Estimation of storage capacity coefficients: Porthos GCS project case study

Concurrent approaches for estimating storage coefficients ( E ) of Geological Carbon Sequestration (GCS) target reservoirs are critically reviewed, and a robust procedure for estimation of such coefficients, which are time-dependent, is proposed. Our method is based on close analogy of what historically is done in hydrocarbon production and reserves estimations using recovery factors ( F ). Typically, F is computed by first estimating the original hydrocarbons in place (OHIP), then the cumulative production to a certain date (of the economic limit) is computed using production forecasting methods. The production forecast provides an estimated ultimate resource (EUR), and then F follows from the ratio EUR/OHIP. We propose to similarly compute the estimated ultimate storage (EUS) or cumulative injection by forward modeling, using Gaussian-based solutions of the pressure diffusivity equation, and after estimating the total storage resource (TSR), the coefficient E follows from the ratio EUS/TSR. The new method is demonstrated in a case study using representative data from the Porthos GCS Project, which repurposes the depleted P18 gas field (offshore, Dutch shelf area) for geological CO 2 sequestration (GCS). The storage coefficient for the P18-6 segment of the Porthos GCS field after 20 years of injection reaches 18 %. In addition to the deterministic storage coefficient estimation, probabilistic values after 20 years of injection for E were estimated: P90-16 %, P50-36 % and P10-59 %. Separately, it is shown how a GCS project in a depleted gas field offers significant operational advantages over storage in saline aquifers. The competitive edge of depleted gas fields over saline aquifers has not been articulated before. The new methods for computing TSR, EUS and E , can handle probabilistic storage resource classification in compliance with the SPE SRMS classification framework for storage resource estimation. Highlights • Rapid method for early-stage assessment of storage capacity in GCS prospects. • Inspired by hydrocarbon recovery models and avoids plume migration modeling. • Uses physics-based injection rates and pressure transients to estimate ultimate storage. • Effectiveness of method demonstrated by application to P18 field (North Sea). • Consistent with SRMS classification framework for storage resource estimation.