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.
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.
Alkaline Surfactant Polymer (ASP) flooding has proven to be an effective method to recover remaining oil after a water flood through numerous laboratory and field tests. Yet, several operational complications limit the broad implementation of ASP technology. Source water requires softening to avoid injectivity issues due to scale formation when alkali is added to the solution. Even when softened water is used to prepare the injected ASP solution, scaling is often an issue in producing wells due to the mixing of injected ASP solution and harder reservoir brine in situ. Scale control through scale inhibitors has been reported to be successful in some cases. Usually, sodium carbonate is used as an alkali in ASP, and carbonate scaling issues are most severe in such a case. However, even if another alkali is used, carbonate scale remains an issue because, at high pH, the bicarbonate present in almost any formation water will be converted to carbonate and subsequently precipitate with the divalent ions present in the formation brine or unsoftened ASP make-up water. Monoethanolamine (MEA) has been used as an alkali in the ASP Phase 1A project in a sandstone reservoir in Southern Oman. The produced water reinjected in the field has a relatively low concentration of divalent ions. It was realized that further ASP implementation could be significantly simplified if softening of the produced water could be avoided. Based on the results of extensive laboratory studies, it was proposed to conduct the scaling inhibitor injection and propagation field trial. The trial's objective is to evaluate the use of a suitable scale inhibitor with the MEA-based ASP formulation as an alternative to water softening under field conditions. This project was executed after completing ASP Phase 1A and lasted about two months. Injection and production results from the trial and implications for future ASP implementation are presented in the paper.
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.
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