CN103306671A - Four-quadrant reservoir stratum type identification method and system - Google Patents

Abstract

The invention discloses a four-quadrant reservoir stratum type identification method and system, wherein the four-quadrant reservoir stratum type identification method comprises the following steps: selecting a core sample, and acquiring the mud filtrate specific resistance and electrical imaging logging information of a well corresponding to the core sample; performing CT scanning imaging on the core sample to generate a CT imaging pore distribution spectrum of the core sample; according to the mud filtrate specific resistance and the imaging logging pixel conductivity, computing an electrical imaging porosity spectrum of the well corresponding to the core sample by utilizing the Archie's formulas; calibrating the electrical imaging porosity spectrum corresponding to a stratum segment by utilizing the CT imaging pore distribution spectrum, and computing the mean value and distribution variance of the calibrated electrical imaging porosity spectrum; performing standardized treatment on the mean value and distribution variance of the calibrated electrical imaging porosity spectrum to generate the standard mean value and distribution variance of porosities; forming a two-dimension plane by the standard mean value and distribution variance of porosities, wherein the standard mean value of porosities is the abscissa axis, and the standard distribution variance of porosities is the ordinate axis; and identifying the reservoir stratum type according to the two-dimension plane.

Description

The recognition methods of a kind of four-quadrant Reservoir type and system

Technical field

The present invention is directed to the complicated seam of heterogeneous body hole reservoir, proposed the recognition methods of a kind of four-quadrant Reservoir type and system.

Background technology

In the oil-gas exploration and development process, evaluating reservoir is comprehensive understanding and the judge to reservoir study.The purpose of Reservoir type research is in order to seek, be familiar with, transform reservoir, giving full play to reservoir potential, to reach the purpose that improves exploration and development benefit.Therefore reasonable, objective, rapidly Reservoir type is divided tool and is of great significance.

Find by research, the method for at present Reservoir type identification has a lot, but can be divided into generally that well-log information is qualitative, two kinds of quantitative assessment and mathematic statistics evaluation of classifications.Well-log information is qualitative, method for quantitatively evaluating origin early, still continued to use by people so far, it mainly changes in conjunction with permeability, the fracture development parameter of imaging logging map as feature or calculating by Logging Curves, with Reservoir type or reservoir grade classification out; Also can in conjunction with experimental datas such as petrographic thin section, pressure mercury, further carry out refinement to Reservoir type in addition.But in production application, the hole reservoir log response of complicated seam often has multi-solution, and this division methods is subject to explaining the restriction of the human factors such as personnel's experience level; On the other hand, China's low hole compact limestone dolomite reservoir generally needs just have industrial production capacity behind the acidified fracturing reform, and great changes have occured reservoir properties before and after the acid fracturing, and this method exists obviously not enough to production effect after estimating reservoir reconstruction.Use gray theory, comprehensively pass judgment on the mathematic statistics sorting techniques such as function method, PCA, cluster analysis and pattern-recongnition method, analytic hierarchy process (AHP), neutral net and carry out method that Reservoir levels divides and obtained fast-developing and be widely used at Oil Field in recent years.The essence of these class methods is at first to choose the sample point of large quantities of representative different reservoir classifications, adds up the log response feature (can increase in case of necessity the information that reservoir parameter calculates) of each sample point; Next selects suitable mathematical method that sample point is learnt, certainly predicted and checks, and determines best predicting reservoir statistic of classification function or probability function; On this basis, use the Mathematical Modeling of having determined that the sample point of the unknown is predicted, thereby determine the classification of reservoir.But the shortcoming of this method is obvious equally: 1) need the representative sample point of a large amount of known Reservoir levels, this is not suitable for the use of exploration phase at oil field initial stage to a certain extent; 2) it is sex-limited that the method has stronger sectional center, and applicability is relatively poor, is difficult to apply at different blocks; It is the key factor that the method also fails to find impact and restriction Reservoir levels to divide at all.

Summary of the invention

For solving the deficiency of said method, this case utilizes the degree of porosity spectrum of the degree of porosity spectrum scale electricity imaging of CT imaging, calculates standard degree of porosity average and standard hole distribution variance parameter, carries out the identification of complicated seam hole Reservoir type with this in four-quadrant.

For achieving the above object, the present invention proposes the recognition methods of a kind of four-quadrant Reservoir type, said method comprising the steps of: choose core sample, and obtain mud filtrate resistivity and the electric imaging logging data of core sample corresponding well; Described core sample is carried out the CT scan imaging, generate the CT imaging hole distribution profile of described core sample; According to described mud filtrate resistivity and electric imaging logging data, utilize Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well; Utilize the described electric imaging degree of porosity spectrum of the corresponding interval of described CT imaging hole distribution profile scale, and calculate average and the distribution variance of the electric imaging degree of porosity spectrum behind the scale; Average and distribution variance to the electric imaging degree of porosity spectrum behind the described scale are carried out standardization, generate standard degree of porosity average and standard hole distribution variance; Consist of two dimensional surface by described standard degree of porosity average and standard hole distribution variance, wherein, described standard degree of porosity average is transverse axis, and standard hole distribution variance is the longitudinal axis; According to described two dimensional surface, Reservoir type is identified.

For achieving the above object, the invention allows for a kind of four-quadrant Reservoir type recognition system, described system comprises that rock core chooses module, CT imaging hole distribution profile extraction module, electric imaging degree of porosity spectrum computing module, degree of porosity spectrum calibration block, standardization module, two dimensional surface generation module and Reservoir type identification module; Wherein, described rock core is chosen module, is used for choosing core sample, and obtains mud filtrate resistivity and the electric imaging logging data of core sample corresponding well; Described CT imaging hole distribution profile extraction module is used for described core sample is carried out the CT scan imaging, generates the CT imaging hole distribution profile of described core sample; Described electric imaging degree of porosity spectrum computing module is used for according to described mud filtrate resistivity and electric imaging logging data, utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well; Described degree of porosity spectrum calibration block utilizes the described electric imaging degree of porosity spectrum of the corresponding interval of described CT imaging hole distribution profile scale, and calculates average and the distribution variance of the electric imaging degree of porosity spectrum behind the scale; Described standardization module is used for average and the distribution variance of the electric imaging degree of porosity spectrum behind the described scale are carried out standardization, generates standard degree of porosity average and standard hole distribution variance; Described two dimensional surface generation module is used for consisting of two dimensional surface by described standard degree of porosity average and standard hole distribution variance, and wherein, described standard degree of porosity average is transverse axis, and standard hole distribution variance is the longitudinal axis; Described Reservoir type identification module is used for according to described two dimensional surface, and Reservoir type is identified.

Four-quadrant Reservoir type of the present invention recognition methods and system are easy to realize technically; The distribution of pores spectrum scale electricity imaging degree of porosity spectrum of utilizing little CT scan to obtain can more accurately reflect the formation rock pore structure characteristic; The four-quadrant Reservoir type recognition methods that proposes among the present invention and system carry out deep excavation with electric imaging degree of porosity spectrum information, and under the scale of CT distribution of pores spectrum, electric imaging degree of porosity spectrum behind the scale that can accurately reflect the formation rock architectural feature and average, variance parameter have quantitatively been calculated, obtain standard degree of porosity average and distribution variance through standardization, and utilize two dimensional surface to carry out the division of reservoir, realized that to Reservoir type division, modification measures and the establishment of the project oilfield prospecting developing is had higher engineering using value.The Image Logging Data of utilizing four-quadrant Reservoir type of the present invention recognition methods and system to process, process layer position recognition result and imaging logging image feature, rock core Characteristic Contrast have improved the coincidence rate that Reservoir type is divided.

Description of drawings

Accompanying drawing described herein is used to provide a further understanding of the present invention, consists of the application's a part, does not consist of limitation of the invention.In the accompanying drawings:

Fig. 1 is the flow chart of the four-quadrant Reservoir type recognition methods of one embodiment of the invention.

Fig. 2 is the structural representation of the four-quadrant Reservoir type recognition system of one embodiment of the invention.

Fig. 3 A is the schematic diagram of CT imaging hole distribution profile of a depth point of the present invention's one specific embodiment.

Fig. 3 B is the schematic diagram that the electric imaging degree of porosity of a depth point of the present invention's one specific embodiment is composed.

Fig. 4 is the schematic diagram of the two dimensional surface reservoir division of the present invention's one specific embodiment.

Fig. 5 is the Southwest Oil LGX well Types of Carbonate Reservoir identification achievement schematic diagram of the present invention's one specific embodiment.

The specific embodiment

For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing the embodiment of the invention is described in further details.At this, illustrative examples of the present invention and explanation thereof are used for explanation the present invention, but not as a limitation of the invention.

Fig. 1 is the flow chart of the four-quadrant Reservoir type recognition methods of one embodiment of the invention.As shown in Figure 1, may further comprise the steps:

Step S101 chooses core sample, and obtains mud filtrate resistivity and the electric imaging logging data of core sample corresponding well;

Step S102 carries out the CT scan imaging to core sample, generates the CT imaging hole distribution profile of core sample;

Step S103 according to mud filtrate resistivity, imaging logging pixel electrical conductivity, utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of core sample corresponding well;

Step S104, utilize the electric imaging degree of porosity spectrum of the corresponding interval of CT imaging hole distribution profile scale, acquisition can accurately reflect the electric imaging degree of porosity spectrum behind the scale of formation rock pore structure characteristic, and calculates average and the distribution variance of electric imaging degree of porosity spectrum behind the scale;

Step S105 carries out standardization to average and the distribution variance of electric imaging degree of porosity spectrum behind the scale, generates standard degree of porosity average and standard hole distribution variance;

Step S106 consists of two dimensional surface by described standard degree of porosity average and standard hole distribution variance, and wherein, described standard degree of porosity average is transverse axis, and standard hole distribution variance is the longitudinal axis;

Step S107 according to described two dimensional surface, identifies Reservoir type.

In step S101, at first want rock core information and the electric imaging logging data in collection research work area, choose the rock that representative typical core sample should comprise different reservoir space types, also want simultaneously to represent whole research and obtain general features.

In step S102, the representative typical core that combing goes out is carried out the high-resolution computed tomography imaging, adopted the method for full diameter, the test of little rock sample two-fold high-resolution ct.At first full diameter rock sample is carried out the little CT of high-resolution test, then drill through plunger or the fine grained chippings rock sample carries out the higher little CT test of resolution ratio at full-hole core.Overcome the secondary pore architectural feature that simple full-hole core CT test can only obtain the large-sizes such as crack, solution cavity, can not obtain the pore structure of matrix; And simple little rock sample CT test can only obtain the pore character of matrix, can't obtain in the defective than the secondary pore architectural feature in the large space.To the CT scan imaging results, in three dimensions, observe Rock Matrix hole and secondary pore architectural feature, and quantitatively extract the distribution of pores spectrum of rock core.Comprehensive CT scan imaging features, imaging logging image feature and rock core show, determine the corresponding interval Reservoir type of rock core.

In step S103, the calculating of electric imaging degree of porosity spectrum utilizes Archie equation to carry out.Imaging logging instrument adopts button electrode system to paste the borehole wall and measures the stratum electrical property feature, the electrical conductivity of its image change reaction rinse band borehole wall behind the shallow resistivity scale, the electric imaging pixel degree of porosity that utilizes Archie formula to calculate the core sample corresponding well can calculate with formula (1);

${\mathrm{\φ}}_{\mathrm{FMI}-i}=\sqrt[m]{a\·b\·R_{\mathrm{mf}}\·C_i}---\left(1\right)$

Wherein, φ _FMI-iThe degree of porosity of a certain pixel in the electric imaging, dimensionless;

A, b are the factor of proportionality in the Archie formula, dimensionless;

M is the formation cementation index, dimensionless;

R _MfThe mud filtrate resistivity of described core sample corresponding well, Ω m;

C _iThe a certain pixel electrical conductivity in the described electric imaging logging data, S/m.

In step S104, utilize step S102 to generate CT imaging hole distribution profile the electric imaging degree of porosity spectrum that step S103 generates is carried out exact scale.To this, the present invention introduces first average and expresses the degree that main peak in CT imaging hole distribution profile and the electric imaging degree of porosity spectrum departs from baseline, with variance express spectra deformation (dispersiveness); According to the electric imaging degree of porosity spectrum that formula (1) among the step S103 generates, utilize formula (2), (3) to calculate average and the distribution variance that generates described electric imaging degree of porosity spectrum;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}}=\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}/\mathrm{NFMI};---\left(2\right)$

${\mathrm{\σ}}_{\mathrm{FMI}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{FMI}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}})}^2}{\mathrm{NFMI}}};---\left(3\right)$

Wherein, Be the average of electric imaging degree of porosity spectrum, dimensionless;

σ _FMIBe the distribution variance of electric imaging degree of porosity spectrum, dimensionless;

φ _FMI-iThe degree of porosity of pixel in the electric imaging that is obtained by formula (1), dimensionless;

NFMI is that the electric imaging logging DATA REASONING that comprises in each data cell in the electric imaging is counted;

According to the CT imaging hole distribution profile that step S102 generates, utilize formula (4), (5) to calculate average and the distribution variance that generates CT imaging hole distribution profile;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}}=\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{CT}-i}/\mathrm{NCT};---\left(4\right)$

${\mathrm{\σ}}_{\mathrm{CT}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{CT}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}})}^2}{\mathrm{NCT}}};---\left(5\right)$

Wherein,

Be the average of CT imaging hole distribution profile, dimensionless;

σ _CTBe the distribution variance of CT imaging hole distribution profile, dimensionless;

φ _CT-iThe degree of porosity of statistic window in the CT scan imaging, dimensionless;

NCT utilizes the statistic window sum that comprises in each data cell in the CT scan imaging;

Degree of porosity φ according to pixel in the electric imaging _FMI-i, electric imaging degree of porosity spectrum average

, electric imaging degree of porosity spectrum distribution variance σ _FMI, CT imaging hole distribution profile average

And the distribution variance σ of CT imaging hole distribution profile _CT, utilize the electric imaging degree of porosity spectrum after formula (6) calculates the generation scale;

${{\mathrm{\φ}}_{\mathrm{FMI}-i}}^{\′}=\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}+{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{CT}}-\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{FMI}};---\left(6\right)$

Wherein, φ _FMI-iThe degree of porosity of pixel in the electric imaging that obtains of formula (1), dimensionless;

The average of the electric imaging degree of porosity spectrum that obtains of formula (2), dimensionless;

σ _FMIThe distribution variance of the electric imaging degree of porosity spectrum that obtains of formula (3), dimensionless;

The average of the CT imaging hole distribution profile that obtains of formula (4), dimensionless;

σ _CTThe distribution variance of the CT imaging hole distribution profile that obtains of formula (5), dimensionless;

φ _FMI-i' be the degree of porosity of pixel in the electric imaging behind the scale, dimensionless;

The degree of porosity φ of pixel in the electric imaging behind the scale that obtains according to formula (6) _FMI-i', utilize formula (2), (3) to calculate the average of the electric imaging degree of porosity spectrum behind the scale With distribution variance σ ' _FMI

In step S105, generating standard degree of porosity average and standard hole distribution variance is to utilize formula (7) and (8), and formula is as follows:

${\mathrm{\φ}}_a=(\stackrel{\‾}{{{\mathrm{\φ}}^{\′}}_{\mathrm{FMI}}}-{\mathrm{\φ}}_0)/\mathrm{\α};---\left(7\right)$

σ _a=(σ' _FMI-σ ₀)/β； （8）

Wherein, φ _aThe standard degree of porosity average that calculates, dimensionless;

The average of electric imaging degree of porosity spectrum behind the described scale, dimensionless;

φ ₀Be the regional reservoir porosity factor, determined dimensionless by regional reservoir characteristic;

α is hole average normalization factor, is determined by regional reservoir quality, and perhaps value 200, dimensionless;

σ _aThe standard hole distribution variance that calculates, dimensionless;

σ ' _FMIThe distribution variance of electric imaging degree of porosity spectrum behind the scale, dimensionless;

σ ₀Be that regional reservoir is communicated with index, determined dimensionless by regional reservoir characteristic;

β is the distribution of pores variance criterion factor, is determined by regional reservoir quality, and perhaps value 50, dimensionless.

In step S106, utilize the result of calculation of step S105, the present invention proposes to carry out the identification of four-quadrant Reservoir type at the two dimensional surface that is made of standard degree of porosity average and standard hole distribution variance, and wherein abscissa represents standard degree of porosity average, and positive direction represents the reservoir pore space enhancing; Ordinate represents standard hole distribution variance, and positive direction represents reservoir communication and strengthens.Reservoir type according to the definite typical sample respective layer position of step S102 has proposed four-quadrant Reservoir type division methods, simultaneously 4 provincial characteristicss of compacted zone, pore type, crack-hole type and slit formation reservoir has been carried out detailed elaboration;

Not only hole is larger for reservoir in the I quadrant, and it is also relatively good to be communicated with effect, belongs to crack-hole type reservoir, even such reservoir is not taked the acid fracturing measure, also can form effective natural production capacity;

The interior reservoir pore space of II quadrant is less but reservoir communication is better, belongs to the slit formation reservoir, by taking fracturing methods, can improve the connectedness of reservoir, forms effective pay;

Less and the poor connectivity of reservoir pore space generally belongs to compacted zone in the III quadrant, even take acidifying, pressure measure effect also not obvious;

Reservoir pore space is larger in the IV quadrant, but it is relatively poor to be communicated with effect, belongs to hole type reservoir, can link up different interstitial spaces by the acidifying measure, improves reservoir productivity.

Fig. 2 is the structural representation of the four-quadrant Reservoir type recognition system of one embodiment of the invention.The system of the present embodiment has realized quantitatively calculating based on the degree of porosity spectrum of electric imaging logging by open the computing module of electricity imaging degree of porosity spectrum at CIFLog integrated logging well interpretation software platform; Native system comprises: rock core is chosen module 21, CT imaging hole distribution profile extraction module 22,, electric imaging degree of porosity spectrum computing module 23, degree of porosity spectrum calibration block 24, standardization module 25, two dimensional surface generation module 26 and Reservoir type identification module 27; Wherein,

Rock core is chosen module 21, is used for choosing core sample, and obtains mud filtrate resistivity and the electric imaging logging data of core sample corresponding well;

CT imaging hole distribution profile extraction module 22 is used for described core sample is carried out the CT scan imaging, generates the CT imaging hole distribution profile of described core sample;

Electricity imaging degree of porosity spectrum computing module 23 is used for according to described mud filtrate resistivity and electric imaging logging data, utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well;

Degree of porosity spectrum calibration block 24, utilize the described electric imaging degree of porosity spectrum of the corresponding interval of described CT imaging hole distribution profile scale, acquisition can accurately reflect the electric imaging degree of porosity spectrum behind the scale of formation rock pore structure characteristic, and calculates average and the distribution variance of the electric imaging degree of porosity spectrum behind the scale;

Standardization module 25 is used for average and the distribution variance of the electric imaging degree of porosity spectrum behind the described scale are carried out standardization, generates standard degree of porosity average and standard hole distribution variance;

Two dimensional surface generation module 26 is used for consisting of two dimensional surface by described standard degree of porosity average and standard hole distribution variance, and wherein, described standard degree of porosity average is transverse axis, and standard hole distribution variance is the longitudinal axis;

Reservoir type identification module 27 is used for according to described two dimensional surface, and Reservoir type is identified.

In the present embodiment, CT imaging hole distribution profile extraction module 22 has adopted the method for full diameter, the test of little rock sample two-fold high-resolution ct; At first full diameter rock sample is carried out the little CT of high-resolution test, then drill through plunger or the fine grained chippings rock sample carries out the higher little CT test of resolution ratio at full-hole core.Overcome the secondary pore architectural feature that simple full-hole core CT test can only obtain the large-sizes such as crack, solution cavity, can not obtain the pore structure of matrix; And simple little rock sample CT test can only obtain the pore character of matrix, can't obtain in the defective than the secondary pore architectural feature in the large space.To the CT scan imaging results, in three dimensions, observe Rock Matrix hole and secondary pore architectural feature, and quantitatively extract the distribution of pores spectrum of rock core.Comprehensive CT scan imaging features, imaging logging image feature and rock core show, determine the corresponding interval Reservoir type of rock core.

In the present embodiment, electric imaging degree of porosity spectrum computing module 23 formula that utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well is:

${\mathrm{\φ}}_{\mathrm{FMI}-i}=\sqrt[m]{a\·b\·R_{\mathrm{mf}}\·C_i};---\left(1\right)$

Wherein, φ _FMI-iBe the degree of porosity of pixel in the electric imaging, dimensionless;

A, b are the factor of proportionality in the Archie formula, dimensionless;

M is the formation cementation index, dimensionless;

R _MfBe the mud filtrate resistivity of core sample corresponding well, Ω m;

C _iBe the pixel electrical conductivity in the electric imaging logging data, S/m.

In the present embodiment, degree of porosity spectrum calibration block 24 utilizes CT imaging hole distribution profile extraction module 22 generation CT imaging hole distribution profiles that the electric imaging degree of porosity spectrum that electric imaging degree of porosity spectrum computing module 23 generates is carried out exact scale.To this, the present invention introduces first average and expresses the degree that main peak in CT imaging hole distribution profile and the electric imaging degree of porosity spectrum departs from baseline, with variance express spectra deformation (dispersiveness); Compose φ according to the electric imaging degree of porosity that electric imaging degree of porosity spectrum computing module 23 generates _FMI-i, utilize formula (2), (3) to calculate average and the distribution variance (before the scale) that generates electric imaging degree of porosity spectrum;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}}=\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}/\mathrm{NFMI};---\left(2\right)$

${\mathrm{\σ}}_{\mathrm{FMI}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{FMI}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}})}^2}{\mathrm{NFMI}}};---\left(3\right)$

Wherein,

Be the average of electric imaging degree of porosity spectrum, dimensionless;

σ _FMIBe the distribution variance of electric imaging degree of porosity spectrum, dimensionless;

φ _FMI-iThe degree of porosity of pixel in the electric imaging that is obtained by formula (1), dimensionless;

NFMI is that the electric imaging logging DATA REASONING that comprises in each data cell in the described electric imaging degree of porosity spectrum computing module is counted;

According to the CT imaging hole distribution profile that described CT imaging hole distribution profile extraction module 22 generates, utilize formula (4), (5) to calculate average and the distribution variance that generates CT imaging hole distribution profile;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}}=\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{CT}-i}/\mathrm{NCT};---\left(4\right)$

${\mathrm{\σ}}_{\mathrm{CT}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{CT}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}})}^2}{\mathrm{NCT}}};---\left(5\right)$

Wherein,

Be the average of CT imaging hole distribution profile, dimensionless;

σ _CTBe the distribution variance of CT imaging hole distribution profile, dimensionless;

φ _CT-iThe degree of porosity of statistic window in the CT scan imaging, dimensionless;

NCT is the statistic window sum that comprises in each data cell in the described CT imaging hole distribution profile extraction module;

Degree of porosity φ according to pixel in the electric imaging _FMI-i, electric imaging degree of porosity spectrum average

, electric imaging degree of porosity spectrum distribution variance σ _FMI, CT imaging hole distribution profile average

And the distribution variance σ of CT imaging hole distribution profile _CT, utilize the electric imaging degree of porosity spectrum after formula (6) calculates the generation scale;

${{\mathrm{\φ}}_{\mathrm{FMI}-i}}^{\′}=\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}+{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{CT}}-\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{FMI}}---\left(6\right)$

Wherein, φ _FMI-iThe degree of porosity of pixel in the electric imaging that obtains of formula (1), dimensionless;

The average of the electric imaging degree of porosity spectrum that obtains of formula (2), dimensionless;

σ _FMIThe distribution variance of the electric imaging degree of porosity spectrum that obtains of formula (3), dimensionless;

The average of the CT imaging hole distribution profile that obtains of formula (4), dimensionless;

σ _CTThe distribution variance of the CT imaging hole distribution profile that obtains of formula (5), dimensionless;

φ _FMI-i' be the degree of porosity of pixel in the electric imaging behind the scale, dimensionless;

The degree of porosity φ of pixel in the electric imaging behind the scale that obtains according to formula (6) _FMI-i', utilize formula (2), (3) to calculate the average of the electric imaging degree of porosity spectrum behind the scale With distribution variance σ ' _FMI

In the present embodiment, average and the distribution variance of the electric imaging degree of porosity spectrum behind 25 pairs of scales of standardization module are carried out standardization, and the formula that generates standard degree of porosity average and the utilization of standard hole distribution variance is:

${\mathrm{\φ}}_a=(\stackrel{\‾}{{{\mathrm{\φ}}^{\′}}_{\mathrm{FMI}}}-{\mathrm{\φ}}_0)/\mathrm{\α}---\left(7\right)$

σ _a=(σ' _FMI-σ ₀)/β （8）

Wherein, φ _aThe standard degree of porosity average that calculates, dimensionless;

The average of the electric imaging degree of porosity spectrum behind the scale that generates of described degree of porosity spectrum calibration block, dimensionless;

φ ₀Be the regional reservoir porosity factor, determined dimensionless by regional reservoir characteristic;

α is hole average normalization factor, is determined by regional reservoir quality, and perhaps value 200, dimensionless;

σ _aThe standard hole distribution variance that calculates, dimensionless;

σ ' _FMIThe distribution variance of the electric imaging degree of porosity spectrum behind the scale that generates of described degree of porosity spectrum calibration block, dimensionless;

σ ₀Be that regional reservoir is communicated with index, determined dimensionless by regional reservoir characteristic;

β is the distribution of pores variance criterion factor, is determined by regional reservoir quality, and perhaps value 50, dimensionless.

In the present embodiment, standard degree of porosity average and standard hole distribution variance that two dimensional surface generation module 26 utilizes standardization module 25 to generate consist of two dimensional surface, and wherein abscissa represents standard degree of porosity average, and positive direction represents the reservoir pore space enhancing; Ordinate represents standard hole distribution variance, and positive direction represents reservoir communication and strengthens;

Described two dimensional surface comprises four quadrants, and not only hole is larger for reservoir in the I quadrant, and it is also relatively good to be communicated with effect, belongs to crack-hole type reservoir, even such reservoir is not taked the acid fracturing measure, also can form effective natural production capacity;

The interior reservoir pore space of II quadrant is less but reservoir communication is better, belongs to the slit formation reservoir, by taking fracturing methods, can improve the connectedness of reservoir, forms effective pay;

Less and the poor connectivity of reservoir pore space generally belongs to compacted zone in the III quadrant, even take acidifying, pressure measure effect also not obvious;

Reservoir pore space is larger in the IV quadrant, but it is relatively poor to be communicated with effect, belongs to hole type reservoir, can link up different interstitial spaces by the acidifying measure, improves reservoir productivity.

In conjunction with Fig. 1 and Fig. 2, the below illustrates four-quadrant Reservoir type of the present invention recognition methods and system with a specific embodiment.

Integrating step S101 at first screens lithology sample representative in the Southwest Oil block target zone position and imaging, Using Conventional Logs, and its core sample has comprised the main lithology of this block, reservoir space type feature;

According to step S102, based on the little CT measuring technique of laboratory high-resolution, the representative typical core that combing goes out is carried out the CT scan imaging.Adopted the method for full diameter, the test of little rock sample two-fold, at first full-hole core is carried out the CT test, then resolution ratio drill through a fritter rock sample that can reflect the matrix feature at full diameter rock sample and carry out the high-resolution ct test about 50 microns, and resolution ratio is about 20 microns.Full diameter rock sample CT analysis result mainly reflects the secondary pore architectural feature, and little rock sample CT analysis result mainly reflects the pore structure characteristic of matrix.At last, the scanning imagery result of different resolution is integrated, observing Rock Matrix hole and secondary pore architectural feature in three dimensions, and quantitatively extract the distribution of pores spectrum of the total rock heart, is the schematic diagram of certain depth point rock core CT imaging hole distribution profile as shown in Figure 3A.Comprehensive CT scan imaging features, imaging logging image feature and rock core show, determine the corresponding interval Reservoir type of rock core.

Integrating step S103 calculates based on the degree of porosity of electric imaging logging spectrum according to formula (1), is depicted as the schematic diagram of electric imaging degree of porosity spectrum of certain depth point of corresponding diagram 3A such as Fig. 3 B.

Integrating step S104, step S105, according to the above-mentioned electric imaging degree of porosity spectrum of above-mentioned CT imaging hole distribution profile scale, utilize standard degree of porosity average and standard hole distribution variance after formula (2) to (8) calculates standardization, acquisition can accurately reflect the electric imaging degree of porosity spectrum behind the scale of formation rock pore structure characteristic.

According to step S106, in two dimensional surface, carry out the division of Reservoir type, in this specific embodiment, as shown in Figure 4, sample point is 44 carbonate reservoir sections of 22 mouthfuls of wells of oil gas field southwest, determine 8 of slit formation reservoir sample points by imaging logging image feature and core analysis, 3 of hole type reservoir sample points, 21 in crack-hole type reservoir sample point, 12 of compacted zones.As can be seen from the figure, being in clearly in four zoness of different of coordinate plane of dissimilar reservoir sampling point, each regional typical reservoir characteristic is obviously different on the CT scan image.Wherein the I quadrant represents standard degree of porosity average and standard hole distribution variance just is, and the CT scan image shows reservoir corrosion hole, fracture development, belongs to crack-hole type reservoir; The II quadrant represent standard degree of porosity average for negative, standard hole distribution variance for just, CT scan image demonstration reservoir fissure development belongs to the slit formation reservoir; The III quadrant represents standard degree of porosity average and standard hole distribution variance and is negative, and the CT scan image shows reservoir corrosion hole, the equal agensis in crack, generally belongs to compacted zone; The IV quadrant represent standard degree of porosity average for just, standard hole distribution variance is for negative, the CT scan image shows that reservoir corrosion hole grows, and belongs to hole type reservoir.Its substance and intension is: to the reservoir of different reservoir space types, the hole composition that comprises is different, and reservoir pore space composition and distribution influence Reservoir and the connectivity of reservoir, also reflect Reservoir type and validity.

According to step S107, in conjunction with shown in Figure 5, the Reservoir type of Southwest Oil is identified.The 6th road is electric imaging degree of porosity spectrum, the 8th road and the 9th road is respectively calculating standard degree of porosity average and standard hole distribution variance result, the 10th road are four-quadrant Reservoir type recognition result among Fig. 5.This Reservoir Section standard degree of porosity average and standard hole distribution variance value just are as seen from the figure, and sample point is positioned at first quartile, show that this Reservoir Section porosity and connectedness are all better, belong to hole-slit formation reservoir.The imaging logging image shows Reservoir Section corrosion hole and fracture development, and reservoir formation testing daily gas 111.7 ten thousand sides conform to the Reservoir type recognition result.

In the present embodiment, utilize the present invention to process the Image Logging Data of 22 mouthfuls of wells of Southwest Oil, through 44 layer position recognition results and imaging logging image feature, rock core Characteristic Contrast, Reservoir type is divided and is correctly amounted to 41 layers, 3 layers of mistakes, and coincidence rate is 93.2%.

Four-quadrant Reservoir type of the present invention recognition methods and system are easy to realize technically; The distribution of pores spectrum scale electricity imaging degree of porosity spectrum of utilizing little CT scan to obtain can more accurately reflect the formation rock pore structure characteristic; The four-quadrant Reservoir type recognition methods that proposes among the present invention and system carry out deep excavation with electric imaging degree of porosity spectrum information, and under the scale of CT distribution of pores spectrum, electric imaging degree of porosity spectrum behind the scale that can accurately reflect the formation rock architectural feature and average, variance parameter have quantitatively been calculated, obtain standard degree of porosity average and distribution variance through standardization, and utilize two dimensional surface to carry out the division of reservoir, realized that to Reservoir type division, modification measures and the establishment of the project oilfield prospecting developing is had higher engineering using value.The Image Logging Data of utilizing four-quadrant Reservoir type of the present invention recognition methods and system to process, process layer position recognition result and imaging logging image feature, rock core Characteristic Contrast have improved the coincidence rate that Reservoir type is divided.

Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is specific embodiments of the invention; the protection domain that is not intended to limit the present invention; within the spirit and principles in the present invention all, any modification of making, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (10)

1. the recognition methods of a four-quadrant Reservoir type is characterized in that, may further comprise the steps:

Choose core sample, and obtain mud filtrate resistivity and the electric imaging logging data of core sample corresponding well;

Described core sample is carried out the CT scan imaging, generate the CT imaging hole distribution profile of described core sample;

According to described mud filtrate resistivity and electric imaging logging data, utilize Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well;

Utilize the described electric imaging degree of porosity spectrum of the corresponding interval of described CT imaging hole distribution profile scale, and calculate average and the distribution variance of the electric imaging degree of porosity spectrum behind the scale;

Average and distribution variance to the electric imaging degree of porosity spectrum behind the described scale are carried out standardization, generate standard degree of porosity average and standard hole distribution variance;

Consist of two dimensional surface by described standard degree of porosity average and standard hole distribution variance, wherein, described standard degree of porosity average is transverse axis, and standard hole distribution variance is the longitudinal axis;

According to described two dimensional surface, Reservoir type is identified.

2. method according to claim 1 is characterized in that, the described formula that utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well is:

${\mathrm{\φ}}_{\mathrm{FMI}-i}=\sqrt[m]{a\·b\·R_{\mathrm{mf}}\·C_i};---\left(1\right)$

Wherein, φ _FMI-iBe the degree of porosity of pixel in the electric imaging, dimensionless;

A, b are the factor of proportionality in the Archie formula, dimensionless;

M is the formation cementation index, dimensionless;

R _MfBe the mud filtrate resistivity of described core sample corresponding well, Ω m;

C _iBe the pixel electrical conductivity in the described electric imaging logging data, S/m.

3. method according to claim 1 is characterized in that, the described described electric imaging degree of porosity spectrum of utilizing the corresponding interval of described CT imaging hole distribution profile scale, and the average and the distribution variance that calculate the electric imaging degree of porosity spectrum behind the scale comprise:

According to the described electric imaging degree of porosity spectrum that formula (1) obtains, utilize formula (2), (3) to calculate average and the distribution variance that generates described electric imaging degree of porosity spectrum;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}}=\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}/\mathrm{NFMI};---\left(2\right)$

${\mathrm{\σ}}_{\mathrm{FMI}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{FMI}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}})}^2}{\mathrm{NFMI}}};---\left(3\right)$

Wherein,

Be the average of electric imaging degree of porosity spectrum, dimensionless;

σ _FMIBe the distribution variance of electric imaging degree of porosity spectrum, dimensionless;

φ _FMI-iThe degree of porosity of pixel in the electric imaging that is obtained by formula (1), dimensionless;

NFMI is that the electric imaging logging DATA REASONING that comprises in each data cell in the electric imaging is counted;

According to described CT imaging hole distribution profile, utilize formula (4), (5) to calculate average and the distribution variance that generates CT imaging hole distribution profile;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}}=\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{CT}-i}/\mathrm{NCT};---\left(4\right)$

${\mathrm{\σ}}_{\mathrm{CT}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{CT}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}})}^2}{\mathrm{NCT}}};---\left(5\right)$

Wherein,

Be the average of CT imaging hole distribution profile, dimensionless;

σ _CTBe the distribution variance of CT imaging hole distribution profile, dimensionless;

φ _CT-iThe degree of porosity of statistic window in the CT scan imaging, dimensionless;

NCT is the statistic window sum that comprises in each data cell in the CT scan imaging;

According to distribution variance, the average of CT imaging hole distribution profile and the distribution variance of CT imaging hole distribution profile of the degree of porosity of pixel in the described electric imaging, the average of electric imaging degree of porosity spectrum, electric imaging degree of porosity spectrum, utilize the electric imaging degree of porosity spectrum after formula (6) calculates the generation scale;

${{\mathrm{\φ}}_{\mathrm{FMI}-i}}^{\′}=\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}+{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{CT}}-\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{FMI}};---\left(6\right)$

Wherein, φ _FMI-iThe degree of porosity of pixel in the electric imaging that obtains of formula (1), dimensionless;

The average of the electric imaging degree of porosity spectrum that obtains of formula (2), dimensionless;

σ _FMIThe distribution variance of the electric imaging degree of porosity spectrum that obtains of formula (3), dimensionless;

The average of the CT imaging hole distribution profile that obtains of formula (4), dimensionless;

σ _CTThe distribution variance of the CT imaging hole distribution profile that obtains of formula (5), dimensionless;

φ _FMI-i' be the degree of porosity of pixel in the electric imaging behind the scale, dimensionless;

The degree of porosity φ of pixel in the electric imaging behind the scale that obtains according to formula (6) _FMI-i', utilize formula (2), (3) to calculate the average of the electric imaging degree of porosity spectrum behind the scale

With distribution variance σ ' _FMI

4. method according to claim 1 is characterized in that, described average and distribution variance to the electric imaging degree of porosity spectrum behind the described scale carried out standardization, and the formula that generates standard degree of porosity average and the utilization of standard hole distribution variance is:

${\mathrm{\φ}}_a=(\stackrel{\‾}{{{\mathrm{\φ}}^{\′}}_{\mathrm{FMI}}}-{\mathrm{\φ}}_0)/\mathrm{\α};---\left(7\right)$

σ _a=(σ' _FMI-σ ₀)/β； （8）

Wherein, φ _aThe standard degree of porosity average that calculates, dimensionless;

The average of the electric imaging degree of porosity spectrum behind the described scale, dimensionless;

φ ₀Be the regional reservoir porosity factor, determined dimensionless by regional reservoir characteristic;

α is hole average normalization factor, is determined by regional reservoir quality, and perhaps value 200, dimensionless;

σ _aThe standard hole distribution variance that calculates, dimensionless;

σ ' _FMIThe distribution variance of electric imaging degree of porosity spectrum behind the scale, dimensionless;

σ ₀Be that regional reservoir is communicated with index, determined dimensionless by regional reservoir characteristic;

β is the distribution of pores variance criterion factor, is determined by regional reservoir quality, and perhaps value 50, dimensionless.

5. method according to claim 1 is characterized in that, comprises four quadrants in the described two dimensional surface, wherein,

Described standard degree of porosity average and the standard hole distribution variance of I quadrant just are, and are crack-hole type reservoir;

The described standard degree of porosity average of II quadrant for negative, standard hole distribution variance for just, be the slit formation reservoir;

The described standard degree of porosity average of III quadrant and standard hole distribution variance are negative, are compacted zone;

The described standard degree of porosity average of IV quadrant for just, standard hole distribution variance is for negative, is hole type reservoir.

6. the recognition system of a four-quadrant Reservoir type, it is characterized in that, comprise that rock core chooses module, CT imaging hole distribution profile extraction module, electric imaging degree of porosity spectrum computing module, degree of porosity spectrum calibration block, standardization module, two dimensional surface generation module and Reservoir type identification module; Wherein,

Described rock core is chosen module, is used for choosing core sample, and obtains mud filtrate resistivity and the electric imaging logging data of core sample corresponding well;

Described CT imaging hole distribution profile extraction module is used for described core sample is carried out the CT scan imaging, generates the CT imaging hole distribution profile of described core sample;

Described electric imaging degree of porosity spectrum computing module is used for according to described mud filtrate resistivity and electric imaging logging data, utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well;

Described degree of porosity spectrum calibration block utilizes the described electric imaging degree of porosity spectrum of the corresponding interval of described CT imaging hole distribution profile scale, and calculates average and the distribution variance of the electric imaging degree of porosity spectrum behind the scale;

Described standardization module is used for average and the distribution variance of the electric imaging degree of porosity spectrum behind the described scale are carried out standardization, generates standard degree of porosity average and standard hole distribution variance;

Described two dimensional surface generation module is used for consisting of two dimensional surface by described standard degree of porosity average and standard hole distribution variance, and wherein, described standard degree of porosity average is transverse axis, and standard hole distribution variance is the longitudinal axis;

Described Reservoir type identification module is used for according to described two dimensional surface, and Reservoir type is identified.

7. system according to claim 6 is characterized in that, the formula that described electric imaging degree of porosity spectrum computing module utilizes Archie formula to calculate the electric imaging degree of porosity spectrum of described core sample corresponding well is:

${\mathrm{\φ}}_{\mathrm{FMI}-i}=\sqrt[m]{a\·b\·R_{\mathrm{mf}}\·C_i};---\left(1\right)$

Wherein, φ _FMI-iBe the degree of porosity of pixel in the electric imaging, dimensionless;

A, b are the factor of proportionality in the Archie formula, dimensionless;

M is the formation cementation index, dimensionless;

R _MfBe the mud filtrate resistivity of described core sample corresponding well, Ω m;

C _iBe the imaging logging pixel electrical conductivity in the described electric imaging logging data, S/m.

8. system according to claim 6, it is characterized in that, described degree of porosity spectrum calibration block utilizes the described electric imaging degree of porosity spectrum of the corresponding interval of described CT imaging hole distribution profile scale, and the average and the distribution variance that calculate the electric imaging degree of porosity spectrum behind the scale comprise:

According to the electric imaging degree of porosity spectrum that described electric imaging degree of porosity spectrum computing module generates, utilize formula (2), (3) to calculate average and the distribution variance that generates electric imaging degree of porosity spectrum;

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}}=\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}/\mathrm{NFMI};---\left(2\right)$

${\mathrm{\σ}}_{\mathrm{FMI}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NFMI}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{FMI}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{FMI}}})}^2}{\mathrm{NFMI}}};---\left(3\right)$

Wherein,

Be the average of electric imaging degree of porosity spectrum, dimensionless;

σ _FMIBe the distribution variance of electric imaging degree of porosity spectrum, dimensionless;

φ _FMI-iThe degree of porosity of pixel in the electric imaging that is obtained by formula (1), dimensionless;

NFMI is that the electric imaging 5 log data measurements that comprise in each data cell in the described electric imaging degree of porosity spectrum computing module are counted;

CT imaging hole distribution profile according to described CT imaging hole distribution profile extraction module generates calculates average and the distribution variance that generates CT imaging hole distribution profile according to formula (4), (5);

$\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}}=\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{\mathrm{\φ}}_{\mathrm{CT}-i}/\mathrm{NCT};---\left(4\right)$

${\mathrm{\σ}}_{\mathrm{CT}}=\sqrt{\frac{\underset{i=1}{\overset{\mathrm{NCT}}{\mathrm{\Σ}}}{({\mathrm{\φ}}_{\mathrm{CT}-i}-\stackrel{\‾}{{\mathrm{\φ}}_{\mathrm{CT}}})}^2}{\mathrm{NCT}}};---\left(5\right)$

Wherein, Be the average of CT imaging hole distribution profile, dimensionless;

σ _CTBe the distribution variance of CT imaging hole distribution profile, dimensionless;

φ _CT-iThe degree of porosity of statistic window in the CT scan imaging, dimensionless;

NCT is the statistic window sum that comprises in each data cell in the described CT imaging hole distribution profile extraction module;

According to distribution variance, the average of CT imaging hole distribution profile and the distribution variance of CT imaging hole distribution profile of the degree of porosity of pixel in the described electric imaging, the average of electric imaging degree of porosity spectrum, electric imaging degree of porosity spectrum, utilize the electric imaging degree of porosity spectrum after formula (6) calculates the generation scale;

${{\mathrm{\φ}}_{\mathrm{FMI}-i}}^{\′}=\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\mathrm{\φ}}_{\mathrm{FMI}-i}+{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{CT}}-\frac{{\mathrm{\σ}}_{\mathrm{CT}}}{{\mathrm{\σ}}_{\mathrm{FMI}}}{\stackrel{\‾}{\mathrm{\φ}}}_{\mathrm{FMI}}---\left(6\right)$

Wherein, φ _FMI-iThe degree of porosity of pixel in the electric imaging that obtains of formula (1), dimensionless;

The average of the electric imaging degree of porosity spectrum that obtains of formula (2), dimensionless;

σ _FMIThe distribution variance of the electric imaging degree of porosity spectrum that obtains of formula (3), dimensionless;

The average of the CT imaging hole distribution profile that obtains of formula (4), dimensionless;

σ _CTThe distribution variance of the CT imaging hole distribution profile that obtains of formula (5), dimensionless;

φ _FMI-i' be the degree of porosity of pixel in the electric imaging behind the scale, dimensionless;

Utilize the degree of porosity φ of pixel in the electric imaging behind the scale that formula (6) obtains _FMI-i', calculate the average of the electric imaging degree of porosity spectrum behind the scale according to formula (2), (3)

With distribution variance σ ' _FMI

9. system according to claim 6, it is characterized in that, average and the distribution variance of the electric imaging degree of porosity spectrum of described standardization module after to described scale are carried out standardization, and the formula that generates standard degree of porosity average and the utilization of standard hole distribution variance is:

${\mathrm{\φ}}_a=(\stackrel{\‾}{{{\mathrm{\φ}}^{\′}}_{\mathrm{FMI}}}-{\mathrm{\φ}}_0)/\mathrm{\α};---\left(7\right)$

σ _a=(σ' _FMI-σ ₀)/β； （8）

Wherein, φ _aThe standard degree of porosity average that calculates, dimensionless;

The average of the electric imaging degree of porosity spectrum behind the scale that generates of described degree of porosity spectrum calibration block, dimensionless;

φ ₀Be the regional reservoir porosity factor, determined dimensionless by regional reservoir characteristic;

α is hole average normalization factor, is determined by regional reservoir quality, and perhaps value 200, dimensionless;

σ _aThe standard hole distribution variance that calculates, dimensionless;

σ ' _FMIThe distribution variance of the electric imaging degree of porosity spectrum behind the scale that generates of described degree of porosity spectrum calibration block, dimensionless;

σ ₀Be that regional reservoir is communicated with index, determined dimensionless by regional reservoir characteristic;

β is the distribution of pores variance criterion factor, is determined by regional reservoir quality, and perhaps value 50, dimensionless.

10. system according to claim 6 is characterized in that, the two dimensional surface that described two dimensional surface generation module generates comprises four quadrants, wherein,

Described standard degree of porosity average and the standard hole distribution variance of I quadrant just are, and are crack-hole type reservoir;

The described standard degree of porosity average of II quadrant for negative, standard hole distribution variance for just, be the slit formation reservoir;

The described standard degree of porosity average of III quadrant and standard hole distribution variance are negative, are compacted zone;

The described standard degree of porosity average of IV quadrant for just, standard hole distribution variance is for negative, is hole type reservoir.

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