Reservoir characterization and forecasting require the construction of reliable and history-matched reservoir models. The goal of all history matching for the reservoir model is to generate accurate predictions. History matching can be carried out either by standard history matching techniques (composed of a fixed geological model with global modifications and local adjustments), or by automatic computation of a set of parameter values so as to minimize the prescribed cost function.
An important aspect of history matching results is to quantify the contribution of uncertainties in underlying parameters such as porosity, permeability, structure, fault system, aquifer support, heterogeneous limit, etc. Thus, a detailed geological understanding of reservoirs will have a significant effect on history matching results.
This paper describes the integration of mathematical models (SIMPLEX and Steepest Descent algorithm) with global modification (through deeply geological studies) to increase the efficiency of history matching. Finally, the authors apply the above method to the Lower Miocene reservoir at White Tiger field, a highly heterogeneous reservoir, and have been producing under naturally edge-water mechanism. A model that covers the upper formation of White Tiger field with 27 production wells, 8 injection wells is conditioned to 23 years of production history. Otherwise, there is no an absolute reservoir model and an error between history data and simulation results often occur. So, the authors supposed an experimental method to estimate this error. These obtained results can be used to optimize the history matching procedures. This research is also an effective approach for history matching oil-gas reservoir models.
In recent years, optimization methods with application to reservoir characterization and uncertainty assessment received an increasing interest. Reservoir simulation is used in several important tasks in the petroleum industry. Since the computer system were limited in the past, it is difficult to solve certain problems in complex real fields. However, nowadays computer sciences, mathematics algorithm and automation tools have developed significantly. Numerical reservoir simulation has become an important tool for several researches and practical activities in the petroleum industry. Although there are several limitations and errors involved in the process of reservoir modeling using available numerical simulators, they are still the most viable and reliable way to perform reservoir behavior and production forecasting.
History matching is absolutely necessary and important for reservoir simulation. It is an inverse problem to partial differential equation in mathematics (Figure 1). Usually the limited static data on the geological and geophysics background of the reservoir are available from well tests, seismic surveys, logs etc. Applications of reservoir simulations, which intend to reproduce measured well production data in the basis of unknown model parameters, define a procedure to solve the inverse problem of reservoir modeling (R.W. Schulze-Riegert et al, 2001)10.
Traditional history matching comprises the adjustment of reservoir parameters in the model, such as porosity, permeability, net thickness, fluid properties, structure, etc, until the simulated performance matches the measured data. This is the trial-and-error procedure; the qualities of history matching procedures depend on so much of the knowledge and experience of the reservoir engineer.
