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Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Modeling and Similitude01:12

Modeling and Similitude

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Related Experiment Video

Updated: Mar 31, 2026

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

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A General Surrogate Model for CO2 Flooding Dynamic Prediction Based on Dimensionless Features and Implicit Time.

Changfu Li1, Xiang Wang1, Wenjie Yu1

  • 1School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China.

ACS Omega
|March 30, 2026
PubMed
Summary

A new Transformer-based surrogate model efficiently predicts CO2 flooding performance, overcoming computational costs in reservoir simulations. This approach enhances optimization for carbon capture, utilization, and storage-enhanced oil recovery (CCUS-EOR) projects.

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Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

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Area of Science:

  • Petroleum Engineering and Reservoir Simulation
  • Artificial Intelligence in Energy
  • Carbon Capture, Utilization, and Storage (CCUS)

Background:

  • Traditional numerical simulations for CO2 flooding are computationally intensive due to complex phase interactions and nonlinear flow.
  • High computational costs limit the efficiency of optimizing large-scale reservoir development schemes.
  • Existing models struggle with generalization across different reservoir geological scales.

Purpose of the Study:

  • To develop a general surrogate model for efficient and high-precision prediction of cumulative oil production and CO2 storage capacity in CO2 flooding reservoirs.
  • To address the computational burden and generalization difficulties in traditional reservoir simulations.
  • To provide a computationally efficient tool for CCUS-Enhanced Oil Recovery (EOR) development assessment.

Main Methods:

  • Proposed a Transformer architecture-based surrogate model.
  • Constructed a dimensionless feature system using Pore Volume (PV) normalization to decouple reservoir geological scales.
  • Integrated implicit time parameterization and Bayesian adaptive optimization, replacing physical time with cumulative fluid injection volume.

Main Results:

  • Achieved R-squared values of 0.9986 for oil production and 0.9968 for CO2 storage predictions across 280,000 samples.
  • Demonstrated significant computational efficiency, achieving a speedup of nearly 60 times compared to numerical simulations for a 5-year prediction.
  • The model exhibits time independence and excellent generalizability across different reservoir scales.

Conclusions:

  • The proposed Transformer-based surrogate model effectively mitigates the computational bottleneck of long-cycle reservoir simulations.
  • The dimensionless feature system and continuous time mapping enhance model generalizability and physical universality.
  • This approach offers robust technical support for rapid assessment and optimization in CCUS-EOR development.