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Leveraging stable diffusion to reconstruct missing core intervals.

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This study uses the Stable Diffusion Model (SDM) to reconstruct missing core data, improving subsurface reservoir characterization. The AI model effectively restores rock textures and bedding but struggles with abrupt facies changes in heterogeneous formations.

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Area of Science:

  • Geoscience
  • Petroleum Engineering
  • Artificial Intelligence in Earth Sciences

Background:

  • Core data are vital for subsurface reservoir characterization, providing direct rock observations.
  • Missing core intervals due to extraction or sampling lead to interpretation challenges, especially in heterogeneous formations.
  • Accurate reservoir modeling relies on spatial continuity, making reconstruction of lost core data crucial.

Purpose of the Study:

  • To investigate the application of the Stable Diffusion Model (SDM) for reconstructing missing core intervals and plugs.
  • To evaluate SDM's effectiveness in diverse depositional environments for core data restoration.
  • To identify the limitations of SDM in handling complex geological formations.

Main Methods:

  • Utilized the Stable Diffusion Model (SDM), known for conditioned image inpainting.
  • Systematically evaluated SDM performance across various depositional environments.
  • Tested the model's ability to reconstruct homogeneous intervals, gradual facies transitions, and abrupt facies changes.

Main Results:

  • SDM demonstrated effectiveness in reconstructing homogeneous intervals and gradual facies transitions.
  • The model successfully restored sedimentary textures and bedding architectures in core images.
  • SDM faced challenges in accurately predicting the precise locations of sharp facies boundaries in complex heterogeneous zones.

Conclusions:

  • The SDM approach enhances the utility of incomplete core images for reservoir evaluation.
  • The study highlights SDM's strengths in texture and structure restoration but notes limitations with abrupt facies changes.
  • Findings offer insights for reducing reservoir assessment uncertainties and suggest potential improvements for SDM applications.