CN108533254A - Reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method - Google Patents
Reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method Download PDFInfo
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- CN108533254A CN108533254A CN201810228038.2A CN201810228038A CN108533254A CN 108533254 A CN108533254 A CN 108533254A CN 201810228038 A CN201810228038 A CN 201810228038A CN 108533254 A CN108533254 A CN 108533254A
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- 238000004458 analytical method Methods 0.000 title claims abstract description 33
- 229930195733 hydrocarbon Natural products 0.000 title claims abstract description 23
- 150000002430 hydrocarbons Chemical class 0.000 title claims abstract description 23
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 19
- 238000005516 engineering process Methods 0.000 claims abstract description 18
- 238000005259 measurement Methods 0.000 claims abstract description 5
- 230000010354 integration Effects 0.000 claims abstract description 4
- 238000012360 testing method Methods 0.000 claims abstract description 4
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 3
- 238000004587 chromatography analysis Methods 0.000 claims description 13
- 238000003672 processing method Methods 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 3
- 239000011435 rock Substances 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 238000002207 thermal evaporation Methods 0.000 claims description 2
- 206010037660 Pyrexia Diseases 0.000 claims 1
- 238000000034 method Methods 0.000 abstract description 9
- 239000012530 fluid Substances 0.000 description 7
- 239000003209 petroleum derivative Substances 0.000 description 3
- 238000000197 pyrolysis Methods 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 201000004569 Blindness Diseases 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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- Engineering & Computer Science (AREA)
- Geology (AREA)
- Mining & Mineral Resources (AREA)
- Physics & Mathematics (AREA)
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- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The present invention relates to reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis methods, which is characterized in that includes the following steps：Step 1 compiles the various characteristic parameters that each single technology in geochemical logging measures, and collects the corresponding formation testing conclusion of every group of characteristic parameter, and then the data of integration are cleaned and verified；Step 2, standardization；Step 3, the ReliefF algorithm models analyzed for geochemical logging measurement by using Matlab software programming ReliefF feature selecting programs, foundation；Data Jing Guo standardization are input in ReliefF algorithm models by step 4, and the weighted value of the various characteristic parameters of single technology is calculated using RelefF algorithm models；Step 5, setting threshold value, screen character subset, extract geochemical logging sensitive parameter.This method is different from Grey Incidence Analysis emphasis, overcomes disadvantage of the Grey Incidence Analysis in weight analysis, belongs to supervised learning.
Description
Technical field
The present invention relates to a kind of reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis methods, suitable for being recorded by ground
The characteristic parameter strong to sample set separating capacity is selected in well experiment analytical method record parameter, in favor of evaluating petroleum gas
Fluid properties in reservoir.
Background technology
Geochemical logging experiment analytical method often obtains many measurement parameters, such as lighter hydrocarbons chromatography, more than 100
A compound, to select the characteristic parameter strong to sample set separating capacity in more compounds of comforming, needing can using one kind
In the method for carrying out right assessment to different characteristic parameter, characteristic parameter weighted value is calculated, setting threshold value chooses well logging sensitivity ginseng
Number.
It is usually that one or more parameter combination is selected to carry out reservoir fluid from numerous geochemical logging parameters at present
Property Quality Research, this is affected by experience and human factor, it is difficult to choose the characteristic parameter strong to sample set separating capacity.
106991245 A of CN disclose a kind of method identifying properties of fluid in bearing stratum based on grey correlation analysis, this method
Using grey correlation methods calculate gas survey, pyrolysis, quantitative fluorescence, six porosity, permeability and interval transit time parameter values power
Weight, but its there are still following disadvantages in weight analysis：Grey correlation analysis is too strong with subjectivity while part index number is optimal
Value is difficult to determining disadvantage, and requires gray system, belongs to unsupervised learning.Therefore it in petroleum gas reservoir for flowing
The analysis of volume property is still undesirable.
Invention content
The purpose of the invention is to provide a kind of more reasonable effective reservoir hydrocarbons water layer logging parameters Dominated Factors
Weight analysis method, this method is different from Grey Incidence Analysis emphasis, overcomes Grey Incidence Analysis in weight
Disadvantage in analysis, belongs to supervised learning.
The technical scheme is that：
A kind of reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method, which is characterized in that include the following steps：
Step 1 is compiled the various characteristic parameters that each single technology in geochemical logging to be analyzed measures, and is collected
Then the data of integration are cleaned and are verified, the single technology includes rock by the corresponding formation testing conclusion of every group of characteristic parameter
Stone pyrolysis analysis technology, lighter hydrocarbons chromatographic technique and thermal evaporation component chromatographic technique, the characteristic parameter are individual event skill
The data target that art measures；
Step 2 is standardized the data after cleaning and verification, and the standardization includes Min-max marks
Standardization, normalizing standardization and z-score standardize three kinds of processing methods, by each single technology measurement parameter of geochemical logging
Test is selected optimal processing method and is standardized, and removes the unit limitation of data, is translated into nondimensional pure
Numerical value；
Step 3, by using Matlab software programming ReliefF feature selecting programs, establish and measured for geochemical logging
The ReliefF algorithm models of analysis；
Data Jing Guo standardization are input in ReliefF algorithm models by step 4, use RelefF algorithm moulds
Type calculates the weighted value of the various characteristic parameters of single technology；
Step 5, setting threshold value, screen character subset, extract geochemical logging sensitive parameter.
Above-mentioned reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method, step 1) is middle to delete Invalid parameter,
It is indicated respectively with alphabetical A, B, C, D differentiation including null value and zero, and by oil-containing water layer, oil-water common-layer, oil reservoir, water layer data,
In case ReliefF algorithms use.
Above-mentioned reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method establishes ReliefF algorithms in step 3
The flow of model is：
1), from sample set D, a sample R is taken out at randomi；
2), with sample RiIn the sample group of same category, k closest sample H are taken outj；
3), in every other and sample RiIn the sample group of different classifications, k nearest samples M is also taken out respectivelyj(C)；
4) weight for, calculating each characteristic parameter, there is the weight of each characteristic parameter：
Wherein：D is sample set, and m is iterations, and k is closest number of samples, and W (A) is characterized parameters weighting value, P
(C) it is the ratio of the category, P (Class (R)) is the ratio of the classification of certain sample randomly selected.
The beneficial effects of the invention are as follows：
1, the present invention considers the classification information in geochemical logging parameter, using in Data Dimensionality Reduction feature selecting algorithm
ReliefF algorithms, the correlation of feature and classification is separating capacity of the feature based to short distance sample in algorithm, is then assigned
The different weight of feature, it is comprehensive which calculates weight more science compared with grey correlation, overcomes grey correlation in weight
Disadvantage in analysis belongs to supervised learning, while avoiding the conventional blindness for selecting sensitive parameter.
2, the present invention selects Min-max standardization, normalizing standardization and z-score to standardize three kinds of data normalization processing
Optimal processing method is changed logging data and is standardized over the ground in method, such as in lighter hydrocarbons chromatography, different characteristic
The numerical value of parameter has a long way to go, and effect of the higher index of numerical value in comprehensive analysis can be protruded by being directly used in analysis, opposite to cut
The effect of the horizontal relatively low index of weak numerical value, can be happened, while data pass through standard using normalized analysis to avoid such
It can be with the convergence rate of lift scheme and the precision of lift scheme after change processing.
3, the method for the invention is applied in the geochemical logging of more measurement parameters, each characteristic parameter, what is referred to is exactly every
The data target that one single technology measures, as included S inside rock pyrolysis technology0,S1,S2,S3Four parameters (use three peaks
Analysis program measures), lighter hydrocarbons chromatography includes nCH4、nC2H6、nC3H8、iC4H10、nC4H10Deng 120 multiple parameters, data are dug
It is referred to as these parameters in pick and is characterized parameter.Because of identification of the different characteristic parameters to properties of fluid in bearing stratum in each single technology
Play the role of difference, can accurately judge each characteristic parameter to identification properties of fluid in bearing stratum using ReliefF algorithms
Weighted value, the big characteristic parameter of weight is namely to fluid identification of reservoir than more sensitive parameter.In the every single technology of analysis
When the characteristic parameter measured, the method for the invention can quickly analyze the weighted value of each characteristic parameter, be more advantageous to selection and close
Manage the research work that parameter carries out fluid properties in petroleum gas reservoir.
Description of the drawings
Fig. 1 be in embodiment lighter hydrocarbons chromatography well logging analysis in each characteristic parameter weighted value distribution map.
Specific implementation mode
The reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method is applied to lighter hydrocarbons chromatography well logging and analyzes, tool
Steps are as follows for body：
Step 1, the characteristic parameter for arranging lighter hydrocarbons chromatography well logging to be analyzed, and collect the corresponding examination of every group of characteristic parameter
Then oily conclusion is cleaned and is verified to the data of integration, specific such as table 1：
1 lighter hydrocarbons chromatography well logging initial data of table
Step 2 is standardized the data after cleaning and verification, compares by analysis using z-score standards
Change processing method to be standardized data, it is specific such as table 2：
2 lighter hydrocarbons chromatography well logging of table handles data through z-score
Step 3, by using Matlab software programming ReliefF feature selecting programs, establish and measured for geochemical logging
The ReliefF algorithm models of analysis, flow are：
1), from sample set D, a sample R is taken out at randomi；
2), with sample RiIn the sample group of same category, k closest sample H are taken outj；
3), in every other and sample RiIn the sample group of different classifications, k nearest samples M is also taken out respectivelyj(C)；
4) weight for, calculating each characteristic parameter, there is the weight of each characteristic parameter：
Wherein：D is sample set, and m is iterations, and k is closest number of samples, and W (A) is characterized parameters weighting value, P
(C) it is the ratio of the category, P (Class (R)) is the ratio of the classification of certain sample randomly selected.
Data Jing Guo standardization are input in ReliefF algorithm models by step 4, use RelefF algorithm moulds
Type calculates the weighted value of various characteristic parameters, such as table 3：
3 each characteristic parameter weighted value of lighter hydrocarbons chromatography well logging of table
| nCH4 | nC2H6 | nC3H8 | … | 22DMC4 | CYC5 | 23DMC4 | … | t13DMCYC6 | nC8H18 |
| 0.0205 | 0.0330 | 0.0911 | … | 0.0238 | 0.0336 | 0.0392 | … | 0.0255 | 0.0154 |
Step 5, setting threshold value are 0.062, filter out sensitive parameter and are followed successively by nC3H8、nC4H10、ctc123TMCYC5、
t1E2MCYC5、234TMC5、iC5H12, corresponding weighted value be 0.0911,0.0828,0.0777,0.0691,0.0766 and
0.0636.Above 6 sensitive parameters are selected from numerous characteristic parameters, are conducive to make oil in next step by this 6 parameters
The work of Gas-Water Contant interpretation and evaluation.
Claims (3)
1. a kind of reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method, which is characterized in that include the following steps：
Step 1 compiles the various characteristic parameters that each single technology in geochemical logging to be analyzed measures, and collects every group
Then the data of integration are cleaned and are verified, the single technology includes rock fever by the corresponding formation testing conclusion of characteristic parameter
Analytical technology, lighter hydrocarbons chromatographic technique and thermal evaporation component chromatographic technique are solved, the characteristic parameter is surveyed for single technology
The data target obtained；
Step 2 is standardized the data after cleaning and verification, the standardization include Min-max standardization,
Normalizing standardizes and z-score standardizes three kinds of processing methods, by being tested in each single technology measurement parameter of geochemical logging
It selects optimal processing method to be standardized, removes the unit limitation of data, be translated into nondimensional pure values；
Step 3, by using Matlab software programming ReliefF feature selecting programs, establish and measure analysis for geochemical logging
ReliefF algorithm models；
Data Jing Guo standardization are input in ReliefF algorithm models by step 4, use RelefF algorithm model meters
Calculate the weighted value of the various characteristic parameters of single technology；
Step 5, setting threshold value, screen character subset, extract geochemical logging sensitive parameter.
2. reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method according to claim 1, it is characterised in that：
Delete Invalid parameter, including null value and zero in step 1), and by oil-containing water layer, oil-water common-layer, oil reservoir, water layer data respectively with
Alphabetical A, B, C, D, which are distinguished, to be indicated, in case ReliefF algorithms use.
3. reservoir hydrocarbons water layer logging parameters Dominated Factors weight analysis method according to claim 1, it is characterised in that：
The flow that ReliefF algorithm models are established in step 3 is：
1), from sample set D, a sample R is taken out at randomi；
2), with sample RiIn the sample group of same category, k closest sample H are taken outj；
3), in every other and sample RiIn the sample group of different classifications, k nearest samples M is also taken out respectivelyj(C)；
4) weight for, calculating each characteristic parameter, there is the weight of each characteristic parameter：
Wherein：D is sample set, and m is iterations, and k is closest number of samples, and W (A) is characterized parameters weighting value, and P (C) is
The ratio of the category, P (Class (R)) are the ratio of the classification of certain sample randomly selected.
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Application publication date: 20180914 |