The diffusivity equation is a partial differential equation (PDE) which can be used for fluid flow modeling in porous media. Determining reservoir parameters from pressure data (i.e., pressure ...transient analysis) is one of the most important steps in the process of field development. This initial evaluation can be used to make decisions about future developments. Wireline Formation Testing (WFT) is one of the most popular techniques for parameter estimation and has received significant attention in recent years. The main problem plaguing WFT is a phenomenon known as the “supercharging effect,” which essentially refers to mud invasion, and this, in turn, alters pressure distribution across the system.
In this study, an analytical solution for fluid flow modeling in spherical coordinates with non-uniform initial pressure is presented. This new procedure takes into account the effect of mud invasion, or, in other words, the supercharging effect. The accuracy of this derivation was validated using previous semi-analytical solutions (the Laplace method) in addition to field data. New type curves and dimensionless parameters, which can be used for pressure transient analysis, are also proposed. This procedure is applied to the WFT data that was obtained from an oil field in the south of Iran, and an excellent agreement (less than 10% error) was observed. In addition, there is considerable uncertainty regarding the radius of investigation for spherical flow. This is important as this parameter greatly affects the applicability of WFT. The analytical derivation of this study was used to determine a reasonable value for this parameter as well.
Reservoir simulation is a powerful predictive tool used in reservoir management. Constructing a simulation model involves subsurface uncertainties which can greatly affect prediction results. ...Quantifying such uncertainties for a field under development necessitates history matching that is a difficult inverse problem with non-unique solutions. History matching is used to minimize the difference between the observed field data and the simulation results and requires numerous simulation runs. In many engineering simulation-based optimization problems, the number of function evaluations is a prohibitive factor limited by time or cost. History matching in hydrocarbon reservoir simulation is one of such computationally expensive problems which pose challenges in the field of global optimization. One way to overcome this difficulty is to use an artificial neural network (ANN) as a surrogate model.
This article presents an ANN-based global optimization method that is used for history matching problem. The method has been applied to an Iranian fractured oil reservoir and the famous Brugge field benchmark. Computational results confirm the success of this method in history matching. We compare history matching results obtained by the proposed method with those of manual history matching and those obtained by simulation based direct optimization algorithm. The results compares favourably with manual history matching in terms of matching quality. The proposed method is superior than the simulation based direct optimization algorithm in finding multiple matched scenarios in less computation time.
•An ANN-based method for assisted history matching process has been developed.•The method was applied successfully on Brugge field and an Iranian reservoir.•ANN is a good substitute for numerical simulator in the history matching process.•The method generates multiple matched scenarios with less computational time.
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