AI/ML Augmented Subsurface Data Workflows for Low Carbon Datasets

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🔬研究者:Highlights the need for FAIR-compliant datasets to enable reliable AI/ML models in low-carbon subsurface projects.

🏢実務担当者:Provides a framework for data aggregation and standardization to improve AI/ML outcomes in geothermal and CCS projects.

Abstract AI/ML workflows in subsurface energy projects depend on access to high-quality, well-curated geotechnical data managed according to modern data management practices. This enables critical business decisions, interoperating with digital data to support low-carbon energy projects like geothermal, mineral extraction, and carbon sequestration, using high quality, well-curated, and easily accessible data. Recent experiences with generative AI/ML have highlighted geotechnical data sets for low-carbon projects that can lack integration, governance, and data deliverability biases, and gaps. Published government datasets may have as little as 20% of geoscience data that is fully Findable, Accessible, Interoperable, and Reusable (FAIR), while spatial and temporal biases in geotechnical datasets can misrepresent the true distribution of earth properties critical to supporting business decisions. Value added aggregation, standardization, and curation of these datasets can ensure that they are measurably more fit for purpose for AI/ML workflows. This helps avoid outcomes in which models reveal that underlying datasets are insufficiently curated or standardized to support reliable data-driven queries. Mitigating the impact of fragmented data sources, inconsistent data quality and insufficient data interoperability will improve AI/ML deployments for re-use in low carbon energy projects using aggregated geotechnical data sets. Accuracy of responses from AI/ML Augmented SQL queries can be increased by automating metadata extraction and supporting the use of open-source industry standard data models. Data quality and ingestion workflows allow analysis of metadata patterns and data set usage behaviors to improve deliverability. Subsurface energy data aggregators and publishers can deploy optimum industry accepted data management standards to support AI/ML. Deployments can be evaluated for data availability and deliverability, measuring how delivering FAIR data can improve AI metrics. Key data types available on platforms that deliver FAIR data can increase the amount of value-added time available on projects. This can be leveraged to increase the volumes of data that are fit-for-purpose for subsurface multi-resource data-driven models.

• openalex https://doi.org/10.2118/232914-msfirst seen 2026-05-17 05:34:14