摘要：Natural oil and gas reservoirs are the main assets of petroleum exploration and production industries. Proper characterization of properties of the reservoirs and reliable estimation of their future performance is therefore of immense importance. In the future, one of the most important problems in quantitative reservoir modeling is characterization of the carbonate reservoirs. These reservoirs, as one of the major hydrocarbon settings, include heterogeneous pore spaces with unknown and irregular distributions. In this study, a carbonate reservoir in southern Iran is selected as a test bed for application of a novel characterization method. Monitoring of velocity values from sonic logs has exhibited inversion in this reservoir. We attribute this inversion to the change in pore sizes for reasons that will be explained in the paper. To obtain real values of dry rock bulk modulus as an indicator of pore sizes, assuming an identifiable model, we devised a genetic algorithm to optimize the Gassmann equation. Our results show that an appropriately designed genetic algorithm can reliably predict the values of the dry rock bulk modulus accurately. Consequently, a proposal for modification of the Gassmann equation is presented by introducing a new coefficient representing the effects of pore sizes.
