Search

Assisted history matching procedures are usually implemented within an optimization procedure, where the goal is to minimize the object function, which is written in terms of the error between observed and simulated data. One of the problems of the process is that the objective function represented by a single value is not sufficient to express the complexity of the problem. The errors that are normally measured for each well and for all production rates and pressure are converted to a single value; usually the norm of all error vectors. If the problem is well behaved and the objective function quickly converges within a desired tolerance, this would not be a problem, but this is usually not the case for complex history matching processes, since it is very difficult to find a model (or a set of them) that matches with a reasonable tolerance the production profiles for every wells. This work proposes a new approach to deal simultaneously with several objective functions (for instance, well rates and pressure). The methodology follows a probabilistic approach where several simulation models are handled during the entire procedure. The initial step is to generate several simulation models by combining the most important uncertainties of the reservoir. Then, an iterative procedure is performed to iteratively change the reservoir attributes and filter the models that are closer to history data. This procedure encompass a re-characterization step, where the probability of the attributes are updated, global multipliers are applied and local changes are made around problematic wells in order to provide a set of model that yield production and pressure curves with better dispersion compared to history data for every well. The key point of the methodology is that for every iteration the errors of each model, well and objective function can be visualized in a very concise plot that is based on the normalized quadratic error of each curve. The plot clearly shows global and local problems of the set of simulation models, so it is a good indicator of the changes to be made in the next iteration. Another point to highlight is that the same type of iterative procedure is performed to integrate 4D seismic into the process. Thus, after selecting a set of models with a good representation of well history data the same iterative process is repeated to generate a new set of models that match 4D seismic data as well. Thus, the proposed methodology integrates 4D seismic and well history data to reduce uncertainties with a probabilistic approach. To validate the methodology an application is shown where it can be seen the gradual improvement achieved for the models along the iterations.