Reservoir Fluid Geodynamics (RFG) is a novel thermodynamic methodology that integrates pressure-volume-temperature (PVT), geochemical fingerprinting (GCFP) and reservoir geology with downhole fluid analysis (DFA) data to understand the evolution of reservoir fluids over geologic time. RFG enables the enhancement of reservoir description, estimation of reservoir fluid properties, and optimization of data acquisition plans. Deep-water reservoirs comprise multiple uncertainties in reservoir connectivity, viscous oil and flow assurance. This paper demonstrates the development of digital fluid sampling techniques for deep-water fields using the RFG workflow to predict fluid properties and distribution, to address compartmentalization uncertainties and flow assurance risks, as well as to redefine the well-logging program.
Identifying key reservoir concerns is the first step during the implementation of the RFG workflow. Five questions define key reservoir concerns: Do optical density measurements explain the impact of biogenic methane on fluid behavior? Is it feasible to characterize baffling and fault compartmentalization? Can we predict reservoir fluid properties and assess flow assurance risks based on fluid behavior? Is it possible to identify all this in real time? How could we optimize future fluid sampling programs? The next step is to collect the available DFA data and to integrate it with the existing PVT and geochemistry datasets. This paper describes the evaluation of over 150 fluid sampling DFA measurements acquired during the operational history of a Gulf of Mexico field. Fluid behavior and optical density gradients are interpreted from a geological perspective to understand reservoir connectivity. A strong correlation between optical density and asphaltene content enables digital fluid sampling for different PVT and geochemical parameters. Lastly, a general correlation of optical density and asphaltene content is derived for multiple Gulf of Mexico oil fields.
Optical density measurements support a consistent characterization of biogenic methane along the studied deep-water field, suggesting a relation to fluid migration and charging from deeper to shallower reservoirs. Likewise, optical density gradients and its integrated evaluation facilitate the identification of mass transport complex (MTC) baffles in the north part of the field and the characterization of fault compartments in the main reservoir sands. In addition, the RFG workflow reveals the difference in fluid behavior of sampled wells located in the area of a water injection project by identifying asphaltene clustering near the oil-water contact. The correlations of optical density and asphaltene content help to predict fluid properties and to estimate its uncertainty, benefiting risk assessment for asphaltenes deposits and flow assurance in deep water operations. Real time analysis of optical density measurements during fluid sampling permits the characterization of fluid properties and reservoir connectivity, optimizing future fluid sampling programs when fluid contamination reaches 10%. Ultimately, this innovative methodology conveys a general correlation to predict asphaltene content based on optical density measurements for deep-water reservoirs in the Gulf of Mexico, enabling the possibility to predict reservoir fluid properties in real time fluid sampling operations.