CN109190292A - Aquifer water well prediction technique based on well-log information - Google Patents
Aquifer water well prediction technique based on well-log information Download PDFInfo
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- 241001269238 Data Species 0.000 claims abstract description 15
- 238000005192 partition Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 7
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Abstract
The invention discloses a kind of the aquifer water well prediction technique based on well-log information, specific steps are as follows: selection well logging sample；Measure the double time receiving difference datas and core porosity data of well logging sample；The correlativity for establishing core porosity and double time receiving difference datas constructs watery prediction model according to water-bearing layer thickness and core porosity, determines the contribution rate that water-bearing layer thickness and core porosity respectively influence watery in watery prediction model；The numerical value normalizing that the water-bearing layer thickness and core porosity of well logging sample will be chosen, the numerical value of rich water sex index is calculated according to watery prediction model；Watery partition threshold is obtained by Histogram statistics, watery block plan is drawn according to watery partition threshold, select two master control impact factors of water-bearing layer thickness and core porosity, establish watery prediction model, the limitation for reducing single factor test or multifactor evaluation sandstone aquifer watery, improves the accuracy of prediction.
Description
Technical field
The present invention relates to a kind of aquifer water well prediction technique more particularly to a kind of water-bearing layer based on well-log information are rich
Aqueous prediction technique.
Background technique
The watery of sandstone aquifer is of great significance to the prevention and treatment of such water damage, and the prediction of sandstone aquifer is to do water well
The basis of evil prevention and treatment, to the understanding of aquifer water well mainly according to hydrogeological investigation, but it is various detect means it is restricted because
Element is more, and as the increase precision of distance can reduce, existing research personnel are by three dimensional seismic data, log etc. thus
Inversion method sandstone porosity, and be contemplated in the prediction of watery as influence factor；The water of preservation is hole in sandstone
Gap water and crevice water.Most of researchs at present be all by lithologic structure, sand mud than etc. factors indirectly react water-bearing layer
Porosity predicts that the watery in water-bearing layer, prediction result error is slightly larger, complicated for operation.
Summary of the invention
The purpose of the present invention is to solve the above-mentioned problems, and it is pre- to provide a kind of aquifer water well based on well-log information
Survey method, it has prediction result error smaller, facilitates service advantages.
To achieve the goals above, the present invention adopts the following technical scheme:
A kind of aquifer water well prediction technique based on well-log information, specific steps are as follows:
Selection well logging sample；
Measure the double time receiving difference datas and core porosity data of well logging sample；
According to the double time receiving difference datas and core porosity data measured, core porosity and double time receiving difference datas are established
Correlativity:
POR=C1A+C2 (1)
Wherein POR is core porosity, and A is double time receiving difference datas, and C1, C2 are respectively real number；
Influence according to water-bearing layer thickness and core porosity to watery constructs watery prediction model:
W=k1H+k2POR (2)
Wherein k1, k2 are respectively the contribution rate of water-bearing layer thickness and core porosity to W, i.e. weight, and H is aqueous thickness
Degree, POR are the calculating porosity in water-bearing layer；
Determine the contribution rate that water-bearing layer thickness and core porosity respectively influence watery in watery prediction model；
The numerical value normalizing that the water-bearing layer thickness and core porosity of well logging sample will be chosen, according to watery prediction model meter
Calculate the numerical value of rich water sex index；
Watery partition threshold is obtained by Histogram statistics, watery block plan is drawn according to watery partition threshold.
The contribution rate that water-bearing layer thickness and core porosity respectively influence watery in the watery prediction model, meter
Calculation method are as follows:
Weight is carried out to H and POR using entropy assessment to determine:
In formula, m is the quantity of the factor, pijFor the standardized value of i-th of sample value of j-th of factor, xijFor j-th because
I-th of sample value of son is arranged using the data under the factor for j-th of factor, using formula (4), calculates the factor
Entropy ej, it may be assumed that
In formula, takeThen 0≤ej≤ 1, by the standardized value p of each factorijIt brings formula (4) into, obtains each
The entropy e of the factorj, according to entropy ejSize, determine the degree of deviation of j-th of factor, its calculation formula is:
gj=1-ej (5)
The degree of deviation of j-th of factor is standardized, obtained standardized value is the weight of j-th of factor, and j-th
The weight calculation formula of the factor are as follows:
Beneficial effects of the present invention:
There are the influences of Multiple factors the watery of sandstone, if only carrying out watery subregion with single factors, predict
As a result limitation is certainly existed；It is various according to the watery for predicting water-bearing layer compared with the assay method of Multi-factor overlap
Many kinds of, the heavy workload and if the influence relationship between each factor cannot be analyzed accurately of factor, influences whether most
The accuracy of result afterwards, the present invention select two master control impact factors of water-bearing layer thickness and core porosity, it is pre- to establish watery
Model is surveyed, the limitation of single factor test or multifactor evaluation sandstone aquifer watery is reduced, improves the accuracy of prediction.
Detailed description of the invention
Fig. 1 is core porosity prediction model figure of the present invention；
Fig. 2 is core porosity forecast of distribution isogram of the present invention；
Fig. 3 is watery histogram of the present invention；
Fig. 4 is watery block plan；
Fig. 5 is flow chart of the method for the present invention.
Specific embodiment
The invention will be further described with embodiment with reference to the accompanying drawing.
As shown in figure 5, a kind of aquifer water well prediction technique based on well-log information, specific steps are as follows:
The present embodiment well logging rock core sample used is derived from the military camp straight sieve group lower section layer position of moat field with "nine squares" exploratory bore-hole rock core, tests
Instrument is that high temperature and pressure covers pressure hole infiltration analyzer；
The double time receiving difference datas and core porosity data that this layer is are measured, as shown in table 1, drilling well number is that well logging sample is compiled
Number；
1 core porosity experimental data table of table
By 1 data of table, compared according to double time receiving difference datas and core porosity data, draw double time receiving difference datas with
Core porosity entity relationship diagram, as shown in Figure 1, building core porosity prediction model:
POR=-0.2834A+54.4499 (1)
R2=0.8286
Wherein POR is core porosity, and A is double time receiving difference datas, R2For related coefficient, coefficient R2=0.8286, phase
Close coefficients R2It is bigger, illustrate that double time receiving difference datas are more related to core porosity, illustrates that core porosity prediction model is more reasonable,
This layer is calculated in the average core porosity of each well logging position by core porosity prediction model, is obtained using Kriging regression method
To the core porosity forecast of distribution isogram of this layer of position in the plane, as shown in Figure 2；
There are the influences of Multiple factors the watery of sandstone, if only carrying out watery subregion with single factors, predict
As a result limitation is certainly existed；It is various according to the watery for predicting water-bearing layer compared with the assay method of Multi-factor overlap
Many kinds of, the heavy workload and if the influence relationship between each factor cannot be analyzed accurately of factor, influences whether most
The accuracy of result afterwards, the present embodiment selection research it is trivial in do not find tomography, the monoclinal structure less than 3 °, stratum rise and fall
Very little, geological structure are simple, therefore select water-bearing layer thickness and two factors of porosity as the governing factor for influencing watery
To construct watery prediction model:
W=k1H+k2POR (2)
Wherein k1, k2 are respectively the contribution rate of water-bearing layer thickness and core porosity to W, i.e. weight, and H is aqueous thickness
Degree, POR are the calculating porosity in water-bearing layer；
Weight is carried out to H and POR using entropy assessment to determine:
In formula, m is the quantity of the factor, pijFor the standardized value of i-th of sample value of j-th of factor, xijFor j-th because
I-th of sample value of son is arranged using the data under the factor for j-th of factor, using formula (4), calculates the factor
Entropy ej, it may be assumed that
In formula, takeThen 0≤ej≤ 1, by the standardized value p of each factorijIt brings formula (4) into, obtains each
The entropy e of the factorj, according to entropy ejSize, determine the degree of deviation of j-th of factor, calculating formula are as follows:
gj=1-ej (5)
J=1,2, degree of deviation vector: g=(0.224,0.176) is calculated according to formula (5)；
The obtained degree of deviation is standardized, obtained standardized value is the weight of j-th of factor, it, which is reflected, contains
The contribution rate of water layer thickness and core porosity to watery index W, the weight calculation formula of j-th of factor are as follows:
Substitute into the degree of deviation g calculatedj, factor j=1 is obtained, 2 weight vectors: S=(0.56,0.44),
Therefore the weight of water-bearing layer thickness and core porosity in expression formula (2) is k1=0.56, k2=0.44；
The data of water-bearing layer thickness and core porosity in table 1 couple watery index W according to formula (7)
When, the big situation of the numerical series difference of water-bearing layer thickness and core porosity that same rank watery occur, meeting when coupling
There is the case where big number eats decimal, influences the accuracy of result, it is this to solve the problems, such as, the aqueous thickness of well logging sample will be chosen
The numerical value normalizing of degree and core porosity calculates the numerical value of rich water sex index according to formula (7)；
The histogram for drawing rich water sex index, as shown in figure 3, being obtained by the numerical value of Histogram statistics rich water sex index
Rich water sex index partition threshold be 0.6,0.8, draw out watery block plan according to the Histogram statistics of rich water sex index, such as
Shown in Fig. 4.
Verify the accuracy of watery prediction model:
The verifying of 2 bailing test of table
K1-6, K2-4, K4-7, K6-4, K7-5, K7-7 drilling are according to watery as defined in " mine geological hazards detailed rules and regulations " point
Grade is consistent with watery block plan shown in Fig. 4, therefore watery prediction model is reliable.
There are an a wide range of weak hydrous fluids in the research straight sieve group lower section water-bearing layer in area in east, the west and south, northeast, the northwestward and in
Portion's watery is medium, near the k1-6 of northeast, near the k2-1 of the northwestward, watery is stronger near the k6-3 and k9-2 of south.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art
The various modifications or changes that can be made are not needed to make the creative labor still within protection scope of the present invention.
Claims (2)
1. a kind of aquifer water well prediction technique based on well-log information, which is characterized in that specific steps are as follows:
Selection well logging sample；
Measure the double time receiving difference datas and core porosity data of well logging sample；
According to the double time receiving difference datas and core porosity data measured, it is related to double time receiving difference datas to establish core porosity
Relationship:
POR=C1A+C2 (1)
Wherein POR is core porosity, and A is double time receiving difference datas, and C1, C2 are respectively real number；
Influence according to water-bearing layer thickness and core porosity to watery constructs watery prediction model:
W=k1H+k2POR (2)
Wherein k1, k2 are respectively the contribution rate of water-bearing layer thickness and core porosity to W, i.e. weight, and H is water-bearing layer thickness, POR
For the calculating porosity in water-bearing layer；
Determine the contribution rate that water-bearing layer thickness and core porosity respectively influence watery in watery prediction model；
The numerical value normalizing that the water-bearing layer thickness and core porosity of well logging sample will be chosen, calculates rich according to watery prediction model
The numerical value of aqueous index；
Watery partition threshold is obtained by Histogram statistics, watery block plan is drawn according to watery partition threshold.
2. a kind of aquifer water well prediction technique based on well-log information as described in claim 1, which is characterized in that described
The contribution rate that water-bearing layer thickness and core porosity respectively influence watery in watery prediction model, calculation method are as follows:
Weight is carried out to H and POR using entropy assessment to determine:
In formula, m is the quantity of the factor, pijFor the standardized value of i-th of sample value of j-th of factor, xijIt is the of j-th of factor
I sample value is arranged using the data under the factor for j-th of factor, using formula (4), calculates the entropy e of the factorj,
That is:
In formula, takeThen 0≤ej≤ 1, by the standardized value p of each factorijIt brings formula (4) into, obtains each factor
Entropy ej, according to entropy ejSize, determine the degree of deviation of j-th of factor, its calculation formula is:
gj=1-ej (5)
The degree of deviation of j-th of factor is standardized, obtained standardized value is the weight of j-th of factor, j-th of factor
Weight calculation formula are as follows:
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Cited By (2)
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| CN112213792A (en) * | 2020-09-07 | 2021-01-12 | 煤炭科学技术研究院有限公司 | Transient electromagnetic method-based water-containing geologic body water-enrichment prediction method |
| CN114460259A (en) * | 2021-12-28 | 2022-05-10 | 淮北矿业股份有限公司 | Dynamic determination method for water-rich property of loose confined aquifer |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112213792A (en) * | 2020-09-07 | 2021-01-12 | 煤炭科学技术研究院有限公司 | Transient electromagnetic method-based water-containing geologic body water-enrichment prediction method |
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Application publication date: 20190111 |