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 outage (or polymer injection unavailability) is undesirable but also inevitable. When it happens, the question is how to respond to it to minimize its adverse impact on the production. This paper presents the rationale for generating a polymer outage strategy to operate a polymer flood field in the southern area of the Sultanate of Oman. The work presented here is based on field performance and analytical analysis. The diagnostic plots were created from 10 years of polymer flood field response and were used for this operating decision. The pros and cons of two scenarios were discussed. The selected operational strategy is to minimize the short falls of polymer outage. The strategy was implemented in the field. Simultaneous injection and production pause (SIPP) is recommended for the full field polymer outage. It minimizes the impact on polymer incremental oil and hence less deferment. Calibrated with the actual results, analytical method is used to determine when to shut down and whether a short of buffer period of water can be tolerated before SIPP is carried out. The polymer literature focus on polymer mechanisms, modeling, project initiation and implementation but no paper discusses the operational strategy on how to respond to field polymer outages. This paper shares our operational learnings and the field results of various polymer operation modes on polymer incremental oil. The learning from this field may be of interest to other operators who are planning or currently implementing polymer flood in their fields.

Since its commencement in 2010, Marmul polymer EOR has been one of the worldwide successful full field applications. One of the key success factors for the project is maintainingwellhead viscosity at the target, which has been monitored by daily selective wellhead sampling. However, daily sampling covers only 7% of the polymer injectors. Recently, a digitalization project to enhance viscosity monitoring was successfully completed. One of the outcomes is utilizing the digital data available in field to have a live viscosity of all polymer injectors using an empirical power law model along with a calibration factor. Machine learning will handle any deviation of these readings by a well-established sampling program to continually re-calibrate the model.In this paper, the approach and outcomes of this projectare shared. Two polymer injectors are selected as a demonstration of the concept and main outcomes. Statistical evaluation was used to initially select the determining process parameters such as wellhead concentration, flowrate, tubing-head pressure, and tubing-head temperature. It has been concluded that wellhead polymer concentration is highly correlated to measured wellhead viscosity. The measured viscosities in the last two years (2020 and 2021) for each well were divided into; a training set (~65%) and a test set (~35%). The training set is used to calculate the calibration factor, while the test set is used to validate model predictions. Out of 415 date points, the average viscosity of polymer injectors MMPI-1 and MMPI-2 are 20.7 and 23.1 cP, respectively. The standard deviation of the measurements of injectors MMPI-1 and MMPI-2 are 3.3 and 4.8 cP, respectively. Viscosity was correlated to wellhead concentration by a power law model with experimentally obtained constant and law's exponent. Using the training set, a tuning parameter, α, was appliedwith criteria of minimummean absolute error (MAE) for each injector. α determined of MMPI-1 and MMPI-2 is 0.915 and 0.981, respectively. The model resulted in good predictions with an average MAE of around 20%. Furthermore, the model proved to be robust and reliable to be applied for live viscosity readings of all Marmul polymer injectors. Machine learning is essential for future tuning of the model for all polymer injectors in Marmul based established program of wellhead measurements. The outcomes of this digitalization and automation step in polymerflooding has demonstrated significant, positive impact on optimization of chemicals, resources, and the overall reservoir management. This work is setting another milestone in the utilization of data analytics and digitalization of fullfield polymer EOR. Machine learning coupled with excellent metering and data streaming have shown added value to overall project management. This is more critical with the shift towards agile work environment and net zero. Significant opportunities have been already realized as an outcome of this project such as quantification of polymer overdosage, which triggered a work in progress to reduce any value-eroding polymer dosage. In Marmul, the improved surveillance of wellhead viscosity and timely optimization of polymer dosage have already positively impacted project economics, GHG and HSE.