (high-frequency), medium-scale (medium-frequency), and large-scale (low-frequency) components.…”

“…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).…”

“…At present, many methods have been proposed to improve the reconstruction reliability and accuracy of wireline logs. One method uses filtering or multiscale wavelet analysis (MWA) to denoise wireline logs (Chen et al., 2018; Honorio et al., 2012; Yu et al., 2010). Others use histogram calibration to improve the lateral comparability of logs since logging instruments and logging environments are not often the same (Quartero et al., 2014; Ren et al., 2016).…”