A Reservoir Prediction Method Based on Combined Analysis of Wavelet Multi-Extreme Value Features

Abstract

The invention discloses a reservoir prediction method based on joint analysis of wavelet multi-extreme value features. Using the forward modeling of the deterministic wavelet pair wave impedance model to obtain the reflection waveform diagram of the whole sequence and the interface; combined with the multi-extreme value shape characteristics, the forward modeling results were analyzed by the horizontal waveform comparison method and the longitudinal reflection analysis method, and the storage The seismic response characteristics of the reservoir; multiple seismic amplitude attribute maps that can reflect the reservoir are extracted from the seismic data; multiple seismic amplitude attribute maps are jointly interpreted to predict the distribution area of ​​the reservoir. In the process of forward modeling, the analysis is carried out from the perspectives of the whole sequence and the interface, which lays the foundation for the analysis of the waveform changes reflecting the reservoir; at the same time, when determining the position and waveform characteristics of the reservoir and the seismic reflection wave, the horizontal waveform comparison is used The reservoir response characteristics and the reflection structure affecting the reservoir response are analyzed, and the prediction results of the reservoir are more accurate.

Description

technical field [0001] The invention relates to the field of oil and gas exploration and development, in particular to a reservoir prediction method based on joint analysis of wavelet multi-extreme value features. Background technique [0002] In the work of using seismic data for reservoir prediction, it is often due to insufficient understanding of the wavelet characteristics of seismic data, resulting in imprecise well seismic calibration and unclear understanding of the characteristics of the seismic response wave group of the reservoir, thus affecting reservoir prediction accuracy. After the fine well seismic calibration is completed, it is a conventional reservoir prediction method to describe the reservoir distribution with a horizon attribute map, which loses the opportunity to extract more information reflecting the reservoir from a set of seismic reflection waves . There are many methods for reservoir prediction, and how to use seismic data accurately and efficie...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a reservoir prediction method based on joint analysis of wavelet multi-extreme features, to solve the problem of inaccurate prediction results in the prior art when using forward modeling to select seismic events for reservoir prediction

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