The Research Of Turbidite Reservoir Physical Properties Predicting Technology

As the developing of oilfield exploration technology ,the development of major oil fields keeps Increasing. Exploring scientists increasingly show great attention to the lithologic hydrocarbon reservoir. As a complex lithologic,the turbidite reservoir have well prospect in the oil and gas exploration. In this paper,we use turbidite deposition of shinan area as the research object. Carrying out the research of physical properties parameters of the Turbidite reservoir, such as geologic and geophysics characteristics of thickness and porosity. Based on the careful analysis of the seismic, geological, logging and drilling data, the sandstone thickness and porosity of target zone of turbidite and the spatial distribution of physical properties were predicted. It's provided a good basis of seismic prediction for the reservoir physical properties prediction of the turbidite.Based on the researching and mastering of the sedimentary characteristics, structural characteristics and forming law of the turbidite in the Shinan area, in terms of the thin interbedded situation of the sandstone and mudstone in the target layers, thickness and porosity forecast technology method of sandstone the thin interbedded are designed. On the basis of characteristics of seismic response of sand and mud thin interbeds in time and frequency domains, aiming at the difficulties of thin interbeds forecasting ,this article established time-frequency domain target function, carry on research in joint inversion method of thin interbedded reflection coefficient, in constraint of larger constrained sparse reflection coefficient. Aiming at the weakness of randomness of non-linear algorithm, discussed the inverse method of the constraint of the result of L1-L2 norm joint constrained sparse spike deconvolution. In the research of the inversion method of thin interbedded reflection coefficient, puting forward the Simulated Annealing Inversion, greatly improve the computational reliability and efficiency. Based on the results of conversion, we calculated the thickness of sandstone in this area. Compared with the well data, show that the prediction have high precision, and get the good results.According to the characteristics of the physical parameters of the reservoir, we analysis the relation between hard data on physical properties prediction and earthquake soft data of the area, optimized the soft data on the seismic, and select the relevant data in a higher degree. All these operations are based on a large number of statistics of logging data and seismic data in the study area. For this reason ,we study Bayesian-Markov blanket network prediction method for porosity prediction, designed the relevant algorithm and use the algorthm to predict porosity in target stratum of shinan area, aiming at the weakness of bayesian network prediction. By means of vacuating verification for multi-well,the prediction results show reliablely and have higher precision, and it is an effective reservoir parameter prediction method.