Method for predicting underground river filling based on GR frequency-divided inversion

Abstract

The invention discloses a method for predicting the underground river filling degree based on GR frequency-divided inversion. The method comprises the steps of performing frequency division processing on original seismic data by using a Marr wavelet frequency division technology, acquiring frequency-divided data volumes with different frequency bands, respectively extracting frequency division attributes of different frequency bands from the frequency-divided data volumes, building a kernel function according to an amplitude-frequency relation under different thicknesses of a reservoir, performing multiple times of learning by using a support vector machine, establishing a nonlinear mapping relation between the frequency division attributes and well logging GR curves, combining the nonlinear mapping relations between the frequency division attributes of different frequency bands and the well logging GR curves together to acquire a GR frequency-divided inversion body, determining the GR peak distribution probability corresponding to underground river samples with different filling degrees according to a well logging interpretation result, and determining the underground river filling degree of the GR frequency-divided inversion body. The scheme can acquire an inversion result with high resolution, can directly predict muddy filling conditions of a reservoir in an undrilled region, can improve the construction and production rate of a development and adjustment well and provides technical support for formulating an oilfield development scheme.

Description

technical field [0001] The invention relates to the technical field of petroleum exploration seismic reservoir prediction, in particular to a method for predicting underground river filling based on GR frequency division inversion. Background technique [0002] Carbonate fracture-cavity reservoirs have various storage spaces, among which the underground river type caves are developed on a large scale and are the main oil and gas enrichment space of fracture-cavity reservoirs. Currently, the commonly used underground river filling prediction methods include well logging identification method, Seismic attribute prediction method and seismic impedance inversion method. [0003] The logging identification method is to comprehensively evaluate the filling degree and effectiveness through conventional and imaging logging techniques combined with drilling and oil testing data. Through the acoustic wave numerical simulation, the forward modeling data of different filling degrees ar...

AI Technical Summary

Problems solved by technology

When the number of wells is small, the sparse inversion method is used. This method needs to find a wavelet first. It is difficult to extract the wavelet accurately in actual calculation, and the shape of the wavelet extracted has a great influence on the inversion result.

When there are many wells, model inversion is the main method. This inversion depends on an initial model, which cannot represent the complex formation contact relationship, and the inversion results have multiple solutions.

Due to the strong heterogeneity of fractured-cavity carbonate reservoirs, the inversion geological model cannot represent the actual geological model of strong heterogeneity reservoirs, and the inversion results cannot overcome the multi-solution nature, and there is a large discrepancy between the filling prediction results and the actual situation. difference

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