In 2015-2016, the Alkaline-Surfactant-Polymer (ASP) flood Pilot in Marmul was successfully completed with ∼30% incremental oil recovery and no significant operational issues. In parallel to the ASP pilot, several laboratory studies were executed to identify an alternative and cost-efficient ASP formulation with simpler logistics. The studies resulted in a new formulation based on mono-ethanolamine (MEA) as alkali and a blend of commercially available and cheaper surfactants. To expediate the phased full field development, Phase-1 project was started in 2019 with the following main objectives are confirm high oil recovery efficiency of the new ASP formulation and ensure the scalability and further commercial maturation of ASP technology; de-risk the injectivity of new formulation; and de-risk oil-water separation in the presence of produced ASP chemicals. The Phase 1 project was executed in the same well pattern as the Pilot, but at a different reservoir unit that is more heterogeneous and has a smaller pore volume (PV) than those of the Pilot. This set-up allowed comparing the performance of ASP formulations and taking advantage of the existing surface facilities, thus reducing the project cost. The project was successfully finished in December 2020, and the following major conclusions were made: (1) with the estimated incremental recovery of around 15-18% and one of the producers exhibiting water cut reversal of more than 30%, the new ASP formulation is efficient and will be used in the follow-up phased commercial ASP projects; (2) the injectivity was sustained throughout the entire operations within the target rate and below the fracture pressure; (3) produced oil quality met the export requirements and a significant amount of oil-water separation data was collected. With confirmed high oil recovery efficiency for the cheaper and more convenient ASP formulation, the success of ASP flooding in the Phase-1 project paves the way for the subsequent commercial-scale ASP projects in the Sultanate of Oman.

Polymer-based chemical flooding is a mature enhanced oil recovery technology that has proven to result in significant incremental oil recovery that is both cost and GHG emission-competitive compared to the oil recovered by conventional waterflooding. For such chemical flooding projects, controlling the viscosity of injected polymer solution is critical because the polymer cost is one of the most significant cost elements in the project economics. The polymer viscosity is routinely measured in the laboratory using fluid samples taken manually at different sampling points (i.e., polymer preparation facilities, injecting lines, and well heads). However, in the case of large-scale projects, such viscosity monitoring becomes time-consuming and requires dedicated field staff. Moreover, the quality of laboratory-measured viscosity is questionable due to the potential viscosity degradation caused by the oxygen ingress or polymer shearing during sampling, storage, and measurement. The inline viscometers were introduced to improve the reliability of viscosity measurements and have a better quality of viscosity monitoring. Such viscometers are relatively simple devices readily available on the market from several vendors. However, the device comes at additional costs and requires modifications at the tie-in point (bypass line, drainage, and (sometimes) communication and power lines). On top of it, operational costs include regular maintenance that the inline viscometer requires to ensure good data quality. This study introduces a data-driven Virtual Viscosity Meter (VVM) as a tool to augment the inline and laboratory viscosity measurements. Standard injector wells in a field are equipped with gauges that report injection rate, well/tubing head pressure, and temperature of the injected fluid. With such well data and viscosity measurements, calculating the viscosity becomes a machine learning regression problem. Training the machine learning regression methods on the actual inline and laboratory-measured polymer viscosity has demonstrated that VVM is a promising, high-accuracy solution with a low computational cost. The possibility of further implementing this approach to calculate the viscosity of an injected fluid was investigated using the data from several projects. Finally, the application of the VVM tool for viscosity monitoring and the limitations of VVM were discussed.

Polymer injection in the south of Sultanate of Oman has been implemented in Marmul field for the last decade. Recently, alkaline surfactant polymer (ASP) technology has also been piloted in the field, which was technically successful owing to its significant incremental oil production. The current end-game strategy for the field is to follow polymer with ASP flood in order to produce the remaining oil after polymer flood and maximize the ultimate oil recovery factor. This has revealed the need for evaluation of the full-field performance of ASP flood using available tools. Full-field dynamic models are not always best tools for modeling the performance of chemical enhanced oil recovery, primarily due to under-representation of the reservoir heterogeneity, lack of the complementary data, complexity of the process itself, and large computation time. In this paper, we implement a conduit-model approach using field production data from the ASP pilot to assess the ultimate incremental oil recovery. This approach is compared to an analytical model that is based on the modified Koval's method with reservoir heterogeneity as an input parameter. The obtained results are used for preliminary assessment of the difference between polymer and ASP injection in the full field.