Chinese Academy of Sciences

Xiaocai Shan

Wang Zhang

Fei Tian

Wenhao Zheng

Jiangyun Zhang

Guanfang Li

Wenyang Wang

Yongjian Zhou

Boye Fu

A graph neural network (GNN)-based prediction system for total organic carbon (TOC) in shale solves the problem that the existing shale TOC prediction method cannot fully analyze the complex nonlinear relationship between all logging curves and TOC. The prediction system adopts a method including: acquiring and preprocessing a plurality of logging curves of a target well location in a target shale bed to acquire a plurality of standardized logging curves, windowing the plurality of standardized logging curves, and inputting the windowed logging curves and weight matrix into a trained GNN-based TOC prediction network to acquire TOC of the target well location. The prediction system inputs the plurality of logging curves as correlative multi-dimensional dynamic graph data for analysis and can acquire the complex nonlinear relationship between the logging curves and TOC, thus improving the prediction accuracy of TOC.

Graph neural network (GNN)-based prediction system for total organic carbon (TOC) in shale

The invention belongs to the field of environmental monitoring, and in particular relates to a method for optimally selecting a carbon storage site based on multi-frequency band seismic data. The method comprises the steps of: performing seismic wavelet spread spectrum simulation based on three-dimensional post-stack seismic data to obtain spread spectrum simulated wavelets; building an isochronous stratigraphic framework model of a target horizon, and calculating the geometric structure and spatial distribution of a fault-karst; then performing waveform-indicated inversion to obtain a wave impedance inversion data volume, and obtaining a stable stratum wave impedance data volume through a virtual well cross-well wave impedance interpolation; calculating the difference between the stable stratum wave impedance data volume and the wave impedance inversion data volume to obtain an abnormal wave impedance data volume, then obtaining a fault-karst reservoir bed interpretation model, and determining the position of a carbon storage box.

Method and system for optimally selecting carbon storage site based on multi-frequency band seismic data and equipment

The present disclosure belongs to the field of capture, utilization, and storage of carbon dioxide, particularly relates to a method and system for intelligently identifying a carbon storage box based on a GAN network, and aims at solving the problem that the analysis accuracy of a fault zone area in the prior art is insufficient. The method comprises the steps: delineating seismic waveform data of a stable sedimentary area through a GAN network, and removing seismic waveform data points in the fault zone area; obtaining a stable sedimentary background seismic waveform data invertomer; obtaining a three-dimensional wave impedance prediction data volume; making a difference to obtain an abnormal wave impedance data volume; retaining abnormal wave impedance data of fault-karst in the three-dimensional variance attribute volume to obtain a fault-karst wave impedance data volume; and then obtaining a carbon storage box interpretation model.

Method and system for intelligently identifying carbon storage box based on GAN network

A rock brittleness analysis method and system based on mineral content and distribution, and a device are provided. The rock brittleness analysis method includes: performing digital processing on an image of a rock, and performing an autocorrelation analysis on a two-dimensional (2D) array obtained after the digital processing to obtain an autocorrelation coefficient variation curve; using an abscissa value corresponding to an ordinate value of dropping to 1/e on the autocorrelation coefficient variation curve to be an autocorrelation length of the rock; constructing an autocorrelation function for describing a mineral distribution according to the autocorrelation length and the distribution characteristics of a mineral and an elastic modulus in the rock; reconstructing a rock sample of the rock to obtain density and elastic modulus distributions of the rock; and partitioning meshes of the reconstructed rock sample to obtain a brittleness index distribution of the to-be-analyzed rock partitioned in different scales.

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Beijing, China

Xiaocai Shan