An integrated 3D dynamic reservoir geomechanics model can provide a diverse 3D view of depletion-injection-induced field stress changes and the resulting deformation of both reservoir and overburden formations at various field locations. It enables the assessment of reservoir compaction, platform site subsidence, fault reactivation and caprock integrity associated with multiple production and injection reservoirs of the field. We demonstrated this integrated approach for a study field located in the South China Sea, Malaysia, which is planned for water injection for pressure support and EOR scheme thereafter. Reservoir fluid containment during water injection is an important concern because of the intensive geologic faulting and fracturing in the collapsed anticlinal structure, with some faults extending from the reservoirs to shallow depths at or close to the seafloor. Over 30 simulations were done, and most input parameters were systematically varied to gain insight in their effect on result that was of most interest to us: The tendency of fault slip as a function of our operation-induced variations in pore pressure in the reservoir rocks bounding the fault, both during depletion and injection. The results showed that depletion actually reduces the risk of fault slip and of the overburden, while injection-induced increase in pore fluid pressure will lead to a significant increase in the risk of fault slip. Overall, while depletion appears to stabilize the fault and injection appears to destabilize the fault, no fault slip is predicted to occur, not even after a 900psi increase in pore pressure above the pore pressure levels at maximum depletion. We present the model results to demonstrate why depletion and injection have such different effects on fault slip tendency. The interpretation of these geomechanical model results have potential applications beyond the study field, especially for fields with a similar geology and development plan. This is a novel application of 3D dynamic reservoir geomechanics model that cannot be obtained from 1D analytical models alone.
Advances in digital technologies have the potential to enhance model predictive capability and redefine its boundaries at various scale. Digital oil with accurate representation of atomistic components is a powerful tool to analyze both macroscopic properties and microscopic phenomena of crude oil under any thermodynamic conditions. Digital oil model presented in this paper is the key input in molecular chemistry modeling for designing chemical enhanced oil recovery formulation. Hence, it is constructed based on a fit-for purpose strategy focusing in oil components that have large contribution to microemulsion stability. Complete crude oil composition could comprise over 100,000 components. Lengthy simulation time is required to simulate all crude oil components which is impratical, despite the challenges to identify all crude oil components experimentally. Therefore, we established a practical experimental strategy to identify key crude oil components and constructed the digital oil model based on surrogate components. The surrogate components are representative molecules of the volatiles, saturates, aromatics and resins. Two-dimensional digital oil model, with aromaticity on one axis, and the size of the molecules on the other axis was constructed. We developed algorithm to integrate nuclear magnetic resonance response with architecture of the molecular structure. A group contribution method was implemented to ensure reliable representation of the molecular structure. We constructed the digital oil models for a field in Malaysia Basin. We validated the physical properties of the digital oil model with properties measured from experiment, predicted from molecular dynamics simulation and calculated from quantitative property-property relationship method. Good agreement was obtained from the validation, with less than 5% and 13% variance in crude density and Equivalent Alkane Carbon Number respectively, indicating that the molecular characteristic of the digital oil model was captured correctly. We adopted the digital oil model in molecular chemistry modeling to gain insights into microemulsion formation in chemical enhanced oil recovery formulation design. Digital oil is a robust tool to make predictions when information cannot be extracted from experimental data alone. It can be extended for engineering applications involving processing, safety, hazard, and environmental considerations.
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