Prestack Fracture Density Estimation Method Based on Improved Ellipse Fitting

Abstract

The present invention relates to a pre-stack fracture density estimation method based on improved ellipse fitting, comprising the following steps: taking the data of a time point of the pre-stack gather, respectively taking the azimuth and amplitude as the coordinates of ellipse fitting, and using Laida rule to eliminate outliers, establish the objective function of the least square difference between the forward coordinate point and the actual coordinate point, and use the least squares linear fitting algorithm to calculate the ellipse fitting coefficient as the initial value of the improved Gauss-Newton optimization iterative algorithm , using the iterative algorithm to obtain the optimal ellipse fitting coefficient, and calculate the fracture density based on it, and complete the fracture density estimation of the entire pre-stack gather. The estimation method of the invention has the advantages of high calculation efficiency, high fitting precision, strong robustness to noise, etc., and effectively solves the problem that the existing least squares method based on linear estimation cannot achieve the optimal solution and has certain limitations in use.

Description

Technical field: [0001] The invention relates to a pre-stack fracture density estimation method, in particular to a pre-stack fracture density estimation method based on improved ellipse fitting. Background technique: [0002] With the improvement of oil and gas exploration technology, fractured oil and gas reservoirs have become an important new field of exploration. Reservoir fracture identification and accurate description and prediction of fractures are the key to the exploration and development of fractured reservoirs. [0003] At present, fracture prediction techniques can be divided into two categories according to data types, one is post-stack prediction technology, and the other is pre-stack prediction technology. At present, post-stack fracture prediction technologies include coherence analysis technology, image processing technology, curvature analysis technology, dip detection technology, profit analysis technology, etc. They are usually input based on post-stac...

AI Technical Summary

Problems solved by technology

[0005] The current common ellipse fitting algorithm is the least squares method based on the minimum global error. This algorithm establishes an objective function based on the minimum variance between the fitting predicted coordinates and the actual coordinates. The algorithm has the advantages of simple principle and fast calculation efficiency. The least squares method is Linear estimation, the default is a linear relationship, and cannot achieve the optimal solution, so the use has certain limitations

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