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Abstract

Summary

With the urgent need to reduce CO2 emissions, carbon capture and storage (CCS) technologies have become crucial. Shale gas formations offer a promising solution for storing CO2, but interactions between CO2 and shale can affect shale properties during storage and recovery processes. This study delves into how shale absorbs Carbon Dioxide, considering factors like total organic carbon (TOC), temperature, and clay mineralogy. Using machine learning, a new approach in this field, we predict CO2 adsorption on shale surfaces using supervised techniques. The Gradient Boosting Regressor (GBR) is the most effective model, accurately predicting CO2 adsorption, validated with extensive experimental data. Spearman’s correlation analysis reveals important connections between input factors and CO2 adsorption, highlighting the significance of specific surface area (SSA) and TOC with a significant clay mineral percentage. The models’ robustness is confirmed by Root Mean Square Error (RMSE) and Average Absolute Percentage Error (AAPE) values, with GBR showing high reliability. This research introduces an innovative machine-learning framework for precise CO2 adsorption predictions on shale surfaces, enhancing accuracy in gas adsorption estimation across different operational scenarios.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.2024101402
2024-06-10
2026-02-07

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References

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/content/papers/10.3997/2214-4609.2024101402
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Optimizing CO2 Geological Storage: a Data-Driven Approach to Predict the CO2 Adsorption on Shale Reservoir
Conference Proceedings 2024, 1 (2024); https://doi.org/10.3997/2214-4609.2024101402
/content/papers/10.3997/2214-4609.2024101402
/content/papers/10.3997/2214-4609.2024101402
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