Fault variable-scale recognition method based on multi-iteration ant colony algorithm

Abstract

The invention provides a fault variable-scale recognition method based on a multi-iteration ant colony algorithm. The fault variable-scale recognition method comprises the steps that 1, seismic data volumes are preprocessed; 2, an improved coherency algorithm is applied to process actual seismic data according to the pre-processed seismic data volumes; 3, threshold values and weight coefficients of all parameters for different scales of fault recognition are determined by applying the multi-iteration ant colony algorithm and through multiple times of experiments; 4, a fault plane map of a study area is drawn; and 5, application and popularization are conducted, and fault variable-scale recognition is conducted in a semi-quantitative mode. According to the fault variable-scale recognition method based on the multi-iteration ant colony algorithm, the accuracy and reliability of fault interpretation can be effectively improved, and the requirements for fault system interpretation at different exploration and development stages can be met.

Description

technical field [0001] The invention relates to the field of geophysical exploration and comprehensive research, in particular to a variable-scale identification method for faults based on multiple iterative ant colony algorithms. Background technique [0002] Fault interpretation is the key to structural interpretation of oil and gas exploration, and the accuracy and rationality of fault interpretation directly affect the accuracy of structural interpretation. For a long time, researchers have made a lot of efforts on the precise description of the fault system, proposed and applied many description methods, and successfully extracted many discontinuous attribute bodies that highlight fault information from 3D seismic data. The current fault identification methods mainly include the following categories: (1) Seismic attribute identification along the bed, including coherence attribute, dip azimuth angle attribute, remaining amplitude attribute along the bed, etc.; (2) Seism...

AI Technical Summary

Problems solved by technology

Each type of method has its own advantages and disadvantages. For example, coherent volume technology is the most widely used, and it can better detect faults with a large degree of development and has a certain ability to suppress noise. However, for small faults or seismic data with poor data quality, fault detection The effect is still not ideal

The traditional method of fault tracking only comprehensively tracks all scale faults that can be described by the original seismic data, and does not have the ability to perform variable-scale analysis on faults of different levels

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