Imaging porosity spectrum cutoff value processing method and device, electronic equipment, storage medium and product

Abstract

This invention provides a method, apparatus, electronic device, storage medium, and product for processing the cutoff value of an imaging porosity spectrum. The method includes: determining the electrical imaging porosity spectrum of the downhole carbonate formation based on electrical imaging logging data; reconstructing the electrical imaging porosity spectrum of the downhole carbonate formation using a mixture of Gaussian models to obtain multiple target normal distributions, including multiple normal distributions with different variances and means to characterize the electrical imaging porosity spectrum of the downhole carbonate formation; determining the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation based on the multiple target normal distributions of the electrical imaging porosity spectrum; and identifying the primary and secondary porosity of the downhole carbonate formation based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation. This application's solution can analyze the secondary porosity cutoff values ​​of electrical imaging porosity spectra at different stratigraphic levels according to the sequence of Gaussian models.

Description

Technical Field

[0001] This invention relates to the field of microresistivity imaging logging data processing technology, and in particular to a method, apparatus, electronic device, storage medium, and product for processing imaging porosity spectrum cutoff values. Background Technology

[0002] Various types of carbonate rocks are widely developed in some basin areas, and significant breakthroughs in carbonate strata have led to the identification of an important type of oil and gas reservoir. The sedimentary structure and tectonic features of carbonate reservoirs exhibit strong heterogeneity, making well logging interpretation and evaluation, especially porosity, a major challenge.

[0003] The dual-porosity structure of heterogeneous carbonate rocks mainly consists of primary and secondary porosity. To address the discrepancy between porosity calculated by conventional well logging and reservoir testing results, accurate evaluation of secondary porosity based on field well logging data is necessary. Currently, the main methods for evaluating secondary porosity are still detailed analytical experiments such as nuclear magnetic resonance (NMR), dual-energy CT, and scanning electron microscopy (SEM) of core samples. However, these methods are time-consuming and costly, suitable for scientific research but difficult to support actual production tasks. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, storage medium, and product for processing the cutoff value of imaging porosity spectrum, so as to realize the calculation method of Gaussian mixture model and analyze the secondary porosity cutoff value of electrical imaging porosity spectrum of different layers according to the Gaussian model sequence.

[0005] In a first aspect, embodiments of the present invention provide a method for processing the cutoff value of an imaging porosity spectrum, the method comprising:

[0006] Based on electrical imaging logging data of the downhole carbonate rock formation, the electrical imaging porosity spectrum of the downhole carbonate rock formation was determined.

[0007] The electro-imaging porosity spectrum of the downhole carbonate rock formation is reconstructed using a mixture Gaussian model to obtain multiple target normal distributions of the electro-imaging porosity spectrum. These multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electro-imaging porosity spectrum of the downhole carbonate rock formation.

[0008] Based on the normal distribution of multiple targets in the electrical imaging porosity spectrum, the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation is determined.

[0009] Based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation, the primary and secondary porosity of the downhole carbonate rock formation are identified.

[0010] Secondly, embodiments of the present invention also provide an imaging porosity spectrum cutoff value processing device, the device comprising:

[0011] The determination module is used to determine the electrical imaging porosity spectrum of the downhole carbonate rock formation based on electrical imaging logging data of the downhole carbonate rock formation.

[0012] The reconstruction module is used to reconstruct the electrical imaging porosity spectrum of the downhole carbonate rock formation using a mixture Gaussian model to obtain multiple target normal distributions of the electrical imaging porosity spectrum. The multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0013] The detection module is used to determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation based on the normal distribution of multiple targets in the electrical imaging porosity spectrum.

[0014] The identification module is used to determine the primary and secondary porosity of the downhole carbonate rock formation based on the cutoff value of the electrical imaging porosity spectrum.

[0015] Thirdly, this invention also provides an electronic device, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the imaging porosity spectral cutoff value processing method described in any of the above embodiments.

[0019] Fourthly, the present invention also provides a computer-readable medium storing computer instructions, which are used to cause a processor to execute the imaging porosity spectrum cutoff value processing method described in any of the above embodiments.

[0020] Fifthly, this invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the imaging porosity spectrum cutoff value processing method described in any of the above embodiments.

[0021] The technical solution of this invention uses electro-imaging logging data to determine the electro-imaging porosity spectrum, which can intuitively and comprehensively display the porosity distribution of downhole carbonate rock formations. Different porosity values ​​and their corresponding frequencies or probability densities constitute the shape of the porosity spectrum, reflecting the distribution characteristics of pores in different size ranges. By reconstructing the electro-imaging porosity spectrum using a mixture of Gaussian models, multiple target normal distributions with different variances and means are obtained, which can deeply represent the pore distribution law contained in the porosity spectrum. Based on multiple target normal distributions, the cutoff value of the electro-imaging porosity spectrum is determined, providing a key boundary for distinguishing pores of different origins. This cutoff value can reasonably divide the porosity spectrum into two parts with different geological significance. Based on the determined cutoff value, the porosity of downhole carbonate rock formations can be accurately divided into primary porosity and secondary porosity. By using the superposition state of multiple Gaussian models to characterize the porosity spectrum shape, and using the intersection boundary point between the main Gaussian models as the porosity spectrum cutoff value, the accurate calculation of secondary porosity can be achieved efficiently and stably.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0024] Figure 1 This is a schematic flowchart of an imaging porosity spectrum cutoff value processing method provided in an embodiment of the present invention;

[0025] Figure 2 This is a schematic flowchart of an imaging porosity spectrum cutoff value processing method provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of a Gaussian mixture model characterizing the morphological distribution structure of the porosity spectrum in electro-imaging, provided in an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of the calculation result of the cutoff value of the electro-imaging porosity spectrum provided in an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of an imaging porosity spectrum cutoff value processing device provided in an embodiment of the present invention;

[0029] Figure 6This is a schematic diagram of the structure of an electronic device for implementing an imaging porosity spectrum cutoff value processing method provided in an embodiment of the present invention. Detailed Implementation

[0030] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0031] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0032] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0033] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0034] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0035] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0036] Figure 1This is a flowchart illustrating an imaging porosity spectrum cutoff value processing method provided in an embodiment of the present invention. This embodiment of the present invention is applicable to the rapid and accurate identification of primary and secondary porosity in carbonate rock formations. The imaging porosity spectrum cutoff value processing method can be executed by an imaging porosity spectrum cutoff value processing device. This imaging porosity spectrum cutoff value processing device can be implemented in the form of software and / or hardware, and is generally integrated into any electronic device with network communication capabilities, such as a mobile terminal, PC, or server.

[0037] like Figure 1 As shown, the imaging porosity spectrum cutoff value processing method of this invention may include the following process:

[0038] S110. Based on the electrical imaging logging data of the downhole carbonate rock formation, determine the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0039] The sedimentary structure and tectonic features of carbonate reservoirs exhibit strong heterogeneity, making well logging interpretation and evaluation, especially porosity, a significant challenge. The dual-porosity structure of heterogeneous carbonate rocks primarily comprises primary and secondary porosity. Primary porosity refers to pores formed during sedimentation due to gaps between sediment particles or biological remains; these mainly include intercrystalline pores, dissolution pores, and organic pores. Primary pores are mostly distributed between carbonate rock particles, exhibiting porosity and connectivity, but generally have low porosity and permeability. Secondary porosity refers to pores formed after sedimentation due to various geological processes; these mainly include fracture pores, dissolution pores, and compaction pores. Secondary pores are mostly distributed between carbonate minerals, appearing as lines or dots, and serve as the main migration and transport channels for oil and gas.

[0040] Electro-imaging logging (EML) data can provide high-resolution images of the wellbore in carbonate formations, identifying minute fractures, pores, and lithological variations. EML works by transmitting and receiving electrical current around the wellbore, using current variations at different locations to reflect the resistivity distribution of the formation near the wellbore. Since pores and fractures in the formation affect current propagation, causing resistivity changes, these resistivity differences can be used to identify and analyze formation porosity characteristics. EML data utilizes an electrode system attached to the wellbore in carbonate formations to measure micro-resistivity changes, and data processing generates resistivity images of the formation within the wellbore. For example, micro-resistivity scanning imaging (FMI) logging can clearly display geological features such as fractures, pores, and bedding in the wellbore rock.

[0041] Electro-imaging porosity spectra are data representations of porosity distribution characteristics obtained from electro-imaging logging data of downhole carbonate formations, acquired, processed, and analyzed using electro-imaging logging technology. Electro-imaging porosity spectra are typically presented as two-dimensional graphs. The horizontal axis represents porosity values, indicating the size or proportion of pore space in the formation, visually showing the numerical range of porosity. The vertical axis represents physical quantities related to porosity, such as frequency (the number of times a specific porosity value appears in the entire measurement data) and probability density (the probability distribution of porosity around a certain value). Elements in the electro-imaging porosity spectrum, such as curves or bar charts, illustrate the distribution pattern of porosity, such as the presence of a single main peak, multiple peaks, or continuous distribution.

[0042] As an optional but not limited implementation scheme, the electrical imaging porosity spectrum of the downhole carbonate formation is determined based on the electrical imaging logging data of the downhole carbonate formation, including the following steps A1-A2:

[0043] Step A1: Perform well logging data preprocessing on the downhole carbonate rock formation electrical imaging logging data to obtain candidate electrical imaging logging data. Well logging data preprocessing includes at least one of the following types of processing methods: electrode equalization correction, acceleration correction, electrode alignment, azimuth merging, and image calibration.

[0044] Step A2: Calculate the porosity spectrum of the downhole carbonate formation based on the candidate electrical imaging logging data.

[0045] For electrode equalization correction, electrical imaging logging instruments typically contain multiple electrodes. Due to slight differences in instrument manufacturing processes and the influence of the complex downhole environment, the data acquired by each electrode may be inconsistent in terms of amplitude, resolution, etc. Equalization preprocessing aims to adjust the data acquired by each electrode, so that the data measured by different electrodes reach a relatively consistent level in terms of amplitude, resolution, etc., ensuring the comparability of data acquired by different electrodes.

[0046] For acceleration correction, during electrical imaging logging, the speed of the logging instrument is not constant as it moves downhole. The acceleration caused by the speed change can interfere with the measurement signal, leading to deviations in the measurement data. Acceleration correction uses the acceleration information from the measuring instrument and a specific algorithm to compensate for the measurement data, eliminating the influence of acceleration on the measurement results. This allows the electrical imaging logging data to more accurately reflect the actual formation conditions, improving the stability and reliability of the data.

[0047] Electrode alignment ensures that each electrode is in the correct relative position during measurement, allowing data from different electrodes to accurately correspond to their respective locations on the wellbore. This avoids image distortion or data errors caused by electrode misalignment, thus improving the accuracy and clarity of the electro-optical imaging (EIA) images. Azimuth merging combines EIA data obtained from measurements in different azimuths, providing a comprehensive view of the formation around the wellbore. This avoids missing important geological features in certain azimuths, resulting in more complete EIA data that better reflects the overall formation.

[0048] Image calibration can transform an image into multi-channel resistivity data by segmenting pixel values ​​and linearly correlating them with resistivity data. Image calibration can provide a unified calibration standard for electrical imaging data, making data from different depths and measurement points comparable, facilitating accurate identification and analysis of various features in the formation. It also helps to fuse and compare electrical imaging data with other well logging data, improving the application value of the data.

[0049] After the electrical imaging logging data of the downhole carbonate rock formation is preprocessed, the noise, error and other interference factors in the original electrical imaging logging data are removed from the candidate electrical imaging logging data obtained after preprocessing. The data quality is significantly improved, which provides a reliable data basis for subsequent porosity spectrum calculation and can effectively reduce the error and uncertainty of the calculation results.

[0050] Optionally, porosity spectrum calculation can refer to the process of converting electrical imaging logging data into a porosity spectrum through specific algorithms and models. For example, it can extract porosity-related information from candidate electrical imaging logging data and convert the porosity information implicit in the original electrical imaging logging data into a porosity spectrum to present the distribution characteristics of porosity in an intuitive and quantitative way.

[0051] Optionally, the porosity spectrum of the downhole carbonate formation is obtained by calculating the porosity spectrum based on candidate electrical imaging logging data. This includes: using a modified Alzer formula based on the candidate electrical imaging logging data, converting the measurement results of each button electrode into porosity, calculating the porosity of each button electrode, and statistically analyzing the results in the form of a histogram, which is the electrical imaging porosity spectrum of the downhole carbonate formation. The modified Alzer formula is shown below:

[0052]

[0053] in, Porosity, %; m, cementation index, dimensionless; a, lithology-related lithology coefficient, dimensionless; R w R represents the resistivity of formation water, in ohms / mm². i Let be the resistivity of the electrical imaging after the i-th scale, ohmm.

[0054] As an optional but not limited implementation scheme, the electrical imaging logging data of the downhole carbonate rock formation is preprocessed to obtain candidate electrical imaging logging data, including the following steps A11-A13:

[0055] Step A11: After performing acceleration correction and equalization preprocessing on the electrical imaging logging data of the downhole carbonate rock formation, the original electrical imaging logging image information is obtained.

[0056] Step A12: Perform static enhancement processing and dynamic enhancement processing on the original electrical imaging logging image information to obtain the electrical imaging logging processing results. The electrical imaging logging processing results include image information reflecting the changes in rock color throughout the entire well section of the downhole carbonate rock formation and image information reflecting the changes in local rock structure of the downhole carbonate rock formation.

[0057] Step A13: Using a piecewise linear scale, the electrical imaging logging processing results are correlated with the resistivity curve to obtain candidate electrical imaging logging data after image scaling.

[0058] After acceleration correction and equalization preprocessing of the electrical imaging logging data of the downhole carbonate rock formation, the original electrical imaging logging image information is obtained. This image information is converted from the electrical imaging logging data after acceleration correction and equalization preprocessing. The original electrical imaging logging image information initially reflects some electrical characteristics of the downhole carbonate rock formation.

[0059] Optionally, an accelerometer is used to acquire acceleration data of the electrical imaging logging instrument during its downhole movement. Then, the measurement signals in the electrical imaging logging data are corrected to remove interference caused by acceleration. Next, the data collected by each electrode is equalized by adjusting parameters such as amplitude and resolution to make the data of each electrode reach a relatively consistent level. Finally, the corrected and equalized data is converted into image form to obtain the original electrical imaging logging image information.

[0060] The raw electrical imaging logging images still need further optimization in terms of clarity and contrast to more clearly display formation information. Specifically, static enhancement processing can be performed on the raw electrical imaging logging images to adjust the overall characteristics of the images. For example, by changing parameters such as grayscale distribution and contrast, the images can be made clearer and easier to observe, thereby highlighting the characteristics of rock color changes in the downhole carbonate formation on a larger scale. This helps to understand the lithological distribution of the formation from a macroscopic perspective and obtain image information reflecting the rock color changes throughout the well section. In addition, dynamic enhancement processing can be performed on the raw electrical imaging logging images, focusing on adaptive adjustment based on the characteristics of local areas of the image. This can highlight the detailed information of local areas in the image, such as the changes in local rock structure in the downhole carbonate formation, allowing for more detailed observation of subtle structural differences in the formation and obtaining image information reflecting local rock structure changes.

[0061] Optionally, for static enhancement processing, histogram equalization, contrast stretching, and other methods are used to adjust the overall grayscale and contrast of the image based on its overall grayscale distribution to highlight the color variations of the rock throughout the well section. For dynamic enhancement processing, a local adaptive algorithm is used to perform targeted enhancement processing on each local region based on the grayscale statistics of the local image region, highlighting the changes in local rock structure, such as minute cracks, textures, and other detailed features.

[0062] The results of electrical imaging logging are image information obtained after static and dynamic enhancement processing. They integrate macroscopic information reflecting the color changes of the carbonate rock formation throughout the well section and microscopic information reflecting the local rock structure changes, providing more comprehensive and detailed formation image data.

[0063] Piecewise linear scales can be used to correlate image data with resistivity. Specifically, the grayscale range of an image can be divided into multiple intervals, and a linear function can be used to map the grayscale values ​​to the corresponding resistivity values ​​within each interval. This method can more accurately reflect the actual stratigraphic resistivity changes corresponding to different grayscale regions in the image, enhancing the geological interpretability of the image.

[0064] Resistivity curves, obtained through well logging techniques, reflect the resistivity of downhole carbonate formations as a function of depth. Resistivity is a crucial electrical parameter of a formation; formations with different lithologies, porosities, and fluid-bearing properties exhibit varying resistivity values. Combining resistivity curves with electrical imaging logging data can provide more information for geological analysis, helping to determine the lithology, oil-bearing properties, and other characteristics of downhole carbonate formations. Candidate electrical imaging logging data is obtained by correlating the processed electrical imaging logging results with the resistivity curve using a piecewise linear scale.

[0065] Optionally, a piecewise linear scaling method is used to correlate the electrical imaging logging processing results with the resistivity curve. This includes dividing the grayscale value range of the electrical imaging logging processing image into several intervals; then, for each interval, based on the relationship between the actual resistivity value and the image grayscale value provided by the resistivity curve, determining a linear function to map the grayscale value within that interval to the corresponding resistivity value. In this way, a connection is established between the electrical imaging logging image and the actual resistivity physical quantity, resulting in candidate electrical imaging logging data after image scaling.

[0066] S120. The electro-imaging porosity spectrum of the downhole carbonate rock formation is reconstructed using a mixture Gaussian model to obtain multiple target normal distributions of the electro-imaging porosity spectrum. These multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electro-imaging porosity spectrum of the downhole carbonate rock formation.

[0067] A Gaussian mixture model (GMM) is a model that decomposes data into several components based on Gaussian probability density functions (normal distributions). In other words, it assumes that the data is a mixture of multiple Gaussian distributions, each with its own mean, variance, and other parameters. GMM reconstruction refers to determining the parameters of a GMM based on a given electrical imaging porosity spectrum of a downhole carbonate formation and a specific algorithm. This allows the GMM to fit the given electrical imaging porosity spectrum of the downhole carbonate formation as accurately as possible, essentially reconstructing a GMM that reasonably reflects the intrinsic structure of the electrical imaging porosity spectrum of the downhole carbonate formation.

[0068] A Gaussian mixture model (Gaussian mixture model) was used to process the porosity spectrum data from electro-imaging. The parameters of the Gaussian mixture model were adjusted to best fit the porosity spectrum data of downhole carbonate formations, thereby reconstructing a Gaussian mixture model that accurately describes the distribution characteristics of the porosity spectrum in electro-imaging. Essentially, it decomposes the complex porosity spectrum distribution into a linear combination of multiple simple Gaussian distributions, each representing a specific porosity distribution pattern.

[0069] The normal distribution of the electro-imaging porosity spectrum is obtained by reconstructing multiple target normal distributions using a Gaussian mixture model, and is used to accurately characterize the porosity spectrum of downhole carbonate formations. Each target normal distribution has its specific mean and variance. The mean represents the central location of the porosity distribution, and the variance reflects the dispersion of porosity around the mean. Combining multiple target normal distributions can comprehensively and meticulously describe the complex distribution characteristics of the electro-imaging porosity spectrum.

[0070] Optionally, based on the characteristics and practical requirements of the electro-imaging porosity spectrum of downhole carbonate formations, a suitable Gaussian mixture model structure is selected, the number of Gaussian distributions in the model is determined, and the model parameters are initialized, including initial values ​​for the mean, variance, and weights of each Gaussian distribution. Optimization algorithms such as the Expectation-Maximization (EM) algorithm are used to iteratively estimate the parameters of the Gaussian mixture model. In each iteration, the posterior probability of each data point belonging to each Gaussian distribution is first calculated, and then the model parameters are updated based on these posterior probabilities to maximize the likelihood function of the data. Through multiple iterations, the model parameters are gradually adjusted until they converge to the optimal value, thus obtaining a Gaussian mixture model that best fits the electro-imaging porosity spectrum. Evaluation metrics such as mean squared error and likelihood function value are used to assess the fitting effect of the reconstructed Gaussian mixture model on the electro-imaging porosity spectrum of downhole carbonate formations. If the model performance is not ideal, the model structure can be adjusted, parameter initialization values ​​can be reselected, or other optimization algorithms can be used to further optimize the model until a satisfactory fitting result is obtained, and finally, the normal distribution of multiple targets in the electro-imaging porosity spectrum can be obtained.

[0071] The above-described scheme enables the accurate characterization of complex data in electrical imaging porosity spectra, which may contain multiple pore types and distribution characteristics, using multiple normal distributions with different variances and means. This clearly distinguishes the distribution of different porosity ranges, such as the distribution characteristics of large, small, and medium-sized pores, providing more precise information for a deeper understanding of formation pore structure. Furthermore, by using the means and variances of different normal distributions, the main characteristics and variation patterns of porosity in downhole carbonate formations can be highlighted, contributing to improved accuracy in interpreting geological phenomena in downhole carbonate formations.

[0072] S130. Based on the normal distribution of multiple targets in the electrical imaging porosity spectrum, determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0073] Electro-imaging porosity spectrum is a map of porosity distribution in downhole carbonate formations obtained through electro-imaging logging technology. It can visually display the distribution of different porosities in downhole carbonate formations with porosity as the horizontal axis and the frequency, probability density, or other indicators reflecting relative content of the corresponding porosity as the vertical axis.

[0074] The multiple target normal distributions are normal distributions obtained after reconstructing the porosity spectrum of electro-imaging using a Gaussian mixture model. Each target normal distribution has a specific mean, variance, and weight, representing a portion of the porosity distribution characteristics in the electro-imaging porosity spectrum. Together, these multiple target normal distributions constitute a complete description of the electro-imaging porosity spectrum. The mean of each target normal distribution reflects the concentrated porosity region it represents, the variance reflects the degree of dispersion of porosity around the mean, and the weight indicates the proportion of that normal distribution's contribution to the porosity spectrum in the entire mixture model.

[0075] The cutoff value of the electro-imaging porosity spectrum is used to divide the electro-imaging porosity spectrum into different parts, thereby distinguishing porosity of different origins or properties. The determination of the cutoff value of the electro-imaging porosity spectrum is based on the probability density function value of the normal distribution of multiple targets at the mean, so as to divide the porosity spectrum into intervals with different geological significance, providing a boundary basis for subsequent work such as identifying primary porosity and secondary porosity.

[0076] Using the above method, in carbonate rock formations, different pore types have different effects on reservoir storage and permeability. By determining the cutoff value of the electrical imaging porosity spectrum, the porosity spectrum can be clearly divided into different parts, making the subsequent identification of primary and secondary pores more accurate and objective.

[0077] S140. Based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation, identify the primary and secondary porosity of the downhole carbonate formation.

[0078] Specifically, the porosity spectrum of electrical imaging can be divided into two intervals based on a cutoff value. The interval with porosity below the cutoff value is generally considered to mainly contain porosity data related to primary porosity; while the interval with porosity above the cutoff value mainly contains porosity data related to secondary porosity. This division is based on the understanding of the porosity formation mechanism of carbonate strata. Generally speaking, primary porosity exists in the early stages of rock formation and its pore size is relatively small; while secondary porosity is the result of later geological processes and often has a relatively large pore size.

[0079] The porosity components within the interval where the porosity is less than the cutoff value are summed. These porosity components can be the frequency, volume fraction, or other indicators reflecting the relative content of porosity for each porosity value. For example, if frequency is used as the indicator, primary porosity equals the sum of the frequencies corresponding to all porosity values ​​within that interval. This summation yields the value of primary porosity, representing the proportion or size of primary porosity in the total formation porosity. Similar to the calculation of primary porosity, the porosity components within the interval where the porosity is greater than the cutoff value are summed. Again, using frequency as an example, secondary porosity is the sum of the frequencies corresponding to all porosity values ​​within that interval. This value reflects the proportion or size of secondary porosity in the total formation porosity.

[0080] The above method can clearly distinguish between porosity of different origins in downhole carbonate strata, namely primary porosity and secondary porosity. This helps to gain a deeper understanding of the formation process and evolution history of strata porosity, providing important information for geological research. For example, by analyzing the relative magnitudes of primary and secondary porosity, the intensity and type of geological processes experienced by the strata after deposition can be inferred.

[0081] The technical solution of this invention uses electro-imaging logging data to determine the electro-imaging porosity spectrum, which can intuitively and comprehensively display the porosity distribution of downhole carbonate rock formations. Different porosity values ​​and their corresponding frequencies or probability densities constitute the shape of the porosity spectrum, reflecting the distribution characteristics of pores in different size ranges. By reconstructing the electro-imaging porosity spectrum using a mixture of Gaussian models, multiple target normal distributions with different variances and means are obtained, which can deeply represent the pore distribution law contained in the porosity spectrum. Based on multiple target normal distributions, the cutoff value of the electro-imaging porosity spectrum is determined, providing a key boundary for distinguishing pores of different origins. This cutoff value can reasonably divide the porosity spectrum into two parts with different geological significance. Based on the determined cutoff value, the porosity of downhole carbonate rock formations can be accurately divided into primary porosity and secondary porosity. By using the superposition state of multiple Gaussian models to characterize the porosity spectrum shape, and using the intersection boundary point between the main Gaussian models as the porosity spectrum cutoff value, the accurate calculation of secondary porosity can be achieved efficiently and stably.

[0082] Figure 2 This is a flowchart illustrating another method for processing the cutoff value of the imaging porosity spectrum provided by an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of reconstructing the electro-imaging porosity spectrum of the downhole carbonate rock formation using a mixture Gaussian model to obtain multiple target normal distributions of the electro-imaging porosity spectrum based on the technical solution of the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments.

[0083] like Figure 2 As shown, the imaging porosity spectrum cutoff value processing method of this invention may include the following process:

[0084] S210. Based on the electrical imaging logging data of the downhole carbonate rock formation, determine the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0085] S220. Obtain the amplitude of each porosity frequency histogram from the porosity frequency histogram data of the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0086] Porosity frequency histogram data from electro-imaging porosity spectra can be obtained by processing electro-imaging logging data from downhole carbonate formations. Porosity values ​​are divided into several intervals, and the frequency of porosity occurrence within each interval is statistically analyzed. The data, presented as a histogram, visually demonstrates the frequency distribution of different porosity values ​​throughout the formation, serving as crucial foundational data for analyzing porosity distribution characteristics. The amplitude of the porosity frequency histogram can be understood as the height of each bar, representing the frequency of porosity occurrence within the corresponding porosity interval. A higher amplitude indicates a higher frequency of porosity occurrence within that interval throughout the formation.

[0087] Specifically, the porosity frequency histogram data of the electro-imaging porosity spectrum can be read. This porosity frequency histogram data records different porosity intervals and their corresponding frequency information. The frequency value corresponding to each porosity interval is extracted from this data, that is, the height of each bar in the histogram. The amplitude of the histogram is used as the input sequence to describe the distribution of porosity in different intervals.

[0088] S230. Determine the maximum number of Gaussian models when reconstructing the porosity spectrum of an electrical imaging model using a Gaussian mixture model.

[0089] The maximum number of Gaussian models can be the maximum number of Gaussian distributions allowed to be used when reconstructing the porosity spectrum of an electro-imaging system using a Gaussian mixture model. The value of the maximum number of Gaussian models affects the model's complexity and its ability to fit the data. Setting it too low may prevent the model from fully capturing the complex features of the data, while setting it too high may lead to overfitting. The specific value can be set based on prior knowledge or experience of the electro-imaging porosity spectrum. For example, if the electro-imaging porosity spectrum is expected to exhibit a single-peak to three-peak characteristic, it can be set to 3 or 4.

[0090] S240. Based on the amplitude of each porosity frequency histogram and the maximum number of Gaussian models, the maximum expectation algorithm is used for algorithm iteration. The optimal number of Gaussian models is selected when reconstructing the porosity spectrum of electro-imaging using a Gaussian mixture model. The parameters of the Gaussian mixture model corresponding to the optimal number of Gaussian models are determined as the optimal solution that can characterize the amplitude of each porosity frequency histogram.

[0091] The probability density function of a single Gaussian model is:

[0092]

[0093] Where μ is the mean vector (expectation); σ is the standard deviation.

[0094] The probability density function of the Gaussian mixture model is:

[0095]

[0096] Where k is the number of Gaussian models; q j Let be the weights of the j-th Gaussian distribution, and satisfy q j ≥0, μ j σ is the expectation of the j-th Gaussian distribution; j Let be the standard deviation of the j-th Gaussian model.

[0097] For a single Gaussian model, the maximum likelihood estimation method is mainly used to estimate the parameters. However, for a Gaussian mixture model, it is impossible to know which Gaussian distribution the current sample comes from. Therefore, the ExpectationMaximization Algorithm (EM algorithm) is needed to estimate the maximum posterior probability by setting initial values ​​and iterating.

[0098] The Expectation-Maximization (EM) algorithm is used to estimate model parameters in probabilistic models when latent variables exist. In Gaussian mixture model reconstruction, the parameters (mean, variance, and weights) of the Gaussian mixture model are continuously updated by alternately executing the expectation (E) step and the maximization (M) step, making the model fit the data better and better until a certain convergence condition is met.

[0099] The optimal Gaussian model number can be determined through iterative EM algorithm selection from multiple models with different Gaussian model numbers during the reconstruction of the porosity spectrum of electro-imaging using a Gaussian mixture model. The optimal Gaussian model number ensures the best fit to the porosity frequency histogram amplitude data. The model corresponding to the optimal Gaussian model number accurately reflects the distribution characteristics of the data without being overly complex and causing overfitting.

[0100] The parameters of a Gaussian mixture model (GMM) can refer to the mean, variance, and weight of each Gaussian distribution in the GMM. These parameters determine the shape of each normal distribution and its relative importance within the GMM. Iterative optimization of these parameters using the EM algorithm enables the GMM to better fit the electrical imaging porosity spectrum of downhole carbonate formations.

[0101] Optionally, initialization: For each possible number of Gaussian models from 1 to the maximum number of Gaussian models, randomly initialize the parameters of the Gaussian mixture model, including the mean, variance, and weights of each Gaussian distribution. E-step: For the amplitude data of a given porosity frequency histogram, calculate the posterior probability of each data point belonging to each Gaussian distribution based on the current parameters of the Gaussian mixture model. M-step: Based on the calculated posterior probabilities, update the parameters of the Gaussian mixture model by maximizing the likelihood function to determine the new mean, variance, and weights. Iteration: Repeat steps E and M until the parameters of the Gaussian mixture model converge, i.e., the change in parameters between two adjacent iterations is less than a pre-set threshold. Model selection: For each possible number of Gaussian models, after parameter convergence, calculate the evaluation index of the corresponding model (e.g., Bayesian Information Criterion (BIC)). Select the Gaussian model number that optimizes the evaluation index as the optimal number of Gaussian models, and use the parameters corresponding to this model as the optimal solution that characterizes the amplitude of each porosity frequency histogram.

[0102] S250. Based on the optimal solution that can characterize the amplitude of each porosity frequency histogram, obtain multiple target normal distributions of the electro-imaging porosity spectrum that can reconstruct the electro-imaging porosity spectrum of the downhole carbonate formation.

[0103] Among them, multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the porosity spectrum of downhole carbonate rock formations through electrical imaging.

[0104] Based on the determined number of optimal Gaussian models and the corresponding Gaussian mixture model parameters (mean, variance, and weights), multiple normal distributions are constructed. Each normal distribution corresponds to a Gaussian distribution in the Gaussian mixture model, and the parameters of these normal distributions are the optimal solution parameters obtained earlier. These normal distributions are combined to form multiple target normal distributions that can reconstruct the porosity spectrum of downhole carbonate formations using electrical imaging.

[0105] For example, the porosity spectrum of the downhole carbonate formation is reconstructed using a Gaussian mixture model to obtain multiple target normal distributions of the porosity spectrum. The specific process is as follows:

[0106] (1) Define the initial value parameter q j μ j σ j ;

[0107] (2) Calculate the posterior probability that the i-th data belongs to the j-th Gaussian distribution based on the current parameters.

[0108]

[0109] (3) Calculate q based on the posterior probability obtained in step two. j μ jσ j

[0110]

[0111]

[0112] (4) Calculate the error between the current Gaussian mixture model and the original data. If the error is greater than the preset value, jump to (2). If the error meets the requirements, record the standard deviation, expectation and weight of all Gaussian models and exit the iteration.

[0113] S260. Based on the normal distribution of multiple targets in the electrical imaging porosity spectrum, determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0114] See Figure 3 Specifically, the electro-imaging porosity spectrum can be regarded as the sum of multiple normal distribution probability density spectra with different means and variances. The EM algorithm is used for iterative calculation to obtain all normal distribution sequences. The porosity spectrum cutoff value analysis is performed on the sequences, and the imaging porosity spectrum morphology is divided into 6. For each type of porosity spectrum morphology, the corresponding normal distribution boundary point is taken as the cutoff value.

[0115] See Figure 3 Electron imaging porosity spectra typically exhibit single-peak, double-peak, and multi-peak characteristics. Due to the difference in porosity between primary and secondary porosity, the front end of the porosity spectrum is generally considered to represent primary porosity, and the back end to represent secondary porosity. The typical characteristics of the electron imaging porosity spectrum morphology correspond to the contribution proportions of different types of porosity, and can be categorized into the following six cases: Figure 3 When the total porosity of the strata shown in (1) is low and secondary porosity is underdeveloped, it exhibits a single-peak shape at the front end; Figure 3 When the strata shown in (2) have high porosity but poor secondary porosity, they exhibit a single-peak shape in the middle. Figure 3 When only secondary pores are developed in the strata shown in (3), it exhibits a single-peak morphology at the rear end; Figure 3 When the total porosity of the strata shown in (4) is high and both primary and secondary pores are well developed, it exhibits a typical bimodal shape. Figure 3 When the primary and secondary pores of the strata shown in (5) are both well-developed and have fracture and pore features, they exhibit a separated bimodal characteristic. Figure 3 When the various types of pores in the strata shown in (6) are all developed and relatively uniform, they exhibit a three-peak or wide and long multi-peak characteristic.

[0116] As an optional but not limited implementation scheme, based on the normal distribution of multiple targets in the electrical imaging porosity spectrum, the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation is determined, including steps B1-B2:

[0117] Step B1: Sort the normal distributions of each target in the electro-imaging porosity spectrum from smallest to largest according to the expected size of each target normal distribution in the multiple target normal distributions.

[0118] Step B2: When the morphology of the electrical imaging porosity spectrum is multi-peaked, determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation based on the intersection of the highest and second highest peaks in the normal distribution of each target after sorting.

[0119] Multiple target normal distributions are obtained after reconstructing the porosity spectrum of electro-imaging using a Gaussian mixture model. Each target normal distribution has its specific parameters, such as mean (expectation) and variance. These parameters describe the distribution characteristics of porosity within a certain range. Multiple target normal distributions together constitute a complete mathematical description of the electro-imaging porosity spectrum. In a normal distribution, the expectation, or mean, is an important parameter representing the central location of the distribution. In the context of porosity spectrum, the expectation of the target normal distribution represents the average level of porosity for the portion it represents.

[0120] First, for multiple target normal distributions obtained by reconstructing the porosity spectrum of electro-imaging using a Gaussian mixture model, the expected value (mean) of each target normal distribution is extracted. The expected value reflects the location of the porosity concentration represented by the target normal distribution. Then, based on the magnitude of these expected values, all target normal distributions are sorted from smallest to largest.

[0121] Secondly, it is necessary to determine whether the shape of the electro-imaging porosity spectrum is multi-peaked. A multi-peaked shape means that there are multiple obvious peaks in the porosity spectrum, each peak corresponding to a target normal distribution, representing a major type of porosity distribution. In the case of multiple peaks, from the target normal distributions sorted by expected value from smallest to largest, the target normal distributions corresponding to the highest peak (the peak with the largest probability density function value) and the second highest peak (the peak with the probability density function value second only to the highest peak) are determined.

[0122] In multi-peaked electrical imaging porosity spectra, the highest and second-highest peaks typically represent two main pore types. The boundary between the highest and second-highest peaks can reasonably delineate the porosity ranges corresponding to different pore types. Determining this cutoff value provides a quantitative basis for distinguishing porosity from different origins, which helps to accurately identify primary and secondary porosity, thereby gaining a deeper understanding of the pore structure and geological characteristics of downhole carbonate formations and providing important references for reservoir evaluation and oil and gas exploration and development.

[0123] Optionally, when the shape of the electrical imaging porosity spectrum is a single peak, there is theoretically no secondary peak. According to actual needs, such as setting a fixed proportion (e.g., the mean plus or minus a certain multiple of the standard deviation) to determine a similar electrical imaging porosity spectrum cutoff value for downhole carbonate formations.

[0124] Using the aforementioned optional method, based on the normal distribution of multiple targets in the electro-imaging porosity spectrum, and through reasonable sorting and determination of the key peak boundary points under the multi-peak morphology, the cutoff value of the electro-imaging porosity spectrum is obtained. This cutoff value provides a key boundary for subsequent analysis of the origin and properties of downhole carbonate formation porosity, and helps to accurately assess the reservoir pore structure.

[0125] S270. Based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation, identify the primary and secondary porosity of the downhole carbonate formation.

[0126] Optionally, using the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation as the dividing point, the component proportion of the porosity spectrum around the dividing point is calculated. The low porosity portion is accumulated to the primary porosity, and the medium and high porosity portion is accumulated to the secondary porosity, thus obtaining the primary porosity and secondary porosity with varying cutoff values.

[0127] As an optional but not limited implementation, the primary and secondary porosity of the downhole carbonate formation is identified based on the cutoff value of the electrical imaging porosity spectrum, including the following steps C1-C2:

[0128] Step C1: Using the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation as the boundary, divide the electrical imaging porosity spectrum of the downhole carbonate rock formation to obtain a first porosity component and a second porosity component. The first porosity component is the sum of the frequency, probability density, or other relative content indicators corresponding to all porosity data in the electrical imaging porosity spectrum whose porosity values ​​are less than the cutoff value of the electrical imaging porosity spectrum. The second porosity component is the sum of the frequency, probability density, or other relative content indicators corresponding to all porosity data in the electrical imaging porosity spectrum whose porosity values ​​are greater than the cutoff value of the electrical imaging porosity spectrum.

[0129] Step C2: Determine the first porosity component as primary porosity and the second porosity component as secondary porosity.

[0130] The cutoff value of the electrical imaging porosity spectrum is a specific porosity value determined based on the electrical imaging porosity spectrum analysis of downhole carbonate rock formations. As a boundary for dividing the porosity spectrum, the cutoff value typically relies on the study of the distribution characteristics of the porosity spectrum, such as reconstruction using a Gaussian mixture model and analysis of peak characteristics, to distinguish porosity intervals of different origins.

[0131] The first porosity component can refer to the sum of the frequency, probability density, or other indicators that can reflect the relative content of the porosity data in the electro-imaging porosity spectrum whose porosity value is less than the cutoff value of the electro-imaging porosity spectrum. It represents the overall relative content of porosity to the left of the cutoff value and is considered to be related to the original porosity in subsequent analysis.

[0132] The second porosity component can refer to the sum of the frequency, probability density, or other relative content indicators corresponding to the portion of porosity data in the electro-imaging porosity spectrum whose porosity value is greater than the cutoff value of the electro-imaging porosity spectrum. It reflects the overall relative content of porosity to the right of the cutoff value and is usually associated with secondary porosity.

[0133] The entire electro-imaging porosity spectrum is segmented by defining a cutoff value as the boundary. For each porosity data point in the spectrum, the relationship between its porosity value and the cutoff value is determined. All porosity data points with values ​​less than the cutoff value are selected, and their corresponding frequencies, probability densities, or other indicators of relative abundance are summed to obtain the first porosity component. Similarly, the relevant indicators corresponding to porosity data points with values ​​greater than the cutoff value are summed to obtain the second porosity component. For example, if the electro-imaging porosity spectrum data is plotted with porosity value on the x-axis and frequency on the y-axis, and the cutoff value is 10%, then the frequencies corresponding to all data points with porosity less than 10% are summed to obtain the first porosity component, and the frequencies corresponding to all data points with porosity greater than 10% are summed to obtain the second porosity component.

[0134] This method of division allows the electro-imaging porosity spectrum to be clearly divided into two parts based on the relationship between porosity values ​​and cutoff values. These parts are quantified using the first porosity component and the second porosity component, respectively. This provides a direct data basis for subsequent identification of primary and secondary porosity, and clarifies the relative content of different porosity ranges.

[0135] Based on general understanding of geological origins and porosity distribution characteristics, the first porosity component is directly identified as primary porosity, and the second porosity component as secondary porosity. This identification is based on common geological patterns, namely, the smaller porosity component is often related to primary porosity in the early stages of rock formation, while the larger porosity component is usually secondary porosity formed by later geological processes. For example, in many carbonate rock strata, primary porosity, such as intergranular porosity, is relatively small, while secondary porosity, such as dissolution porosity, is relatively large. The two porosity components after division by cutoff values ​​can precisely correspond to the content of these two porosity types, respectively.

[0136] By explicitly identifying the two porosity components as primary porosity and secondary porosity, the transformation from electrical imaging porosity spectrum data to geologically significant porosity types was achieved. This allows geologists to intuitively understand the developmental degree of primary and secondary porosity in downhole carbonate formations, providing crucial data for assessing reservoir performance, seepage characteristics, and geological evolution history, and offering significant guidance for oil and gas exploration and development.

[0137] For example, all first porosity components to the left of the porosity spectrum cutoff value are considered as primary porosity, and all second porosity components to the right of the porosity spectrum cutoff value are considered as secondary porosity. The overall electro-imaging porosity spectrum is a frequency distribution histogram of all porosity within the window length. The total porosity (POR) is calculated using the following formula:

[0138]

[0139] Where n is the number of segments in the frequency distribution histogram; P i The percentage of the i-th data segment in the whole; This represents the porosity value corresponding to the i-th data segment.

[0140] Original porosity (POR) pri The calculation formula is:

[0141]

[0142] Secondary porosity POR sec The calculation formula is:

[0143]

[0144] Where k is the cutoff value segment point corresponding to the porosity spectrum on the frequency distribution histogram; P i The percentage of the i-th data segment in the whole; This represents the porosity value corresponding to the i-th data segment.

[0145] The calculation results of the porosity spectrum cutoff value are as follows: Figure 4 As shown, channel 7 is the porosity spectrum of the electro-imaging model after being characterized by the Gaussian mixture model, and channel 9 is a comparison chart of the secondary porosity calculated by the cutoff value of the porosity spectrum and the secondary porosity calculated by the 3ms cutoff value of the nuclear magnetic resonance logging. The red curve represents the calculation result of the secondary porosity cutoff value of the porosity spectrum, and the black curve represents the interpretation result of the secondary porosity of the nuclear magnetic resonance data.

[0146] By dividing the porosity spectrum using the cutoff value of the electrical imaging porosity spectrum and identifying the two components as primary porosity and secondary porosity, respectively, the genetic types of porosity in downhole carbonate formations can be effectively identified. This identification method provides a quantitative basis for in-depth research on formation pore structure and accurate assessment of reservoir quality, helps optimize oil and gas exploration and development strategies, improves exploration and development efficiency, and plays an important technical supporting role in understanding the geological characteristics and resource potential of underground carbonate formations.

[0147] The technical solution of this invention uses electro-imaging logging data to determine the electro-imaging porosity spectrum, which can intuitively and comprehensively display the porosity distribution of downhole carbonate rock formations. Different porosity values ​​and their corresponding frequencies or probability densities constitute the shape of the porosity spectrum, reflecting the distribution characteristics of pores in different size ranges. By reconstructing the electro-imaging porosity spectrum using a mixture of Gaussian models, multiple target normal distributions with different variances and means are obtained, which can deeply represent the pore distribution law contained in the porosity spectrum. Based on multiple target normal distributions, the cutoff value of the electro-imaging porosity spectrum is determined, providing a key boundary for distinguishing pores of different origins. This cutoff value can reasonably divide the porosity spectrum into two parts with different geological significance. Based on the determined cutoff value, the porosity of downhole carbonate rock formations can be accurately divided into primary porosity and secondary porosity. By using the superposition state of multiple Gaussian models to characterize the porosity spectrum shape, and using the intersection boundary point between the main Gaussian models as the porosity spectrum cutoff value, the accurate calculation of secondary porosity can be achieved efficiently and stably.

[0148] Figure 5 The present invention provides a flow structure schematic of an imaging porosity spectrum cutoff value processing device. This invention is applicable to the rapid and accurate identification of primary and secondary porosity in carbonate rock formations. The imaging porosity spectrum cutoff value processing device can be implemented in software and / or hardware and is generally integrated into any electronic device with network communication capabilities, such as a mobile terminal, PC, or server.

[0149] like Figure 5 As shown, the imaging porosity spectrum cutoff value processing device of this embodiment may include the following:

[0150] The determination module 510 is used to determine the electrical imaging porosity spectrum of the downhole carbonate rock formation based on the electrical imaging logging data of the downhole carbonate rock formation.

[0151] The reconstruction module 520 is used to reconstruct the electrical imaging porosity spectrum of the downhole carbonate rock formation using a mixture Gaussian model to obtain multiple target normal distributions of the electrical imaging porosity spectrum. The multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0152] The detection module 530 is used to determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation based on the normal distribution of multiple targets in the electrical imaging porosity spectrum.

[0153] The identification module 540 is used to determine the primary porosity and secondary porosity of the downhole carbonate rock formation based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0154] Based on the above embodiments, optionally, the electrical imaging porosity spectrum of the downhole carbonate formation is determined based on electrical imaging logging data, including:

[0155] Candidate electrical imaging logging data are obtained by performing logging data preprocessing on electrical imaging logging data of downhole carbonate rock formations. The logging data preprocessing includes at least one of the following types of processing methods: electrode equalization correction, acceleration correction, electrode alignment, azimuth merging, and image calibration.

[0156] The porosity spectrum of the downhole carbonate formation is obtained by calculating the porosity spectrum based on the candidate electrical imaging logging data.

[0157] Based on the above embodiments, optionally, electrical imaging logging data of downhole carbonate formations is preprocessed to obtain candidate electrical imaging logging data, including:

[0158] After performing acceleration correction and equalization preprocessing on the electrical imaging logging data of the downhole carbonate rock formation, the original electrical imaging logging image information is obtained.

[0159] The original electrical imaging logging image information is subjected to static enhancement processing and dynamic enhancement processing to obtain electrical imaging logging processing results. The electrical imaging logging processing results include image information reflecting the changes in rock color throughout the entire well section of the downhole carbonate rock formation and image information reflecting the changes in local rock structure of the downhole carbonate rock formation.

[0160] By using a piecewise linear scale, the electrical imaging logging processing results are correlated with the resistivity curve to obtain candidate electrical imaging logging data after image scaling.

[0161] Based on the above embodiments, optionally, the electrical imaging porosity spectrum of the downhole carbonate formation is reconstructed using a Gaussian mixture model to obtain multiple target normal distributions of the electrical imaging porosity spectrum, including:

[0162] The amplitude of each porosity frequency histogram is obtained from the porosity frequency histogram data of the electrical imaging porosity spectrum of the downhole carbonate rock formation.

[0163] Determine the maximum number of Gaussian models when reconstructing the porosity spectrum of electrical imaging using a Gaussian mixture model;

[0164] Based on the amplitude of each porosity frequency histogram and the maximum number of Gaussian models, the maximum expectation algorithm is used for algorithm iteration. The optimal number of Gaussian models is selected when reconstructing the porosity spectrum of electro-imaging using a Gaussian mixture model. The parameters of the Gaussian mixture model corresponding to the optimal number of Gaussian models are determined as the optimal solution that can characterize the amplitude of each porosity frequency histogram.

[0165] Based on the optimal solution that can characterize the amplitude of each porosity frequency histogram, multiple target normal distributions of the electro-imaging porosity spectrum that can reconstruct the electro-imaging porosity spectrum of the downhole carbonate formation are obtained.

[0166] Based on the above embodiments, optionally, the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation is determined based on multiple target normal distributions of the electrical imaging porosity spectrum, including:

[0167] According to the expected magnitude of each target normal distribution in the multiple target normal distributions of the electro-imaging porosity spectrum, the target normal distributions of the electro-imaging porosity spectrum are sorted from smallest to largest.

[0168] When the morphology of the electrical imaging porosity spectrum is multi-peaked, the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation is determined based on the boundary point between the highest and second highest peaks in the normal distribution of each target after sorting.

[0169] Based on the above embodiments, optionally, the primary and secondary porosity of the downhole carbonate formation are identified according to the cutoff value of the electrical imaging porosity spectrum, including:

[0170] Using the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation as the boundary, the electrical imaging porosity spectrum of the downhole carbonate rock formation is divided into a first porosity component and a second porosity component. The first porosity component is the sum of the frequency, probability density, or other relative content indicators corresponding to all porosity data in the electrical imaging porosity spectrum whose porosity values ​​are less than the cutoff value of the electrical imaging porosity spectrum. The second porosity component is the sum of the frequency, probability density, or other relative content indicators corresponding to all porosity data in the electrical imaging porosity spectrum whose porosity values ​​are greater than the cutoff value of the electrical imaging porosity spectrum.

[0171] The first porosity component is defined as primary porosity, and the second porosity component is defined as secondary porosity.

[0172] The technical solution of this invention uses electro-imaging logging data to determine the electro-imaging porosity spectrum, which can intuitively and comprehensively display the porosity distribution of downhole carbonate rock formations. Different porosity values ​​and their corresponding frequencies or probability densities constitute the shape of the porosity spectrum, reflecting the distribution characteristics of pores in different size ranges. By reconstructing the electro-imaging porosity spectrum using a mixture of Gaussian models, multiple target normal distributions with different variances and means are obtained, which can deeply represent the pore distribution law contained in the porosity spectrum. Based on multiple target normal distributions, the cutoff value of the electro-imaging porosity spectrum is determined, providing a key boundary for distinguishing pores of different origins. This cutoff value can reasonably divide the porosity spectrum into two parts with different geological significance. Based on the determined cutoff value, the porosity of downhole carbonate rock formations can be accurately divided into primary porosity and secondary porosity. By using the superposition state of multiple Gaussian models to characterize the porosity spectrum shape, and using the intersection boundary point between the main Gaussian models as the porosity spectrum cutoff value, the accurate calculation of secondary porosity can be achieved efficiently and stably.

[0173] The imaging porosity spectrum cutoff value processing device provided in this embodiment of the invention can execute the imaging porosity spectrum cutoff value processing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the imaging porosity spectrum cutoff value processing method.

[0174] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of the present invention.

[0175] Figure 6 This is a schematic diagram of an electronic device for implementing an imaging porosity spectrum cutoff value processing method according to an embodiment of the present invention. The following refers to... Figure 6It illustrates an electronic device suitable for implementing embodiments of the present invention (e.g., Figure 6 The diagram below shows the structure of the terminal device (or server) 600. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0176] like Figure 6 As shown, electronic device 600 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of electronic device 600. The processing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. An edit / output (I / O) interface 605 is also connected to bus 604.

[0177] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0178] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the imaging porosity spectral cutoff value processing method shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the imaging porosity spectral cutoff value processing method of the embodiments of the present invention.

[0179] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0180] The electronic device provided in this embodiment of the invention and the imaging porosity spectrum cutoff value processing method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0181] The present invention provides a computer program product, which includes a computer program that, when executed by a processor, implements the imaging porosity spectrum cutoff value processing method provided in the embodiments of the present invention.

[0182] This invention provides a computer storage medium storing a computer program that, when executed by a processor, implements the imaging porosity spectrum cutoff value processing method provided in the above embodiments.

[0183] It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0184] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0185] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0186] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: determine the electrical imaging porosity spectrum of the downhole carbonate formation based on electrical imaging logging data; reconstruct the electrical imaging porosity spectrum of the downhole carbonate formation using a Gaussian mixture model to obtain multiple target normal distributions of the electrical imaging porosity spectrum, wherein the multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electrical imaging porosity spectrum of the downhole carbonate formation; determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation based on the multiple target normal distributions of the electrical imaging porosity spectrum; and identify the primary porosity and secondary porosity of the downhole carbonate formation based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation.

[0187] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0188] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0189] The units described in the embodiments of the present invention can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0190] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0191] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0192] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0193] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0194] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for processing the cutoff value of an imaging porosity spectrum, characterized in that, The method includes: Based on electrical imaging logging data of the downhole carbonate rock formation, the electrical imaging porosity spectrum of the downhole carbonate rock formation was determined. The electro-imaging porosity spectrum of the downhole carbonate rock formation is reconstructed using a mixture Gaussian model to obtain multiple target normal distributions of the electro-imaging porosity spectrum. These multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electro-imaging porosity spectrum of the downhole carbonate rock formation. Based on the normal distribution of multiple targets in the electrical imaging porosity spectrum, the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation is determined. Based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation, the primary and secondary porosity of the downhole carbonate rock formation are identified.

2. The method according to claim 1, characterized in that, Based on electrical imaging logging data of the downhole carbonate formation, the electrical imaging porosity spectrum of the downhole carbonate formation is determined, including: Candidate electrical imaging logging data are obtained by performing logging data preprocessing on electrical imaging logging data of downhole carbonate rock formations. The logging data preprocessing includes at least one of the following types of processing methods: electrode equalization correction, acceleration correction, electrode alignment, azimuth merging, and image calibration. The porosity spectrum of the downhole carbonate formation is obtained by calculating the porosity spectrum based on the candidate electrical imaging logging data.

3. The method according to claim 2, characterized in that, Candidate electrical imaging logging data are obtained by preprocessing electrical imaging logging data from downhole carbonate formations, including: After performing acceleration correction and equalization preprocessing on the electrical imaging logging data of the downhole carbonate rock formation, the original electrical imaging logging image information is obtained. The original electrical imaging logging image information is subjected to static enhancement processing and dynamic enhancement processing to obtain electrical imaging logging processing results. The electrical imaging logging processing results include image information reflecting the changes in rock color throughout the entire well section of the downhole carbonate rock formation and image information reflecting the changes in local rock structure of the downhole carbonate rock formation. By using a piecewise linear scale, the electrical imaging logging processing results are correlated with the resistivity curve to obtain candidate electrical imaging logging data after image scaling.

4. The method according to claim 1, characterized in that, The electrical imaging porosity spectrum of the downhole carbonate formation was reconstructed using a Gaussian mixture model to obtain multiple target normal distributions of the electrical imaging porosity spectrum, including: The amplitude of each porosity frequency histogram is obtained from the porosity frequency histogram data of the electrical imaging porosity spectrum of the downhole carbonate rock formation. Determine the maximum number of Gaussian models when reconstructing the porosity spectrum of electrical imaging using a Gaussian mixture model; Based on the amplitude of each porosity frequency histogram and the maximum number of Gaussian models, the maximum expectation algorithm is used for algorithm iteration. The optimal number of Gaussian models is selected when reconstructing the porosity spectrum of electro-imaging using a Gaussian mixture model. The parameters of the Gaussian mixture model corresponding to the optimal number of Gaussian models are determined as the optimal solution that can characterize the amplitude of each porosity frequency histogram. Based on the optimal solution that can characterize the amplitude of each porosity frequency histogram, multiple target normal distributions of the electro-imaging porosity spectrum that can reconstruct the electro-imaging porosity spectrum of the downhole carbonate formation are obtained.

5. The method according to claim 1, characterized in that, Based on the normal distribution of multiple targets in the electrical imaging porosity spectrum, the cutoff value of the electrical imaging porosity spectrum for downhole carbonate formations is determined, including: According to the expected magnitude of each target normal distribution in the multiple target normal distributions of the electro-imaging porosity spectrum, the target normal distributions of the electro-imaging porosity spectrum are sorted from smallest to largest. When the morphology of the electrical imaging porosity spectrum is multi-peaked, the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation is determined based on the boundary point between the highest and second highest peaks in the normal distribution of each target after sorting.

6. The method according to claim 1, characterized in that, Based on the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate formation, the primary and secondary porosity of the downhole carbonate formation are identified, including: Using the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation as the boundary, the electrical imaging porosity spectrum of the downhole carbonate rock formation is divided into a first porosity component and a second porosity component. The first porosity component is the sum of the frequency, probability density, or other relative content indicators corresponding to all porosity data in the electrical imaging porosity spectrum whose porosity values ​​are less than the cutoff value of the electrical imaging porosity spectrum. The second porosity component is the sum of the frequency, probability density, or other relative content indicators corresponding to all porosity data in the electrical imaging porosity spectrum whose porosity values ​​are greater than the cutoff value of the electrical imaging porosity spectrum. The first porosity component is defined as primary porosity, and the second porosity component is defined as secondary porosity.

7. An imaging porosity spectrum cutoff value processing device, characterized in that, The device includes: The determination module is used to determine the electrical imaging porosity spectrum of the downhole carbonate rock formation based on electrical imaging logging data of the downhole carbonate rock formation. The reconstruction module is used to reconstruct the electrical imaging porosity spectrum of the downhole carbonate rock formation using a mixture Gaussian model to obtain multiple target normal distributions of the electrical imaging porosity spectrum. The multiple target normal distributions include multiple normal distributions with different variances and means used to characterize the electrical imaging porosity spectrum of the downhole carbonate rock formation. The detection module is used to determine the cutoff value of the electrical imaging porosity spectrum of the downhole carbonate rock formation based on the normal distribution of multiple targets in the electrical imaging porosity spectrum. The identification module is used to determine the primary and secondary porosity of the downhole carbonate rock formation based on the cutoff value of the electrical imaging porosity spectrum.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the imaging porosity spectral cutoff value processing method according to any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the imaging porosity spectrum cutoff value processing method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the imaging porosity spectrum cutoff value processing method according to any one of claims 1-6.

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