Lithology identification method and device based on fusion of rock debris fluorescence image and Raman spectrum, and electronic equipment

Abstract

The invention relates to the technical field of lithology identification, in particular to a lithology identification method and device based on fusion of a rock debris fluorescence image and a Raman spectrum, and electronic equipment. The lithology identification method comprises the following steps: dynamically segmenting a fluorescence region by applying a Gaussian weighting method according to a rock debris fluorescence image of which a corresponding rock debris sample is non-petroleum hydrocarbon fluorescence; obtaining high, medium and low fluorescence intensity area images and non-fluorescence area images, determining gray value ranges and fluorescence area proportions of the area images, and substituting the gray value ranges and the fluorescence area proportions into mineral type judgment rules for matching to obtain corresponding mineral types and mineral contents; and matching with the lithology identification rule to obtain a corresponding lithology identification result. According to the lithology identification method, the participation of any experimental apparatus is not needed during lithology identification, the mineral type and the mineral content are directly obtained by directly reflecting the gray value range of the fluorescence intensity and combining the established mapping relation between the fluorescence intensity and the mineral components, the drilling cost can be effectively saved, and the lithology can be rapidly and accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lithology identification, and is a lithology identification method and device based on fusion of a cutting fluorescence image and a Raman spectrum, and an electronic device. BACKGROUND

[0002] Lithology identification is a key link in oil and gas resource exploration and development, but the wide application of PDC drill bits poses a severe challenge to cutting identification in geological logging. Specifically, the PDC drill bit breaks rocks through shearing and cutting, resulting in extremely small cutting particle sizes. In particular, in poor lithology formations, the cuttings are mostly in a paste or dispersed granular state, and the mineral composition and structure are difficult to identify with the naked eye, which reduces the accuracy of lithology identification.

[0003] With the rapid development of computer technology, a large number of researchers have begun to analyze cutting images using intelligent algorithms such as machine learning to identify lithology, or combine image analysis results with cutting mineral and element data to jointly identify lithology. For example: Existing patent document one, CN111709423B, discloses a PDC drill bit condition cutting lithology identification method based on lithology feature library matching, which includes: 1. drilling under the condition of a PDC drill bit, collecting fine cutting particle white light original images and fluorescence images; 2. converting the white light original images and fluorescence images from RGB color space images to HSV color space images; 3. segmenting the fluorescence images using a threshold segmentation formula; 4. segmenting the white light original images using an image segmentation algorithm based on watershed and near neighbor region merging to segment all cutting particles in the entire white light original image; 5. extracting the color and texture features of the cutting particles at the corresponding positions to construct a lithology feature library; 6. calculating the Bhattacharyya similarity distance between the lithology feature library features and the matching cutting features, calculating the area ratio of the characteristic cutting according to the matching results obtained from the similarity distance, and completing the identification of the cutting lithology.

[0004] Existing patent document two, CN119478669A, discloses a hyperspectral image lithology identification method and device based on spatial and spectral features, relating to the technical field of image information processing. The steps of the hyperspectral image lithology identification method based on spatial and spectral features mainly include: preprocessing the hyperspectral data of the study area to obtain a hyperspectral sample data set, dividing the hyperspectral sample data set according to a predetermined proportion and performing data enhancement processing to obtain a training set, a validation set, and a test set; constructing a lithology identification model using a high-level convolutional neural network model; training and verifying the lithology identification model using the training set and the validation set to obtain a trained lithology identification model; and obtaining a lithology identification result using the trained lithology identification model according to the test set.

[0005] The existing published patent document three, the publication number is CN110031493B, discloses a lithology intelligent identification system and method based on image and spectrum technology, including a rock block shape analysis system for collecting shape information of a test sample and preselecting multiple XRF detection surfaces according to the shape information of the test sample, and determining a test sample grinding position according to the grinding workload of different detection surfaces, and transmitting the test sample grinding position and the flatness of the grinding surface to a central analysis control system; the central analysis control system controls the sample processing system to grind the sample according to the determined test sample grinding position until the flatness meets the requirements of X-ray fluorescence analysis, and uses an image recognition system to preliminarily judge the lithology of the ground rock block; after the sample processing system grinds the sample to meet the requirements, the sample processing system grinds the debris generated during the grinding process, and uses an image recognition system to judge whether the rock powder meets the requirements of X-ray diffraction analysis on the size of the rock particles; the spectrum analysis system respectively performs X-ray diffraction analysis and X-ray fluorescence analysis on the rock powder with the required particle size and the sample with the required flatness, and transmits the respective analysis results to the central analysis control system; and the central analysis control system determines the final lithology of the sample according to the rock block recognition result transmitted by the image recognition system and the analysis result transmitted by the spectrum analysis system.

[0006] The above method has the following problems: (1) The existing method of combining cutting image and mineral data to realize lithology identification mostly relies on XRD and XRF instrument equipment, and the XRD and XRF sample analysis period is long, which cannot meet the requirement of rapid analysis on site, resulting in delayed decision-making. (2) Only the lithology can be determined, and the mineral species and content cannot be quantified. SUMMARY

[0007] The present application provides a lithology identification method, device and electronic equipment based on fusion of cutting fluorescence image and Raman spectrum, which overcomes the shortcomings of the prior art, and effectively solves the problems of long XRD and XRF sample analysis period, long time consumption and high cost in the existing lithology identification method which mostly relies on XRD and XRF instrument equipment.

[0008] One of the technical solutions of the present application is realized by the following measures: a lithology identification method based on fusion of cutting fluorescence image and Raman spectrum, comprising: obtaining a cutting fluorescence image, and the fluorescence type of the cutting sample corresponding to the cutting fluorescence image is non-hydrocarbon fluorescence; applying a Gaussian weighting method to dynamically segment the fluorescence region, obtaining high, medium and low fluorescence intensity region images and no-fluorescence region image according to different fluorescence intensity ranges, and determining the gray value range and fluorescence area proportion of each region image, wherein the fluorescence area proportion is the proportion of the number of fluorescence pixels in the total number of image pixels; The gray value range and the fluorescent area proportion of each area image are brought into the mineral type identification rule to obtain the corresponding mineral type and mineral content, wherein the mineral type identification rule is obtained by analyzing the mineral composition of the rock samples in the different fluorescent intensity areas of the historical rock fluorescence images combined with the in-situ Raman spectrum analysis, and the mineral content is the sum of the fluorescent area proportions of the area images corresponding to the same mineral type; The mineral type and the mineral content are brought into the lithology identification rule to obtain the corresponding lithology identification result, wherein the lithology identification rule comprises: (1) determining the basic name When the highest mineral content is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; When all the mineral contents are less than 50%, the mineral type with the highest mineral content is taken as the basic name of the rock; (2) determining the additional noun When the remaining mineral contents are between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; When the remaining mineral contents are between 25% and 10%, the corresponding mineral type is added in the form of containing XX before the basic name; When the remaining mineral content is less than 10%, the corresponding mineral type does not participate in the naming of the rock.

[0009] The following is a further optimization or / and improvement of the above technical solutions: The above application of the Gaussian weighting method dynamically divides the fluorescent area, obtains high, medium and low fluorescent intensity area images and non-fluorescent area images according to different fluorescent intensity ranges, and determines the gray value and the fluorescent area proportion of each area image, comprising: The rock fluorescence image is converted into a gray image and preprocessed; The gray image is dynamically segmented by applying the Gaussian weighting method to obtain the corresponding fluorescent area, and the fluorescent area is further processed by morphological optimization and connected region extraction in turn; Based on the reprocessed fluorescent area, high, medium and low fluorescent intensity area images and non-fluorescent area images are obtained according to different fluorescent intensity ranges; The gray value and the fluorescent area proportion of each area image are determined, wherein the calculation formula of the fluorescent area proportion is as follows: Wherein, is the fluorescent area proportion; is the number of fluorescent pixels of the fluorescent area; is the total number of pixels of the rock fluorescence image.

[0010] The establishment of the above mineral type identification rule comprises: a plurality of historical cutting fluorescence images are acquired, and the cutting samples corresponding to each historical cutting fluorescence image are not of petroleum hydrocarbon fluorescence type; The fluorescence region is dynamically segmented by using a Gaussian weighting method, high, medium and low fluorescence intensity region images and a non-fluorescence region image are obtained according to different fluorescence intensity ranges, and the gray value range and the fluorescence area ratio of each region image are determined, wherein the fluorescence area ratio is the ratio of the number of fluorescence pixels to the total number of image pixels; At least one cutting sample is obtained in each region image, and the mineral composition of each cutting sample is obtained by using in-situ Raman spectrum analysis; Based on the gray value range of each region image and the mineral composition of all cutting samples, the gray value range of each mineral type is determined, and the gray abnormal value of each mineral type is removed by using the 3sigma principle, and finally the gray value range of each mineral type is obtained.

[0011] The above-mentioned cutting fluorescence image is obtained, and the cutting sample corresponding to the cutting fluorescence image is of non-petroleum hydrocarbon fluorescence type, comprising: Any cutting fluorescence image is acquired, and the three-dimensional fluorescence spectrum analysis result of the cutting sample corresponding to the cutting fluorescence image is combined; If the three-dimensional fluorescence spectrum analysis result does not show a fluorescence characteristic region, lithology identification is performed; If the three-dimensional fluorescence spectrum analysis result shows a fluorescence characteristic region, the fluorescence type re-judgment process is triggered; The three-dimensional fluorescence spectrum of the cutting sample corresponding to the cutting fluorescence image and the three-dimensional fluorescence spectrum of the corresponding drilling fluid are obtained, and the fluorescence characteristic regions in the two three-dimensional fluorescence images are detected respectively, and the corresponding fluorescence characteristic region features are extracted, wherein the fluorescence characteristic region features include contour circumscribed rectangle parameters and contour mask region color features; The similarity of the fluorescence characteristic region features in the two three-dimensional fluorescence images is analyzed to obtain a comprehensive similarity; wherein, is the comprehensive similarity; is the region shape similarity, which is obtained by weighting the region number similarity, the position distribution similarity and the shape similarity; is the color distribution similarity; If > 0.8, the fluorescence is caused by drilling fluid additive pollution, lithology identification is performed, and if < 0.8, the fluorescence type is petroleum hydrocarbon fluorescence, and lithology identification is not performed.

[0012] The above-mentioned similarity analysis of the fluorescence characteristic region features in the two three-dimensional fluorescence images obtains a comprehensive similarity, comprising: The hue histogram of the fluorescence characteristic region of the three-dimensional fluorescence spectrum of the rock debris sample and the three-dimensional fluorescence spectrum of the drilling fluid is obtained respectively, the Bhattacharyya distance between them is determined, and the color distribution similarity is obtained; wherein, is the color distribution similarity; is the Bhattacharyya distance; The region shape similarity is obtained by weighting the region number similarity, the position distribution similarity and the shape similarity, including: The region number similarity is determined; wherein, is the region number similarity; , The number of fluorescence characteristic regions in the two three-dimensional fluorescence images is respectively; The position distribution similarity is determined, including: (1) Obtain the three-dimensional fluorescence spectrum point set of the rock debris sample and the three-dimensional fluorescence spectrum point set of the drilling fluid , determine the directed Hausdorff distance between the point sets, wherein the three-dimensional fluorescence spectrum point set is the geometric center point set of each fluorescence characteristic region in the three-dimensional fluorescence spectrum; wherein, is the directed Hausdorff distance from to ; is the directed Hausdorff distance from to ; is one geometric center point in ; is one geometric center point in ; is the Euclidean distance between and ; (2) Take the maximum value as the final distance : (3) The distance is normalized and converted into the position distribution similarity; wherein, is the position distribution similarity; is the maximum value of the size of the two images; is the distance normalized according to the image size; Determine shape similarity, respectively, in the three-dimensional fluorescence spectrum of the drill sample and the three-dimensional fluorescence spectrum of the drilling fluid, the area of the largest fluorescence feature region is obtained, the corresponding Hu moment is determined, and the cosine similarity between the two Hu moments is calculated , and the shape similarity is obtained after normalization ; ; The regional shape similarity is obtained based on the weight; Among them, The regional shape similarity; The regional quantity similarity; The position distribution similarity; The shape similarity.

[0013] The second technical scheme of the present application is realized by the following measures: a lithology identification device based on the fusion of drill fluorescence image and Raman spectrum, comprising: An original image acquisition unit acquires a drill fluorescence image, and the fluorescence type of the drill sample corresponding to the drill fluorescence image is non-petroleum hydrocarbon fluorescence; An image analysis unit applies a Gaussian weighting method to dynamically segment the fluorescence region, obtains high, medium and low fluorescence intensity region images and non-fluorescence region images according to different fluorescence intensity ranges, and determines the gray value range and fluorescence area proportion of each region image, wherein the fluorescence area proportion is the proportion of the number of fluorescence pixels in the total number of image pixels; A mineral analysis unit inputs the gray value range and fluorescence area proportion of each region image into a mineral type discrimination rule for matching to obtain corresponding mineral species and mineral content, wherein the mineral type discrimination rule is obtained by combining in-situ Raman spectrum analysis to analyze the mineral composition of drill samples in different fluorescence intensity regions of a plurality of historical drill fluorescence images, and the mineral content is the sum of the fluorescence area proportions of the region images corresponding to the same mineral species; A lithology identification unit inputs the mineral species and mineral content into a lithology identification rule for matching to obtain a corresponding lithology identification result, wherein the lithology identification rule comprises: (1) Determine the basic name When the highest mineral content is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; When all mineral contents are less than 50%, the mineral type with the highest mineral content is taken as the basic name of the rock; (2) Determine the additional noun When the remaining mineral content is between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; If the rest of the mineral content is between 25% and 10%, the corresponding mineral type is added in the form of XX-containing to the basic name; If the rest of the mineral content is less than 10%, the corresponding mineral type does not participate in the naming of the rock.

[0014] The following is a further optimization or / and improvement of the above technical solutions: The above image analysis unit comprises: A preprocessing module converts the cutting fluorescence image into a gray-scale image and performs preprocessing; A fluorescence region extraction module applies a Gaussian weighting method to dynamically segment the gray-scale image to obtain the corresponding fluorescence region, and sequentially performs fluorescence region reprocessing through morphological optimization and connected region extraction; A fluorescence region reprocessing module, based on the reprocessed fluorescence region, obtains high, medium, and low fluorescence intensity region images and a non-fluorescence region image according to different fluorescence intensity ranges; A fluorescence region quantification module determines the gray-scale values and fluorescence area proportions of each region image, wherein the calculation formula of the fluorescence area proportion is as follows: Wherein, is the fluorescence area proportion; is the number of fluorescence pixels in the fluorescence region; is the total number of pixels in the cutting fluorescence image.

[0015] The above original image acquisition unit comprises: A fluorescence type acquisition module acquires any cutting fluorescence image and combines the three-dimensional fluorescence spectrum analysis result of the cutting sample corresponding to the cutting fluorescence image; A first fluorescence type analysis module, if there is no fluorescence characteristic region displayed in the three-dimensional fluorescence spectrum analysis result, performs lithology identification; A second fluorescence type analysis module, if there is a fluorescence characteristic region displayed in the three-dimensional fluorescence spectrum analysis result, triggers a fluorescence type re-judgment process, including: Acquire the three-dimensional fluorescence spectrum of the cutting sample corresponding to the cutting fluorescence image and the three-dimensional fluorescence spectrum of the corresponding drilling fluid, and detect the fluorescence characteristic regions in the two three-dimensional fluorescence images respectively, and extract the corresponding fluorescence characteristic region features, wherein the fluorescence characteristic region features include contour circumscribed rectangle parameters and contour mask region color features; Perform similarity analysis on the fluorescence characteristic region features in the two three-dimensional fluorescence images to obtain a comprehensive similarity; Wherein, is the comprehensive similarity; For the regional shape similarity, the regional number similarity, the position distribution similarity and the shape similarity are weighted to obtain; For the color distribution similarity; If > 0.8, the fluorescence is caused by drilling fluid additive pollution, and lithology identification is performed, and if < 0.8, the fluorescence type is petroleum hydrocarbon fluorescence, and lithology identification is not performed.

[0016] The third technical solution of the present application is realized by the following measures: an electronic device comprising a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to realize the steps in the lithology identification method based on the fusion of the fluorescence image of the cuttings and the Raman spectrum.

[0017] The beneficial effects of the present application include: The present application combines in-situ Raman spectrum analysis to analyze the mineral composition of the cuttings samples in different fluorescence intensity regions in a plurality of historical fluorescence images of the cuttings, establishes a mapping relationship between the fluorescence intensity and the mineral composition, so that in the lithology identification, without the participation of any experimental instrument, the mineral type and the mineral content can be directly obtained by combining the established mapping relationship between the fluorescence intensity and the mineral composition and the gray value range of the reaction fluorescence intensity, which not only effectively saves the drilling cost, but also quickly and accurately identifies the lithology, and further, in the present embodiment, no conventional XRD, XRF and other instrument devices are used for sample analysis, avoiding the problems of long sample analysis period and not meeting the on-site rapid analysis demand of logging, leading to lagging decision-making; When the fluorescence region characteristics are displayed in the three-dimensional fluorescence spectrum analysis result of the cuttings corresponding to the cuttings sample, based on the regional shape similarity and the color distribution similarity of the fluorescence characteristic region in the three-dimensional fluorescence spectrum of the cuttings and the drilling fluid, the comprehensive similarity between the two three-dimensional fluorescence images is comprehensively evaluated, and it is determined whether it is caused by drilling fluid additive pollution or petroleum hydrocarbon fluorescence, compared with the method of judging whether the fluorescence type is mineral fluorescence by a single factor, the judgment result is more accurate, and a reliable guarantee is provided for the lithology identification method. BRIEF DESCRIPTION OF DRAWINGS

[0018] Attached Figure 1 The lithology identification method flowchart provided for the embodiment 1 of the present application.

[0019] Attached Figure 2 The cuttings fluorescence image acquisition flowchart provided for the embodiment 2 of the present application.

[0020] Attached Figure 3 The gray value and fluorescence area proportion determination flowchart of each region image provided for the embodiment 3 of the present application.

[0021] AttachedFigure 4 The mineral type identification rule establishment flowchart provided for the embodiment 4 of the present application is shown.

[0022] Figure 1 is a schematic diagram of the mineral type identification rule establishment flowchart provided for the embodiment 1 of the present application. Figure 5 The three-dimensional fluorescence spectrum of the rock debris and drilling fluid and the fluorescence characteristic region contour extraction schematic diagram provided for the embodiment 5 of the present application is shown in Figure 2. Figure 5 a is the three-dimensional fluorescence spectrum of the rock debris, and Figure 5 b is the three-dimensional fluorescence spectrum characteristic region contour graph of the rock debris, and Figure 5 c is the three-dimensional fluorescence spectrum of the drilling fluid, and Figure 5 d is the three-dimensional fluorescence spectrum characteristic region contour graph of the drilling fluid, and Figure 5 e is the characteristic region contour comparison graph.

[0023] Figure 3 is a schematic diagram of the rock debris fluorescence image and its different fluorescence intensity region display graph provided for the embodiment 5 of the present application. Figure 6 a is the rock debris fluorescence image, Figure 6 b is the pretreatment gray scale image, Figure 6 c is the high fluorescence intensity region image, Figure 6 d is the medium fluorescence intensity region image, Figure 6 e is the low fluorescence intensity region image, Figure 6 f is the no fluorescence region image. Figure 6 Figure 4 is a schematic diagram of the rock debris mineral Raman spectrum characteristic graph provided for the embodiment 5 of the present application.

[0024] Figure 7 Figure 5 is a schematic diagram of the confusion matrix display graph provided for the embodiment 5 of the present application.

[0025] Figure 6 is a schematic diagram of the lithology identification device structure provided for the embodiment 6 of the present application. Figure 8

[0026] Figure 9 Figure 6 is a schematic diagram of the lithology identification device structure provided for the embodiment 6 of the present application. DETAILED DESCRIPTION

[0027] The present application is not limited by the following embodiments, and the specific implementation can be determined according to the technical solutions of the present application and the actual situation.

[0028] Those skilled in the art can understand that, unless specifically stated, the "module" or "unit" in the embodiments of the present application refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined target, and can be implemented entirely or partially by using software, hardware (such as a processing circuit or a memory) or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the functions of the module or unit.

[0029] ​​​In addition, "a plurality of" in the embodiments of the present application refers to two or more than two, and "first" and "second" and the like are used for distinguishing description and cannot be understood as implying relative importance.

[0030] The embodiments of the present application provide a lithology identification method and device based on fusion of a cutting fluorescence image and a Raman spectrum and electronic equipment. The lithology identification device based on fusion of the cutting fluorescence image and the Raman spectrum can be integrated in a computer device, which can be a server, a terminal or the like; or can be jointly executed by a terminal and a server, and the above examples should not be understood as limiting the present application.

[0031] The terminal can include a mobile phone, a wearable smart device, a tablet computer, a notebook computer, a personal computer (PC), and a vehicle-mounted computer, and the present application does not limit the terminal. The number of terminal devices is not limited.

[0032] The server can be a stand-alone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligence platforms, and the present application does not limit the server.

[0033] For example, the computer device obtains a cutting fluorescence image, and the cutting sample corresponding to the cutting fluorescence image is of a non-petroleum hydrocarbon fluorescence type; a Gaussian weighting method is applied to dynamically segment a fluorescence region, high, medium and low fluorescence intensity region images and a non-fluorescence region image are obtained according to different fluorescence intensity ranges, and a gray value range and a fluorescence area ratio of each region image are determined, wherein the fluorescence area ratio is a ratio of a number of fluorescence pixels to a total number of pixels in the image; the gray value range and the fluorescence area ratio of each region image are input into a mineral type discrimination rule for matching to obtain corresponding mineral types and mineral contents, wherein the mineral type discrimination rule is obtained by combining in-situ Raman spectrum analysis to analyze mineral compositions of cutting samples in different fluorescence intensity regions of a plurality of historical cutting fluorescence images, and the mineral content is a sum of fluorescence area ratios of region images corresponding to the same mineral type; the mineral types and the mineral contents are input into a lithology identification rule for matching to obtain a corresponding lithology identification result.

[0034] Based on this, the technical solutions of the present application will be introduced and described in combination with several examples.

[0035] Embodiment 1: As shown in the accompanying Figure 1 The embodiments of the present application disclose a lithology identification method based on fusion of a cutting fluorescence image and a Raman spectrum, which comprises: Step S110, a cutting fluorescence image is acquired, and the fluorescence type of the cutting sample corresponding to the cutting fluorescence image is non-petroleum hydrocarbon fluorescence; Step S120, a Gaussian weighting method is applied to dynamically segment the fluorescence region, high, medium and low fluorescence intensity region images and a non-fluorescence region image are obtained according to different fluorescence intensity ranges, and the gray value range and the fluorescence area proportion of each region image are determined, wherein the fluorescence area proportion is the proportion of the number of fluorescence pixels in the total number of image pixels; Step S130, the gray value range and the fluorescence area proportion of each region image are brought into the mineral type discrimination rule for matching to obtain the corresponding mineral species and mineral content, wherein the mineral type discrimination rule is obtained by combining in-situ Raman spectrum analysis to analyze the mineral composition of the cutting samples in different fluorescence intensity regions of a plurality of historical cutting fluorescence images, and the mineral content is the sum of the fluorescence area proportions of the region images corresponding to the same mineral species; Step S140, the mineral species and the mineral content are brought into the lithology identification rule for matching to obtain the corresponding lithology identification result.

[0036] The lithology identification rule comprises: (1) determining a basic name When the highest mineral content is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; When all the mineral contents are less than 50%, the mineral type with the highest mineral content is taken as the basic name of the rock; (2) determining an additional noun When the remaining mineral contents are between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; When the remaining mineral contents are between 25% and 10%, the corresponding mineral type is added in the form of containing XX before the basic name; When the remaining mineral content is less than 10%, the corresponding mineral type does not participate in the naming of the rock.

[0037] The fluorescence type of the cutting fluorescence image includes petroleum hydrocarbon fluorescence and mineral fluorescence, but only mineral fluorescence can reflect the mineral type, so the fluorescence type of the cutting fluorescence image used in the embodiment of the present application must be non-petroleum hydrocarbon fluorescence before the lithology identification is performed.

[0038] The mineral type identification rule in step S130 is obtained by analyzing the mineral composition of the rock samples in different fluorescence intensity regions of the historical fluorescence images based on the in-situ Raman spectrum analysis, that is, the Raman spectrum analysis is performed on the rock samples in different fluorescence intensity regions of the historical fluorescence images, a mapping relationship between the fluorescence intensity and the mineral composition is established, then based on the corresponding relationship between the different fluorescence intensity regions and the gray value range, a mapping relationship between the gray value range and the mineral composition is obtained, which is used for subsequent inversion of the mineral type according to the different gray value ranges in the rock fluorescence images.

[0039] The lithology identification rule in step S140 is set according to the rock three-level naming rule, and can include: (1) determining the basic name When the content of the highest mineral is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; When the content of all minerals is less than 50%, the mineral type with the highest content is taken as the basic name of the rock; (2) determining the additional noun When the content of the remaining minerals is between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; When the content of the remaining minerals is between 25% and 10%, the corresponding mineral type is added in the form of containing XX before the basic name; When the content of the remaining minerals is less than 10%, the corresponding mineral type does not participate in the naming of the rock.

[0040] For example, when the content of calcite is greater than 50%, the rock is named as limestone, and when the content of dolomite / ferrodolomite is greater than 50%, the rock is named as dolomite.

[0041] For example, when the content of calcite is greater than 50% and the content of dolomite / ferrodolomite is between 50% and 25%, the rock is named as dolomite limestone, and when the content of calcite is greater than 50% and the content of dolomite / ferrodolomite is between 25% and 10%, the rock is named as dolomite limestone.

[0042] The embodiment of the present application discloses a lithology identification method based on fusion of rock debris fluorescence image and Raman spectrum, combines in-situ Raman spectrum analysis to perform mineral composition analysis on rock debris samples in different fluorescence intensity regions of a plurality of historical rock debris fluorescence images, establishes a mapping relationship between fluorescence intensity and mineral composition, so that in the process of lithology identification, no experimental instrument is needed to participate, the gray value range of the reaction fluorescence intensity can be directly obtained, the mapping relationship between fluorescence intensity and mineral composition is combined to directly obtain the mineral type and mineral content, drilling cost is effectively saved, the lithology can be quickly and accurately identified, and further, in the embodiment, no conventional XRD, XRF and other instrument equipment are used for sample analysis, the problem of long sample analysis period and unsatisfied on-site rapid analysis requirement of mud logging is avoided, and the problem of lagging decision is caused.

[0043] Embodiment 2: as shown in the accompanying Figure 2 The embodiment of the present application is a further optimization of the above-mentioned embodiment, wherein the rock debris fluorescence image is obtained, and the method comprises the following steps: Step S210, any rock debris fluorescence image is obtained, and three-dimensional fluorescence spectrum analysis results of the rock debris sample corresponding to the rock debris fluorescence image are combined; Step S220, if no fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis results, lithology identification is performed; Step S230, if the fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis results, a fluorescence type re-judgment process is triggered; It should be noted that if no fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum of the rock debris fluorescence image, the fluorescence type is mineral fluorescence, if the fluorescence characteristic region is displayed, the fluorescence type may be petroleum hydrocarbon fluorescence or caused by drilling fluid additive pollution, and the fluorescence type needs to be re-judged.

[0044] Step S240, three-dimensional fluorescence spectra of the rock debris sample corresponding to the rock debris fluorescence image and three-dimensional fluorescence spectra of the drilling fluid are obtained, fluorescence characteristic regions in the two three-dimensional fluorescence images are detected respectively, and corresponding fluorescence characteristic region features are extracted, wherein the fluorescence characteristic region features include contour circumscribed rectangle parameters and contour mask region color features; Specifically, detecting the fluorescence characteristic regions in the two three-dimensional fluorescence images comprises: (1) the cv2.cvtColor function of the OpenCV library is used to convert both three-dimensional fluorescence spectra from the BGR color space to the HSV space; (2) the HSV range of the fluorescence characteristic region in the two three-dimensional fluorescence spectra is defined, a binary mask is generated through the cv2.inRange() function, and the pixels in the fluorescence characteristic region in each three-dimensional fluorescence spectrum are marked; (3) Morphology optimization to smooth the boundary of the fluorescence feature region in each three-dimensional fluorescence spectrum, improve connectivity, and specifically use a 5x5 elliptical kernel to perform mask merging, i.e. 2 times of closed operation to fill holes and gaps in the feature region, and 1 time of open operation to eliminate noise; (4) Contour extraction and filtering, for the fluorescence feature region in each three-dimensional fluorescence spectrum, all independent connected regions in the mask are detected by the cv2.findContours() function, and small regions with an area <100 pixels are removed, only the significant regions are retained, and the number of fluorescence feature regions N is obtained by traversing all contours.

[0045] Specifically, the fluorescence feature region features are extracted, including: (1) Obtain the bounding rectangle parameters of the contour, for the fluorescence feature region in each three-dimensional fluorescence spectrum, the coordinates (x, y) of the contour bounding rectangle, the width w, and the height h are extracted by the cv2.boundingRect() function, wherein (x, y) is the left upper corner coordinate of the contour bounding rectangle, w and h are the width and height of the contour bounding rectangle respectively; (2) Obtain the color feature of the contour mask region, and calculate the average BGR value of the pixels in the fluorescence feature region by the cv2.mean() function.

[0046] Step S250, similarity analysis is performed on the fluorescence feature region features in the two three-dimensional fluorescence images, and a comprehensive similarity is obtained; wherein, is the comprehensive similarity; is the region shape similarity, which is obtained by weighting the region number similarity, the position distribution similarity, and the shape similarity; is the color distribution similarity.

[0047] The above indicates that the two three-dimensional fluorescence images are completely identical, > 0.8 indicates that the two three-dimensional fluorescence images are highly similar, > 0.6 indicates that the two three-dimensional fluorescence images are moderately similar, and the rest indicates that the two three-dimensional fluorescence images are quite different.

[0048] Specifically, the similarity analysis is performed on the fluorescence feature region features in the two three-dimensional fluorescence images, including: Step S251, the region shape similarity is obtained by weighting the region number similarity, the position distribution similarity, and the shape similarity, including: Step S2511, determine the region number similarity; wherein, is the region number similarity; , respectively are the number of fluorescent feature regions in two three-dimensional fluorescence images; Here represents that the region number similarity is maximum, i.e., the number of fluorescent feature regions in the two three-dimensional fluorescence images is completely the same, represents that the region number similarity is minimum.

[0049] Step S2512, determining the position distribution similarity, comprising: (1) obtaining a three-dimensional fluorescence spectrum point set of the drill sample and a three-dimensional fluorescence spectrum point set of the drilling fluid , determining the directed Hausdorff distance between the point sets, wherein the three-dimensional fluorescence spectrum point set is a set of geometric center points of each fluorescent feature region in the three-dimensional fluorescence spectrum; wherein, is the directed Hausdorff distance from to ; is the directed Hausdorff distance from to ; is one geometric center point in ; is one geometric center point in ; is the Euclidean distance between and ; It should be noted that the coordinates of the geometric center point of the fluorescent feature region are (x+w / 2, y+h / 2).

[0050] (2) taking the maximum value as the final distance : (3) normalizing the final distance and converting it into a position distribution similarity; wherein, is the position distribution similarity; is the maximum value of the size of the two images; is to normalize the distance according to the size of the image.

[0051] Step S2513, determining the shape similarity, obtaining the fluorescent feature region with the largest area in the three-dimensional fluorescence spectrum of the drill sample and the three-dimensional fluorescence spectrum of the drilling fluid respectively, determining the corresponding Hu moment, and calculating the cosine similarity between the two Hu moments and the shape similarity is obtained after normalization , which maps the range from [-1, 1] to [0, 1]; ; It should be noted that the determination process of the Hu moment can include: calculating the contour moment of the three-dimensional fluorescence spectrum by cv2.moments(), converting the contour moment into the Hu moment by the cv2.Humoments() function, and performing logarithmic transformation on the Hu moment to compress the numerical range and enhance the discrimination degree.

[0052] Step S2514, obtaining the regional shape similarity based on the weighting; wherein, is the regional shape similarity; is the regional quantity similarity; is the position distribution similarity; is the shape similarity.

[0053] Step S252, obtaining the hue histogram of the fluorescence characteristic region of the three-dimensional fluorescence spectrum of the rock sample and the three-dimensional fluorescence spectrum of the drilling fluid respectively, determining the Bhattacharyya distance between the two, and obtaining the color distribution similarity; wherein, is the color distribution similarity; is the Bhattacharyya distance.

[0054] It should be noted that the hue histogram (only H channel is counted) of the fluorescence characteristic region can be realized by the cv2.calcHist() function, and after obtaining the hue histogram of the fluorescence characteristic region of the two, the histogram is further normalized by the cv2.normalize() function, and then the Bhattacharyya distance between the two is calculated by the cv2.compareHist() function. bd ).

[0055] The above indicates that the color distribution similarity is maximum.

[0056] Step S260, if > 0.8, the fluorescence is caused by drilling fluid additive pollution, and lithology identification is performed, and if < 0.8, the fluorescence type is oil hydrocarbon fluorescence, and lithology identification is not performed.

[0057] When the fluorescence region characteristic is displayed in the three-dimensional fluorescence spectrum analysis result of the cuttings sample corresponding to the cuttings fluorescence image, the embodiment of the present application determines whether it is caused by drilling fluid additive pollution or oil hydrocarbon fluorescence based on the region shape similarity and color distribution similarity of the fluorescence characteristic region in the three-dimensional fluorescence spectrum of the cuttings and the drilling fluid, and comprehensively evaluates the comprehensive similarity between the two three-dimensional fluorescence images, so that the judgment result is more accurate compared with the method of judging whether the fluorescence type is mineral fluorescence by a single factor, and a reliable guarantee is provided for the lithology identification method.

[0058] Embodiment 3: As shown in the accompanying Figure 3 The embodiment of the present application is a further optimization of the above-mentioned embodiment, wherein the fluorescence region is dynamically segmented by applying the Gaussian weighting method, the high, medium and low fluorescence intensity region images and the non-fluorescence region image are obtained according to different fluorescence intensity ranges, and the gray value and fluorescence area proportion of each region image are determined, including: Step S310, converting the cuttings fluorescence image into a gray scale image and performing pretreatment; Specifically, the cv2.cvtColor() function is called to convert the cuttings fluorescence image (i.e. RGB fluorescence image) into a gray scale image, and Gaussian filtering (cv2.GaussianBlur) can be used to suppress high-frequency noise, with the filter parameter set to kernel size ksize=(5,5) and standard deviation σ=1.1, effectively eliminating the reflection artifacts on the surface of the cuttings.

[0059] Step S320, applying the Gaussian weighting method to dynamically segment the gray scale image, obtaining the corresponding fluorescence region, and sequentially performing fluorescence region reprocessing through morphological optimization and connected region extraction; The parameter setting in the above-mentioned Gaussian weighting method can include but is not limited to setting the neighborhood size BlockSize=7 and the threshold offset C=5, enhancing the robustness of the algorithm to uneven illumination and noise.

[0060] The above-mentioned sequential fluorescence region reprocessing through morphological optimization and connected region extraction includes: (1) Morphological optimization, performing iterative erosion (cv2.erode, 3 times) through a 3x3 rectangular structure element (kernel=np.ones((3,3), np.uint8)) to eliminate isolated noise points in the binary image, and performing dilation operation (cv2.dilate, 3 times) with the same structure element to fill the broken fluorescence region caused by threshold segmentation; (2) Connected region analysis, using the cv2.findContours() function to extract the connected region contour, screening the interference region based on the area threshold (<50 pixels²), and realizing the rejection of fragmented regions.

[0061] Step S330, based on the reprocessed fluorescent region, high, medium and low fluorescent intensity region images and no fluorescent region image are obtained according to different fluorescent intensity ranges; Step S340, the gray value and the fluorescent area ratio of each region image are determined, and the calculation formula of the fluorescent area ratio is as follows: wherein, is the fluorescent area ratio; is the number of fluorescent pixels of the fluorescent region; is the total number of image pixels of the rock debris fluorescent image.

[0062] Embodiment 4: As shown in the following table, the embodiment of the present application is a further optimization of the above-mentioned embodiments, wherein the establishment of the mineral type discrimination rule includes: Figure 4 Step S410, a plurality of historical rock debris fluorescent images are obtained, and the fluorescent type of the rock debris sample corresponding to each historical rock debris fluorescent image is not petroleum hydrocarbon fluorescence. Step S420, the fluorescent region is dynamically segmented by applying the Gaussian weighting method, high, medium and low fluorescent intensity region images and no fluorescent region image are obtained according to different fluorescent intensity ranges, and the gray value range and the fluorescent area ratio of each region image are determined, wherein the fluorescent area ratio is the ratio of the number of fluorescent pixels to the total number of image pixels;

[0063] The specific implementation process of this step is the same as that of embodiment 3, and will not be described here.

[0064] Step S430, at least one rock debris sample is obtained in each region image, and the mineral composition of each rock debris sample is obtained by in-situ Raman spectrum analysis.

[0065] Step S440, based on the gray value range of each region image and the mineral composition of all rock debris samples, the gray value range of each mineral type is determined, and the gray abnormal value of each mineral type is removed by using the 3sigma principle, and finally the gray value range of each mineral type is obtained.

[0066] Further, the mineral type discrimination rule can also be provided with an update time, and the mineral type discrimination rule is updated according to the update time.

[0067] Embodiment 5: The above-mentioned embodiments are verified by using the embodiment of the present application, and the specific process is as follows: (1) a rock debris fluorescent image is obtained, and the fluorescent type of the rock debris sample corresponding to the rock debris fluorescent image is non-petroleum hydrocarbon fluorescence; (1) rock debris fluorescent type identification, including: Step 1.1, any rock debris fluorescent image is obtained, and the three-dimensional fluorescent spectrum analysis result of the rock debris sample corresponding to the rock debris fluorescent image is combined;​ Step 1.2, if there is a fluorescent characteristic region in the three-dimensional fluorescence spectrum, it is necessary to determine whether the characteristic region is caused by the drilling fluid additive. Read the three-dimensional fluorescence spectrum of the cuttings and the corresponding three-dimensional fluorescence spectrum of the drilling fluid (such as Appendix Figure 5 a, 5c), call the cv2.cvtColor function of the OpenCV library to convert the three-dimensional fluorescence spectrum from the BGR color space to the HSV space, which is convenient for detecting the fluorescent characteristic region.

[0068] Step 1.3, define the HSV range of the fluorescent characteristic region, Appendix Figure 5 a, 5c, the color of the fluorescent characteristic region includes red and orange yellow, set the red HSV range to (H: 0-10, S: 150-255, S: 150-255), and the orange yellow HSV range to (H: 15-30, S: 140-255, S: 150-255), generate a binary mask through the cv2.inRange() function to mark the pixels in the characteristic peak region in the image.

[0069] Step 1.4, morphological optimization to smooth the region boundary and improve connectivity, use a 5x5 elliptical kernel to perform 2 times of closed operation to fill the holes and gaps in the characteristic peak region, and 1 time of open operation to eliminate noise.

[0070] Step 1.5, contour extraction and filtering, detect all independent connected domains in the mask through the cv2.findContours() function, eliminate the small regions with an area <100 pixels, and only keep the significant regions, the contour of the characteristic region of the three-dimensional fluorescence image of the cuttings is shown in Appendix Figure 5 b, the contour of the characteristic region of the three-dimensional fluorescence image of the drilling fluid is shown in Appendix Figure 5 d, the number of contour regions of both is 1, Appendix Figure 5 e is the contour comparison figure of the characteristic regions of both.

[0071] (2) Use the region number similarity, position distribution similarity, and shape similarity to get the region shape similarity, including: Step 2.1, determine the region number similarity; wherein, ; Step 2.2, determine the position distribution similarity; (a) the final distance ; (b) normalize the distance and convert it to the position distribution similarity; wherein, For ; Step 2.3, determine shape similarity; Step 2.4, determine regional shape similarity; (3) For the three-dimensional fluorescence spectrum of the rock sample and the three-dimensional fluorescence spectrum of the drilling fluid, the hue histogram of the fluorescence characteristic region of the two is obtained respectively, the Bhattacharyya distance between the two is determined, and the color distribution similarity is obtained; Wherein, the Bhattacharyya distance ; (4) determine the comprehensive similarity; (5) because , it is indicated that the fluorescence is caused by drilling fluid additive pollution, and lithology identification is performed.

[0072] (Two) Establishing mineral type discrimination rule (1) Quantitative characterization of rock image, load rock fluorescence image (such as attached Figure 6 a), convert it into a gray scale image and pre-process (such as attached Figure 6 b), use the method disclosed in Example 3 to obtain the gray scale value range and fluorescence area ratio of high, medium and low fluorescence intensity region images and non-fluorescence region images (such as attached Figure 6 c to 6f), wherein the gray scale value range of each region image is 198-255, 107-198, 46-107, 0-46.

[0073] (2) In-situ Raman spectrum analysis, in-situ Raman spectrum analysis is performed on the rock sample in the fluorescence region (such as the red cross mark point in attached Figure 6 c to 6f, sample numbers A-H) of the rock fluorescence image to determine the mineral composition, and the results are shown in attached Figure 7 and Table 1. The mineral Raman characteristic peak of the high-medium fluorescence intensity region (see attached Figure 6 c, 6d, gray scale value 107-255) is identified as calcite, and the content is 25.21%. The content of calcite can be determined from the fluorescence area ratio of each region image in step (1), and the mineral content is the sum of the fluorescence area ratios of the region images corresponding to the same mineral type. The mineral of the low fluorescence intensity region (see attached Figure 6 e, gray scale value 46-107) is identified as dolomite, and the content is 42.34%; the mineral of the non-fluorescence region (see attached Figure 6 f, gray scale 0-46) is identified as ankerite, and the content is 32.45%.

[0074] Table 1 Raman characteristic peak shift data of carbonate rock minerals .

[0075] (3) Obtain 10 historical cutting fluorescence images, repeat steps (1) to (2) above, and count the obtained gray value ranges of all calcite, dolomite, ankerite and other carbonate rock minerals, and use the 3 sigma principle to eliminate the gray value outliers of each mineral, and finally obtain the mineral type discrimination rule, which specifically includes: the calcite gray value range is 102 to 255, the dolomite gray value range is 41 to 102, and the ankerite gray value range is 0 to 41.

[0076] (Three) Obtain 80 cutting fluorescence images of the Ordovician system of X well, identify the lithology based on the method disclosed in the present application, and perform Raman spectrum surface scanning on the 80 cutting samples corresponding to the cutting fluorescence image to determine the true lithology. The results show that the coincidence rate of the lithology identification results of the present application and the Raman surface scanning analysis results reaches 92.5%, and the lithology comparison results are shown in a confusion matrix graph (as shown in the accompanying Figure 8 ), and the method disclosed in the present application realizes rapid and accurate identification of lithology.

[0077] Example 6: As shown in the accompanying Figure 9 , the present application discloses a lithology identification device based on fusion of cutting fluorescence images and Raman spectra, which comprises: An original image acquisition unit acquires a cutting fluorescence image, and the fluorescence type of the cutting sample corresponding to the cutting fluorescence image is non-petroleum hydrocarbon fluorescence; An image analysis unit applies a Gaussian weighting method to dynamically segment the fluorescence region, obtains high, medium and low fluorescence intensity region images and non-fluorescence region images according to different fluorescence intensity ranges, and determines the gray value range and fluorescence area proportion of each region image, wherein the fluorescence area proportion is the proportion of the number of fluorescence pixels in the total number of image pixels; A mineral analysis unit inputs the gray value range and fluorescence area proportion of each region image into the mineral type discrimination rule for matching to obtain the corresponding mineral type and mineral content, wherein the mineral type discrimination rule is obtained by combining in-situ Raman spectrum analysis to analyze the mineral composition of the cutting samples in different fluorescence intensity regions of a plurality of historical cutting fluorescence images, and the mineral content is the sum of the fluorescence area proportions of the region images corresponding to the same mineral type; A lithology identification unit inputs the mineral type and mineral content into the lithology identification rule for matching to obtain the corresponding lithology identification result, wherein the lithology identification rule comprises: (1) Determine the basic name When the highest mineral content is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; When all mineral contents are less than 50%, the mineral type with the highest mineral content is taken as the basic name of the rock; (2) Determine the additional noun When the remaining mineral content is between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; When the remaining mineral content is between 25% and 10%, the corresponding mineral type is added in the form of containing XX before the basic name; When the remaining mineral content is less than 10%, the corresponding mineral type does not participate in the naming of the rock.

[0078] Among them, the image analysis unit comprises: The preprocessing module converts the cutting fluorescence image into a gray image and performs preprocessing; The fluorescence region extraction module applies the Gaussian weighting method to the gray image for dynamic segmentation to obtain the corresponding fluorescence region, and sequentially performs fluorescence region reprocessing through morphological optimization and connected region extraction; The fluorescence region reprocessing module obtains high, medium and low fluorescence intensity region images and non-fluorescence region images according to different fluorescence intensity ranges based on the reprocessed fluorescence region; The fluorescence region quantification module determines the gray value and fluorescence area proportion of each region image, wherein the calculation formula of the fluorescence area proportion is as follows: Among them, is the fluorescence area proportion; is the number of fluorescence pixels of the fluorescence region; is the total number of pixels of the cutting fluorescence image.

[0079] Among them, the original image acquisition unit comprises: The fluorescence type acquisition module acquires any cutting fluorescence image, and combines the three-dimensional fluorescence spectrum analysis result of the cutting sample corresponding to the cutting fluorescence image; The first fluorescence type analysis module performs lithology identification if no fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis result; The second fluorescence type analysis module triggers the fluorescence type re-judgment process if the fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis result, including: Obtain the three-dimensional fluorescence spectrum of the cutting sample corresponding to the cutting fluorescence image and the three-dimensional fluorescence spectrum of the corresponding drilling fluid, and detect the fluorescence characteristic regions in the two three-dimensional fluorescence images respectively, and extract the corresponding fluorescence characteristic region features, wherein the fluorescence characteristic region features include the contour circumscribed rectangle parameters and the contour mask region color features; The similarity of the fluorescence characteristic region features in the two three-dimensional fluorescence images is analyzed to obtain the comprehensive similarity; wherein, is the comprehensive similarity; is the regional shape similarity, which is obtained by weighting the regional number similarity, the position distribution similarity and the shape similarity; is the color distribution similarity; If > 0.8, the fluorescence is caused by drilling fluid additive pollution, and lithology identification is performed, and if < 0.8, the fluorescence type is oil hydrocarbon fluorescence, and lithology identification is not performed.

[0080] In an embodiment of the present application, an electronic device is disclosed, which comprises a processor and a memory, and the memory stores a computer program, the computer program is loaded and executed by the processor to realize the lithology identification method based on the fusion of the cutting fluorescence image and the Raman spectrum.

[0081] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. It can also be a combination of computing functions, such as one or more microprocessor combinations, DSP and microprocessor combinations, etc. The memory can include but is not limited to: a U disk, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and various computer program storage media.

[0082] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting languages JavaScript, etc.

[0083] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the variations disclosed herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart block and / or block diagrams can represent a Figure 1 The flowchart and / or block diagram in the variations disclosed herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart block and / or block diagrams can represent a

[0084] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the variations disclosed herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart block and / or block diagrams can represent a Figure 1 The flowchart and / or block diagram in the variations disclosed herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart block and / or block diagrams can represent a

[0085] The above merely provides a specific implementation of the present application, which has strong adaptability and implementation effect. However, the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, equivalent changes made according to the claims of the present application are still covered in the scope of the present application.

Claims

1. A lithology identification method based on fusion of a cutting fluorescence image and a Raman spectrum, characterized by, The method comprises the following steps: obtaining a fluorescence image of the cuttings, and the fluorescence type of the cuttings sample corresponding to the fluorescence image is non-petroleum hydrocarbon fluorescence; applying a Gaussian weighting method to dynamically segment the fluorescence region, obtaining high, medium and low fluorescence intensity region images and no fluorescence region image according to different fluorescence intensity ranges, and determining the gray value range and fluorescence area ratio of each region image, wherein the fluorescence area ratio is the ratio of the number of fluorescence pixels to the total number of image pixels; inputting the gray value range and fluorescence area ratio of each region image into the mineral type discrimination rule for matching to obtain the corresponding mineral type and mineral content, wherein the mineral type discrimination rule is obtained by combining in-situ Raman spectrum analysis to analyze the mineral composition of the cuttings sample in different fluorescence intensity regions of a plurality of historical fluorescence images of the cuttings, and the mineral content is the sum of the fluorescence area ratios of the region images corresponding to the same mineral type; inputting the mineral type and mineral content into the lithology identification rule for matching to obtain the corresponding lithology identification result, wherein the lithology identification rule comprises: (1) determining the basic name when the highest mineral content is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; when all mineral contents are less than 50%, the mineral type with the highest mineral content is taken as the basic name of the rock; (2) determining the additional noun when the remaining mineral content is between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; when the remaining mineral content is between 25% and 10%, the corresponding mineral type is added in the form of containing XX before the basic name; when the remaining mineral content is less than 10%, the corresponding mineral type does not participate in the naming of the rock. 2.The lithology identification method based on fusion of lithodetritus fluorescence image and Raman spectrum according to claim 1, characterized in that, The application of the Gaussian weighting method to dynamically segment the fluorescence region, the obtaining of high, medium and low fluorescence intensity region images and no fluorescence region image according to different fluorescence intensity ranges, and the determination of the gray value and fluorescence area ratio of each region image comprise: converting the fluorescence image of the cuttings into a gray image and performing pretreatment; applying the Gaussian weighting method to the gray image for dynamic segmentation to obtain the corresponding fluorescence region, and sequentially performing fluorescence region reprocessing through morphological optimization and connected region extraction; based on the reprocessed fluorescence region, obtaining high, medium and low fluorescence intensity region images and no fluorescence region image according to different fluorescence intensity ranges; determining the gray value and fluorescence area ratio of each region image, wherein the calculation formula of the fluorescence area ratio is as follows: wherein, is the fluorescent area ratio; is the number of fluorescent pixels of the fluorescent area; is the total number of pixels of the rock debris fluorescent image. 3.The lithology identification method based on fusion of a lithodetritus fluorescence image and a Raman spectrum according to claim 1, characterized in that, The establishment of the mineral type discrimination rule comprises: obtaining a plurality of historical fluorescence images of the cuttings, and the fluorescence type of the cuttings sample corresponding to each historical fluorescence image is not petroleum hydrocarbon fluorescence; applying a Gaussian weighting method to dynamically segment the fluorescence region, obtaining high, medium and low fluorescence intensity region images and no fluorescence region image according to different fluorescence intensity ranges, and determining the gray value range and fluorescence area ratio of each region image, wherein the fluorescence area ratio is the ratio of the number of fluorescence pixels to the total number of image pixels; obtaining at least one cuttings sample in each region image, and obtaining the mineral composition of each cuttings sample by in-situ Raman spectrum analysis; Based on the gray value range of each area image and the mineral composition of all the rock debris samples, the gray value range of each mineral type is determined, and the abnormal gray value of each mineral type is removed by using the 3sigma principle, and finally the gray value range of each mineral type is obtained.

4. The lithology identification method based on fusion of debris fluorescence image and Raman spectrum according to claim 1 or 2 or 3, characterized in that, The rock debris fluorescence image is obtained, and the fluorescence type of the rock debris sample corresponding to the rock debris fluorescence image is non-petroleum hydrocarbon fluorescence. Any rock debris fluorescence image is obtained, and the three-dimensional fluorescence spectrum analysis result of the rock debris sample corresponding to the rock debris fluorescence image is combined. If no fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis result, lithology identification is performed. If the fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis result, the fluorescence type re-judgment process is triggered. The three-dimensional fluorescence spectrum of the rock debris sample corresponding to the rock debris fluorescence image and the three-dimensional fluorescence spectrum of the drilling fluid are obtained, and the fluorescence characteristic regions in the two three-dimensional fluorescence images are detected respectively, and the corresponding fluorescence characteristic region features are extracted, wherein the fluorescence characteristic region features include contour circumscribed rectangle parameters and contour mask region color features. The similarity of the fluorescence characteristic region features in the two three-dimensional fluorescence images is analyzed to obtain a comprehensive similarity. wherein, is a comprehensive similarity; is a region shape similarity, which is weighted by a region number similarity, a position distribution similarity, and a shape similarity; is a color distribution similarity; If > 0.8, the fluorescence is caused by drilling fluid additive contamination, and lithology identification is performed, if < 0.8, the fluorescence type is oil hydrocarbon fluorescence, and lithology identification is not performed. 5.The lithology identification method based on fusion of lithodetritus fluorescence image and Raman spectrum according to claim 4, characterized in that, The similarity of the fluorescence characteristic region features in the two three-dimensional fluorescence images is analyzed to obtain a comprehensive similarity, including: The hue histogram of the fluorescence characteristic region of the three-dimensional fluorescence spectrum of the rock debris sample and the three-dimensional fluorescence spectrum of the drilling fluid is obtained respectively, the Bhattacharyya distance between them is determined, and the color distribution similarity is obtained. wherein, is a color distribution similarity; is a Bhattacharyya distance; The region shape similarity is obtained by weighting the region number similarity, the position distribution similarity and the shape similarity, including: The region number similarity is determined. wherein, is a region quantity similarity; , are the number of fluorescent feature regions in the two three-dimensional fluorescent images, respectively. The position distribution similarity is determined, including: (1) obtaining a three-dimensional fluorescence spectrum point set of a rock sample and a three-dimensional fluorescence spectrum point set of a drilling fluid , determining a directed Hausdorff distance between the point sets, wherein the three-dimensional fluorescence spectrum point set is a set of geometric center points of each fluorescence feature region in the three-dimensional fluorescence spectrum; wherein, is to a directed Hausdorff distance; is to a directed Hausdorff distance; is a geometric center point in is a geometric center point in is and an Euclidean distance; (2) take the maximum value of both as the final distance : (3) the distance Normalization is done and converted into a position distribution similarity; wherein, is a position distribution similarity; is a maximum of two image sizes; is a distance normalized by image size; Determine shape similarity, respectively acquire the largest area fluorescent characteristic region in the three-dimensional fluorescence spectrum of the drilling fluid and the three-dimensional fluorescence spectrum of the drilling fluid, determine the corresponding Hu moment, and calculate the cosine similarity between the two Hu moments , and get shape similarity after normalization ; ； The region shape similarity is obtained by weighting. wherein, is a region shape similarity; is a region number similarity; is a position distribution similarity; is a shape similarity.

6. A lithology identification device based on fusion of debris fluorescence image and Raman spectrum using the method according to any one of claims 1 to 5, characterized in that, It includes: The rock debris fluorescence image is obtained, and the fluorescence type of the rock debris sample corresponding to the rock debris fluorescence image is non-petroleum hydrocarbon fluorescence. The image analysis unit applies the Gaussian weighting method to dynamically segment the fluorescence region, obtains high, medium and low fluorescence intensity region images and non-fluorescence region image according to different fluorescence intensity ranges, and determines the gray value range and fluorescence area proportion of each region image, wherein the fluorescence area proportion is the proportion of the number of fluorescence pixels in the total number of image pixels. The mineral analysis unit inputs the gray value range and fluorescence area proportion of each region image into the mineral type discrimination rule for matching to obtain the corresponding mineral species and mineral content, wherein the mineral type discrimination rule is obtained by combining the in-situ Raman spectrum analysis to analyze the mineral composition of the rock debris samples in different fluorescence intensity regions of a plurality of historical rock debris fluorescence images, and the mineral content is the sum of the fluorescence area proportions of the region images corresponding to the same mineral species. The lithology identification unit inputs the mineral species and mineral content into the lithology identification rule for matching to obtain the corresponding lithology identification result, wherein the lithology identification rule includes: (1) determining the basic name When the highest mineral content is greater than 50%, the corresponding mineral type is taken as the basic name of the rock; When all mineral contents are less than 50%, the mineral type with the highest mineral content is taken as the basic name of the rock; (2) determine the additional noun If the remaining mineral content is between 50% and 25%, the corresponding mineral type is added in the form of XX quality before the basic name; If the remaining mineral content is between 25% and 10%, the corresponding mineral type is added in the form of containing XX before the basic name; If the remaining mineral content is less than 10%, the corresponding mineral type does not participate in rock naming. 7.The lithology identification device based on fusion of lithodetritus fluorescence image and Raman spectrum according to claim 6, characterized in that, The image analysis unit comprises: A preprocessing module converts the cutting fluorescence image into a gray-scale image and performs preprocessing; A fluorescence region extraction module applies a Gaussian weighting method to the gray-scale image for dynamic segmentation to obtain corresponding fluorescence regions, and sequentially performs fluorescence region reprocessing through morphological optimization and connected region extraction; A fluorescence region reprocessing module obtains high, medium and low fluorescence intensity region images and a non-fluorescence region image according to different fluorescence intensity ranges based on the reprocessed fluorescence regions; A fluorescence region quantification module determines the gray-scale values and fluorescence area proportions of each region image, wherein the calculation formula of the fluorescence area proportion is as follows: wherein, is the fluorescent area proportion; is the number of fluorescent pixels of the fluorescent area; is the total number of pixels of the rock debris fluorescent image. 8.The lithology identification device based on fusion of lithodetritus fluorescence image and Raman spectrum according to claim 6 or 7, characterized in that, The original image acquisition unit comprises: A fluorescence type acquisition module acquires any cutting fluorescence image and combines the three-dimensional fluorescence spectrum analysis result of the cutting sample corresponding to the cutting fluorescence image; A first fluorescence type analysis module performs lithology identification if no fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis result; A second fluorescence type analysis module triggers a fluorescence type rejudgment process if a fluorescence characteristic region is displayed in the three-dimensional fluorescence spectrum analysis result, including: Acquiring the three-dimensional fluorescence spectrum of the cutting sample corresponding to the cutting fluorescence image and the three-dimensional fluorescence spectrum of the corresponding drilling fluid, detecting the fluorescence characteristic regions in the two three-dimensional fluorescence images, and extracting the corresponding fluorescence characteristic region features, wherein the fluorescence characteristic region features include contour circumscribed rectangle parameters and contour mask region color features; Performing similarity analysis on the fluorescence characteristic region features in the two three-dimensional fluorescence images to obtain a comprehensive similarity; wherein, is a comprehensive similarity; is a region shape similarity, which is weighted by a region number similarity, a position distribution similarity, and a shape similarity; is a color distribution similarity; If > 0.8, the fluorescence is caused by drilling fluid additive contamination, and lithology identification is performed, if < 0.8, the fluorescence type is oil hydrocarbon fluorescence, and lithology identification is not performed.

9. An electronic device, comprising: The device comprises a processor and a memory, and the memory stores a computer program which is loaded and executed by the processor to realize the steps in the method of any one of claims 1 to 5. The device comprises a processor and a memory, and the memory stores a computer program which is loaded and executed by the processor to realize the steps in the method of any one of claims 1 to 5.

Images