Well logging rock debris identifying and naming method and device

Abstract

The invention provides a logging rock debris identifying and naming method and device. The method comprises the following steps: acquiring a rock debris picture; segmenting the rock debris photo to enable each rock debris particle to have an independent area, and obtaining a segmented image; identifying the segmented image by using a rock debris particle identification model, and determining particle information in the rock debris photo according to an identification result; and performing rock debris naming on the rock debris photo according to the particle information in the rock debris photo. By means of the scheme, identification and naming of the rock debris on the microcosmic particle level can be achieved, and the logging efficiency and accuracy are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration, and particularly relates to a method and device for identifying and naming mud logging cuttings. BACKGROUND

[0002] At present, the method for identifying mud logging cuttings is that workers collect sample cuttings, and then a geologist identifies and names the cuttings by naked eye according to his knowledge. The real-time performance and accuracy of the identification of the cuttings are completely controlled by the workers and the geologist. In addition, with the increasing speed of drilling and the decreasing size of the cuttings, the efficiency of manual identification cannot meet the requirements of mud logging under the new form. SUMMARY

[0003] The present application provides a method and device for identifying and naming mud logging cuttings, so as to realize the identification and naming of the cuttings at the microscopic particle level and improve the efficiency and accuracy of mud logging.

[0004] To this end, the present application provides the following technical scheme:

[0005] A method for identifying and naming mud logging cuttings, the method comprising:

[0006] obtaining a photo of the cuttings;

[0007] segmenting the photo of the cuttings so that each cutting particle has an independent area, to obtain a segmented image;

[0008] identifying the segmented image by using a cutting particle identification model, and determining the particle information in the photo of the cuttings according to the identification result;

[0009] naming the cuttings in the photo of the cuttings according to the particle information in the photo of the cuttings.

[0010] Optionally, the photo of the cuttings comprises a white light photo and a fluorescent photo.

[0011] The particle information corresponding to the white light photo comprises any one or more of the following: color, mineral name, roundness or broken shape, cement, and particle characteristics.

[0012] The particle information corresponding to the fluorescent photo comprises oiliness.

[0013] Optionally, the segmentation of the photo of the cuttings so that each cutting particle has an independent area comprises:

[0014] segmenting the photo of the cuttings by using a watershed algorithm;

[0015] performing region identification on the segmented image, and dividing each particle into a segmentation frame;

[0016] If the segmentation frame is a conglutination image, segmentation of the conglutination image is continued until each particle has its own segmentation frame.

[0017] Optionally, the method further comprises:

[0018] Combining information of different types of detrital particles to establish a naming logical rule table;

[0019] The naming of the detrital particles in the detrital photo according to the information of the particles in the detrital photo comprises:

[0020] Sequentially matching rules in the naming logical rule table according to the information of the particles in the detrital photo to obtain a matching result;

[0021] Naming the detrital particles in the detrital photo according to the matching result.

[0022] Optionally, the method further comprises:

[0023] Collecting a large number of detrital photos;

[0024] Labeling each particle in the detrital photo to generate a training sample with labeling information;

[0025] Training a detrital particle recognition model using the training sample.

[0026] Optionally, the method further comprises:

[0027] Saving the training sample to a sample library;

[0028] Judging whether the recognition result is correct;

[0029] If the recognition result is incorrect and the labeling information of the detrital photo is incorrect, correcting the labeling information of the detrital photo and synchronously saving to the sample library.

[0030] A logging detrital recognition and naming device, the device comprising:

[0031] A photo acquisition module for acquiring a detrital photo;

[0032] A segmentation module for segmenting the detrital photo so that each detrital particle has an independent area to obtain a segmented image;

[0033] A recognition module for recognizing the segmented image using a detrital particle recognition model and determining information of particles in the detrital photo according to a recognition result;

[0034] A naming module for naming the detrital particles in the detrital photo according to the information of the particles in the detrital photo.

[0035] Optionally, the segmentation module includes:

[0036] A segmentation unit is used to segment the rock debris photograph using the watershed algorithm;

[0037] The region recognition unit is used to perform region recognition on the segmented image and divide each particle into a segmentation box.

[0038] The judgment unit is used to determine whether the segmentation box is attached to the image. When the segmentation box is attached to the image, the segmentation unit is triggered to continue segmenting the attached image until each particle has its own segmentation box.

[0039] Optionally, the device further includes:

[0040] The rule table creation module is used to combine information on different types of rock cutting particles to create a naming logic rule table.

[0041] The naming module sequentially matches the rules in the naming logic rule table with the particle information in the rock cuttings photo to obtain the matching result; and names the rock cuttings in the rock cuttings photo according to the matching result.

[0042] Optionally, the device further includes: a model training module for training a rock cuttings particle recognition model; the model training module includes:

[0043] The photo acquisition unit is used to acquire a large number of rock debris photos;

[0044] The sample generation unit is used to label each particle in the rock debris photograph and generate training samples with labeling information.

[0045] The training unit is used to train a rock debris particle recognition model using the training samples.

[0046] Optionally, the device further includes:

[0047] A sample library is used to store the training samples;

[0048] The identification result detection module is used to determine whether the identification result is correct; if it is incorrect and the annotation information of the rock cuttings photo is wrong, the annotation information of the rock cuttings photo is corrected and saved to the sample library simultaneously.

[0049] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to perform the steps of the logging cuttings identification and naming method.

[0050] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the logging cuttings identification and naming method.

[0051] The logging cuttings identification and naming method and apparatus provided by this invention segment cuttings photographs, giving each cuttings particle an independent region. A cuttings particle identification model is used to identify the segmented images, obtaining the cuttings identification results at the microscopic particle level, i.e., particle information. Then, the cuttings in the photographs are named based on the particle information. Using this invention, on-site cuttings identification can be digitized, unified, and standardized, achieving intelligent lithology identification and significantly improving the efficiency of logging field work. Furthermore, this solution can reduce the workload on-site, providing technical support for future automated and unmanned logging.

[0052] Furthermore, by combining information on different types of rock cuttings and establishing a naming logic rule table, the naming of rock cuttings can be made more standardized.

[0053] Furthermore, by collecting a large number of rock debris photos and annotating them at the micro-particle level, training samples with annotation information are generated. The rock debris particle recognition model is trained using the training samples, and this model can be used to achieve finer-grained rock debris recognition.

[0054] Furthermore, through manual evaluation of the rock cuttings identification results, errors can be detected, corrected, and added to the database in a timely manner. By continuously expanding the sample database, the rock cuttings identification model can be continuously optimized to achieve better rock cuttings identification results. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of a logging cuttings identification and naming method provided in an embodiment of the present invention;

[0057] Figure 2 This is a flowchart of a rock chip particle recognition model trained in an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of rock cuttings photographs with annotation information in an embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram of a logging cuttings identification and naming device provided in an embodiment of the present invention;

[0060] Figure 5This is another structural schematic diagram of the logging cuttings identification and naming device provided in this embodiment of the invention;

[0061] Figure 6 This is a schematic diagram of a model training module in one embodiment of the present invention;

[0062] Figure 7 This is another structural schematic diagram of the logging cuttings identification and naming device provided in an embodiment of the present invention. Detailed Implementation

[0063] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0064] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0065] like Figure 1 The diagram shown is a flowchart of a logging cuttings identification and naming method provided by the present invention, which includes the following steps:

[0066] Step 101: Obtain photos of rock cuttings.

[0067] Specifically, photographic equipment can be used to acquire various rock debris images. To ensure image clarity and improve the accuracy of rock debris identification, certain camera parameters can be set. For example, in a non-limiting embodiment, the following camera parameters can be set: a 65-megapixel camera with 7.2x optical zoom, capable of clearly imaging rock debris particles with diameters ranging from 0.05mm to 4.0mm within a 60mm × 40mm field of view, with an image size of 9344 pixels × 7000 pixels.

[0068] The rock cuttings photographs may include: white light photographs and / or fluorescent photographs. Using white light photographs, the rock cuttings particle information that can be identified includes, but is not limited to, any one or more of the following: color, mineral name, rounded or broken shape, cement, particle characteristics, etc. Using fluorescent photographs, the rock cuttings particle information that can be identified mainly includes: oiliness.

[0069] Step 102: Segment the rock debris photograph so that each rock debris particle has an independent region, and obtain the segmented image.

[0070] Whether it's a white light photograph or a fluorescence photograph, the segmentation method is similar. For example, in a non-limiting embodiment, a watershed algorithm can be used to segment a rock debris photograph. The segmented image is then used for region identification, assigning each particle to a segmentation box (e.g., a rectangle). The segmentation boxes are checked to see if they are contiguous images; if so, the contiguous images are further segmented until each particle has its own segmentation box. In other words, each particle is separated from the overall photograph, resulting in multiple segmentation boxes, each containing one particle. Therefore, the region defined by each segmentation box corresponds to a segmented image.

[0071] A single photograph of rock debris can be segmented into multiple bounding boxes.

[0072] Step 103: Use the rock debris particle recognition model to identify the segmented image, and determine the particle information in the rock debris photo based on the recognition result.

[0073] The rock debris particle recognition model is a microscopic particle level rock debris recognition model that can automatically identify each segmented image, that is, automatically identify the particles within the segmentation box to obtain the particle information in the image.

[0074] Since the oil content of rock cuttings can be determined based on their color, separate models for identifying the lithology and oil content of rock cutting particles can be established. In other words, the rock cutting particle identification model can include both a lithology identification model and / or an oil content identification model. Using a white-light photograph of rock cuttings and the lithology identification model, the lithological information of the particles in the photograph can be obtained; using a fluorescent photograph of rock cuttings and the oil content identification model, the oil content of the particles in the photograph can be obtained.

[0075] Specifically, a single white-light photograph of a rock fragment can be segmented into multiple grain images. These grain images are then individually identified using a rock fragment grain lithology identification model, yielding the identification result for each grain image. Combining these results allows us to obtain the lithological information of most grains in the rock fragment photograph. For example, the identified grain lithology information includes: quartz-gray-neutral-sub-angular. Furthermore, the proportion of each lithological feature can be calculated. For instance, the combined result might include: "intersection of coarse sand (i.e., grain size 0.5-1.0 mm) with quartz, feldspar, and quartzite blocks," with the total amount denoted as A. The content of coarse sandstone blocks is B, and the total content of sandstone grains (i.e., selecting all sandstone components) is C. Then, the proportion of coarse sandstone content is: (A+B) / C×100%.

[0076] Similarly, using the fluorescence photograph of the rock fragment, multiple particle images can be obtained. The oil content identification model of the rock fragment particles can be used to identify these particle images one by one to obtain the oil content identification result for each particle image. By combining these identification results, the oil content information of most particles in the rock fragment photograph can be obtained, such as the oil content identification result being that it contains heavy oil.

[0077] The lithological and oil content identification results were combined to obtain the particle information of the rock fragment: heavy oil-quartz-gray-nothing-sub-angular.

[0078] Step 104: Name the rock fragments in the rock fragment photograph based on the particle information in the rock fragment photograph.

[0079] To ensure consistent naming, in a non-limiting embodiment, rock fragment information can be pre-combined to establish a naming logic rule table. It should be noted that separate naming rule tables for clastic rock lithology and for clastic rock color and oil content can be established.

[0080] For example, a lithological naming rule table for clastic rocks may include: names that can be named, and corresponding information such as rock fragment characteristics (content) and rock fragment characteristics (increment). Nameable rocks may include, for example, conglomerate, sandstone conglomerate, gravelly inequigranulated sandstone, gravelly coarse sandstone, gravelly medium sandstone, gravelly fine sandstone, gravelly siltstone, coarse sandstone, medium sandstone, fine sandstone, siltstone, argillaceous siltstone, mudstone, etc. Rock fragment characteristics are used to describe the lithological information of each type of rock fragment.

[0081] The rock fragment characteristics (content) refer to those analyzed from this rock fragment photograph, such as quartz accounting for 70%. The rock fragment characteristics (increase) are the increase or decrease obtained by comparing the content with the previous rock fragment photograph. For example, if quartz accounted for 80% in the previous rock fragment photograph and 70% in this one, then the quartz (increase) is -10%. Increment information is also an important reference for naming, used to judge subtle changes in characteristic grains. For example, if the increase in mudstone exceeds 5%, it can be directly named mudstone.

[0082] For example, the naming rules table for clastic rock color and oil content can include: names that can be named, and corresponding information such as rock fragment characteristics (content) and rock fragment characteristics (increment). Names that can be named can include, for example, oil-rich, oil spots, oil traces, fluorescence, no oil, and variegated. The rock fragment characteristics are used to describe the oil content information of each type of rock fragment.

[0083] Accordingly, after identifying the particle information in the rock cuttings photograph, the rules in the naming logic rule table can be matched sequentially according to the particle information in the rock cuttings photograph to obtain the matching result; the rock cuttings photograph is named according to the matching result, thereby realizing the automatic identification and naming of rock cuttings.

[0084] For example, in a non-limiting embodiment, the naming is divided into three parts: color + oil content + lithology.

[0085] The naming and matching process is illustrated below using a relatively complex example of variegated oil-bearing gravelly sandstone with uneven grain size.

[0086] First, the lithology is determined based on the grain information in the rock cuttings photographs. Assuming that the conditions of "gravelly inequigranular sandstone" (item 3 in the naming logic rule table) are met, it is determined to be gravelly inequigranular sandstone.

[0087] Then, by using rules numbered 3.1-3.5, if the oil content meets the conditions of "oil stain" in 3.3, it can be identified as an oil stain.

[0088] Finally, according to rule number 3.6, if there are three or more colors and the percentage of a single color in the named particles is greater than 10%, it can be classified as mixed color.

[0089] Based on the above rules, the rock fragments were finally named: variegated oil-bearing gravelly sandstone with uneven grain size.

[0090] Furthermore, custom rule modules and formula editor modules can be developed based on the naming rule table. The lithology of the rock fragment can be automatically determined by combining the particle information such as composition, color, oil content, roundness, and cementation obtained through model identification with the calculated particle size and proportion. This can be done through logical combinations such as "AND", "OR", "NOT", and "None".

[0091] For example, according to rule 1.7 in the naming logic rule table, if the gray increment is 15% and the gray content is 60%, the dark gray increment is 10% and the dark gray content is 20%, and the increments of other colors are <10%, the gray with the largest content will be automatically selected as the color naming result, i.e., gray.

[0092] If the result of (oil-bearing particles and oil-containing particles content) ÷ sandstone content is 35%, which meets the conditions of Rule 1.2 "oil spots", then the oil-bearing particles are defined as oil spots.

[0093] The calculated content of conglomerate is 80% > 75%, and its increment is 20% > 5%, which meets Rule 1 and is therefore named conglomerate.

[0094] By combining the above color, oil content grade, and lithology, the name will be automatically generated: gray oil conglomerate.

[0095] This invention also provides a method for constructing a rock fragment identification model. The rock fragment identification model is a rock fragment identification model at the micro-particle level. For the lithology and oil content of rock fragments, corresponding identification models can be constructed respectively, namely, a rock fragment lithology identification model and a rock fragment oil content identification model.

[0096] For the lithology identification model of rock fragments, a large number of white light photos of rock fragments can be collected and manually labeled. The white light photos with labeled information can be used as training samples to train the lithology identification model of rock fragments.

[0097] Similarly, for the oiliness identification model of rock fragments, a large number of fluorescent photographs of rock fragments can be collected and manually labeled. The fluorescent photographs with labeled information can be used as training samples to train the rock fragment oiliness identification model.

[0098] Since the two identification models with different characteristics are constructed in a similar way, the following description will use the rock debris particle identification model to uniformly represent the two identification models with different characteristics, and will no longer describe them separately.

[0099] Reference Figure 2 The flowchart illustrating the construction of a rock debris particle identification model in an embodiment of the present invention includes the following steps:

[0100] Step 201: Collect a large number of rock debris photographs.

[0101] Specifically, photographic equipment can be used to acquire various rock debris images. To ensure image clarity and improve the accuracy of the particle recognition model, certain camera parameters can be set. These camera parameters can be set according to actual application needs, and this embodiment of the invention does not impose specific limitations on them.

[0102] For example, a 65-megapixel camera is used to photograph dry rock cuttings. A blue rock cutting background is required, and particles with a diameter of 0.05-4.0 mm can be clearly imaged.

[0103] Step 202: Label each particle in the rock debris photograph to generate a training sample with labeled information.

[0104] With the help of some existing image annotation tools, each particle in the rock debris photo can be manually segmented and labeled. The segmentation box can be a rectangle, and the labeling information for each rectangle can include, but is not limited to, any one or more of the following: mineral name, composition, oil content, color, rounding (fracture morphology), cement, particle characteristics (information related to naming or description), etc.

[0105] like Figure 3 The image shown is an example of a photograph of rock debris with annotation information.

[0106] By manually labeling a large number of rock cuttings, a file containing the labeling information of the rock cuttings can be obtained, such as a JSON file.

[0107] For each rock debris photograph, a training sample with labeled information can be generated.

[0108] Step 203: Use the training samples to train a rock debris particle recognition model.

[0109] In this embodiment of the invention, a rock debris particle recognition model can be obtained by training training samples using a deep learning algorithm. The specific structure of the rock debris particle recognition model is not limited in this embodiment; any conventional training method adapted to the specific structure of the model can be used.

[0110] Using the rock debris particle recognition model provided in this embodiment of the invention, it is possible to locate and detect rock debris particles with a particle size in the range of 0.1-4.0 mm in the entire photo, and obtain particle information.

[0111] Furthermore, the rock debris particle identification model provided by this invention was tested and verified, as follows:

[0112] A total of 400 images from 6 categories were collected for testing. After preprocessing, the images were tested, and the overall naming accuracy rate was 84.89%. The specific test results are shown in Table 1 below:

[0113]

[0114] In some embodiments, the training samples may also be saved to a sample library. Accordingly, in Figure 1 After the segmented image is identified using the rock debris particle recognition model in step 103, manual assistance can be used to determine whether the recognition result is correct. If it is incorrect and the annotation information of the rock debris photo is wrong, the annotation information of the rock debris photo can be corrected and simultaneously saved to the sample library. This can update and improve the sample quality of the sample library. In addition, for particles that cannot be identified, they can be manually labeled and added to the sample library at any time. Correspondingly, the current rock debris particle recognition model can be optimized through newly added and / or updated samples to further improve the accuracy of the rock debris particle recognition model.

[0115] The logging cuttings identification and naming method provided by this invention segments logging cuttings photographs, giving each cuttings particle an independent region. A cuttings particle identification model is then used to identify the segmented images, obtaining the cuttings identification results at the microscopic particle level, i.e., particle information. Finally, the logging cuttings are named based on this particle information in the photographs. Using this invention, on-site logging cuttings identification can be digitized, unified, and standardized, achieving intelligent lithology identification and significantly improving the efficiency of logging field work. Furthermore, this method can reduce the workload on-site, providing technical support for future automated and unmanned logging.

[0116] The logging cuttings identification and naming method provided in this invention is based on the information of each cuttings particle in the cuttings photograph, refined to the particle level, combining the proportion and combination relationship of different information of cuttings particles, and then creating a naming logic rule table based on the cuttings naming standard. The naming result is unique, avoiding the multiple interpretations and human experience factors that occur in traditional logging where naming relies on geologists. Compared with traditional logging that relies solely on geologists' eyes and experience to estimate percentage content, this method not only improves accuracy but also greatly enhances logging efficiency.

[0117] Accordingly, embodiments of the present invention also provide a logging cuttings identification and naming device, such as... Figure 4 The diagram shown is a structural schematic of the device.

[0118] The logging cuttings identification and naming device 400 includes:

[0119] Photo acquisition module 401 is used to acquire photos of rock cuttings;

[0120] The segmentation module 402 is used to segment the rock debris photograph so that each rock debris particle has an independent region, thereby obtaining a segmented image;

[0121] The identification module 403 is used to identify the segmented image using a rock debris particle identification model, and to determine the particle information in the rock debris photograph based on the identification result;

[0122] The naming module 404 is used to name the rock cuttings in the rock cuttings photograph based on the particle information in the rock cuttings photograph.

[0123] One specific structure of the segmentation module may include the following units:

[0124] A segmentation unit is used to segment the rock debris photograph using the watershed algorithm;

[0125] The region recognition unit is used to perform region recognition on the segmented image and divide each particle into a segmentation box.

[0126] The judgment unit is used to determine whether the segmentation box is attached to the image. When the segmentation box is attached to the image, the segmentation unit is triggered to continue segmenting the attached image until each particle has its own segmentation box.

[0127] like Figure 5 As shown, in a non-limiting embodiment, the logging cuttings identification and naming device 400 may further include: a rule table establishment module 405, used to combine different types of cuttings particle information to establish a naming logic rule table.

[0128] Accordingly, the naming module 404 can sequentially match the rules in the naming logic rule table according to the particle information in the rock cuttings photo to obtain the matching result; and name the rock cuttings in the rock cuttings photo according to the matching result.

[0129] In this embodiment of the invention, the rock cuttings identification model can be established by a corresponding model training module. This model training module can be part of the logging rock cuttings identification and naming device of the present invention, or it can be independent of the logging rock cuttings identification and naming device. This embodiment of the invention does not limit this.

[0130] The rock debris particle recognition model can employ a deep neural network model, and one specific structure of the model training module is as follows: Figure 6 As shown.

[0131] The model training module 600 includes the following units:

[0132] Photo acquisition unit 601 is used to acquire a large number of rock debris photos;

[0133] The sample generation unit 602 is used to label each particle in the rock debris photograph and generate training samples with labeling information.

[0134] Training unit 603 is used to train a rock debris particle recognition model using the training samples.

[0135] In order to continuously optimize the rock debris particle identification model, such as Figure 7 As shown, in another non-limiting embodiment of the logging cuttings identification and naming device of the present invention, it may further include: a sample library 406 and an identification result detection module 407. Wherein:

[0136] Sample library 406 is used to store the training samples;

[0137] The identification result detection module 407 is used to determine whether the identification result is correct; if it is incorrect and the annotation information of the rock cuttings photo is wrong, the annotation information of the rock cuttings photo is corrected and saved to the sample library simultaneously.

[0138] Accordingly, in this embodiment, the model training module 600 can optimize the rock debris particle recognition model using newly added and / or updated samples, such as by performing optimization training at certain time intervals or after a certain number of new samples have been accumulated. Through continuous model optimization, the accuracy of the rock debris particle recognition model can be further improved.

[0139] The specific implementation methods of the above modules can be referred to the description in the previous embodiments of the present invention, and will not be repeated here.

[0140] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0141] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus can be implemented in other ways.

[0143] The present invention also provides a storage medium, which is a computer-readable storage medium storing a computer program thereon, the computer program being executable when it runs. Figure 1 and Figure 2 The method shown may include some or all of the steps. The storage medium may include read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc. The storage medium may also include non-volatile memory or non-transitory memory, etc.

[0144] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means.

[0145] The embodiments of the present invention have been described in detail above. Specific implementation methods have been used to illustrate the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and systems of the present invention, and are merely some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention, and the content of this specification should not be construed as a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying and naming logging cuttings, characterized in that, The method includes: Obtain photos of rock cuttings; The rock debris photograph is segmented so that each rock debris particle has an independent region, resulting in a segmented image; The segmented image is identified using a rock debris particle recognition model, and the particle information in the rock debris photograph is determined based on the recognition results. The rock debris is named based on the particle information in the rock debris photograph.

2. The logging cuttings identification and naming method according to claim 1, characterized in that, The rock cuttings photographs include white light photographs and fluorescent photographs; The particle information corresponding to the white light photograph includes any one or more of the following: color, mineral name, rounded or broken shape, cement, and particle characteristics; The particle information corresponding to the fluorescence photograph includes: oiliness.

3. The logging cuttings identification and naming method according to claim 1, characterized in that, The segmentation of the rock debris photograph, so that each rock debris particle has an independent region, includes: The rock debris images were segmented using the watershed algorithm; Region recognition is performed on the segmented image to classify each particle into a segmentation box; If the segmentation box is a contiguous image, then continue segmenting the contiguous image until each particle has its own segmentation box.

4. The logging cuttings identification and naming method according to claim 1, characterized in that, The method further includes: Combine information on different types of rock cuttings to establish a naming logic rule table; The step of naming the rock debris in the rock debris photograph based on the particle information in the rock debris photograph includes: Based on the particle information in the rock cuttings photograph, the rules in the naming logic rule table are matched sequentially to obtain the matching result; The rock debris is named based on the matching results in the rock debris photograph.

5. The method for identifying and naming logging cuttings according to any one of claims 1 to 4, characterized in that, The method further includes: Collect a large number of rock debris photographs; Each particle in the rock debris photograph is labeled to generate training samples with labeled information; A rock debris particle recognition model was trained using the training samples.

6. The logging cuttings identification and naming method according to claim 5, characterized in that, The method further includes: Save the training samples to the sample library; Determine whether the recognition result is correct; If the labeling information of the rock cuttings photograph is incorrect, then the labeling information of the rock cuttings photograph is corrected and simultaneously saved to the sample library.

7. A logging cuttings identification and naming device, characterized in that, The device includes: The photo acquisition module is used to acquire photos of rock cuttings; The segmentation module is used to segment the rock debris photograph so that each rock debris particle has an independent region, resulting in a segmented image; The identification module is used to identify the segmented image using a rock debris particle identification model, and to determine the particle information in the rock debris photograph based on the identification result; The naming module is used to name the rock debris in the rock debris photograph based on the particle information in the rock debris photograph.

8. The logging cuttings identification and naming device according to claim 7, characterized in that, The segmentation module includes: A segmentation unit is used to segment the rock debris photograph using the watershed algorithm; The region recognition unit is used to perform region recognition on the segmented image and divide each particle into a segmentation box. The judgment unit is used to determine whether the segmentation box is attached to the image. When the segmentation box is attached to the image, the segmentation unit is triggered to continue segmenting the attached image until each particle has its own segmentation box.

9. The logging cuttings identification and naming device according to claim 7, characterized in that, The device further includes: The rule table creation module is used to combine information on different types of rock cutting particles to create a naming logic rule table. The naming module sequentially matches the rules in the naming logic rule table with the particle information in the rock cuttings photo to obtain the matching result; and names the rock cuttings in the rock cuttings photo according to the matching result.

10. The logging cuttings identification and naming device according to any one of claims 7 to 9, characterized in that, The device further includes: a model training module for training a rock debris particle recognition model; the model training module includes: The photo acquisition unit is used to acquire a large number of rock debris photos; The sample generation unit is used to label each particle in the rock debris photograph and generate training samples with labeling information. The training unit is used to train a rock debris particle recognition model using the training samples.

11. The logging cuttings identification and naming device according to claim 10, characterized in that, The device further includes: A sample library is used to store the training samples; The identification result detection module is used to determine whether the identification result is correct; if it is incorrect and the annotation information of the rock cuttings photo is wrong, the annotation information of the rock cuttings photo is corrected and saved to the sample library simultaneously.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the logging cuttings identification and naming method according to any one of claims 1 to 6.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the logging cuttings identification and naming method according to any one of claims 1 to 6.

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