The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization

In this study, a carbonate oil reservoir located in the southeast part of Turkey was characterized by the use of kriging and the fractal geometry. The three-dimensional porosity and permeability distributions were generated by both aforementioned methods by using the wireline porosity logs and core plug permeability measurements taken from six wells of the field. Since classical regression (lognormal or polynomial) and geostatistical techniques (cross variograms) fail to estimate permeability from wireline log-porosity data, the use of artificial neural networks (ANNs) is proposed in this study to generate permeability data at uncored intervals of porosity logs. For both of the methods, kriging and fractal techniques, the validation of the estimated/simulated data with known wellbore data resulted with acceptable agreements, especially for porosity. Also the comparison of both methods at unsampled locations show better agreements for porosity than permeability

The use of genetic algorithms for determining the transport parameters of core experiments

From hydrocarbon reservoirs, brine is produced as a waste material, which may be injected into the ground or discharged at the surface. When the wastewater is injected into the ground, it may be mixed with fresh-water sources by several processes. Groundwater contamination from leakage, spills, or the injection of hazardous or toxic materials is widely regarded as one of the leading environmental problems. This study presents the use of genetic algorithms (GAs) as a viable means of estimating the transport parameters such as dispersivity, retardation factor, and diffusion coefficient of water-saturated porous media. The unknown transport parameters of advective-dispersive contaminant equations for homogeneous, linear, radial, and fractured systems are predicted by the use of GAs coupled with the experimental data. The parameter estimation study is considered as a constrained optimisation problem by minimising the total error between the calculated and the measured effluent concentrations satisfying state equations, boundary conditions, and limits on parameters