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Water alternating gas (WAG) is a well-known strategy to improve the mobility issues during gas injection. However, WAG was identified still having some challenges during implementation at oilfield with high reservoir heterogeneity and high permeable zones in the reservoir and will cause unfavorable mobility ratio. Enproperties of the selected core samplehancement of WAG (EWAG) using foam and surfactant has been research to solve its issue and has success stories. This paper will describe the work process of EWAG to be Pilot at Malaysian oilfield, focusing on numerical investigation during upscaling process. Foam treatment has role for gas mobility control, delaying gas breakthrough and diverting gas to unswept zones. Meanwhile, the surfactant was utilized to reduce the IFT between gas and liquid to enable gas dispersion into liquid phase. An in-house foaming surfactant has been developed and used for coreflooding experiment at harsh environment. It was used to generate stable foam in contact with gas and it caused a mobility reduction which was suitable for mobilizing trapped oil and hence improving oil recovery. Coreflood experiment was performed on native core and all experimental results were consolidated and checked for the quality prior model calibration in the reservoir simulator. Once coreflood model was constructed, base case was run using default foam parameters. It aimed initially to test whether the model run smoothly and to observe the matching quality using the default values. Once satisfactory matchings were achieved, the process continued with foam parameters upscaling. During scale-up process the velocity of the fluids and pressure drop were conserved as laboratory data. The important scale-up parameters and the corresponding scale-up ratio were investigated. Mobility Reduction Factor (MRF) was calculated by dividing average DP for each foam cycle with base differential pressure (DP) in the prior gas injection. MRF values for both lower and higher rate show increasing MRF values. Regardless, these values are lower in lower flowrates sequences compared to ones for higher flowrates. This corresponds to MRF values calculated in the laboratory analysis. Therefore, stronger and more stabilized foam were generated using higher injection rates. Lower and higher flowrates had distinctive set of foam parameters. The acceptable matches for differential pressure, oil, water, and gas were achieved. for lower flowrate. Based on this study, model was able to capture production trends depicted in the laboratory analysis. The foam parameter set from higher flowrates have more potential for further upscaling and modeling in full-field scale.
Water alternating gas (WAG) is a well-known strategy to improve the mobility issues during gas injection. However, WAG was identified still having some challenges during implementation at oilfield with high reservoir heterogeneity and high permeable zones in the reservoir and will cause unfavorable mobility ratio. Enproperties of the selected core samplehancement of WAG (EWAG) using foam and surfactant has been research to solve its issue and has success stories. This paper will describe the work process of EWAG to be Pilot at Malaysian oilfield, focusing on numerical investigation during upscaling process. Foam treatment has role for gas mobility control, delaying gas breakthrough and diverting gas to unswept zones. Meanwhile, the surfactant was utilized to reduce the IFT between gas and liquid to enable gas dispersion into liquid phase. An in-house foaming surfactant has been developed and used for coreflooding experiment at harsh environment. It was used to generate stable foam in contact with gas and it caused a mobility reduction which was suitable for mobilizing trapped oil and hence improving oil recovery. Coreflood experiment was performed on native core and all experimental results were consolidated and checked for the quality prior model calibration in the reservoir simulator. Once coreflood model was constructed, base case was run using default foam parameters. It aimed initially to test whether the model run smoothly and to observe the matching quality using the default values. Once satisfactory matchings were achieved, the process continued with foam parameters upscaling. During scale-up process the velocity of the fluids and pressure drop were conserved as laboratory data. The important scale-up parameters and the corresponding scale-up ratio were investigated. Mobility Reduction Factor (MRF) was calculated by dividing average DP for each foam cycle with base differential pressure (DP) in the prior gas injection. MRF values for both lower and higher rate show increasing MRF values. Regardless, these values are lower in lower flowrates sequences compared to ones for higher flowrates. This corresponds to MRF values calculated in the laboratory analysis. Therefore, stronger and more stabilized foam were generated using higher injection rates. Lower and higher flowrates had distinctive set of foam parameters. The acceptable matches for differential pressure, oil, water, and gas were achieved. for lower flowrate. Based on this study, model was able to capture production trends depicted in the laboratory analysis. The foam parameter set from higher flowrates have more potential for further upscaling and modeling in full-field scale.
A major Malaysian offshore oilfield, which is currently operating under waterflooding for a quite long time and declining in oil production, plan to convert as chemical enhanced oil recovery (CEOR) injection. The CEOR journey started since the first oil production in year 2000 and proximate waterflooding, with research and development in determining suitable method, encouraging field trial results and a series of field development plans to maximize potential recovery above waterflooding and prolong the production field life. A comprehensive EOR study including screening, laboratory tests, pilot evaluation, and full field reservoir simulation modelling are conducted to reduce the project risks prior to the full field investment and execution. Among several EOR techniques, Alkaline-Surfactant (AS) flooding is chosen to be implemented in this field. Several CEOR key parameters have been studied and optimized in the laboratory such as chemical concentration, chemical adsorption, interfacial tension (IFT), slug size, residual oil saturation (Sor) reduction, thermal stability, flow assurance, emulsion, dilution, and a chemical injection scheme. Uncertainty analysis on CEOR process was done due to the large well spacing in the offshore environment as compared to other CEOR projects, which are onshore with shorter well spacing. The key risks and parameters such as residual oil saturation (Sorw), adsorption and interfacial tension (IFT) cut-off in the dynamic chemical simulator have been investigated via a probabilistic approach on top of deterministic method. The laboratory results from fluid-fluid and rock-fluid analyses ascertained a potential of ultra-low interfacial tension of 0.001 dyne/cm with adsorption of 0.30 mg/gr-of-rock, which translated to a 50-75% reduction in Sor after waterflooding. The results of four single well chemical tracer tests (SWCTT) on two wells validated the effectiveness of the Alkaline Surfactant by a reduction of 50-80% in Sor. The most suitable chemical formulation was found 1.0 wt. % Alkali and 0.075 wt. % Surfactant. The field trial results were thenceforth upscaled to a dynamic chemical simulation; from single well to full field modeling, resulting an optimal chemical injection of three years or almost 0.2 effective injection pore volume, coupled with six months of low salinity treated water as pre-flush and post-flush injection. The latest field development study results yield a technical potential recoverable volume of 14, 16, and 26 MMstb (above waterflooding) for low, most likely, and high cases, respectively, which translated to an additional EOR recovery factor up to 5.6 % for most-likely case by end of technical field life. Prior to the final investment decision stage, Petronas’ position was to proceed with the project based on the techno-commerciality and associated risks as per milestone review 5, albeit it came to an agreement to have differing interpretations towards the technical basis of the project in the final steering committee. Subsequently, due to the eventual plunging global crude oil price, the project was then reprioritized and adjourned correspondingly within Petronas’ upstream portfolio management. Further phased development including a producing pilot has been debated with the main objective to address key technical and business uncertainties and risks associated with applying CEOR process.
Mature fields already account for about 70% of the hydrocarbon liquids produced globally. Since the average recovery factor for oil fields is 30 to 35%, there is substantial quantities of remaining oil at stake. Conventional simulation-based development planning approaches are well established, but their implementation on large, complex mature oil fields remains challenging given their resource, time, and cost intensity. In addition, increased attention towards reduce carbon emissions makes the case for alternative, computationally-light techniques, as part of a global digitalisation drive, leveraging modern analytics and machine learning methods. This work describes a modern digital workflow to identify and quantify by-passed oil targets. The workflow leverages an innovative hybrid physics-guided data-driven, which generates historical phase saturation maps, forecasts future fluid movements and locate infill opportunities. As deliverables, a fully probabilistic production forecast is obtained for each drilling location, as a function of the well type, its geometry, and position in the field. The new workflow can unlock remaining potential of mature fields in a shorter time-frame and generally very cost-effectively compared to the advanced dynamic reservoir modelling and history-match workflows. Over the last 5 years, this workflow has been applied to more than 30 mature oil fields in Europe, Africa, the Middle East, Asia, Australia, and New Zealand. Three case studies’ examples and application environments of applied digital workflow are described in this paper. This study demonstrates that it is now possible to deliver digitalized locating the remaining oil projects, capturing the full uncertainty ranges, including leveraging complex multi-vintage spatial 4D datasets, providing reliable non-simulation physics-compliant data-driven production forecasts within weeks.
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