Natural gas hydrate saturation prediction method, device, equipment, medium and product

Abstract

The present application discloses a method, device, equipment, medium and product for predicting natural gas hydrate saturation, which relates to the field of exploration. The method comprises: analyzing and processing well logging data to obtain imaging well logging images and natural gas hydrate reservoir conditions; performing natural gas hydrate identification processing on the imaging well logging image using a preset algorithm to obtain a natural gas hydrate image; calculating the hydrate surface ratio based on the natural gas hydrate image; performing experimental tests based on the natural gas hydrate reservoir conditions, calculating a second natural gas hydrate saturation based on the experimental test data, and determining the relationship between the second natural gas hydrate saturation and resistivity; establishing a hydrate saturation prediction model based on the relationship between the first natural gas hydrate saturation, the hydrate surface ratio and the second natural gas hydrate saturation and resistivity in the well logging data; and determining the natural gas hydrate saturation corresponding to the well to be predicted using the hydrate saturation prediction model, thereby improving the prediction accuracy.

Description

Technical Field

[0001] The present application relates to the field of exploration, and specifically to a method, device, equipment, medium and product for predicting the saturation of natural gas hydrate. Background Art

[0002] Natural gas hydrate is a cage-shaped compound formed by water and natural gas under low temperature and high pressure conditions. It is widely distributed in marine sediments and terrestrial permafrost, and is easy to decompose. It can only remain stable under certain temperature and pressure conditions. Saturation estimation is an important issue that needs to be solved in natural gas hydrate exploration, and is also one of the main parameters for reserve estimation. It plays a positive role in promoting the precise positioning and quantification of natural gas hydrates and accelerating actual production and development.

[0003] Geophysical logging is an important means of quantitative evaluation of natural gas hydrate reservoirs. Geophysical logging technology can be used to detect the physical properties of the reservoir. Through logging interpretation technology, parameters such as rock porosity, hydrate saturation, and reservoir thickness can be further obtained, providing necessary information for the quantitative evaluation of natural gas hydrate reservoirs. Studies have shown that hydrate-containing sedimentary layers have higher resistivity than ordinary surrounding rock layers, and resistivity logging has become an effective method for evaluating hydrate reservoir parameters. However, due to the influence of various factors such as the formation and the type and distribution of porous fluids, the direct use of electrical data will lead to inaccurate prediction of reservoir parameters. Therefore, a natural gas hydrate saturation prediction method combined with actual geological conditions is urgently needed. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a natural gas hydrate saturation prediction method, device, equipment, medium and product that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of an embodiment of the present application, a method for predicting natural gas hydrate saturation is provided, comprising:

[0006] The logging data are analyzed and processed to obtain imaging logging images and natural gas hydrate reservoir conditions, wherein the logging data include: first natural gas hydrate saturation;

[0007] Using a preset algorithm to perform natural gas hydrate identification processing on the imaging well logging image to obtain a corresponding natural gas hydrate image, wherein the natural gas hydrate image is a binary image;

[0008] Calculate the hydrate surface ratio based on the gas hydrate image;

[0009] According to the natural gas hydrate reservoir conditions, a natural gas hydrate generation experimental test is performed, the second natural gas hydrate saturation is calculated according to the experimental test data, and the relationship between the second natural gas hydrate saturation and the resistivity is determined;

[0010] A hydrate saturation prediction model is established based on the relationship between the first gas hydrate saturation, the hydrate surface porosity, and the second gas hydrate saturation and resistivity;

[0011] The hydrate surface porosity and resistivity corresponding to the well to be predicted are determined, and the hydrate surface porosity and resistivity are input into the hydrate saturation prediction model to predict the hydrate saturation, thereby obtaining the natural gas hydrate saturation corresponding to the well to be predicted.

[0012] Furthermore, according to the natural gas hydrate image, calculating the hydrate surface ratio further includes:

[0013] Performing region marking processing on the natural gas hydrate image to identify and mark each natural gas hydrate region;

[0014] Determine the regional contour corresponding to the marked natural gas hydrate region, and generate a contour chain code sequence corresponding to the regional contour;

[0015] The hydrate surface ratio is calculated based on the contour chain code sequence.

[0016] Furthermore, determining the region contour corresponding to the marked natural gas hydrate region further includes:

[0017] For each foreground pixel in any marked natural gas hydrate region, determine whether there is a background pixel in the eight neighborhoods of the foreground pixel;

[0018] If yes, the foreground pixel is determined as a contour pixel of the marked natural gas hydrate region, and the contour pixel is subjected to target marking processing;

[0019] A contour image is generated according to the contour pixels processed by the target mark, wherein the contour part in the contour image is the foreground image and the rest is the background image.

[0020] Furthermore, calculating the hydrate surface ratio according to the profile chain code sequence further includes:

[0021] Traverse the contour chain code sequence corresponding to any marked natural gas hydrate region and calculate the total length of the contour corresponding to the regional contour;

[0022] Calculate the area of ​​the marked natural gas hydrate region according to the total length of the contour and the coordinate information of each pixel point in the regional contour;

[0023] The hydrate surface ratio is calculated based on the number of natural gas hydrate regions within a unit window length, the calculated area of ​​each natural gas hydrate region, and the window length area.

[0024] Furthermore, performing region marking processing on the natural gas hydrate image, identifying and marking each natural gas hydrate region further includes:

[0025] Based on the equivalent pair numbering algorithm, each natural gas hydrate area in the natural gas hydrate image is identified and numbered.

[0026] Furthermore, using a preset algorithm to perform natural gas hydrate identification processing on the imaging well logging image to obtain a corresponding natural gas hydrate image further includes:

[0027] Convert the imaging log image into the original grayscale image;

[0028] Perform morphological opening and closing filtering on the original grayscale image, and convert the filtered grayscale image into an initial binary image;

[0029] Performing opening and closing operations on the initial binary image based on a structure element of a preset size and a target structure element;

[0030] The image processed by the opening and closing operations is segmented using a preset segmentation threshold to obtain the corresponding natural gas hydrate image.

[0031] According to another aspect of an embodiment of the present application, a natural gas hydrate saturation prediction device is provided, the device comprising:

[0032] The processing module is suitable for analyzing and processing the well logging data to obtain the imaging well logging image and the natural gas hydrate reservoir conditions, wherein the well logging data includes: the first natural gas hydrate saturation;

[0033] An identification module is adapted to perform natural gas hydrate identification processing on the imaging well logging image using a preset algorithm to obtain a corresponding natural gas hydrate image, wherein the natural gas hydrate image is a binary image;

[0034] A calculation module, adapted to calculate the hydrate surface ratio based on the natural gas hydrate image;

[0035] The experimental test module is suitable for performing an experimental test on the formation of natural gas hydrate according to the conditions of the natural gas hydrate reservoir, calculating the second natural gas hydrate saturation according to the experimental test data, and determining the relationship between the second natural gas hydrate saturation and the resistivity;

[0036] Establishing a module, suitable for establishing a hydrate saturation prediction model according to the first natural gas hydrate saturation, the hydrate surface porosity and the relationship between the second natural gas hydrate saturation and resistivity;

[0037] The prediction module is suitable for determining the hydrate surface area fraction and resistivity corresponding to the well to be predicted, inputting the hydrate surface area fraction and resistivity into the hydrate saturation prediction model to perform hydrate saturation prediction, and obtaining the natural gas hydrate saturation corresponding to the well to be predicted.

[0038] According to another aspect of the embodiment of the present application, there is provided a computing device, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0039] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned natural gas hydrate saturation prediction method.

[0040] According to another aspect of the embodiments of the present application, a computer storage medium is provided, in which at least one executable instruction is stored, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned natural gas hydrate saturation prediction method.

[0041] According to another aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned natural gas hydrate saturation prediction method.

[0042] According to the technical solution provided in the present application, the influence of resistivity and the occurrence state of natural gas hydrates is comprehensively considered, and a more comprehensive hydrate saturation prediction model is established, so that the natural gas hydrate saturation predicted by the model is more accurate, which has certain guiding significance for the subsequent exploration and development of natural gas hydrates. The watershed algorithm can effectively suppress the interference of dark spot noise, accurately separate the target hydrate from the complex background, and the extracted hydrate distribution outline is clear and has good integrity.

[0043] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0045] Figure 1 A schematic diagram of a process for predicting natural gas hydrate saturation according to an embodiment of the present application is shown;

[0046] Figure 2 A schematic diagram showing the result of natural gas hydrate image identification using watershed algorithm;

[0047] Figure 3 This is a schematic diagram of the Freeman chain code;

[0048] Figure 4 is the calculation result of natural gas hydrate area;

[0049] Figure 5 This is a schematic diagram of the resistivity test results of the hydrate experiment;

[0050] Figure 6 is the correlation diagram between the resistivity, surface area ratio and saturation of natural gas hydrate;

[0051] Figure 7 This is a comparison chart between the hydrate saturation prediction results and the core data;

[0052] Figure 8 A structural block diagram of a natural gas hydrate saturation prediction device according to an embodiment of the present application is shown;

[0053] Fig. 9 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0054] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0055] Figure 1 A flow chart of a method for predicting natural gas hydrate saturation according to an embodiment of the present application is shown. Figure 1 As shown, the method comprises the following steps:

[0056] Step S101, analyzing and processing the well logging data to obtain an imaging well logging image and natural gas hydrate reservoir conditions, wherein the well logging data includes: a first natural gas hydrate saturation.

[0057] Well logging data includes well logging data collected during the actual well logging process and some result data based on well logging data analysis. Well logging data can be preprocessed first, such as data cleaning and data correction, to ensure data quality and consistency. Then, well logging data can be converted into imaging well logging images using preset algorithms. For example, direct mapping method, interpolation method, imaging logging technology, multi-parameter fusion and three-dimensional reconstruction algorithms can be used for conversion. By sorting out well logging data, natural gas hydrate reservoir conditions can be obtained, such as temperature range, pressure range, etc. In addition, well logging data also includes the first natural gas hydrate saturation, which is the hydrate saturation measured by the on-site core and is the original natural gas hydrate saturation.

[0058] Step S102, using a preset algorithm to perform natural gas hydrate identification processing on the imaging well logging image to obtain a corresponding natural gas hydrate image, wherein the natural gas hydrate image is a binary image.

[0059] For example, the watershed algorithm can be used to segment the imaging logging image. Through segmentation, the natural gas hydrate segment can be effectively identified from the complex background to obtain the natural gas hydrate image. The natural gas hydrate image is a binary image, and the natural gas hydrate in the image is the foreground image (the corresponding pixel value is 1), and the other parts are the background image (the corresponding pixel value is 0). Among them, the watershed algorithm method includes a series of morphological operations, for example, including the optimal opening and closing operation area selection, filter selection, smoothing area selection and optimal extended minimum transformation area selection, and finally a better natural gas hydrate image is obtained.

[0060] Specifically, the imaging logging image can be first converted into an original grayscale image, and then the original grayscale image can be subjected to morphological opening and closing filtering, and the grayscale image after filtering can be converted into an initial binary image, and then the initial binary image can be subjected to opening and closing operations based on a structure element of a preset size and a target structure element; finally, the image after the opening and closing operations can be segmented using a preset segmentation threshold to obtain the corresponding natural gas hydrate image. The above processing can effectively avoid over-segmentation.

[0061] More specifically, the electrical imaging logging image is converted into an original grayscale image, and the original grayscale image is filtered using a log operator filter. This process will produce a smoother image; then the filtered grayscale image is converted into an initial binary image through a threshold algorithm; then based on the matlab platform, a 3×3 size structural element and an optimal structural element (disk, line, rectangle, diamond) are selected to open and close the initial binary image, and the foreground target is determined based on the Euclidean distance transformation algorithm to highlight the shape characteristics of the hydrate; finally, the morphologically based extended minimum transformation is introduced to reduce the over-segmented area by setting a threshold, and the hydrate target area is marked. After a large number of experimental tests, by comparing and analyzing the watershed transformation image with the original electrical imaging logging image, it is determined that when the preset segmentation threshold is 5, the hydrate can be better identified.

[0062] Figure 2 The figure shows the result of the natural gas hydrate image identified by the watershed algorithm. It can be seen from the figure that this method can effectively merge small areas, ensure the integrity of the hydrate morphology, and the segmented hydrate has good connectivity and clear boundaries, which is more conducive to hydrate identification and subsequent parameter calculation.

[0063] Step S103, calculating the hydrate surface ratio according to the natural gas hydrate image.

[0064] The gas hydrate image is an image with gas hydrate as the target. Therefore, the area of ​​each gas hydrate region can be calculated based on the gas hydrate image, so the hydrate surface ratio can be calculated by combining the area per unit window length and the number of gas hydrate regions within the unit window length.

[0065] For example, the following method can be used to calculate the hydrate surface ratio: perform region labeling processing on the natural gas hydrate image to identify and label each natural gas hydrate region; determine the region contour corresponding to the labeled natural gas hydrate region, and calculate the hydrate surface ratio based on the region contour, for example, generate a contour chain code sequence corresponding to the region contour; and calculate the hydrate surface ratio based on the contour chain code sequence.

[0066] There are many methods for region labeling. For example, the gas hydrate regions in the gas hydrate image can be identified and numbered based on an equivalent pair numbering algorithm. The gas hydrate regions can be identified and numbered based on morphological operations (such as corrosion, expansion, opening operation, and closing operation). The gas hydrate regions can be identified and marked using a deep learning model. Other methods can also be used to identify and mark the gas hydrate regions. Through the region labeling process, it is possible to identify which regions in the gas hydrate image are gas hydrate regions and number them, so that the total number of gas hydrate regions in the gas hydrate image can be counted.

[0067] After identifying and determining the natural gas hydrate area, it is necessary to determine the regional contour corresponding to the marked natural gas hydrate area. The regional contour can be determined by the following methods: using edge detection algorithms, contour tracking, regional growing and other algorithms to determine the regional contour corresponding to the marked natural gas hydrate area.

[0068] In an optional implementation, the regional contour corresponding to the marked natural gas hydrate region may be determined by generating a contour image. Specifically, for each foreground pixel in any marked natural gas hydrate region, it is determined whether there is a background pixel in the eight neighborhoods of the foreground pixel.

[0069] If so, it means that the foreground image belongs to the contour of the natural gas hydrate area, the foreground pixels are determined as the contour pixels of the marked natural gas hydrate area, and the contour pixels are subjected to target labeling processing, a special labeling; a contour image is generated according to the contour pixels after the target labeling processing, and the generated contour image is a binary image containing the contours of all natural gas hydrate areas, and the binary image only retains the contour of the natural gas hydrate area, wherein the contour part in the contour image is the foreground image, for example, the pixel value is represented by 1 (white), and the rest is the background image (including the original background image and the internal pixels of the natural gas hydrate area), for example, the pixel value is represented by 0 (black).

[0070] Specifically, the labeled gas hydrate image is scanned a second time. The purpose of the second scan is to extract the contour. Each pixel of the image is scanned again from left to right and from top to bottom. For each foreground pixel, check whether there is a background pixel in its eight-neighborhood. If there is a background pixel, the foreground pixel belongs to the contour of the gas hydrate area, and the detected contour pixel is specially marked. For example, color marking is performed, or the contour pixels are marked with specific labels, etc., and the final contour image is generated based on the marked contour pixels, and a binary image containing the contours of all gas hydrate areas is output. This image can be used for further analysis or morphological processing. The final output binary image only retains the contour of the target object, and the rest of the image (including the background and internal pixels) is 0 (black), and the contour part is 1 (white).

[0071] Then, the hydrate surface ratio is calculated according to the regional contour. For example, a contour chain code sequence corresponding to the regional contour can be generated. Specifically, the pixel boundary of the regional contour can be encoded using the Freeman chain code, and a set of predefined direction values ​​can be used to describe the connection relationship between adjacent pixels of the contour. The direction and shape of the boundary of the object in the discrete image can be accurately described. Specifically, a contour pixel point is selected as the starting point from the regional contour of each connected domain, and the coordinates of this pixel point are recorded at the same time. The pixels adjacent to it are respectively assigned direction values ​​0 to 7 in a counterclockwise direction at 8 different positions (such as Figure 3 As shown). Starting from the starting point of the contour, traverse along the contour in a clockwise or counterclockwise direction, record the relative direction between each two adjacent pixels in turn, and generate a Freeman chain code sequence. In each traversal process, the corresponding direction code is found according to the position difference between the two adjacent pixels before and after, forming a chain code sequence. For example, if the movement between the current pixel and the next pixel is to the right, it is recorded as "0"; if it is to the lower left, it is recorded as "5". It should be noted that other chain code methods can also be used to encode the pixel boundaries of the regional contour, which is not specifically limited here.

[0072] After the contour chain code sequence is generated, the hydrate surface ratio can be calculated according to the contour chain code sequence. Specifically, the following method can be used: traverse the contour chain code sequence corresponding to any marked natural gas hydrate area, and calculate the total contour length corresponding to the area contour; calculate the area of ​​the marked natural gas hydrate area according to the total contour length and the coordinate information of each pixel point in the area contour; calculate the hydrate surface ratio according to the number of natural gas hydrate areas within the unit window length, the calculated area of ​​each natural gas hydrate area and the window length area.

[0073] Specifically, the movement distance corresponding to each direction in the Freeman chain code is different. Directions "0", "2", "4", and "6" correspond to horizontal or vertical movement, and the movement distance is 1 pixel unit. Directions "1", "3", "5", and "7" correspond to diagonal directions, and the movement distance is Therefore, by traversing the entire contour chain code sequence, the total length (perimeter) L of the area contour is calculated using the following formula:

[0074]

[0075] Among them, N SL N is the number of chain codes in the horizontal or vertical direction; DL is the number of chain codes in the diagonal direction.

[0076] After calculating the total length of the contour of the gas hydrate area, for any marked gas hydrate area, all the chain code elements in the corresponding contour chain code sequence are traversed, and the area of ​​the gas hydrate area can be calculated based on the direction coding of the Freeman chain code. For a closed polygon composed of pixel boundaries, the area of ​​the gas hydrate area can be calculated using the following formula:

[0077]

[0078] In the formula, is the coordinate of the pixel point in the area contour, L is the total length of the area contour, and each marked natural gas hydrate area is traversed to calculate its area, which is recorded as A1, A2, A3, ... A i .

[0079] After calculating the area of ​​the natural gas hydrate region, the surface ratio of the natural gas hydrate can be calculated, that is, the area of ​​the hydrate region within the unit window length. The result is as follows: Figure 4 Specifically, the face rate C can be calculated using the following formula:

[0080]

[0081] Where: A i is the area of ​​the i-th natural gas hydrate region within the unit window length, m 2 ; A w is the window length area, m 2 ; N is the number of natural gas hydrate regions within the window length. w Window length area refers to the area of ​​the electrical imaging logging image within the unit window length after image segmentation processing.

[0082] It should be noted that in addition to using the chain code method to encode the regional contour to calculate the area of ​​the natural gas hydrate region and then calculate the hydrate surface ratio, the integration method, pixel counting method, rasterization method and other methods can also be used to calculate the area of ​​the natural gas hydrate region and then calculate the hydrate surface ratio.

[0083] Step S104, performing a natural gas hydrate formation experimental test according to the natural gas hydrate reservoir conditions, calculating a second natural gas hydrate saturation according to the experimental test data, and determining a relationship between the second natural gas hydrate saturation and resistivity.

[0084] Since it is difficult to obtain core samples containing natural gas hydrates in nature and they are unstable at room temperature and pressure, the physical properties of natural gas hydrate sediments are mainly studied by conducting simulation experiments on natural gas hydrate sediment samples synthesized indoors. Methane hydrates are often used in laboratory simulation experiments, but since methane hydrates are prone to preferential generation near the gas injection port during the generation process, and methane gas is difficult to enter other pores after the generated hydrate blocks the throat, the upper limit of the hydrate saturation in the methane hydrate generation experiment is only about 40%. The physical properties of tetrahydrofuran hydrate are similar to those of methane hydrate. Compared with the conditions for the generation of natural gas hydrates from methane, the generation conditions of tetrahydrofuran hydrate are milder and easier to observe, and the hydrate saturation is easy to quantitatively control. Therefore, it is often used to replace natural gas hydrates for rock physics experiments. Therefore, two preparation methods for hydrates are designed: uniform distribution and non-uniform distribution. The non-uniform distribution is prepared by preparing methane hydrates, and the uniform distribution is prepared by preparing tetrahydrofuran mixed solution.

[0085] According to the conditions of the gas hydrate reservoir, mainly the temperature and pressure conditions, the experimental test temperature range is between 0℃~20℃, and the pressure range is between 0Mpa~10Mpa. Natural sea sand with a particle size of 0.18~0.25mm was added with 10% montmorillonite to simulate sediments and carry out hydrate formation experimental tests. The corresponding second gas hydrate saturation was calculated according to the measured temperature and pressure, and the relationship between the second gas hydrate saturation and resistivity was determined, such as drawing the second gas hydrate saturation and resistivity change curve (such as Figure 5 as shown).

[0086] Step S105, establishing a hydrate saturation prediction model according to the first natural gas hydrate saturation, the hydrate surface porosity, and the relationship between the second natural gas hydrate saturation and resistivity;

[0087] Through the above-mentioned natural gas hydrate formation experimental test results, the relationship between the second natural gas hydrate saturation and resistivity under different conditions can be established, and then the saturation can be calculated by resistivity. However, there is a certain error in predicting the natural gas hydrate saturation only by resistivity, so the hydrate surface porosity obtained in step S103 is combined to establish a new hydrate saturation prediction model.

[0088] Through research, it was found that the resistivity of some wells was abnormally high, and they were divided into two modes: low resistance-low saturation and high resistance-high saturation. Since the maximum saturation under the methane hydrate experimental conditions can only reach 40%, the methane hydrate experimental results were combined with the low resistance-low saturation mode; while under the tetrahydrofuran hydrate experimental conditions, the maximum saturation can reach 100%, so the tetrahydrofuran hydrate experimental results were combined with the high resistance-high saturation mode.

[0089] According to the experimental results of methane hydrate and tetrahydrofuran hydrate ( Figure 5 ), the relationship between natural gas hydrate saturation and resistivity is:

[0090] The first resistance range (low resistance (≤5 )):

[0091]

[0092] Second resistance range (high resistance (>5 )):

[0093]

[0094] Among them, S h is the experimentally measured second natural gas hydrate saturation, %; RT is the corresponding resistivity, .

[0095] However, the hydrate saturation value measured in the laboratory cannot reflect the actual situation of the hydrate reservoir, and the conventional resistivity logging technology is also affected by many factors such as the formation and the type and distribution of pore fluids. If a single resistivity data is directly used, the prediction of natural gas hydrate saturation will be inaccurate. Therefore, the combined resistivity data and imaging logging data can make the predicted saturation parameters mutually constrained, which is more in line with the actual formation conditions.

[0096] Through experiments, the second gas hydrate saturation can be calculated based on resistivity. Combined with the first gas hydrate saturation measured on site and the surface porosity calculated from the imaging logging image at the same depth point, a linear relationship between the three can be obtained ( Figure 6 ). The actual hydrate saturation prediction model is obtained through linear regression analysis:

[0097] In the first resistance range, the hydrate saturation prediction model is:

[0098]

[0099] In the second resistance range, the hydrate saturation prediction model is:

[0100]

[0101] In the formula, S H is the natural gas hydrate saturation, %; C is the hydrate surface area, decimal.

[0102] According to the above analysis results, the saturation is calculated using the GVR imaging while drilling logging images and resistivity logging data in the study area. The prediction results are as follows: Figure 7 As shown in the figure, the discrete points (circles) represent the saturation values ​​estimated based on the anomaly method of chloride ion concentration in the pore water of the on-site core. It can be seen from the figure that the hydrate saturation prediction model established by the above method has high accuracy and is consistent with the actual data.

[0103] Step S106, determining the hydrate surface porosity and resistivity corresponding to the well to be predicted, inputting the hydrate surface porosity and resistivity into the hydrate saturation prediction model to perform hydrate saturation prediction, and obtaining the natural gas hydrate saturation corresponding to the well to be predicted.

[0104] When the natural gas hydrate saturation of a well needs to be predicted, the hydrate surface porosity and resistivity corresponding to the well to be predicted can be determined first, and then the hydrate surface porosity and resistivity are input into the hydrate saturation prediction model for hydrate saturation prediction. Specifically, it is determined whether the resistance is high resistance or low resistance first, and then the hydrate surface porosity and resistivity are substituted into the corresponding model to calculate the natural gas hydrate saturation corresponding to the well to be predicted. Among them, the resistivity can be obtained from the well logging data of the well to be predicted, and the hydrate surface porosity can be determined using the above method, which will not be repeated here.

[0105] The natural gas hydrate saturation prediction method provided in this application is a natural gas hydrate saturation prediction method based on image segmentation and rock physics experiments, which has the following characteristics, mainly manifested as follows:

[0106] (1) The watershed algorithm can effectively suppress the interference of dark spot noise and accurately separate natural gas hydrates from the complex background. The extracted hydrate distribution has clear outlines and good integrity.

[0107] (2) The effects of resistivity and hydrate occurrence state were comprehensively considered, and a more comprehensive hydrate saturation prediction model was established. The hydrate saturation predicted by this model is highly accurate and has certain guiding significance for hydrate exploration and development research.

[0108] (3) This method solves the time-consuming and labor-intensive shortcomings of electrical imaging image recognition. It can more accurately identify hydrate reservoirs and determine hydrate saturation more precisely. In addition, the method combined with rock physics experiments has higher accuracy and has room for promotion and application.

[0109] According to the natural gas hydrate saturation prediction method provided in the embodiment of the present application, the influence of resistivity and the occurrence state of natural gas hydrates is comprehensively considered, and a more comprehensive hydrate saturation prediction model is established, thereby making the natural gas hydrate saturation predicted by the model more accurate, which has certain guiding significance for the subsequent exploration and development of natural gas hydrates. The watershed algorithm can effectively suppress the interference of dark spot noise, accurately separate the target hydrate from the complex background, and the extracted hydrate distribution outline is clear and has good integrity.

[0110] Figure 8 FIG. 4 shows a structural block diagram of a natural gas hydrate saturation prediction device according to an embodiment of the present application. Figure 8 As shown, the device comprises:

[0111] The processing module 801 is adapted to analyze and process the well logging data to obtain an imaging well logging image and natural gas hydrate reservoir conditions, wherein the well logging data includes: first natural gas hydrate saturation;

[0112] The identification module 802 is adapted to perform natural gas hydrate identification processing on the imaging well logging image using a preset algorithm to obtain a corresponding natural gas hydrate image, wherein the natural gas hydrate image is a binary image;

[0113] A calculation module 803, adapted to calculate the hydrate surface ratio according to the natural gas hydrate image;

[0114] The experimental test module 804 is adapted to perform an experimental test on the formation of natural gas hydrates according to the conditions of the natural gas hydrate reservoir, calculate the second natural gas hydrate saturation according to the experimental test data, and determine the relationship between the second natural gas hydrate saturation and the resistivity;

[0115] Establishing module 805, adapted to establish a hydrate saturation prediction model according to the first natural gas hydrate saturation, the hydrate surface porosity and the relationship between the second natural gas hydrate saturation and resistivity;

[0116] The prediction module 806 is adapted to determine the hydrate surface porosity and resistivity corresponding to the well to be predicted, input the hydrate surface porosity and resistivity into the hydrate saturation prediction model to perform hydrate saturation prediction, and obtain the natural gas hydrate saturation corresponding to the well to be predicted.

[0117] Optionally, the calculation module is further adapted to: perform region marking processing on the natural gas hydrate image to identify and mark each natural gas hydrate region;

[0118] Determine the regional contour corresponding to the marked natural gas hydrate region, and generate a contour chain code sequence corresponding to the regional contour;

[0119] The hydrate surface ratio is calculated based on the contour chain code sequence.

[0120] Optionally, the calculation module is further adapted to: for each foreground pixel in any marked natural gas hydrate region, determine whether there is a background pixel in an eight-neighborhood of the foreground pixel;

[0121] If yes, the foreground pixel is determined as a contour pixel of the marked natural gas hydrate region, and the contour pixel is subjected to target marking processing;

[0122] A contour image is generated according to the contour pixels processed by the target mark, wherein the contour part in the contour image is the foreground image and the rest is the background image.

[0123] Optionally, the calculation module is further adapted to: traverse the contour chain code sequence corresponding to any marked natural gas hydrate region, and calculate the total contour length corresponding to the region contour;

[0124] Calculate the area of ​​the marked natural gas hydrate region according to the total length of the contour and the coordinate information of each pixel point in the regional contour;

[0125] The hydrate surface ratio is calculated based on the number of natural gas hydrate regions within a unit window length, the calculated area of ​​each natural gas hydrate region, and the window length area.

[0126] Optionally, the calculation module is further adapted to: identify and number each natural gas hydrate region in the natural gas hydrate image based on an equivalent pair numbering algorithm.

[0127] Optionally, the recognition module is further adapted to: convert the imaging logging image into an original grayscale image;

[0128] Perform morphological opening and closing filtering on the original grayscale image, and convert the filtered grayscale image into an initial binary image;

[0129] Performing opening and closing operations on the initial binary image based on a structure element of a preset size and a target structure element;

[0130] The image processed by the opening and closing operations is segmented using a preset segmentation threshold to obtain the corresponding natural gas hydrate image.

[0131] The description of each module above refers to the corresponding description in the method embodiment and will not be repeated here.

[0132] According to the natural gas hydrate saturation prediction device provided in the embodiment of the present application, the influence of resistivity and the occurrence state of natural gas hydrates is comprehensively considered, and a more comprehensive hydrate saturation prediction model is established, thereby making the natural gas hydrate saturation predicted by the model more accurate, which has certain guiding significance for the subsequent exploration and development of natural gas hydrates. The watershed algorithm can effectively suppress the interference of dark spot noise, accurately separate the target hydrate from the complex background, and the extracted hydrate distribution outline is clear and has good integrity.

[0133] An embodiment of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the natural gas hydrate saturation prediction method in any of the above method embodiments.

[0134] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the natural gas hydrate saturation prediction method in any of the above method embodiments.

[0135] Fig. 9 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the computing device.

[0136] like Fig. 9 As shown, the computing device may include: a processor 902 , a communication interface 904 , a memory 906 , and a communication bus 908 .

[0137] The processor 902 , the communication interface 904 , and the memory 906 communicate with each other via a communication bus 908 .

[0138] The communication interface 904 is used to communicate with other devices such as clients or other servers.

[0139] The processor 902 is used to execute the program 910, and specifically can execute the relevant steps in the above-mentioned natural gas hydrate saturation prediction method embodiment.

[0140] Specifically, the program 910 may include program codes, which include computer operation instructions.

[0141] The processor 902 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0142] The memory 906 is used to store the program 910. The memory 906 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0143] Program 910 can be specifically used to enable processor 902 to execute the natural gas hydrate saturation prediction method in any of the above method embodiments. The specific implementation of each step in program 910 can refer to the corresponding descriptions in the corresponding steps and units in the above natural gas hydrate saturation prediction method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0144] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is for the purpose of disclosing the best mode of implementation of the application.

[0145] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0146] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims, the inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present application.

[0147] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0148] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0149] The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present application. The present application may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0150] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A method for predicting natural gas hydrate saturation, characterized in that: The method comprises: The logging data are analyzed and processed to obtain imaging logging images and natural gas hydrate reservoir conditions, wherein the logging data include: first natural gas hydrate saturation; Performing natural gas hydrate identification processing on the imaging well logging image using a preset algorithm to obtain a corresponding natural gas hydrate image, wherein the natural gas hydrate image is a binary image; Calculating the hydrate surface ratio according to the natural gas hydrate image; According to the gas hydrate reservoir conditions, a gas hydrate generation experimental test is performed, a second gas hydrate saturation is calculated according to the experimental test data, and a relationship between the second gas hydrate saturation and resistivity is determined; Establishing a hydrate saturation prediction model according to the first natural gas hydrate saturation, the hydrate surface porosity, and the relationship between the second natural gas hydrate saturation and resistivity; The hydrate surface porosity and resistivity corresponding to the well to be predicted are determined, and the hydrate surface porosity and resistivity are input into the hydrate saturation prediction model to perform hydrate saturation prediction, thereby obtaining the natural gas hydrate saturation corresponding to the well to be predicted.

2. The method for predicting natural gas hydrate saturation according to claim 1, characterized in that: The calculating the hydrate surface ratio according to the natural gas hydrate image further comprises: Performing region marking processing on the natural gas hydrate image to identify and mark each natural gas hydrate region; Determine a region contour corresponding to the marked natural gas hydrate region, and generate a contour chain code sequence corresponding to the region contour; The hydrate surface ratio is calculated based on the profile chain code sequence.

3. The method for predicting natural gas hydrate saturation according to claim 2, characterized in that: Determining the area contour corresponding to the marked natural gas hydrate area further includes: For each foreground pixel in any marked natural gas hydrate region, determining whether there is a background pixel in the eight neighborhoods of the foreground pixel; If yes, the foreground pixel is determined as a contour pixel of the marked natural gas hydrate region, and a target marking process is performed on the contour pixel; A contour image is generated according to the contour pixels after target label processing, wherein the contour part of the contour image is a foreground image and the rest is a background image.

4. The natural gas hydrate saturation prediction method according to claim 2 or 3, characterized in that: The calculating of the hydrate surface porosity according to the profile chain code sequence further comprises: Traverse the contour chain code sequence corresponding to any marked natural gas hydrate region and calculate the total length of the contour corresponding to the regional contour; Calculate the area of ​​the marked natural gas hydrate region according to the total length of the contour and the coordinate information of each pixel point in the regional contour; The hydrate surface ratio is calculated based on the number of natural gas hydrate regions within a unit window length, the calculated area of ​​each natural gas hydrate region, and the window length area.

5. The method for predicting natural gas hydrate saturation according to claim 2 or 3, characterized in that: The performing region marking processing on the natural gas hydrate image to identify and mark each natural gas hydrate region further comprises: Each natural gas hydrate region in the natural gas hydrate image is identified and numbered based on an equivalent pair numbering algorithm.

6. The method for predicting natural gas hydrate saturation according to any one of claims 1 to 3, characterized in that: The using a preset algorithm to perform natural gas hydrate identification processing on the imaging well logging image to obtain a corresponding natural gas hydrate image further includes: Converting the imaging logging image into an original grayscale image; Performing morphological opening and closing filtering on the original grayscale image, and converting the filtered grayscale image into an initial binary image; Performing an opening and closing operation on the initial binary image based on a structure element of a preset size and a target structure element; The image processed by the opening and closing operations is segmented using a preset segmentation threshold to obtain the corresponding natural gas hydrate image.

7. A natural gas hydrate saturation prediction device, characterized in that: The device comprises: The processing module is suitable for analyzing and processing the well logging data to obtain the imaging well logging image and the natural gas hydrate reservoir conditions, wherein the well logging data includes: the first natural gas hydrate saturation; an identification module, adapted to perform natural gas hydrate identification processing on the imaging well logging image using a preset algorithm to obtain a corresponding natural gas hydrate image, wherein the natural gas hydrate image is a binary image; A calculation module, adapted to calculate the hydrate surface ratio according to the natural gas hydrate image; An experimental test module, adapted to perform an experimental test on gas hydrate formation according to the gas hydrate reservoir conditions, calculate a second gas hydrate saturation according to the experimental test data, and determine a relationship between the second gas hydrate saturation and resistivity; An establishment module, adapted to establish a hydrate saturation prediction model according to the first natural gas hydrate saturation, the hydrate surface porosity, and the relationship between the second natural gas hydrate saturation and resistivity; The prediction module is suitable for determining the hydrate surface porosity and resistivity corresponding to the well to be predicted, inputting the hydrate surface porosity and resistivity into the hydrate saturation prediction model to perform hydrate saturation prediction, and obtaining the natural gas hydrate saturation corresponding to the well to be predicted.

8. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the natural gas hydrate saturation prediction method according to any one of claims 1-6.

9. A computer storage medium, characterized in that The computer storage medium stores at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the natural gas hydrate saturation prediction method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the natural gas hydrate saturation prediction method according to any one of claims 1 to 6.

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