Core retrieval method and device

Abstract

The present invention relates to the field of oil and gas exploration, and in particular to a core homing method and device, which comprises the following steps: data collection and data preparation; determining a preset range, wherein the initial core section depth is included in the preset range; within the preset range, selecting at least two target sections, extracting the logging curves in each target section, and calculating the comprehensive correlation coefficient under the target section, wherein the length of the target section is equal to the length of the core section, and the comprehensive correlation coefficient is used to reflect the correlation between the physical property parameters of the core section sample and the logging curve; comparing the calculated comprehensive correlation coefficients, the target section depth corresponding to the largest comprehensive correlation coefficient is the optimal homing depth of the core section under the corresponding homing accuracy. The core homing method provided by the present invention finds the area with the greatest correlation with the core section through the comprehensive correlation coefficient, so that the core section can be more accurately relocated to the optimal homing depth, making the core homing result more objective.

Description

Technical Field

[0001] The invention relates to the field of oil and gas exploration, and in particular to a core homing method and device. Background Art

[0002] Core section data is the most accurate and intuitive data for obtaining geological information of the target layer in oil and gas exploration drilling. It is the basis of reservoir logging evaluation research. The accuracy of the core section depth directly affects the accuracy and precision of logging reservoir evaluation. During the drilling and coring process, logging and other operations will be performed while drilling, so that the depth of a core section can be obtained, which is the initial core section depth. Due to the error between the core section recovery rate, drilling, logging and logging depth, the initial core section depth obtained is often inaccurate and differs greatly from the actual depth of the core section. Therefore, after obtaining the porosity data of the core section experimental analysis in the laboratory, the logging data will be compared with the experience and judgment of geological professionals, and the logging curves associated with the porosity comparison of the core section experimental analysis will be qualitatively moved and reset as a whole to restore the actual depth of the core section.

[0003] The traditional method of core movement and relocation is mainly based on the experience of geological professionals, which is a subjective and qualitative factor adjustment: after using software to draw a map, the core segment relocation adjustment is manually performed by observing the correlation between the physical property data and the associated logging curves. Although this method is highly flexible and easy to operate, it relies on the experience of geological professionals. At the same time, even if the same person relocates different samples of the same core segment, different relocation depths may be obtained. This brings great difficulties to the reservoir evaluation of logging. For a long time, the research work on core segment relocation has lacked an effective and objective method to guide it. Summary of the invention

[0004] The purpose of the present invention is to overcome the problem in the prior art that the core segment relocation research is greatly affected by the subjective factors of geological professionals, and to provide a core relocation method and device.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] A core retrieval method comprises the following steps:

[0007] Information collection and data preparation;

[0008] determining a preset range, wherein the initial core section depth is contained within the preset range;

[0009] Within a preset range, at least two target sections are selected, the logging curves in each target section are extracted, and the comprehensive correlation coefficient under the target section is calculated, wherein the length of the target section is equal to the length of the core section, and the comprehensive correlation coefficient is used to reflect the correlation between the physical property parameters of the core section sample and the logging curve;

[0010] Compare the calculated comprehensive correlation coefficients, and the depth corresponding to the largest comprehensive correlation coefficient is the optimal homing depth of the core segment under the corresponding homing accuracy. The core segment homing results obtained by this method are not limited by the operator's experience and subjective factors, and inexperienced operators can also obtain reliable core segment homing results. The homing accuracy is related to the number of target segments within the preset range. The more target segments there are, the higher the corresponding homing accuracy.

[0011] As a preferred embodiment of the present invention, in the data collection and data preparation steps, the well logging curves related to the core segment physical property curves are screened out through the correlation between the core physical property parameters and various existing well logging curves, and the screened well logging curves are used to establish the core segment homing map.

[0012] As a preferred embodiment of the present invention, the preset range is determined by the following method:

[0013] Taking the initial core segment depth as a reference, the core segment is moved upward by a preset distance, and the top depth of the core segment after the movement is taken as the highest point of the preset range;

[0014] Taking the initial core segment depth as a reference, the core segment is moved downward by a preset distance, and the bottom depth of the core segment after the movement is taken as the lowest point of the preset range;

[0015] The preset distance is not less than 5 meters.

[0016] As a preferred embodiment of the present invention, the target segment is selected by the following method:

[0017] Starting from the state where the highest point of the preset range coincides with the top of the core segment, the core segment is moved in sequence with the logging step length as the interval. Each time the core segment is moved, the area between the top depth value and the bottom depth value corresponding to the core segment is a target segment, until the bottom depth of the core segment coincides with the lowest point of the preset range. The number of times the core segment is moved is the number of target segments within the preset range. When in use. The size of the logging step length can be adjusted according to the use requirements, and then the accuracy of the core segment homing can be adjusted, which is conducive to achieving high-precision core segment homing operations.

[0018] As a preferred embodiment of the present invention, the core physical property parameters include porosity, permeability, TOC organic carbon content or particle size.

[0019] As a preferred embodiment of the present invention, the logging curve includes one or more of acoustic wave, neutron or resistivity.

[0020] As a preferred solution of the present invention, the comprehensive correlation coefficient includes the correlation coefficient between the porosity and the acoustic logging response value.

[0021] As a preferred embodiment of the present invention, the comprehensive correlation coefficient also includes the correlation coefficient between porosity and neutron logging response value, and the correlation coefficient between porosity and resistivity logging response value. The calculation method of the comprehensive correlation coefficient is:

[0022] R=a×r 声波-孔隙度 +b×r 中子-孔隙度 +c×|r 电阻率-孔隙度 |;

[0023] a+b+c=1;

[0024] Among them, R is the comprehensive correlation coefficient;

[0025] r 声波-孔隙度 is the correlation coefficient between porosity and acoustic logging response value;

[0026] r 中子-孔隙度 is the correlation coefficient between porosity and neutron logging response value;

[0027] |r 电阻率-孔隙度 | is the correlation coefficient between porosity and resistivity logging response values;

[0028] a is the comprehensive correlation coefficient r 声波-孔隙度 The weight of

[0029] b is the comprehensive correlation coefficient r 中子-孔隙度 The weight of

[0030] c is the comprehensive correlation coefficient | r 电阻率-孔隙度 The weight of | can be used to measure the core segment return results by comprehensive correlation coefficient, which can achieve the quantification of core segment return.

[0031] As a preferred embodiment of the present invention, the correlation coefficient between the porosity and each logging response value is calculated by the following formula:

[0032] ;

[0033] Among them, x i is the porosity corresponding to a certain depth;

[0034] is the average porosity of a core section;

[0035] y iIt is the acoustic logging response value corresponding to a certain depth, or the neutron logging response value corresponding to a certain depth, or the resistivity logging response value corresponding to a certain depth;

[0036] It is the average value of the sonic logging response of a core section, or the average value of the neutron logging response of a core section; or the average value of the resistivity logging response of a core section.

[0037] S x is the porosity standard deviation;

[0038] S y is the standard deviation of the sonic logging response value, or the standard deviation of the neutron logging response, or the standard deviation of the resistivity logging response:

[0039]

[0040]

[0041] A device for core retrieval includes at least one processor and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0042] Compared with the prior art, the present invention has the following beneficial effects: In the core homing method provided by the present invention, by comparing the comprehensive correlation coefficients under different target segments within a preset range, the area with the greatest correlation with the core segment is found, so that the core segment can be more accurately relocated to the optimal relocation depth under the corresponding relocation accuracy. In the core segment classification method provided by the present invention, since the correlation comparison under at least two target segments is performed within the preset range, there is no need to make judgments based on the experience of professionals, and even inexperienced personnel can obtain reliable core segment relocation results, making the relocation results objective and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention provides a flow chart of the core retrieval method. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with test examples and specific implementation methods. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention. Example

[0045] The present invention provides a core homing method, please refer to Figure 1 , this core retrieval method includes the following steps:

[0046] Step 1: Data collection and data preparation, and establishment of core homing chart;

[0047] In step 1, the data to be collected include core segment data and well logging data. Core segment data is the most accurate and intuitive data for obtaining geological information of the target layer in oil and gas exploration drilling, and is the basis for reservoir logging evaluation research. Well logging data includes various well logging curves of the corresponding depth section in the well where the core segment is obtained. After obtaining the above data, it is necessary to determine which well logging curves are related to the core segment data among the existing well logging curves, and exclude irrelevant well logging curves.

[0048] The exclusion of well logging curves is carried out in the following ways:

[0049] Establish the intersection chart of each well logging curve and core test data, analyze the correlation between each curve and core test data, so as to obtain the well logging curve with good correlation with the core test data. Good correlation with core test data means that the relationship between the well logging curve value and the core test data can be described by or basically by a univariate function. If the relationship between a well logging curve value and the core test data cannot be described by a univariate function, then there is no correlation between the well logging curve and the core test data.

[0050] The logging curve related to the core test data is used, and the logging curve and the core test data are loaded into the logging software to form a core segment homing chart, so that the logging curve presents a continuous curve. The core segment homing chart is: a logging curve chart related to the core test data formed in the logging software.

[0051] Step 2: determining a preset range, wherein the initial core section depth is included in the preset range;

[0052] The initial core section depth is the core section depth obtained during the drilling and logging process.

[0053] Specifically, in step 2, based on the initial core segment depth, the core segment is moved upward by a preset distance, and the top depth of the core segment after the movement is used as the highest point of the preset range; based on the initial core segment depth, the core segment is moved downward by a preset distance, and the bottom depth of the core segment after the movement is used as the lowest point of the preset range.

[0054] Through the core segment experiment in the prior art, the initial core segment depth can be obtained, but the initial core segment depth often has a certain error. Therefore, based on the initial core segment depth, a preset range is selected, and it can be considered that the optimal homing depth of the core segment is within the preset range. Then, by comparing and searching within the preset range, the optimal homing depth of the core segment can be found.

[0055] When the depth of the core section is corrected, the vertical movement distance of the core section is usually within a range of five meters. Therefore, the preset distance is not less than five meters. In this embodiment, the preset distance is five meters.

[0056] Step 3: Select at least two target segments within a preset range and calculate the comprehensive correlation coefficient under the target segments, wherein the length of the target segment is equal to the length of the core segment, and the comprehensive correlation coefficient is used to reflect the correlation between the core physical property parameters and the logging curve;

[0057] In step three, the target segment is selected by:

[0058] Starting from the state where the highest point of the preset range coincides with the top of the core segment, the core segment is moved at intervals of the logging step length. Each time the core segment is moved, the area between the top and bottom of the core segment is a target segment until the bottom depth of the core segment coincides with the lowest point of the preset range.

[0059] When in use, the logging step length can be selected according to the required accuracy. In this embodiment, the logging step length is selected to be 0.1 meters. That is, in step 3, the state where the highest point of the preset range coincides with the top of the core segment is taken as the starting point, and the core segment is moved at intervals of 0.1 meters. Each time the core segment is moved, the area between the top and bottom of the core segment is a target segment, until the bottom of the core segment coincides with the lowest point of the preset range.

[0060] Under each target section, a comprehensive correlation coefficient is calculated, and the comprehensive correlation coefficient is used to reflect the correlation between the core physical property parameters and the well logging curve. The core physical property parameters include porosity, permeability, TOC organic carbon content or particle size. The well logging curve includes one or more of acoustic wave, neutron or resistivity. In this embodiment, the core physical property parameter is porosity, and the well logging curve includes an acoustic well logging response curve, a neutron well logging response curve and a resistivity well logging response curve. In this embodiment, the comprehensive correlation coefficient is a quantity that comprehensively considers the correlation between the acoustic well logging response curve, the neutron well logging response curve and the resistivity well logging response curve and the porosity.

[0061] When calculating the comprehensive correlation coefficient, a section of the logging curve corresponding to the target section depth is selected for calculation.

[0062] Further:

[0063] R=a×r 声波-孔隙度 +b×r 中子-孔隙度 +c×|r 电阻率-孔隙度 |;

[0064] a+b+c=1;

[0065] Among them, R is the comprehensive correlation coefficient;

[0066] r 声波-孔隙度 is the correlation coefficient between porosity and acoustic logging response value;

[0067] r 中子-孔隙度 is the correlation coefficient between porosity and neutron logging response value;

[0068] |r 电阻率-孔隙度 | is the correlation coefficient between porosity and resistivity logging response values;

[0069] a is the comprehensive correlation coefficient r 声波-孔隙度 The weight of

[0070] b is the comprehensive correlation coefficient r 中子-孔隙度 The weight of

[0071] c is the comprehensive correlation coefficient | r 电阻率-孔隙度 |The weight of.

[0072] Since for most regions and formations, in the reservoir logging evaluation research, through experimental data, it is found that the correlation between sonic logging and porosity is better than other logging curves, the linear correlation between sonic logging and core experimental analysis of porosity is often used as a porosity logging interpretation model. Since the present invention uses porosity and three parameters of sonic waves, neutrons and resistivity respectively, a more accurate logging evaluation system can be obtained. According to the experiment, the correlation coefficient ratio of its porosity and sonic waves is a=0.5-0.8, the correlation coefficient ratio of porosity and neutrons is neutron b=0.1-0.25, and the correlation coefficient ratio of porosity and resistivity is c=0.1-0.25, depending on the actual logging curve; in this embodiment, the correlation coefficient between porosity and sonic logging response value has a larger weight, taking a=0.6, b=0.2, c=0.2.

[0073] but:

[0074] R=0.6×r 声波-孔隙度 +0.2×r 中子-孔隙度 +0.2×|r 电阻率-孔隙度 |;

[0075] The correlation coefficient between porosity and each logging response value is calculated by the following formula:

[0076] ;

[0077] Among them, x i is the porosity corresponding to a certain depth;

[0078] is the average porosity of a core section;

[0079] y iIt is the acoustic logging response value corresponding to a certain depth, or the neutron logging response value corresponding to a certain depth, or the resistivity logging response value corresponding to a certain depth;

[0080] It is the average value of the sonic logging response of a core section, or the average value of the neutron logging response of a core section; or the average value of the resistivity logging response of a core section.

[0081] S x is the porosity standard deviation;

[0082] S y is the standard deviation of the sonic logging response value, or the standard deviation of the neutron logging response, or the standard deviation of the resistivity logging response:

[0083]

[0084] ;

[0085] Step 4: Compare the calculated comprehensive correlation coefficients. The target segment corresponding to the largest comprehensive correlation coefficient is the optimal homing depth of the core segment under the corresponding homing accuracy.

[0086] The following is a further description of the core homing method provided by the present invention through an operation example of homing a core segment:

[0087] In this operation example, the basic data are the porosity of the core section experimental analysis of the 4991.98-5002.2m coring section of a certain well, and the logging curve of the corresponding depth section of the well.

[0088] Step 1: Data collection and data preparation, and establishment of core segment homing chart;

[0089] In this example, the core physical property parameter is porosity. The logging curves that are not related to the core section data are excluded. In this example, the sonic logging response curve, the neutron logging response curve, and the resistivity logging response curve are retained.

[0090] Step 2: extracting the logging curve value within a preset range, wherein the initial core section depth is included in the preset range;

[0091] The initial core section depth is 4991.98-5002.2m. Based on the initial core section depth, the core section is moved upward by 5m to obtain the highest point of the preset range of 4986.98m. At this time, the core section range is 4986.98~4997.2m;

[0092] On the basis of the initial core section depth, the core section is moved down by 5 m to obtain the lowest point of the preset range of 5007.2 m. At this time, the core section range is 4996.98 to 5007.2 m.

[0093] Step 3: Select at least two target segments within a preset range and calculate the comprehensive correlation coefficient under the target segments, wherein the comprehensive correlation coefficient is used to reflect the correlation between the core physical property parameters and the logging curve;

[0094] The top depth of the core segment is 4986.95 m as the starting point, with a step length of 0.1 m. Each time a movement is made, a target segment is obtained. Thus, 100 target segments can be obtained within the preset range.

[0095] The logging curve value corresponding to each target section is extracted and the comprehensive correlation coefficient R is calculated.

[0096] For example, extract a section of the well logging curve corresponding to 4987.08-4997.3m, and calculate the correlation parameters between the well logging curve and the porosity of the core section. Perform the above calculation once for each target section, and thus 100 comprehensive correlation coefficients R can be obtained.

[0097] Step 4: Compare the calculated comprehensive correlation coefficients. The depth corresponding to the largest comprehensive correlation coefficient is the optimal homing depth of the core section under the corresponding homing accuracy.

[0098] For the core section in this operation example, the maximum comprehensive correlation coefficient calculated is R max =0.75, the corresponding depth is: the top depth of the core section is 4992.68m. Therefore, it can be obtained that the optimal homing depth of the core section under the corresponding homing accuracy is 4992.68m.

[0099] The difference between it and the initial core section depth is the core section depth return correction Δ:

[0100] Δ=4992.68m-4991.98m=0.7m

[0101] The core homing method provided by the embodiment of the present invention has the following beneficial effects:

[0102] 1. The core segment homing results obtained by this method are not limited by the operator's experience and subjective factors, and inexperienced operators can also obtain reliable core segment homing results;

[0103] 2. The core segment return result can be measured by comprehensive correlation coefficient, which can realize the quantification of core segment return;

[0104] 3. The accuracy of core segment homing can be adjusted according to usage requirements, which is conducive to achieving high-precision core segment homing operation.

[0105] It should be noted that:

[0106] In this embodiment, the porosity is selected for calculating the comprehensive correlation coefficient because the porosity is a conventional rock physical parameter and thus can be easily calculated. In other embodiments of the present invention, other rock physical parameters may also be selected to calculate the comprehensive correlation coefficient.

[0107] In this embodiment, the acoustic logging response curve, the neutron logging response curve and the resistivity logging response curve are retained. In other embodiments of the present invention, other logging response curves may also be selected for use.

[0108] In this embodiment, the target depth is selected by moving the core section depth by the logging step length. In other embodiments of the invention, those skilled in the art may also select the target depth by other methods according to the usage, such as random selection within a preset range, etc. As long as the calculation of the comprehensive correlation coefficients at different target sections within the preset range can be realized and the optimal homing depth can be obtained by the maximum comprehensive correlation coefficient.

[0109] In this embodiment, each comprehensive correlation coefficient is obtained first, and then the comprehensive correlation coefficients are compared. In other embodiments of the present invention, those skilled in the art can make appropriate adjustments to the above order, for example: first obtain the comprehensive correlation coefficients R1 and R2 at two target segment depths, and after a comparison, retain the larger comprehensive correlation coefficient, and then calculate the comprehensive correlation coefficient R3 at the next target segment depth, compare the larger value of R1 and R2 with R3, retain the larger comprehensive correlation coefficient, and then perform the next calculation and comparison, and so on. Therefore, the present invention does not limit the above calculation and comparison order, as long as the maximum comprehensive correlation coefficient can be obtained in the end.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A core homing method, characterized in that: The following steps are involved: Data collection and data preparation: The data to be collected include core section data and well logging data. Well logging data include various well logging curves of the corresponding depth section in the well where the core section is obtained. After obtaining the above data, it is necessary to determine which well logging curves are related to the core section data among the existing well logging curves, and exclude irrelevant well logging curves; determining a preset range, wherein the initial core section depth is contained within the preset range; The preset range is determined by the following operations: Taking the initial core segment depth as a reference, the core segment is moved upward by a preset distance, and the top depth of the core segment after the movement is taken as the highest point of the preset range; Taking the initial core segment depth as a reference, the core segment is moved downward by a preset distance, and the bottom depth of the core segment after the movement is taken as the lowest point of the preset range; The preset distance is not less than 5 meters; Within a preset range, at least two target segments are selected, logging curves in each target segment are extracted, and a comprehensive correlation coefficient under the target segment is calculated, wherein the length of the target segment is equal to the length of the core segment, and the comprehensive correlation coefficient under each target segment is used to reflect the correlation between the physical property parameters of the core sample and the logging curve in the target segment; The comprehensive correlation coefficient includes the correlation coefficient between porosity and acoustic logging response value; The comprehensive correlation coefficient also includes the correlation coefficient between porosity and neutron logging response value, and the correlation coefficient between porosity and resistivity logging response value; The calculation method of the comprehensive correlation coefficient is: R=a×r 声波-孔隙度 +b×r 中子-孔隙度 +c×|r 电阻率-孔隙度 |; a+b+c=1; Among them, R is the comprehensive correlation coefficient; r 声波-孔隙度 is the correlation coefficient between porosity and acoustic logging response value; r 中子-孔隙度 is the correlation coefficient between porosity and neutron logging response value; |r 电阻率-孔隙度 | is the correlation coefficient between porosity and resistivity logging response values; a is the comprehensive correlation coefficient r 声波-孔隙度 The weight of b is the comprehensive correlation coefficient r 中子-孔隙度 The weight of c is the comprehensive correlation coefficient | r 电阻率-孔隙度 |weight; The core segment return result can be measured by comprehensive correlation coefficient, which can realize the quantification of core segment return; By comparing the calculated comprehensive correlation coefficients, the target section depth corresponding to the largest comprehensive correlation coefficient is the optimal homing depth of the core section under the corresponding homing accuracy.

2. The core homing method according to claim 1, characterized in that: In the data collection and data preparation steps, the well logging curves related to the core physical property parameters are screened out through the correlation between the core physical property parameters and various existing well logging curves, and the screened well logging curves are used to establish the core section homing chart.

3. The core homing method according to claim 2, characterized in that: The target segment is selected by: Starting from the state where the highest point of the preset range coincides with the top of the core segment, the core segments are moved in sequence with the logging step length as the interval. Each time the core segment is moved, the area between the top depth value and the bottom depth value corresponding to the core segment is a target segment, until the bottom depth of the core segment coincides with the lowest point of the preset range. The number of times the core segment is moved is the number of target segments in the preset range.

4. The core homing method according to claim 3, characterized in that: The core physical property parameters include porosity, permeability, TOC organic carbon content or particle size.

5. The core homing method according to claim 4, characterized in that: The logging curve includes one or more of acoustic wave, neutron or resistivity.

6. The core homing method according to claim 1, characterized in that: The correlation coefficient r between the porosity and each logging response value xy Calculated by the following formula: ； Among them, x i is the porosity corresponding to a certain depth; is the average porosity of a core section; i It is the acoustic logging response value corresponding to a certain depth, or the neutron logging response value corresponding to a certain depth, or the resistivity logging response value corresponding to a certain depth; is the average value of the acoustic logging response of a core section, or the average value of the neutron logging response of a core section; or the average value of the resistivity logging response of a core section; S x is the porosity standard deviation; S y is the standard deviation of the sonic logging response value, or the standard deviation of the neutron logging response, or the standard deviation of the resistivity logging response: 。 7. A device for core homing, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 6.

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