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

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...

AI Technical Summary

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

One of the important reasons is the lack of big data processing platforms and corresponding big data technologies to make full use of these computing and storage resources.

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