Method for identifying and analyzing formation mechanism of deep heat storage weak structure

Abstract

The invention provides a deep heat storage weak structure formation mechanism identification and analysis method, and relates to the technical field of deep high-temperature geothermal reservoir exploration and development. A plurality of exploration well positions are selected in a deep heat storage exploration and development area, a well drilling steel pipe is used for sample collection, and in the drilling process, the deep heat storage weak structure formation mechanism is identified and analyzed. And the rock sample is conveyed to a ground sample collection position through the spiral conveying device in the steel pipe. And the length of the sample can be measured in real time, and the depth of the sample is recorded in combination with the steel pipe driving sequence, so that the depth interval of the sample can be accurately divided, the surface characteristics and component information of the sample are analyzed in detail, and the position, scale and property of the deep heat storage weak structure can be accurately identified. And a database generated by combining the data can accurately grasp a deep heat storage weak structure formation mechanism, so that deep heat storage resources can be developed more efficiently, and clean energy supply is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of exploration and development of deep high-temperature geothermal reservoirs, and particularly relates to a method for identifying and analyzing the genetic mechanism of weak structures in deep geothermal reservoirs. Background Art

[0002] Under the background of the accelerating transformation of the global energy structure towards clean energy, deep geothermal reservoirs, as a highly potential clean energy source, have become the focus of research and development in the energy field due to their rich reserves, high stability, environmental friendliness, etc. The efficient development and utilization of deep geothermal reservoir resources depends on the accurate identification and in-depth study of weak structures in deep geothermal reservoirs, which is directly related to the safety of drilling engineering, the efficiency of geothermal reservoir exploitation, and the quality of energy output. In the actual exploration and development process, it is necessary to collect and analyze samples in the deep geothermal reservoir area to clarify the location, scale, and nature of weak structures, so as to provide a scientific basis for the formulation of exploitation plans.

[0003] Currently, in the field of research on weak structures in deep geothermal reservoirs, traditional sample collection and analysis methods have many limitations. On the one hand, in the link of determining the sample depth, most only divide the sample depth interval based on the approximate drilling range of the drilling steel pipe, without fully considering the difference in the actual source depth of rock samples in the steel pipe, resulting in inaccurate sample depth positioning and unable to accurately reflect the characteristics of deep geological structures; on the other hand, in terms of sample feature analysis, the analysis means for the surface features and composition information of samples are relatively single, making it difficult to comprehensively capture the feature information related to weak structures and unable to accurately identify the location and nature of weak structures.

[0004] Therefore, how to accurately determine the spatial distribution and formation mechanism of weak structures has become an urgent problem to be solved today. Summary of the Invention

[0005] The present invention provides a method for identifying and analyzing the genetic mechanism of weak structures in deep geothermal reservoirs, which can accurately determine the spatial distribution and formation mechanism of weak structures.

[0006] In a first aspect of the present invention, there is provided a method for identifying and analyzing the genetic mechanism of weak structures in deep geothermal reservoirs, including: Obtaining a sample set collected from the area of weak structures in deep geothermal reservoirs, and dividing the depth interval of the sample set according to the collection order and length measurement results of the sample set to obtain sub-intervals of each sub-sample; Identifying image data to obtain the surface features of each sub-sample, and marking the sub-samples corresponding to the surface features that meet the sampling conditions; Generating deep geological parameters by combining the component information and sub-intervals of the sub-samples corresponding to fixed nodes and marked nodes; Associating and integrating the deep geological parameters of the sub-samples to construct a comprehensive database.

[0007] Optionally, in a possible implementation of the first aspect, according to the acquisition order and length measurement results of the sample set, dividing the depth interval of the sample set to obtain the sub-intervals of each sub-sample includes: Based on the acquisition order and length measurement results, perform cumulative depth recording on each sub-sample based on the depth interval to determine the initial depth interval when there is no interval for each sub-sample; When the total measured length of each sub-sample is less than the total length of the depth interval, compare the surface features corresponding to the image data of adjacent sub-samples, and mark the sub-sample groups with intervals, where the surface features include edge features and color features; Determine the predicted spacing of the sub-sample group according to the feature difference degree in the image data; Insert the predicted spacing into the initial depth interval corresponding to the sub-sample group, and recalculate the cumulative depth of each sub-sample to obtain the sub-intervals of each sub-sample.

[0008] Optionally, in a possible implementation of the first aspect, comparing the surface features corresponding to the image data of adjacent sub-samples and marking the sub-sample groups with intervals includes: When comparing edge features, extract the edge contours in the image data of adjacent sub-samples; Obtain the concave points and convex points in each edge contour, calculate the concave point difference and convex point difference in each edge contour, and when the concave point difference and / or convex point difference is greater than the quantity threshold, determine that the sub-sample group has an interval and mark it; When both the concave point difference and the convex point difference are less than the quantity threshold, obtain the matching set of the concave point and convex point with the smallest distance in each edge contour; Statistical the total distance of each matching set, and when the total distance is greater than the fit threshold, determine that the sub-sample group has an interval and mark it.

[0009] Optionally, in a possible implementation of the first aspect, obtaining the matching set of the concave point and convex point with the smallest distance in each edge contour includes: For each concave point of each edge contour, calculate its distance from each convex point in the other edge contour, and select the convex point with the smallest distance to generate a matching set; For each convex point of each edge contour, calculate its distance from each concave point in the other edge contour, and select the concave point with the smallest distance to generate a matching set.

[0010] Optionally, in a possible implementation of the first aspect, comparing the surface features corresponding to the image data of adjacent sub-samples and marking the sub-sample groups with intervals includes: When comparing color features, extract the sample contours in the image data of adjacent sub-samples; Obtain the target areas in each sample contour where the pixel values are in the same pixel interval and are located in the edge part; Compare the target areas of each sample contour with the target areas of another sample contour. If there are no target areas with the same pixel interval, it is determined that there is an interval in the sub-sample group and it is marked.

[0011] Optionally, in a possible implementation manner of the first aspect, determining the predicted spacing of the sub-sample group according to the feature difference degree in the image data includes: For edge features, determine the preset difference degree corresponding to the maximum concave point difference or convex point difference as the feature difference degree, or determine the preset difference degree corresponding to the total distance of the matching set as the feature difference degree; For color features, obtain the minimum color difference corresponding to multiple target areas of adjacent sub-samples, and determine the preset difference degree corresponding to the minimum color difference as the feature difference degree; Obtain the length difference between the total length of the depth interval and the total measured length of each sub-sample, and divide the length difference proportionally according to the ratio between the feature difference degrees of the sub-sample groups to obtain the predicted spacing of each sub-sample group.

[0012] Optionally, in a possible implementation manner of the first aspect, identifying the surface features of each sub-sample from the image data and marking the sub-samples corresponding to the surface features that meet the sampling conditions includes: Select each sub-sample in sequence according to the acquisition order, and identify the color features included in the surface features of the image data of the sub-sample; Based on the color features, determine the pixel mean value of the sub-sample, and calculate the pixel difference between the pixel mean value of the current sub-sample and the pixel mean value of the previous sub-sample; When the pixel difference is within the abnormal difference interval, it is determined that the current sub-sample meets the sampling conditions and is marked.

[0013] Optionally, in a possible implementation manner of the first aspect, generating deep geological parameters by combining the component information and sub-intervals of the sub-samples corresponding to the fixed nodes and marked nodes includes: According to the corresponding relationship between the component information and the temperature, determine the predicted temperature corresponding to the corresponding sub-interval, and generate deep geological parameters according to the mapping relationship between the sub-interval and the predicted temperature.

[0014] Optionally, in a possible implementation manner of the first aspect, it further includes: Obtain the scanning information of the sample data of each well position in the deep geothermal reservoir soft structure area by the inspection equipment; Determine the blank depth interval with missing sample data according to the scanning information, select the sub-regions adjacent to the blank depth interval, and obtain the filling data of the blank depth interval by combining the content differences of their constituent elements.

[0015] Optionally, in a possible implementation manner of the first aspect, selecting the sub-regions adjacent to the blank depth interval and obtaining the filling data of the blank depth interval by combining the content differences of their constituent elements includes: Select the sub-samples corresponding to the sub-intervals adjacent to the blank depth interval as the target samples; Calculate the content differences between the same constituent elements in each of the target samples, and obtain the content mean values corresponding to the constituent elements whose content differences are less than the content difference threshold; Determine the content mean values as the filling values of the corresponding constituent elements within the blank depth interval, and obtain the filling data of the blank depth interval according to the filling values.

[0016] In the second aspect of the present invention, there is provided a system for identifying and analyzing the genetic mechanism of deep geothermal reservoir weak structures, including: A sample module for obtaining a sample set collected from the deep geothermal reservoir weak structure area, and dividing the depth interval of the sample set according to the collection order and length measurement results of the sample set to obtain the sub-intervals of each sub-sample; An identification module for identifying the surface features of each of the sub-samples from the image data and marking the sub-samples corresponding to the surface features that meet the sampling conditions; A parameter module for generating deep geological parameters by combining the component information and sub-intervals of the sub-samples corresponding to the fixed nodes and marked nodes; An integration module for associating and integrating the deep geological parameters of the sub-samples to construct a comprehensive database.

[0017] The beneficial effects of the present invention are as follows: 1. Through the collection order and sample length measurement results, the present invention makes a refined division of the sample depth interval. It not only determines the initial depth interval when there is no interval between sub-samples, but also identifies the intervals between samples by comparing the edge features and color features of adjacent sub-samples, and determines the predicted interval according to the feature difference degree, and finally obtains the accurate sub-intervals of the sub-samples. It can more truly reflect the actual situation of the deep strata, provides a reliable data basis for in-depth analysis of the deep geological structure, and helps researchers more accurately grasp the distribution characteristics of deep geothermal reservoir weak structures at different depths.

[0018] 2. Using image recognition technology, the present invention deeply analyzes the surface features of samples. By setting reasonable sampling conditions, such as changes in color features, etc., it can accurately screen out sub-samples that may be related to weak structures. At the same time, combined with component analysis equipment to obtain sample component information and generate deep geological parameters. From surface features to component information, it excavates sample features in multiple dimensions and at a deep level, greatly enhancing the ability to identify weak structures in deep geothermal reservoirs, and thus more accurately determining the location, scale, and nature of weak structures.

[0019] 3. The present invention systematically correlates and integrates deep geological parameters such as the component information, precise depth, and predicted temperature of sub-samples to construct a comprehensive integrated database. The required information can be quickly obtained from the integrated database. By comparing the parameters of different sub-samples, the potential connections between data can be mined, and the formation mechanism and distribution law of weak structures in deep geothermal reservoirs can be studied in depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic diagram of an application scenario provided by an embodiment of the present invention; Figure 2 is a schematic flow diagram of a method for identifying and analyzing the formation mechanism of weak structures in deep geothermal reservoirs provided by an embodiment of the present invention; Figure 3 is a schematic structural diagram of a system for identifying and analyzing the formation mechanism of weak structures in deep geothermal reservoirs provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Refer to Figure 1 , which is a schematic diagram of an application scenario provided by an embodiment of the present invention. The present invention will select multiple exploration well positions in the exploration and development area of deep geothermal reservoirs and use drilling steel pipes for sample collection. During the drilling process, rock samples are transported to the ground sample collection area through the spiral conveyor device inside the steel pipe. And the sample length will also be measured in real time, and the sample depth will be recorded in combination with the order of the steel pipe driving, so as to accurately divide the sample depth interval, detailedly analyze the surface features and component information of the samples, and accurately identify the location, scale, and nature of weak structures in deep geothermal reservoirs. And the database generated by combining these data can accurately grasp the formation mechanism of weak structures in deep geothermal reservoirs, which is helpful for more efficient development of deep geothermal reservoir resources and increasing the supply of clean energy.

[0023] See Figure 2 , which is a schematic flow chart of a method for identifying and analyzing the genetic mechanism of deep geothermal reservoir soft structures provided by an embodiment of the present invention. Figure 2 The execution subject of the method shown can be a software and / or hardware device. The execution subject of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. Among them, user equipment can include, but is not limited to, computers, smart phones, personal digital assistants (Personal Digital Assistant, abbreviated as: PDA), and the above-mentioned electronic equipment, etc. Network equipment can include, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Among them, cloud computing is a type of distributed computing, which consists of a group of loosely coupled computers forming a super virtual computer. This embodiment does not make any restrictions on this. It includes steps S1 to S4, specifically as follows: S1. Obtain a sample set collected from the deep geothermal reservoir soft structure area, and divide the depth interval of the sample set according to the collection order and length measurement results of the sample set to obtain the sub-intervals of each sub-sample.

[0024] Accurately determining the specific depth position of each rock sample underground can lay a foundation for subsequent analysis of the relationship between geological conditions at different depths and sample characteristics, and help accurately identify the distribution of deep geothermal reservoir soft structures. Although each section of steel pipe has a general drilling range, there are differences in the depth of the internal rock sources. Refining the depth interval can accurately locate the formation position corresponding to the sample, and then study the relationship between different formation characteristics and soft structures.

[0025] The sample set refers to the sum of all rock samples collected by each splicable steel pipe in the deep geothermal reservoir soft structure area; the collection order is the order in which the samples are collected on the ground when the steel pipe drills and collects samples section by section; the length measurement result is the data obtained by measuring the length of each rock sample obtained from the steel pipe; the depth interval is the general depth range covered by each section of steel pipe during the drilling process. For example, each 10-meter-long steel pipe corresponds to a specific depth range after being drilled into the ground. For example, the 5th section of steel pipe may correspond to a sample interval of 40 - 50 meters; the sub-sample refers to a single rock sample within the sample set; the sub-interval is the precise depth interval corresponding to each sub-sample determined according to the collection order and sample length.

[0026] Based on the above embodiment, the specific implementation method of "dividing the depth interval of the sample set according to the collection order and length measurement results of the sample set to obtain the sub-intervals of each sub-sample" in step S1 can be: S11. Based on the acquisition sequence and the length measurement results, perform cumulative depth recording for each of the sub-samples based on depth intervals, and determine the initial depth intervals when there are no gaps between the sub-samples.

[0027] To provide basic depth data, the underground position range of each sub-sample can be initially determined without considering possible gaps between samples, which facilitates subsequent more precise depth adjustment and analysis, and understanding the distribution of samples underground.

[0028] Cumulative depth recording is the depth record of the sample calculated by successively accumulating according to the acquisition sequence, the length of each steel pipe section, and the position of the sample within the steel pipe. The initial depth interval is the depth range of the sub-sample initially determined according to the acquisition sequence and the sample length without considering the gaps between samples.

[0029] For example, in the data preparation stage, it is known that each steel pipe is 10 meters long, and sampling starts at a cumulative depth of 300 meters after driving 30 steel pipe sections. Samples with lengths of 2 meters, 3 meters, and 1 meter are taken out in sequence. According to the acquisition sequence and the length measurement results, the cumulative depth of the first sample starts from 300 meters and its length is 2 meters, so its initial depth interval is 300 - 302 meters; the second sample follows the first sample, and its initial depth interval is 302 - 305 meters; the initial depth interval of the third sample is 305 - 306 meters.

[0030] S12. When the total measured length of the sub-samples is less than the total length of the depth interval, compare the surface features corresponding to the image data of adjacent sub-samples, and mark the sub-sample groups with gaps. The surface features include edge features and color features.

[0031] In actual exploration, there may be gaps between samples. For example, if the total length of this depth interval is assumed to be 10 meters and the total measured length of three sub-samples is 6 meters, which is less than the total length of the depth interval, it indicates that there may be gaps. By comparing the surface features of adjacent sub-samples to discover these gaps and marking the sub-sample groups with gaps, the depth interval of the samples can be accurately adjusted to make the sample depth information more in line with the actual geological situation.

[0032] The total measured length of the sub-samples is the sum of the lengths of all the collected sub-samples; the total length of the depth interval is the length of the continuous depth range involved in this exploration; the surface features include the edge features and color features of the sub-samples; the sub-sample group refers to a group of adjacent sub-samples that are judged to have gaps.

[0033] The reason for determining whether there is a gap between rocks through edge features and color features is that these features can intuitively reflect the differences in geological processes during rock formation and transportation, thus providing important clues for judging the gap. In the underground geological environment, under normal circumstances, if rocks are continuously deposited or in close contact, their edges should have a certain degree of coherence and fit. When rocks are affected by tectonic movements, erosion, etc. and gaps appear, the edge features will change significantly. For example, if one sample has a flat edge and the other is uneven and cannot be naturally joined, it is very likely that they were not originally closely connected and there were other geological processes in between that caused separation and a gap to appear. The color of rocks mainly depends on their mineral composition and formation environment. Rocks formed at different depths and under different geological conditions often have different colors. If the colors of adjacent rocks are significantly different and not gradually transitional, it indicates that there is likely a gap between them. For example, if the color of the first sample suddenly changes from gray to brown, this may mean that during the formation of these two rocks, the geological environment has changed significantly, such as changes in redox conditions, different sources of sedimentary materials, etc. Such changes are usually accompanied by sedimentary discontinuities or other geological events, indicating the existence of a gap between the rocks.

[0034] In some embodiments, the surface features corresponding to the image data of adjacent said sub-samples can be compared through the following steps to mark the sub-sample groups with gaps, including: When comparing edge features, extract the edge contours in the image data of adjacent said sub-samples; obtain the concave points and convex points in each of the edge contours, calculate the concave point difference and convex point difference in each of the edge contours, and when the concave point difference and / or convex point difference is greater than the quantity threshold, determine that the sub-sample group has a gap and mark it; when both the concave point difference and convex point difference are less than the quantity threshold, obtain the matching sets of the concave points and convex points with the smallest distance in each of the edge contours; calculate the total distance of each of the matching sets, and when the total distance is greater than the fit threshold, determine that the sub-sample group has a gap and mark it.

[0035] The edge contour contains the shape and structural information of the sub-sample edge and is the basis for analyzing the differences in edge features. Concave points and convex points are important feature points of the edge contour, and the changes in their quantity and distribution can reflect the irregularity and differences of the edge. By calculating the concave point difference and convex point difference and comparing them with the quantity threshold, the degree of difference in the edge features of adjacent sub-samples can be quickly judged. When the difference is too large, it is very likely that there is a gap between the sub-sample groups, so these sub-sample groups can be marked.

[0036] When the differences between concave points and convex points are small, it is difficult to determine whether there is a gap in the sub-sample group only by the quantity difference. At this time, it is necessary to further analyze the positional relationship between concave points and convex points in the edge contour. Obtaining the matching set of the concave point and convex point with the smallest distance can further judge the degree of fit of the edge from the perspective of position matching, and more accurately judge whether there is a gap in the sub-sample group. The total distance of the matching set reflects the overall degree of fit between concave points and convex points on the edge contours of adjacent sub-samples. By comparing the total distance with the fit threshold, the alignment degree of the edge can be judged as a whole. When the total distance is too large, it indicates that the edge fit degree is low, and there is likely a gap in the sub-sample group. Therefore, these sub-sample groups can be marked.

[0037] Edge features refer to the characteristics such as the shape and smoothness presented by the sample edge; the edge contour is the shape contour of the sample edge in the sample image; a concave point is a point where the edge contour is indented inward, and its curvature is negative; a convex point is a point where the edge contour protrudes outward, and its curvature is positive; the concave point difference is a quantitative value of the difference in the number of concave points in the edge contours of adjacent sub-samples; the convex point difference is a quantitative value of the difference in the number of convex points in the edge contours of adjacent sub-samples; the quantity threshold is a preset critical value for judging whether the concave point difference or convex point difference is significant; the matching set is a set composed of the concave point and convex point with the smallest distance in each edge contour, used to measure the degree of fit of adjacent sample edges; the fit threshold is a preset critical value for judging whether the total distance is too large.

[0038] Specifically, when analyzing adjacent sub-samples, image processing techniques can be used to extract the edge contour from the acquired image data. After obtaining the edge contour, according to a series of points obtained by contour discretization, calculate the curvature of each point. Determine concave points and convex points according to the positive and negative of the curvature. For example, a point with negative curvature is a concave point, and a point with positive curvature is a convex point. Then count the number of concave points and convex points in the edge contours of adjacent sub-samples, and calculate their differences. Suppose the edge contour of sub-sample A has 8 concave points, and the edge contour of sub-sample B has 3 concave points, and the concave point difference is 5. If the preset quantity threshold is 3, and 5 is greater than 3, then it is determined that there is a gap in the sub-sample group where sub-sample A and B are located, and they are marked.

[0039] When both the concave point difference and the convex point difference are less than the quantity threshold, the matching set can be determined through the following steps: For each concave point of each of the edge contours, calculate its distance from each convex point in the other edge contour, and select the convex point with the smallest distance to generate a matching set; for each convex point of each of the edge contours, calculate its distance from each concave point in the other edge contour, and select the concave point with the smallest distance to generate a matching set.

[0040] For each convex point of one of the contours, calculate its distances from all the concave points of the other contour, and select the concave point with the minimum distance to form the first convex point - concave point matching set; for each concave point of this contour, calculate its distances from all the convex points of the other contour, and select the convex point with the minimum distance to form the second concave point - convex point matching set. For example, there is a convex point P on the edge contour of subsample C. Calculate the distances from P to all the concave points on the edge contour of subsample D, and find the concave point Q with the minimum distance. Add (P, Q) to the first convex point - concave point matching set.

[0041] By obtaining the matching sets, the judgment basis is supplemented from the perspective of position matching, improving the accuracy of judging whether there is a gap in the subsample group. Even if the difference in the number of concave and convex points is not obvious, potential gap situations can be discovered through the positional relationship.

[0042] After calculating the matching sets, according to the set distance calculation method, such as the Manhattan distance, calculate the total distance of all point pairs. Suppose there are 5 point pairs in the first convex point - concave point matching set and the second concave point - convex point matching set, and the calculated Manhattan distances of these 5 point pairs are 2, 3, 4, 3, 2 respectively, and the total distance is 14. If the preset fitness threshold is 10 and 14 is greater than 10, then it is determined that there is a gap in the subsample group and it is marked.

[0043] By statistically calculating the total distance and comparing it with the fitness threshold, it is possible to accurately judge whether there is a gap in the subsample group as a whole. Marking the subsample groups with gaps helps improve the accuracy of deep geothermal reservoir research.

[0044] In some other embodiments, the surface features corresponding to the image data of adjacent said subsamples can also be compared through the following steps to mark the subsample groups with gaps, including: When comparing color features, extract the sample contours in the image data of adjacent said subsamples; obtain the target regions where the pixel values in each said sample contour are in the same pixel interval and are located in the edge part; compare the target regions of each said sample contour with the target regions of another sample contour. If there is no target region with the same pixel interval, it is determined that there is a gap in the said subsample group and it is marked.

[0045] Color features can reflect information such as the formation environment and compositional changes of rocks. In normal continuous sedimentation or closely contacted geological situations, the color features of adjacent sub-samples should have a certain coherence. If the color feature differences between adjacent sub-samples are obvious and there is no target area with the same pixel interval in the edge part, this is likely to mean that there are sedimentation discontinuities, geological structure changes, etc. between them, resulting in intervals between sub-samples. In this way, it can assist in judging whether there are intervals in the sub-sample group, provide a basis for determining the true distribution of rock samples in the deep thermal reservoir, and further study the genetic mechanism of the weak structure of the deep thermal reservoir more accurately.

[0046] The color feature refers to the characteristics of the sub-sample in terms of color presented in the image; the sample contour refers to the boundary contour of the sample in the sample image; the target area refers to the area in the sample contour where the pixel values are in the same pixel interval and are located in the edge part, which is the key area for comparing color features because the edge part can better reflect the relationship between adjacent samples.

[0047] Suppose adjacent sub-samples A and sub-sample B are collected. First, extract the sample contours of sub-sample A and sub-sample B from the collected image data. Then, obtain the area with the same pixel value. For example, pixel values between 100 - 120 represent a certain specific color (assumed to be light gray). In the edge part of the sample contour of sub-sample A, find the area with pixel values between 100 - 120 to obtain the target area A1; similarly, find the area with the same pixel interval in the edge part of the sample contour of sub-sample B. If no area with pixel values between 100 - 120 can be found in the edge part of sub-sample B, that is, there is no target area with the same pixel interval in the edge parts of sub-sample A and sub-sample B, then it is determined that there is an interval in the sub-sample group where sub-sample A and sub-sample B are located, and this sub-sample group is marked. For example, if the edge of sub-sample A shows light gray while the edge of sub-sample B shows dark gray, and the corresponding pixel values are completely different, this indicates that there may be an interval between them.

[0048] S13. Determine the predicted spacing of the sub-sample group according to the feature difference degree in the image data.

[0049] To accurately adjust the depth interval of the sample, it is necessary to determine the distance between the intervals of the sample groups. By analyzing the feature difference degree in the image data to quantify the interval size, it can provide a basis for inserting an appropriate interval distance in the initial depth interval.

[0050] The feature difference degree is a quantitative value of the difference degree obtained by comparing the surface features of adjacent sub-samples, which is used to measure the size of the interval between sub-sample groups; the predicted spacing is the predicted value of the interval distance between sub-sample groups calculated according to the feature difference degree.

[0051] In some embodiments, the predicted spacing of the sub - sample groups can be obtained through the following steps: For edge features, determine the preset degree of difference corresponding to the largest concave - point difference or convex - point difference as the feature degree of difference, or determine the preset degree of difference corresponding to the total distance of the matching set as the feature degree of difference; for color features, obtain the minimum color difference corresponding to multiple target regions of adjacent sub - samples, and determine the preset degree of difference corresponding to the minimum color difference as the feature degree of difference; obtain the length difference between the total length of the depth interval and the total measured length of each sub - sample, and divide the length difference proportionally according to the ratio between the feature degrees of difference of the sub - sample groups to obtain the predicted spacing of each sub - sample group.

[0052] Accurately determining the interval distance between sub - sample groups helps to precisely divide the sample depth interval, thereby more accurately understanding the distribution of rock samples in the deep geothermal reservoir, and providing a reliable basis for studying the genetic mechanism of the weak structure in the deep geothermal reservoir. By analyzing the degree of difference in edge features and color features and combining depth - related data, the interval between sample groups can be quantified, improving the accuracy and scientific nature of the research.

[0053] The preset degree of difference corresponding to the concave - point difference or convex - point difference is a value preset for different concave - point differences and convex - point differences, representing the corresponding degree of difference. The larger the difference value, the higher the preset degree of difference, which means the greater the difference in edge features, the greater the possibility that there is an interval between sample groups, and the larger the possible interval. The preset degree of difference corresponding to the total distance of the matching set refers to setting the preset degree of difference for different total distance values after calculating the total distance of the matching set composed of the concave points and convex points with the smallest distance in the edge contour. The larger the total distance, the higher the preset degree of difference, indicating the lower the degree of fit of the edge contour and the greater the possibility that there is an interval between sample groups. The preset degree of difference corresponding to the minimum color difference is to obtain the minimum color difference corresponding to multiple target regions when comparing the color features of adjacent sub - samples, and set the preset degree of difference for different minimum color differences. The larger the minimum color difference, the higher the preset degree of difference, indicating the greater the color difference and the greater the possibility that there is an interval between sample groups.

[0054] When calculating the predicted spacing, assume that the total length of the depth interval corresponding to the sub - sample group is 20 meters, the total measured length of each sub - sample is 16 meters, and the length difference is 4 meters. The feature degree of difference of the sub - sample group based on edge features is 0.7, and the feature degree of difference based on color features is 0.3, and the ratio between them is 7:3. Divide the length difference of 4 meters according to this ratio. The predicted spacing corresponding to edge features is 2.8 meters, and the predicted spacing corresponding to color features is 1.2 meters. The sum of the ratios between the feature degrees of difference of each sub - sample group is 1.

[0055] S14. Insert the predicted spacing into the initial depth interval corresponding to the sub-sample group, and recalculate the cumulative depth of each sub-sample to obtain the sub-intervals of each sub-sample.

[0056] After determining the predicted spacing between sample groups, inserting it into the initial depth interval and recalculating the cumulative depth of sub-samples can obtain a more accurate depth interval for sub-samples.

[0057] For example, originally the depth of the first sample was 300 - 302 meters, and the depth of the second sample was 302 - 305 meters. After determining that the interval distance between the two samples was 0.15 meters, insert this interval into the initial depth intervals of the first and second samples. After adjustment, the depth of the first sample remains 300 - 302 meters, the depth of the interval part is 302 - 302.15 meters, and the depth of the second sample becomes 302.15 - 305 meters. Then recalculate the cumulative depth to ensure that the total depth of all samples and intervals is consistent with the actual detection depth. After such processing, more accurate sub-intervals for each sub-sample are obtained.

[0058] By inserting the predicted spacing and recalculating the cumulative depth, the obtained sub-intervals are more in line with the actual geological situation, improving the accuracy of sample depth information.

[0059] S2. Identify the surface features of each sub-sample from the image data, and mark the sub-samples corresponding to the surface features that meet the sampling conditions.

[0060] The formation of weak structures in deep geothermal reservoirs often causes special surface features in the surrounding rocks. Identifying the surface features of sub-samples and marking the sub-samples that meet specific conditions can screen out samples that may be related to weak structures, avoid wasting too much effort on a large number of ordinary samples, thereby improving the research efficiency and finding clues related to weak structures faster.

[0061] Specifically, after the samples are collected on the ground and fall into the sample collection box, the high-definition image acquisition device will automatically take multi-angle photos of each sub-sample to obtain image data and analyze the image data. Assume that the sampling condition is met when the color of the sub-sample changes, for example, suddenly changes from light gray of the surrounding samples to dark gray. If a certain sub-sample collected from the 9th section of the steel pipe shows the above characteristic change, the system will automatically mark this sub-sample as a special sample and record the sub-interval where it is located.

[0062] The image data is the digital image information obtained by using the high-definition image acquisition device installed above the sample collection box to take multi-angle photos of the sub-samples collected on the ground. The surface features include the external manifestation features such as the color and edge shape of the sub-samples. The sampling conditions are pre-set criteria for judging whether the surface features of the sub-samples are special and whether they may be related to the weak structure of the deep thermal reservoir. For example, features such as color mutation may be set as sampling conditions.

[0063] On the basis of the above embodiments, the specific implementation manner of step S2 may be: Select each of the sub-samples in sequence according to the collection order, identify the color features included in the surface features of the image data of the sub-sample; determine the pixel mean value of the sub-sample based on the color features, and calculate the pixel difference between the current sub-sample and the previous sub-sample; when the pixel difference is within the abnormal difference interval, determine that the current sub-sample meets the sampling conditions and mark it.

[0064] By analyzing the color features of the sub-samples in the collection order, calculating the pixel mean difference, and comparing it with the abnormal difference interval, sub-samples with abnormal color feature changes can be screened out. These sub-samples may represent mutations in geological conditions and are related to the formation or distribution of the weak structure of the deep thermal reservoir. Marking these sub-samples helps to conduct key research on them subsequently, improving the research efficiency and accuracy.

[0065] The abnormal difference interval refers to the pre-set range for judging whether the pixel difference is abnormal. If the pixel difference is within this interval, it is considered that the color change of the sub-sample is significant and may be related to the special geological conditions of the deep thermal reservoir.

[0066] Suppose in a series of sub-samples collected, in the collection order, the first sub-sample is selected first. Through image recognition technology, its image data is analyzed to extract color features. Suppose the calculated pixel mean value of this sub-sample is 120. Then the second sub-sample is selected, and the color features are also extracted and the pixel mean value is calculated, suppose it is 150. Calculate the pixel difference between these two sub-samples as 30. The pre-set abnormal difference interval is [20, 40]. Since 30 is within this abnormal difference interval, it is determined that the second sub-sample meets the sampling conditions, and the system automatically marks it. Subsequently, researchers can further analyze the composition, structure and other features of this marked sub-sample to explore its relationship with the weak structure of the deep thermal reservoir.

[0067] Through the screening method based on color features, sub-samples with abnormal color feature changes can be accurately found, avoiding unnecessary in-depth analysis of a large number of ordinary samples and saving time.

[0068] S3. Combine the component information and sub-intervals corresponding to the sub-samples of the fixed nodes and marked nodes to generate deep geological parameters.

[0069] Integrate various information such as the component information and precise depth of the sub-samples to generate parameters that can intuitively reflect the deep geological conditions, which can provide key data support for constructing a deep geothermal reservoir geological database.

[0070] The fixed node is the position of the sub-sample corresponding to a specific depth specified by the researcher according to the needs, which is used to focus on the geological conditions at this depth. The marked node is the position of the sub-sample marked because the surface characteristics meet the sampling conditions. The component information is the data of the chemical composition and the content of each component obtained by analyzing the sub-samples of the fixed node and the marked node through a component analysis device such as a spectrometer. The deep geological parameter is a parameter generated by integrating various information such as the component information and sub-interval of the sub-sample, which is used to describe the deep geological characteristics. Common ones include predicted temperature, etc.

[0071] The generated deep geological parameters organically combine various attributes of the sub-samples, providing intuitive and quantitative data for studying the geological structure, geothermal reservoir characteristics, and the origin of weak structures of the deep geothermal reservoir. Researchers can analyze the internal relationships between samples of different depths and different components and the weak structures of the deep geothermal reservoir based on these parameters. For example, study under what temperature conditions rocks with a specific component combination are more likely to form weak structures, providing a scientific basis for the exploration and development of the deep geothermal reservoir.

[0072] Based on the above embodiments, the specific implementation manner of step S3 can be: Determine the predicted temperature corresponding to the corresponding sub-interval according to the correspondence between the component information and the temperature, and generate deep geological parameters according to the mapping relationship between the sub-interval and the predicted temperature.

[0073] The geological characteristics of the deep geothermal reservoir are complex and diverse. Component information and temperature are two key elements among them. By establishing the correspondence between component information and temperature, determining the predicted temperature of different sub-intervals, and generating deep geological parameters, the information such as the composition, depth, and temperature of rock samples can be integrated. This helps researchers understand the deep geological conditions of the deep geothermal reservoir more comprehensively and deeply, providing key data support for exploring the genetic mechanism of the weak structures in the deep geothermal reservoir.

[0074] The component information refers to the data of the chemical composition and the content of each component obtained by detecting rock samples through a component analysis device (such as a spectrometer), and these information reflect the material composition of the rock. The predicted temperature refers to the temperature value of a specific sub-interval inferred for the component of the rock sample in the sub-interval according to the correspondence between the component information and the temperature. Since it is difficult to actually measure the temperature at different positions of the deep geothermal reservoir, the predicted temperature can provide an important reference for research.

[0075] Integrating composition information, depth and temperature information into deep geological parameters enables researchers to understand the geological characteristics of deep heat reservoirs more intuitively and comprehensively, avoiding the fragmentation and isolation of information, and helping to discover the potential connections between different geological elements. The generated deep geological parameters not only provide an important basis for the exploration and development of deep heat reservoirs. They also provide key data for exploring the causal mechanism of weak structures in deep heat reservoirs. Different temperature and composition conditions affect the physical and mechanical properties of rocks. By analyzing deep geological parameters, we can study how these factors interact with each other, thereby affecting the formation and distribution of weak structures, and promoting a deeper understanding of the geological processes of deep heat reservoirs.

[0076] S4, correlating and integrating the deep geological parameters of the sub-samples to construct a comprehensive database.

[0077] The scattered deep geological parameters are systematically integrated and stored in a database, which facilitates centralized data management, efficient query and in-depth analysis. Researchers can quickly obtain the required information from the comprehensive database, compare the parameters of different sub-samples, explore the potential connections between data, and conduct in-depth research on the formation mechanism and distribution law of the weak structure of deep thermal reservoirs.

[0078] The comprehensive database is a collection specially used to store deep geological parameters of all sub-samples. It has functions such as data storage, classification management, and rapid query, and can meet the needs of researchers for processing and analyzing large amounts of geological data.

[0079] For example, the deep geological parameters of all previously generated subsamples can be entered into the database with the number of steel pipe sections and subintervals corresponding to the subsamples as indexes. For example, the database records information such as subsample 1 (from the 6th steel pipe section, subinterval [59.2,59.5] meters, main components quartz and mica, predicted temperature 48°C), subsample 2 (from the 13th steel pipe section, subinterval [129.1,129.4] meters, mainly feldspar, predicted temperature 62°C). Researchers can quickly query other parameter information of related subsamples by entering multiple conditions such as the number of steel pipe sections, subinterval range, and component type.

[0080] In addition, based on the above embodiments, this solution also includes the following embodiments: Obtain scanning information of sample data of each well in the deep thermal reservoir soft structure area by inspection equipment; determine the blank depth interval where sample data is missing based on the scanning information, select the sub-area adjacent to the blank depth interval, and obtain the filling data of the blank depth interval in combination with the content difference of its component elements.

[0081] It is understandable that sample data is crucial for analyzing geological structures and genetic mechanisms. However, due to various reasons such as equipment failures and complex geological conditions, there may be cases where sample data is missing. Obtaining the scanning information of the inspection equipment and processing the missing data can supplement the complete sample data, ensure the accuracy and continuity of the research, and enable researchers to explore the relevant characteristics and laws of the weak structures in the deep geothermal reservoir based on more comprehensive data.

[0082] The inspection equipment refers to the equipment used to scan and collect sample data at each well position in the area of the weak structure of the deep geothermal reservoir, such as an inspection robot, which can obtain various information of the sample, such as composition, image, etc. The scanning information refers to the digital information obtained after the inspection equipment scans the sample data at each well position, including various attribute data of the sample. The blank depth interval refers to the depth range corresponding to the missing sample data. The content difference of the component elements refers to the difference value of the content of the same component element in the samples of adjacent sub-regions, which is used to evaluate the component change in the adjacent regions and provide a basis for filling the data in the blank depth interval. The filled data refers to the data estimated and generated for the blank depth interval based on information such as the content difference of the component elements in the samples of adjacent sub-regions to supplement the missing sample data.

[0083] In some embodiments, the following steps can be used to select the sub-regions adjacent to the blank depth interval and obtain the filled data for the blank depth interval in combination with the content difference of their component elements, including: Select the sub-samples corresponding to the sub-intervals adjacent to the blank depth interval as the target samples; calculate the content differences between the same component elements in each of the target samples, and obtain the content mean values corresponding to the component elements whose content differences are less than the content difference threshold; determine the content mean values as the filled values of the corresponding component elements in the blank depth interval, and obtain the filled data for the blank depth interval according to the filled values.

[0084] Suppose there is a blank depth interval of 200 - 210 meters. The sub - intervals adjacent to this blank depth interval are 190 - 200 meters and 210 - 220 meters respectively. The corresponding sub - samples A (from the 190 - 200 - meter sub - interval) and sub - sample B (from the 210 - 220 - meter sub - interval) are selected as target samples. Component analysis is carried out on these two target samples, and it is found that the samples contain component elements such as silicon, aluminum, and iron. The content of silicon element in sub - sample A is calculated to be 30%, and in sub - sample B is 32%, with a content difference of 2%; the content of aluminum element in sub - sample A is 20%, and in sub - sample B is 25%, with a content difference of 5%; the content of iron element in sub - sample A is 10%, and in sub - sample B is 11%, with a content difference of 1%. The pre - set content difference threshold is 3%. The content differences of silicon and iron elements are less than the content difference threshold. The average content of silicon element is calculated to be 31%, and the average content of iron element is 10.5%. It is determined that 31% is the filling value of silicon element within the blank depth interval, 10.5% is the filling value of iron element. Similarly, the filling values of other component elements that meet the conditions are determined, so as to obtain the filling data for the blank depth interval of 200 - 210 meters.

[0085] Component elements refer to various chemical elements that make up rock samples, such as silicon, aluminum, iron, etc. The content of different component elements reflects the characteristics of the rock. The content difference refers to the difference value of the content of the same component element in each target sample, which is used to measure the change degree of the content of component elements in adjacent sub - samples. The content difference threshold refers to a pre - set value used to judge whether the content difference is within an acceptable range. When the content difference is less than this threshold, it is considered that the content change of this component element in adjacent sub - samples is small and can be used to calculate the average content.

[0086] By obtaining scanning information to determine the blank depth interval and using the data of adjacent sub - regions for filling, the missing sample data is effectively supplemented, ensuring the integrity of the data.

[0087] See Figure 3 , which is a schematic structural diagram of a system for identifying and analyzing the genetic mechanism of weak structures in deep geothermal reservoirs provided by an embodiment of the present invention. The system for identifying and analyzing the genetic mechanism of weak structures in deep geothermal reservoirs includes: A sample module, used to obtain a sample set collected from the area of weak structures in deep geothermal reservoirs, and divide the depth interval of the sample set according to the collection order and length measurement results of the sample set to obtain the sub - intervals of each sub - sample; An identification module, used to identify image data to obtain the surface features of each sub - sample, and mark the sub - samples corresponding to the surface features that meet the sampling conditions; A parameter module, configured to generate deep geological parameters by combining the component information and sub-intervals corresponding to the sub-samples of fixed nodes and marked nodes. An integration module, configured to associate and integrate the deep geological parameters of the sub-samples to construct a comprehensive database.

[0088] Figure 3 The device of the illustrated embodiment can correspondingly be used to execute Figure 2 the steps in the method embodiment shown, and the implementation principle and technical effects are similar, which will not be elaborated here.

[0089] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying and analyzing the genetic mechanism of weak structures in deep thermal reservoirs, characterized in that, Including: Obtain a sample set collected from the weak structural area of the deep geothermal reservoir. According to the collection order and length measurement results of the sample set, divide the depth interval of the sample set to obtain the sub-intervals of each sub-sample; Identify the surface features of each of the sub-samples from the image data, and mark the sub-samples corresponding to the surface features that meet the sampling conditions; Combine the component information and sub-intervals of the sub-samples corresponding to the fixed nodes and marked nodes to generate deep geological parameters; Associate and integrate the deep geological parameters of the sub-samples to construct a comprehensive database.

2. The method according to claim 1, wherein: Dividing the depth interval of the sample set to obtain the sub-intervals of each sub-sample according to the collection order and length measurement results of the sample set includes: Based on the collection order and length measurement results, perform cumulative depth recording on each of the sub-samples based on the depth interval to determine the initial depth interval when there is no interval for each of the sub-samples; When the total measured length of each of the sub-samples is less than the total length of the depth interval, compare the surface features corresponding to the image data of adjacent sub-samples, and mark the sub-sample groups with intervals. The surface features include edge features and color features; Determine the predicted spacing of the sub-sample group according to the feature difference degree in the image data; Insert the predicted spacing into the initial depth interval corresponding to the sub-sample group, and recalculate the cumulative depth of each of the sub-samples to obtain the sub-intervals of each sub-sample.

3. The method according to claim 2, wherein: Comparing the surface features corresponding to the image data of adjacent sub-samples and marking the sub-sample groups with intervals includes: When comparing the edge features, extract the edge contours in the image data of adjacent sub-samples; Obtain the concave points and convex points in each of the edge contours, calculate the concave point difference and convex point difference in each of the edge contours. When the concave point difference and / or convex point difference is greater than the quantity threshold, determine that the sub-sample group has an interval and mark it; When both the concave point difference and the convex point difference are less than the quantity threshold, obtain the matching set of the concave point and convex point with the smallest distance in each of the edge contours; Statistically calculate the total distance of each of the matching sets. When the total distance is greater than the fitting degree threshold, determine that the sub-sample group has an interval and mark it.

4. The method according to claim 3, wherein: Obtaining the matching set of the concave point and convex point with the smallest distance in each of the edge contours includes: For each concave point of each of the edge contours, calculate its distance from each convex point in the other edge contour, and select the convex point with the smallest distance to generate a matching set; For each convex point of each of the edge contours, calculate its distance from each concave point in the other edge contour, and select the concave point with the smallest distance to generate a matching set.

5. The method according to claim 2, wherein: Comparing the surface features corresponding to the image data of adjacent sub-samples and marking the sub-sample groups with intervals includes: When comparing the color features, extract the sample contours in the image data of adjacent sub-samples; Obtain the target areas in each of the sample contours where the pixel values are in the same pixel interval and are located in the edge part; Compare the target regions of each of the sample profiles with the target regions of another sample profile. If there are no target regions with the same pixel intervals, it is determined that there is an interval in the sub-sample group and it is marked.

6. The method according to claim 2, wherein determining the predicted spacing of the sub-sample group according to the feature difference degree in the image data includes: For edge features, determine the preset difference degree corresponding to the largest concave point difference or convex point difference as the feature difference degree, or determine the preset difference degree corresponding to the total distance of the matching set as the feature difference degree; For color features, obtain the minimum color difference corresponding to multiple target regions of adjacent sub-samples, and determine the preset difference degree corresponding to the minimum color difference as the feature difference degree; Obtain the length difference between the total length of the depth interval and the total measured length of each sub-sample, and divide the length difference proportionally according to the ratio between the feature difference degrees of the sub-sample groups to obtain the predicted spacing of each sub-sample group.

7. The method according to claim 1, wherein identifying the surface features of each sub-sample from the image data and marking the sub-samples corresponding to the surface features that meet the sampling conditions includes: Select each sub-sample in sequence according to the acquisition order, and identify the color features included in the surface features in the image data of the sub-sample; Determine the pixel mean value of the sub-sample based on the color feature, and calculate the pixel difference between the pixel mean values of the current sub-sample and the previous sub-sample; When the pixel difference is within the abnormal difference interval, it is determined that the current sub-sample meets the sampling conditions and is marked.

8. The method according to claim 1, wherein generating deep geological parameters by combining the component information and sub-intervals of the sub-samples corresponding to the fixed nodes and marked nodes includes: Determine the predicted temperature corresponding to the corresponding sub-interval according to the correspondence between the component information and the temperature, and generate deep geological parameters according to the mapping relationship between the sub-interval and the predicted temperature.

9. The method according to claim 1, wherein It also includes: Obtain the scanning information of the sample data of each well position in the weak structure area of the deep geothermal reservoir by the inspection equipment; Determine the blank depth interval where the sample data is missing according to the scanning information, select the sub-region adjacent to the blank depth interval, and combine the content difference of its component elements to obtain the filling data of the blank depth interval.

10. The method according to claim 9, wherein selecting the sub-region adjacent to the blank depth interval and combining the content difference of its component elements to obtain the filling data of the blank depth interval includes: Select the sub-sample corresponding to the sub-interval adjacent to the blank depth interval as the target sample; Calculate the content difference between the same component elements in each of the target samples, and obtain the content mean value corresponding to the component elements whose content difference is less than the content difference threshold; Determine the content mean value as the filling value of the corresponding component element in the blank depth interval, and obtain the filling data of the blank depth interval according to the filling value.

Images