Stratum interface pickup method and device, electronic equipment and storage medium

Abstract

The invention discloses a formation interface pickup method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a gamma logging curve corresponding to a target mine, and determining at least one to-be-applied formation boundary point based on the gamma logging curve; determining at least one target stratum boundary point based on the to-be-applied stratum boundary point and the gamma logging curve; and determining at least one to-be-applied stratum interface based on the target stratum boundary point, and determining a target stratum interface based on the to-be-applied stratum interface. On the basis of the technical scheme, formation interface pickup is performed on the basis of the correlation of the data in all directions in the gamma logging data, and the division result is corrected based on statistical analysis evaluation. Therefore, the result of the single curve is further optimized and processed, and the efficiency and accuracy of stratum interface pickup are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a formation interface picking method, device, electronic equipment and storage medium. Background Art

[0002] With the continuous development of exploration technology, in order to quickly obtain the stratigraphic data corresponding to the exploration area, the stratigraphic information of the exploration area is determined by analyzing the stratigraphic data of the exploration area based on the collected gamma logging data.

[0003] However, the existing formation interface picking method is to manually determine the formation interface in the exploration area based on gamma images, which results in the accuracy of formation interface determination being too dependent on the experience of the staff and the accuracy of formation interface picking being unable to be guaranteed. In addition, the manual processing method makes the interface picking efficiency low. Summary of the invention

[0004] The present invention provides a formation interface picking method, device, electronic device and storage medium, which picks the formation interface based on the correlation of various orientation data in gamma logging data, improves the efficiency of formation interface picking, and solves the problem of low formation interface picking efficiency in the prior art.

[0005] According to one aspect of the present invention, a method for picking up a stratum interface is provided, the method comprising:

[0006] Acquire a gamma logging curve corresponding to the target mine, and determine at least one formation boundary point to be applied based on the gamma logging curve;

[0007] Determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve;

[0008] At least one to-be-applied formation interface is determined based on the target formation boundary point, and a target formation interface is determined based on the to-be-applied formation interface.

[0009] According to another aspect of the present invention, a stratum interface pickup device is provided, the device comprising:

[0010] A module for determining a boundary point of a formation to be applied, used for acquiring a gamma logging curve corresponding to a target mine, and determining at least one boundary point of a formation to be applied based on the gamma logging curve;

[0011] A target formation boundary point determination module, used to determine at least one target formation boundary point based on the formation demarcation point to be applied and the gamma logging curve;

[0012] The target formation interface determination module is used to determine at least one formation interface to be applied based on the target formation boundary point, and to determine the target formation interface based on the formation interface to be applied.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the formation interface picking method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the formation interface picking method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention is to obtain a gamma logging curve corresponding to a target mine, determine at least one formation boundary point to be applied based on the gamma logging curve, and determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve, and then determine at least one formation interface to be applied based on the target formation boundary point, and determine the target formation interface based on the formation interface to be applied. Based on the above technical solution, formation interfaces are picked based on the correlation of various orientation data in the gamma logging data, and the division results are corrected by using statistical analysis and evaluation. In this way, the results of a single curve are further optimized, and the efficiency and accuracy of formation interface picking are improved.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1It is a flow chart of a formation interface picking method provided by an embodiment of the present invention;

[0022] Figure 2 is a flow chart of a formation interface picking method provided by an embodiment of the present invention;

[0023] Figure 3 It is a structural block diagram of a formation interface pickup device provided by an embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 It is a flow chart of a formation interface picking method provided by an embodiment of the present invention. This embodiment can be applied to the situation where a target formation boundary point is determined based on the gamma logging data of a target mine, and then a target formation interface is determined based on the target formation boundary point. The method can be executed by a formation interface picking device, and the formation interface picking device can be implemented in the form of hardware and / or software. The formation interface picking device can be configured in an electronic device, and the electronic device can be a server, a terminal device, etc.

[0029] like Figure 1 As shown, the method includes:

[0030] S110, acquiring a gamma logging curve corresponding to the target mine, and determining at least one formation boundary point to be applied based on the gamma logging curve.

[0031] The target mine may be a mine where the stratigraphic interface needs to be picked, for example, a mine selected from the target area according to the demand. The gamma logging curve may be a logging curve generated by a gamma logging device after detecting the target mine. The stratigraphic boundary point to be applied may be a boundary point of the stratigraphic interface that is initially determined.

[0032] Specifically, a gamma logging curve corresponding to a target mine is obtained, and at least one stratum boundary point to be applied is determined based on the gamma logging curve. For example, a user may select a target mine from the mines to be selected, and obtain a gamma logging curve corresponding to the target mine. A gamma logging curve matching the target mine may be searched in a database based on the target mine, and then at least one stratum boundary point to be applied corresponding to the target mine may be determined based on the gamma logging curve. It should be noted that the gamma logging curve may be a gamma curve obtained after detecting the target mine by a natural gamma logging device. For example, the measuring device of the natural gamma logging instrument is composed of a downhole instrument and a ground instrument. The downhole instrument is composed of a gamma ray detector (composed of a scintillation crystal and a photomultiplier tube), a pulse amplitude discriminator, a frequency divider, a shaper, a cable driver and other circuits. After obtaining the gamma logging curve, the stratum boundary point to be applied may be calculated based on wavelet analysis, gradient method, Wall transform method, activity method, and the like.

[0033] On the basis of the above technical solution, the method of determining at least one formation boundary point to be applied based on the gamma logging curve includes: determining an activity curve corresponding to the current gamma logging curve based on a preset depth step, and determining an activity threshold corresponding to the current gamma logging curve based on the activity curve; determining the formation boundary point to be applied corresponding to the current gamma logging curve based on the activity threshold and the activity curve.

[0034] The preset depth step may be a preset depth value, such as 10 m, 20 m, etc. The activity curve may be understood as an activity value curve obtained based on a gamma logging curve, and the activity value may be used to indicate the severity of the gamma value change. The activity threshold may be an activity value used to determine the boundary point of the formation to be applied.

[0035] Specifically, the activity curve corresponding to the current gamma logging curve is determined based on the preset depth step, and the activity threshold corresponding to the current gamma logging curve is determined based on the activity curve, and then the formation boundary point to be applied corresponding to the current gamma logging curve is determined based on the activity threshold and the activity curve. For example, the current gamma logging curve can be divided based on the preset depth step, and then the activity threshold is determined based on the gamma data in each depth window, and the maximum point of the activity in each depth window is determined, and the point where the maximum point is greater than the activity threshold is used as the formation boundary point to be applied. It should be noted that the activity threshold can be obtained based on a weighted method. For example, after determining the maximum activity value corresponding to the target mine, the proportion of the gamma data in the current depth window to the total gamma data is used as a weight, and then the activity threshold can be obtained based on the maximum activity value and the weight. In addition, the activity is calculated by the following formula:

[0036] Among them, E(H0,L) represents the activity value of the logging curve at the depth point H0 with a window length of L; X(H) represents the gamma value at the depth H; X(H,L) represents the average value in the depth range of H0-L / 2 to H0+L / 2; the larger the activity value, the more drastic the change in the gamma value. It is generally believed that when the detector is exactly at the interface of two strata with different radioactivity, the gamma value changes most drastically, corresponding to the maximum value of the activity value. The activity curves under different window lengths are used, and then the results of different window lengths are weighted averaged to obtain the final activity value. Then, the activity value greater than the threshold is selected as the stratum boundary point.

[0037] S120. Determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve.

[0038] The target stratum boundary point may be a stratum boundary point obtained after screening the stratum boundary points to be applied.

[0039] Specifically, at least one target formation boundary point is determined based on the formation boundary point to be applied and the gamma logging curve. For example, gamma data associated with the formation boundary point to be applied is obtained, and the formation boundary point to be applied is screened based on the gamma data to obtain the target formation boundary point.

[0040] On the basis of the above technical solution, the gamma logging curve includes a reference gamma curve and an azimuthal gamma curve; the method of determining at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve includes: determining at least one benchmark boundary point based on the reference gamma curve, and determining at least one sampling window based on the benchmark boundary point and a preset sampling step; determining the target formation boundary point based on the sampling window, the formation boundary point to be applied, the azimuthal gamma curve and the reference gamma curve.

[0041] Among them, the reference gamma curve can be a total gamma curve corresponding to the target mine. The azimuth gamma curve can be understood as the gamma logging curve corresponding to different logging azimuths during the logging process. The logging azimuth can be the logging angle value corresponding to the azimuth gamma curve. The benchmark demarcation point can be understood as the boundary point of the formation to be applied selected from the boundary points of the formation to be applied in the reference gamma curve. The preset sampling step size can be the sampling length of the preset curve. The sampling window can be understood as each sampling window corresponding to each benchmark demarcation point, and the length of the sampling window is equal to the preset sampling step size.

[0042] Specifically, the gamma logging curve includes a reference gamma curve and an azimuth gamma curve, and when determining the target formation boundary point, at least one benchmark demarcation point can be determined based on the reference gamma curve, and at least one sampling window can be determined based on the benchmark demarcation point and a preset sampling step, and then the target formation boundary point can be determined based on the sampling window, the formation boundary point to be applied, the azimuth gamma curve and the reference gamma curve.

[0043] On the basis of the above technical solution, the target formation boundary point is determined based on the sampling window, the formation boundary point to be applied, the azimuth gamma curve and the reference gamma curve, including: determining a correlation value corresponding to the current sampling window based on the azimuth gamma curve and the reference gamma curve; determining the most relevant position point corresponding to the current sampling window based on the correlation value, and determining the target formation boundary point based on the most relevant position point and the formation boundary point to be applied.

[0044] The correlation value may be used to indicate the correlation between the azimuth gamma curve and the reference gamma curve in the current sampling window. The most correlated position point may be the point where the correlation value between the azimuth gamma curve and the reference gamma curve is the smallest in the current sampling window.

[0045] Specifically, the correlation value corresponding to the current sampling window is determined based on the azimuth gamma curve and the reference gamma curve, the most relevant position point corresponding to the current sampling window is determined based on the correlation value, and the target stratum boundary point is determined based on the most relevant position point and the stratum boundary point to be applied. It should be noted that the correlation is a quantity that describes the difference in shape and size between two segments of the curve. When crossing the stratum, although the depth points at which the gamma curves of different azimuths start to respond are different, the curves of gamma value changes are similar. Therefore, if the similarity of two gamma curves of different azimuths with a small depth difference in the same measurement is high, it can be judged that there is a high possibility that they are responses to the same stratum boundary surface. When calculating the correlation, the data in the window with the depth point H0 as the center and the length L on the reference curve is taken and calculated with the data in the window with the depth point H'i as the center and the length L on the i-azimuth gamma curve. The result obtained is called the correlation between the reference gamma curve H0 and the i-azimuth gamma curve H'i point. The smaller the calculated result value is, the better the correlation between the two points is. The correlation value can be obtained by the correlation formula: Wherein, C(H0,H'i) represents the correlation between the reference gamma curve H0 and the i-position gamma curve H'i point; X(H0+s) represents the gamma value of the depth H0+s on the reference gamma curve; X'(H'i+s) represents the gamma value of the depth H'i+s on the i-position gamma curve; and L is the selected window length.

[0046] On the basis of the above technical solution, the method of determining the target formation boundary point based on the most relevant position point and the formation boundary point to be applied includes: determining the distance value between each formation boundary point to be applied and the most relevant position point; and determining the target formation boundary point from the formation boundary points to be applied based on the distance value.

[0047] The distance value can be understood as the distance between the boundary point of the to-be-applied stratum and the most relevant position point, which can be determined by the distance formula between two points.

[0048] Specifically, the distance value between each of the to-be-applied formation boundary points and the most relevant position point is determined, and the target formation boundary point is determined from the to-be-applied formation boundary points based on the distance value. For example, after the relevant position point is determined, the most relevant position point is not necessarily the to-be-applied formation boundary point. Therefore, it is necessary to obtain all the to-be-applied formation boundary points in the current sampling window, and determine the distance value between the to-be-applied formation boundary point and the most relevant position point, and use the to-be-applied formation boundary point with the smallest distance value as the target formation boundary point. It should be noted that if there is no to-be-applied formation boundary point in the current sampling window, the most relevant position point can be used as the target formation boundary point corresponding to the current logging orientation in the sampling window.

[0049] S130: Determine at least one to-be-applied stratum interface based on the target stratum boundary point, and determine a target stratum interface based on the to-be-applied stratum interface.

[0050] The to-be-applied stratum interface may be a stratum interface that needs to be screened, and the target stratum interface may be understood as the final stratum boundary result obtained after filtering the to-be-applied stratum interface.

[0051] Specifically, at least one formation interface to be applied is determined based on the target formation boundary points, and the target formation interface is determined based on the formation interface to be applied. For example, the obtained target formation boundary points can be directly connected in sequence to obtain at least one formation interface to be applied, and the formation interface to be applied is filtered to obtain the target formation interface.

[0052] On the basis of the above technical solution, the method of determining at least one formation interface to be applied based on the target formation boundary point includes: fitting each of the target formation boundary points based on a fitting function, determining at least one formation interface to be processed, and obtaining an interface fitting error corresponding to each of the formation interfaces to be processed; determining the formation interface to be applied from each of the formation boundaries to be processed based on the interface fitting error and a preset error threshold.

[0053] The fitting function may be a preset function for fitting the boundary points of the target formation, such as a sine function. The interface fitting error may be understood as the error value of each boundary point of the target formation during the fitting process, such as a depth difference. The preset error threshold may be a pre-set error value, or may be the resolution of the logging device, etc.

[0054] Specifically, each target formation boundary point is fitted based on a fitting function to determine at least one formation interface to be processed, and an interface fitting error corresponding to each formation interface to be processed is obtained, and the formation interface to be applied is determined from each formation boundary to be processed based on the interface fitting error and a preset error threshold. For example, after determining the target formation boundary point, each target formation boundary point is fitted based on a fitting function to obtain at least one formation interface to be processed, and then the minimum depth difference between each formation interface to be processed can be calculated, and the minimum depth difference can be compared with the resolution of the logging device to determine whether it is necessary to filter the formation interface to be processed.

[0055] On the basis of the above technical solution, the target formation interface is determined based on the formation interface to be applied, including: determining the formation characteristic data corresponding to the formation interface to be applied based on the gamma logging curve; determining the classification result according to the formation characteristic data and a preset classification model, and processing the formation interface to be applied according to the classification result to determine the target formation interface.

[0056] The formation characteristic data may be gamma data corresponding to the current formation interface to be applied, such as the gamma maximum value, the gamma minimum value, and the statistical distribution frequency of the gamma value. The preset classification model may be understood as a pre-trained neural network model, whose input may be the formation characteristic data corresponding to each formation interface to be applied, and the output may be the classification result corresponding to the current formation interface to be applied. The classification result may be understood as the type of the current formation interface to be applied, such as whether it can be divided again.

[0057] Specifically, the formation characteristic data corresponding to the formation interface to be applied is determined based on the gamma logging curve, the classification result is determined according to the formation characteristic data and a preset classification model, and the formation interface to be applied is processed according to the classification result to determine the target formation interface. For example, the formation can be divided based on the formation boundary surface, and then the attributes of the data in each formation are extracted and analyzed, including: maximum value, minimum value, statistical distribution of gamma values ​​and other attributes. The judgment standard can be set based on experience and machine learning methods to determine whether the current formation can continue to be divided. If it can continue to be divided, a formation interface is divided by minimum variance, contour tracking and other methods.

[0058] The technical solution of the embodiment of the present invention is to obtain a gamma logging curve corresponding to a target mine, determine at least one formation boundary point to be applied based on the gamma logging curve, and determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve, and then determine at least one formation interface to be applied based on the target formation boundary point, and determine the target formation interface based on the formation interface to be applied. Based on the above technical solution, formation interfaces are picked based on the correlation of various orientation data in the gamma logging data, and the division results are corrected by using statistical analysis and evaluation. In this way, the results of a single curve are further optimized, and the efficiency and accuracy of formation interface picking are improved.

[0059] Embodiment 2

[0060] Figure 2A flow chart of a formation interface picking method provided by an embodiment of the present invention. This embodiment further optimizes the formation interface picking method based on the above embodiment. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here.

[0061] like Figure 2 The method of the embodiment of the present invention includes:

[0062] Determine the formation boundary points to be applied: Specifically, the formation boundary points to be applied are calculated for the gamma curves of each orientation. The formation division method for processing a single curve is used to process the gamma curve (including the total gamma) measured in each orientation. The formation boundary points calculated in this way are called the formation boundary points to be applied in this orientation. It should be noted that the formation boundary points to be applied can also be calculated using wavelet analysis, gradient method, Wall transform method, activity method, etc. The technical solution provided in the embodiment of the present invention uses an activity method under multi-window scales. Compared with the traditional method of only calculating the activity under one window length to determine the formation boundary points, this method can accurately divide the formation boundary points under poor curve quality and more complex conditions. The activity is calculated using the following formula: Among them, E(H0,L) represents the activity value of the logging curve at the depth point H0 with a window length of L; X(H) represents the gamma value at the depth H; X(H,L) represents the average value in the depth range of H0-L / 2 to H0+L / 2. It should be noted that the larger the activity value, the more drastic the change in the gamma value. It is generally believed that when the detector is exactly at the interface of two strata with different radioactivity, the gamma value changes most drastically, corresponding to the maximum value of the activity value. The activity curves under different window lengths are calculated by the activity method under multi-window scales, and then the results of different window lengths are weighted averaged to obtain the final activity value, and then the activity value greater than the threshold is selected as the boundary point of the stratum to be applied.

[0063] Determine the target formation boundary point: Specifically, through the above steps, the formation boundary point to be applied on the total gamma curve and the azimuthal gamma curve can be obtained, and when the azimuthal gamma logging while drilling enters another formation from one formation, the gamma in each azimuth will change, that is, each time it crosses the formation boundary, each azimuthal gamma curve will produce a mutation, which is the formation boundary point to be applied found in the above steps. Since the angles of the azimuthal gamma logging while drilling across the formation boundary surface are different, the gamma curve in the closest azimuth starts to change first, and the gamma curve in the farthest azimuth starts to change last, so the depths of the boundary points corresponding to the azimuthal gamma curves are also different.

[0064] Exemplarily, first select a curve (generally a reference gamma curve) to be applied on the boundary point of the formation as the benchmark demarcation point, determine the boundary point of the formation to be applied on the gamma curve of each orientation that is most similar to the benchmark demarcation point through correlation calculation, and use it as the target formation boundary point, and then determine whether the boundary surface is reasonable based on the number of null values ​​and the fitting error of the positive selection function to retain the corresponding boundary surface. Finally, a series of formation boundary surface information is calculated, and the formation can be divided based on the formation boundary surface. It should be noted that correlation is a quantity that describes the shape and size difference of two curves. When crossing the formation, although the depth points at which the gamma curves of different orientations begin to respond are different, the curves of gamma value changes are similar. Therefore, if the similarity of two gamma curves of different orientations with little difference in depth in the same measurement is high, it can be judged that there is a high possibility of responding to the same formation boundary surface. When calculating the correlation, the data in the window with a length of L and a depth point H0 as the center on the reference curve is calculated with the data in the window with a length of L and a depth point H'i as the center on the i-azimuth gamma curve. The result is called the correlation between the reference gamma curve H0 and the i-azimuth gamma curve H'i. The smaller the calculated result value is, the better the correlation between the two points is.

[0065] The correlation calculation formula is as follows: Among them, C(H0,H'i) represents the correlation between the reference gamma curve H0 and the i-position gamma curve H'i point; X(H0+s) represents the gamma value of the depth H0+s on the reference gamma curve; X'(H'i+s) represents the gamma value of the depth H'i+s on the i-position gamma curve; L is the selected window length. The technical solution provided by the embodiment of the present invention corresponds each reference stratigraphic boundary point on the total gamma curve to the most relevant point on each azimuth curve, selects the stratigraphic boundary point closest to the most relevant curve, and fits the information of a stratigraphic interface based on this.

[0066] Determine the target stratum interface: Specifically, the target stratum boundary point is fitted based on a preset fitting function to obtain the stratum interface to be applied, and then the attributes of the strata corresponding to each stratum interface to be applied and the statistical attributes of the data within the stratum are obtained to evaluate the stratum, so as to determine whether a stratum needs to be further divided and whether two adjacent strata need to be merged.

[0067] First, the formation boundaries that are too close can be corrected. The dividing points in all directions provided by the formation boundary information can be fitted to obtain a sine function to describe a formation boundary surface, thereby calculating the minimum depth difference between the two formation boundary surfaces. By comparing the minimum depth difference between the two formation boundary surfaces and the parameters of the sine function describing the boundary surface, it is determined whether the two boundary surfaces need to be merged. This operation is because sometimes the distance between the boundary surfaces found is too close, and the formation thickness described between the two is less than the resolution of the instrument. It is physically impossible to find such a thin layer, so it is obviously an unreasonable situation. At the same time, by comparing the parameters of the sine function describing the two formation boundary surfaces, it can be obtained whether the shapes of the two boundary surfaces are similar. If the shapes are similar, it may be due to factors such as statistical errors that cause two boundary points to be obtained on one boundary, so the two boundaries are merged. If the shape difference is large, the cause cannot be determined and both boundary surfaces are deleted. If a stratigraphic boundary exists here, it can be re-divided through subsequent stratigraphic data analysis. The stratigraphic layers can also be divided based on the stratigraphic boundary surface, and then the attributes of the data in each stratigraphic layer are extracted and analyzed, including maximum and minimum values, statistical distribution of gamma values ​​and other attributes. The judgment criteria can be set based on experience and machine learning methods to determine whether the current stratigraphic layer can continue to be divided. If it can continue to be divided, a stratigraphic interface is divided by minimum variance, contour tracking and other methods.

[0068] The technical solution of the embodiment of the present invention is to obtain a gamma logging curve corresponding to a target mine, determine at least one formation boundary point to be applied based on the gamma logging curve, and determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve, and then determine at least one formation interface to be applied based on the target formation boundary point, and determine the target formation interface based on the formation interface to be applied. Based on the above technical solution, formation interfaces are picked based on the correlation of various orientation data in the gamma logging data, and the division results are corrected by using statistical analysis and evaluation. In this way, the results of a single curve are further optimized, and the efficiency and accuracy of formation interface picking are improved.

[0069] Embodiment 3

[0070] Figure 3 The structure block diagram of a stratum interface pickup device provided by an embodiment of the present invention. Figure 3 As shown, the device includes: a to-be-applied stratum boundary point determination module 310 , a target stratum boundary point determination module 320 , and a target stratum interface determination module 330 .

[0071] A to-be-applied formation boundary point determination module 310 is used to obtain a gamma logging curve corresponding to a target mine, and determine at least one to-be-applied formation boundary point based on the gamma logging curve;

[0072] A target formation boundary point determination module 320, configured to determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve;

[0073] The target formation interface determination module 330 is used to determine at least one formation interface to be applied based on the target formation boundary point, and to determine the target formation interface based on the formation interface to be applied.

[0074] On the basis of the above technical solution, the module for determining the boundary point of the formation to be applied is used to determine the activity curve corresponding to the current gamma logging curve based on a preset depth step, and determine the activity threshold corresponding to the current gamma logging curve based on the activity curve; and determine the boundary point of the formation to be applied corresponding to the current gamma logging curve based on the activity threshold and the activity curve.

[0075] On the basis of the above technical solution, the gamma logging curve includes a reference gamma curve and an azimuth gamma curve; the target formation boundary point determination module is used to determine at least one benchmark demarcation point based on the reference gamma curve, and to determine at least one sampling window based on the benchmark demarcation point and a preset sampling step; the target formation boundary point is determined based on the sampling window, the formation boundary point to be applied, the azimuth gamma curve and the reference gamma curve.

[0076] On the basis of the above technical solution, the target formation boundary point determination module is used to determine the correlation value corresponding to the current sampling window based on the azimuth gamma curve and the reference gamma curve; determine the most relevant position point corresponding to the current sampling window based on the correlation value, and determine the target formation boundary point based on the most relevant position point and the formation boundary point to be applied.

[0077] On the basis of the above technical solution, the target stratum boundary point determination module is used to determine the distance value between each of the stratum boundary points to be applied and the most relevant position point; and determine the target stratum boundary point from the stratum boundary points to be applied based on the distance value.

[0078] On the basis of the above technical solution, a target formation interface determination module is provided, which is used to fit each of the target formation boundary points based on a fitting function, determine at least one formation interface to be processed, and obtain an interface fitting error corresponding to each of the formation interfaces to be processed; and determine the formation interface to be applied from each of the formation boundaries to be processed based on the interface fitting error and a preset error threshold.

[0079] On the basis of the above technical solution, a target formation interface determination module is provided, which is used to determine the formation characteristic data corresponding to the formation interface to be applied based on the gamma logging curve; determine the classification result according to the formation characteristic data and a preset classification model; and process the formation interface to be applied according to the classification result to determine the target formation interface.

[0080] The technical solution of the embodiment of the present invention is to obtain a gamma logging curve corresponding to a target mine, determine at least one formation boundary point to be applied based on the gamma logging curve, and determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve, and then determine at least one formation interface to be applied based on the target formation boundary point, and determine the target formation interface based on the formation interface to be applied. Based on the above technical solution, formation interfaces are picked based on the correlation of various orientation data in the gamma logging data, and the division results are corrected by using statistical analysis and evaluation. In this way, the results of a single curve are further optimized, and the efficiency and accuracy of formation interface picking are improved.

[0081] The formation interface picking device provided in the embodiment of the present invention can execute the formation interface picking method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0082] Embodiment 4

[0083] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0084] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0085] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0086] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a formation interface picking method.

[0087] In some embodiments, the formation interface picking method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the formation interface picking method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the formation interface picking method in any other appropriate manner (for example, by means of firmware).

[0088] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0090] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0091] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0092] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0093] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0094] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0095] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for picking up a stratum interface, characterized in that: include: Acquire a gamma logging curve corresponding to the target mine, and determine at least one formation boundary point to be applied based on the gamma logging curve; Determine at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve; At least one to-be-applied formation interface is determined based on the target formation boundary point, and a target formation interface is determined based on the to-be-applied formation interface.

2. The method according to claim 1, characterized in that The step of determining at least one formation boundary point to be applied based on the gamma logging curve comprises: Determining an activity curve corresponding to a current gamma logging curve based on a preset depth step, and determining an activity threshold corresponding to the current gamma logging curve based on the activity curve; A formation boundary point to be applied corresponding to the current gamma logging curve is determined based on the activity threshold and the activity curve.

3. The method according to claim 1, characterized in that The gamma logging curve includes a reference gamma curve and an azimuth gamma curve; The determining at least one target formation boundary point based on the formation boundary point to be applied and the gamma logging curve comprises: Determine at least one reference demarcation point based on the reference gamma curve, and determine at least one sampling window based on the reference demarcation point and a preset sampling step size; The target formation boundary point is determined based on the sampling window, the to-be-applied formation boundary point, the azimuth gamma curve and the reference gamma curve.

4. The method according to claim 3, characterized in that The determining the target formation boundary point based on the sampling window, the formation boundary point to be applied, the azimuth gamma curve and the reference gamma curve comprises: determining a correlation value corresponding to a current sampling window based on the azimuth gamma curve and the reference gamma curve; The most relevant position point corresponding to the current sampling window is determined based on the correlation value, and the target formation boundary point is determined based on the most relevant position point and the formation boundary point to be applied.

5. The method according to claim 4, characterized in that The determining the target stratum boundary point based on the most relevant position point and the stratum boundary point to be applied comprises: Determine the distance value between each of the boundary points of the to-be-applied stratum and the most relevant position point; The target stratum boundary point is determined from the stratum boundary points to be applied based on the distance value.

6. The method according to claim 1, characterized in that The determining at least one stratum interface to be applied based on the target stratum boundary point comprises: Fitting each of the target formation boundary points based on the fitting function, determining at least one formation interface to be processed, and obtaining an interface fitting error corresponding to each of the formation interfaces to be processed; The to-be-applied formation interface is determined from each of the to-be-processed formation boundaries based on the interface fitting error and a preset error threshold.

7. The method according to claim 1, characterized in that The step of determining a target stratum interface based on the stratum interface to be applied comprises: Determine formation characteristic data corresponding to the formation interface to be applied based on the gamma logging curve; A classification result is determined according to the formation characteristic data and a preset classification model, and the formation interface to be applied is processed according to the classification result to determine the target formation interface.

8. A stratum interface pickup device, characterized in that: include: A module for determining a boundary point of a formation to be applied, used for acquiring a gamma logging curve corresponding to a target mine, and determining at least one boundary point of a formation to be applied based on the gamma logging curve; A target formation boundary point determination module, used to determine at least one target formation boundary point based on the formation demarcation point to be applied and the gamma logging curve; The target formation interface determination module is used to determine at least one formation interface to be applied based on the target formation boundary point, and to determine the target formation interface based on the formation interface to be applied.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the formation interface picking method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the formation interface picking method according to any one of claims 1 to 7 when executed.

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