Advantages of a Compositional Framework for Well and Surface Network Modeling

Abstract

Abstract Integrated asset models are being constructed for all ADNOC assets as part of a production optimization initiative supported by a significant digitization effort. Contrary to the standard industry practice of utilizing black-oil formulations to capture fluid behavior, a compositional modeling framework was selected to address some key challenges: Compositional variation at reservoir-level, either lateral or verticalInjection of gas (immiscible as well as near-miscible) causing mass transferBlending of different fluids in the surface network at line conditionsOperational requirement to maintain the bottom-hole pressure above saturation pressureValidation of raw well test data before shrinkage correction (line conditions)Investigation of impact of changing separator settings (affecting shrinkage correction)Tracking of fluid composition in produced streams The compositional framework is very comprehensive. For each of the 100+ producing reservoirs, one or several equation of state (EOS) models was developed. In every well, an initial fluid composition estimate was provided as an anchoring point, which was subsequently adjusted in a tailor-made workflow to match the solution gas-oil ratio measured in the field by performing an isothermal flash of the original composition and then recombining the flashed oil and gas streams to the field gas-oil ratio. The workflow offers a number of advantages, one of which is that the path-to-surface correction can be imposed directly on field measurements. This has resulted in much improved allocation factors for oil, gas, and water. Fit-for-purpose algorithms have been developed to perform well test validation for different well types based on raw data at line conditions.