A method for improving vertical resolution of element logging based on logging calibration

Abstract

The present invention discloses a method for improving the vertical resolution of element logging based on logging calibration, comprising the following steps: S1, comparing and analyzing the relationship between conventional logging data and element logging data, and selecting a curve with good correlation between conventional logging data and element logging data according to the comparison and analysis results, performing correlation fitting using the least square method, and establishing a quantitative calibration relationship between the two; S2, comparing and analyzing the relationship between element logging data and element logging data, and selecting a curve with good correlation between element logging data and element logging data according to the comparison and analysis results, performing correlation fitting using the least square method, and establishing a quantitative calibration relationship between the two; S3, combining the results of the quantitative calibration of logging data and the quantitative calibration of logging data to improve the vertical resolution of element logging data. The present invention has the advantages that a small amount of element logging data can be combined with conventional logging data to improve the vertical resolution of element logging data, the operation is simple, and it is easy to promote and apply.

Description

Technical Field

[0001] The invention relates to the field of oil and gas field exploration, and in particular to a method for improving the vertical resolution of element logging based on logging calibration. Background Art

[0002] The formation temperature of the buried hill reservoir is high, and it is difficult for the element logging instrument to measure data normally in the target layer, resulting in difficulty in obtaining data. The element logging data is not affected by this, but its vertical resolution is not high. The target layer section has only one data point of 5m or even 10m, which cannot meet the requirements of on-site fine interpretation. At present, the main method to improve the resolution of element logging data for buried hill reservoirs is to use logging data while drilling. Its vertical resolution is one data point of 1m. Compared with the logging data of 0.125m per data point, its vertical accuracy is still insufficient. Based on the above situation, a method of using logging data to improve the vertical resolution of element logging data is urgently needed to meet the requirements of on-site fine interpretation. Summary of the invention

[0003] The object of the present invention is to provide a method for improving the vertical resolution of element logging based on logging calibration, so as to solve the problems existing in the background technology.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] A method for improving the vertical resolution of element logging based on logging calibration comprises the following steps:

[0006] S1. Compare and analyze the relationship between conventional logging data and element logging data, and select the curve with better correlation between conventional logging data and element logging data according to the comparative analysis results, use the least squares method to perform correlation fitting, and establish a quantitative calibration relationship between conventional logging data and element logging data;

[0007] S2. Compare and analyze the relationship between element logging data and element logging data, and select the curve with better correlation between element logging data and element logging data according to the comparative analysis results, use the least square method to perform correlation fitting, and establish a quantitative calibration relationship between element logging data and element logging data;

[0008] S3. Combine the quantitative calibration results of logging data and recording data to improve the vertical resolution of element logging data.

[0009] Preferably: in the step S1, conventional logging curves and element logging curves are first drawn, and then conventional logging data and element logging data having correlation are qualitatively determined according to the overall change trends of the two curves;

[0010] The compensated density logging data in the conventional logging data and the Fe and Si elements in the element logging data have a good correlation as a whole.

[0011] Preferably: in the step S1, specifically, firstly, by extracting respectively the compensated density logging data in the conventional logging data with correlation and the Fe and Si data in the corresponding element logging data, and then using the least square method to perform correlation fitting analysis on DEN and Fe, and DEN and Si, respectively, so as to achieve quantitative calibration between the conventional logging data and the element logging data;

[0012] Among them, DEN represents the compensated density logging data in conventional logging data.

[0013] Preferably, the specific method for quantitative calibration between conventional logging data and element logging data is as follows:

[0014] (1) Extract and construct the data set to be fitted:

[0015] After determining the relevant conventional logging data and element logging data through curve comparison, the relevant data of the two are extracted to form a set of data to be fitted:

[0016] (x1,y1),(x2,y2),…(x n ,y n )

[0017] In this set of data, x1, x2, ... x n are the measured values ​​of conventional logging data extracted, y1, y2, ... y n is the measured value of the extracted element logging data;

[0018] (2) Construct the fitting relationship between conventional logging data and element logging data:

[0019] The least square method is used to fit a set of data extracted in (1) and establish a fitting relationship between conventional logging data and element logging data:

[0020] y=ax+b (1)

[0021] In formula (1), x is the measured value of conventional well logging, y is the calculated value of element well logging; a and b are fitting coefficients, and the final least square fitting relationship can be obtained by calculating a and b;

[0022] (3) Solve the fitting coefficients a and b in the fitting equation of step (2):

[0023] The extracted element logging value y iThe minimum value of the sum of squares of deviations between (i=1, 2...n) and the final fitting formula calculated value y is the "optimization criterion" to determine the fitting coefficients a and b, where the specific calculation formula for the sum of squares of deviations is as follows:

[0024]

[0025] In formula (2): is the sum of squares of deviations; i is the measured value of element logging; y is the calculated value of element logging;

[0026] Since y in formula (2) is the calculated value calculated by the final fitting formula y=ax+b in step (2), formula (2) can be replaced by the following formula (3):

[0027]

[0028] In formula (3): is the sum of squares of deviations; i is the measured value of element logging; x i It is the measured value of conventional well logging;

[0029] When the sum of squared deviations When it is the smallest, that is, the first-order partial derivatives of a and b are zero, the fitting degree is the highest. Therefore, the fitting coefficients a and b are solved when the first-order partial derivatives of a and b are zero. The specific solution process is as follows:

[0030] The first step is to find the first-order partial derivatives of a and b respectively:

[0031]

[0032]

[0033] The second step is to set the first-order partial derivatives of a and b to zero, and assume:

[0034]

[0035]

[0036] Then we have:

[0037]

[0038] After sorting, we get the following system of equations:

[0039]

[0040] Right now:

[0041]

[0042] The third step is to convert the measured value of element logging y i (i=1, 2……n) is substituted into the above equation group (10), the fitting coefficients a and b can be solved, and finally the fitting relationship is obtained;

[0043] Through the above method, the fitting relationship between the compensated density logging data and the element logging data in the conventional logging data is obtained as follows:

[0044] y Si =-46.614x DEN +155.76 R 2 =0.5443 (11)

[0045] y Fe =9.0484x DEN -21.816 R 2 =0.7322 (12)

[0046] In formulas (11) and (12): Si is the Si content in the fitted element logging data; y Fe is the Fe content in the fitted element logging data; x DEN It is the compensated density logging data in the regular logging data, and the unit is g / cm3; R 2 Represents the correlation coefficient.

[0047] Preferably, in step S3, the process of extracting data with relatively good correlation between element logging data and element logging data, and establishing a quantitative calibration relationship between the two using the least squares method is the same as the process of establishing a quantitative calibration relationship between conventional logging data and element logging data in step S1, and the fitting relationship between the element logging data and the element logging data obtained thereby is specifically as follows:

[0048] y Si =0.5249x Si +4.9559 R 2 =0.6563 (13)

[0049] y Fe =1.1989x Fe -0.1467 R 2 =0.573 (14)

[0050] In formulas (13) and (14): Si The Si content of the element logging data is fitted; Fe is the Fe content of the element logging data fitted; x Si is the Si content in the element logging data; x Fe is the Fe content in the element logging data; R2 Represents the correlation coefficient.

[0051] Compared with the prior art, the advantages and effective effects of the present invention are: 1) a small amount of element logging data in the work area can be used to quantitatively calibrate the results of the logging data and combine them with conventional logging data to improve the vertical resolution of the element logging data; 2) the operation is simple and easy to promote and apply, and it is suitable for on-site geologists to conduct rapid and detailed evaluation of formation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 To compare and analyze the relationship between conventional logging data and element logging data;

[0053] Figure 2a ˉ2b is a scatter plot of conventional logging data and element logging data with correlation extracted, and a fitting relationship between conventional logging data and element logging data obtained by least squares fitting;

[0054] Figure 3a ˉ3b is a comparison diagram between the element logging data calibrated by the fitting relationship between the conventional logging data and the element logging data obtained in S1 and the original element logging data;

[0055] Figure 4a ˉ4b is a scatter plot of the extracted correlated element logging data and element logging data, and the fitting relationship between the element logging data and the element logging data obtained by the least squares fitting method;

[0056] Figure 5a ˉ5b is a comparison diagram between the original element logging data and the element logging data after the element logging data is calibrated to improve the vertical resolution using the element logging data and element logging data calibration formula determined in S2;

[0057] Figure 6 This is a result diagram of improving the vertical resolution of element logging data obtained by the present invention;

[0058] Figure 7 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0060] See also Figures 1 to 7 The present invention provides a method for improving the vertical resolution of element logging based on logging calibration, which specifically includes the following steps:

[0061] S1. Compare and analyze the relationship between conventional logging data and element logging data, and select the curve with better correlation between conventional logging data and element logging data according to the comparative analysis results, use the least squares method to perform correlation fitting, and establish the quantitative calibration relationship between conventional logging data and element logging data (see Figure 2a-2b shown);

[0062] That is: First, by drawing the conventional logging curve and the element logging curve (see Figure 1 As shown in Figure 2, the conventional logging data and element logging data with correlation are qualitatively determined based on the overall trend of the two curves. Usually, there is no strict correlation between the curves, and it is sufficient if the curves reflect a certain correlation as a whole. Figure 1 The above qualitative analysis results show that the Fe and Si elements in the compensated density logging data in the conventional logging data and the element logging data have a good correlation as a whole.

[0063] Then, based on the aforementioned correlation qualitative analysis results, the compensated density logging data and the corresponding element logging data Fe and Si data in the conventional logging data with correlation are extracted respectively;

[0064] Finally, the least square method was used to perform correlation fitting analysis on DEN and Fe, and DEN and Si, respectively, so as to achieve quantitative calibration between conventional logging data and element logging data; where DEN represents the compensated density logging data in conventional logging data.

[0065] Among them, the specific method of using the least squares method to establish quantitative calibration between conventional logging data and element logging data is as follows:

[0066] (1) Extract and construct the data set to be fitted:

[0067] After determining the relevant conventional logging data and element logging data through curve comparison, the relevant data of the two are extracted to form a set of data to be fitted:

[0068] (x1,y1),(x2,y2),…(x n ,y n )

[0069] In this set of data, x1, x2, ... x n are the measured values ​​of conventional logging data extracted, y1, y2, ... y n is the measured value of the extracted element logging data;

[0070] (2) Construct the fitting relationship between conventional logging data and element logging data:

[0071] The least square method is used to fit a set of data extracted in (1) and establish a fitting relationship between conventional logging data and element logging data:

[0072] y=ax+b (1)

[0073] In formula (1), x is the measured value of conventional well logging, y is the calculated value of element well logging; a and b are fitting coefficients, and the final least square fitting relationship is obtained by calculating a and b;

[0074] (3) Solve the fitting coefficients a and b in the fitting equation of step (2):

[0075] The extracted element logging value y i The minimum value of the sum of squares of deviations between (i=1, 2...n) and the final fitting formula calculated value y is the "optimization criterion" to determine the fitting coefficients a and b, where the specific calculation formula for the sum of squares of deviations is as follows:

[0076]

[0077] In formula (2): is the sum of squares of deviations; i is the measured value of element logging; y is the calculated value of element logging;

[0078] Since y in formula (2) is the calculated value calculated by the final least squares fitting formula y=ax+b in step (2), formula (2) can be replaced as follows:

[0079]

[0080] In formula (3): is the sum of squares of deviations; i is the measured value of element logging; x i It is the measured value of conventional well logging;

[0081] When the sum of squared deviations When it is the smallest, that is, the first-order partial derivatives of a and b are zero, the fitting degree is the highest. Therefore, the fitting coefficients a and b are solved when the first-order partial derivatives of a and b are zero. The specific solution process is as follows:

[0082] The first step is to find the first-order partial derivatives of a and b respectively:

[0083]

[0084]

[0085] The second step is to set the first-order partial derivatives of a and b to zero, and assume:

[0086]

[0087]

[0088] Then we have:

[0089]

[0090] After sorting, we get the following system of equations:

[0091]

[0092] Right now:

[0093]

[0094] The third step is to convert the measured value of element logging y i Substituting (i=1, 2...n) into the above equation group (10), the fitting coefficients a and b can be solved, and finally the fitting relationship can be obtained.

[0095] Through the above method, the fitting relationship between the compensated density logging data and the element logging data in the conventional logging data is obtained, which is as follows:

[0096] y Si =-46.614x DEN +155.76 R 2 =0.5443 (11)

[0097] y Fe =9.0484x DEN -21.816 R 2 =0.7322 (12)

[0098] In formulas (11) and (12): Si is the Si content in the fitted element logging data; y Fe is the Fe content in the fitted element logging data; x DEN It is the compensated density logging data in conventional logging data, and the unit is g / cm 3 Represents; R 2 Represents the correlation coefficient.

[0099] The actual data were processed using the fitting relationship between the compensated density logging data and the element logging data obtained by S1. The processing results show that the elements Si and Fe calculated by the fitting relationship are generally consistent with the measured element logging data Si and Fe. Figure 3a ˉAs shown in 3b.

[0100] S2. Compare and analyze the relationship between element logging data and element logging data, and select the curve with better correlation between element logging data and element logging data according to the comparative analysis results, use the least square method to perform correlation fitting, and establish a quantitative calibration relationship between the element logging data and the element logging data;

[0101] In this step S2, the data with relatively good correlation between the element logging data and the element logging data are extracted, and the process of establishing the quantitative calibration relationship between the two using the least squares method is the same as the process of establishing the quantitative calibration relationship between the conventional logging data and the element logging data in step S1, so it will not be repeated here. The fitting relationship between the element logging data and the element logging data obtained by this method in this step S2 is as follows:

[0102] y Si =0.5249x Si +4.9559 R 2 =0.6563 (13)

[0103] y Fe =1.1989x Fe -0.1467 R 2 =0.573 (14)

[0104] In formulas (13) and (14): Si is the Si content in the fitted element logging data; y Fe is the Fe content in the fitted element logging data; x Si is the Si content in the element logging data; x Fe is the Fe content in the element logging data; R 2 Represents the correlation coefficient.

[0105] The actual data were processed using the fitting relationship between the element logging data and the element logging data obtained by S2. The processing results show that the elements Si and Fe calculated by the fitting relationship are generally consistent with the measured element logging data Si and Fe. Figure 5a ˉ5b.

[0106] S3. Combine the quantitative calibration results of logging data and recording data to improve the vertical resolution of element logging data;

[0107] After calculating S1 and S2, we can get the element curves Si and Fe with higher vertical resolution, which are consistent with the measured element logging Si and Fe as a whole. Figure 6 .

[0108] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A method for improving the vertical resolution of element logging based on logging calibration, characterized in that: The following steps are involved: S1. Compare and analyze the relationship between conventional logging data and element logging data, and select the curve with better correlation between conventional logging data and element logging data according to the comparative analysis results, use the least squares method to perform correlation fitting, and establish a quantitative calibration relationship between conventional logging data and element logging data; S2. Compare and analyze the relationship between element logging data and element logging data, and select the curve with better correlation between element logging data and element logging data according to the comparative analysis results, use the least square method to perform correlation fitting, and establish a quantitative calibration relationship between element logging data and element logging data; S3. Combine the quantitative calibration results of logging data and recording data to improve the vertical resolution of element logging data; In the step S1, specifically, conventional logging curves and element logging curves are first drawn, and then conventional logging data and element logging data having correlation are qualitatively determined according to the overall change trends of the two curves; The compensated density logging data in the conventional logging data and the Fe and Si elements in the element logging data have a good correlation as a whole; In the step S1, specifically, firstly, the compensated density logging data and the Fe and Si data in the corresponding element logging data with correlation are extracted respectively, and then the correlation fitting analysis between DEN and Fe, and DEN and Si is performed respectively by the least square method, so as to realize the quantitative calibration between the conventional logging data and the element logging data; Among them, DEN represents the compensated density logging data in conventional logging data.

2. The method for improving the vertical resolution of element logging based on logging calibration according to claim 1, characterized in that: The specific method for quantitative calibration between conventional logging data and element logging data is as follows: (1) Extract and construct the data set to be fitted: After determining the relevant conventional logging data and element logging data through curve comparison, the relevant data of the two are extracted to form a set of data to be fitted: (x1,y1),(x2,y2),…(x n ,y n ) In this set of data, x1, x2, ... x n are the measured values ​​of conventional logging data extracted, y1, y2, ... y n is the measured value of the extracted element logging data; (2) Construct the fitting relationship between conventional logging data and element logging data: The least square method is used to fit a set of data extracted in (1) and establish a fitting relationship between conventional logging data and element logging data: y=ax+b (1) In formula (1), a and b are fitting coefficients, x is the measured value of conventional well logging, and y is the calculated value of element well logging; (3) Solve the fitting coefficients a and b in the fitting equation of step (2): The extracted element logging value y i , i = 1, 2...n; the minimum value of the sum of squares of deviations from the final fitting formula calculated value y is the "optimization criterion", and the fitting coefficients a and b are determined, where the specific calculation formula for the sum of squares of deviations is as follows: In formula (2): is the sum of squares of deviations; i is the measured value of element logging; y is the calculated value of element logging; Since y in formula (2) is the calculated value calculated by the final fitting formula y=ax+b in step (2), formula (2) can be replaced by the following formula (3): In formula (3): is the sum of squares of deviations; i is the measured value of element logging; x i is the measured value of conventional well logging; a and b are fitting coefficients; When the sum of squared deviations When it is the smallest, that is, the first-order partial derivatives of a and b are zero, the fitting degree is the highest. Therefore, the fitting coefficients a and b are solved when the first-order partial derivatives of a and b are zero. The specific solution process is as follows: The first step is to find the first-order partial derivatives of a and b respectively: The second step is to set the first-order partial derivatives of a and b to zero, and assume: Then we have: After sorting, we get the following system of equations: Right now: The third step is to convert the measured value of element logging y i , i = 1, 2...n; Substituting into the above equation group (10), the fitting coefficients a and b can be solved, and the fitting relationship between the compensated density logging data and the element logging data in the conventional logging data can be obtained, which is as follows: the Si =-46.614x DEN +155.76 R 2 =0.5443 (11) y Fe =9.0484x DEN -21.816 R 2 =0.7322 (12) In formulas (11) and (12): Si is the Si content in the fitted element logging data; y Fe is the Fe content in the fitted element logging data; x DEN It is the compensated density logging data in conventional logging data, expressed in g / cm3; R 2 represents the correlation coefficient; Among them, the compensated density logging data in conventional logging data is represented by DEN.

3. A method for improving the vertical resolution of element logging based on logging calibration according to claim 2, characterized in that: In step S2, the data with relatively good correlation between element logging data and element logging data are extracted, and the process of establishing a quantitative calibration relationship between the two using the least squares method is the same as the process of establishing a quantitative calibration relationship between conventional logging data and element logging data in step S1. The fitting relationship between the element logging data and the element logging data obtained thereby is specifically as follows: y Si =0.5249x Si +4.9559 R 2 =0.6563 (13) y Fe =1.1989x Fe -0.1467 R 2 =0.573 (14) In formulas (13) and (14): y Si is the Si content in the fitted element logging data; y Fe is the Fe content in the fitted element logging data; x Si is the Si content in the element logging data; x Fe is the Fe content in the element logging data; R 2 Represents the correlation coefficient.

Images