Method and device for identifying diabase in carbonate rocks and electronic equipment

Abstract

The invention provides a method and device for identifying diabase in carbonate rocks and electronic equipment, and the method comprises the steps: obtaining logging curve data of to-be-identified carbonate rocks, the logging curve data comprising interval transit time curve data, carbonate content curve data, drilling time curve data and drilling pressure curve data; according to the change rate of the carbonate content along with the depth in the carbonate content curve data, the change rate of the drilling time along with the depth in the drilling time curve data and the change rate of the drilling pressure along with the depth in the drilling pressure curve data, the weight corresponding to each curve data is determined according to a preset rule; performing weighted reconstruction on the interval transit time curve data based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and respective corresponding weights; and performing geostatistical inversion based on the interval transit time curve after weighted reconstruction, and identifying the diabase invading body according to a model obtained by inversion. According to the method, the diabase invading body in the carbonate rock can be identified more accurately.

Description

Technical Field

[0001] The invention belongs to the technical field of rock mass identification, and in particular relates to a method, a device and electronic equipment for identifying diabase in carbonate rock. Background Art

[0002] Carbonate rocks are important oil and gas reservoirs, and the intrusion of diabase may affect the pore structure and permeability of carbonate rocks, thereby affecting the migration and storage of oil and gas. Therefore, identifying the distribution of diabase is of great significance to the exploration and development of oil and gas fields.

[0003] At present, the identification of different rock types is mainly achieved through the sonic time difference logging method, that is, the rock type is identified by using the difference in the propagation speed of sound waves in different rock types. However, there is no obvious difference in the sonic time difference between diabase and carbonate rock, so it is difficult to accurately identify diabase only through the sonic time difference curve.

[0004] Therefore, how to accurately identify diabase in carbonate rocks becomes a problem to be solved. Summary of the invention

[0005] In order to solve the problem of inaccurate identification of diabase in carbonate rocks, the present invention provides a method, device and electronic equipment for identifying diabase in carbonate rocks.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for identifying diabase in carbonate rock, comprising:

[0008] Acquire logging curve data of the carbonate rock to be identified, wherein the logging curve data includes acoustic wave time difference curve data, carbonate content curve data, drilling time curve data and drilling pressure curve data;

[0009] Determine the weight corresponding to each curve data according to the change rate of carbonate content with depth in the carbonate content curve data, the change rate of drilling time with depth in the drilling time curve data, and the change rate of drilling pressure with depth in the drilling pressure curve data, and preset rules; the preset rules include the corresponding relationship between the change rate and the weight;

[0010] The acoustic time difference curve data is weightedly reconstructed based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights;

[0011] Geostatistical inversion is performed based on the weighted reconstructed acoustic time difference curve data, and the diabase intrusion is identified according to the inverted model.

[0012] Optionally, weighted reconstruction of the acoustic time difference curve data is performed based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights, including:

[0013] Performing denoising and reconstruction processing on the acoustic wave time difference curve data;

[0014] The de-noised and reconstructed acoustic time difference curve data is weightedly reconstructed based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights, including:

[0015]

[0016] Among them, DTcg(n) represents the sound wave time difference curve data after weighted reconstruction, n represents the sampling depth, DTqz(n) represents the sound wave time difference curve data after denoising and reconstruction, and w i represents weights, w1 represents the weight of carbonate content curve data, w2 represents the weight of drilling time curve data, w3 represents the weight of drilling pressure curve data, s(n i ) represents curve data, s(n1) represents carbonate content curve data, s(n2) represents drilling time curve data, and s(n3) represents drilling pressure curve data.

[0017] Optionally, performing denoising and reconstruction processing on the acoustic wave time difference curve data includes:

[0018] Performing normalization processing on the acoustic wave time difference curve;

[0019] The normalized acoustic wave time difference curve is filtered using a low-pass filter and a high-pass filter to obtain the scale coefficient and the wavelet coefficient;

[0020] Iteratively decomposing the scale coefficient until a preset scale is reached;

[0021] Reconstructing the acoustic wave time difference curve based on the scale coefficient obtained by iterative decomposition and the corresponding wavelet coefficient to obtain a denoised and normalized acoustic wave time difference curve;

[0022] The denoised and normalized acoustic wave time difference curve is denormalized to obtain the denoised and reconstructed acoustic wave time difference curve.

[0023] Optionally, a geostatistical inversion with well constraints is performed based on the weighted reconstructed acoustic time difference curve, and the diabase intrusion is identified according to the model obtained by the inversion, including:

[0024] Based on the weighted reconstructed acoustic time difference curve, the geostatistical inversion without wells is performed to determine the target signal-to-noise ratio with the highest inversion resolution;

[0025] Based on the target signal-to-noise ratio, well geostatistical inversion is performed, and the diabase intrusion is identified according to the model obtained by the inversion.

[0026] Optionally, a wellless geostatistical inversion is performed based on the weighted reconstructed acoustic time difference curve, including:

[0027] Synthesize seismic records based on the weighted reconstructed acoustic time difference curves;

[0028] Acquire actual seismic data, and perform amplitude correction and time-depth conversion based on the synthetic seismic record and the actual seismic data;

[0029] Wellless geostatistical inversion is performed based on amplitude-corrected and time-depth converted seismic data.

[0030] Optionally, obtaining well logging curve data of the carbonate rock to be identified includes:

[0031] Draw curve graphs respectively according to the carbonate content curve data, drilling time curve data and drilling pressure curve data of the carbonate rock to be identified;

[0032] Determine the target depth range based on the changing trend of the curve graph;

[0033] Acquire well logging curve data within the target depth range.

[0034] The present invention also provides a device for identifying diabase in carbonate rock, comprising:

[0035] An acquisition module, used to acquire logging curve data of the carbonate rock to be identified, wherein the logging curve data includes acoustic wave time difference curve data, carbonate content curve data, drilling time curve data and drilling pressure curve data;

[0036] A weight determination module is used to determine the weight corresponding to each curve data according to the change rate of carbonate content with depth in the carbonate content curve data, the change rate of drilling time with depth in the drilling time curve data, and the change rate of drilling pressure with depth in the drilling pressure curve data, and preset rules; the preset rules include the corresponding relationship between the change rate and the weight;

[0037] A weighted reconstruction module, used for weighted reconstruction of the acoustic time difference curve data based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and the corresponding weights;

[0038] The identification module is used to perform geostatistical inversion based on the weighted reconstructed acoustic time difference curve data, and identify the diabase intrusion according to the inversion model.

[0039] The present invention also provides a computer device, including a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to perform the above-mentioned method for identifying diabase in carbonate rock.

[0040] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for identifying diabase in carbonate rock is implemented.

[0041] The method for identifying diabase in carbonate rock provided by the present invention has the following beneficial effects:

[0042] Since carbonate rock and diabase have obvious differences in carbonate content, drilling time and drilling pressure, carbonate content curve data, drilling time curve data and drilling pressure curve data are more sensitive to diabase. The present invention determines the weights corresponding to each curve data according to the change rate of carbonate content with depth, the change rate of drilling time with depth in the drilling time curve data, the change rate of drilling pressure with depth in the drilling pressure curve data, and preset rules, and can determine a reasonable weight according to the sensitivity of each curve data to diabase. The acoustic wave time difference curve data is reconstructed based on the reasonable weight, and pseudo acoustic wave time difference curve data sensitive to diabase can be constructed, thereby highlighting the low acoustic wave time difference characteristics of the diabase intrusion segment, that is, the high-speed logging response characteristics. Furthermore, geostatistical inversion is performed based on the weighted reconstructed acoustic wave time difference curve, so that the diabase intrusion can be identified more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. 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.

[0044] Figure 1 A schematic flow chart of a method for identifying diabase in carbonate rock provided in an embodiment of the present invention;

[0045] Figure 2 A wellless geostatistical inversion signal-to-noise ratio test result provided by an embodiment of the present invention; wherein, Figure 2 Figure (a) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 6db. Figure 2 Figure (b) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 10db. Figure 2 Figure (c) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 8 db. Figure 2Figure (d) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 12db;

[0046] Figure 3 A schematic diagram of a geostatistical inversion and concatenation section of an acoustic time difference curve provided in an embodiment of the present invention before weighted reconstruction;

[0047] Figure 4 A schematic diagram of a geostatistical inversion and cross-section after weighted reconstruction of an acoustic time difference curve provided in an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the structure of a device for identifying diabase in carbonate rock provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0050] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the technical solutions of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0051] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise clearly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0052] Example 1

[0053] Figure 1 A schematic flow chart of a method for identifying diabase in carbonate rock provided in an embodiment of the present invention.

[0054] For example, Figure 1 As shown, the following steps are included:

[0055] S1, obtaining the logging curve data of the carbonate rock to be identified.

[0056] The logging curve data include acoustic time difference (DT) curve data, carbonate content curve data, drilling time curve data and drilling pressure curve data.

[0057] It should be understood that the acoustic time difference (DT) curve data can reflect the change of acoustic time difference with sampling depth, the carbonate content curve data can reflect the change of carbonate content with sampling depth, the drilling time curve can reflect the change of drilling time with sampling depth, and the drilling pressure curve can reflect the change of drill bit pressure with sampling depth.

[0058] Optionally, a curve graph can be drawn based on the carbonate content curve data, the drilling time curve data and the drilling pressure curve data, and the target depth range can be determined based on the changing trend of the curves in the curve graph, and then the logging curve data of the target depth range can be obtained for subsequent analysis to reduce the amount of data processing.

[0059] S2, determining the weight corresponding to each curve data according to the change rate of carbonate content with depth in the carbonate content curve data, the change rate of drilling time with depth in the drilling time curve data, the change rate of drilling pressure with depth in the drilling pressure curve data, and the preset rules.

[0060] It should be understood that the rate of change of carbonate content with depth can reflect the sensitivity of carbonate content curve data to diabase, the rate of change of drilling time with depth can reflect the sensitivity of drilling time curve data to diabase, and the rate of change of drilling pressure with depth can reflect the sensitivity of drilling pressure curve data to diabase.

[0061] Taking the carbonate content curve data as an example, if the carbonate content suddenly drops sharply at a certain sampling depth, it means that the carbonate content curve data is highly sensitive to diabase, and a higher weight can be set for it, such as 0.25 to 0.45.

[0062] For curve data with low sensitivity to diabase, a lower weight may be set, for example, below 0.2. The specific weight of each curve data may be determined according to a preset rule, which includes a corresponding relationship between a change rate and a weight.

[0063] S3, weighted reconstruction of the acoustic time difference curve based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights.

[0064] In order to obtain more reliable data, the acoustic time difference curve can be denoised and reconstructed first, and then the sampling rates of the carbonate content curve data, drilling time curve data and drilling pressure curve data can be unified with the sampling rate of the acoustic time difference curve data, and then the denoised and reconstructed acoustic time difference curve can be weighted reconstructed.

[0065] Exemplarily, the process of denoising and reconstructing the acoustic wave time difference curve is as follows:

[0066] First, the acoustic time difference curve is normalized, and the formula is:

[0067]

[0068] DT1(n) is the normalized acoustic time difference curve data, n represents the sampling depth, DT(n) is the original acoustic time difference curve, DT min is the minimum value in the original sound wave time difference curve, DT max It is the maximum value in the original sound wave time difference curve.

[0069] Then you can use MATLAB software to perform wavelet transform on the acoustic time difference data through the Mallat algorithm to denoise and reconstruct the acoustic time difference data. The steps are as follows:

[0070] Initialization signal: The normalized acoustic wave time difference curve data is recorded as f(t).

[0071] The Mallat algorithm is used to perform multi-scale decomposition of the acoustic time difference curve data. The decomposition steps are as follows:

[0072] First-level decomposition: Use a low-pass filter (scaling function) and a high-pass filter (wavelet function) to filter the acoustic time difference curve data to obtain the scaling coefficient and wavelet coefficient.

[0073] Specifically, let the scaling function be φ(t), the corresponding wavelet coefficient be ψ(t), and decompose by multi-resolution, then any f(t)∈V j-1 In space V j-1 The expression is:

[0074]

[0075] Among them, c j-1,k It represents the scale coefficient of the kth spatial position in the j-1th level wavelet decomposition, and t represents the depth.

[0076] Decomposing f(t) once, we get:

[0077] f(t)=∑c j-1,k 2 (-j+1) / 2 φ(2 -j+1 tk)+∑dj-1,k 2 -j / 2 φ(2 -j tk)

[0078] c j-1,k Represents the wavelet coefficient at the kth spatial position in the j-1th level wavelet decomposition.

[0079] According to the above formula, we can get the scale coefficient c j,k and wavelet coefficient d j,k The expression is:

[0080]

[0081] Where * represents complex conjugation, and the scaling factor c j,k Reflects the low-frequency part of the acoustic time difference curve data, the wavelet coefficient d j,k It reflects the high-frequency part of the acoustic time difference curve data.

[0082] Iterative decomposition: The obtained scale coefficients are iteratively decomposed, and each decomposition is performed only on the low-frequency part until the preset scale m is reached, and a series of scale coefficients and wavelet coefficients are obtained.

[0083] After iterative decomposition, the scaling coefficients and wavelet coefficients are:

[0084]

[0085] h * (m-2k) is the low-pass filter function, g * (m-2k) is the high-pass filter function.

[0086] Then, the acoustic time difference curve data is reconstructed based on the scale coefficients and corresponding wavelet coefficients obtained by iterative decomposition to obtain denoised and normalized acoustic time difference curve data.

[0087] Specifically, through the above iterative decomposition, the expression of the finite resolution decomposition of the acoustic time difference curve data as a discrete signal can be obtained as follows:

[0088]

[0089] The scale coefficient is used for reconstruction to obtain the normalized acoustic time difference curve after denoising, and its formula is:

[0090] DTqz1(n)=f(n)=c m,k

[0091] DTqz1(n) is the denoised and normalized acoustic wave time difference curve data.

[0092] Furthermore, the denoised and normalized sound wave time difference curve data is denormalized to obtain the denoised and reconstructed sound wave time difference curve data, and the formula is as follows:

[0093] DTqz(n)=DT min +DTqz1(n)(DT max -DT min ).

[0094] DTqz(n) is the acoustic time difference curve data after denoising and reconstruction.

[0095] After obtaining the denoised and reconstructed acoustic wave time difference curve data through the above process, weighted reconstruction is performed, and the reconstruction formula is:

[0096]

[0097] Among them, DTcg(n) represents the sound wave time difference curve data after weighted reconstruction, DTqz(n) represents the sound wave time difference curve data after denoising and reconstruction, and w i represents weight, w1 represents weight of carbonate content curve data, w2 represents weight of drilling time curve data, w3 represents weight of drilling pressure curve data, s(n) represents curve data, s(n1) represents carbonate content curve data, s(n2) represents drilling time curve data, and s(n3) represents drilling pressure curve data.

[0098] S4, geostatistical inversion is performed based on the weighted reconstructed acoustic time difference curve data, and the diabase intrusion is identified according to the inversion model.

[0099] Geostatistical Inversion is an important method in earth science, which combines geostatistics and inversion theory to estimate underground geological structure and properties.

[0100] The process is mainly based on the establishment of an inversion model based on the weighted reconstructed acoustic time difference curve data, and the diabase intrusion is identified according to the inverted model.

[0101] Optionally, S4 may include the following steps:

[0102] Step 1: Perform wellless geostatistical inversion based on the weighted reconstructed acoustic time difference curve data to determine the target signal-to-noise ratio with the highest inversion resolution.

[0103] Wellless Geostatistical Inversion is a geophysical exploration technology that does not rely on wellbore data, but instead uses geostatistical principles and seismic data to estimate the distribution of rock properties in underground strata.

[0104] First, the weighted reconstructed acoustic time difference curve data is used to synthesize seismic records. Then, the actual seismic data is obtained, and amplitude correction (synthetic proposal) is performed based on the synthesized seismic records and the actual seismic data to establish a well-seismic relationship (time-depth conversion). Finally, wellless geostatistical inversion is performed based on the amplitude-corrected and time-depth converted seismic data.

[0105] Specifically, the above process may include the following steps:

[0106] Wave impedance calculation: Wave impedance (or acoustic impedance) is calculated using density logs and weighted reconstructed acoustic time difference curve data, because wave impedance is directly related to seismic reflection coefficient.

[0107] Synthetic seismic record production: Use wave impedance to calculate the reflection coefficient sequence; use seismic wavelets (usually extracted from seismic data) to convolve the reflection coefficient sequence to synthesize seismic records.

[0108] Synthetic calibration: Compare synthetic seismic records with actual seismic data for time alignment and amplitude matching.

[0109] Establish the relationship between well and seismic data: Analyze the correspondence between synthetic seismic records and actual seismic data, and establish a time-depth conversion model (time-depth conversion curve) between well and seismic data.

[0110] Furthermore, wellless geostatistical inversion was performed based on the amplitude-corrected and time-depth converted seismic data, the signal-to-noise ratio was tested, and the signal-to-noise ratio with the highest inversion resolution was selected.

[0111] Figure 2 A wellless geostatistical inversion signal-to-noise ratio test result provided by an embodiment of the present invention.

[0112] in, Figure 2 Figure (a) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 6db. Figure 2 Figure (b) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 10db. Figure 2 Figure (c) shows the signal-to-noise ratio test result of geostatistical inversion without wells corresponding to a signal-to-noise ratio of 8 db. Figure 2 Figure (d) shows the signal-to-noise ratio test result of wellless geostatistical inversion corresponding to a signal-to-noise ratio of 12db.

[0113] like Figure 2 As shown, when the signal-to-noise ratio is 8 dB, the inversion resolution is the highest.

[0114] Step 2: Perform well geostatistical inversion based on the target signal-to-noise ratio and identify the diabase intrusion according to the inverted model.

[0115] Well-Based Geostatistical Inversion (WBI) is a geostatistical method that combines wellbore data with seismic data to estimate the distribution of subsurface rock properties.

[0116] Specifically, through well seismic calibration, that is, by extracting seismic wavelets and convolving them with the reflection coefficients near the well obtained from the weighted reconstructed acoustic wave time difference curve data, synthetic seismic records are obtained.

[0117] The initial geological statistical model is established based on the well logging data and the actual seismic data. The well logging data is used as a hard constraint condition. The target signal-to-noise ratio is set as one of the inversion parameters in the inversion algorithm. The model parameters are continuously adjusted through the iterative algorithm to obtain a high-precision velocity model or wave impedance model to match the synthetic seismic data with the actual seismic data. The inversion model is then used to predict the distribution and range of diabase.

[0118] It should be understood that for diabase in the carbonate rock background, it is difficult to identify it in seismic analysis because there is little difference between diabase and carbonate rock in seismic wave propagation characteristics; in well logging, since the DT logging curve is not sensitive to diabase, the DT curve can be reconstructed using sensitive curves (carbonate content curve, drilling time curve and drilling pressure curve) to construct a pseudo DT curve that is sensitive to diabase, and then the information in the pseudo DT curve is applied to the seismic data interpretation or inversion process, and part of the information in the pseudo DT curve that reflects the characteristics of diabase intrusive rocks is integrated into the interpretation or inversion process of seismic data, so that the seismic velocity or wave impedance model obtained by inversion reflects the diabase more obviously, which is more conducive to finding the spatial distribution morphology of diabase.

[0119] In summary, the present invention performs wavelet denoising on the acoustic wave curve through curve reconstruction technology, and extracts and reconstructs the medium and low frequency information by combining the curve data that is sensitive to the carbonate content, drilling time and drilling pressure on the intrusive rock, and then selects a reasonable weighting coefficient to reconstruct the acoustic wave time difference curve data, highlighting the low acoustic wave time difference characteristics of the diabase intrusive body section, that is, the high-speed logging response characteristics, and then uses the reconstructed acoustic wave time difference curve data to perform synthetic calibration, establish a well-seismic relationship, and then perform a geostatistical inversion test without a well to determine a reasonable signal-to-noise ratio value, and then perform a geostatistical inversion with a well constraint, thereby achieving the purpose of effectively identifying and predicting the diabase intrusive body in the carbonate rock layer.

[0120] Figure 3 A schematic diagram of a geostatistical inversion and concatenation section of an acoustic time difference curve provided in an embodiment of the present invention before weighted reconstruction, Figure 4 A schematic diagram of a geostatistical inversion and cross-section after weighted reconstruction of an acoustic time difference curve provided by an embodiment of the present invention, Figure 4The green part in the middle shows the distribution of diabase. Figure 3 and Figure 4 By comparison, it can be seen that after weighted reconstruction, the inverted wave impedance value has a more obvious effect on the diabase intrusive rock. It can identify and depict the distribution of the diabase intrusive rock in the horizontal direction and more accurately identify the diabase intrusive body in the carbonate rock.

[0121] Based on the same inventive concept, an embodiment of the present invention also provides a device for identifying diabase in carbonate rock.

[0122] For example, Figure 5 As shown, the identification device for diabase in carbonate rock includes:

[0123] The acquisition module 51 is used to acquire the logging curve data of the carbonate rock to be identified, and the logging curve data includes the acoustic wave time difference curve data, the carbonate content curve data, the drilling time curve data and the drilling pressure curve data.

[0124] The weight determination module 52 is used to determine the weight corresponding to each curve data according to the change rate of carbonate content with depth in the carbonate content curve data, the change rate of drilling time with depth in the drilling time curve data, and the change rate of drilling pressure with depth in the drilling pressure curve data, as well as preset rules; the preset rules include the corresponding relationship between the change rate and the weight.

[0125] The weighted reconstruction module 53 is used to perform weighted reconstruction on the acoustic time difference curve data based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and the corresponding weights.

[0126] The identification module 54 is used to perform geostatistical inversion based on the weighted reconstructed acoustic time difference curve data, and identify the diabase intrusion according to the model obtained by the inversion.

[0127] Each module in the above-mentioned device for identifying diabase in carbonate rock can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0128] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps in the embodiment of the method for identifying diabase in carbonate rock. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0129] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, and a computer program is stored on the storage medium. For example, a memory containing instructions, the above instructions can be executed by a processor of a computer device to complete the above method. For example, the non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, the steps in the embodiment of the method for identifying diabase in carbonate rocks can be implemented. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0130] It will be appreciated by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0134] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A method for identifying diabase in carbonate rock, characterized in that: include: Acquire logging curve data of the carbonate rock to be identified, wherein the logging curve data includes acoustic wave time difference curve data, carbonate content curve data, drilling time curve data and drilling pressure curve data; Determine the weight corresponding to each curve data according to the change rate of carbonate content with depth in the carbonate content curve data, the change rate of drilling time with depth in the drilling time curve data, and the change rate of drilling pressure with depth in the drilling pressure curve data, and preset rules; the preset rules include the corresponding relationship between the change rate and the weight; The acoustic time difference curve data is weightedly reconstructed based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights; Geostatistical inversion is performed based on the weighted reconstructed acoustic time difference curve data, and the diabase intrusion is identified according to the inverted model.

2. The method for identifying diabase in carbonate rock according to claim 1, characterized in that: The acoustic time difference curve data is weightedly reconstructed based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights, including: Performing denoising and reconstruction processing on the acoustic wave time difference curve data; The de-noised and reconstructed acoustic time difference curve data is weightedly reconstructed based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and their corresponding weights, including: Among them, DTcg(n) represents the sound wave time difference curve data after weighted reconstruction, n represents the sampling depth, DTqz(n) represents the sound wave time difference curve data after denoising and reconstruction, and w i represents weights, w1 represents the weight of carbonate content curve data, w2 represents the weight of drilling time curve data, w3 represents the weight of drilling pressure curve data, s(n i ) represents curve data, s(n1) represents carbonate content curve data, s(n2) represents drilling time curve data, and s(n3) represents drilling pressure curve data.

3. The method for identifying diabase in carbonate rock according to claim 2, characterized in that: The acoustic wave time difference curve data is subjected to denoising and reconstruction processing, including: Performing normalization processing on the acoustic wave time difference curve data; The normalized acoustic wave time difference curve data is filtered using a low-pass filter and a high-pass filter to obtain the scale coefficient and the wavelet coefficient; Iteratively decomposing the scale coefficient until a preset scale is reached; Reconstructing the acoustic wave time difference curve data based on the scale coefficient obtained by iterative decomposition and the corresponding wavelet coefficient to obtain denoised and normalized acoustic wave time difference curve data; The denoised and normalized acoustic wave time difference curve data are de-normalized to obtain denoised and reconstructed acoustic wave time difference curve data.

4. The method for identifying diabase in carbonate rock according to any one of claims 1 to 3, characterized in that: Based on the weighted reconstructed acoustic time difference curve data, geostatistical inversion is performed, and the diabase intrusion is identified according to the inversion model, including: Based on the weighted reconstructed acoustic time difference curve data, the geostatistical inversion without wells is performed to determine the target signal-to-noise ratio with the highest inversion resolution; Based on the target signal-to-noise ratio, well geostatistical inversion is performed, and the diabase intrusion is identified according to the model obtained by the inversion.

5. The method for identifying diabase in carbonate rock according to claim 4, characterized in that: Wellless geostatistical inversion based on weighted reconstructed acoustic time difference curves, including: Synthesize seismic records based on weighted reconstructed acoustic time difference curve data; Acquire actual seismic data, and perform amplitude correction and time-depth conversion based on the synthetic seismic record and the actual seismic data; Wellless geostatistical inversion is performed based on amplitude-corrected and time-depth converted seismic data.

6. The method for identifying diabase in carbonate rock according to any one of claims 1 to 3, characterized in that: Obtain the well logging curve data of the carbonate rock to be identified, including: Draw curve graphs respectively according to the carbonate content curve data, drilling time curve data and drilling pressure curve data of the carbonate rock to be identified; Determine the target depth range based on the changing trend of the curve graph; Acquire well logging curve data within the target depth range.

7. A device for identifying diabase in carbonate rock, characterized in that: include: An acquisition module, used to acquire logging curve data of the carbonate rock to be identified, wherein the logging curve data includes acoustic wave time difference curve data, carbonate content curve data, drilling time curve data and drilling pressure curve data; A weight determination module is used to determine the weight corresponding to each curve data according to the change rate of carbonate content with depth in the carbonate content curve data, the change rate of drilling time with depth in the drilling time curve data, and the change rate of drilling pressure with depth in the drilling pressure curve data, and preset rules; the preset rules include the corresponding relationship between the change rate and the weight; A weighted reconstruction module, used for weighted reconstruction of the acoustic time difference curve data based on the carbonate content curve data, the drilling time curve data, the drilling pressure curve data and the corresponding weights; The identification module is used to perform geostatistical inversion based on the weighted reconstructed acoustic time difference curve data, and identify the diabase intrusion according to the inversion model.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying diabase in carbonate rock as claimed in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for identifying diabase in carbonate rock according to any one of claims 1 to 6 is implemented.

Images