Abstract

Most static modeling workflows deal with stochastic simulations of the uncertain subsurface parameters on a base case model, although recent studies highlighted usefulness of discrete deterministic multiple geological scenario-based modeling. This paper illustrates the benefits of capturing the principal geological uncertainties through discrete subsurface scenarios, through a case study from the Vijaya and Vandana (V&V) field, Barmer Basin, northwest India. The 12 exploration and appraisal wells have established seven stratigraphically trapped oil pools with the maximum resources confined in the V&V mounds, consisting of turbidite sandstones and conglomerates in a shale background, inferred to be deposited in a deep lacustrine environment. Hydraulic fracturing of these sandstones resulted in significant production increase. Detailed subsurface analysis suggests that the V&V mounds consist of two channel complexes represented by a laterally migrating network of turbidite channels with a maximum thickness of 4–5 m for individual sandstones. Multiattribute seismic studies indicate that delineation of these channel sands, controlled mainly by the channel geometries, cannot be resolved by seismic signature/attribute studies alone, which necessitates the iteration of the facies model into five different scenarios. Each of the facies scenarios is further iterated with other key uncertain input parameters for STOIIP calculation (namely saturation, porosity, contact, etc.) to result in 50 deterministic static realizations that capture the wide uncertainty range of in-place volumes, through a cumulative distribution function plot. In the absence of a defined concept, our model highlights the importance of deterministic depiction of subsurface concepts (geologic, geophysical, petrophysical, and dynamic) through a scenario-based approach. This workflow captures a wide range of various high-impact uncertainties in an integrated manner and links discrete, deterministic, scenario-based outcomes to probabilistic reporting. This will help in the decision-making process by linking the model outcome with long-term well testing and ultimately the concept underlying the development plan.

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