bayesian method

Determination of inter-seismic deformations such as fault slip-rate can usually be achieved by using geodetic observations, earthquake geology and paleo-seismology, as well as mechanical, empirical and numerical modeling. In these models, combination of the fault seismic parameters and the GPS data can help estimate the fault slip-rate, the elastic thickness of the lithosphere, the earthquakes recurrence time, the relaxation time of the asthenosphere, the elapsed time of earthquake and the locking depth of the fault. In this study, we utilize the geodetic data of the North Tabriz Fault (NTF) by using random Bootstrap sampling and conducting numerical modeling by code writing in the R and MATLAB softwares. In this concern, the fault slip-rate and elastic layer thickness are estimated to be ~4-6.5±1 mm/yr and ~5-25 km, respectively, for the NW segment of the NTF. Similarly, model results for the SE segment of the fault indicate a slip-rate of ~3.5-5.5±1 mm/yr and elastic layer thickness of ~8-16 km. For the NW segment of the NTF, the asthenosphere relaxation time, earthquake recurrence time and elapsed time are estimated to be ~160-185 years, ~650-950 years and ~200-1400 years, respectively. Model results for the SE segments of the NTF indicate an asthenosphere relaxation time of ~220-340 years, an earthquake recurrence time of ~750-1050 years and an elapsed time of ~200-1500 years, respectively. The results are well consistent with the other paleoseismological and geological results.

Due to subsurface heterogeneity and existing vagueness in geophysical interpretation, identifying and interpretation of facies in wellbores is always prone to uncertainty and risk. Nowadays several methods have developed for quantitative facies interpretation. These methods are generally divided into deterministic and stochastic categories. Deterministic methods, in spite of their simple modeling procedure, cannot expose the amount of error or accuracy of the model. On the other hand, stochastic methods, in addition to quantifying the error of the model, can provide the probability of the model’s accuracy in each point of the reservoir. The Bayesian approach is one of the stochastic methods that use conditional probabilities for modeling. This approach, as well as probabilistic modeling of hydrocarbon facies, quantitatively computes the effect of additional data in decreasing the error of the classification. Information entropy theory, by quantifying the intrinsic uncertainty in each model input parameter, can easily provide the selection of valuable parameters. The present study was carried out on one of the wells of Mansuri oil field, south of Iran. After generation of training data by using rock physics techniques and Gassmann’s relation, the value of each input parameter was identified by entropy analysis. Then, by use of Bayesian analysis and valuable parameters, oil facies classification and discrimination was implemented. The five optimum parameters were elastic impedance, compressional wave velocity, shear wave velocity, density and porosity. The amount of error in this method is approximated to be 11 percent. This investigation also showed that gamma ray parameter does not have a drastic positive effect on identification and discrimination procedure of oil facies, which has a good agreement with the results of entropy analysis.