Method for identifying carbonate rock fluid based on fuzzy C mean cluster

Abstract

The invention provides a method for identifying carbonate rock fluid based on a fuzzy C mean cluster in oil exploration. According to the method, chaotic quantum particle swarm optimization (CQPSO) and a fuzzy C mean (FCM) algorithm are organically bonded, chaotic particle swarm optimization is utilized to initialize a membership matrix, the problem that a traditional fuzzy C mean algorithm is sensitive to initialization can be effectively solved, high capability for searching global optimal solution is possessed, and fuzzy classification capability is remarkably improved. The method is introduced into carbonate rock fluid identification, the problem that rock physical analysis results and seismic inversion results are not matched due to frequency dispersion of seismic data can be effectively solved, and identification accuracy of the carbonate rock fluid is improved. Besides, by means of the method, probability of properties of various fluids can be calculated, evaluation on indeterminacy of fluid identification can be conducted so that exploration risks can be effectively reduced, and a new research thought for fully utilizing various prestack elastic information to achieve carbonate rock reservoir fluid identification is provided.

Description

technical field [0001] The invention belongs to the field of petroleum exploration, and relates to the combination of a chaotic quantum particle swarm optimization algorithm and a fuzzy C-means clustering algorithm, and introduces it into carbonate rock fluid identification, so as to fully utilize various pre-stack elastic information to realize carbonate rock Fluid identification provides a new research idea. [0002] Background technique [0003] Fluid identification using seismic data is the most important research work in oil and gas exploration. Because pre-stack AVO / AVA inversion retains the shear wave information of the formation, it is sensitive to the characteristics of reservoir fluid changes. Therefore, AVO technology has become an important geophysical tool for fluid detection. One of the means. [0004] Some scholars at home and abroad have done a lot of in-depth research on fluid identification using pre-stack elastic information, and proposed the use of mult...

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Problems solved by technology

[0005] Compared with the post-stack fluid identification method, this method has made great progress, but the actual application effect in the Tarim carbonate rock fluid identification is not very satisfactory

The main reasons are as follows: ①Petrophysical research is the basic guarantee for pre-stack inversion, but the secondary pore structure (dissolution pores, caves, and fractures) of Tarim carbonate reservoir is complex, which will affect the carbon The petrophysical analysis of salt rock reservoirs and the prediction of P- and S-wave velocities bring considerable difficulty, and errors are inevitable in the calculation process, and their accuracy will have an important impact on the results of pre-stack inversion;② The logging data on which rock physics forward modeling is based has a high frequency (usually 1kHZ-20kHz), while seismic data often have a large dispersion in deep layers, resulting in a low frequency (usually 10HZ-80Hz). There will be some differences in the numerical range of the inversion results, so the intersection data used in the actual fluid identification is extracted from the pre-stack inversion results, resulting in the delineated in the sensitive fluid factor intersection map containing different fluid properties The range delineated in the petrophysical forward modeling analysis is different. In order to obtain a better fluid identification effect, it must be calibrated according to the fluid information revealed by known wells, and the fluid distribution range in the intersection map must be continuously modified to achieve The best matching effect will inevitably bring human influence factors during this operation; ③ A variety of pre-stack elastic parameters can be calculated through pre-stack AVO / AVA inversion, which contains a wealth of fluid information, but the conventional stack The pre-fluid identification method finally selects the intersection of two pre-stack parameters that are relatively sensitive to fluid identification, so as to predict the fluid distribution, but fails to make full use of the rich pre-stack information, resulting in low fluid identification accuracy

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