CN102033247A - Method for calculating apparent formation water resistivity spectrum and parameters by point-by-point scale electrical imaging data - Google Patents

Abstract

The invention relates to a method for calculating a resistivity spectrum and parameters of apparent formation water by point-by-point graduation electric imaging data; point-by-point calibration: aligning the electrical imaging data with the shallow resistivity depth with a logging data editing tool; calculating the average current value of the electric imaging data image; for each image frame, calculating a scale coefficient by using the conductivity values corresponding to the shallow resistivity values of the same depth points; calculating the apparent formation water resistivity spectrum of the electrical imaging data: the electric imaging through the shallow resistivity scale is substantially a conductivity image of a well wall washing zone, and the apparent formation water resistivity of one pixel point of electric imaging data is defined; calculating the apparent formation water resistivity spectrum parameters of the electrical imaging data: introducing the degree of deviation of a main peak from a base line in a mean expression apparent formation water resistivity distribution spectrum; expressing the width of the apparent formation water resistivity distribution spectrum by using variance; the shallow resistivity point-by-point scale electrical imaging data are applied, the electrical imaging data apparent formation water resistivity spectrum is calculated, and the mean value and the variance of the apparent formation water resistivity spectrum are extracted to evaluate the reservoir fluid property, so that the method has a remarkable application effect.

Description

A kind of pointwise scale electricity imaging data is calculated the method for apparant formation water resistivity spectrum and parameter

Technical field

The present invention relates to the logging technology of petroleum geology exploration, groundwater exploration.Particularly a kind of electric imaging logging data that is used for measuring according to oil well logging is discerned carbonate reservoir, the method for igneous reservoirs and low-porosity crack property sandstone reservoir fluid properties.

Background technology

The electric imaging logging instrument measures near the well stratum with the microresistivity image information data of change in depth, the variation of many curves reflection stratum microconductivities of measurement by being installed in button-electrode on the pole plate in pit shaft.Because near the conductivity difference of the plastid differently borehole wall, thereby Image Logging Data is with geological phenomenons such as near the bedding on the stratum form reflection borehole wall of image, crack, corrosion holes.The resistivity of mud stone shale band is low, the corrosion hole, and also the resistivity than matrix rock is low for the geological phenomenon resistivity relevant with reservoir such as crack.For expressing these different geological phenomenons, on the image of imaging data, express with different colors.Light color expression low conductivity, dark expression high conductivity.

Electricity imaging data will be carried out according to following steps in formation evaluation: the hole deviation of measuring during 1. according to well logging, magnetic azimuth, logging speed, data gain etc. are proofreaied and correct raw data; 2. because the button-electrode of electric imaging logging instrument system is a non-focusing electrode system, just its measured value only with the borehole wall near the proportional variation of conductivity of geologic body, thereby to demarcate with the shallow resistivity well-log information.The application of 3. electric imaging data.

Shallow resistivity is the important step that actual quantification is analyzed to the demarcation of electric imaging data.Existing shallow resistivity scaling method is the piecewise linearity method.At first calculate an average current curve, carry out degree of depth coupling with this average current curve and shallow resistivity curve according to Image Logging Data; According to the log value of degree of depth match point correspondence, be horizontal ordinate with the average current then, shallow resistivity is that ordinate is drawn X plot.On X plot, obtain a piecewise linear relationship by man-machine interaction.At last, according to the be converted to conductivity value of this piecewise linear relationship with original measurement.The problem one that this method exists is that the more software of step realizes that workload is big, the 2nd, and the scale effect of imaging data depends on the sectional linear fitting effect.And the quality of calibration results directly influences electric imaging data effect on this basis.

In the application facet of electric imaging data, present great majority are used the qualitative evaluation aspect that mainly concentrates on reservoir, as crack identification, stratigraphic dip explanation etc.; Aspect the reservoir quantitative evaluation, the work of seeing has PorSpect ⁽¹⁾The factor of porosity analysis of spectrum is used to estimate primary crack and secondary pores, fracture width calculating etc.

Summary of the invention

The objective of the invention is to propose a kind of shallow resistivity data pointwise scale electricity imaging data of using, calculate the method for looking local water spectrum and spectrum average and variance according to calibration results and discern low-porosity carbonate reservoir, igneous reservoirs, low-porosity crack sandstone reservoir fluid properties.

Pre-service for the electric imaging logging data, more in order to overcome the step that existing shallow resistivity data scale electricity imaging document method exists, the software work amount reaches problems such as scale effect greatly, invented the method for shallow resistivity pointwise scale imaging data, and the method evaluation properties of fluid in bearing stratum that calculates electric imaging data apparant formation water resistivity spectrum and parameter on this basis.

1 pointwise scale method step

(1) with the well-log information edit tool the electric imaging data and the shallow resistivity degree of depth are drawn together;

(2) average current value of the electric imaging source map picture of calculating;

$C_{\mathrm{aver}}\left(\mathrm{dep}\right)=\underset{i=1}{\overset{n_i}{\mathrm{\Σ}}}\underset{j=1}{\overset{n_j}{\mathrm{\Σ}}}\underset{k=1}{\overset{n_k}{\mathrm{\Σ}}}C(j,k,i)/(n_i\·n_j\·n_k)---\left(1\right)$

N in the formula _iBe counting on the frames images degree of depth; n _jBe the pole plate number of imager, different instruments is different; n _kBe the button number of each pole plate of instrument, different instruments is different.(j, k i) are the measurement current value of the i depth point j pole plate k button of frames images to C; C _Aver(dep) be the average current that calculates.

(3) to each frames images (depth point of conventional logging data), by calibration factor of conductivity value calculating of same depth point shallow resistivity value correspondence;

$\mathrm{Coef}\left(\mathrm{dep}\right)=\frac{1000}{\mathrm{RS}\left(\mathrm{dep}\right)}/C_{\mathrm{aver}}\left(\mathrm{dep}\right)---\left(2\right)$

C in the formula _Aver(dep) be the average current that (1) formula is calculated; RS (dep) is the shallow resistivity log value of the corresponding degree of depth; Coefi (dep) is the calibration factor of the corresponding degree of depth of imaging source map frame.

(4) the imaging data of a depth point of usefulness calibration factor scale corresponding diagram frame;

C_scale(j，k，i)＝Coef(dep)*C(j，k，i) (3)

(j, k i) are the j pole plate of input image frame to C in the formula, k button, the measurement current value of i depth point; C_scale (j, k, i) the scale pixel value of corresponding point.

By top pointwise scale step as can be known, the advantage of this method is that the scale process does not need manual intervention, directly with the shallow resistivity data of depth match electric imaging data is carried out careful scale.The prerequisite of this scale method is electric imaging data and shallow resistivity depth match, has relatively high expectations for depth match.

The apparant formation water resistivity spectrum of 2 electric imaging data is calculated

Come down to the conductivity map picture of borehole wall flushed zone through the electric imaging of shallow resistivity scale.Define the apparant formation water resistivity of a pixel of electric imaging data.

R _wa(j，k，i)＝φ(dep)[RS(dep)·C_scale(j，k，i)] ^1/m(dep)/C_scale(j，k，i)

(4)

(i, j k) are the conductivity value of i depth point j pole plate k button through scale, s/m to C_scale in the formula; M (dep) is the cementation exponent in the Archie equation, adopts three porosity Model Calculation [ref 3]; RS (dep) is a flushed zone resistivity.The total porosity that φ (dep) calculates for conventional logging.Can calculate the apparant formation water resistivity value of electric each pixel of imaging data according to (4) formula.

For a frames images, then can obtain the apparant formation water resistivity spectrum according to its histogram of result of calculation primary system meter of each pixel, be used to discern fluid properties.

The apparant formation water resistivity spectrum calculation of parameter of 3 electric imaging data

For the difference on quantitative evaluation oil gas interval and the water layer section apparant formation water resistivity distribution profile, main peak departs from the degree of baseline in the introducing average expression apparant formation water resistivity distribution profile; Express the width (dispersiveness) of apparant formation water resistivity distribution profile with variance (second moment).A depth point apparant formation water resistivity spectrum average is defined as follows

$\stackrel{\‾}{R_{\mathrm{wa}}}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{R}_{\mathrm{wai}}{P}_{R_{\mathrm{wai}}}/\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{P}_{R_{\mathrm{wai}}}---\left(5\right)$

R in the formula _WaiBe the apparant formation water resistivity value, Be that corresponding apparant formation water resistivity is R _WaiFrequency (pixel number).Apparant formation water resistivity spectrum variance

${\mathrm{\σ}}_{R_{\mathrm{wa}}}=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{P}_{R_{\mathrm{wai}}}{(R_{\mathrm{wai}}-\stackrel{\‾}{R_{\mathrm{wa}}})}^2}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{P}_{R_{\mathrm{wai}}}}}---\left(6\right)$

By above-mentioned definition as can be seen, the average of apparant formation water resistivity distribution is expressed the degree that departs from baseline; Variance has been expressed the dispersion degree (width of main peak) that the apparant formation water resistivity spectrum distributes

The feature of the apparant formation water resistivity spectrum of hydrocarbon zone is " value is big and wide "; The feature of the apparant formation water resistivity spectrum of water layer is " value is little and narrow ".For the average and the variance of a certain depth calculation, hydrocarbon zone is that the both increases; The feature that the water layer both is little.

Invention is also used the different electric imaging data of many mouthfuls of wells of different regions, and effect is remarkable.

Use shallow resistivity pointwise scale electricity imaging data, calculate electric imaging data apparant formation water resistivity spectrum on this basis, the average of extraction apparant formation water resistivity spectrum and variance are estimated properties of fluid in bearing stratum significant effect.

Description of drawings

Fig. 1 is an apparant formation water resistivity spectrum discrimination fluid properties synoptic diagram.Wherein, a figure is the Rwa spectrum synoptic diagram of water layer; B figure is the Rwa spectrum synoptic diagram of oil reservoir.Ordinate P is a frequency in the accompanying drawing 1, and horizontal ordinate is the size of apparant formation water resistivity.

Fig. 2 pointwise scale electric imaging logging data is calculated the process flow diagram of electric imaging apparant formation water resistivity spectrum and parameter.

Embodiment

At Si Lunbeixie Geoframe ⁽¹⁾3.8 environment and Cifsun ⁽⁴⁾Realize the foregoing invention content in the environment, developed the corresponding program module.

At Geoframe ⁽¹⁾The environment performing step as shown in Figure 2.

1) at Geoframe ⁽¹⁾Load routine and Image Logging Data in the software;

2) utilize three porosity Model Calculation conventional logging data factor of porosity Φ (dep) and m (dep) value;

3) use Geoframe ⁽¹⁾BorEID in the software ⁽¹⁾Module is carried out pre-service to the imaging data;

4) realize formula (1) (2) (3) (4) (5) on the basis of calculating and result in front

(6) program module;

5) result apparant formation water resistivity spectrum and parameter are carried out statistical computation and drawing.

Wherein realize above-mentioned steps 4) mode as follows.

I) to the FMI Data Processing, n in formula (1) _k=8, n _j=24, n _i=50; Realize formula (2), (3), (4) then.

II) statistics 8*24*50 point α R _Wa(j, k, histogram i), α=3.3 are coefficient, obtain a depth point apparant formation water resistivity spectrum

III) by formula depth point apparant formation water resistivity of (5) and (6) statistics is composed

Average and variance.

IV) draw result of calculation such as accompanying drawing 2, accompanying drawing 3.The 4th road is an imaging data apparant formation water resistivity spectrum, and the 5th road is the average and the variance of apparant formation water resistivity spectrum.

V) to the EMI Data Processing, n in formula (1) _k=6, n _j=25, n _i=50; Realize formula (2), (3), (4) then.

VI) statistics 6*25*50 point α R _Wa(j, k, histogram i), α=3.3 are coefficient, obtain a depth point apparant formation water resistivity spectrum

VII) by formula depth point apparant formation water resistivity of (5) and (6) statistics is composed

Average and variance.

(note:

(1) Geoframe, FMI, BorEID, PorSpect, the Schlumberge house mark

(2) EMI, Haliburton Logging Services house mark

(3) STARII, Atlas Wireline Services house mark

(4) Cifsun, MCI, the CNPC house mark)

Claims (1)

1. pointwise scale electricity imaging data is calculated the method for apparant formation water resistivity spectrum and parameter, it is characterized in that:

1) pointwise scale step

(1) with the well-log information edit tool the electric imaging data and the shallow resistivity degree of depth are drawn together;

(2) average current value of the electric imaging source map picture of calculating;

$C_{\mathrm{aver}}\left(\mathrm{dep}\right)=\underset{i=1}{\overset{n_i}{\mathrm{\Σ}}}\underset{j=1}{\overset{n_j}{\mathrm{\Σ}}}\underset{k=1}{\overset{n_k}{\mathrm{\Σ}}}C(j,k,i)/(n_i\·n_j\·n_k)---\left(1\right)$

N in the formula _iBe counting on the frames images degree of depth; n _jBe the pole plate number of imager, different instruments is different; n _kBe the button number of each pole plate of instrument, different instruments is different.(j, k i) are the measurement current value of the i depth point j pole plate k button of frames images to C; C _Aver(dep) be the average current that calculates;

(3) to each frames images, by calibration factor of conductivity value calculating of same depth point shallow resistivity value correspondence;

$\mathrm{Coef}\left(\mathrm{dep}\right)=\frac{1000}{\mathrm{RS}\left(\mathrm{dep}\right)}/C_{\mathrm{aver}}\left(\mathrm{dep}\right)---\left(2\right)$

C in the formula _Aver(dep) be the average current that (1) formula is calculated; RS (dep) is the shallow resistivity log value of the corresponding degree of depth; Coef (dep) is the calibration factor of the corresponding degree of depth of imaging source map frame;

(4) the imaging data of a depth point of usefulness calibration factor scale corresponding diagram frame;

C_scale(j，k，i)＝Coef(dep)*C(j，k，i) (3)

(j, k i) are the j pole plate of input image frame to C in the formula, k button, the measurement current value of i depth point; C_scale (j, k, i) the scale pixel value of corresponding point;

2) apparant formation water resistivity of electric imaging data spectrum is calculated

The electric imaging of process shallow resistivity scale comes down to the conductivity map picture of borehole wall flushed zone, defines the apparant formation water resistivity of a pixel of electric imaging data;

R _wa(j，k，i)＝φ(dep)[RS(dep)·C_scale(j，k，i)] ^1/m(dep)/C_scale(j，k，i)

(4)

(i, j k) are the conductivity value of i depth point j pole plate k button through scale, s/m to C_scale in the formula; M (dep) is the cementation exponent in the Archie equation, adopts three porosity Model Calculation [ref 3]; RS (dep) is a flushed zone resistivity.The total porosity that φ (dep) calculates for conventional logging; Can calculate the apparant formation water resistivity value of electric each pixel of imaging data according to (4) formula;

For a frames images, then can obtain the apparant formation water resistivity spectrum according to its histogram of result of calculation primary system meter of each pixel, be used to discern fluid properties;

3) apparant formation water resistivity of electric imaging data spectrum calculation of parameter

Main peak departs from the degree of baseline in the introducing average expression apparant formation water resistivity distribution profile; With the width of variance expression apparant formation water resistivity distribution profile, a depth point apparant formation water resistivity spectrum average is defined as follows

$\stackrel{\‾}{R_{\mathrm{wa}}}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{R}_{\mathrm{wai}}{P}_{R_{\mathrm{wai}}}/\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{P}_{R_{\mathrm{wai}}}---\left(5\right)$

R in the formula _WaiBe the apparant formation water resistivity value,

Be that corresponding apparant formation water resistivity is R _WaiFrequency (pixel number), apparant formation water resistivity spectrum variance

${\mathrm{\σ}}_{R_{\mathrm{wa}}}=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{P}_{R_{\mathrm{wai}}}{(R_{\mathrm{wai}}-\stackrel{\‾}{R_{\mathrm{wa}}})}^2}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{P}_{R_{\mathrm{wai}}}}}---\left(6\right).$

Images