A mutual interference spectrum logging method based on logging curves

Abstract

The present invention relates to the technical field, and in particular to a mutual interference spectrum logging method based on logging curves, comprising: obtaining a plurality of conventional logging curves; calculating the mutual correlation coefficients between the plurality of conventional logging curves to obtain a correlation coefficient matrix; screening the mutual correlation coefficients that highlight the difference between rock formations in the correlation coefficient matrix; arranging and adjusting the mutual correlation coefficients to obtain a mutual interference curve; combining the mutual interference curves to obtain a mutual interference spectrum; analyzing the mutual interference spectrum to obtain the characteristics of each fluid reservoir; and playing a key or auxiliary identification role in fluid division by analyzing and summarizing the difference performance characteristics of each fluid reservoir in the mutual interference spectrum, thereby providing a new method for fluid identification using conventional logging curves. Compared with other conventional fluid identification methods and auxiliary identification methods, the present invention requires low data complexity, low acquisition difficulty, and low difficulty in personnel training.

Description

Technical Field

[0001] The invention relates to the technical field of well logging methods, and in particular to a mutual interference spectrum well logging method based on well logging curves. Background Art

[0002] Well logging is a geophysical method that uses a special probe to observe the corresponding signal within a certain range around the wellbore by moving the downhole instrument along the well axis. It has the characteristics of multiple curve types and rich information, which can directly reflect the physical information of the corresponding well depth section, and interpret the formation, lithology, pore structure and fluid properties through physical information. However, as the current oil and gas exploration targets shift from traditional structural conventional oil and gas reservoirs to unconventional oil and gas reservoirs with complex lithological structures, it is difficult to directly use the physical response characteristics of the logging curve to identify the reservoir and its fluid properties.

[0003] Taking tight sandstone reservoirs as an example, due to their strong heterogeneity, complex lithology and pore structure characteristics, as well as low porosity and permeability, fluid identification for this type of reservoir is very difficult, which is specifically manifested in the unclear response of conventional logging curves. At present, the traditional methods for conventional logging fluid identification mainly include overlap method, ratio method, crossplot method and plate method, etc., and their accuracy cannot achieve satisfactory results. Although special imaging logging methods such as array acoustic logging and nuclear magnetic resonance logging have good fluid identification effects, they have problems such as complex processing and interpretation, long time consumption, high cost, and little actual data. Summary of the invention

[0004] The purpose of the present invention is to provide a mutual interference spectrum logging method based on logging curves, which can solve the problem of difficulty in fluid logging identification in complex pore structures.

[0005] To achieve the above object, the present invention provides a mutual interference spectrum logging method based on logging curves, comprising:

[0006] Obtain multiple conventional logging curves;

[0007] Calculating the mutual correlation coefficients between the plurality of conventional logging curves to obtain a correlation coefficient matrix;

[0008] screening the correlation coefficient matrix for correlation coefficients that highlight differences in rock formations;

[0009] Arrange and adjust the mutual correlation coefficients to obtain a mutual correlation curve;

[0010] Combining the mutual interference curves to obtain a mutual interference spectrum;

[0011] The mutual coherence spectrum is analyzed to obtain the characteristics of each fluid reservoir.

[0012] The plurality of conventional logging curves include a mud logging curve, a porosity logging curve and a resistivity logging curve.

[0013] The calculation of the mutual correlation coefficients between the plurality of conventional logging curves is performed using the Pearson correlation coefficient formula.

[0014] The specific steps of arranging and adjusting the mutual correlation coefficients to obtain the mutual correlation curve include:

[0015] sorting the mutual correlation coefficients;

[0016] The sorted mutual correlation coefficients are mirror-inverted and spliced ​​to obtain mutual correlation curves.

[0017] The method for sorting the mutual correlation coefficients is ascending order or descending order.

[0018] The present invention is a mutual interference spectrum logging method based on logging curves. On the basis of conventional logging curve data, it innovatively proposes to use the mutual interference values ​​between the upper and lower sampling points of different conventional logging curves to form a mutual interference spectrum, and to highlight and reflect the mathematical differences of reservoirs and surrounding rocks with different fluid properties in the mutual interference spectrum through the morphology, trend or characteristic parameters of the mutual interference spectrum. By analyzing and summarizing the difference performance characteristics of each fluid reservoir in the mutual interference spectrum, it plays a key or auxiliary identification role in fluid division, and provides a new method for fluid identification using conventional logging curves. Compared with other conventional fluid identification methods and auxiliary identification methods, the present invention requires low data complexity, low difficulty in acquisition, and low difficulty in personnel training. Since the mutual interference spectrum has strong visualization and a more intuitive effect, it is easy to operate and reduces the subjectivity of personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 The present invention is a flow chart of a mutual coherence spectrum logging method based on logging curves.

[0021] Figure 2 It is a flow chart of the present invention for arranging and adjusting the mutual correlation coefficients to obtain the mutual interference curve.

[0022] Figure 3 This is a diagram of the gas layer treatment results of a certain well.

[0023] Figure 4 This is a diagram of the gas-water same-layer treatment results for a certain well.

[0024] Figure 5 This is a diagram of the poor gas layer processing results of a certain well.

[0025] Figure 6 This is a diagram of the water layer treatment results of a certain well.

[0026] Figure 7 This is a diagram of the dry layer treatment results of a certain well. DETAILED DESCRIPTION

[0027] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0028] See also Figure 1 to Figure 7 The present invention provides a mutual interference spectrum logging method based on logging curves, comprising:

[0029] S1 obtains multiple conventional logging curves;

[0030] The existing method is used to obtain multiple conventional logging curves, including mud logging curves (photoelectric absorption cross-section index logging curve PE, natural gamma logging curve GR, natural potential logging curve SP), porosity logging curves (acoustic time difference logging curve AC, compensated neutron logging curve CNL, density logging curve DEN), and resistivity logging curves (deep lateral resistivity logging curve RLLD, shallow lateral resistivity logging curve RLLS).

[0031] S2 calculates the mutual correlation coefficients between the plurality of conventional logging curves to obtain a correlation coefficient matrix;

[0032] Input the start and end depths of the processing depth segment, calculate the mutual correlation coefficients between the conventional logging curves within the processing window length corresponding to each depth point, and obtain the correlation coefficient matrix; use the Pearson correlation coefficient formula to calculate the mutual correlation coefficients between the eight curves of mud logging curve (photoelectric absorption cross-section index logging curve PE, natural gamma logging curve GR, natural potential logging curve SP), porosity logging curve (acoustic time difference logging curve AC, compensated neutron logging curve CNL, density logging curve DEN), and resistivity logging curve (deep lateral resistivity logging curve RLLD, shallow lateral resistivity logging curve RLLS) to form a correlation coefficient matrix; before calculation, some curve segments with small changes can be encrypted, and the encryption multiple can be determined according to the actual situation. When performing mutual interference calculation on them, it is necessary to set the processing window length for mutual correlation coefficient calculation. The window length cannot be too small or too large. It is recommended to select a window length as 1 / 2-2 times the reservoir width as much as possible, and use the sampling interval of the logging curve as the moving step.

[0033] S3 screening the correlation coefficient matrix for the mutual correlation coefficients that highlight the differences in rock formations;

[0034] The calculated coefficient matrix contains autocorrelation coefficients (1 elements) and filling coefficients (0 elements). The autocorrelation coefficient is the correlation value of the data point itself, and the filling coefficient is meaningless. Therefore, it is necessary to screen the coefficient matrix, remove the 0 elements and 1 elements in the correlation matrix, and leave the mutual correlation coefficients that can highlight the difference between the upper and lower rock layers.

[0035] S4 arranges and adjusts the mutual correlation coefficients to obtain a mutual correlation curve;

[0036] The specific steps include:

[0037] S41 sorts the mutual correlation coefficients;

[0038] After screening the correlation coefficients, since the original correlation coefficients are not arranged regularly and cannot reflect the differences between different fluid reservoirs, the correlation coefficients need to be reordered and processed. First, the original irregular mutual correlation coefficients are arranged in descending or ascending order.

[0039] S42 performs mirror inversion and splicing on the sorted mutual correlation coefficients to obtain a mutual correlation curve;

[0040] The sorted mutual correlation coefficients are mirror-inverted and spliced, thereby highlighting the morphological characteristics of the mutual correlation coefficients and improving the difference characteristics of different fluid reservoirs, thereby forming a mutual correlation curve.

[0041] S5 combines the mutual interference curves to obtain a mutual interference spectrum;

[0042] Finally, the mutual interference curves in the processing depth segment covering the target reservoir are combined and output as a mutual interference spectrum.

[0043] S6 analyzes the mutual interference spectrum to obtain characteristics of each fluid reservoir;

[0044] By analyzing the proportion of concave areas in the mutual interference curves in the reservoir, the speed of boundary descent, the steepness of changes and the amplitude of the concave areas, the fluid properties can be distinguished, which can be used as a key or auxiliary means for gas and water identification and division. Figure 3 The spectrum analysis of the gas layer processing result diagram shown in the figure shows that the gas layer and the surrounding rock are obviously different, the concave area accounts for a large proportion in the mutual interference spectrum, and the edge boundary of the concave area changes very steeply and drops rapidly. Figure 4 The spectrum analysis of the gas-water same layer processing result diagram shown in the figure shows that the gas-water same layer is quite different from the surrounding rock, the concave area accounts for almost the entire inter-correlation curve, the concave area boundary changes slowly, and the concave area is deep. Figure 5 The spectrum analysis of the poor gas layer processing result diagram shown in the figure shows that there is a certain difference between the poor gas layer and the surrounding rock, the concave area accounts for a small proportion, and there is a concave area at the upper interface of the reservoir, but the change at the concave area interface is slow and the amplitude is small. Figure 6 The spectral analysis of the water layer processing result graph shows that there are certain differences between the water layer and the surrounding rock. The concave area accounts for a small proportion on the upper interface of the reservoir, the boundary changes steeply, and the amplitude of the concave area is large; the concave area accounts for a large proportion near the lower interface of the reservoir, but the boundary changes relatively slowly, and the amplitude of the concave area is small. Figure 7 From the spectral analysis of the dry layer processing result diagram shown in the figure, it can be seen that the dry layer is slightly different from the surrounding rock, the concave area accounts for a small proportion, and the changes at the boundary of the concave area are slow and the amplitude is small.

[0045] The present application discloses a mutual interference spectrum logging method based on logging curves. On the basis of conventional logging curve data, it innovatively proposes to use the mutual interference values ​​between upper and lower sampling points of different conventional logging curves to form a mutual interference spectrum, and to highlight and reflect the mathematical differences between reservoirs and surrounding rocks with different fluid properties in the mutual interference spectrum through the morphology, trend or characteristic parameters of the mutual interference spectrum. By analyzing and summarizing the differential performance characteristics of each fluid reservoir in the mutual interference spectrum, it plays a key or auxiliary identification role in fluid division, and provides a new method for fluid identification using conventional logging curves. Compared with other conventional fluid identification methods and auxiliary identification methods, the data required for the present invention is less complex, less difficult to obtain, and less difficult to train personnel. Since the mutual interference spectrum has strong visualization and a more intuitive effect, it is less difficult to operate and reduces personnel subjectivity.

[0046] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A mutual interference spectrum logging method based on logging curves, characterized in that: include: Obtain multiple conventional logging curves; Calculating the mutual correlation coefficients between the plurality of conventional logging curves to obtain a correlation coefficient matrix; screening the correlation coefficient matrix for correlation coefficients that highlight differences in rock formations; Arrange and adjust the mutual correlation coefficients to obtain a mutual correlation curve; Combining the mutual interference curves to obtain a mutual interference spectrum; Analyzing the mutual interference spectrum to obtain the characteristics of each fluid reservoir; The calculation of the mutual correlation coefficients between the plurality of conventional well logging curves is performed using the Pearson correlation coefficient formula; The specific steps of arranging and adjusting the mutual correlation coefficients to obtain the mutual correlation curve include: sorting the mutual correlation coefficients; The sorted mutual correlation coefficients are mirror-inverted and spliced ​​to obtain mutual correlation curves.

2. A mutual interference spectrum logging method based on logging curves according to claim 1, characterized in that: The plurality of conventional logging curves include a mud logging curve, a porosity logging curve and a resistivity logging curve.

3. A mutual interference spectrum logging method based on logging curves as claimed in claim 1, characterized in that: The method of sorting the mutual correlation coefficients is ascending order or descending order.

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