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Abstract

Summary

Carbon Capture and Sequestration (CCS) is one of the vital components in keeping the global temperature rise within the 1.5 degrees Celsius target. Depleted oil and gas reservoirs are suitable locations for sequestering CO2 owing to their rock and structural properties and easy access to required infrastructure. Abandoned wells in these reservoirs can be used to inject CO2 saving both time and cost. Understanding the well integrity is important for CO2 containment and leakage prevention. It requires significant effort from a subject matter expert(SME) to identify well integrity information in legacy well documents which often results in longer lead times of up to an year for a CO2 sequestration site to mature. Transformer based pre-trained state-of-the-art models are utilized to inject useful information extracted from the well documents, which can be used instantly by SMEs to classify a well as potential high or low risk to inject CO2. Fine-tuning these models on generic datasets and less than 50 domain-specific examples shows that we can infer relevant well information not provided by traditional rule-based approaches. Reducing the lead time in maturing a site for CO2 injection could contribute to faster CCS project delivery timelines and broader goal of achieving net-zero targets.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202332009
2023-03-20
2024-04-27

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202332009
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Transformers for Site Assessment for Carbon Capture and Sequestration using Legacy Well Data
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202332009
/content/papers/10.3997/2214-4609.202332009
/content/papers/10.3997/2214-4609.202332009
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