Uncertainty Management on a Reservoir Workflow

摘要

In this paper, we determine a future development plan of a mature field to maximize the cumulated oil production. First, a probabilistic history matching of the simulation model is performed by integrating dynamic data. As a result, a posterior distribution of the uncertain parameters is obtained. To perform this step, an innovative statistical technique that combines non-parametric response surface modeling with adaptive design method is used to approximate the objective function. The adaptive design method allows us to reduce the number of necessary simulations while producing an accurate and predictive response surface in the areas of the input space where the OF is low (acceptable solutions region). This OF approximation is then used in a Markov Chain Monte Carlo algorithm to compute the posterior distribution of the parameters that respect the production data. At the end of the history period, a new development plan is performed and therefore new controllable parameters are added to the model. To optimize the new development plan under the remaining uncertainty, a new response surface model is built using both uncertain and controllable parameters. Then, the set of controllable parameters are optimized to maximize the cumulative oil production (or the Net Present Value) while minimizing the risk using the response surface model. In our test case, we show that the advanced statistical methods used to obtain the response surfaces allows us to address highly non-linear problems in an uncertain framework while doing an acceptable number of calls to the expensive fluid flow simulator.