CN106372402A - Parallelization method of convolutional neural networks in fuzzy region under big-data environment - Google Patents

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

Parallelization method of fuzzy area convolution neural network in big data environment

Technical Field

The invention relates to the technical field of petroleum logging, in particular to the field of big data logging.

Background

The logging information and the deposition are reflection and control factors of formation rock physical properties, so the logging information is always used as an important information source for the research of oil and gas reservoir sedimentology, and the logging phase is a bridge between the logging information and reservoir sedimentology characteristics. For most oil and gas wells, the logging information is the only comprehensive information source covering the whole well interval stratum, so the logging phase identification analysis method is always used as the most important research means in oil and gas exploration and geological research development.

However, the logging information has the characteristic of ambiguity, and has geologically significant multi-solution and ambiguity. Therefore, the identification and analysis of the logging facies must be established on the basis of a large amount of existing comprehensive depth analysis of sedimentary feature and logging parameter relations (logging response), and meanwhile, the accurate identification of the logging facies can be realized only by selecting a modeling method suitable for geological features by referring to results of field outcrop, core logging and seismic analysis.

In addition, due to the lack of effective automatic identification methods and technologies for logging facies, the current logging facies identification is mainly realized through manual identification of geological workers, and due to factors such as personnel experience difference, subjective difference and system difference of logging data, the data volume faced by the geological workers is large and the workload is heavy. Moreover, the traditional logging phase identification accuracy is greatly reduced due to factors such as experience difference of geologists, subjective factors, system difference of logging data of different instruments in different periods and the like.

The application of advanced technologies such as big data analysis and deep learning to oil and gas geological research is exploration and attempt for solving the problem that big data analysis resources in the current petroleum industry are idle. In recent years, a large number of cloud data centers are established in the petroleum industry, but the utilization rate is not high, and resources are seriously wasted. One important reason for this is the lack of large data processing platforms and corresponding large data technologies to fully utilize these computing and storage resources.

The establishment of an efficient and accurate well logging phase identification method is an urgent need of the current oil and gas geological research.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a parallelization method of a fuzzy area convolution neural network in a big data environment.

The technical scheme of the invention is realized as follows:

a parallelization method of a fuzzy area convolution neural network under a big data environment comprises the steps of firstly, constructing the fuzzy area convolution neural network, putting a given target hypothesis area and target identification into the same network, sharing convolution calculation, and updating the weight of the whole network in a training process;

next, dividing the input logging data set into a plurality of small data sets, parallelizing a plurality of workflows, performing convolution and pooling operations through a fuzzy region convolution neural network, and training each small data set by independently utilizing gradient descent; after training is finished, outputting the result to a waiting queue, reading the output queue after one round of training is finished, performing synchronous updating operation of shared weight, and performing the next round of training after updating is finished; in each training, the calculation of each segmented small data set is carried out asynchronously on a distributed basis, each time the gradient value is calculated, the gradient value is added to a list, after all the small data sets are calculated, the weight and the offset value of the fuzzy area convolutional neural network are synchronously updated, and then the next training is carried out; in the aspect of parallelization identification, collecting logging data by the Spout, then distributing the data to all Bolt nodes to perform logging phase identification in parallel, and inputting an identification result into the next Bolt node by each Bolt node to count object information in the Bolt nodes;

the method comprises the following steps of performing convolution and pooling operation on each small data set through a fuzzy area convolution neural network, and specifically comprises the following steps: the method comprises the steps that a convolutional layer and a pooling layer are interacted, fuzzy operation is conducted on the convolutional layer and the pooling layer, the number of layers of fuzzification is gradually increased from the first layer of a fuzzy area convolutional neural network, the number of layers of fuzzification is adjusted according to different data sets, a feature vector is obtained from the last layer of the fuzzy area convolutional neural network, the feature vector maps features to a low-dimensional vector through a sliding window, then the features are input into two full-connection layers, one full-connection layer is used for positioning, and the other full-connection layer is used for classification.

Optionally, the convolutional layer is formulated as:

$v_{ij}^{xy}=f({\stackrel{\‾}{b}}_{ij}+{\Σ}_m{\Σ}_{p=0}^{P_i-1}{\Σ}_{q=0}^{Q_i-1}{\stackrel{\‾}{W}}_{ijm}^{pq}{v}_{(i-1)m}^{(x+p)(y+q)})$

the pooling layer formula is expressed as:

$x_j=f\left({\stackrel{\‾}{\mathrm{\β}}}_{ij}down\right(x_{i-1j})+{\stackrel{\‾}{b}}_{ij})$

wherein, is offsetAnd weightAre fuzzy numbers, here symmetric triangular fuzzy numbers are used,is a vector of fuzzy numbers, the jth fuzzy numberThe membership function of (a) is:

${\stackrel{\‾}{W}}_j\left(w\right)=\max \left\{1-\frac{|w-w_j|}{{\hat{w}}_j},0\right\}.$

optionally, in the training process of the fuzzy area convolutional neural network, a joint loss function is defined:

$L\left(\left\{p_i\right\},\left\{t_i\right\}\right)=\frac{1}{N_{cls}}{\Σ}_i{L}_{cls}(p_i,p_i^{*})+\mathrm{\λ}\frac{1}{N_{reg}}{\Σ}_i{p}_i^{*}{L}_{reg}(t_i,t_i^{*})$

wherein p is_iIs the predicted probability that this sample is the log profile,is a label for the sample, and, if the corresponding log configuration,is 1, otherwiseIs 0, N_clsIs a two-classification logic loss; t is t_iIs a vector of four parameters that predicts the object boundaries,the vectors formed by the parameters of the marked areas are respectively as follows:

t_x＝(x-x_a)/w_at_h＝(y-y_a)/h_a

t_w＝log(w/w_a) t_h＝log(h/h_a)

$\begin{array}{cc}{t}_w^{*}=(w^{*}/w_a)/w_a& t_y^{*}=(y*-y_a)/h_a\end{array}$

$\begin{array}{cc}{t}_w^{*}=log(w^{*}/w_a)& t_h^{*}=log(h*/h_a)\end{array}$

wherein x, y, w and h represent the center coordinate, width and length of the object, respectively, and x, x_a，x^*Respectively representing prediction region, anchor region and label region, regression lossR is flatSlip loss function

${\mathrm{smooth}}_{L_1}\left(x\right)=\left\{\begin{array}{cc}0.5x^2& if\left|x\right|<1\\ \left|x\right|-0.5& otherwise\end{array}\right.$

Indicating that only if the anchor region is a positive sampleThen, the regression loss is calculated, otherwiseNot calculating, normalizing parameter N_clsAnd N_regRepresenting the length of the low-dimensional vector mapped from the feature vector and the number of anchor regions, respectively.

Optionally, the well logging data is normalized, the original data is converted into non-dimensionalized mapping evaluation values, and the mapping evaluation values are in the same quantity level, and then comprehensive evaluation analysis is performed.

Optionally, the normalization of the well log data is performed by the following normalization method:

Sx＝(x-M)/S,x∈{GR，AC，DEN，CNL，SDN，...}

wherein x represents the data of each logging curve, Sx represents the normalized logging curve data, M is the mean value of the corresponding logging curve data, and S is the standard deviation of each logging curve data.

The invention has the beneficial effects that:

(1) according to the characteristic of data fuzziness in big logging data, a fuzzy theory is integrated, a fuzzy area convolutional neural network FR-CNN is provided, a progressive fuzzy method is provided, the number of layers of fuzziness is gradually increased from the first layer of the fuzzy area convolutional neural network, so that the network structure and parameters are optimized, and better analysis performance and precision are realized;

(2) the number of layers of FR-CNN fuzzification is adjusted according to different logging data sets, so that the extracted characteristics can better reflect the characteristics of the oil and gas reservoir, and the problem of the fuzziness of the logging data can be solved;

(3) the invention utilizes the multiple GPUs to perform parallel training and execution of the FR-CNN so as to improve the efficiency of the FR-CNN.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic diagram of a fuzzy area convolutional neural network according to the present invention;

FIG. 2 is a schematic diagram of symmetric triangular fuzzy number coordinates;

FIG. 3 is a schematic diagram of parallelizing real-time data processing of the fuzzy area convolutional neural network according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The well logging data has the characteristic of ambiguity, the reasons for the ambiguity are manifold, including data space pollution of the well logging data caused by noise, inconsistency, imperfection and the like, and systematic data differences caused by well logging in different periods and different instruments, and the ambiguity of the well logging data caused by the problems restricts accurate identification of a well logging phase.

The invention provides a parallelization method of a Fuzzy regional convolutional neural network in a big data environment, which constructs a multidimensional data space for logging data, fuses a Fuzzy theory and a deep learning network R-CNN, provides an identification method for solving a logging phase under the condition of the Fuzzy data, fuses the Fuzzy theory according to the characteristic of data fuzziness in the logging big data, provides a Fuzzy regional convolutional neural network FR-CNN (Fuzzy R-CNN), further provides a progressive Fuzzy method, gradually increases the number of layers of fuzziness from the first layer of the convolutional neural network, optimizes a network structure and parameters, and finally establishes the theory and the method of the FR-CNN to realize better analysis performance and precision.

The design of a proper fuzzy area convolutional neural network is the key point of the invention, and the construction of the fuzzy area convolutional neural network of the invention is explained in detail below.

The fuzzy area convolution neural network FR-CNN is established on the basis of the deep learning network R-CNN, as shown in figure 1, the FR-CNN puts a given target hypothesis area and target identification into the same network, shares convolution calculation, avoids complex calculation steps, can update the weight of the whole network only by one training process, and simultaneously accelerates the detection speed and achieves the purpose of rapid processing.

In FIG. 1, the well log data is passed through a fuzzy area convolutional neural network for convolution and pooling operations. The core of fuzzy area convolutional neural network training is the interaction of the convolutional layer and the pooling layer, so that fuzzy operation is performed on the convolutional layer and the pooling layer. In order to avoid excessive information loss caused by excessive blurring and consider that the refinement degree of the feature extracted by the fuzzy area convolutional neural network is reduced layer by layer, the blurring of each layer by the traditional fuzzy neural network is changed, the invention provides a progressive blurring method, namely, the number of the blurring layers is gradually increased from the first layer of the fuzzy area convolutional neural network, and the number of the blurring layers is adjusted according to different data sets, so that the extracted feature better reflects the characteristic of a well logging curve, the optimal recognition result is obtained, and the recognition efficiency is improved.

The last layer of the fuzzy area convolutional neural network gets the feature vector, which maps the feature to a low-dimensional vector through a small sliding window, and then inputs the feature to two fully-connected layers, one for localization and the other for classification. Several target hypothesis regions, which may be referred to as anchor regions, are simultaneously given at each sliding window, and the regions are centered on the sliding window and have different aspect ratios and scales.

The convolutional layer formula of the convolutional neural network R-CNN can be expressed as:

$v_{ij}^{xy}=f(b_{ij}+{\&Sigma;}_m{\&Sigma;}_{p=0}^{P_i-1}{\&Sigma;}_{q=0}^{Q_i-1}{W}_{ijm}^{pq}{v}_{(i-1)m}^{(x+p)(y+q)})---\left(1\right)$

wherein,representing the value at the (x, y) position of the jth feature vector for the ith layer neuron,to representThe weight of the convolution kernel connected to the mth eigenvector at position (p, q). P_iAnd Q_iRespectively representing the height and width of the convolution kernel, b_ijFor the bias term, f (x) represents the activation function of the neuron.

The R-CNN pooling layer formula is expressed as:

x_ij＝f(β_ijdown(x_i-1j)+b_ij) (2)

down (.) represents a down-sampling function, and a typical operation is to sum all the information of different n × n blocks of input data, so that the output data is reduced by n times in both dimensions, and each output map corresponds to a multiplicative offset β and an additive offset b belonging to itself.

The input and calculation processes of the convolutional neural network are real numbers, the obtained results are deterministic, and for the condition of data blurring such as data missing, the fuzzy area convolutional neural network of the invention introduces a fuzzy theory, and the improved formula is as follows:

the convolutional layer formula is expressed as:

$v_{ij}^{xy}=f({\stackrel{\&OverBar;}{b}}_{ij}+{\&Sigma;}_m{\&Sigma;}_{p=0}^{P_i-1}{\&Sigma;}_{q=0}^{Q_i-1}{\stackrel{\&OverBar;}{W}}_{ijm}^{pq}{v}_{(i-1)m}^{(x+p)(y+q)})---\left(3\right)$

the pooling layer formula is expressed as:

$x_j=f\left({\stackrel{\&OverBar;}{\mathrm{\&beta;}}}_{ij}down\right(x_{i-1j})+{\stackrel{\&OverBar;}{b}}_{ij})---\left(4\right)$

wherein is offsetAnd weightAre fuzzy numbers, here symmetric triangular fuzzy numbers are used,is a vector of fuzzy numbers, the jth fuzzy numberIs a membership function of

${\stackrel{\&OverBar;}{W}}_j\left(w\right)=\max \left\{1-\frac{|w-w_j|}{{\hat{w}}_j},0\right\}---\left(5\right)$

As shown in FIG. 2, w_jIs the center of symmetry of the blur number,is half the length of the blur number,representing the degree of membership at w.

In the training process of the fuzzy area convolution neural network, a joint loss function is defined:

$L\left(\left\{p_i\right\},\left\{t_i\right\}\right)=\frac{1}{N_{cls}}{\&Sigma;}_i{L}_{cls}(p_i,p_i^{*})+\mathrm{\&lambda;}\frac{1}{N_{reg}}{\&Sigma;}_i{p}_i^{*}{L}_{reg}(t_i,t_i^{*})---\left(6\right)$

wherein p is_iIs the predicted probability that this sample is the log profile,is a label for the sample, and, if the corresponding log configuration,is 1, otherwiseIs 0, N_clsIs a two classification (0 or 1) logical loss.

t_iIs a vector of four parameters that predicts the object boundaries,the vectors formed by the parameters of the marked areas are respectively as follows:

t_x＝(x-x_a)/w_at_h＝(y-y_a)/h_a(7)

t_w＝log(w/w_a)t_h＝log(h/h_a)

$\begin{array}{cc}{t}_x^{*}=(x^{*}-x_a)/w_a& t_y^{*}=(y^{*}-y_a)/h_a\end{array}$

$\begin{array}{cc}{t}_w^{*}=log(w^{*}/w_a)& t_h^{*}=log(h*/h_a)\end{array}$

wherein x, y, w and h represent the center coordinate, width and length of the object, respectively, and x, x_a，x^*Respectively representing a prediction region, an anchor region and a labeling region (y, w, h are the same). Loss of returnR is a smoothing loss function

${\mathrm{smooth}}_{L_1}\left(x\right)=\left\{\begin{array}{cc}0.5x^2& if\left|x\right|<1\\ \left|x\right|-0.5& otherwise\end{array}\right.---\left(8\right)$

Meaning that only when the anchor region is a positive sampleThe regression loss is calculated, otherwiseNo calculation is performed. Normalization parameter N_clsAnd N_regRepresenting the length of the low-dimensional vector mapped from the feature vector and the number of anchor regions, respectively.

Different logging tools are used to generate different data. If natural Gamma (GR), compensated acoustic wave (AC), compensated Density (DEN), Compensated Neutron (CNL), neutron apparent porosity and density apparent porosity difference (SDN) and the like are adopted and have different dimensions, data have no comparability, therefore, the invention needs to firstly carry out standardization of logging data, convert all original data into non-dimensionalized index mapping evaluation values, namely, all index mapping evaluation values are on the same quantity level, and then carry out comprehensive evaluation analysis.

The following normalization method was used:

Sx＝(x-M)/S,x∈{GR，AC，DEN，CNL，SDN，...}

wherein x represents the data of each logging curve, and Sx represents the normalized logging curve data; m is the mean of the corresponding log data, and S is the standard deviation of each log data.

The invention carries out calibration from the existing logging data, establishes a training data set of FR-CNN, and on the basis, because the information disclosed by different logging methods is different, selects the combination of different logging data as the input of FR-CNN, thereby determining the optimal logging data combination of FR-CNN and optimizing the network parameters and structure of FR-CNN when carrying out logging phase identification.

The FR-CNN has two types of full connection layers more than the traditional convolutional neural network, and also has more operations such as calculation of area coordinates and the like, and the calculation amount of the operations is large. In fuzzy neural networks, fuzzy operations exist at each level of the network, i.e., the deeper the network, the more computation is added, which makes the network cumbersome, which would otherwise require heavy computation. The increase of the calculated amount leads to the great increase of the training time of the network, the updating period of the network model is prolonged, the flexibility of the system is weakened, and meanwhile, the detection time is prolonged.

The invention improves the FR-CNN training and operating efficiency through parallelization, firstly, an input logging data set is divided into a plurality of small data sets, a plurality of workflows operate simultaneously, and each part is trained by independently utilizing gradient descent. And after the training is finished, outputting the result to a waiting queue, and after one round of training is finished, reading the output queue to perform synchronous updating operation of the shared weight. And after the updating is completed, carrying out the next round of training.

In each training round, the calculation of each divided small data set is carried out asynchronously on a distributed basis, each time the gradient value is calculated, the gradient value is added to a list, after all the small data sets are calculated, the weight and the offset value of the network are synchronously updated, and then the next training round is carried out.

As shown in fig. 3, in terms of parallelization identification, the adopted solution is: and collecting logging data by the spitout, distributing the data to all Bolt nodes to perform logging phase identification in parallel, and inputting an identification result into the next Bolt node by each Bolt node to count object information in the Bolt nodes.

According to the characteristics of data fuzziness in big logging data, a fuzzy theory is integrated, a fuzzy area convolutional neural network FR-CNN is provided, a progressive fuzzy method is provided, the number of layers of fuzziness is gradually increased from the first layer of the fuzzy area convolutional neural network, so that the network structure and parameters are optimized, and better analysis performance and precision are realized; in addition, the invention adjusts the number of layers of FR-CNN fuzzification aiming at different logging data sets, so that the extracted characteristics better reflect the characteristics of the oil and gas reservoir, and the problem of the fuzzification of the logging data can be solved; aiming at the problem of large operation calculation amount, the invention utilizes multiple GPUs to perform parallel training and execution of FR-CNN so as to improve the efficiency of FR-CNN.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. A parallelization method of fuzzy area convolution neural network under big data environment is characterized in that,

firstly, constructing a fuzzy area convolution neural network, putting a given target hypothesis area and target identification into the same network, sharing convolution calculation, and updating the weight of the whole network in a training process;

next, dividing the input logging data set into a plurality of small data sets, parallelizing a plurality of workflows, performing convolution and pooling operations through a fuzzy region convolution neural network, and training each small data set by independently utilizing gradient descent; after training is finished, outputting the result to a waiting queue, reading the output queue after one round of training is finished, performing synchronous updating operation of shared weight, and performing the next round of training after updating is finished; in each training, the calculation of each segmented small data set is carried out asynchronously on a distributed basis, each time the gradient value is calculated, the gradient value is added to a list, after all the small data sets are calculated, the weight and the offset value of the fuzzy area convolutional neural network are synchronously updated, and then the next training is carried out; in the aspect of parallelization identification, collecting logging data by the Spout, then distributing the data to all Bolt nodes to perform logging phase identification in parallel, and inputting an identification result into the next Bolt node by each Bolt node to count object information in the Bolt nodes;

the method comprises the following steps of performing convolution and pooling operation on each small data set through a fuzzy area convolution neural network, and specifically comprises the following steps: the method comprises the steps that a convolutional layer and a pooling layer are interacted, fuzzy operation is conducted on the convolutional layer and the pooling layer, the number of layers of fuzzification is gradually increased from the first layer of a fuzzy area convolutional neural network, the number of layers of fuzzification is adjusted according to different data sets, a feature vector is obtained from the last layer of the fuzzy area convolutional neural network, the feature vector maps features to a low-dimensional vector through a sliding window, then the features are input into two full-connection layers, one full-connection layer is used for positioning, and the other full-connection layer is used for classification.

2. The method for parallelizing the fuzzy area convolutional neural network in the big data environment according to claim 1, wherein the convolutional layer formula is expressed as:

$v_{ij}^{xy}=f({\stackrel{\&OverBar;}{b}}_{ij}+{\mathrm{\&Sigma;}}_m{\mathrm{\&Sigma;}}_{p=0}^{P_i-1}{\mathrm{\&Sigma;}}_{q=0}^{Q_i-1}{\stackrel{\&OverBar;}{W}}_{ijm}^{pq}{v}_{(i-1)m}^{(x+p)(y+q)})$

the pooling layer formula is expressed as:

$x_j=f\left({\stackrel{\&OverBar;}{\mathrm{\&beta;}}}_{ij}down\right(x_{i-1j})+{\stackrel{\&OverBar;}{b}}_{ij})$

wherein, is offsetAnd weightAre fuzzy numbers, here symmetric triangular fuzzy numbers are used,is a vector of fuzzy numbers, the jth fuzzy numberThe membership function of (a) is:

${\stackrel{\&OverBar;}{W}}_j\left(w\right)=\max \{1-\frac{\left|w-w_j\right|}{{\hat{w}}_j},0\}.$

3. the method according to claim 2, wherein in the training process of the fuzzy area convolutional neural network, a joint loss function is defined:

$L\left(\right\{p_i\},\{t_i\left\}\right)=\frac{1}{N_{cls}}{\mathrm{\&Sigma;}}_i{L}_{cls}(p_i,p_i^{*})+\mathrm{\&lambda;}\frac{1}{N_{reg}}{\mathrm{\&Sigma;}}_i{p}_i^{*}{L}_{reg}(t_i,t_i^{*})$

wherein p is_iIs the predicted probability that this sample is the log profile,is a label for the sample, and, if the corresponding log configuration,is 1, otherwiseIs 0, N_clsIs a two-classification logic loss; t is t_iIs a vector of four parameters that predicts the object boundaries,the vectors formed by the parameters of the marked areas are respectively as follows:

t_x＝(x-x_a)/w_at_h＝(y-y_a)/h_a

t_w＝log(w/w_a) t_h＝log(h/h_a)

$t_x^{*}=(x^{*}-x_a)/w_a,t_y^{*}=(y^{*}-y_a)/h_a$

$t_w^{*}=\log (w^{*}/w_a),t_h^{*}=\log (h^{*}/h_a)$

wherein x, y, w and h represent the center coordinate, width and length of the object, respectively, and x, x_a，x^*Respectively representing prediction region, anchor region and label region, regression lossR is a smoothing loss function

${\mathrm{smooth}}_{L_1}\left(x\right)=\left\{\begin{array}{cc}0.5x^2& if\left|x\right|<1\\ \left|x\right|-0.5& otherwise\end{array}\right.$

Indicating that only if the anchor region is a positive sampleThen, the regression loss is calculated, otherwiseNot calculating, normalizing parameter N_clsAnd N_regRepresenting the length of the low-dimensional vector mapped from the feature vector and the number of anchor regions, respectively.

4. The method according to any one of claims 1 to 3, wherein the normalization of the well logging data is performed first, the raw data are all converted into dimensionless index values, and the index values are all at the same quantitative level, and then the comprehensive evaluation analysis is performed.

5. The method for parallelizing the fuzzy regional convolutional neural network in the big data environment according to claim 4, wherein the normalization of the logging data is performed by adopting a normalization method as follows:

Sx＝(x-M)/S,x∈{GR，AC，DEN，CNL，SDN，...}

wherein x represents the data of each logging curve, Sx represents the normalized logging curve data, M is the mean value of the corresponding logging curve data, and S is the standard deviation of each logging curve data.