|The EVOLVE® workflow solves a longstanding and difficult problem in reservoir management: quantifying the uncertainty in NPV ensuing from uncertainties related to geological and simulation parameters, forecast scenarios, and economic conditions. The uniqueness of the EVOLVE workflow lies in its selective use of streamlines, distance-based generalized sensitivity analysis (dGSA), calibrating to field- and well-responses efficiently, and the use of multidimensional scaling (MDS) with clustering to extract representative model ensembles. EVOLVE is embedded in a robust and friendly software environment, usable by expert and junior geo-engineers alike. It encapsulates many years of R&D but retains a level of practicality that is indispensable for making timely and informed decisions.|
The EVOLVE workflow is a four-stage, linear workflow and stands in sharp contrast to the traditional idea of a big loop that continuously produces new models until a stopping criteria is met. The novel idea is the repeated evolution and reduction of ensembles in a cascading fashion as new parameters are injected at various stages of the workflow. The final ensemble is robust and practical for forecasting.
|The EVOLVE workflow is linear. It evolves and reduces model ensembles in a cascading fashion to create a final ensemble that is robust and practical for forecasting.|
The goal of the first stage is to extract a smaller ensemble of geomodels that is representative of the diversity of a much larger ensemble. The concern is not how models compare to measured production data, rather how model responses compare to each other. Model diversity is identified through multidimensional scaling (MDS) along with cluster analysis. An efficient flow modeling proxy to compare model responses is essential at this stage.
|Screening multiple geomodels through 3DSL, followed by clustering, and then extracting a representative subset.|
The geo-ensemble from Stage 1 are combined with global flow simulation uncertainties (such as OWC depth, relperm functions, PVT properties,...) increasing again the ensemble size. The goal of this stage is to reduce the size of the ensemble while minimizing the error to historical data and maximizing input parameter diversity. This an optimization problem.
|Include uncertainty in flow simulation parameters such as PVT properties, contact depths, and relperms. Then optimize on these flow parameters and extract a set of models that are close to history while retaining input parameter diversity.|
Stage 2 will yield an ensemble of models that display an acceptable match of the field response. Individual well responses, however, are not guaranteed to exhibit the same match. At this stage, a novel well-level history matching algorithm is used to modify inter-well geology to improve well matches for all models of the ensemble, or a selected subset.
|Automatically select the worst well in each model and then perform well-level history matching on these wells, such that each model in the ensemble has both a good field-level and well-level match.|
The final step of the EVOLVE workflow is to use the ensemble of models extract from stage 3 for forecasting and ecomomic analysis. The ensemble of models is considered robust and practical: robust because the models exhibit diversity, practical because the number of models remains manageable for computing purposes. The ensemble can now be used to investigate short and long term optimization strategies under various economic scenarios.
|For each history matched model in the final ensemble, attach multiple forecast scenarios (base case, infill drilling, polymer injection, etc). Then for each forecast scenario include uncertainties in NPV by including multiple economic scenarios.|