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A Seismic Attribute Fusion Method Based on Data Property Space Dimension Ascension

A technology of seismic attributes and fusion methods, which is applied in seismology, seismic signal processing, geophysical measurement, etc., and can solve problems such as incompleteness, missing information, data point identification and classification errors, etc.

Active Publication Date: 2018-06-26
NORTHEAST GASOLINEEUM UNIV
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

When the data is displayed one-dimensionally in its property space, the displayed information is missing and incomplete, which leads to errors in the identification and classification of some data points

Method used

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  • A Seismic Attribute Fusion Method Based on Data Property Space Dimension Ascension
  • A Seismic Attribute Fusion Method Based on Data Property Space Dimension Ascension
  • A Seismic Attribute Fusion Method Based on Data Property Space Dimension Ascension

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Embodiment Construction

[0077] Provide a specific embodiment of the present invention below in conjunction with accompanying drawing, in order to further illustrate the present invention:

[0078] The data in this example are taken from the western part of the South 1 area of ​​Daqing Oilfield. The research target layer is the Saertu oil layer. The fracture system of this layer is complex, the reservoir is thin, and the interbeds are serious. The prediction effect of the reservoir using a single attribute is poor.

[0079] The seismic attribute fusion method based on data property space dimension increase described in the present invention, the method comprises the following steps:

[0080] (1) Extract seismic attributes, and select the attribute A with the best correlation with the reservoir in the study area 1 , A 2 .

[0081] (2) For the preferred attributes, the dimensionality reduction processing of the space where the data is located is firstly carried out, and then standardized on this basis...

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Abstract

A seismic attribute fusion method based on data property space dimensionality enhancement. The method belongs to the fields of oil and gas prediction from seismic data, exploration of oil and gas resources, and reservoir prediction combined with well and seismic data. The main steps of the method are: high-dimensional expansion of multiple single attributes in one-dimensional property space, and establishment of curve equations in the high-dimensional property space, under the control of reservoir prediction coincidence rate, to search for reservoir domains in partitions. This method improves the accuracy and breadth of seismic attribute data analysis, and realizes seismic attribute analysis research in high-dimensional data property space, so that reservoir prediction can be performed more accurately, especially in fault complex areas, for thin interbeds. , the prediction effect of thin sand layer is better.

Description

Technical field: [0001] The invention relates to a seismic multi-attribute fusion method applied in the fields of seismic data oil-gas prediction, oil-gas resource exploration and well-seismic combined reservoir prediction. Background technique: [0002] Seismic attributes were proposed in the 1870s, and developed rapidly in the 1990s. Due to the introduction of a large number of advanced algorithms, seismic attributes have been developed rapidly and well. At the same time, the combination with sequence stratigraphy has made seismic attribute analysis techniques gradually It has become an important part of reservoir geophysics and plays an important role in reservoir prediction and other aspects. Seismic attributes are extracted from 3D seismic data. Affected by the quality of the data, there are often a large number of outliers in the seismic attributes; in complex fault areas, the seismic attributes are also severely disturbed by faults, which makes the reservoir predictio...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V1/30
CPCG01V1/307
Inventor 李婷婷马世忠许承武范广娟文慧俭丛琳王岁宝
Owner NORTHEAST GASOLINEEUM UNIV
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