Rock core sample automatic processing method and equipment and storage medium

Abstract

The invention discloses a core sample automatic processing method and equipment and a storage medium. The method comprises the following steps: receiving an initial rock core sample, and performing infrared thermal imaging on the initial rock core sample; preliminarily cutting the initial rock core sample to obtain segmented rock cores; performing preliminary measurement on the segmented core to obtain segmented core data; constructing a three-dimensional model of the rock core sample on a data server; axially cutting the segmented rock core to obtain a half-section rock core; performing rock core color measurement and spectral measurement on the half-section rock core; and generating a rock core report in the data server according to the thermal imaging data, the segmented rock core data and the rock core profile data. According to the invention, core first-hand information can be automatically collected, core drilling initial data is reserved, and the manual participation degree is reduced; meanwhile, automatic warehousing can be realized, a core report is generated in time, and the scientific research efficiency and the large-scale line production efficiency of the core are effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of core drilling, and in particular to an automated processing method, equipment and storage medium for core samples. Background Art

[0002] Core drilling is a commonly used exploration method for solid mineral geological exploration. The cylindrical drill bit and drilling tools break the rock in a circular shape at the bottom of the hole, leaving a columnar core in the center of the bottom of the hole. The core sample is taken out of the hole to study the geological characteristics of the local land, so it is called core drilling. Different from the core drilling on land, the marine core drilling is that the drilling ship drills holes into the seabed for exploration and obtains the core samples on the seabed.

[0003] In order to analyze the geological characteristics of core samples, the core samples need to be transported to the laboratory for cutting and instrument measurement. In traditional practices, core cutting and measurement operations mainly rely on manual operations. When there are a large number of core samples, operational errors are prone to occur, resulting in unqualified core data. At the same time, it also makes the storage efficiency of core samples and core data low, hindering the realization of large-scale flow operations. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a core sample automated processing method, device and storage medium.

[0005] A first aspect of the present invention provides a method for automatically processing a core sample, comprising the following steps:

[0006] receiving an initial core sample, and performing infrared thermal imaging on the initial core sample to obtain thermal imaging data;

[0007] Upload thermal imaging data to the data server;

[0008] Preliminarily cutting the initial core sample to obtain segmented cores;

[0009] Performing preliminary measurements on the segmented cores to obtain segmented core data; the preliminary measurements include microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection;

[0010] Uploading the segmented core data to a data server, and constructing a three-dimensional model of the core sample on the data server;

[0011] The segmented core is cut axially to obtain a half-cut core;

[0012] Performing core color measurement and spectrum measurement on the half-cut core to obtain core profile data;

[0013] Uploading the core profile data to a data server;

[0014] A core report is generated in the data server according to the thermal imaging data, the segmented core data and the core profile data.

[0015] Furthermore, after receiving the initial core sample, the following steps are also included:

[0016] Generate a core code for the core sample, the coding format is: date_voyage ID_station ID_core random code;

[0017] Hydrogen sulfide deposition in the core samples was excluded.

[0018] Furthermore, the infrared thermal imaging of the initial core sample is performed by a natural gas hydrate infrared thermal imager; the thermal imaging data includes thermal imaging data with depth marks, and the depth marks are used to represent the combined temperature of the core.

[0019] Further, after the step of uploading the thermal imaging data to the data server, the data server classifies the thermal imaging data through a hydrate classification neural network;

[0020] The hydrate classification neural network is trained by the following steps:

[0021] Collect historical thermal imaging data;

[0022] Classify and label historical thermal imaging data, and divide them into multiple categories according to the degree of hydrate enrichment; divide each category into training set and validation set according to a preset ratio;

[0023] Training the hydrate classification neural network based on the training set, and using the validation set to validate the training effect of the hydrate classification neural network, wherein the training effect is represented by accuracy and recall;

[0024] The current score of the hydrate classification neural network is calculated according to the accuracy and recall rate obtained by verification. When the current score of the hydrate classification neural network exceeds a preset score threshold, it is determined that the hydrate classification neural network has completed training.

[0025] Furthermore, the microbial detection specifically includes the following steps:

[0026] Performing microbial sampling on the segmented cores by using a microbial sampler to obtain microbial samples;

[0027] The microbial sample is tested by a micropaleontology experimental instrument to obtain microbial information;

[0028] The pore water detection specifically comprises the following steps:

[0029] Get pore water from the core through a sampler to obtain a pore water sample;

[0030] Using an organic chemical instrument to test the chemical composition of the pore water sample to obtain pore water information;

[0031] The core image acquisition specifically includes the following steps:

[0032] The segmented core is photographed by a CCD camera to obtain several core images;

[0033] classifying the core image by a core image classification neural network;

[0034] The X-ray scanning is specifically performed by scanning the segmented core with a CT scanner to obtain a plurality of core slices;

[0035] The physical and chemical information detection is specifically used to obtain the mineral content, magnetic susceptibility, remanent magnetic intensity, and demagnetization rate of the segmented core as the physical and chemical data of the core;

[0036] The microbial information, pore water information, core images, core slices and physical and chemical data are output as segmented core data.

[0037] Furthermore, before outputting the microbial information, pore water information, core image, core slice and physical and chemical data as segmented core data, the following steps are also included:

[0038] Generate a microbial code for the microbial information, the coding format is: date_voyage ID_station ID_core random code_microbial information ID;

[0039] A pore water code is generated for the pore water information, and the coding format is: date_voyage ID_station ID_core random code_pore water information ID.

[0040] Furthermore, constructing the three-dimensional model of the core sample on the data server specifically includes the following steps:

[0041] According to the core slices, three-dimensional restoration is performed on the segmented core to obtain a preliminary model;

[0042] Adding the microorganism information and pore water information to the preliminary three-dimensional model;

[0043] The core surface of the preliminary model is restored according to the core image to obtain a three-dimensional model of the core sample.

[0044] Furthermore, after the step of generating a core report, the following steps are also included:

[0045] Sending a core report to an object subscribing to the core report;

[0046] Waiting for a preset modification time, if no feedback is received from the object within the preset modification time, confirming that the core report data is accurate;

[0047] If feedback is received from the subject within the preset modification time, the half-cut core is re-measured through manual operation.

[0048] A second aspect of the present invention discloses an electronic device, comprising a processor and a memory;

[0049] The memory is used to store programs;

[0050] The processor executes the program to implement an automated core sample processing method.

[0051] A third aspect of the present invention discloses a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement an automated core sample processing method.

[0052] The embodiments of the present invention have the following beneficial effects: the disclosed method, device and storage medium for automated processing of core samples can automatically collect first-hand information of cores, retain initial data of drilling cores, and reduce the degree of manual participation; at the same time, it can also realize automatic storage and timely generate core reports, effectively improving the scientific research efficiency of cores and the efficiency of large-scale flow operations.

[0053] Additional aspects and advantages of the present invention will be given in part in the following description, and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 This is a basic implementation flow chart of a core sample automated processing method, device and storage medium of the present invention;

[0056] Figure 2 This is a core storage flow chart in a core sample automated processing method, device and storage medium of the present invention;

[0057] Figure 3 It is a schematic diagram of core coding in a core sample automated processing method, device and storage medium of the present invention;

[0058] Figure 4It is a flow chart of a neural network training for hydrate classification in a core sample automated processing method, equipment and storage medium of the present invention;

[0059] Figure 5 This is a flow chart of microbial information collection in a core sample automated processing method, device and storage medium of the present invention;

[0060] Figure 6 This is a flow chart of pore water information collection in a core sample automated processing method, equipment and storage medium of the present invention;

[0061] Figure 7 It is a schematic diagram of a core image in a core sample automated processing method, device and storage medium of the present invention;

[0062] Figure 8 It is a schematic diagram of core slice data in a core sample automated processing method, device and storage medium of the present invention;

[0063] Fig. 9 It is a schematic diagram of a three-dimensional model of a core sample in a core sample automated processing method, device and storage medium of the present invention;

[0064] Fig.10 The invention discloses a core sample automatic processing method, equipment and core report sending flow chart in a storage medium. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0067] Before explaining the embodiments of the present invention in detail, some industry terms involved in the present invention are first explained:

[0068] Core drilling: Core drilling is a commonly used exploration method for solid mineral geological exploration. Different from core drilling on land, marine core drilling is a drilling ship drilling holes into the seabed to obtain seabed cores. Through core analysis, the microbial characteristics, pore water characteristics, physical and chemical characteristics, etc. of the geological area where the core is located can be examined to further determine the stratigraphic age and conduct stratigraphic comparison; by observing the lithology and sedimentary structure of the core, the sedimentary environment of the geological area where the core is located can be determined, and the structure and fault conditions, such as the dip angle of the formation, the contact relationship of the formation, the location of the fault, etc. can be understood. It can be seen that core drilling is an important geological exploration method for studying and understanding underground geology and mineral conditions.

[0069] Drilling vessel: A drilling vessel is a ship dedicated to drilling submarine geological structures. It is mainly used for marine geological exploration, among which core drilling is an important application scenario of marine geological exploration. A drilling vessel obtains a large number of cores every time it sails. Taking the USS Resolution drilling ship as an example, according to statistics, the average voyage of the USS Resolution drilling ship in the ocean drilling program is 2,000 meters, and about 1,500 core samples are collected each time (roughly estimated at 1.5 meters). The total length of the cores in the IODP342 voyage is 5,700 meters, and there are nearly 4,000 core samples (roughly estimated at 1.5 meters). It can be seen that in the face of so many core samples, if each step in the core storage process requires manual participation, it is easy to cause human errors and affect the storage efficiency.

[0070] To solve the problem of high manual involvement in the core storage process, Figure 1 As shown, the embodiment of the present invention provides an automated core sample processing method, comprising the following steps:

[0071] S1. receiving an initial core sample, performing infrared thermal imaging on the initial core sample, and obtaining thermal imaging data;

[0072] S2. Upload the thermal imaging data to the data server;

[0073] S3. Perform preliminary cutting on the initial core sample to obtain segmented cores;

[0074] S4. Conduct preliminary measurements on the segmented cores to obtain segmented core data; the preliminary measurements include microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection;

[0075] S5. Upload the segmented core data to the data server and build a three-dimensional model of the core sample on the data server;

[0076] S6. Axially cut the segmented core to obtain a half-cut core;

[0077] S7. Performing core color measurement and spectrum measurement on the half-cut core to obtain core profile data;

[0078] S8. Upload the core profile data to the data server;

[0079] S9. Generate a core report in the data server based on the thermal imaging data, the segmented core data and the core profile data.

[0080] The core storage process in the embodiment of the present invention is as follows Figure 2 As shown, the drilling ship uses a cylindrical drill bit and drilling tools to break the rock in a circular shape at the bottom of the hole, retaining a columnar core in the center of the bottom of the hole, and then takes the core out of the hole and transports it to the laboratory for geological characteristic measurement through core conveyors and other equipment; after the laboratory completes the geological characteristic measurement of the core, it will generate a core report as a measurement result output to scientific researchers for reading, and the scientific researchers will further analyze the geological conditions based on the core report.

[0081] S1. receiving an initial core sample, performing infrared thermal imaging on the initial core sample, and obtaining thermal imaging data.

[0082] In the embodiment of the present invention, after receiving the initial core sample, a core code is first generated for the core sample. Figure 3 As shown in the figure, the core code consists of "date_voyage ID_station ID_core random code". The date indicates the sampling time of the core sample; the voyage ID indicates the voyage of the exploration ship that collected the core sample; the station ID indicates the well location where the core sample was collected; and the core random code is the unique ID of the core generated by the server. Generating core codes for core samples is helpful for the subsequent cataloging of core samples. Various forms and reports generated by the subsequent cataloging work are associated with the core codes.

[0083] In an embodiment of the present invention, after receiving the initial core sample, hydrogen sulfide deposits in the core sample must also be removed. There are a large number of sulfur and hydrogen elements in animal and plant organisms, which can generate hydrogen sulfide compounds through high temperature and high pressure or bacterial action. Especially in an anaerobic environment, microorganisms can convert organic sulfur and inorganic sulfur into hydrogen sulfide; therefore, from the production environment of hydrogen sulfide, it can be found that the interior of the formation is an excellent place for hydrogen sulfide production and storage, and the core samples obtained from the formation may also contain hydrogen sulfide. This embodiment uses a core hydrogen sulfide removal device to remove hydrogen sulfide from the core sample, effectively protecting the safety of scientific researchers and experimental instruments.

[0084] In this embodiment, the core sample transported from the core conveyor is about 950 cm long and has a maximum diameter of 15 cm. In this embodiment, a natural gas hydrate infrared thermal imager is used to collect thermal imaging data of the distribution of natural gas hydrates inside the core, and the thermal imager is accurately positioned by a slide rail system; after the thermal imaging data is collected, thermal imaging data with depth marks are obtained, where the depth marks on the thermal imaging data are used to represent the combined temperature of the core.

[0085] S2. Upload the thermal imaging data to the data server;

[0086] The collected thermal imaging data is uploaded to the data server, which can draw images in formats such as JPEG based on the obtained data and save them in the database. This embodiment uses a MySQL database to store thermal imaging data, and converts unstructured data into base64 format for storage.

[0087] In this embodiment, a hydrate classification neural network is preloaded on the data server for image classification. Specifically, Figure 4 As shown, the image classification process is divided into two parts, namely the model generation stage and the data verification stage. The specific process is as follows:

[0088] Step 1-1: Collect existing thermal imaging data. This part of the data mainly comes from the existing thermal imaging data stored in the database. A certain amount of thermal imaging data is retrieved from the database for model training;

[0089] Step 1-2: Researchers mark and classify the collected thermal imaging data. The classification can be binary based on whether it contains hydrates, or multiple based on the degree of hydrate enrichment (for example, no hydrates are classified as category 1, average hydrate enrichment is classified as category 2, high hydrate enrichment is classified as category 3, etc.). Currently, it is classified into binary based on whether it contains hydrates. The data will be divided into a test set and a validation set. The test set is used for training, and the validation set is used for testing the model.

[0090] Step 1-3: Use Convolutional Neural Networks (CNN) to train based on the training set, and then use the validation set to verify the model effect. Repeat this process. Each validation will get the precision rate P and recall rate R, and then calculate the comprehensive score Score = 2*(P*R) / (P+R). If the Score reaches the preset score threshold, it means that the model effect meets the requirements and can be used for thermal imaging data classification. In this embodiment, the preset score threshold is 0.5;

[0091] Step 1-4: Generate the latest hydrate classification neural network. In this embodiment, when the ship is not in operation, the model will be retrained based on the newly collected data to generate a replacement model. The model cycle update time can be freely configured (such as daily, weekly, or monthly updates). In this embodiment, the hydrate classification neural network starts to be updated at 1 a.m. every day;

[0092] Step 1-5: After the data server receives the thermal imaging data generated by the core sample, it uses the hydrate classification neural network to classify it;

[0093] Step 1-6: The hydrate classification neural network will automatically generate classification data and store the classification results and images in the database;

[0094] Step 1-7: The thermal imaging data in the library will eventually be output together in the final generated core report for researchers to read;

[0095] Step 1-8: After reading the core report, the researcher may point out that the thermal imaging data is misclassified. If the data server receives the researcher's feedback, the latest data will be used for the next model training update. If there is no feedback, the default model classification result is correct.

[0096] S3. Perform preliminary cutting on the initial core sample to obtain segmented cores.

[0097] In most cases, the core sample is about 950 cm in length and 15 cm in diameter at most; in this embodiment, the core sample is cut into segmented cores of about 150 cm for subsequent measurement.

[0098] S4. Conduct preliminary measurements on the segmented cores to obtain segmented core data; preliminary measurements include microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection.

[0099] Microbial detection: Microorganisms living in the environment below the surface are called lithobiot ic microorganisms. Lithological microorganisms have a wide distribution range around the world. Where microorganisms exist, biochemical reactions promoted by microbial enzymes will occur. Therefore, lithobiot ic microorganisms play an important role in the circulation of chemical substances above and below the surface. At the same time, lithobiot ic microorganisms also have environmental benefits. For example, lithobiot ic microorganisms on the seabed may play a role in carbon cycling and global warming because the methane gas produced by the metabolism of lithobiot ic microorganisms on the seabed has a potential impact on the climate; therefore, the extraction and detection of lithobiot ic microorganisms that may be attached to the core has important scientific research value.

[0100] like Figure 5As shown, the microbial detection in this embodiment mainly includes the following steps:

[0101] S4-a1. Microbial sampling is performed on the segmented cores through a microbial sampler to obtain a microbial sample. Since the number of microorganisms in the core is very small, and the calcium compounds in the core have a high adsorption effect on microorganisms, it is more efficient to extract microorganisms using calcium salts in the core as a target. Existing microbial sampling methods include enzyme lysis, freeze-thaw, and frozen grinding. This embodiment is implemented by a lysis method, in which lysozyme, proteinase K, and poly-(A) are added to the microbial sampler to reduce the adsorption of microorganisms on calcium compounds in the core, and then the SDS lysis method is used to sample the microorganisms in the core as microbial samples.

[0102] S4-a2. Use micropaleontology experimental instruments to test microbial samples and obtain microbial information.

[0103] The micropaleontology experimental instrument of this embodiment analyzes the information of foraminifera, calcareous ultra-microfossils, etc. reflected by the microorganisms in the core; the experimental means used include preparing the microorganism samples into specimens and using an optical stereo microscope to observe the surface and internal details of the microorganism samples; using CT scanning of the microorganism samples to obtain their internal structure, etc. The study of the microorganism samples in the core is helpful to determine the age of the rock layer described in the core and the possibility of oil storage, etc., and can be used as an indicator for petroleum geologists.

[0104] Pore ​​water detection: The pores in the core may be formed during mineral deposition or in certain geological processes after sedimentation and diagenesis. Therefore, chemical composition analysis of the pore water in the core can effectively reflect the chemical reactions between the solid and liquid phases of the sediments and the exchange and diffusion of substances during the early diagenesis process.

[0105] like Figure 6 As shown, the pore water detection in this embodiment mainly includes the following steps:

[0106] S4-b1. Obtain pore water from the core through a sampler to obtain a pore water sample;

[0107] S4-b2. Use an organic chemical instrument to detect the chemical composition of the pore water sample to obtain pore water information;

[0108] In this embodiment, the chemical composition detection of the pore water sample mainly includes isotope analysis of various anions and cations in the pore water. For example, the anion Cl - 、SO 4 2- and cation K + 、Na + , Ca 2+ Mg2+ The concentration analysis was carried out by ion chromatography. Before the determination, dilute HNO 3 The pore water sample was diluted to an appropriate multiple, and then Rh was added as an internal standard and measured using an ion chromatograph. The carbon and oxygen isotopes were measured using a gas isotope mass spectrometer. The pore water sample was first reacted with 100% phosphoric acid for a period of time at room temperature, and the released CO 2 The gas is sent to the gas isotope mass spectrometer for measurement. The determination of boron isotopes and chlorine isotopes is carried out by positive thermal ionization mass spectrometry. After the pore water sample is sent to the mass spectrometer, it is vacuumed inside the mass spectrometer and then measured using the static double-receiver method.

[0109] After obtaining the microbial information and pore water information through measurement, this embodiment generates a microbial code for the microbial information and a pore water code for the pore water information. Specifically, the encoding format of the microbial code is: date_voyage ID_station ID_core random code_microbial information ID; the encoding format of the pore water code is: date_voyage ID_station ID_core random code_pore water information ID. It can be seen from the encoding format that the microbial code and the pore water code are associated with the core code, so the core of their source can be retrieved through the microbial code and the pore water code.

[0110] In this embodiment, core image acquisition specifically includes the following steps:

[0111] S4-c1. Segmented cores are photographed by a CCD camera to obtain several core images;

[0112] S4-c2. Classify core profile data through core image classification neural network.

[0113] In this embodiment, the core is photographed by a CCD camera with a six-degree-of-freedom bracket. The CCD camera is installed at the end of the bracket that can achieve six degrees of freedom. The six-degree-of-freedom bracket enables the CCD camera to achieve six degrees of freedom (i.e., three translational degrees of freedom and three rotational degrees of freedom) within a certain range, and further photographs the core image showing the surface characteristics of the segmented core. The core image schematic diagram is shown in FIG. Figure 7 As shown in the figure, after the CCD camera captures the core image, the real-time data will be transmitted to the data server via the CCD camera. A core image classification neural network is preloaded in the data server for image classification. The core image classification neural network is similar to the training and verification process of the hydrate classification neural network mentioned above. Both use the existing core image data for manual identification and classification, and divide it into training sets and verification sets to train the core image classification neural network. The comprehensive score is used to judge whether the core image classification neural network can be put into actual work.

[0114] In this embodiment, X-ray scanning is specifically performed by scanning the segmented core with a CT scanner to obtain a number of core slices. CT (Computed Tomography) scanning refers to dividing the segmented core into a plurality of core slices, and performing three-dimensional reconstruction of the core sample through the plurality of core slices. The core slice effect is as follows: Figure 8 As shown. Since the components of the core are different, the absorption intensity of X-rays is also different accordingly. Therefore, by performing X-ray scanning on the segmented core, the corresponding grayscale value can be generated according to the intensity of the rays absorbed by each part of the core to characterize different structural components; the denser the part of the segmented core, the more X-rays it absorbs, so the grayscale value on the core slice is larger. Exemplarily, the CT scanner in this embodiment includes an X-ray source, a stage, and an X-ray detector for collecting scanned rays. The X-ray source emits X-rays to the segmented core on the stage, and after a series of interactions with the segmented core, it is received by the X-ray detector, which processes and converts it into an electrical signal to form a core slice that records the grayscale information at different positions. CT scanning of core samples is helpful in understanding the bedding and direction of core samples on the one hand, and on the other hand, it is helpful in screening for subsequent analysis, that is, screening segmented cores with relatively complete structures for subsequent research, which can improve the core recovery rate and other effects.

[0115] In this embodiment, the physical and chemical information detection is specifically used to obtain the mineral content, magnetic susceptibility, residual magnetic intensity, demagnetization rate and other indicators of the segmented core, and this data is written into the report as the physical and chemical data of the core. For example, the mineral quality of the core sample is obtained by γ measurement, which can be used to analyze the mineral reserves of the rock layer where the core sample is located. The measurement of the magnetic susceptibility, residual magnetic intensity, demagnetization rate and other indicators of the segmented core can obtain information such as the type, content, particle size and particle size arrangement direction of the magnetic minerals in the segmented core. Since the movement, deposition and transformation of magnetic minerals are closely related to the changes in the sedimentary environment and the evolution of paleoclimate, the magnetic minerals in the segmented core can be analyzed as a substitute indicator of the rock layer environment changes and climate processes described in the core sample, providing a basis for paleomagnetic chronology research.

[0116] S5. Upload the segmented core data to the data server, and construct a three-dimensional model of the core sample on the data server. In this embodiment, the three-dimensional reconstruction of the core sample is mainly based on the core slices. The core slices are reconstructed in the data server to obtain a three-dimensional model of the core sample. The constructed three-dimensional model example Fig. 9 Constructing a three-dimensional model of the core sample on the data server specifically includes the following steps:

[0117] S5-1. Perform three-dimensional restoration of the segmented core according to the core slices to obtain a preliminary model;

[0118] S5-2. Add microbial information and pore water information to the preliminary 3D model;

[0119] S5-3. Restore the core surface of the preliminary model based on the core image to obtain a three-dimensional model of the core sample.

[0120] S6. Axially cut the segmented core to obtain a half-cut core.

[0121] In some embodiments, after the segmented core is axially cut, a half-cut core data code is further generated for the half-cut core. The coding format is: date_voyage ID_station ID_core random code_half-cut core ID. The half-cut core ID is recorded in sequence, that is, the first half-cut core of the same core sample is recorded as 1, the second half-cut core is recorded as 2... and so on to obtain the data code of the half-cut core so as to realize the association between the half-cut core and the core sample.

[0122] S7. Performing core color measurement and spectrum measurement on the half-cut core to obtain core profile data;

[0123] S8. Upload the core profile data to the data server;

[0124] Half-cutting the core can obtain vertical change information of the core sample and determine the distribution and content of the main minerals in the core. Specifically, the mineral spectrum usually contains a series of characteristic absorption bands, and the characteristic band information extracted from different minerals is different. The absorption characteristics of minerals to light can be characterized by spectral absorption characteristic parameters, such as the wavelength position, depth, width, symmetry, area, etc. of the absorption band, from which qualitative and quantitative information of various minerals can be extracted. After obtaining the core profile data, the embodiment of the present invention uses the core spectrum classification neural network on the data server to perform image classification. The core spectrum classification neural network is similar to the training and verification process of the above-mentioned hydrate classification neural network. Both use the existing core profile data for manual identification and classification, and are divided into training sets and verification sets to train the core spectrum classification neural network. The comprehensive score is used to judge whether the core spectrum classification neural network can be put into actual work.

[0125] S9. Generate a core report in the data server based on the thermal imaging data, the segmented core data and the core profile data.

[0126] After the step S9 of generating a core report in this embodiment, the following steps are also included:

[0127] S9-1. Send the core report to the object that subscribes to the core report;

[0128] S9-2. Wait for the preset modification time. If no feedback is received from the object within the preset modification time, confirm that the core report data is accurate;

[0129] S9-3. If feedback is received from the subject within the preset modification time, the half-cut core is re-measured through manual operation.

[0130] like Fig.10 As shown, in this embodiment, the data server generates a core report based on the existing thermal imaging data, segmented core data and core profile data. The report is an online document and supports editing and review functions, which are pushed to the researchers who subscribe to the report. The core report information specifically includes the basic information of the core (such as voyage, station, etc.), as well as further information obtained by analysis such as hydrate infrared images and classifications, core images, core profile images, core three-dimensional models, pore water, microorganisms and other biochemical information. In this embodiment, the core report is based on a subscription publishing model, that is, interested researchers can subscribe to the initial report of a specific core sample according to the core code. After the report is generated and archived, the data server will actively remind the scientist to read it through timely software and text messages.

[0131] In some embodiments, if the scientific researcher has objections to certain data (eg incorrect graphic classification results, abnormal biochemical indicators, etc.), feedback can be provided to the data server, and the feedback here will be processed according to the authority of the scientific researcher. If the user has the authority to edit and correct, the corresponding modification opinions will be processed directly on the data server, otherwise an application for modification needs to be initiated, and it will be processed on the data server after the final approval. There is a default delay time here (the delay time in this embodiment supports active configuration, and the default is 1 day). If the default delay time is exceeded, the data of the initial report is normal. At the same time, a reminder message is sent at each established time interval within this time range to remind scientific researchers to read the core report. In this embodiment, the delayed feedback time of the core report is associated with the update time of the neural network model. For the core report information that has not received feedback, it will be added to the database later to update the neural network model.

[0132] In summary, the embodiment of the present invention realizes the automatic collection of first-hand information of the core after the initial cutting and section cutting of the core, retains the initial data of the drilling core, and automatically stores it in the database, and uses methods such as convolutional neural networks to timely process the data, generate reports, and tap the data potential. The embodiment of the present invention realizes the automatic processing of core samples, reduces the degree of human participation, and improves the efficiency of large-scale flow operations.

[0133] Exemplarily, the automated processing flow of core samples according to an embodiment of the present invention is described as follows:

[0134] Step 1: Obtain core samples through drilling vessels and transport them through core conveyors; Use natural gas hydrate infrared thermal imagers to detect the distribution of natural gas hydrates inside core samples, and actively upload the obtained thermal imaging data with depth marks to the data server; After the data server uses the hydrate classification neural network for classification, it draws a JPEG format image based on the data and saves it in the database.

[0135] Step 2: Generate core code. The coding format is date_voyage ID_station ID_core random code.

[0136] Step 3: Use the core hydrogen sulfide removal device to remove H in the core sample 2 S.

[0137] Step 4: Perform preliminary cutting of the core samples into segmented cores with a length of about 150 cm.

[0138] Step 5: Use a microbial sampler to collect microbial samples from the segmented cores.

[0139] Step 6: Use micropaleontology experimental instruments to analyze the information of foraminifera, calcareous nanofossils, etc. on the microbial samples.

[0140] Step 7: Encode the microbial information in the format of date_voyage ID_station ID_core random code_microbial information ID. After encoding, upload the microbial information to the data server for storage.

[0141] Step 8: Use a sampler to obtain pore water from the segmented core.

[0142] Step 9: Use organic chemistry experimental instruments to analyze the main components of pore water (such as trace elements, oxygen and carbon isotope determination, etc.).

[0143] Step 10: Generate data code for the pore water information in the format of date_voyage ID_station ID_core random code_pore water information ID. After coding, upload the pore water information to the data server for storage.

[0144] Step 11: Use a CCD camera to collect core images; upload the collected core images to the data server, and the data server uses the core image classification neural network to classify the core images and save them in the database.

[0145] Step 12: Use an X-ray CT scanner to scan the segmented core to obtain two-dimensional data of the core slices; upload the core slice data to the data server, and the data server reconstructs the core sample in three dimensions based on the core slice data.

[0146] Step 13: Conduct physical and chemical information detection on the segmented cores, and store the collected parameters such as mineral content, magnetic susceptibility, remanent magnetic intensity, demagnetization rate, etc. in the data server.

[0147] Step 14: Cut the segmented core along the axial direction to obtain a half-cut core.

[0148] Step 15: Generate half-cut core data code for the half-cut core. The coding format is date_voyage ID_station ID_core random code_half-cut core ID.

[0149] Step 16: Conduct core color and spectrum measurements on the half-section; and further measure pore water and microorganisms based on three-dimensional data.

[0150] Step 17: The half-section core data is uploaded to the data server, which classifies it through a convolutional neural network and stores it in the database.

[0151] Step 18: Generate a core report based on thermal imaging data, segmented core data and core profile data.

[0152] Step 19: Send core reports to subscribed researchers and receive feedback from them; update reports and model parameters based on feedback from researchers.

[0153] Step 20: After the previous steps are completed, the preliminary measurements of the core samples from drilling are all completed. Next, more detailed measurements will be carried out in the laboratory based on the feedback from researchers, and finally the core samples will be sent to the storage.

[0154] The present application embodiment also provides an electronic device. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (centra l processing un its, CPU) and one or more memories, wherein at least one program code is stored in the above memory, and the above at least one program code is loaded and executed by the above processor to implement the core sample automation processing method provided in the above method embodiment. Of course, the electronic device may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface, so as to perform input and output, and the electronic device may also include other components for realizing the device functions, which will not be repeated here.

[0155] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a program code, and the program code can be executed by a processor in a computer device to complete the core sample automated processing method in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0156] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0157] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0158] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0159] The memory may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 Not shown, usually referred to as a "hard drive"). A disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus via one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0160] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0161] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0162] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0164] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0165] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A core sample automated processing method, characterized in that: The following steps are involved: receiving an initial core sample, and performing infrared thermal imaging on the initial core sample to obtain thermal imaging data; Upload thermal imaging data to the data server; Preliminarily cutting the initial core sample to obtain segmented cores; Performing preliminary measurements on the segmented cores to obtain segmented core data; the preliminary measurements include microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection; Uploading the segmented core data to a data server, and constructing a three-dimensional model of the core sample on the data server; The segmented core is cut axially to obtain a half-cut core; Performing core color measurement and spectrum measurement on the half-cut core to obtain core profile data; Uploading the core profile data to a data server; A core report is generated in the data server according to the thermal imaging data, the segmented core data and the core profile data.

2. The method for automated processing of core samples according to claim 1, characterized in that: After receiving the initial core sample, the method further comprises the following steps: Generate a core code for the core sample, the coding format is: date_voyage ID_station ID_core random code; Hydrogen sulfide deposition in the core samples was excluded.

3. The method for automated processing of core samples according to claim 1, characterized in that: The infrared thermal imaging of the initial core sample is performed by a natural gas hydrate infrared thermal imager; the thermal imaging data includes thermal imaging data with depth marks, and the depth marks are used to represent the combined temperature of the core.

4. The method for automated processing of core samples according to claim 1, characterized in that: After the step of uploading the thermal imaging data to the data server, the data server classifies the thermal imaging data by a hydrate classification neural network; The hydrate classification neural network is trained by the following steps: Collect historical thermal imaging data; Classify and label historical thermal imaging data, and divide them into multiple categories according to the degree of hydrate enrichment; divide each category into training set and validation set according to a preset ratio; Training the hydrate classification neural network based on the training set, and using the validation set to validate the training effect of the hydrate classification neural network, wherein the training effect is represented by accuracy and recall; The current score of the hydrate classification neural network is calculated according to the accuracy and recall rate obtained by verification. When the current score of the hydrate classification neural network exceeds a preset score threshold, it is determined that the hydrate classification neural network has completed training.

5. The method for automated processing of core samples according to claim 1, characterized in that: The microbial detection specifically comprises the following steps: Performing microbial sampling on the segmented cores by using a microbial sampler to obtain microbial samples; The microbial sample is tested by a micropaleontology experimental instrument to obtain microbial information; The pore water detection specifically comprises the following steps: Get pore water from the core through a sampler to obtain a pore water sample; Using an organic chemical instrument to test the chemical composition of the pore water sample to obtain pore water information; The core image acquisition specifically includes the following steps: The segmented core is photographed by a CCD camera to obtain several core images; classifying the core image by a core image classification neural network; The X-ray scanning is specifically performed by scanning the segmented core with a CT scanner to obtain a plurality of core slices; The physical and chemical information detection is specifically used to obtain the mineral content, magnetic susceptibility, remanent magnetic intensity, and demagnetization rate of the segmented core as the physical and chemical data of the core; The microbial information, pore water information, core images, core slices and physical and chemical data are output as segmented core data.

6. The method for automated processing of core samples according to claim 5, characterized in that: Before outputting the microbial information, pore water information, core image, core slice and physical and chemical data as segmented core data, the following steps are also included: Generate a microbial code for the microbial information, the coding format is: date_voyage ID_station ID_core random code_microbial information ID; A pore water code is generated for the pore water information, and the coding format is: date_voyage ID_station ID_core random code_pore water information ID.

7. The method for automated processing of core samples according to claim 5, characterized in that: The three-dimensional model of the core sample is constructed on the data server, specifically comprising the following steps: According to the core slices, three-dimensional restoration is performed on the segmented core to obtain a preliminary model; Adding the microorganism information and pore water information to the preliminary three-dimensional model; The core surface of the preliminary model is restored according to the core image to obtain a three-dimensional model of the core sample.

8. The method for automated processing of core samples according to claim 1, characterized in that: After the step of generating a core report, the following steps are also included: Sending a core report to an object subscribing to the core report; Waiting for a preset modification time, if no feedback is received from the object within the preset modification time, confirming that the core report data is accurate; If feedback is received from the subject within the preset modification time, the half-cut core is re-measured through manual operation.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 8.

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