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Method and system for predicting dolomite reservoir

A prediction method and technology of carbonate rock reservoirs, applied in the field of oil and gas field exploration and development, can solve the problems of deep burial, difficult identification of dolomite lithology, weak signal, etc.

Inactive Publication Date: 2014-01-22
PEKING UNIV
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Problems solved by technology

[0004] (1) Deep burial and weak signal;
[0005] (2) Unlike the Ordovician limestone caves and beads, the reflection characteristics are obvious;
[0006] (3) Dolomite, limestone, gypsum salt, and clastic rock interact with each other, and the lithology is complex and diverse, but it is very difficult to identify the lithology of dolomite;

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  • Method and system for predicting dolomite reservoir
  • Method and system for predicting dolomite reservoir
  • Method and system for predicting dolomite reservoir

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[0112] In order to make the above objects, features and advantages of the present application more obvious and comprehensible, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0113] Seismic lateral prediction of dolomite reservoirs has always been a difficult problem in geophysical exploration, especially in the description of small pores, karst caves, and fractured reservoirs. The resolution is low, and it is difficult to identify; the second is that the oil content is random; the third is that the buried depth is generally deep, and the quality of seismic data is poor.

[0114] One of the core ideas of this application is that the dolomite reservoir prediction technology is based on high signal-to-noise ratio, high amplitude, and high-resolution seismic imaging technology, and the high-resolution imaging technology with wide azimuth, small bins, and large offset Three-dimen...

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Abstract

The invention provides a method for predicting a dolomite reservoir. The method includes acquiring seismic attributes of a target stratum, increasing the signal-to-noise ratio of the seismic attributes, adopting the seismic attributes with the signal-to-noise ratio increased to perform post-stack wave impedance inversion; adopting the seismic attributes with the signal-to-noise ratio increased to perform pre-stack elastic parameter inversion; adopting the results of the post-stack wave impedance inversion and / or the pre-stack elastic parameter inversion to predict the dolomite reservoir of the target stratum. By an effective predicting method technical system of seismic data quality evaluation, pre-stack depth processing, post-stack attribute analysis and post-stack wave impedance inversion lithologic identification, pre-stack attribute analysis and pre-stack elastic parameter inversion of a high-quality reservoir and hydrocarbon detection of the dolomite reservoir, the dolomite reservoir can be effectively predicted.

Description

technical field [0001] The present application relates to the technical field of oil and gas field exploration and development, in particular to a dolomite reservoir prediction method and a dolomite reservoir prediction system. Background technique [0002] With the continuous development of oil and gas exploration and development, the existence of unconventional oil and gas has broken the traditional theory and thinking mode of oil and gas accumulation, and has shown great potential under the existing economic and technological conditions. Unconventional oil and gas include tight gas, shale gas, coalbed methane, tight oil, shale oil and oil sands. Tight oil is the abbreviation of tight reservoir oil, and generally refers to tight sandstone, tight carbonate rock, tight volcanic rock, tight metamorphic rock and other reservoirs with a permeability less than 1×10-3 μm2 interbedded with or closely adjacent to the oil-generating rock series accumulated oil resources. Tight oil...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): E21B49/00G01V1/28
Inventor 师永民张玉广王磊师巍锋徐蕾师翔郭馨蔚师俊峰熊文涛师春爱吴洛菲方媛媛刘乐盛英帅杜书恒师锋吴文娟秦小双李晓敏柴智
Owner PEKING UNIV
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