“… Figure 1 ( a ) Geological map of the Bohai Bay Basin in Northeast China and ( b ) schematic cross section of the stratigraphy of the study area. (Modified from Boateng et al 32 ). …”
Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from poststack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio-deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.
“… Figure 1 ( a ) Geological map of the Bohai Bay Basin in Northeast China and ( b ) schematic cross section of the stratigraphy of the study area. (Modified from Boateng et al 32 ). …”
Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from poststack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio-deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.
“…A comprehensive review of core-scale numerical reconstruction methods with detailed applications can be found in Ji et al [15]. The most typical algorithms of objectbased techniques result from random media theory described by mathematical tools such as autocorrelation functions and power spectral density functions [16]- [22]. However, these methods are only suitable for a single-scale or singlecomponent reconstruction of digital cores and are less accurate for strongly heterogeneous shales.…”
Organic shales usually present significant heterogeneities in rock textures and reservoir properties due to differing kerogen contents and morphologies, subsequently impacting shale elastic properties and acoustic responses. Numerical upscaling of digital organic shales to evaluate effective elastic properties and acoustic responses has important implications for source rocks and unconventional reservoir characterization. We propose a modeling framework that includes the multiscale reconstruction of kerogen distributions, the numerical modeling of effective elastic properties, and the acoustic response to evaluate the contribution of organic matter. Based on digitized images of the microstructure of Longmaxi black shale samples obtained by X-ray CT, the kerogen components are identified and decomposed into different-level slices in terms of organic matter sizes and morphologies. Multiscale random media reconstruction is applied to these kerogen slices, with synthetic kerogen distributions validated by original counterparts. A finiteelement method is used to model the effective elastic properties of digital organic shales, by which we investigate the effect of different kerogen contents and organic matter morphologies. We use a rotated staggered-grid finite-difference method to simulate ultrasonic wave propagation in digital organic shales to evaluate the response of different kerogen contents and organic matter morphologies. Numerical examples show that the multiscale random media method can be applicable to natural organic shales for the reconstruction of kerogen distributions. The elastic properties mainly depend on kerogen contents, with less influence by organic matter morphologies. The ultrasonic scattering effects become stronger for higher kerogen contents with smaller rounding coefficients. Our results confirm the applicability of the proposed modeling framework to support unconventional reservoir characterization. The purpose of this study is to provide the possibility of indicating the sweet point of shale. INDEX TERMS Digital organic shales, effective elastic properties, kerogen contents, multiscale reconstructions, organic matter morphologies, ultrasonic responses.
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