Thin reservoir prediction method and thin reservoir prediction system

Abstract

The application provides a thin reservoir prediction method and a thin reservoir prediction system. The method comprises the following steps: acquiring seismic data and log data; performing consistency treatment on the log data; constructing a geological model according to the seismic data and the log data subjected to consistency treatment; performing depositional microfacies and strata sequence framework partitioning on the geological model; performing strata sequence restriction geostatistic inversion according to the geological model subjected to depositional microfacies and strata sequence framework partitioning; screening the results of the geostatistic inversion, and performing strata slicing on the screened results, thereby obtaining a planar distribution rule of a thin reservoir. According to the thin reservoir prediction method and the thin reservoir prediction system provided by the embodiment of the application, on the basis of improving the resolution ratio by use of a geophysical prospecting means, the geological cognition is fused into a geostatistical approach, so that a restriction effect is achieved, the final prediction result conforms with the geological rules, and the thin reservoir predication precision is improved.

Description

technical field [0001] The present application relates to the technical field of geophysical exploration, in particular to a thin reservoir prediction method and system. Background technique [0002] Thin reservoirs generally refer to reservoirs whose thickness is less than 1 / 4 of the seismic wave wavelength and cannot be distinguished by seismic waves, and their thickness is often less than 20m. This type of reservoir usually has the characteristics of poor lateral connectivity and fast lateral pinch-out. Therefore, it is very difficult to compare and trace seismic interpretation of this type of reservoir. Conventional reservoir prediction methods are also difficult to identify such reservoirs due to their limited resolution. [0003] At present, there are many prediction methods for thin reservoirs, generally including: sedimentary cycle analysis method, colored inversion slice method, lithofacies sequence pattern analysis method, and reservoir prediction technology with...

AI Technical Summary

Problems solved by technology

However, the geophysical techniques selected for these methods have limitations

For example, the spectral decomposition technology mainly converts the seismic data volume from the time domain to the frequency domain, generates the amplitude spectrum and phase spectrum, and identifies the thickness change of the stratum through the amplitude spectrum. However, different spectral decomposition algorithms may be used for the same seismic data volume. will have different effects, leading to inaccuracies in the final forecast results

On the other hand, these methods only identify reservoirs through geophysical prospecting techniques of mathematical operations, without considering the geological laws of the distribution of thin reservoirs, which will also lead to inaccuracies in the final prediction results

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