Chunbi Jiang

ORCID: 0000-0003-0926-7820
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About
Contact & Profiles
Research Areas
  • Hydrocarbon exploration and reservoir analysis
  • Hydraulic Fracturing and Reservoir Analysis
  • Geological Modeling and Analysis
  • Seismic Imaging and Inversion Techniques
  • Geochemistry and Geologic Mapping
  • Advanced Computational Techniques and Applications
  • Image Processing and 3D Reconstruction
  • Advanced Decision-Making Techniques
  • Evaluation Methods in Various Fields
  • Drilling and Well Engineering
  • Advanced Numerical Analysis Techniques

Shenzhen Institutes of Advanced Technology
2021-2022

Peng Cheng Laboratory
2021

Huafon Group (China)
2010

Lithology identification is of great importance in reservoir characterization. Recently, many researchers have applied machine-learning techniques to solve lithology problems from well-log curves, and their works indicate three main characteristics. First, most predict lithofacies using features measured during logging, whereas very few consider adding stratigraphic sequence information that available prior drilling this problem. Second, studies properties one depth point, take the influence...

10.1190/geo2020-0676.1 article EN Geophysics 2021-06-24

Well logging is a common method that used to obtain the rock properties of formation. It relatively frequent, however, log information incomplete due cost limitations or borehole problems. Existing models predict missing well logs from fixed combination other available logs. However, vary well. We have proposed using gated graph neural network (GNN) handle values in well-log curves. takes sequential data, predicting each measurement data not only variables measured at same depth but also...

10.1190/geo2022-0028.1 article EN Geophysics 2022-10-05

Lithofacies is a key parameter in reservoir characterization. With advances machine learning, many researchers have attempted to predict lithofacies from well-log curves by using machine-learning algorithm. However, existing models are built purely on data, which do not provide interpretability. In addition, distribution highly imbalanced. We incorporate domain knowledge into gated recurrent unit network force the model learn data and knowledge. The that we use expressed as first-order logic...

10.1190/geo2022-0770.1 article EN Geophysics 2023-09-27
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