CN109800863B - Logging phase identification method based on fuzzy theory and neural network - Google Patents
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Abstract
The invention provides a logging phase identification method based on a fuzzy theory and a neural network, firstly, a fuzzy area convolutional neural network is constructed, a given target hypothesis area and target identification are put into the same network, convolution calculation is shared, and the weight of the whole network is updated in a training process; and then, carrying out convolution and pooling operation on the logging data through a fuzzy area convolutional neural network, carrying out fuzzy operation on the convolution layer and the pooling layer in an interaction mode, starting from the first layer of the fuzzy area convolutional neural network, gradually increasing the number of fuzzy layers, adjusting the fuzzy layers according to different data sets, obtaining a feature vector at the last layer of the fuzzy area convolutional neural network, mapping the feature vector into a low-dimensional vector through a sliding window, and then inputting the feature into two fully-connected layers, wherein one fully-connected layer is used for positioning, and the other fully-connected layer is used for classifying.
Description
Statement of divisional application
The application is a divisional application of Chinese invention patent application with the application number of 201610762101.1, and the name of the invention is 'parallelization method of a fuzzy area convolutional neural network under a big data environment' submitted by the year 2016 and the month 08.
Technical Field
The invention relates to the technical field of petroleum logging, in particular to the field of big data logging.
Background
Logging information and depositions are reflecting and controlling factors of formation petrophysical properties, so logging information has been used as an important information source for the basis of the study of oil and gas reservoir depositions, and logging phases are bridges between logging information and reservoir depositional characteristics. For most oil and gas wells, logging data is the only comprehensive information source covering all well formations, so the logging phase identification analysis method has been used as the most important research means in oil and gas exploration and development geological research.
However, logging information is characterized by ambiguity, with geologic polynomials and ambiguity. Therefore, the identification and analysis of the logging phases are required to be established on the basis of comprehensive depth analysis of a large number of existing sediment characteristics and logging parameter relations (logging response), and meanwhile, modeling methods suitable for geological characteristics are selected by referring to results of field outcrop, core logging and seismic analysis, so that the accurate identification of the logging phases can be realized.
In addition, due to the lack of an effective automatic identification method and technology of the logging facies, the current logging facies identification is mainly realized through manual identification of geological staff, and due to factors such as personnel experience differences, subjective differences, systematic differences of logging data and the like, the geological staff is large in data volume and heavy in workload. Moreover, due to factors such as experience differences of geological personnel, subjective factors, systematic differences of logging data of different instruments in different periods and the like, the conventional logging phase identification accuracy is greatly reduced.
The advanced technologies such as big data analysis, deep learning and the like are applied to oil-gas geology research, so that the problem of idle exploration and attempt of big data analysis resources in the current petroleum industry is solved. In recent years, the petroleum 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 large data processing platforms and corresponding large data technologies to fully utilize these computing and storage resources.
Establishing an efficient and accurate logging phase identification method is an urgent need for current oil and gas geology research.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a parallelization method of a fuzzy area convolutional neural network in a big data environment.
The technical scheme of the invention is realized as follows:
the parallelization method of the fuzzy area convolution neural network under the 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 an input logging data set into a plurality of small data sets, parallelizing a plurality of workflows, performing convolution and pooling operations through a fuzzy area convolutional neural network, and training each small data set by gradient descent alone; after training is completed, outputting the result to a waiting queue, reading the output queue after one round of training is completed, performing synchronous updating operation of the shared weight, and performing the next round of training after updating is completed; in each training round, the calculation of each divided small data set is asynchronously carried out on a distributed basis, each gradient value calculated 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 round is carried out; in the aspect of parallelization identification, collecting logging data by Spout, distributing the data to each Bolt node for parallelization logging phase identification, inputting an identification result to the next Bolt node by each Bolt node, and counting object information in the identification result;
each small data set is subjected to convolution and pooling operation through a fuzzy area convolutional neural network, and the method specifically comprises the following steps of: the convolution layer and the pooling layer are interacted, fuzzy operation is carried out on the convolution layer and the pooling layer, the number of fuzzy layers is gradually increased from the first layer of the fuzzy area convolution neural network, the number of fuzzy layers is adjusted for different data sets, the last layer of the fuzzy area convolution neural network obtains a feature vector, the feature vector maps features into a low-dimensional vector through a sliding window, then the features are input into two fully connected layers, one fully connected layer is used for positioning, and the other fully connected layer is used for classifying.
Optionally, the convolution layer formula is expressed as:
the pooling layer formula is expressed as:
wherein the bias is
And weight->
Are all fuzzy numbers, here using symmetric triangular fuzzy numbers,>
the j-th fuzzy number is a vector composed of fuzzy numbers>
The membership functions of (a) are:
optionally, during training of the fuzzy region convolutional neural network, a joint loss function is defined:
wherein ,pi Is the predicted probability that this sample is the log morphology,
a label of the sample, if it is the corresponding log morphology,/->
1, otherwise->
Is 0, L cls Is a two-class logical loss; t is t i Is a vector of four parameters predicting the boundary of an object,/->
For the vector composed of the labeling area parameters, they are respectively:
t x ＝(x-x a )/w a t h ＝(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 coordinates, width and length of the object, x, respectively a ，x * Respectively representPrediction region, anchoring region and labeling region, regression loss
R is the smooth loss function->
Optionally, normalization of logging data is performed first, original data are converted into dimensionless index evaluation values, the index evaluation values are all in the same number level, and comprehensive evaluation analysis is performed.
Optionally, the normalization of the logging data is performed using the following normalization method:
Sx＝(x-M)/S,x∈{GR，AC，DEN，CNL，SDN，...}
wherein x represents the data of each log, sx represents normalized log data, M is the mean value of the corresponding log data, and S is the standard deviation of each log data.
The beneficial effects of the invention are as follows:
(1) According to the characteristic of data blurring in logging big data, a blurring theory is integrated, a blurring region convolution neural network FR-CNN is provided, a progressive blurring method is provided, and the number of blurring layers is gradually increased from the first layer of the blurring region convolution neural network, so that the network structure and parameters are optimized, and better analysis performance and precision are realized;
(2) The FR-CNN fuzzification layer number is adjusted for different logging data sets, so that the extracted characteristics better reflect the characteristics of an oil and gas reservoir, and the problem of logging data fuzziness can be solved;
(3) The invention uses multiple GPUs to perform the parallel training and execution of the FR-CNN so as to improve the efficiency of the FR-CNN.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a fuzzy region convolutional neural network of the present invention;
FIG. 2 is a schematic diagram of a symmetric triangle fuzzy number coordinate;
FIG. 3 is a schematic diagram of the parallel processing of real-time data by the fuzzy region convolutional neural network of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The ambiguity of the logging data is caused by various reasons including data space pollution of the logging data caused by noise, inconsistency, incompleteness and the like, and systematic data differences caused by logging of different instruments in different periods, and the ambiguity of the logging data caused by the problems restrict the accurate identification of logging phases.
The invention provides a parallelization method of a Fuzzy area convolutional neural network in a big data environment, a multidimensional data space is constructed for logging data, a Fuzzy theory and a deep learning network R-CNN are fused, a method for identifying logging phases under the condition of solving the Fuzzy data is provided, the Fuzzy theory is fused according to the characteristic of data blurring in the logging big data, the Fuzzy area convolutional neural network FR-CNN (Fuzzy R-CNN) is provided, a progressive Fuzzy method is further provided, the number of Fuzzy layers is gradually increased from a first layer of the convolutional neural network, network structures and parameters are optimized, and finally, the theory and the method of the FR-CNN are established, so that better analysis performance and accuracy are achieved.
The design of a suitable fuzzy area convolutional neural network is an important point of the invention, and the construction of the fuzzy area convolutional neural network is described in detail below.
The fuzzy region convolution neural network FR-CNN is established on the basis of the deep learning network R-CNN, as shown in fig. 1, the FR-CNN puts the given target hypothesis region and target identification into the same network, so as to share convolution calculation, avoid complex calculation steps, update the weight of the whole network only by one training process, and accelerate the detection speed at the same time, thereby achieving the purpose of rapid processing.
In fig. 1, logging data is rolled and pooled through a fuzzy region convolutional neural network. The core of the fuzzy area convolutional neural network training is the interaction of a convolutional layer and a pooling layer, so that fuzzy operation is carried out on the convolutional layer and the pooling layer. In order to avoid excessive information loss caused by excessive blurring, and considering that the refinement degree of the extracted features of the fuzzy region convolution neural network is reduced layer by layer, the blurring of each layer of the traditional fuzzy neural network is changed.
The last layer of the fuzzy region convolutional neural network gets feature vectors that map features into a low-dimensional vector through a small sliding window, then input features into two fully connected layers, one for localization and the other for classification. At each sliding window several target hypothesis regions, which may be referred to as anchor regions, are simultaneously presented, centered on the sliding window, with different aspect ratios and scales.
The convolutional layer formula for convolutional neural network R-CNN can be expressed as:
wherein ,
representing the value at the (x, y) position of the j-th eigenvector of the i-th layer neuron,/->
Representation->
The convolution kernel connected to the mth eigenvector weights at locations (p, q). P (P) i and Qi Representing the height and width of the convolution kernel, b ij For 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 ij down(x i-1j )+b ij ) (2)
down (-) represents a downsampling function, typically by summing all information of different n x n blocks of input data, such that the output data is scaled down n times in both dimensions, each output map corresponding to an own multiplicative offset β and an additive offset b.
The input and calculation processes of the convolutional neural network are real numbers, the obtained results are deterministic, and for the condition of data ambiguity such as data loss, the fuzzy theory is introduced into the fuzzy region convolutional neural network, and the improved formula is as follows:
the convolution layer formula is expressed as:
the pooling layer formula is expressed as:
wherein the bias is
And weight->
Are all fuzzy numbers, here using symmetric triangular fuzzy numbers,>
the j-th fuzzy number is a vector composed of fuzzy numbers>
Membership function of +.>
As shown in FIG. 2, w j Is the center of symmetry of the fuzzy number,
is half length of the fuzzy number, +.>
Representing the membership at w.
In the training process of the fuzzy area convolutional neural network, a joint loss function is defined:
wherein pi Is the predicted probability that this sample is the log morphology,
a label of the sample, if it is the corresponding log morphology,/->
1, otherwise->
Is 0, N cls Is a binary class (0 or 1) logic loss.
t i Is a vector of four parameters predicting object boundaries,
for the vector composed of the labeling area parameters, they are respectively:
t x ＝(x-x a )/w a t h ＝(y-y a )/h a (7)
t w ＝log(w/w a ) t h ＝log(h/h a )
wherein (x, y), w and h each representCenter coordinates, width and length of object, x a ，x * Representing the predicted region, the anchor region and the labeled region, respectively (y, w, h are the same). Regression loss
R is a smooth loss function
Different logging tools may be used to generate different data. If natural Gamma (GR), compensating sound wave (AC), compensating Density (DEN), compensating Neutrons (CNL), neutron apparent porosity and density apparent porosity difference (SDN) are adopted, and the like, the data are of different dimensions, and no comparability exists between the data, therefore, the invention needs to normalize logging data, convert original data into dimensionless index evaluation values, namely, all index evaluation values are in the same number level, and then perform comprehensive evaluation analysis.
The following normalization method is adopted:
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 value of the corresponding log data, and S is the standard deviation of each log data.
The invention performs calibration from the existing logging data, establishes the training data set of the FR-CNN, and on the basis, as the information disclosed by different logging methods is different, selects the combination of different logging data as the input of the FR-CNN, thereby determining the optimal logging data combination of the FR-CNN and optimizing the network parameters and the structure of the FR-CNN when logging phase identification is performed.
The FR-CNN has more two types of full-connection layers than the traditional convolutional neural network, and has more operations such as calculation of region coordinates, and the calculation amount of the operations is large. In the fuzzy neural network, fuzzy operation exists in each layer of the network, that is, the deeper the network is, the more calculation amount is increased, which makes the network which is required to be heavy and calculation is heavy. The increase of the calculated amount leads to the great increase of the training time of the network, prolongs the updating period of the network model, weakens the flexibility of the system, and prolongs the detection time.
According to the invention, FR-CNN training and operation efficiency are improved through parallelization, an input logging data set is firstly divided into a plurality of small data sets, a plurality of workflows are operated simultaneously, and each part is trained by gradient descent alone. After training is completed, the result is output to a waiting queue, and after one round of training is completed, the output queue is read, and synchronous updating operation of the sharing weight is carried out. After the updating is completed, the next training round is performed.
In each training round, the calculation of each divided small data set is asynchronously performed on a distributed basis, each gradient value calculated is added to the list, and after all the small data sets are calculated, the weight and the bias value of the network are synchronously updated, and then the next training round is performed.
As shown in fig. 3, in the aspect of parallelization identification, the solution adopted is: collecting logging data by Spout, distributing the data to each Bolt node for logging phase identification in parallel, inputting an identification result to the next Bolt node by each Bolt node, and counting object information in the next Bolt node.
According to the characteristic of data blurring in logging big data, a blurring theory is integrated, a blurring region convolution neural network FR-CNN is provided, a progressive blurring method is provided, the number of blurring layers is gradually increased from the first layer of the blurring region convolution neural network, and therefore network structures and parameters are optimized, and better analysis performance and accuracy are achieved; in addition, the invention adjusts the FR-CNN fuzzification layer number aiming at different logging data sets, so that the extracted characteristics better reflect the characteristics of an oil and gas reservoir, and the problem of logging data fuzziness can be solved; aiming at the problem of large operation calculation amount, the invention utilizes multiple GPUs to carry out the parallel training and execution of the FR-CNN so as to improve the efficiency of the FR-CNN.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. A logging phase identification method based on fuzzy theory and neural network is characterized in that,
firstly, constructing a fuzzy region convolutional neural network, putting a given target hypothesis region and target identification into the same network, sharing convolutional calculation, and updating the weight of the whole network in a training process;
then, the logging data is subjected to convolution and pooling operation through a fuzzy area convolutional neural network, the convolution layer and the pooling layer are interacted, fuzzy operation is performed on the convolution layer and the pooling layer, the number of fuzzy layers is gradually increased from the first layer of the fuzzy area convolutional neural network, the number of fuzzy layers is adjusted for different data sets, the last layer of the fuzzy area convolutional neural network obtains a feature vector, the feature vector maps features into a low-dimensional vector through a sliding window, then the features are input into two fully connected layers, one fully connected layer is used for positioning, and the other fully connected layer is used for classifying;
the convolution layer formula is expressed as:
wherein ,
representing the value at the (x, y) position of the j-th eigenvector of the i-th layer neuron, P i and Qi Respectively representing the height and width of the convolution kernel, f (x) representing the activation function of the neuron;
the pooling layer formula is expressed as:
where down (-) represents a downsampling function, offset
And weight->
All are fuzzy numbers +.>
Representation->
Weights at positions (p, q) connected to the convolution kernel of the mth eigenvector, where symmetric triangle ambiguity is used, < >>
The j-th fuzzy number is a vector composed of fuzzy numbers>
The membership functions of (a) are:
wherein wj Is the center of symmetry of the fuzzy number,
is half length of the fuzzy number, +.>
Represents the membership at w; in the training process of the fuzzy area convolutional neural network, a joint loss function is defined:
wherein ,pi Is the predicted probability that this sample is the log morphology,
a label of the sample, if it is the corresponding log morphology,/->
1, otherwise->
Is 0, L cls Is a two-class logical loss; t is t i Is a vector of four parameters predicting the boundary of an object,/->
For the vector composed of the labeling area parameters, they are respectively:
t x ＝(x-x c )/w a t h ＝(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 coordinates, width and length of the object, x, respectively a ，x * Representing the predicted region, the anchor region and the labeled region, respectively, regression loss
R is the smooth loss function->
2. The method for identifying the logging phases based on the fuzzy theory and the neural network according to claim 1, wherein the normalization of logging data is performed first, raw data are converted into dimensionless index evaluation values, the index evaluation values are all in the same number level, and then comprehensive evaluation analysis is performed.
3. The method for identifying the logging phases based on the fuzzy theory and the neural network as claimed in claim 2, wherein the normalization of the logging data is carried out by adopting the following normalization method:
Sx＝(x-M)/S,x∈{GR，AC，DEN，CNL，SDN，...}
wherein x represents data of each log, sx represents normalized log data, M is an average value of the corresponding log data, S is a standard deviation of each log data, GR is natural gamma, AC is a compensating sound wave, DEN is a compensating density, CNL is a compensating neutron, and SDN is a density viewing porosity difference.
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| CN109171774B (en) * | 2018-09-30 | 2021-07-06 | 合肥工业大学 | Personality analysis method and system based on multi-channel data |
| CN109920013B (en) * | 2019-01-30 | 2020-11-10 | 北京交通大学 | Image reconstruction method and device based on progressive convolution measurement network |
| CN109919313B (en) * | 2019-01-31 | 2021-06-08 | 华为技术有限公司 | Gradient transmission method and distributed training system |
| CN109816042B (en) * | 2019-02-01 | 2020-11-24 | 北京达佳互联信息技术有限公司 | Data classification model training method and device, electronic equipment and storage medium |
| CN111236932B (en) * | 2020-01-13 | 2022-12-13 | 中国石油天然气股份有限公司大港油田分公司 | Shale oil lithology evaluation method based on indication curve reconstruction |
| CN111462060A (en) * | 2020-03-24 | 2020-07-28 | 湖南大学 | Method and device for detecting standard section image in fetal ultrasonic image |
| CN112784980B (en) * | 2021-01-05 | 2024-05-28 | 中国石油天然气集团有限公司 | Intelligent logging horizon dividing method |
| CN113537458B (en) * | 2021-06-21 | 2022-12-30 | 中国科学技术大学 | Rational function neural network construction method, system and readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217225A (en) * | 2014-09-02 | 2014-12-17 | 中国科学院自动化研究所 | A visual target detection and labeling method |
| CN104573669A (en) * | 2015-01-27 | 2015-04-29 | 中国科学院自动化研究所 | Image object detection method |
| CN104657748A (en) * | 2015-02-06 | 2015-05-27 | 中国石油大学(华东) | Vehicle type recognition method based on convolutional neural network |
| CN105654729A (en) * | 2016-03-28 | 2016-06-08 | 南京邮电大学 | Short-term traffic flow prediction method based on convolutional neural network |
| CN105825511A (en) * | 2016-03-18 | 2016-08-03 | 南京邮电大学 | Image background definition detection method based on deep learning |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1758398A1 (en) * | 2005-08-23 | 2007-02-28 | Syneola SA | Multilevel semiotic and fuzzy logic user and metadata interface means for interactive multimedia system having cognitive adaptive capability |
| US20080033991A1 (en) * | 2006-08-03 | 2008-02-07 | Jayanta Basak | Prediction of future performance of a dbms |
| US11256982B2 (en) * | 2014-07-18 | 2022-02-22 | University Of Southern California | Noise-enhanced convolutional neural networks |
| CN105354568A (en) * | 2015-08-24 | 2016-02-24 | 西安电子科技大学 | Convolutional neural network based vehicle logo identification method |
-
2016
- 2016-08-30 CN CN201910042398.8A patent/CN109800863B/en active Active
- 2016-08-30 CN CN201610762101.1A patent/CN106372402B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217225A (en) * | 2014-09-02 | 2014-12-17 | 中国科学院自动化研究所 | A visual target detection and labeling method |
| CN104573669A (en) * | 2015-01-27 | 2015-04-29 | 中国科学院自动化研究所 | Image object detection method |
| CN104657748A (en) * | 2015-02-06 | 2015-05-27 | 中国石油大学(华东) | Vehicle type recognition method based on convolutional neural network |
| CN105825511A (en) * | 2016-03-18 | 2016-08-03 | 南京邮电大学 | Image background definition detection method based on deep learning |
| CN105654729A (en) * | 2016-03-28 | 2016-06-08 | 南京邮电大学 | Short-term traffic flow prediction method based on convolutional neural network |
Non-Patent Citations (3)
| Title |
|---|
| 基于 BP 神经网络的测井相分析及沉积相识别;吴灿灿等;《煤田地质与勘探》;20120229;第 40 卷(第 1 期);全文 * |
| 应用神经网络模式识别技术进行测井沉积学研究;张福明等;《石油勘探与开发》;20030630;第30卷(第3期);全文 * |
| 苏北盆地测井层序与沉积相研究;程道解;《中国优秀硕士学位论文全文数据库基础科学辑》;20090615;全文 * |
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