2016
DOI: 10.1007/s11770-016-0531-7
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The application study on the multi-scales integrated prediction method to fractured reservoir description

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Cited by 18 publications
(7 citation statements)
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“…Fault parameters, including length, fault displacement, and occurrence, are obtained by interpreting 3D seismic data. Large-scale fractures come from the outcrops and post-stack attributes (coherence, curvature, and variance) of 3D seismic data (Chen et al, 2016;Xu et al, 2020). Meso-and small-scale fracture parameters, including strike, dip, density, aperture, and length, are obtained through outcrops, cores, and imaging logs (Hennings et al, 2000;Khoshbakht et al, 2012).…”
Section: Data Set and Methodologymentioning
confidence: 99%
“…Fault parameters, including length, fault displacement, and occurrence, are obtained by interpreting 3D seismic data. Large-scale fractures come from the outcrops and post-stack attributes (coherence, curvature, and variance) of 3D seismic data (Chen et al, 2016;Xu et al, 2020). Meso-and small-scale fracture parameters, including strike, dip, density, aperture, and length, are obtained through outcrops, cores, and imaging logs (Hennings et al, 2000;Khoshbakht et al, 2012).…”
Section: Data Set and Methodologymentioning
confidence: 99%
“…It is usually identified by seismic azimuth anisotropy technology and prestack attenuation attribute. Small-scale fractures are much smaller than 1% seismic wavelength, and their seismic reflection characteristics are weak, making it difficult to identify using seismic data (Chen et al, 2016;Dai et al, 2017;Wang et al, 2018). Second, according to the identifying data, fractures are divided into seismic scale, production dynamic data scale, logging scale, core scale, and BHI scale fractures (Strijker et al, 2012;Xue et al, 2014;Sun et al, 2019).…”
Section: Multi-scale Fracture Classification Standardmentioning
confidence: 99%
“…As the degree of global oil and gas exploration increases, fractured marlstone hydrocarbon reservoirs have become one of the important new fields of exploration. Years of exploration practice have shown that marlstone reservoirs are more difficult to study than clastic reservoirs (Chen et al 2016). The reservoir space of marlstone is dominated by secondary pores, which determines that it has stronger reservoir heterogeneity, and it is more likely to be affected by late diagenetic transformation, thus increasing the difficulty of reservoir prediction.…”
Section: Introductionmentioning
confidence: 99%