Abstract: A hierarchical modeling tool or process is provided that represents the fill range of behavior of a hydrocarbon system, accurately including uncertainties and potential events affecting the system. The potential events may include actions taken and information learned. The hierarchical modeling tool may be embedded within a decision support system or used in a stand-alone fashion. Disclosed aspects may link from accurate (high-detail) physics models to an accurate uncertainty representation, and then reduce the accurate uncertainty representation to a high-speed representation of both the physics model and the uncertainty that can be used in an optimizer.

Abstract: Methods and systems are provided for generating a development plan for a hydrocarbon asset. A high-fidelity computer model of a hydrocarbon asset is created. A low-fidelity computer model of the hydrocarbon asset is created. The low-fidelity computer model is iterated on to an interim solution. A comparison is generated of the interim solution to a solution obtained from a simulation of the high-fidelity computer model at the variables of the interim solution. The low-fidelity computer model is calibrated based, at least in part, on the comparison. The development plan for the hydrocarbon asset is generated based, at least in part, on a result from the calibrated low-fidelity computer model. The low-fidelity computer model is a mixed-integer nonlinear programming problem with complementarity.

Abstract: A hierarchical modeling tool or process is provided that represents the fill range of behavior of a hydrocarbon system, accurately including uncertainties and potential events affecting the system. The potential events may include actions taken and information learned. The hierarchical modeling tool may be embedded within a decision support system or used in a stand-alone fashion. Disclosed aspects may link from accurate (high-detail) physics models to an accurate uncertainty representation, and then reduce the accurate uncertainty representation to a high-speed representation of both the physics model and the uncertainty that can be used in an optimizer.

Abstract: One or more methods for optimizing reservoir development planning include a source of characterized input data, an optimization model, a baseline model for simulating the reservoir, a modified model, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The modified model can systematically address uncertain data, for example comprehensively or even taking all uncertain data into account. Accordingly, the modified model is optimized to flexible or robust solutions. Final reservoir development plans are generated based on optimized model results.

Abstract: Methods and systems to reduce or eliminate numerical oscillations in solutions that occur when using conventional MPFA when modeling flow in a reservoir are provided. The technique may be referred to as enriched multi-point flux approximation (EMPFA) and may be used to improve the consistency and accuracy in constructing pressure interpolations in cells for the purpose of determining flux equations used in predicting flow in a reservoir.

Abstract: Methods and systems to reduce or eliminate numerical oscillations in solutions that occur when using conventional MPFA when modeling flow in a reservoir are provided. The technique may be referred to as enriched multi-point flux approximation (EMPFA) and may be used to improve the consistency and accuracy in constructing pressure interpolations in cells for the purpose of determining flux equations used in predicting flow in a reservoir.