Quantitative subsurface prediction and uncertainty analysis are critical success factors in all phases of exploration, development, and production projects, including regional evaluation, leasing, prospect maturation and drilling, appraisal and development planning, system selection, and reservoir management. Today, the E&P industry is moving into more frontier oil and gas provinces, such as deepwater and very deep plays, where well costs are very high. Consequently, the regional and in-field well densities are generally very low compared with historical exploration and development. Such sparseness of well control poses a challenge for quantitative subsurface analysis because it is difficult to establish spatial correlations of rock and reservoir properties with confidence, when the distances between wells are too large to assume geostatistical stationarity of earth model parameters.An effective approach to reduce subsurface uncertainty due to well sparseness is to build a 3D shared earth model using seismic data as "spatial glue." Under this approach, self-consistent 3D models are built for all interdependent earth model parameters that span a wide range of spatial scale and subsurface disciplines, and are constrained by physical laws and geologic scenarios. The self-consistency conditions of a 3D shared earth model provide tighter constraints and improved reservoir, rock and fluid property predictions, and more robust uncertainty analysis. This approach has been piloted in a number of play settings Figure 1. Pressure prediction workflow, incorporating all geophysical, geologic, and petrophysical data and analysis.Figure 2. Traverse through Mars-Ursa Basin (Mars-Princess-Ursa-Crosby) showing the original PSDM velocity model used as input to pressure prediction and an assortment of pressure diagnostic cubes that follow. (Color scales run from low to high values from bottom to top.)
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