Method, device and computing equipment for preprocessing through-casing density logging information

Abstract

The present application discloses a method, device and computing equipment for preprocessing density logging information through casing, the method comprising: obtaining the count rate in the designated address window of each depth from the energy spectrum data of each depth detected by the density detector, and constructing the first associated data; performing differential operation according to the first associated data to obtain the second associated data; the first associated data records the correlation relationship between the depth and the count rate in the designated address window, and the second associated data records the correlation relationship between the depth and the differential operation result; identifying the target depth interval where the casing collar is located according to the second associated data; deleting the count rate corresponding to the target depth interval in the first associated data, and performing count rate compensation processing on the target depth interval. Through the above method, the measurement noise caused by the casing collar can be removed while retaining the original formation density characteristics, and the count rate data of the real formation conditions of the reaction casing density measurement can be obtained.

Description

Technical Field

[0001] The present application relates to the field of well logging technology, and in particular to a method, device, computing equipment, computer storage medium and computer program product for preprocessing through-casing density logging information. Background Art

[0002] The development of logging technology provides important parameters such as porosity, permeability, and oil and gas saturation for reservoir evaluation, which is of great significance to the exploitation of oil and gas reservoir resources. Density logging curve is one of the important logging data, which can be used to evaluate reservoir properties and lay the foundation for reservoir evaluation.

[0003] In conventional open-hole wells, it is difficult to record reservoir data through measurement while drilling, and the operational risks in complex wells cannot be effectively controlled. In order to improve the safety of logging, the through-casing measurement technology has been widely used. The method of logging after installing casing and pouring cement in the well can effectively prevent the collapse of the hole wall, and at the same time can isolate the formation and seal the leaking layer and oil and gas layer, providing a stable channel for drilling. Usually, casing is installed and cement is poured after a part of the drilling is completed.

[0004] In the cased density measurement, the casing in the well is often assembled from a series of casings, and the two adjacent casings are connected by casing collars to prevent oil and gas leakage or mud seepage. However, the connection of the casing collar will increase the thickness and there may be oil or scale at the casing collar. When the instrument passes through the casing collar for measurement, the detector count rate will be abnormal, which will lead to inaccurate formation density calculation, thus affecting the cased measurement.

[0005] In summary, in the case of post-casing measurement, conventional density logging preprocessing methods cannot meet the requirements of through-casing density measurement, and a reliable and efficient preprocessing method for post-casing density instrument measurement data is urgently needed. Summary of the invention

[0006] In view of the above problems, the present application is proposed to provide a method, apparatus, computing device, computer storage medium and computer program product for preprocessing through-casing density logging information that overcomes the above problems or at least partially solves the above problems.

[0007] According to one aspect of the present application, a method for preprocessing through-casing density logging information is provided, comprising:

[0008] Obtaining the count rate within the designated channel address window at each depth from the energy spectrum data at each depth detected by the density detector, and constructing first correlation data;

[0009] Performing a differential operation on the first associated data to obtain second associated data;

[0010] The first associated data records the associated relationship between the depth and the count rate in the specified channel address window, and the second associated data records the associated relationship between the depth and the differential operation result;

[0011] identifying, according to the second associated data, a target depth interval in which the casing collar is located;

[0012] The count rate corresponding to the target depth interval in the first associated data is deleted, and count rate compensation processing is performed on the target depth interval.

[0013] Optionally, performing count rate compensation processing on the target depth interval further includes:

[0014] For each depth within the target depth interval, interpolation processing is performed based on the count rate within the specified channel address window of the adjacent depth of the depth to obtain the count rate compensation result of the depth; wherein the adjacent depth is outside the target depth interval.

[0015] Optionally, the differential operation includes: a first-order differential operation or a second-order differential operation.

[0016] Optionally, according to the second associated data, identifying the target depth interval where the casing collar is located further comprises:

[0017] Based on the second associated data, determine a first depth interval in which the differential operation result is within a first range, and determine a second depth interval in which the differential operation result is within a second range; wherein the first range is determined according to a lower threshold and an upper threshold, and the second range is determined according to the opposite number of the upper threshold and the opposite number of the lower threshold;

[0018] The target depth interval in which the casing collar is located is determined according to the first depth interval and the second depth interval whose depth distance is less than a preset distance threshold.

[0019] Optionally, determining the target depth interval where the casing collar is located according to the first depth interval and the second depth interval whose depth distances are less than a preset distance threshold further includes:

[0020] Performing depth interval merging processing according to the first depth interval and the second depth interval to obtain an initial depth interval;

[0021] The initial depth interval is expanded to obtain the target depth interval where the casing collar is located.

[0022] Optionally, the designated track window includes: a cement-related track window and a formation-related track window; before obtaining the count rate in the designated track window at each depth, the method further includes:

[0023] According to the sample energy spectrum data, formation parameters and cement parameters, correlation analysis is performed; according to the correlation analysis results, cement-related channel windows and formation-related channel windows are divided.

[0024] According to another aspect of the present application, a through-casing density logging information preprocessing device is provided, comprising:

[0025] An acquisition module, adapted to acquire the count rate within the designated channel address window at each depth from the energy spectrum data at each depth detected by the density detector, and construct first associated data;

[0026] A calculation module, adapted to perform a differential operation according to the first associated data to obtain second associated data;

[0027] The first associated data records the associated relationship between the depth and the count rate in the specified channel address window, and the second associated data records the associated relationship between the depth and the differential operation result;

[0028] an identification module, adapted to identify a target depth interval where the casing collar is located according to the second associated data;

[0029] The processing module is adapted to delete the count rate corresponding to the target depth interval in the first associated data and perform count rate compensation processing on the target depth interval.

[0030] Optionally, the processing module is further adapted to:

[0031] For each depth within the target depth interval, interpolation processing is performed based on the count rate within the specified address window of the adjacent depth of the depth to obtain the count rate compensation result of the depth, wherein the adjacent depth is outside the target depth interval.

[0032] Optionally, the differential operation includes: a first-order differential operation or a second-order differential operation.

[0033] Optionally, the identification module is further adapted to:

[0034] Based on the second associated data, determine a first depth interval in which the differential operation result is within a first range, and determine a second depth interval in which the differential operation result is within a second range; wherein the first range is determined according to a lower threshold and an upper threshold, and the second range is determined according to the opposite number of the upper threshold and the opposite number of the lower threshold;

[0035] The target depth interval in which the casing collar is located is determined according to the first depth interval and the second depth interval whose depth distance is less than a preset distance threshold.

[0036] Optionally, the identification module is further adapted to:

[0037] Performing depth interval merging processing according to the first depth interval and the second depth interval to obtain an initial depth interval;

[0038] The initial depth interval is expanded to obtain the target depth interval where the casing collar is located.

[0039] Optionally, the designated address window includes: a cement related address window and a stratum related address window;

[0040] The device further comprises: a division module, adapted to perform correlation analysis according to sample energy spectrum data, formation parameters and cement parameters; and to divide cement-related channel address windows and formation-related channel address windows according to the correlation analysis results.

[0041] According to another aspect of the present application, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0042] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned through-casing density logging information preprocessing method.

[0043] According to another aspect of the present application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for preprocessing through-casing density logging information.

[0044] According to another aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for preprocessing through-casing density logging information.

[0045] According to the method, device, computing equipment, computer storage medium and computer program product for preprocessing the through-casing density logging information provided by the embodiment of the present application, the method includes: obtaining the count rate in the designated address window of each depth from the energy spectrum data of each depth detected by the density detector, and constructing the first associated data; performing differential operation according to the first associated data to obtain the second associated data; the first associated data records the correlation relationship between the depth and the count rate in the designated address window, and the second associated data records the correlation relationship between the depth and the result of the differential operation; according to the second associated data, identifying the target depth interval where the casing collar is located; deleting the count rate corresponding to the target depth interval in the first associated data, and performing count rate compensation processing on the target depth interval. In the above manner, the measurement noise caused by the casing collar can be removed while retaining the original formation density characteristics, and the count rate data of the real formation conditions of the reaction casing density measurement can be obtained.

[0046] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0048] Figure 1 A flow chart of a method for preprocessing through-casing density logging information provided by an embodiment of the present application is shown;

[0049] Figure 2 A flow chart showing a method for preprocessing through-casing density logging information provided by another embodiment of the present application is shown;

[0050] Figure 3 A schematic diagram of a post-casing density logging scenario is shown;

[0051] Figure 4a A schematic diagram of a first curve in an example is shown;

[0052] Figure 4b A schematic diagram showing a curve segment of a target depth interval where a casing collar is located in a first curve;

[0053] Figure 4c A schematic diagram of a second curve in the example is shown;

[0054] Figure 4d A schematic diagram showing a casing collar characteristic curve segment and a coal seam characteristic curve segment in a second curve identified in an example;

[0055] Figure 4e A schematic diagram of a first curve after preprocessing in an example is shown;

[0056] Figure 5 A functional structure diagram of a through-casing density logging information preprocessing device provided in an embodiment of the present application is shown;

[0057] Figure 6 A schematic diagram of the structure of a computing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0059] First, the density logging technology involved in the embodiments of the present application is explained.

[0060] Figure 3 The schematic diagram of the post-casing density logging scenario is shown. Both the post-casing density logging and the conventional compensation or lithology density logging use the classic chemical source Cs-137 (corresponding to structure 9), which emits gamma rays with an energy of 0.66 MeV. The post-casing density measurement instrument is an array density instrument (corresponding to structure 5), which contains four detectors. According to the spatial position distribution of the detectors and the chemical source, they can be divided into detector BSP, detector SSP, detector MSP, and detector LSP, corresponding to structure 4, structure 3, structure 2, and structure 1, respectively. After the gamma ray is emitted from the source at a certain angle, it passes through the linear combination of the three media of casing, cement, and formation (corresponding to structure 6, structure 7, and structure 8, respectively) in the case of post-casing density logging.

[0061] During the gamma ray transmission, it will react with the formation material. Under this energy condition, the reaction types of most photons are Compton effect and photoelectric effect. The reaction will change the energy and angle of the photon, and this process carries the information of the relevant materials. After experiencing one or more scattering, the photon will be received by the detector. The scintillation detector used uses the flash caused by the radiation in the scintillator to convert it into an electrical pulse through a photomultiplier tube to record the radiation. When the gamma ray enters the scintillator crystal, it is absorbed to produce secondary electrons. The secondary electrons lose energy in the scintillator, causing a large number of molecules or atoms to be excited or ionized. These excited molecules or atoms emit photons when they return from the excited state to the ground state. The fluorescence emitted by the scintillator is incident on the photocathode of the photomultiplier tube to produce photoelectrons. The photoelectrons are multiplied in the photomultiplier tube and finally collected at the anode to output an electrical signal. The signal output by the photomultiplier tube is proportional to the kinetic energy of the secondary electron. Scintillator crystals can be used to detect both the intensity and energy of rays. Finally, the energy spectra of different detectors are obtained. These gamma counts and energy spectra are the basis for processing and inverting formation information.

[0062] Figure 1 FIG. 2 is a flow chart showing a method for preprocessing through-casing density logging information provided by an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0063] Step S110, obtaining the count rate in the designated channel window at each depth from the energy spectrum data at each depth detected by the density detector, and constructing first correlation data.

[0064] In the actual measurement process, the array density instrument is gradually lifted up with the carrier, and collects information every time it passes a certain distance, specifically collecting the energy spectrum data of each density detector. In other words, the array density instrument essentially samples the formation at intervals, and based on the sampled data, a density logging curve can be drawn with the horizontal axis being the depth and the vertical axis being the density detector count rate.

[0065] The method of the embodiment of the present application is a method for processing energy spectrum data obtained by a single density detector. For each density detector, its energy spectrum data includes the count rates of multiple addresses. The count rate in the specified address window can reflect the relevant properties of the formation. Therefore, the count rate in the specified address window at each depth is extracted for subsequent processing. If the specified address window contains multiple addresses, the count rates of the multiple addresses at any depth are accumulated to obtain the count rate in the specified address window at the depth.

[0066] The first associated data records the relationship between the depth and the count rate in the specified address window, that is, the first associated data is used to record the trend of the count rate in the specified address window following the depth. A curve is drawn based on the first associated data to obtain a first curve in which the horizontal axis represents the depth and the vertical axis represents the count rate in the specified address window.

[0067] Step S120: Perform a differential operation on the first associated data to obtain second associated data, wherein the second associated data records the association relationship between the depth and the differential operation result.

[0068] A differential operation is performed according to the count rate in the designated channel address window at each depth to obtain the differential operation result corresponding to each depth, and then the second associated data is obtained. The second associated data records the association between the depth and the differential operation result, that is, the second associated data is used to record the change trend of the differential operation result following the depth. A curve is drawn according to the second associated data to obtain a second curve in which the horizontal axis represents the depth and the vertical axis represents the differential operation result.

[0069] Step S130: identifying the target depth interval where the casing collar is located according to the second associated data.

[0070] The count rate of the density detector generally changes continuously with depth. However, in the post-casing measurement method, the casing is often made of iron-like materials such as stainless steel. Compared with the formation and cement, its density is very high, and the two adjacent casings are connected by casing couplings. The high-density nature of the casing coupling connection and the casing itself leads to a decrease in the count rate. Therefore, the data outliers can be used to identify the position of the casing coupling. In addition, since the length of the casing is relatively regular, the characteristics of the casing coupling in the count rate are often a periodic downward trend. Accordingly, after the first associated data is differentiated, the characteristics of the casing coupling in the differential operation result will also appear periodically, and the differential operation result in the target depth interval where the casing coupling is located also has a specific change trend.

[0071] At the same time, the actual logging situation is often complex and changeable. For example, if a low-density mineral is encountered during logging, the count rate will suddenly increase, and some heavy minerals will also cause the count rate to suddenly decrease. Therefore, it is necessary to perform feature division and accurately identify the target depth interval where the casing collar is located, so as to eliminate the influence of the casing collar as much as possible and retain the relevant characteristics of the formation.

[0072] In the method of the embodiment of the present application, the characteristics of the casing collar and the target depth interval in which it is located are identified based on the depth recorded in the second associated data and the associated differential operation result.

[0073] Step S140: deleting the count rate corresponding to the target depth interval in the first associated data, and performing count rate compensation processing on the target depth interval.

[0074] After identifying the target depth interval where the casing collar is located, all count rates corresponding to the target depth interval in the first associated data are deleted, and then the count rate compensation value of each depth in the target interval is calculated through count rate compensation calculation, thereby achieving the effect of removing the measurement noise caused by the casing collar and retaining the original formation density characteristics.

[0075] In summary, according to the preprocessing method of through-casing density logging information provided by this embodiment, the count rate in the designated address window of each depth is obtained from the energy spectrum data of each depth detected by the density detector, and the first associated data is constructed; the differential operation is performed according to the first associated data to obtain the second associated data; the first associated data records the correlation relationship between the depth and the count rate in the designated address window, and the second associated data records the correlation relationship between the depth and the differential operation result; according to the second associated data, the target depth interval where the casing collar is located is identified; the count rate corresponding to the target depth interval in the first associated data is deleted, and the count rate compensation processing is performed on the target depth interval. In the above manner, the measurement noise caused by the casing collar can be removed while retaining the original formation density characteristics, and the count rate data reflecting the real formation conditions of the post-casing density measurement can be obtained.

[0076] Figure 2 FIG. 2 is a flow chart showing a method for preprocessing through-casing density logging information provided by another embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0077] Step S210, obtaining the count rate in the designated channel window of each depth from the energy spectrum data of each depth detected by the density detector, and constructing the first correlation data.

[0078] In an optional manner, after obtaining the energy spectrum data of each depth detected by the density detector, data optimization processing is performed on it; and the counting rate within the specified channel address window of each depth is obtained from the optimized data.

[0079] Among them, data optimization processing includes: background subtraction processing, abnormal data elimination processing and / or depth correction processing. Background subtraction processing can eliminate the impact of instrument background on energy spectrum and counts; abnormal data elimination processing is to set negative abnormal data in measured data to zero; depth correction processing is to solve the problem of depth mismatch, using depth and other curves to translate and match the data.

[0080] Preferably, the background subtraction process is performed first, then the abnormal value is reset to zero, and then the depth correction process is performed. In this way, the problem of abnormal counting rate of a small number of channels appearing in actual measurement can be solved.

[0081] In an optional manner, the designated address windows include: a cement-related address window and a formation-related address window. The cement-related address window refers to an address window in which the counting rate has a high correlation with cement characteristics, also known as a cement mixing information window. The formation-related address window refers to an address window in which the counting rate has a high correlation with formation characteristics, also known as a density window.

[0082] Specifically, the implementation method for determining the designated channel window is as follows: performing correlation analysis based on sample energy spectrum data, formation parameters and cement parameters; dividing cement-related channel windows and formation-related channel windows according to the correlation analysis results. At each channel, analyzing the correlation between the count rate and the formation parameters and cement parameters respectively, and determining in which channel ranges the count rate has a higher correlation with the formation parameters and in which channel ranges the count rate has a higher correlation with the cement parameters according to the correlation calculation results, thereby determining the cement-related channel window and the formation-related channel window.

[0083] In an optional manner, the correlation is calculated as follows:

[0084]

[0085] Where X represents the sample energy spectrum data, that is, the sample channel count rate matrix, Y represents the formation parameter matrix or cement parameter matrix, represents the covariance of X and Y, represents the variance of X, represents the variance of Y, Indicates the correlation between X and Y. A positive correlation indicates a positive correlation, and the closer the value is to 1, the higher the correlation is; a negative correlation indicates a negative correlation, and the closer the value is to -1, the higher the correlation is.

[0086] For example, the energy of the chemical source Cs-137 is 0.662 MeV, which is divided into 256 channels, and different channels correspond to different energy ranges. Through correlation calculation, the analysis results are as follows: in the range of 70-90 channels, the counting rate of the density detector is more correlated with the formation parameters; in the range of 100-120 channels, the counting rate of the density detector is more correlated with the cement parameters. Based on the above correlation analysis results, the formation-related channel window is [70, 90], and the cement-related channel window is [100, 120].

[0087] In the embodiment of the present application, compared with conventional compensated lithology density measuring instruments, it is not only necessary to comply with the basic principles of gamma rays, but also necessary to perform correlation and sensitivity analysis on cement and formations to obtain more features to be solved, thereby obtaining better measurement results.

[0088] The first associated data is constructed according to the counting rate in the designated address window at each depth. The first associated data records the association between the depth and the counting rate in the designated address window, that is, the first associated data is in the form of a multidimensional array. By drawing a curve based on the first associated data, a first curve of the counting rate changing with the depth can be obtained.

[0089] Figure 4aA schematic diagram of the first curve in the example is shown, where the abscissa represents the depth and the ordinate represents the count rate within a specified track address window. Figure 4b A schematic diagram of the curve segment of the target depth interval where the casing collar is located in the first curve is shown. Generally, the count rate and density in the designated address window are inversely proportional, and due to the high-density nature of the casing collar connection and the casing itself, the count rate in the target depth interval where the casing collar is located shows a downward trend. At the same time, since the lengths of the casings are comparable, the characteristics of the casing collar in the count rate appear periodically. Therefore, the curve segment of the target depth interval where the casing collar is located on the first curve is regularly distributed. Region 41 contains the curve segment of the target depth interval where the casing collar is located, and region 42 contains the curve segment of the depth where the low-density strata (such as coal seams) are located. The count rate at the depth where the low-density minerals are located is higher.

[0090] Step S220: performing a differential operation on the first associated data to obtain second associated data.

[0091] The second associated data records the associated relationship between the depth and the differential operation result. Specifically, according to the depth and count rate included in the first associated data, the differential operation result is calculated for each depth to obtain the second associated data including the associated depth and differential operation result.

[0092] In an optional manner, the differential operation includes a first-order differential operation or a second-order differential operation.

[0093] In an optional manner, the differential operation is specifically a first-order differential operation, and the operation formula is as follows:

[0094]

[0095] in, It represents the differential operation result of the depth of the i-th measuring point in the measuring direction. Indicates the count rate in the specified channel window at the depth of the i+1th measurement point in the measurement direction. Indicates the count rate in the specified channel window at the depth of the i-th measurement point in the measurement direction. The differential operation result is used to reflect the trend of the count rate change in the depth direction. The closer the absolute value of the differential operation result is to 0, the lower the change rate of the count rate in the specified channel window at adjacent depths. If the absolute value is too high or too low, it means that the data is abnormal.

[0096] Draw a curve based on the second correlation data to obtain a second curve in which the differential operation result changes with the depth. Figure 4c FIG. 1 shows a schematic diagram of the second curve in an example, in which a first-order differential operation is used. Figure 4cAs shown in the figure, the differential calculation results at the casing coupling position are significantly different from those at the non-casing coupling position. The periodic curve segment that first decreases and then increases is the characteristic corresponding to the casing coupling, while the differential calculation results corresponding to the depth interval where the coal seam is located are generally higher and show a trend of first increasing and then decreasing.

[0097] Since the density detector counts have statistical characteristics and satisfy the normal distribution, within the entire measurement depth range, since multiple casing collars are evenly distributed, the count rates corresponding to the multiple casing collars appear as periodic abnormal data. Therefore, the target depth interval where the casing collars are located can be identified based on the characteristics of the periodic changes in the count rates, thereby eliminating the influence of the casing collars.

[0098] In an optional manner, a differential operation is performed on the first associated data to obtain the second associated data. The specific implementation method of this step is: a first-order differential operation is performed on the first associated data to obtain the initial second associated data; if the initial second associated data does not meet the preset requirements, a first-order differential operation is performed on the initial second associated data to obtain the second associated data. In this manner, a first-order differential operation is first performed on the first associated data, and a concavity and convexity judgment and feature distinction are performed on the initial second associated data. If the feature data corresponding to the casing coupling cannot be distinguished from the initial second associated data (i.e., it does not meet the preset requirements), a first-order differential operation is performed on the initial second associated data again to obtain the second associated data.

[0099] In an optional manner, a differential operation is performed on the first correlation data to obtain intermediate second correlation data, and a filtering and smoothing operation is performed on the intermediate second correlation data to obtain the second correlation data. In this manner, noise data can be reduced and data quality can be improved, so that a smooth second curve can be drawn according to the second correlation data.

[0100] Step S230, based on the second associated data, determine the first depth interval in which the differential operation result is within the first range, and determine the second depth interval in which the differential operation result is within the second range; determine the target depth interval in which the casing coupling is located according to the first depth interval and the second depth interval whose depth distances are less than a preset distance threshold.

[0101] The first range and the second range are symmetrical about the origin. The first range is determined according to the lower threshold and the upper threshold, and the second range is determined according to the inverse of the upper threshold and the inverse of the lower threshold. The upper threshold is greater than the lower threshold and both are positive numbers. The first depth interval and the second depth interval both contain the depths of multiple consecutive measurement points.

[0102] The reason for setting the upper threshold is that the differential operation results of the depth of the low-density formation are higher overall. The upper limit is set to distinguish the low-density formation characteristics from the periodic casing collar characteristics, and the lower limit is set to distinguish the normal formation density fluctuation and mutation. The upper and lower thresholds are combined for identification to accurately distinguish the target depth interval of the casing collar and the depth interval of the low-density formation.

[0103] In the method of the embodiment of the present application, for the second associated data, each first depth interval is identified according to the first range, and the differential operation results corresponding to each depth included in the first depth interval are all within the first range; each second depth interval is determined according to the second range, and the differential operation results corresponding to each depth included in the second depth interval are all within the second range; then, according to a preset distance threshold, one or more groups of adjacent first depth intervals and second depth intervals are searched, and the preset distance threshold can be set according to actual needs. The depth distance between the first depth interval and the second depth interval can refer to: the minimum value or maximum value of the depth distance between each depth in the first depth interval and each depth in the second depth interval; then, according to the first depth interval and the second depth interval, the target depth interval where the casing coupling is located is determined.

[0104] Among them, according to the first depth interval and the second depth interval whose depth distance is less than the preset distance threshold, the target depth interval where the casing collar is located is determined. The specific implementation method of this step is: according to the first depth interval and the second depth interval, the depth interval is merged to obtain the initial depth interval; the initial depth interval is expanded to obtain the target depth interval where the casing collar is located. Among them, the initial depth interval is obtained by the depth interval merging process, and the first depth interval and the second depth interval both belong to the initial depth interval. The initial depth interval is expanded to obtain a target depth interval whose interval length is larger than the interval length of the initial depth interval. Specifically, the start and end depths of the target depth interval are calculated according to the start and end depths of the initial depth interval and the expansion coefficient, and the start depth of the target depth interval is equal to the start depth of the initial depth interval minus the expansion coefficient, and the end depth of the target depth interval is equal to the end depth of the initial depth interval plus the expansion coefficient. For example, the second depth interval is [a,b], the first depth interval is [c,d], c>b, then the merged initial depth interval is [a,d], then the expanded target depth interval is [at,d+t], where t is the expansion coefficient.

[0105] by Figure 4cTaking the second curve shown as an example, assuming that the first range is [45, 120] and the second range is [-120, -45], that is, the lower threshold is 45 and the upper threshold is 120. On the second curve, multiple first curve segments can be determined by the first range, and the depth interval corresponding to the first curve segment is the first depth interval. Multiple second curve segments can be determined by the second range, and the depth interval corresponding to the second curve segment is the second depth interval. Figure 4c The curve segments in the dotted box are the first curve segments and the second curve segments identified according to the first range and the second range. Then, a group of first curve segments and second curve segments whose depth distance is less than the preset distance threshold are searched, and the depth intervals corresponding to the first curve segments and the second curve segments are merged to obtain the initial depth interval, and then the initial depth interval is expanded by a certain range to obtain the target depth interval where the casing collar is located.

[0106] In an optional manner, a target depth interval of a casing coupling is determined based only on a set of first depth intervals and second depth intervals, and then the target depth intervals of all casing couplings within the entire measurement depth range are determined based on the length of the casing. This method is suitable for the case where the lengths of the casings are consistent. In this case, the casing couplings are evenly distributed within the entire measurement depth range. By determining the depth interval of a casing coupling, the depth intervals of all casing couplings can be calculated. This method can improve the efficiency of locating the position of the casing couplings.

[0107] Figure 4d A schematic diagram showing the casing collar characteristic curve segment and the coal seam characteristic curve segment in the second curve identified in the example is shown, for details, see Figure 4d In the orange-yellow curve, the differential operation results are set to zero for all depths except the target depth interval where the casing collar is located and the depth interval where the coal seam is located, thereby highlighting the abnormal data. Among them, the depth interval where the coal seam is located is determined in a similar way. The third depth interval is determined according to the upper threshold value, and the fourth depth interval is determined according to the inverse of the upper threshold value. The adjacent third depth interval and fourth depth interval are merged and then expanded to obtain the depth interval where the coal seam is located. The differential operation results corresponding to each continuous depth in the third depth interval all reach the upper threshold value, and the differential operation results corresponding to each continuous depth in the fourth depth interval do not reach the inverse of the upper threshold value.

[0108] Step S240, the count rate corresponding to the target depth interval in the first associated data is deleted, and for each depth in the target depth interval, interpolation processing is performed based on the count rate in the designated address window of the adjacent depth of the depth to obtain the count rate compensation result of the depth, wherein the adjacent depth is outside the target depth interval.

[0109] In an optional manner, interpolation processing is performed in the following manner:

[0110]

[0111] in, Indicates the measurement direction. The count rate compensation result of the depth of each measurement point is: Indicates the measurement direction. The count rate in the specified channel address window at the depth of the sth measurement point before the sth measurement point, Indicates the measurement direction. The measurement point after In this method, the similarity between adjacent depth measurement points is used to interpolate the count rate in the target depth interval where the casing collar is located using the count rates of the previous and next depths.

[0112] Figure 4e A schematic diagram of the preprocessed first curve in the example is shown. The first associated data is preprocessed using the method of an embodiment of the present application. The preprocessed first curve is drawn based on the preprocessed first associated data. The blue curve segments are the curve segments of each identified target depth interval, and the orange-yellow curve is the preprocessed first curve, wherein the count rate of each depth in each target depth interval is obtained through interpolation compensation processing.

[0113] In practical applications, after acquiring the energy spectrum data detected by the density detector, it is first processed according to the conventional data preprocessing method, then the channel address window is divided, and then the count rate curve is differentiated to obtain the differential curve. The target depth interval where the casing coupling is located is identified according to the differential curve, and the identified target depth interval is cross-validated according to the CCL curve (magnetic positioning curve) to evaluate the recognition accuracy. If the recognition accuracy does not meet the requirements, the lower and upper thresholds are readjusted until the recognition accuracy meets the requirements. At this time, the count rate of the target depth interval where the casing coupling is located is removed and compensated to obtain the final preprocessing result.

[0114] In summary, according to the preprocessing method of through-casing density logging information provided by this embodiment, firstly, based on the traditional density logging information steps, the data detected by the density detector is processed for depth adjustment, background subtraction, outlier processing, window division, etc. The processed data is subjected to feature analysis and identification, which not only solves the measurement noise caused by the casing collar but also retains the original formation density characteristics, significantly improves the data quality, and facilitates the analysis of the subsequent through-casing density solution algorithm.

[0115] Figure 5The functional structure diagram of the through-casing density logging information preprocessing device provided in the embodiment of the present application is shown. Figure 5 As shown, the device comprises:

[0116] An acquisition module 51 is adapted to acquire the count rate in a designated channel window at each depth from the energy spectrum data at each depth detected by the density detector, and construct first associated data;

[0117] A calculation module 52, adapted to perform a differential operation according to the first associated data to obtain second associated data;

[0118] The first associated data records the associated relationship between the depth and the count rate in the specified channel address window, and the second associated data records the associated relationship between the depth and the differential operation result;

[0119] an identification module 53, adapted to identify the target depth interval where the casing collar is located according to the second associated data;

[0120] The processing module 54 is adapted to delete the count rate corresponding to the target depth interval in the first associated data, and perform count rate compensation processing on the target depth interval.

[0121] In an optional manner, the processing module 54 is further adapted to:

[0122] For each depth within the target depth interval, interpolation processing is performed based on the count rate within the specified address window of the adjacent depth of the depth to obtain the count rate compensation result of the depth, wherein the adjacent depth is outside the target depth interval.

[0123] In an optional manner, the differential operation includes: a first-order differential operation or a second-order differential operation.

[0124] In an optional manner, the identification module 53 is further adapted to:

[0125] Based on the second associated data, determine a first depth interval in which the differential operation result is within a first range, and determine a second depth interval in which the differential operation result is within a second range; wherein the first range is determined according to a lower threshold and an upper threshold, and the second range is determined according to the opposite number of the upper threshold and the opposite number of the lower threshold;

[0126] The target depth interval in which the casing collar is located is determined according to the first depth interval and the second depth interval whose depth distance is less than a preset distance threshold.

[0127] In an optional manner, the identification module 53 is further adapted to:

[0128] Performing depth interval merging processing according to the first depth interval and the second depth interval to obtain an initial depth interval;

[0129] The initial depth interval is expanded to obtain the target depth interval where the casing collar is located.

[0130] In an optional manner, the designated address window includes: a cement-related address window and a stratum-related address window;

[0131] The device further comprises: a division module, adapted to perform correlation analysis according to sample energy spectrum data, formation parameters and cement parameters; and to divide cement-related channel address windows and formation-related channel address windows according to the correlation analysis results.

[0132] In summary, according to the through-casing density logging information preprocessing device provided by this embodiment, the count rate in the designated address window of each depth is obtained from the energy spectrum data of each depth detected by the density detector, and the first associated data is constructed; the differential operation is performed according to the first associated data to obtain the second associated data; the first associated data records the correlation relationship between the depth and the count rate in the designated address window, and the second associated data records the correlation relationship between the depth and the differential operation result; according to the second associated data, the target depth interval where the casing collar is located is identified; the count rate corresponding to the target depth interval in the first associated data is deleted, and the count rate compensation processing is performed on the target depth interval. In the above manner, the measurement noise caused by the casing collar can be removed under the premise of retaining the original formation density characteristics, and the count rate data of the real formation conditions of the post-casing density measurement can be obtained.

[0133] An embodiment of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, which enables a processor to execute operations corresponding to the through-casing density logging information preprocessing method in any of the above method embodiments.

[0134] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, which can enable a processor to execute operations corresponding to the through-casing density logging information preprocessing method in any of the above method embodiments.

[0135] Figure 6 A schematic diagram of the structure of an embodiment of a computing device of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the computing device.

[0136] like Figure 6 As shown, the computing device may include: a processor (processor) 602 , a communications interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0137] The processor 602, the communication interface 604, and the memory 606 communicate with each other via a communication bus 608. The communication interface 604 is used to communicate with other devices such as a client or other server network elements. The processor 602 is used to execute a program 610, which can specifically execute the relevant steps in the above embodiment of the method for preprocessing the through-casing density logging information of the computing device.

[0138] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0139] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0140] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0141] The program 610 can be specifically used to enable the processor 602 to execute the through-casing density logging information preprocessing method in any of the above method embodiments. The specific implementation of each step in the program 610 can refer to the corresponding descriptions in the corresponding steps and units in the through-casing density logging information preprocessing embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0142] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0143] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0144] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present application.

[0145] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0146] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0147] The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components according to the embodiments of the present application. The present application may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0148] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for preprocessing through-casing density logging information, characterized in that: The method comprises: Conduct correlation analysis based on sample energy spectrum data, formation parameters and cement parameters; According to the correlation analysis results, the cement-related channel window and the stratum-related channel window are divided; From the energy spectrum data of each depth detected by the density detector, the count rate in the designated channel window of each depth is obtained, and the first associated data is constructed; wherein the designated channel window includes: a cement-related channel window and a stratum-related channel window; Performing a differential operation on the first associated data to obtain second associated data; The first associated data records the associated relationship between the depth and the count rate in the specified channel address window, and the second associated data records the associated relationship between the depth and the differential operation result; identifying, according to the second associated data, a target depth interval in which the casing collar is located; The count rate corresponding to the target depth interval in the first associated data is deleted, and count rate compensation processing is performed on the target depth interval.

2. The method for preprocessing through-casing density logging information according to claim 1, characterized in that: The counting rate compensation processing for the target depth interval further comprises: For each depth within the target depth interval, interpolation processing is performed according to the count rate within the specified address window of the adjacent depth of the depth to obtain the count rate compensation result of the depth; wherein the adjacent depth is located outside the target depth interval.

3. The method for preprocessing through-casing density logging information according to claim 1, characterized in that: The differential operation includes: a first-order differential operation or a second-order differential operation.

4. The method for preprocessing through-casing density logging information according to claim 1, characterized in that: The step of identifying the target depth interval where the casing collar is located according to the second associated data further comprises: Based on the second associated data, determine a first depth interval in which the differential operation result is within a first range, and determine a second depth interval in which the differential operation result is within a second range; wherein the first range is determined according to a lower threshold and an upper threshold, and the second range is determined according to the opposite number of the upper threshold and the opposite number of the lower threshold; The target depth interval in which the casing collar is located is determined according to the first depth interval and the second depth interval whose depth distance is less than a preset distance threshold.

5. The method for preprocessing through-casing density logging information according to claim 4, characterized in that: The step of determining the target depth interval where the casing collar is located according to the first depth interval and the second depth interval whose depth distance is less than the preset distance threshold further comprises: Performing depth interval merging processing according to the first depth interval and the second depth interval to obtain an initial depth interval; The initial depth interval is expanded to obtain a target depth interval where the casing collar is located.

6. A through-casing density logging information preprocessing device, characterized in that: The device comprises: The division module is suitable for performing correlation analysis based on sample energy spectrum data, formation parameters and cement parameters; and dividing cement-related channel address windows and formation-related channel address windows according to the correlation analysis results; An acquisition module is adapted to acquire the count rate in a designated channel window at each depth from the energy spectrum data at each depth detected by the density detector, and construct first associated data; wherein the designated channel window includes: a cement-related channel window and a stratum-related channel window; A calculation module, adapted to perform a differential operation according to the first associated data to obtain second associated data; The first associated data records the associated relationship between the depth and the count rate in the specified channel address window, and the second associated data records the associated relationship between the depth and the differential operation result; an identification module, adapted to identify a target depth interval where a casing collar is located according to the second associated data; The processing module is adapted to delete the count rate corresponding to the target depth interval in the first associated data, and perform count rate compensation processing on the target depth interval.

7. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the through-casing density logging information preprocessing method according to any one of claims 1-5.

8. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and wherein the executable instruction enables a processor to execute operations corresponding to the method for preprocessing through-casing density logging information according to any one of claims 1 to 5.

9. A computer program product, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the method for preprocessing through-casing density logging information according to any one of claims 1 to 5.

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