جستجوی مقالات مرتبط با کلیدواژه "نگارهای تخلخل" در نشریات گروه "علوم پایه"

Based on the extensive studies, undoubtedly, the shear wave velocity (Vs) plays a fundamental role in hydrocarbon reservoir evaluation. Using Vs often allows us to identify the seismic signatures of lithology, pore fluid type and pore pressure, efficiently. Unfortunately Vs data is not available in all reservoirs and it is necessary to predict it. To date, intelligent systems have been utilized as powerful and routine tools for this purpose. In this study, PSO algorithm that is one of the artificial intelligence system used for Vs estimation. The algorithm is utilized in linear and nonlinear ways by intelligently derived Equations. This study have a total 3190 data points from Asmari reservoir from two wells in one of the oilfields, SW Iran. All data points have porosity log and measurement Vs by Dipole Shear Sonic Imager. These data are divided into two parts, one part included 2090 data points are used for constructing model and the other part included 1100 data points are used for testing and validation model. Shear wave velocity is predicted by PSO algorithm and results compared with real Vs measurements by DSI. Regression between predicted Vs by Linear PSO algorithm and measured Vs is about 0.92, whereas it is about 0.95 by using nonlinear PSO algorithm. Results of this study and comparison with other studies confirm that particle swarm optimization is suitable for predicting Vs in other oilfields.

The shear and compressional wave velocities (Vs and Vp, respectively) have many applications in petrophysical, geophysical and geomechanical studies. Vp is very easily obtained from sonic logs that are available in most of oil and gas wells, but some wells (especially old wells) may not have Vs data. In this study Vs was predicted from porosity well log data (neutron, density and sonic) using fuzzy logic and neuro-fuzzy techniques. For this purpose a total of 3910 data points from Sarvak carbonate reservoir which have Vs and porosity log data were utilized. These data were divided into two parts, one part included 2046 data points used for constructing models and the other part included 1864 data points used for testing models. The results show that fuzzy logic and neuro-fuzzy techniques were useful methods for prediction of Vs in this carbonate oil reservoir.