CN103850679A - Method for reconstructing interval transit time curve by virtue of multiple logging curves - Google Patents
Method for reconstructing interval transit time curve by virtue of multiple logging curves Download PDFInfo
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Abstract
The invention relates to a method for reconstructing an interval transit time curve by virtue of multiple logging curves. The method mainly comprises the steps of selecting a plurality of logging curves obvious in response to reservoir characteristics by performing reservoir sensitivity and correlation analysis on the logging curves; performing discrete wavelet decomposition on the logging curves together with a sonic wave curve to form 8 decomposition layers; enabling all the layers of high-frequency decomposition results of other logging curves except the sonic curve to form matrices respectively, obtaining the corresponding characteristic values and characteristic vectors of the matrices; taking the characteristic vectors corresponding to different characteristic values as new components, which, at the moment, have orthogonality (correlation ); performing multivariate regression analysis on the high-frequency components of the corresponding layer of wavelet decomposition of the sonic curve by utilizing characteristic vectors of each layer, calculating a weighting coefficient corresponding to each vector, returning a regression significance analysis result and determining the quality of regression; next, determining the number of layers of the sonic high-frequency components to be reconstructed by virtue of the characteristic vectors according to the regression significance analysis result, selecting multiple regression results as the high-frequency components for a part having more layers than selected layers, and remaining the high-frequency decomposition results of the sonic logging curves for a part having less layers than the selected layer, and then carrying out curve reconstruction by using the low-frequency components of the sonic logging curves and the high-frequency components obtained through regression so as to obtain a final sonic reconstructed curve.
Description
Technical field:
The present invention relates to a kind of method of utilizing well logs to be reconstructed interval transit time curve.
Background technology:
Sound wave curve reconstruct is the common method in the middle of wave impedance inversion.When underground reservoir velocities and the mud stone speed difference opposite sex is little, registration is high problem; The result of directly carrying out wave impedance inversion can not accurately reflect the difference of reservoir and country rock, causes wave impedance inversion result and drilling well result not to be inconsistent.Therefore need acoustic logging to be reconstructed, in acoustic logging, add lithological information, improve the resolution of acoustic logging to lithology, to carry out reservoir prediction more accurately.
Multiple regression analysis is the regression analysis of relation between the multiple variablees of research, regression analysis (referred to as " one-to-many " regression analysis) and the multiple dependent variable regression analysis (referred to as " multi-to-multi " regression analysis) to multiple independents variable of a dependent variable to multiple independents variable can be divided into by the quantity corresponding relation of dependent variable and independent variable, linear regression analysis and nonlinear regression analysis can be divided into by regression model type.
F checks (F-test), and the most frequently used another name is called joint hypothesis inspection (English: joint hypotheses test), also claims in addition variance ratio test, homogeneity test of variance.It is a kind of under null hypothesis (null hypothesis, H0), and statistical value is obeyed the inspection that F-distributes.It is normally used for analyzing has used the statistical model that exceedes a parameter, to judge whether the whole or parameter in this model is applicable to for estimating parent.
In wavelet transformation, for non-stationary signal (logging signal is non-stationary signal), need time-frequency window to there is adjustable character, require to there is good temporal resolution characteristic at HFS, and there is good frequency resolution characteristic in low frequency part.Log is carried out wavelet analysis and can be identified the curve cycle of different frequency in log, the corresponding high frequency of the curve cycle of high frequency be the cycle of sedimentation of short-term, the corresponding low frequency of the curve cycle of low frequency be the long-term cycle of sedimentation, therefore can divide with the log cycle of different frequency the cycle of sedimentation of different cycles, the Sequence Stratigraphic Units of corresponding different stage.
Summary of the invention:
The present invention relates to a kind of method of utilizing well logs to be reconstructed interval transit time curve, mainly comprise by log is carried out to reservoir sensitivity and correlation analysis, choose significantly many logs of reservoir characteristic response; These logs are carried out together with sound wave curve to discrete wavelet decomposition, decomposing the number of plies is 8 layers; Each other logs except sound wave floor height frequency decomposition result is formed respectively to matrix, ask for matrix characteristic of correspondence value and characteristic vector, new component using different characteristic value characteristic of correspondence vector as signal reconstruction, now these components have orthogonality (uncorrelated); Each layer of characteristic vector carried out multiple regression analysis to the radio-frequency component of sound wave curve wavelet decomposition respective layer, calculates the corresponding weight coefficient of each vector, and returns to recurrence significance analysis result, and judgement returns quality; Judge according to returning significance analysis result the number of plies of utilizing characteristic vector reconstruct sound wave radio-frequency component, select multiple regression result as radio-frequency component to the part more than selected number of plies, the part below the selected number of plies is retained to the high-frequency decomposition result of acoustic logging; The radio-frequency component that the low-frequency component of utilization acoustic logging and recurrence obtain carries out curve Reconstruction and obtains final sound wave reconstruct curve.
Brief description of the drawings
Fig. 1. utilize well logs to be reconstructed flow chart to interval transit time curve
Fig. 2. characteristic vector multiple regression significance analysis result
Fig. 3. Sonic Log Data Rebuilding result
Detailed description of the invention:
As shown in Figure 1, the implementation step of method is described in detail as follows:
Step 1, uses reservoir porosity, shale content data and log to carry out correlation analysis, selection and shale content, the log that degree of porosity correlation is higher;
Step 2, carries out db wavelet decomposition by these and shale content, log that degree of porosity correlation is higher, and decomposing the number of plies is 8 layers;
Step 3, each other logs except sound wave floor height frequency decomposition result is formed respectively to matrix, ask for matrix characteristic of correspondence value and characteristic vector, new component using different characteristic value characteristic of correspondence vector as signal reconstruction, now these components have orthogonality (uncorrelated);
Step 4, use these characteristic vectors to carry out multiple regression analysis to sound wave curve wavelet decomposition radio-frequency component, in regression equation, the coefficient of each is exactly the corresponding weight of this characteristic vector, uses F to detect judgement recurrence quality and can return to two parameter F H and FV; FH is the judgement for multiple regression credible result degree; For given confidence alpha, the F being checked in by F distribution table
α(m, n-m-1) value and FV compare, as FV > F
αmultiple regression credible result is described, FH=1, otherwise FH=0 when (m, n-m-1); FV value is larger, and popualtion regression effect more remarkable (Fig. 2) is described;
Step 5, check the result of returning according to F, all credible at every one deck regression result, be in the situation of FH=1, select that minimum one deck of FV value, sound wave curve high-frequency decomposition result to this one deck and above all layers thereof replaces by multiple regression result, as the radio-frequency component of sound wave curve reconstruct.
Step 6, the radio-frequency component that the low-frequency component of utilization acoustic logging and recurrence obtain carries out curve Reconstruction and obtains final sound wave reconstruct curve (Fig. 3).
Claims (6)
1. a method of utilizing well logs to be reconstructed interval transit time curve, mainly comprises by log is carried out to reservoir sensitivity and correlation analysis, chooses significantly many logs of reservoir characteristic response; These logs are carried out together with sound wave curve to discrete wavelet decomposition, decomposing the number of plies is 8 layers; Each other logs except sound wave floor height frequency decomposition result is formed respectively to matrix, ask for matrix characteristic of correspondence value and characteristic vector, new component using different characteristic value characteristic of correspondence vector as signal reconstruction, now these components have orthogonality (uncorrelated); Each layer of characteristic vector carried out multiple regression analysis to the radio-frequency component of sound wave curve wavelet decomposition respective layer, calculates the corresponding weight coefficient of each vector, and returns to recurrence significance analysis result, and judgement returns quality; Judge according to returning significance analysis result the number of plies of utilizing characteristic vector reconstruct sound wave radio-frequency component, select multiple regression result as radio-frequency component to the part more than selected number of plies, the part below the selected number of plies is retained to the high-frequency decomposition result of acoustic logging; The radio-frequency component that the low-frequency component of utilization acoustic logging and recurrence obtain carries out curve Reconstruction and obtains final sound wave reconstruct curve.
2. according to claim 1 by log is carried out to reservoir sensitivity and correlation analysis, choose significantly many logs of reservoir characteristic response, it is characterized in that using reservoir porosity, shale content data and log to carry out correlation analysis, selection and shale content, the log that degree of porosity correlation is higher.
3. according to claim 1ly these logs are carried out together with sound wave curve to discrete wavelet decomposition, decomposing the number of plies is 8 layers, it is characterized in that decomposing the number of plies is 8 layers to carrying out db wavelet decomposition with shale content, log that degree of porosity correlation is higher described in claim 2.
According to claim 1 by each other logs except sound wave floor height frequently decomposition result form respectively matrix, ask for matrix characteristic of correspondence value and characteristic vector, new component using different characteristic value characteristic of correspondence vector as signal reconstruction, now these components have orthogonality (uncorrelated), it is characterized in that, by the radio-frequency component composition matrix of every one deck in the wavelet decomposition result described in claim 3, forming altogether 8 matrixes; Ask for each matrix characteristic of correspondence value and characteristic vector, between the characteristic vector after asking for, there is orthogonality.
5. according to claim 1 each layer of characteristic vector carried out to multiple regression analysis to the radio-frequency component of sound wave curve wavelet decomposition respective layer, calculate the corresponding weight coefficient of each vector, and return and return significance analysis result, judgement returns quality, it is characterized in that using the characteristic vector described in claim 4 to carry out multiple regression analysis to sound wave curve wavelet decomposition radio-frequency component, in regression equation, the coefficient of each is exactly the corresponding weight of this characteristic vector, uses F to detect judgement recurrence quality and can return to two parameter F H and FV; FH is the judgement for multiple regression credible result degree; For given confidence alpha, the F being checked in by F distribution table
α(m, n-m-1) value and FV compare, as FV > F
αmultiple regression credible result is described, FH=1, otherwise FH=0 when (m, n-m-1); FV value is larger, illustrates that popualtion regression effect is more remarkable.
6. the number of plies of utilizing characteristic vector reconstruct sound wave radio-frequency component according to the judgement of recurrence significance analysis result according to claim 1, select multiple regression result as radio-frequency component to the part more than selected number of plies, part below the selected number of plies is retained to the high-frequency decomposition result of acoustic logging, it is characterized in that checking according to F the result of returning, all credible at every one deck regression result, be in the situation of FH=1, select that minimum one deck of FV value, sound wave curve high-frequency decomposition result to this one deck and above all layers thereof replaces with the regression result described in claim 5, as the radio-frequency component of sound wave curve reconstruct.
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Cited By (10)
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| CN104533400A (en) * | 2014-11-12 | 2015-04-22 | 中国海洋石油总公司 | Method for reconstructing logging curve |
| CN106707344A (en) * | 2016-12-12 | 2017-05-24 | 中国石油天然气股份有限公司 | Stratigraphic sequence division method and device |
| CN107339099A (en) * | 2017-07-19 | 2017-11-10 | 中国石油天然气集团公司 | A kind of method and apparatus for determining reservoir lithology |
| CN108072903A (en) * | 2016-11-09 | 2018-05-25 | 中国石油化工股份有限公司 | A kind of Well logging curve reconstruction method |
| CN108756867A (en) * | 2018-05-11 | 2018-11-06 | 中国地质调查局油气资源调查中心 | The method that pressure break selects layer is carried out based on acoustic logging and Resistivity log |
| CN109061729A (en) * | 2018-08-22 | 2018-12-21 | 西安石油大学 | A kind of high temperature and pressure gas reservoir gassiness sensitivity curve reconstructing method |
| CN109343120A (en) * | 2018-10-17 | 2019-02-15 | 吉林大学 | Incorporate the sound wave curve reconstructing method of constrained sparse spike inversion inverting low-frequency compensation |
| CN109633553A (en) * | 2019-01-18 | 2019-04-16 | 浙江大学 | Moving sound based on dynamic programming algorithm reaches delay time estimation method |
| CN111827966A (en) * | 2020-03-25 | 2020-10-27 | 大庆油田有限责任公司 | Multi-well acoustic logging curve consistency processing method and device and storage medium |
| CN112012726A (en) * | 2019-05-30 | 2020-12-01 | 中石化石油工程技术服务有限公司 | Lithology identification method |
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| CN104533400B (en) * | 2014-11-12 | 2017-05-17 | 中海油能源发展股份有限公司 | Method for reconstructing logging curve |
| CN104533400A (en) * | 2014-11-12 | 2015-04-22 | 中国海洋石油总公司 | Method for reconstructing logging curve |
| CN108072903A (en) * | 2016-11-09 | 2018-05-25 | 中国石油化工股份有限公司 | A kind of Well logging curve reconstruction method |
| CN106707344A (en) * | 2016-12-12 | 2017-05-24 | 中国石油天然气股份有限公司 | Stratigraphic sequence division method and device |
| CN106707344B (en) * | 2016-12-12 | 2019-01-18 | 中国石油天然气股份有限公司 | A kind of division of stratigraphic sequence method and device |
| CN107339099B (en) * | 2017-07-19 | 2020-06-09 | 中国石油天然气集团公司 | Method and device for determining reservoir lithology |
| CN107339099A (en) * | 2017-07-19 | 2017-11-10 | 中国石油天然气集团公司 | A kind of method and apparatus for determining reservoir lithology |
| CN108756867A (en) * | 2018-05-11 | 2018-11-06 | 中国地质调查局油气资源调查中心 | The method that pressure break selects layer is carried out based on acoustic logging and Resistivity log |
| CN108756867B (en) * | 2018-05-11 | 2021-11-19 | 中国地质调查局油气资源调查中心 | Method for fracturing and selecting layer based on acoustic logging curve and resistivity logging curve |
| CN109061729A (en) * | 2018-08-22 | 2018-12-21 | 西安石油大学 | A kind of high temperature and pressure gas reservoir gassiness sensitivity curve reconstructing method |
| CN109343120A (en) * | 2018-10-17 | 2019-02-15 | 吉林大学 | Incorporate the sound wave curve reconstructing method of constrained sparse spike inversion inverting low-frequency compensation |
| CN109343120B (en) * | 2018-10-17 | 2019-10-01 | 吉林大学 | Incorporate the sound wave curve reconstructing method of constrained sparse spike inversion inverting low-frequency compensation |
| CN109633553A (en) * | 2019-01-18 | 2019-04-16 | 浙江大学 | Moving sound based on dynamic programming algorithm reaches delay time estimation method |
| CN112012726A (en) * | 2019-05-30 | 2020-12-01 | 中石化石油工程技术服务有限公司 | Lithology identification method |
| CN112012726B (en) * | 2019-05-30 | 2023-12-12 | 中石化石油工程技术服务有限公司 | Lithology recognition method |
| CN111827966A (en) * | 2020-03-25 | 2020-10-27 | 大庆油田有限责任公司 | Multi-well acoustic logging curve consistency processing method and device and storage medium |
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