--> Application of Geostatistical Inversion in the Thin Sand Body Prediction: A Case Study in Yangqian 19 Area, Nanxiang Basin
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Application of Geostatistical Previous HitInversionNext Hit in the Thin Sand Body Prediction: A Case Study in Yangqian 19 Area, Nanxiang Basin

Abstract

Abstract

Yangqian 19 area, located in Nanxiang Basin, is characterized by rapid facies change, strong heterogeneity, and small thickness of single sand body. As the earthquake-band of the conventional deterministic Previous HitinversionNext Hit is limited, the vertical resolution of the Previous HitseismicNext Hit Previous HitinversionNext Hit body is very low, and hence it is often difficult to identify the thin sand body. The geostatistical Previous HitinversionNext Hit method based on stochastic modeling technique can effectively integrate geological, logging and Previous HitseismicNext Hit Previous HitdataNext Hit, which can greatly improve the Previous HitseismicNext Hit vertical resolution, and identify the thin sand layers more accurately. Thus, the geostatistical Previous HitinversionNext Hit method is used to predict the sand body spatial distribution in this research.

First, a correlation analysis is carried out between lithology (from core Previous HitdataNext Hit) and well logging Previous HitdataNext Hit, and resistivity log shows the best correlation with lithology. Then the wave impedance Previous HitdataNext Hit body is acquired by using constrained sparse spike Previous HitinversionNext Hit, which is used to get the horizontal variogram, while the vertical variogram is acquired based on the well Previous HitdataNext Hit. After a correlation analysis between resistivity and wave impedance at the well point, resistivity logs are discretized to lithological Previous HitdataNext Hit from 30 wells which are used for the Previous HitinversionNext Hit. Next, regarding resistivity value as principal variable, whilst the wave impedance Previous HitdataNext Hit body as covariate, MCMC method is used to inverse principal variable and several equal probability realizations of resistivity invertomer are obtained finally. Then by calculating root mean square (RMS) of all probabilities, the Previous HitdataNext Hit body of RMS is the final resistivity invertomer, and then transformed into the lithology invertomer, so as to forecast sand body distribution. Results of Previous HitinversionNext Hit are consistent with well Previous HitdataNext Hit by verification wells and vertical resolution is significantly improved. In addition, forward model is also established for further evaluating Previous HitinversionNext Hit results. By comparison, synthetic seismogram and original Previous HitseismicNext Hit trace is highly matched. In conclusion, stochastic Previous HitseismicNext Hit Previous HitinversionTop has good effect on this research.