History Matching in the Context of Closed-loop Reservoir Management
Figure 1. Schematic layout of closed-loop reservoir management.
Figure 2. Data matches and predictions of injection rate of a well from unconditional realizations. Solid red curve shows the truth, red circles show the observed data, dash-dot blue curves show P10, P50 and P90; gray curves are from prior realizations.
Mehrdad Gharib Shirangi
In reservoir management, computational models are built to simulate the future reservoir behaviour. Geological models for the subsurface reservoir are generated based on the available geological, geophysical and production data. As there is always new data from the reservoir, development of efficient methods for history matching is of significant interest. History matching is an important step of closed-loop reservoir management (CLRM). CLRM provides a framewrok that different history matching methods can be compared.
In this research, efficient gradient-based history matching techniques are developed for applications in closed-loop reservoir management. Figures 2 and 3, respectively, show data matches and predictions from prior and conditional RML realizations for an injector well. Data matches and predictions for the wells not shown are similar. As figure 4 shows, all RML realizations are generated in about 50 simulation runs which shows the efficiency of our gradient-based history matching technique.
Figure 3. Data matches and predictions of injection rate of a well from conditional RML realizations. Dashed vertical line shows end of history matching; Solid red curve shows the truth, red circles show the observed data, dash-dot blue curves show P10, P50 and P90; gray curves are from prior realizations.
Figure 4. Normalized (history matching) objective function versus number of simulation runs.