Developing a Coupled Static-Dynamic Algorithm for Optimization of Reservoir Characterization.

Reservoir simulation is the combination of physics, mathematics, reservoir engineering, and computer programming for predicting the performance of a hydrocarbon reservoir under various operating conditions. The necessity of optimizing oil extraction methods is to increase the efficiency of a hydrocarbon reservoir under various operating conditions. Therefore, in a hydrocarbon recovery project that may involve hundreds of millions of dollars in investments, the risk associated with using the best method for the desired outcome must be evaluated and minimized. Finding an optimal model for static reservoir simulation through fluid flow model is the main goal of this paper. For this purpose, first of all, artificial data of porosity and permeability was generated, and then these data were used for geostatistical simulation. Sequential Gaussian Simulation (SGS) method was used for static simulation of a region and for each of two static parameters, 25 realizations were obtained. The realizations of SGS were randomly entered in the dynamic model of the reservoir. Dynamic model was coded by Python for slightly-compressible fluid flow equations in three dimensions and the pressure in each of the blocks in the oil production reservoir was determined. For each category, the least Sum of Squared Errors (SSE) was determined and then the corresponding static model was selected as the desired static model. With a reliable static model, it is possible to define different locations for designing injection and oil production wells, potential intelligent wells areas, as well as optimizing the management of a reservoir, which leads to costs reduction and, at the same time, increase in oil production. [ABSTRACT FROM AUTHOR]