Chinese Academy of Sciences

Jiangyun Zhang

Fei Tian

Wenhao Zheng

Yongyou Yang

Wenxiu Zhang

Qingyun Di

Zhongxing Wang

Wang Zhang

Xiaocai Shan

Liang Huang

Shoudong Huo

Xuhui Zhou

Shengqiang Mu

Guoxu Shu

Mingran Liu

Wenchao Su

A method for determining hydraulic fractures in the near-wellbore region, comprising the following steps: constructing a model with hydraulic fractures, a wellbore, and a formation as main components, and discretizing the model using non-uniform grid technology. This data subset is inverted using the time-lapse full-waveform inversion (TL-FWI) algorithm to achieve high-precision reconstruction of the hydraulic fractures and to obtain key attributes such as fracture-wellbore connectivity, extent of fracture propagation, and fracture geometry.

Quantitative evaluation method for hydraulic fractures in near-wellbore region

The present description relates to the field of geological exploration, and discloses a method and system for extracting features of logging while drilling and drilling-time logging based on Unet dual-channel output, aiming at solving the problem that the prior art 5 cannot integrate various types of information for lithology classification. The present invention comprises: acquiring stratum data of different wells; eliminating outliers, normalizing and resampling; performing data reconstruction via a self-encoder-based artificial neural network based on the pre-trained data to obtain stratum reconstructed data for training; inputting the data to be analyzed from shallow to deep in segments, performing 10 dynamic optimization while drilling on the pre-trained self-encoder-based artificial neural network by a quantile loss function; and extracting the independent components output by the encoder dual-channel for unsupervised clustering, and fitting to obtain a stratum lithology determination result corresponding to the current data to be analyzed.

Method and system for extracting features of logging while drilling and drilling-time logging based on UNET dual-channel output

The present invention belongs to the field of geological exploration and specifically relates to the fault characterization method and system for precise navigation of deep oil and gas based on image fusion, aiming to solve the problem that faults in deep formations are difficult to characterize with conventional seismic interpretation methods. The present invention includes: obtaining amplitude gradient images by calculating amplitude gradient vectors; calculating dip attribute images based on the enhanced seismic data; fusing gradient amplitude attribute fault confidence region with dip angle attribute data volume that defines the fault position through a hierarchical wavelet transform method to obtain a superimposed fault attribute map; dividing bead-like structures based on superimposed fault attribute maps; calculating the score of branch fault data points based on the center point position of bead-like structures, and dividing and analyzing the dominant fault areas.

Fault characterization method and system for precise navigation of deep oil and gas based on image fusion

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 invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for analyzing filling for a karst reservoir based on spectrum decomposition and machine learning, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

Method and system for analyzing filling for karst reservoir based on spectrum decomposition and machine learning

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

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for evaluating the filling characteristics of a deep paleokarst reservoir through well-to-seismic integration, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

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Beijing, China

Jiangyun Zhang