A value of information analysis of permeability data in a carbon, capture and storage project



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Carbon dioxide capture and storage (CCS) is considered one of the key technologies for reducing atmospheric emissions of CO₂ from human activities (IPCC, 2005). The scale of potential deployment of CCS is enormous spanning manufacturing, power generation and hydrocarbon extraction worldwide. Uncertainty, cost-benefit challenges, market barriers and failures, and promotion and regulation of infrastructure are the main obstacles for deploying CCS technology in a broad scale. In a CCS project, it is the operator’s responsibility to guarantee the CO₂ containment while complying with environmental regulations and CO₂ contractual requirements with the source emitter. Acquiring new information (e.g. seismic, logs, production data, etc.) about a particular field can reduce the uncertainty about the reservoir properties and can (but not necessarily) influence the decisions affecting the deployment of a CCS project. The main objective of this study is to provide a decision-analysis framework to quantify the Value of Information (VOI) in a CCS project that faces uncertainties about permeability values in the reservoir. This uncertainty translates into risks of CO₂ migration out of the containment zone (or lease zone), non-compliance with contractual requirements on CO₂ storage capacity, and leakage of CO₂ to sources of Underground Source of Drinking Water (USDW). The field under analysis has been idealized based on a real project located in Texas. Subsurface modeling of the upper Frio Formation (injection zone) was conducted using well logs, field-specific GIS data, and other relevant published literature. The idealized model was run for different scenarios with different permeability distributions. The VOI was quantified by defining prior scenarios based on the current knowledge of a reservoir, contractual requirements, and regulatory constraints. The project operator has the option to obtain more reliable estimates of permeability, which will help to reduce the uncertainty of the CO₂ behavior and storage capacity of the formation. The accuracy of the information gathering activities is then applied to the prior probabilities (Bayesian inference) to infer the value of such data.