Density logging data optimization method and device, storage medium and processor
By performing Stockwell transformation and inverse Q-filter compensation on density logging data, the density logging data was optimized, the problem of low accuracy of density logging curves was solved, the identification and evaluation capabilities of thin sandy reservoirs were improved, and more accurate reservoir parameters were provided.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-11-21
- Publication Date
- 2026-05-22
AI Technical Summary
Density logging data is greatly affected by the surrounding rock, resulting in low accuracy of density logging curves for thin sand reservoirs, making it difficult to accurately identify and evaluate thin and ultra-thin oil layers.
Stockwell transform is used to process density logging data. The first and second inverse Q filtering methods are combined to perform filtering compensation in the time and frequency domain. Amplitude compensation is performed by determining different gain limits inside and outside the target frequency band. Finally, inverse Stockwell transform is performed to optimize the density logging data.
It improves the accuracy of density logging curves, enhances the ability to identify and evaluate thin sandy reservoirs, provides more accurate reservoir parameters, and provides reliable data support for oilfield reserve recalculation and comprehensive logging interpretation.
Smart Images
Figure CN122072673A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas exploration and development technology, specifically to a density logging data optimization method, a density logging data optimization device, a machine-readable storage medium, and a processor. Background Technology
[0002] As oil fields gradually enter the middle and late stages of production, exploiting thin and even ultra-thin oil layers becomes crucial for increasing output. Therefore, accurately identifying and evaluating thin and ultra-thin oil layers is of paramount importance.
[0003] However, in actual exploration, due to the thin sand layer reservoirs corresponding to thin oil layers and ultra-thin oil layers, the density logging data is greatly affected by the surrounding rock, resulting in low accuracy of density logging curves, making it difficult to identify and evaluate thin sand layer reservoirs using density logging curves. Summary of the Invention
[0004] The purpose of this invention is to overcome the problem of low accuracy of density logging curves in thin sandy reservoirs in the prior art, and to provide a density logging data optimization method, device, storage medium and processor.
[0005] To achieve the above objectives, the present invention provides a density logging data optimization method, comprising:
[0006] The density logging data is subjected to Stockwell transform to obtain the spectrum of the density logging data in the time-frequency domain.
[0007] The target frequency band range is determined from the spectrum diagram. Within the target frequency band range, the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method. Outside the target frequency band range, the density logging data after Stockwell transformation is filtered and compensated using the second inverse Q filtering method.
[0008] The density logging data after Stockwell transformation and filtering compensation is subjected to inverse Stockwell transformation to obtain optimized density logging data.
[0009] In this embodiment of the application, the Stockwell transformation of the density logging data includes:
[0010] Fourier transform of the density logging data corresponding to each depth point is performed using a Gaussian window function.
[0011] In the embodiments of this application, the gain limit of the first inverse Q filtering method is a fixed gain limit, and the gain limit of the second inverse Q filtering method is a variable gain limit.
[0012] In this embodiment of the application, before filtering and compensating the density logging data after Stockwell transformation, the optimization method further includes: obtaining the target Q value;
[0013] Within the target frequency band, a first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation; outside the target frequency band, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation, including:
[0014] Within the target frequency band, based on the target Q value, the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method.
[0015] Outside the target frequency band, based on the target Q value, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation.
[0016] In this embodiment of the application, the step of using a first inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation within the target frequency band, and using a second inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation outside the target frequency band, includes:
[0017] Within the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method.
[0018] Outside the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the second inverse Q filtering method.
[0019] In this embodiment of the application, within the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated based on the following formula:
[0020]
[0021] Where S1 and B are the results of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; ω is the frequency in MHz, d is the logging depth in m; and Q is the quality factor of the density logging curve.
[0022] In this embodiment of the application, outside the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated based on the following formula:
[0023]
[0024] Wherein, S2 is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the second inverse Q filtering method; B is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; c is the time-varying gain limit corresponding to the cutoff frequency of the target frequency band; ω is the frequency in MHz; and d is the logging depth in m.
[0025] A second aspect of the present invention provides a density logging data optimization device, comprising:
[0026] The Stockwell transformation module is used to perform Stockwell transformation on density logging data to obtain the spectrum of the density logging data in the time-frequency domain.
[0027] The filtering compensation module is used to determine the target frequency band range from the spectrum diagram. Within the target frequency band range, the first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation. Outside the target frequency band range, the second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation.
[0028] The Stockwell inverse transform module is used to perform Stockwell inverse transform on density logging data that has undergone Stockwell transform and filtering compensation to obtain optimized density logging data.
[0029] A third aspect of this application provides a processor configured to execute the density logging data optimization method described above.
[0030] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned density logging data optimization method.
[0031] The above technical solution includes: performing a Stockwell transform on density logging data to obtain a spectrum of the density logging data in the time-frequency domain; determining a target frequency band from the spectrum; within the target frequency band, applying a first inverse Q filter to filter and compensate the Stockwell-transformed density logging data; outside the target frequency band, applying a second inverse Q filter to filter and compensate the Stockwell-transformed density logging data; and performing an inverse Stockwell transform on the density logging data after Stockwell transformation and filtering compensation to obtain optimized density logging data. Since the solution provided in this application can optimize density logging data to obtain optimized density logging data, the density logging curve drawn based on the optimized density logging data has higher accuracy, which can improve the ability of the density logging curve to identify and evaluate thin sandstone reservoirs.
[0032] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description
[0033] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:
[0034] Figure 1 The illustration shows a flowchart of a density logging data optimization method according to an embodiment of this application;
[0035] Figure 2 This illustration schematically shows a spectrum of density logging data after Stockwell transformation according to an embodiment of this application.
[0036] Figure 3 This illustration schematically shows a comparison between an optimized density logging curve and an initial density logging curve according to an embodiment of this application.
[0037] Figure 4 This illustration schematically shows a calculation result of a modified porosity model according to an embodiment of this application;
[0038] Figure 5 This schematic diagram illustrates a structural block diagram of a density logging data optimization device according to an embodiment of this application;
[0039] Figure 6 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.
[0040] Explanation of reference numerals in the attached figures
[0041] 210 - Stockwell Transform Module; 220 - Filter Compensation Module; 230 - Stockwell Inverse Transform Module; A01 - Processor; A02 - Network Interface; A03 - Internal Memory; A04 - Display Screen; A05 - Input Device; A06 - Non-Volatile Storage Medium; B01 - Operating System; B02 - Computer Program. Detailed Implementation
[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0043] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.
[0044] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.
[0045] As described in the background section, as oil fields gradually enter the middle and late stages of production, exploiting thin or even ultra-thin oil layers becomes crucial for increasing output. Therefore, accurately identifying and evaluating thin and ultra-thin oil layers is essential. However, in actual exploration, due to the thin reservoir thickness of these layers, density logging data is significantly affected by the surrounding rock, resulting in lower accuracy of density logging curves. For example, in thin sandstone reservoirs, the thin reservoir thickness leads to limited accuracy in reservoir property evaluation due to the influence of the surrounding rock on the density logging curves, making it difficult to identify and evaluate thin sandstone reservoirs using density logging curves.
[0046] To address this, this application provides a density logging data optimization method, which can be used to optimize density logging data, thereby improving the accuracy of density logging curves and enhancing their ability to identify and evaluate thin sandstone reservoirs. Figure 1 As shown, this density logging data optimization method may include the following steps:
[0047] Step 101: Perform Stockwell transform on the density logging data to obtain the spectrum of the density logging data in the time-frequency domain.
[0048] Specifically, Stockwell transformation is performed on the density logging data, which can be done on the data corresponding to the density logging curve. The spectrum of the density logging data in the time-frequency domain can reflect the joint characteristics of the density logging data in the time and frequency domains. The time domain (i.e., time) in the time-frequency domain corresponds to the logging depth.
[0049] The Stockwell transform, or S-transform for short, is a combination of Fourier transform and wavelet transform, incorporating the advantages of both the Short-Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT). Compared to the Continuous Wavelet Transform, the S-transform has the same time resolution across all frequencies. Unlike the STFT, whose frequency resolution is fixed at different frequencies, the S-transform, through its Fourier transform mechanism, does not fix the window size across frequencies, thus offering more flexible frequency compensation. In the S-transform, the window function can be a Gaussian window function.
[0051] The formula for performing the S-transform on a discrete signal can be shown below:
[0052]
[0053] In formula (1) above, x[m] is a discrete signal; ω[mn] is the discrete form of the Gaussian window function corresponding to time n; N is the total number of samples; e -j2πmk / N It is the kernel of the Discrete Fourier Transform; n is the time index; k is the frequency index.
[0054] It is understandable that when performing S-transformation on density logging data, it can be based on the above formula (1).
[0055] Furthermore, in this embodiment, performing a Stockwell transform on the density logging data may include: applying a Gaussian window function to perform a Fourier transform on the density logging data corresponding to each depth point. Specifically, the density logging data corresponding to each depth point may be the density logging data corresponding to each depth point of the density logging curve.
[0056] In practice, a frequency f can be assigned to the density logging data, and a Gaussian window function can be used to perform a local Fourier transform on the density logging data at depth x. This step is repeated for the density logging data corresponding to each depth point on the density logging curve, thus obtaining the time-frequency representation of the density logging data, and subsequently, the spectrum of the density logging data in the time-frequency domain. Here, frequency f can be the maximum frequency across the entire frequency range in the spectrum, such as for... Figure 2 The frequency f can be 70MHz.
[0057] It is understandable that when using the Gaussian window function to perform Fourier transform on the density logging data corresponding to each depth point, it can be based on the above formula (1).
[0058] In practical applications, the output of the S-transform is a complex number, which can provide not only signal energy information, such as amplitude information, but also signal phase information. Specifically, the amplitude can be calculated based on the following formula (2) and the phase can be calculated based on the following formula (3):
[0059] Magnitude = |S(x,f)| (2)
[0060] Phase=arg{S(x,f)} (3)
[0061] In the above formula, Magnitude is the amplitude, Phase is the phase, x is the time (corresponding to the logging depth), and f is the frequency.
[0062] Step 102: Determine the target frequency band range from the spectrum diagram. Within the target frequency band range, use the first inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation. Outside the target frequency band range, use the second inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation.
[0063] Specifically, the target frequency band range refers to the effective frequency band range. In practical implementation, the cutoff frequency ω of the effective frequency band range can be determined from the frequency spectrum. m Frequency ω ≤ cutoff frequency ω m The portion that is within the effective frequency band is where frequency ω > cutoff frequency ω. m The portion that is outside the effective frequency band range.
[0064] The gain limit of the first inverse Q filtering method is a fixed gain limit, and it can also be called a stable factor inverse Q filtering method. The gain limit of the second inverse Q filtering method is a variable gain limit, and more preferably, it is an adaptive gain limit. That is, a fixed gain limit inverse Q filtering method is used for filtering compensation within the effective frequency band, and an adaptive gain limit inverse Q filtering method is used for filtering compensation outside the effective frequency band.
[0065] In this embodiment of the application, within the target frequency band, the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method. Specifically, this may include: within the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method.
[0066] The amplitude compensation function corresponding to the first inverse Q filtering method can be as follows, that is, the amplitude of the density logging data after Stockwell transformation can be filtered and compensated based on the following formula:
[0067]
[0068] In the above formula (4), S1 and B are the amplitude compensation functions corresponding to the first inverse Q filtering method, that is, the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation by using the first inverse Q filtering method; ω is the frequency in MHz, d is the logging depth in m; Q is the density logging curve quality factor, which can be obtained by comparing and iterating the core density data and the compensated density logging curve.
[0069] In this embodiment of the application, outside the target frequency band, the second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation. Specifically, it may include: outside the target frequency band, the second inverse Q filtering method is used to filter and compensate the amplitude corresponding to the density logging data after Stockwell transformation.
[0070] In practical implementation, to suppress high-frequency noise outside the effective frequency band, the amplitude compensation function S2(d,ω) can be set to gradually decrease as the frequency increases outside the effective frequency band. Therefore, when using the adaptive gain-limited inverse Q-filtering method for filtering compensation, the gain limit of the amplitude compensation function S2(d,ω) satisfies:
[0071] c(d)=B[d,ω m (5);
[0072] In the above formula (5), where ω mω is the cutoff frequency in MHz; d is the logging depth in meters; c(d) is the time-varying gain limit of the amplitude compensation function S2(d,ω), and the cutoff frequency ω of the effective bandwidth. m Correspondingly, c(d) can be used to suppress high-frequency noise and avoid the Gibbs effect, thus making the filtered data more stable and reliable.
[0073] Due to ω m It is time-varying (i.e., it varies depending on the density logging curve), so the gain limit c(d) is also time-varying, and in this case, the gain limit c(d) adapts to the outside of the effective frequency band.
[0074] In this case, the amplitude compensation function S2(d,ω) corresponding to the second inverse Q filtering method can be as follows, that is, the amplitude corresponding to the density logging data after Stockwell transformation can be filtered and compensated based on the following formula:
[0075]
[0076] Wherein, S2 is the amplitude compensation function corresponding to the second inverse Q filtering method, that is, the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the second inverse Q filtering method.
[0077] Furthermore, based on formulas (4) and (6), the formula for inverse Q filtering of the amplitude of density logging data after Stockwell transformation can be obtained:
[0078]
[0079] In practical applications, applying the first inverse Q filtering method across all frequency bands cannot accurately compensate for amplitude attenuation caused by thin reservoir layers. However, the solution provided in this application, by using the first inverse Q filtering method for filtering compensation within the effective frequency band and the second inverse Q filtering method for filtering compensation outside the effective frequency band, improves the vertical accuracy and amplitude of density logging data while better reducing the impact of overcompensation on high-frequency components, effectively avoiding the Gibbs effect and thus improving the accuracy of subsequent density logging curves.
[0080] In this embodiment of the application, to obtain better filtering compensation results, before filtering and compensating the density logging data after Stockwell transformation, the density logging data optimization method provided in this embodiment may further include: obtaining a target Q value. Then, step 102 can specifically be: within the target frequency band, based on the target Q value, using a first inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation; outside the target frequency band, based on the target Q value, using a second inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation. It can be understood that the target Q value is an optimal density logging curve quality factor.
[0081] In practice, different Q values can be selected, and the density logging data after Stockwell transformation can be filtered and compensated based on these different Q values (specifically, within the target frequency band, the first inverse Q filtering method is used for filtering and compensation; outside the target frequency band, the second inverse Q filtering method is used for filtering and compensation). Then, the filtering and compensation results corresponding to each Q value for the Stockwell-transformed density logging data are obtained. Each filtering and compensation result is converted into a density logging curve and compared with the core density data. When the root mean square error between the density logging curve and the core density data is minimized, the Q value corresponding to that density logging curve is the target Q value applicable to the current study area. The filtering and compensation result corresponding to this target Q value can be considered the optimal filtering and compensation result. Therefore, this optimal filtering and compensation result can be considered as obtained by filtering and compensating the Stockwell-transformed density logging data within the target frequency band using the first inverse Q filtering method based on the target Q value; and by filtering and compensating the Stockwell-transformed density logging data outside the target frequency band using the second inverse Q filtering method based on the target Q value.
[0082] Step 103: Perform inverse Stockwell transformation on the density logging data after Stockwell transformation and filtering compensation to obtain optimized density logging data.
[0083] The formula for the Stockwell inverse transform is as follows:
[0084]
[0085] In the above formula (8), the meaning of each symbol can be found in the relevant explanation in formula (1), and will not be repeated here.
[0086] It is understood that the density logging data optimization method provided in this application includes: performing a Stockwell transform on the density logging data to obtain a spectrum of the density logging data in the time-frequency domain; determining a target frequency band range from the spectrum; within the target frequency band range, applying a first inverse Q filter to filter and compensate the density logging data after Stockwell transform; outside the target frequency band range, applying a second inverse Q filter to filter and compensate the density logging data after Stockwell transform; and performing an inverse Stockwell transform on the density logging data after Stockwell transform and filtering compensation to obtain optimized density logging data. Since the scheme provided in this application can optimize density logging data to obtain optimized density logging data, the density logging curve drawn based on the optimized density logging data has higher accuracy, which can improve the ability of the density logging curve to identify and evaluate thin sandstone reservoirs.
[0087] Furthermore, reservoir parameter modeling based on high-precision density logging curves offers higher reliability and provides more accurate reservoir parameters for oilfield reserve recalculation and comprehensive logging interpretation and evaluation. This allows for the development of logging interpretation and evaluation techniques applicable to thin sandstone reservoirs. Moreover, the scheme provided in this application requires fewer resources for computation, offering significant advantages for widespread application.
[0088] The density logging data optimization method provided in this application will be described below with specific examples. It should be understood that the following examples are only some specific implementation methods and do not imply an improper limitation of the solution in this application.
[0089] Example 1
[0090] Step 1: Perform a Stockwell transform on the density logging data to obtain its time-frequency spectrum. The spectrum of the density logging data after the Stockwell transform is shown below. Figure 2 As shown.
[0091] Step 2: Determine the effective frequency band range from the spectrum diagram.
[0092] like Figure 2 As shown, the cutoff frequency ω of the effective frequency band. m It is 30MHz.
[0093] Step 3: Select different Q values to obtain the corresponding filtering compensation results. The Q value is 30 when the root mean square error between the density logging curve and the core density data corresponding to each filtering compensation result is the smallest, i.e., the target Q value is 30. Within the effective frequency band, based on the target Q value, a fixed gain-limited inverse Q-filtering method is used to filter and compensate the density logging data after Stockwell transformation. Outside the effective frequency band, based on the target Q value, an adaptive gain-limited inverse Q-filtering method is used to filter and compensate the density logging data after Stockwell transformation.
[0094] Step 4: Perform inverse Stockwell transformation on the density logging data after Stockwell transformation and filtering compensation to obtain optimized density logging data.
[0095] The descriptions and explanations of steps one through four can be found in the foregoing content and will not be repeated here.
[0096] Step 5: Plot density logging curves based on the optimized density logging data, such as... Figure 3 As shown.
[0097] exist Figure 3 In this process, a density logging curve is plotted based on the optimized density logging data, which is simply referred to as the optimized density logging curve, such as... Figure 3 As shown by the dashed line, the density logging curve is plotted based on unoptimized density logging data, and is referred to as the initial density logging curve. Figure 3 As shown by the solid line in the middle.
[0098] Furthermore, the porosity model in the reservoir parameters was corrected using optimized density logging data. It was found that in thin sandy reservoir sections, the calculated porosity model results were more consistent with the results from drilling coring. Figure 4 As shown.
[0099] Based on the same inventive concept, such as Figure 5 As shown, Figure 5 A schematic diagram illustrates a structural block diagram of a density logging data optimization device according to an embodiment of this application. In one embodiment, a density logging data optimization device 200 is provided, including a Stockwell transformation module 210, a filter compensation module 220, and a Stockwell inverse transformation module 230, wherein:
[0100] Stockwell transformation module 210 is used to perform Stockwell transformation on density logging data to obtain the spectrum of the density logging data in the time-frequency domain.
[0101] The filtering compensation module 220 is used to determine the target frequency band range from the spectrum diagram. Within the target frequency band range, the first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation. Outside the target frequency band range, the second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation.
[0102] The Stockwell inverse transformation module 230 is used to perform Stockwell inverse transformation on density logging data that has undergone Stockwell transformation and filtering compensation to obtain optimized density logging data.
[0103] In one embodiment, the Stockwell transform module 210 is used to perform a Fourier transform on the density logging data corresponding to each depth point using a Gaussian window function.
[0104] In one embodiment, the gain limit of the first inverse Q filtering method is a fixed gain limit, and the gain limit of the second inverse Q filtering method is a variable gain limit.
[0105] In one embodiment, before filtering and compensating the density logging data after Stockwell transformation, the filtering and compensation module 220 is further configured to obtain a target Q value. Then, within the target frequency band, the filtering and compensation module 220 is configured to perform filtering and compensation on the density logging data after Stockwell transformation using a first inverse Q filtering method based on the target Q value; outside the target frequency band, based on the target Q value, the filtering and compensation on the density logging data after Stockwell transformation using a second inverse Q filtering method.
[0106] In one embodiment, the filtering compensation module 220 is used to perform filtering compensation on the amplitude corresponding to the density logging data after Stockwell transformation using a first inverse Q filtering method within the target frequency band; and to perform filtering compensation on the amplitude corresponding to the density logging data after Stockwell transformation using a second inverse Q filtering method outside the target frequency band.
[0107] In one embodiment, within the target frequency band, the filtering compensation module 220 is used to perform filtering compensation on the amplitude corresponding to the Stockwell transformed density logging data based on the following formula:
[0108]
[0109] Where S1 and B are the results of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; ω is the frequency in MHz, d is the logging depth in m; and Q is the quality factor of the density logging curve.
[0110] In one embodiment, outside the target frequency band, the filtering compensation module 220 is used to filter and compensate the amplitude corresponding to the Stockwell-transformed density logging data based on the following formula:
[0111]
[0112] Wherein, S2 is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the second inverse Q filtering method; B is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; c is the time-varying gain limit corresponding to the cutoff frequency of the target frequency band; ω is the frequency in MHz; and d is the logging depth in m.
[0113] The density logging data optimization device includes a processor and a memory. The Stockwell transformation module 210, the filter compensation module 220, and the Stockwell inverse transformation module 230 are all stored in the memory as program units. The processor executes the program modules stored in the memory to implement the corresponding functions.
[0114] A processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured, and by adjusting the core parameters, fast and efficient computation can be achieved at the entire chip scale.
[0115] Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM.
[0117] The memory includes at least one memory chip.
[0118] This application provides a machine-readable storage medium storing a program that, when executed by a processor, implements the aforementioned density logging data optimization method.
[0119] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a density logging data optimization method. The display screen A04 can be an LCD screen or an e-ink display screen. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.
[0120] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0121] In one embodiment, the density logging data optimization device provided in this application can be implemented as a computer program, which can be implemented in the form of, for example, Figure 6 The computer device shown runs on this system. The computer device's memory can store the various program modules that make up the intelligent scheduling device for this construction task, for example... Figure 5 The Stockwell transformation module 210, filter compensation module 220, and Stockwell inverse transformation module 230 are shown. The computer program comprised of these modules causes the processor to execute the steps in the density logging data optimization methods of the various embodiments of this application described in this specification.
[0122] Figure 6 The computer device shown can be used as follows Figure 5 The execution method of Stockwell transformation module 210, filter compensation module 220 and Stockwell inverse transformation module 230 in the density logging data optimization device shown.
[0123] This application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps:
[0124] The density logging data is subjected to Stockwell transform to obtain the spectrum of the density logging data in the time-frequency domain.
[0125] The target frequency band range is determined from the spectrum diagram. Within the target frequency band range, the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method. Outside the target frequency band range, the density logging data after Stockwell transformation is filtered and compensated using the second inverse Q filtering method.
[0126] The density logging data after Stockwell transformation and filtering compensation is subjected to inverse Stockwell transformation to obtain optimized density logging data.
[0127] In one embodiment, performing Stockwell transformation on the density logging data includes:
[0128] Fourier transform of the density logging data corresponding to each depth point is performed using a Gaussian window function.
[0129] In one embodiment, the gain limit of the first inverse Q filtering method is a fixed gain limit, and the gain limit of the second inverse Q filtering method is a variable gain limit.
[0130] In one embodiment, before filtering and compensating the density logging data after Stockwell transformation, the optimization method further includes: obtaining the target Q value;
[0131] Within the target frequency band, a first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation; outside the target frequency band, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation, including:
[0132] Within the target frequency band, based on the target Q value, the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method.
[0133] Outside the target frequency band, based on the target Q value, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation.
[0134] In one embodiment, the step of applying a first inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation within the target frequency band, and applying a second inverse Q filtering method to filter and compensate the density logging data after Stockwell transformation outside the target frequency band, includes:
[0135] Within the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method.
[0136] Outside the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the second inverse Q filtering method.
[0137] In one embodiment, within the target frequency band, the amplitude corresponding to the Stockwell-transformed density logging data is filtered and compensated based on the following formula:
[0138]
[0139] Where S1 and B are the results of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; ω is the frequency in MHz, d is the logging depth in m; and Q is the quality factor of the density logging curve.
[0140] In one embodiment, outside the target frequency band, the amplitude corresponding to the Stockwell-transformed density logging data is filtered and compensated based on the following formula:
[0141]
[0142] Wherein, S2 is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the second inverse Q filtering method; B is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; c is the time-varying gain limit corresponding to the cutoff frequency of the target frequency band; ω is the frequency in MHz; and d is the logging depth in m.
[0143] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.
[0144] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0145] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0146] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0147] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0148] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0149] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0150] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0151] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for optimizing density logging data, characterized in that, The optimization method includes: The density logging data is subjected to Stockwell transform to obtain the spectrum of the density logging data in the time-frequency domain. The target frequency band range is determined from the spectrum diagram. Within the target frequency band range, the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method. Outside the target frequency band range, the density logging data after Stockwell transformation is filtered and compensated using the second inverse Q filtering method. The density logging data after Stockwell transformation and filtering compensation is subjected to inverse Stockwell transformation to obtain optimized density logging data.
2. The density logging data optimization method according to claim 1, characterized in that, The Stockwell transformation of density logging data includes: Fourier transform of the density logging data corresponding to each depth point is performed using a Gaussian window function.
3. The density logging data optimization method according to claim 1, characterized in that, The gain limit of the first inverse Q filtering method is a fixed gain limit, while the gain limit of the second inverse Q filtering method is a variable gain limit.
4. The density logging data optimization method according to claim 1, characterized in that, Before filtering and compensating the density logging data after Stockwell transformation, the optimization method further includes: obtaining the target Q value; Within the target frequency band, a first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation; outside the target frequency band, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation, including: Within the target frequency band, based on the target Q value, a first inverse Q filtering method is used to... Density logging data after Stockwell transformation is filtered and compensated. Outside the target frequency band, based on the target Q value, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation.
5. The density logging data optimization method according to claim 3, characterized in that, Within the target frequency band, a first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation; outside the target frequency band, a second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation, including: Within the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the first inverse Q filtering method. Outside the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated using the second inverse Q filtering method.
6. The density logging data optimization method according to claim 5, characterized in that, Within the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated based on the following formula: Where S1 and B are the results of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; ω is the frequency in MHz, d is the logging depth in m; and Q is the quality factor of the density logging curve.
7. The density logging data optimization method according to claim 5, characterized in that, Outside the target frequency band, the amplitude corresponding to the density logging data after Stockwell transformation is filtered and compensated based on the following formula: Wherein, S2 is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the second inverse Q filtering method; B is the result of filtering and compensating the amplitude of the density logging data after Stockwell transformation using the first inverse Q filtering method; c is the time-varying gain limit corresponding to the cutoff frequency of the target frequency band; ω is the frequency in MHz; and d is the logging depth in m.
8. A density logging data optimization device, characterized in that, include: The Stockwell transformation module is used to perform Stockwell transformation on density logging data to obtain the spectrum of the density logging data in the time-frequency domain. The filtering compensation module is used to determine the target frequency band range from the spectrum diagram. Within the target frequency band range, the first inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation. Outside the target frequency band range, the second inverse Q filtering method is used to filter and compensate the density logging data after Stockwell transformation. The Stockwell inverse transform module is used to perform Stockwell inverse transform on density logging data that has undergone Stockwell transform and filtering compensation to obtain optimized density logging data.
9. A processor, characterized in that, It is configured to perform the density logging data optimization method according to any one of claims 1 to 7.
10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the density logging data optimization method according to any one of claims 1 to 7.