Logging facies identification and analysis method based on fuzzy depth learning in big data environment

A technology of deep learning and analysis methods, applied in the direction of neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as low utilization rate, waste of resources, lack of big data processing platforms, etc., to improve efficiency and facilitate analysis Performance and accuracy, the effect of resolving ambiguities

Active Publication Date: 2017-03-22
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

In recent years, the oil industry has established a large number of cloud data centers, but the utilization rate is not high, and resources are seriously wasted
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  • Logging facies identification and analysis method based on fuzzy depth learning in big data environment
  • Logging facies identification and analysis method based on fuzzy depth learning in big data environment
  • Logging facies identification and analysis method based on fuzzy depth learning in big data environment

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Embodiment Construction

[0045]The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0046] Well logging data is characterized by ambiguity, and there are many reasons for this ambiguity, including the data space pollution of well logging data caused by noise, inconsistency, incompleteness, etc. The systematic data differences brought about by these problems and the ambiguity of logging data caused by these problems all restrict the accurate identification of logging facies.

[0047] The present invention proposes a parallelization method of fu...

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Abstract

The present invention provides a logging facies identification and analysis method based on fuzzy depth learning in a big data environment. The method comprises: constructing a fuzzy area convolution nerve network, putting a given target hypothesis area and target identification into the same network to share the convolution calculation, and employing a training process to update the whole network weight; dividing input logging data to a plurality of small data sets, performing convolution and pooling operation steps of each small data set through the fuzzy area convolution nerve network; and employing classified features to construct a logging facies-sedimentary facies knowledge base which is configured to establish a corresponding knowledge base including the sedimentary facies, the sedimentary subfacies and the sedimentary microfacies based on the unambiguous logging data and the sedimentary facies fusion method to support the association analysis of the sedimentary facies and the logging facies so as to establish the logging facies-sedimentary facies knowledge base and determine the corresponding relation of the current logging data and the sedimentary facies.

Description

technical field [0001] The invention relates to the technical field of petroleum logging, in particular to the field of big data logging. Background technique [0002] Well logging information and deposition are the reflection and controlling factors of formation rock physical properties, so well logging data has always been regarded as the basic and important source of information in the study of oil and gas reservoir sedimentology, and logging facies are logging information and reservoir deposition bridges between academic features. For most oil and gas wells, well logging data is the only comprehensive information source covering the whole well section, so the logging facies identification analysis method has always been the most important research method in the geological research of oil and gas exploration and development. [0003] However, well logging information has the characteristics of ambiguity, multi-solution and ambiguity in geological significance. Therefore...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/043G06N3/045
Inventor 李忠伟张卫山宋弢卢清华崔学荣刘昕赵德海何旭
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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