Deep thin interbedded reservoir quantitative characterization method based on seismic grading sensitive attribute fusion

Abstract

The invention discloses a deep thin interbedded reservoir quantitative characterization method based on seismic grading sensitive attribute fusion, which effectively increases the efficiency of analyzing deep thin interbedded reservoir seismic attributes, and greatly increases prediction precision. The deep thin interbedded reservoir quantitative characterization method comprises the steps of: extracting reasonable small time window segment attributes of a geologic target of an interest interval in a regional mode on the geological background of large time window segment, carving a favorable reservoir finely to be fused in the overall background, and acquiring an attribute prediction map of the geologic target. The deep thin interbedded reservoir quantitative characterization method comprises two key steps that: 1, performing multi-attribute correlation dimension reduction by utilizing conventional seisms and optimizing target sensitive attributes of the interest interval; 2, grading the attributes based on seismic grading firstly, determining reasonable small time window segments of the target region on different backgrounds, refusing the attributes, and predicting reservoir range in a research region quantitatively by refusing the graded sensitive attributes under small time window parameters. The grading and fusion are implemented by adopting a support vector machine (SVM) algorithm under real drilling data constraints.

Description

technical field [0001] The invention relates to the field of geophysical exploration and comprehensive research, in particular to a method for predicting deep thin mutual reservoirs based on a fusion method of seismic classification sensitive attributes. Background technique [0002] Seismic attributes are physical quantities that characterize seismic wave geometry, kinematics, dynamics, and statistics. It is an important aspect of reservoir prediction in the current comprehensive research on petroleum exploration. Due to the limited use of a single attribute to predict reservoirs, the prediction results are often highly multi-solution, and this multi-solution problem can be improved by multi-attribute fusion. On the basis of well logging data, study the characteristics of seismic attributes, analyze the sensitivity of each attribute to the reservoir, apply the multi-attribute fusion method to integrate several attributes, and then use the well location to calculate the fusi...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a deep thin interbed reservoir based on the fusion of seismic classification sensitivity attributes using seismic classification (zone classification and time window classification) for the problem of low prediction accuracy of deep thin interbedded reservoirs in the current technology The quantitative characterization method effectively improves the efficiency of seismic attribute analysis for deep thin interbedded reservoirs, and greatly improves the prediction accuracy

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