CN103777243A - Sand-mud stone thin interbed reservoir thickness prediction method - Google Patents
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Abstract
The invention provides a sand-mud stone thin interbed reservoir thickness prediction method. The sand-mud stone thin interbed reservoir thickness prediction method includes the following steps that: performing fine horizon interpretation; extracting a plurality of kinds of seismic attributes of a thin interbed; optimizing the plurality of kinds of seismic attributes to obtain optimized seismic attributes; performing multiple-attribute fitting; and calculating sand body thickness distribution and outputting results. With the sand-mud stone thin interbed reservoir thickness prediction method adopted, problems such as difficult thin interbed oil reservoir structure interpretation, instable sand body transverse distribution and difficult transverse prediction, can be solved, and distribution trends of thin layers (sand layer groups) on a plane can be precisely described, and relative relationships between thinness and thickness can be indicated.
Description
Technical field
The present invention relates to earthquake multiattribute electric powder prediction, particularly relate to a kind of thin sand-mud interbed reservoir thickness prediction method.
Background technology
Thin interbed oil reservoir is the important porspecting type of continental faulted basins, due to its form mechanism and regularity of distribution complexity, difficulties in exploration large, technical requirement is high, the research of describing evaluation technique, new method is the inevitable approach of this class reservoir exploration difficulty of solution.But the technical method of current introduction software application is still difficult to meet the needs of thin interbed reservoir exploration, mainly there is following problem: the one, often structural trapping condition is poor for thin interbed oil reservoir, and minor fault and microstructures are grown, structure elucidation difficulty.The 2nd, reservoir development is poor, layer is thin, the resolution of existing seismic data is inadequate, the thickness of oily sandstone reservoir is generally less than the seismic resolution limit, make the response of earthquake oil gas more difficult definite with the corresponding relation of actual oily sandstone thin (storage) layer, be difficult to identification form sand body, the research difficulty of earthquake oil gas response modes is very large, sand body cross direction profiles is unstable, lateral prediction difficulty.The 3rd, reservoir thin interbed, oil-gas-water layer identification difficulty is large.
Present stage, explain the various attribute volumes as Typical Representative such as (instantaneous amplitude, phase place, frequency), coherent body analysis, wave resistance antibody analysis take " three winks ", they are in specific situation, there is certain detectability for lithological change ability, common seismic is explained and greatly improved a step, tentatively forward lithologic interpretation to from structure elucidation, made seismic prospecting become the powerful of geological personnel.But their defect is, is all single attributive analysis, make the geologic interpretation result of earthquake have multi-solution.In recent years, along with the introducing of the frontier such as maturation and mathematics, the information science knowledge of seismic technology, the seismic properties of extracting from geological data is more and more abundanter, and new attribute is also continuing to bring out, the appearance of a large amount of new attributes, has produced multiattribute Conjoint Analysis technology.
In three dimensional seismic data, containing abundant earthquake information, application Seismic attribute analysis technology can disclose and in original seismic section, be difficult for found geologic anomaly phenomenon and oily situation.The method and the software that have occurred in recent years many kind research attributes, seismic properties can be extracted several large classes such as amplitude, frequency, phase place, energy, waveform, and existing software extracts attribute type can reach more than 60 kinds, and new attribute type is being continually developed application.But in real work, often ignored the validity of attributes extraction, that especially aspect thin layer information extraction mode, studies is not deep enough.Research shows, different attributes is different to the sensitivity of different lithology, in the time describing different objects, role is also different, simultaneously, some seismic properties may be irrelevant with zone of interest itself, and reflected that shallow-layer disturbs or the variation of stratum to seismic event absorption, if introduce without analyse the various seismic properties relevant with reservoir prediction, can bring adverse influence for reservoir prediction.In Seismic Reservoir Prediction process, due to the restriction of complicated seismogeology, single seismic properties often can not accurately reflect the reservoir development situation of underground reality, need to extract multiple attribute, and seismic properties combination generally has larger difference, in a large amount of attributes, be certain to comprising many factors that are relative to each other, some attributes may also play interference effect to prediction classification, therefore must be in numerous seismic properties preferred those Useful Informations.
Address the above problem, must, take geophysics, petroleum geology, geology seismic model theory etc. as basis, start with from model investigation poststack seismic properties feature, and then research optimization is selected the method for poststack seismic properties.From dozens or even hundreds of kind of seismic properties, select the seismic properties the most favourable to thin-inter bed reservoir prediction, effect is optimum, propose to be applicable to the best of breed of Seismic attribute analysis technology, and carry out regretional analysis, reach the object of utilizing seismic properties predicting reservoir parameter, raising reservoir prediction precision, improving exploratory development efficiency.
Thin sand-mud interbed reservoir thickness prediction method is not only applicable to the prediction of shore Vlei thin interbed sand body, the prediction of other subtle reservoir targets is also had to important directive significance simultaneously, also will offer reference and reference for the reservoir descriptive study of other deposition sand body.
Summary of the invention
The object of this invention is to provide one and can portray thin layer (layer of sand group) distribution trend in the plane, the thin sand-mud interbed reservoir thickness prediction method of the relativeness of indication thickness.
Object of the present invention can be achieved by the following technical measures: thin sand-mud interbed reservoir thickness prediction method, and this thin sand-mud interbed reservoir thickness prediction method comprises: step 1, carry out the explanation of detailed level position; Step 2, the multiple seismic properties of extraction thin interbed; Step 3, carry out this multiple seismic properties preferably, obtain the seismic properties after preferential; Step 4, carries out multiattribute matching; And step 5, calculate sand thickness and distribute, and export result of calculation.
Object of the present invention also can be achieved by the following technical measures:
In step 1, by the demarcation of composite traces, according to time location corresponding to type formation and reflectance signature, and corresponding time location and the reflectance signature of reservoir, and in conjunction with the actual geological condition in work area, complete the demarcation of required reference layer position.
In step 2, from pseudo-entropy, fractal, chaos, high-order statistic, select to be suitable for, can reflect this multiple seismic properties of thin interbed type, when Rational choice, window extracts this multiple seismic properties.
In step 2, by this multiple seismic properties respectively with well in sand thickness carry out cross analysis, judge the degree of correlation between this multiple seismic properties and sand thickness, analytical calculation error.
In step 3, from this multiple seismic properties, select best seismic properties or combinations of attributes, thereby obtain this seismic properties after preferably.
In step 3, this multiple seismic properties of ground and zone thickness or oil-gas possibility geologic parameter are analyzed, and in conjunction with work area actual conditions, select a few class seismic properties that related coefficient is higher, certain class seismic properties is being done to correlation analysis again, to there being larger correlativity to belong to again the seismic properties of a generic attribute together, preferably one, reduce redundancy, obtain this seismic properties after preferably.
In step 4, according to regression coefficient, regression equation and multiple regression significance test computing method, set up the relation formula between multiattribute and thin interbed sand body, form the multiple regression mathematical model that is applicable to thin-inter bed reservoir prediction, set up the predictor formula of sand thickness.
In step 4, this multiattribute fitting formula is:
。
Wherein, H is zone thickness parameter, is this seismic properties after preferably, for this corresponding weight coefficient of seismic properties after preferably, is a constant.
In step 5, according to this seismic properties after preferably in step 3, integrating step 1 interpretation horizon that obtains, and this multiattribute fitting formula of obtaining of step 4, carry out sand thickness and distribute and calculate, and export result of calculation.
Thin sand-mud interbed reservoir thickness prediction method in the present invention, the problems such as the resolution of thin interbed oil reservoir thin thickness, existing seismic data is low are solved, under the prerequisite of layer position Fine structural interpretation, select window when rational, extract the seismic properties of objective interval number of different types, comprise RMS amplitude, reflection strength, average frequency, instantaneous phase, instantaneous acceleration, instant bandwidth, wave impedance relatively, and for attributes such as the pseudo-entropy of thin interbed prediction, chaos, fractal, high-order statistics; Then add up the property value of these seismic properties at emphasis Jing Chu, set up the quantitative calculated relationship of optimization between emphasis well place's reservoir thickness and seismic properties.Thin sand-mud interbed reservoir thickness prediction method in the present invention can be portrayed thin layer distribution trend in the plane, the thickness of prediction thin interbed reservoir.
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Accompanying drawing explanation
Fig. 1 is the process flow diagram of a specific embodiment of thin sand-mud interbed reservoir thickness prediction method of the present invention;
Fig. 2 is multiple regression coefficient calculations figure;
Fig. 3 is that multiple regression coefficient formulas is set up schematic diagram.
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Embodiment
For above and other object of the present invention, feature and advantage can be become apparent, cited below particularly go out preferred embodiment, and coordinate appended graphicly, be described in detail below.
As shown in Figure 1, Fig. 1 is the process flow diagram of thin sand-mud interbed reservoir thickness prediction method of the present invention.In step 101, carry out the explanation of detailed level position.Explain detailed level position is the basis of carrying out seismic attributes analysis, by the demarcation of composite traces, specify time location and reflectance signature that type formation is corresponding, know time location and reflectance signature that reservoir is corresponding, the actual geological condition in binding work area, completes the demarcation of required reference layer position simultaneously.Flow process enters into step 102.
In step 102, extract thin interbed seismic properties.For thin interbed, selection is suitable for, can reflect the seismic properties such as the pseudo-entropy of the seismic properties of thin interbed Reservoir type, fractal, chaos, high-order statistic, when Rational choice, window extracts seismic properties, by they respectively with well in sand thickness carry out cross analysis, judge the degree of correlation between each attribute and sand thickness, analytical calculation error.
Wherein, the concept of entropy originates from thermodynamic argument, and in thermodynamics, entropy represents the confusion degree of molecular motion.If the state of molecular motion is more chaotic in a thermodynamic system, the entropy of this system is just larger so.Use for reference maximum entropy deconvolution disposal route, proposed the concept of pseudo-entropy.It is not completely suitable that the entropy of seismic trace should be tending towards minimum hypothesis, and in fact it should regular variation occur along deposition direction.Under normal circumstances, in petroclastic rock area, sedimentation coarse size, thickness in monolayer is also thicker, the high value of pseudo-entropy; Sedimentation fine size, thickness in monolayer is also thinner, pseudo-entropy low value; In carbonate rock area, crack, solution cavity and dissolution pore are relatively grown, and it is complicated that seismic waveshape is tending towards, and pseudo-entropy is low value.Therefore, more conventional seismic properties, pseudo-entropy attribute has clearer and more definite geology indicative significance.
The basic thought of fractal theory is: objective things have the hierarchical structure of self similarity, and local have the similarity in statistical significance with entirety at aspects such as form, function, information, time, spaces, becomes self-similarity.For example, the every part in a magnet all has the two poles of the earth, north and south as entirety, constantly cuts apart down, and every part all has the magnetic field identical with integrated magnets.The hierarchical structure of this self similarity, the suitable physical dimension that zooms in or out, total is constant.The many things of objective occurring in nature, have " level " structure of self similarity, in the ideal case, even have infinite level.The suitable physical dimension that zooms in or out, total does not change.Many complicated physical phenomenons, are exactly the fractal geometry that is reflecting this class hierarchy behind, are dimension for portraying the index of seismic event fractal characteristic.
Chaos can be understood as deterministic randomness, and determinacy is to be produced by immanent cause because of it, and randomness is because it is irregular, uncertain behavior.At present, maximum Lyapunov exponent spectral technology is an important method of research chaos system dynamic characteristic.The method refers to by calculating the average index rate that in phase space, adjacent tracks is dispersed or restrained, carry out rapidly Difference test, thereby the predicted possibility of the to-be that makes system disappears, and has reflected that system is from moving towards in order unordered, from simple to complicated intensity of variation.Generally, in the time that oil gas is contained on stratum, can cause the variation of seismic waveshape, the complexity that adopts maximum Lyapunov exponent can delineate seismic sequence, discloses its horizontal change, finds oily position.
Classical signal disposal route is using second-order statistic as tool of mathematical analysis, and as time domain adopts related function, frequency domain adopts power spectrum.But in non-Gaussian signal process field, mainly adopt Higher Order Cumulants as main analytical tools, claim again Higher Order Cumulants treatment technology.High-order statistic has the remarkable advantage of following 3 aspects compared with second-order statistic (autocorrelation function): 1. to have constant to Gaussian noise be zero feature to high-order statistic, thereby can be used for extracting the non-Gaussian signal in Gaussian noise; 2. high-order statistic contains systematic phase information, thereby can be used for the identification of non-minimum phase system; 3. high-order statistic can be used for the non-linear of detection and descriptive system, as detected gaussian signal or non-Gaussian signal.Non-Gaussian signal is processed and is adopted Higher Order Cumulants as main analytical tools, as the character of a function point of differential geometry research, in the time that first order derivative is not enough, with regard to Consideration of Second Order derivative, three order derivatives etc.
Flow process enters into step 103.
In step 103, carry out seismic properties preferred.Extracting on the basis of thin interbed seismic properties, the geologic parameters such as these attributes and zone thickness or oil-gas possibility are done to correlation analysis, in conjunction with work area to be studied actual conditions, select several generic attributes that related coefficient is higher, certain generic attribute is done to correlation analysis again, to there being larger correlativity to belong to again the seismic properties of a generic attribute together, preferably one, reduce redundancy, thus a certain concrete practical problems that is finally resolved preferably after seismic properties.
In the time carrying out Seismic Reservoir Prediction, conventionally introduce the various seismic properties relevant with reservoir prediction.The introducing of seismic properties conventionally will through one from less to more, again from many to few process.What is called from less to more, refers to that initial stage in design prediction scheme lists various attributes that may be relevant with reservoir prediction should try one's best more.Can make full use of so various Useful Informations, absorb each side expert's experience, improve the effect of reservoir prediction.But the unlimited increase of attribute also can bring adverse influence for reservoir prediction, with regard to pattern-recognition, in the time that sample number is fixing, attribute number too much can cause the deterioration of classifying quality.
Therefore, for particular problem, must from numerous seismic properties, select some best seismic properties or combinations of attributes, carry out from how to analyze to few seismic properties optimization.Seismic properties method for optimization analysis is a lot, can be divided into substantially the mapping of seismic properties dimensionality reduction and select two large class methods with seismic properties: 1. seismic properties dimensionality reduction mapping; 2. seismic properties is selected.
At present, for the overall selection principle of the seismic properties of pattern-recognition, reservoir description be: 1. different survey regions should, according to the geology characteristic of local area, be selected corresponding attribute on the basis of test; 2. need the geologic objective solving as differences such as lithology, stratum, oil-gas possibility, zone of fracture, the attribute of selection should be different; 3. select the attribute that reflection off-note is the most responsive, physical significance is the clearest and the most definite to participate in computing or be used as synthetic study; 4. in numerous seismic properties, reflect in several similar parameters of off-note, only select one of them; 5. according to practice and experience, participating in the comprehensive attribute of analyzing or process is generally good at 3 to 9.
By a large amount of tests and practical application, sum up a set of effective seismic properties method for optimization analysis in actual production process for thin interbed, choose a large amount of seismic properties, do correlation analysis with the geologic parameter such as zone thickness or oil-gas possibility, select several generic attributes that related coefficient is higher, certain generic attribute is done to correlation analysis again, to there being larger correlativity to belong to again the seismic properties of a generic attribute together, preferably one, reduce redundancy, thus a certain concrete practical problems that is finally resolved preferably after seismic properties.
Flow process enters into step 104.
In step 104, carry out multiattribute matching.What linear regression was studied is the regression problem between a Dependent variable, and an independent variable, but, in many practical problemss of field of seismic exploration, affect often more than one of the independent variable of Dependent variable,, but multiple, therefore need to carry out the regretional analysis between a Dependent variable, and multiple independent variable, it is multiple regression analysis, and it is wherein the simplest, conventional and what have basic character is multiple linear regression analysis, many non-linear regressions and polynomial regression can turn to multiple linear regression and solve, thereby multiple linear regression analysis has a wide range of applications.Research multiple linear regression analysis thought, method and principle and linear regression analysis basic identical, but wherein to relate to some new concepts and carry out finer analysis, particularly calculating on than linear regression analysis complexity many.
The basic task of multiple linear regression analysis comprises: set up the multiple linear regression equations of Dependent variable, to multiple independents variable according to the actual observed value of Dependent variable, and multiple independents variable; Check, analyze the conspicuousness of each independent variable to the comprehensive linear effect according to independent variable; Check, analyze the conspicuousness of the simple linear effect of each independent variable to Dependent variable,, select only Dependent variable, to be had the independent variable of remarkable linear effect, set up the best multiple linear regression equation; Evaluate relative importance and the irrelevance of mensuration the best multiple linear regression equation etc. of each independent variable on Dependent variable, impact.
Fig. 2 is multiple regression coefficient calculations figure; Fig. 3 is that multiple regression coefficient formulas is set up schematic diagram.As can be seen from Figure 2, by the preferred attribute at well point place is carried out to multiple regression, can obtain multiple regression coefficients; As can be seen from Figure 3, by multiple attributes are carried out to matching, finally obtain thin interbed thickness data.
Systematic study multiple regression forecasting computing method, the computing method such as regression coefficient, regression equation and multiple regression significance test are furtherd investigate, set up the relation formula between multiattribute and thin interbed sand body, form the multiple regression mathematical model that is applicable to thin-inter bed reservoir prediction, set up the multiattribute fitting formula of sand thickness.
Wherein: H is zone thickness parameter, for the seismic properties that step (3) optimizes, be the corresponding weight coefficient of optimized seismic properties, be a constant.Flow process enters into step 105.
In step 105, calculate sand thickness and distribute.The seismic properties optimizing according to step 103, integrating step 101 interpretation horizon that obtains, and the multiattribute fitting formula that obtains of step 104, carry out sand thickness and distribute and calculate, and export result of calculation.
Thin sand-mud interbed reservoir thickness prediction method in the present invention, can provide a set of simple and effective thin interbed earthquake multiattribute forecasting techniques method for geological research personnel.By extracting the multiple seismic properties that can react thin interbed, and a basic enterprising step is carried out the preferred of seismic properties again, the final multiple regression mathematical model of predicting for thin interbed of setting up by multiattribute multiple regression procedure, this mathematical model can be carried out the quantitative calculating of thin interbed thickness.
Claims (9)
1. thin sand-mud interbed reservoir thickness prediction method, is characterized in that, this thin sand-mud interbed reservoir thickness prediction method comprises:
Step 1, carries out the explanation of detailed level position;
Step 2, the multiple seismic properties of extraction thin interbed;
Step 3, carry out this multiple seismic properties preferably, obtain the seismic properties after preferential;
Step 4, carries out multiattribute matching; And
Step 5, calculates sand thickness and distributes, and export result of calculation.
2. thin sand-mud interbed reservoir thickness prediction method according to claim 1, it is characterized in that, in step 1, by the demarcation of composite traces, according to time location corresponding to type formation and reflectance signature, and corresponding time location and the reflectance signature of reservoir, and in conjunction with the actual geological condition in work area, complete the demarcation of required reference layer position.
3. thin sand-mud interbed reservoir thickness prediction method according to claim 1, it is characterized in that, in step 2, from pseudo-entropy, fractal, chaos, high-order statistic, select to be suitable for, this multiple seismic properties that can reflect thin interbed type, when Rational choice, window extracts this multiple seismic properties.
4. thin sand-mud interbed reservoir thickness prediction method according to claim 3, it is characterized in that, in step 2, by this multiple seismic properties respectively with well in sand thickness carry out cross analysis, judge the degree of correlation between this multiple seismic properties and sand thickness, analytical calculation error.
5. thin sand-mud interbed reservoir thickness prediction method according to claim 1, is characterized in that, in step 3, from this multiple seismic properties, selects best seismic properties or combinations of attributes, thereby obtains this seismic properties after preferably.
6. thin sand-mud interbed reservoir thickness prediction method according to claim 4, it is characterized in that, in step 3, this multiple seismic properties of ground and zone thickness or oil-gas possibility geologic parameter are analyzed, and in conjunction with work area actual conditions, select a few class seismic properties that related coefficient is higher, certain class seismic properties is being done to correlation analysis again, to there being larger correlativity to belong to again the seismic properties of a generic attribute together, preferably one, reduce redundancy, obtain this seismic properties after preferably.
7. thin sand-mud interbed reservoir thickness prediction method according to claim 1, it is characterized in that, in step 4, according to regression coefficient, regression equation and multiple regression significance test computing method, set up the relation formula between multiattribute and thin interbed sand body, form the multiple regression mathematical model that is applicable to thin-inter bed reservoir prediction, set up the predictor formula of sand thickness.
8. thin sand-mud interbed reservoir thickness prediction method according to claim 7, is characterized in that, in step 4, this multiattribute fitting formula is:
Wherein, H is zone thickness parameter, is this seismic properties after preferably, for this corresponding weight coefficient of seismic properties after preferably, is a constant.
9. thin sand-mud interbed reservoir thickness prediction method according to claim 1, it is characterized in that, in step 5, according to this seismic properties after preferably in step 3, integrating step 1 interpretation horizon that obtains, and this multiattribute fitting formula of obtaining of step 4, carry out sand thickness and distribute and calculate, and export result of calculation.
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