Assessing the value of permeability data in a carbon capture and storage project

Abstract Acquiring new field information can reduce the uncertainty about the reservoir properties and can (but not necessarily) alter decisions affecting the deployment of a CCS project. The main objective of this paper is to provide a decision-analytic framework to quantify the value of acquiring additional information regarding reservoir permeability. Uncertainty in reservoir characterization translates into risks of CO 2 migration out of the containment zone (or lease zone) and non-compliance with contractual requirements on CO 2 storage capacity. The field we consider is based on an actual, and mature, field located in Texas. Subsurface modeling of the injection zone was conducted using well logs, field-specific GIS data, and other relevant published literature. The value of information (VOI) was quantified by defining prior scenarios based on the current knowledge of the reservoir, contractual requirements, and regulatory constraints. The project operator has the option to obtain more reliable estimates of permeability, which will help reduce the uncertainty of the CO 2 plume behavior and storage capacity of the formation. The reliability of the information-gathering activities is then applied to the prior probabilities (Bayesian inference) to infer the value of such data.