A Generalized Partial Molar Volume Algorithm Provides Fast Estimates of CO_2 Storage Capacity in Depleted Oil and Gas Reservoirs

摘要

Understanding fluid behavior is critical for the success of any gas injection project into a reservoirrnwhether this is to recover additional oil or to sequester and store greenhouse gases including CO_2. Fluidrnphase behavior of mixtures resulting from the injected streams and reservoir fluids determine the designrnof production and injection schemes and facilities.rnThis manuscript presents an analytical method to estimate the ultimate CO_2 storage capacity in depletedrnoil and gas reservoirs by implementing a volume-constrained thermodynamic equation of state (EOS) andrnusing average reservoir pressure and fluid compositions. This method can handle all impurities containedrnin the injection stream by defining and applying a generalized partial molar volume calculation.rnThe developed algorithm provides fast and thermodynamically consistent estimates of storage capacityrnand enables the selection of candidate storage reservoirs, schedule injection strategies, and design ofrnsurface facilities including compressors and tubulars.rnResults from this analytical method are in excellent agreement with those from a commercial reservoirrnsimulator. A total of 24 numerical runs were conducted to evaluate scenarios with large pressure andrncompositional gradients while injecting. The reservoir used was heterogeneous, had a five-spot injectionrnpattern and local grid refinement in the neighborhood of wells. CO_2 storage capacity was predicted withrnan average difference of 1.26 wt% between analytical and numerical methods; the average oil, gas, andrnwater saturations at the end of injection were also matched within 2.35% difference. Additionally, thernanalytical algorithm performed several orders of magnitude faster than numerical simulation, with anrnaverage of 5 seconds per run.