Abstract

Full-field reservoir models provide key input to annual business plans and reserve booking. They support the long-term field development plan by enabling well target optimization, identification of infill opportunities, water-flood management, and well-surveillance and intervention strategies. It is crucial to constrain the model with all available static and dynamic data to improve its predictive power for confident decision making. Across Shell's global deepwater portfolio, a model-based probabilistic seismic amplitude-variation-with-offset (AVO) inversion methodology is used to constrain reservoir properties as part of a comprehensive quantitative seismic reservoir modeling workflow. Promise, a proprietary probabilistic inversion tool, estimates values of reservoir properties and quantifies their uncertainties through repeated forward modeling and automated quality checking of synthetic against recorded seismic data. During workflow execution, available geologic, petrophysical, and geophysical data are incorporated. As a consequence, the reservoir models are consistent with all relevant subsurface data following their update through inversion. Model-based inversion establishes a direct link between static model properties and elastic impedances. Probabilistic inversion output is an ensemble of posterior static models. The inversion process automatically sorts through the ensemble. It can directly provide low, mid, and high cases of the inverted models that are ready to be used in hydrocarbon volume estimation and multiscenario dynamic modeling for history matching and production forecasting. For successful and efficient delivery of full-field reservoir models with uncertainty assessment using model-based probabilistic AVO inversion, early integration of interdisciplinary subsurface data and cross-business collaboration are key.

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