Risks and Uncertainties Evaluation of Reservoir Models as a Way to Optimal Decisions
- Evgenii Sazonov (Bashneft-Dobycha) | Alfiya Nugaeva (Bashneft-Dobycha) | Anton Muryzhnikov (Rock Flow Dynamics) | Dmitry Eydinov (Rock Flow Dynamics)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Petroleum Technology Conference, 26-28 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2015. Society of Petroleum Engineers
- 7.3 Strategic Planning and Management, 3 Production and Well Operations, 7.2 Risk Management and Decision-Making, 1.6.6 Directional Drilling, 7.2.1 Risk, Uncertainty and Risk Assessment, 5.5 Reservoir Simulation, 5.1.5 Geologic Modeling, 7.1 Asset and Portfolio Management, 7.1.9 Project Economic Analysis, 5.5.8 History Matching, 7.6.6 Artificial Intelligence, 5 Reservoir Desciption & Dynamics, 7.6 Information Management and Systems, 1.6 Drilling Operations, 7.1.10 Field Economic Analysis, 7.3.3 Project Management, 7 Management and Information, 3 Production and Well Operations
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When horizontal wells are being planned, reservoir engineers have specific requirements to the quality of prediction. Knowledge about the reservoir structure and properties, number of reservoir beds and the other parameters in the areas far from the well bores is critical for decision-making. However, all geological modeling tools can only estimate the reservoir properties in the interwell space with some uncertainty. The modern modeling workflows with ensembles of simulation models allow uncertainty and risk analysis with validation of the available data. That helps to investigate different development scenarios and maximize the profit.
In this work, we discuss the workflows for assisted history matching, uncertainty and risk assessment as well as the impact it has on the field development. The paper shares practical experience of application of the model ensembles for history matching and prediction. Multiple solution history matching is obtained with assisted history matching tools. Differential evolution optimization algorithm is used for minimizing the mismatch between the simulation results and the observed data. Tornado diagrams representing the impact of the individual parameters on the objective function value was used for sensitivity analysis and ranging the uncertainty parameters. Eventually, based on the multiple history matching solutions, engineers propose field development plan that is less sensitive to the uncertainty and minimizes the risks.
Multiple solutions are found with evolutionary algorithms, which are based on multiple realizations of simulation models with stochastic distribution of the parameter values. Some practical aspects of application of assisted history matching software are discussed in details. The authors share their experience with the workflows and share some recommendations about the basic steps, such as sensitivity analysis, automatic history matching with optimizers, identifying the key parameters, and ranging the multiple solutions obtained.
The standard company workflows in ANK Bashneft assume that multiple solutions found at the history matching stage are then used for optimization of the new wells or workovers on the existing wells. In this work, we carried out prediction with uncertainty assessment for optimizing operational regimes for injection and production wells, infill drilling scheme, well trajectory optimization, EOR's, oil in place localization and optimization of the lab research program to reduce the model uncertainty. It is clearly demonstrated that the uncertainty quantification helps to make decisions that are more efficient and significantly improve the project economics.
|File Size||2 MB||Number of Pages||16|
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