Parallelization method of convolutional neural networks in fuzzy region under big-data environment

Abstract

The invention discloses a parallelization method of convolutional neural networks in a fuzzy region under a big-data environment. The parallelization method comprises the following steps: firstly, constructing the convolutional neural networks in the fuzzy region, putting a given target assumption region and object identification into the same network, carrying out convolutional calculation, and updating the weight of the whole network in a training process; and secondly, dividing an input log data set into a plurality of small data sets, introducing multiple workflows to pass through the convolutional neural networks in the fuzzy region in parallel for convolution and pooling, and independently training each small data set by virtue of gradient descent. By virtue of the parallelization method, a network structure and parameters are optimized, and relatively good analysis performance and precision are realized; furthermore, the number of FR-CNN obfuscation layers is adjusted aiming at different log data sets, so that the extracted features can well reflect the characters of oil-gas reservoirs, and the fuzzification problem of the log data can be solved; and the parallel training and execution of FR-CNN are carried out by virtue of multiple GPUs, so that the efficiency of the FR-CNN is improved.

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.

Images