Method for identifying fluid of multi-attribute fusion reservoirs on basis of well information attribute optimization

Abstract

The invention provides a method for identifying fluid of multi-attribute fusion reservoirs on the basis of well information attribute optimization. The method includes extracting various seismic attributes by the aid of software tools, analyzing correlations of the seismic attributes, the reservoir, oil-gas bearing properties and the like and optimizing sensitive seismic attributes; classifying wells according to geological data of the sample wells, respectively determining numerical ranges of sensitive attributes according to classification of the wells, carrying out crossing on attributes of selected numerical intervals pairwise and carrying out multi-attribute crossing fusion to obtain spatial distribution for indicating the favorable reservoirs and the favorable oil-gas bearing properties. The various extracted seismic attributes include amplitudes, frequencies, phases, attenuation and the like. The method has the advantages that the sensitive seismic attributes can be quickly and effectively optimized by the aid of the method, identification modes for the reservoirs, the oil-gas bearing properties and the like of the sample wells and the seismic attributes can be established, and human intervention can be reduced; anomaly of the oil-gas spatial distribution for indicating the favorable reservoirs can be obtained, and the determinacy of the oil-gas bearing properties can be indicated.

Description

technical field [0001] The invention relates to the technical field of oil and gas exploration, in particular to a multi-attribute fusion reservoir fluid identification method based on well data attribute optimization. Background technique [0002] With the increasing demand for oil and gas resources in modern society, reservoir fluid prediction is becoming more and more important. In nature, changes in any underground geological body and its properties will cause changes in seismic attributes. A large number of studies and events have shown that seismic wave amplitude, frequency, velocity and other attribute parameters are comparable to formation lithology, physical properties, and oil and gas properties. Therefore, the use of seismic attributes to study the lithology and oil-gas properties of underground reservoirs has great prospects. Seismic attributes have developed from the initial amplitude attribute to dozens of commonly used ones. These seismic attributes reflect t...

AI Technical Summary

Problems solved by technology

At present, in the oil and gas exploration and development, the attribute fusion method used is generally to make a variety of attribute maps first, and the project personnel combine the well data and geological experience to delineate the approximate range of favorable areas with the naked eye, or use third-party software tools to combine the two to The superposition of the three attributes shows that the method of determining the favorable area of ​​the plane is very cumbersome and time-consuming, and the seismic attributes used are relatively limited

Some methods such as neural network are used to establish the relationship between reservoir parameters and seismic attributes, and predict the plane distribution of reservoir oil and gas. These methods require less human intervention, but due to the complexity of the relationship between seismic attributes, The accuracy of the prediction results obtained is not ideal, and the application is not widespread

Some of the methods mentioned above have too much human intervention, are time-consuming and cumbersome, use relatively limited seismic attribute information, and some prediction results are not accurate, and they are all qualitative predictions. Generally, only the approximate range of favorable areas is delineated.

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