CN110320557A - Multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning - Google Patents
Multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning Download PDFInfo
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Abstract
The multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning that the invention discloses a kind of, it is related to geologic feature detection fusion technical field, itself the following steps are included: S1, reservoir evaluation parameter output, S1.01, more data volumes according to input, seismic data, well logging data generate monomer and more body union feature data, are labeled to characteristic.The multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning, the respective learning method and data structure that can be used for different data, different evolution and study are realized according to different data, different models can be predicted and be assessed for different geological conditions, the self-correcting of meeting implementation model and improvement in learning process, and the assessment of parameter, learning process is without fixed model and mode, the reservoir parameter ultimately generated has more the meaning of geology, also can be closer to actual conditions for the prediction of reservoir parameter.
Description
Technical field
It is specially a kind of based on deep learning and evolutionary learning the present invention relates to geologic feature detection fusion technical field
Multiple dimensioned geologic feature detection fusion method.
Background technique
Some companies and research institution both domestic and external in recent years achieve some new in terms of the research of layer description
Progress.Reservoir detecting method of the patent based on seismic data deep learning of 2017 of Cao Junxing et al.；2016 year's harvest states build
Deng using deep learning algorithm research rock image procossing；The application convolutional neural networks such as Duan Youxiang in 2016 predict geologic reservoir
Parameter；Zheng Yu wise man applies deep learning Study In Reservoir physical parameter within 2018；Smith in 2017 etc. is counted using deep learning
According to control fusion etc..
Reservoir Description is a kind of technology explained applied to petroleum industry seismic data, which utilizes seismic data
Amplitude, frequency and phase information extract the porositys of underground petroleum reservoirs, water saturation and oily at
Point, in addition it can calculate the density that oil and gas reservoir is contained in underground, the characteristics such as speed of seimic wave propagation.Currently based on well logging number
The voidage of entirely accurate identification reservoir is unable to according to the analysis method with seismic data inversion, fluid composition and oil gas contain
Amount, the prior art of deep learning is mainly applied and carries out reservoir prediction in oil field by current application study, for intelligence
The essence that can learn is motivated there is no in-depth study,
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, the multiple dimensioned geology based on deep learning and evolutionary learning that the present invention provides a kind of
Feature detection fusion method solves and is unable to entirely accurate knowledge currently based on the analysis method of log data and seismic data inversion
The content of the voidage of other reservoir, fluid composition and oil gas and current application study are mainly showing deep learning
There is technical application to carry out reservoir prediction in oil field, for essence excitation the asking there is no in-depth study of intelligence learning
Topic.
(2) technical solution
To achieve the above objectives, the technical solution adopted by the present invention is that: it is a kind of more based on deep learning and evolutionary learning
Scale geologic feature detection fusion method, comprising the following steps:
The output of S1, reservoir evaluation parameter.
S1.01, more data volumes according to input, seismic data, well logging data generate monomer and more body union features
Data are labeled characteristic.
S1.02, destination layer position information is found according to relevant geologic data, destination layer position is marked, including joint objective
Layer position mark and relevant layers position mark.
S1.03, multiple layers of position data and data are formed to new structural body combination, structured data is combined.
S1.04, relevant structured data is extracted, utilizes newborn area data configuration parameter.
The algorithm and network structure that S1.05, input learn.
S1.06, the learning outcome and characteristic for extracting area data.
S1.07, characteristic and learning outcome are re-entered into combinatory analysis.
S1.08, analysis result is inputted into evaluation function, and generates the result of evaluation.
S1.09, by evaluation result input step S1.03, step S1.03 to step S1.08 is repeated, until evaluation result is steady
It is fixed.
S1.010, the result of evaluation is input to analytic function, exports the evaluation parameter of reservoir.
S2, forecast analysis is carried out to the evaluation parameter of reservoir.
S2.01, to input a variety of data volumes according to order using input function include earthquake and well logging, logging data, to each
From data carry out value region decompose and synthesis.
S2.02, data fusion is carried out for the data after decomposing and is decomposed again.
S2.03, to after abundant solution data extract characteristic, be based on data the characteristics of, again matched data generate phase
Close more volume datas and joint volume data.
S2.04, according to data and characteristic, target equipotential is automatically marked and is identified.
S2.05, destination layer position and multivariate data body are identified and is combined, building study architecture generates new
Structural body.
S2.06, new area data and parameter are generated according to structured data.
S2.07, in new region input feature vector data and intelligence learning algorithm is run.
S2.08, the result of intelligence learning is inputted into combination evaluation function, is determined according to the output result of evaluation function into one
Walk region and the data of study.
S2.09, the result of intelligence learning is input to information analytic function, information analytic function is exported to each reservoir
Parameter evaluation and prediction.
Preferably, the method for intelligence learning includes machine learning and deep learning, and adaptive in the step S2
It practises, mainly extract and learn relevant data and extracts some relevant features, determined according to different features different
Learning method.
Preferably, the evolutionary learning includes interpreted data, seismic data, seismic attributes data and the earthquake of input
Geologic feature mining data, Design evolution learning network structure, test data, real data application and big data parallel computation
Model training and application.
(3) beneficial effect
The beneficial effects of the present invention are:
1, it is somebody's turn to do the multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning, technology path is more multiple
It is miscellaneous, data relativity is had more, for respective learning method and data structure that different data can be used, according to different
Data realize different evolution and study, and different models can be predicted and be assessed for different geological conditions, learnt
The self-correcting of meeting implementation model and improvement and the assessment of parameter in process, learning process is without fixed model and mould
Formula, the reservoir parameter ultimately generated have more the meaning of geology, also can be closer to actual conditions for the prediction of reservoir parameter
2, it is somebody's turn to do the multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning, wherein different depth
The method of study, which analyzes different data, can generate different effects, and the present invention will utilize more data volumes and synthesis
The intellectual technology of current comparative maturity is applied to layer description, and generates the method for objective Stability Assessment by data characteristics.
Detailed description of the invention
Fig. 1 is the whole geologic feature detection fusion method flow schematic diagram of the present invention；
Fig. 2 is evolutionary learning schematic network structure of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figs. 1-2, the present invention provides a kind of technical solution: a kind of multiple dimensioned based on deep learning and evolutionary learning
Geologic feature detection fusion method, comprising the following steps:
The output of S1, reservoir evaluation parameter.
S1.01, more data volumes according to input, seismic data, well logging data generate monomer and more body union features
Data are labeled characteristic.
S1.02, destination layer position information is found according to relevant geologic data, destination layer position is marked, including joint objective
Layer position mark and relevant layers position mark.
S1.03, multiple layers of position data and data are formed to new structural body combination, structured data is combined.
S1.04, relevant structured data is extracted, utilizes newborn area data configuration parameter.
The algorithm and network structure that S1.05, input learn.
S1.06, the learning outcome and characteristic for extracting area data.
S1.07, characteristic and learning outcome are re-entered into combinatory analysis.
S1.08, analysis result is inputted into evaluation function, and generates the result of evaluation.
S1.09, by evaluation result input step S1.03, step S1.03 to step S1.08 is repeated, until evaluation result is steady
It is fixed.
S1.010, the result of evaluation is input to analytic function, exports the evaluation parameter of reservoir.
S2, forecast analysis is carried out to the evaluation parameter of reservoir.
S2.01, to input a variety of data volumes according to order using input function include earthquake and well logging, logging data, to each
From data carry out value region decompose and synthesis.
S2.02, data fusion is carried out for the data after decomposing and is decomposed again.
S2.03, to after abundant solution data extract characteristic, be based on data the characteristics of, again matched data generate phase
Close more volume datas and joint volume data.
S2.04, according to data and characteristic, target equipotential is automatically marked and is identified.
S2.05, destination layer position and multivariate data body are identified and is combined, building study architecture generates new
Structural body.
S2.06, new area data and parameter are generated according to structured data.
S2.07, in new region input feature vector data and intelligence learning algorithm is run.
S2.08, the result of intelligence learning is inputted into combination evaluation function, is determined according to the output result of evaluation function into one
Walk region and the data of study.
S2.09, the result of intelligence learning is input to information analytic function, information analytic function is exported to each reservoir
Parameter evaluation and prediction, the method for intelligence learning include machine learning and deep learning and adaptive learning, are mainly extracted
Data relevant with study and some relevant features of extraction, different learning methods is determined according to different features, is evolved
Study includes inputting interpreted data, seismic data, seismic attributes data and seismic geologic feature mining data, design into
Chemistry practises network structure, test data, real data application and the training of big data parallel computational model and application, wherein evolving
In learning network structural schematic diagram the Chinese meaning of Input, Outputs, Actions and Program Nodes be respectively input,
Output, behavior and item nodes.
The present invention utilizes unconventional seismic data attribute, in conjunction with the image of deep learning and evolutionary learning, at data
Reason and the method for Fusion Features detection fine geology feature include thin interbed crack and Special complex lithology.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not limited to this hair the foregoing is merely a specific embodiment of the invention
Bright, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the present invention
Protection scope within.
Claims (3)
1. a kind of multiple dimensioned geologic feature detection fusion method based on deep learning and evolutionary learning, it is characterised in that: including
Following steps:
The output of S1, reservoir evaluation parameter；
S1.01, more data volumes according to input, seismic data, well logging data generate monomer and more body union feature data,
Characteristic is labeled；
S1.02, destination layer position information is found according to relevant geologic data, destination layer position is marked, including joint objective layer position
Mark and relevant layers position mark；
S1.03, multiple layers of position data and data are formed to new structural body combination, structured data is combined；
S1.04, relevant structured data is extracted, utilizes newborn area data configuration parameter；
The algorithm and network structure that S1.05, input learn；
S1.06, the learning outcome and characteristic for extracting area data；
S1.07, characteristic and learning outcome are re-entered into combinatory analysis；
S1.08, analysis result is inputted into evaluation function, and generates the result of evaluation；
S1.09, by evaluation result input step S1.03, step S1.03 to step S1.08 is repeated, until evaluation result stabilization；
S1.010, the result of evaluation is input to analytic function, exports the evaluation parameter of reservoir；
S2, forecast analysis is carried out to the evaluation parameter of reservoir；
S2.01, to input a variety of data volumes according to order using input function include earthquake and well logging, logging data, to respective
Data carry out value region and decompose and synthesize；
S2.02, data fusion is carried out for the data after decomposing and is decomposed again；
S2.03, the characteristics of extracting characteristic to the data after abundant solution, be based on data, matched data generates related more again
Volume data and joint volume data；
S2.04, according to data and characteristic, target equipotential is automatically marked and is identified；
S2.05, destination layer position and multivariate data body are identified and is combined, building study architecture generates new structure
Body；
S2.06, new area data and parameter are generated according to structured data；
S2.07, in new region input feature vector data and intelligence learning algorithm is run；
S2.08, the result of intelligence learning is inputted into combination evaluation function, is determined according to the output result of evaluation function and is further learned
The region of habit and data；
S2.09, the result of intelligence learning is input to information analytic function, information analytic function exports the parameter to each reservoir
Evaluation and prediction.
2. the multiple dimensioned geologic feature detection fusion method according to claim 1 based on deep learning and evolutionary learning,
It is characterized by: the method for intelligence learning includes machine learning and deep learning and adaptive learning, master in the step S2
If extracting and learning relevant data and extract some relevant features, different study sides is determined according to different features
Method.
3. the multiple dimensioned geologic feature detection fusion method according to claim 1 based on deep learning and evolutionary learning,
It is characterized by: the evolutionary learning includes interpreted data, seismic data, seismic attributes data and the Seismology and Geology of input
Feature mining data, Design evolution learning network structure, test data, real data application and big data parallel computational model
Training and application.
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