Rapid laser scanning confocal image analysis method for oiliness and rock brittleness of well site rock debris

Abstract

The invention discloses a rapid laser scanning confocal image analysis method for oiliness and rock brittleness of well site rock debris, and relates to the field of petroleum geology exploration and development. The rapid laser scanning confocal image analysis method for the oiliness and the rock brittleness of the well site rock debris comprises the following steps: S1, rapidly freezing and packaging a collected well site rock debris sample, and preparing the well site rock debris sample into a slice; s2, converting the rock debris sample slice into a fluorescence image; s3, automatically judging the oiliness of the rock debris; and S4, evaluating the brittleness of the rock according to the crack characteristics. According to the method, the problems of complex flow, long time consumption and difficulty in meeting the requirement of rapid drilling during rock debris analysis in the prior art are solved, the oiliness of the rock debris is automatically analyzed according to the extracted feature data of the rock debris, the oiliness of the rock debris can be rapidly judged, the working efficiency is improved, the rock brittleness is evaluated, and the method is suitable for popularization and application. The method does not need to be sent to a laboratory for analysis, and is simple in process and short in time consumption.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum geological exploration and development, in particular to a fast laser scanning confocal image analysis method for oil content and rock brittleness of well site rock cuttings. Background Art

[0002] The rock cuttings analysis device for oil logging refers to an instrument used to analyze the rock cuttings obtained during the logging process to understand the properties, composition and mineral composition of the rocks. The obtained rock cuttings can be observed under a microscope, photographed, classified, counted and other operations. The user can obtain information such as the mineral composition and particle size distribution of oil and gas-bearing rocks through the rock cuttings analysis device, providing valuable reference data for exploration and development. Before using the rock cuttings analysis device, the rock cuttings samples must be cut and ground to prepare specimens suitable for observation.

[0003] The Chinese patent with the publication number CN103927547B discloses a method for rapid analysis of rock cuttings fluorescence images, which includes the following steps: 1) oil component recognition training based on RGB three-dimensional color space to obtain a clustering file; 2) oil component analysis of the rock cuttings fluorescence image to be analyzed according to the clustering file obtained in step 1), specifically including the following steps: 2.1) color feature extraction of the input rock cuttings fluorescence image to be analyzed, and generating a feature vector array; 2.2) analysis of each feature vector in the feature vector array; 2.3) until the analysis of all feature vectors in the vector array is completed, the oil components contained in the rock cuttings fluorescence image and the proportion of each component are calculated. The patent's rock cuttings fluorescence hierarchical clustering method based on non-uniform color space color difference calculation has accurate and fast results in oil component recognition and rock cuttings fluorescence image analysis, which can meet the needs of intelligent logging of offshore platform geologists.

[0004] The above patent analyzes rock cuttings by collecting samples of the rock cuttings and then sending them to the laboratory. The process is complicated and time-consuming, and it is difficult to meet the needs of rapid drilling. Therefore, it does not meet the existing needs. We have proposed a rapid laser scanning confocal image analysis method for the oil content and rock brittleness of well site rock cuttings. Summary of the invention

[0005] The purpose of the present invention is to provide a fast laser scanning confocal image analysis method for the oil content and rock brittleness of well site cuttings. By extracting characteristic data of the cuttings and automatically analyzing the oil content of the cuttings based on the extracted characteristic data of the cuttings, the oil content of the cuttings can be quickly determined, thereby improving work efficiency and realizing the assessment of rock brittleness without sending the cuttings to a laboratory for analysis. The process is simple and time-saving, thereby meeting the requirements for drilling of the oil content and rock brittleness of the cuttings and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a fast laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings, comprising the following steps:

[0007] S1: Collect rock cuttings samples from the well site, and quickly freeze and package the collected rock cuttings samples through a sample rapid freezing device to make thin slices;

[0008] S2: Process the thin-sectioned rock cuttings samples using a portable confocal microscope system to convert the rock cuttings thin sections into fluorescent images;

[0009] S3: Analyze the color and intensity attributes of each pixel in the fluorescent image, analyze and count the ratios of fluorescent pixels of different colors and intensities, identify the fluorescence level and content of the cuttings, and automatically determine the oil content of the cuttings;

[0010] S4: The fracture characteristics of the rock cuttings were analyzed using a portable confocal microscope, and the brittleness of the rock was evaluated based on the fracture characteristics obtained from the analysis.

[0011] Preferably, the rapid freezing and packaging of the rock cuttings samples specifically includes:

[0012] Put the rock samples collected on site into a sample rapid freezing device, and the rock samples collected on site are core samples or rock cuttings samples collected on site;

[0013] The rock samples are quickly frozen in a sample rapid freezing device, using liquid nitrogen for external cooling and propane for internal cooling to produce rock cutting sample slices.

[0014] Preferably, the portable confocal microscopy system specifically comprises:

[0015] The light source module is a vertical cavity surface emitting laser array, which is used to generate multiple lights, and each light is provided with a corresponding spectrum;

[0016] An optical signal receiving device is used to perform multi-point simultaneous detection on the rock cutting sample slice, control the light source module during multi-point detection, and transmit the detection results to the industrial control computer;

[0017] An industrial control computer is used to process the signal received by the optical signal receiving device and obtain the maximum point of the measured light intensity;

[0018] A beam expansion and collimation lens, used for expanding and collimating the light beam output by the light source module;

[0019] a beam splitter for directing the reflected and scattered beams of light from the thin slice of rock sample to be analyzed into a long-tube microscope objective;

[0020] A long-tube microscope objective lens is used to converge the output light beam of the light-emitting module onto the rock chip sample slice and to perform tomographic scanning on the rock chip sample slice;

[0021] The imaging module is used to convert the thin-section tomographic image of the rock cuttings sample scanned by the long-tube microscope objective into a fluorescent image for display.

[0022] Preferably, controlling the light source module during multi-point detection includes:

[0023] Adjust the intensity, direction and frequency of the light source module in real time according to detection requirements;

[0024] Extracting intensity information corresponding to the intensity of the light adjusted each time;

[0025] Extracting direction information corresponding to the direction of light adjusted each time;

[0026] Extracting frequency information corresponding to the frequency of light adjusted each time;

[0027] Obtaining an intensity adjustment parameter, a direction adjustment parameter, and a frequency adjustment parameter using intensity information corresponding to the intensity of the light, direction information corresponding to the direction of the light, and frequency information corresponding to the frequency of the light;

[0028] The intensity adjustment parameter, the direction adjustment parameter and the frequency adjustment parameter are used to obtain the adjustment operation evaluation parameter of the light source module, wherein the adjustment operation evaluation parameter is obtained by the following formula:

[0029]

[0030] Wherein, Q represents the adjustment operation evaluation parameter; Q0 represents the preset reference parameter value; B represents the intensity adjustment parameter; Ψ represents the direction adjustment parameter; F represents the frequency adjustment parameter;

[0031] When the adjustment operation evaluation parameter is lower than a preset parameter threshold, an adjustment operation abnormality alarm is issued.

[0032] Preferably, obtaining the intensity adjustment parameter, the direction adjustment parameter and the frequency adjustment parameter using the intensity information corresponding to the intensity of the light, the direction information corresponding to the direction of the light and the frequency information corresponding to the frequency of the light includes:

[0033] The intensity information corresponding to the intensity of the light adjusted each time is retrieved, and the intensity adjustment parameter is obtained according to the intensity information corresponding to the intensity of the light adjusted each time, wherein the intensity adjustment parameter is obtained by the following formula:

[0034]

[0035] Wherein, B represents the intensity adjustment parameter; b represents the first evaluation coefficient; n represents the number of times the light intensity is adjusted; Bi Indicates the intensity value corresponding to the actual adjustment of the intensity of the i-th light when the adjustment is completed; B mi Indicates the target intensity value corresponding to the intensity adjustment of the i-th light; E b Indicates the preset maximum allowable strength error value; exp indicates the exponential function operation with e as the base;

[0036] The direction information corresponding to the direction of the light adjusted each time is retrieved, and the direction adjustment parameter is obtained according to the direction information corresponding to the direction of the light adjusted each time, wherein the direction adjustment parameter is obtained by the following formula:

[0037]

[0038] Where, Ψ represents the direction adjustment parameter; P b represents the overall change rate of the amplitude of the intensity adjustment parameter; ω represents the second evaluation coefficient; m represents the number of light direction adjustments; α i Indicates the direction deviation angle value when the actual adjustment corresponding to the i-th direction adjustment is completed; α mi represents the target angle value corresponding to the i-th direction adjustment; E α Indicates the preset maximum allowable direction angle error value; exp indicates the exponential function operation with e as the base;

[0039] The frequency information corresponding to the frequency of the light adjusted each time is retrieved, and the frequency adjustment parameter is obtained according to the frequency information corresponding to the frequency of the light adjusted each time, wherein the frequency adjustment parameter is obtained by the following formula:

[0040]

[0041] Where F represents the frequency adjustment parameter; k represents the number of times the frequency of light is adjusted; f i represents the frequency value when the actual adjustment corresponding to the ith frequency adjustment is completed; f mi Indicates the target frequency value corresponding to the i-th frequency adjustment; E f Indicates the preset maximum allowable frequency error value; exp indicates the exponential function operation with e as the base.

[0042] Preferably, the oil content of the cuttings is analyzed, specifically including:

[0043] Receiving the fluorescence image of the rock cuttings sample slice through the rock cuttings fluorescence image automatic recognition module, and obtaining the color chromatogram of the rock cuttings sample slice according to the fluorescence image of the rock cuttings sample slice;

[0044] The color of the rock chip area and the characteristics of the fluorescent pixels are analyzed based on the color chromatogram of the rock chip sample thin section.

[0045] A clustering algorithm is used to perform hierarchical training on the color of the rock cuttings area and the features of the fluorescent pixels to generate a training model, marking points in different areas as light oil, medium oil and heavy oil, and recording the color waiting features of the categories;

[0046] The boundaries between the particles of the rock cuttings are extracted from the fluorescent image, and then the rock cutting targets in the rock cuttings are identified based on the texture characteristics of the rock cuttings and mudstone.

[0047] The cuttings targets are classified by training the model to obtain the oil-containing component analysis of the cuttings fluorescence image. The oil content of the cuttings is analyzed based on the analysis results of the oil-containing components combined with the color characteristics.

[0048] Preferably, the step of extracting the boundaries of particles between rock cuttings from the fluorescent image specifically includes:

[0049] The particles in the rock cuttings image are divided into a plurality of closed regions, the pixel points in the plurality of closed regions are determined, and the flow direction of each pixel point pointing to the nearest edge of the pixel point is determined;

[0050] Then the fluorescence image of the rock cutting sample slice is transformed into color space from RGB space to Lab space, and then the edge flow vector is constructed at a single scale.

[0051] Calculate the edge flow vector of the current point and the probability of finding an edge in each direction, determine the point vector, traverse the complete fluorescent image, and draw an edge flow vector map. The opposite direction of the edge flow vector in the vector map is the location of the target edge in the image.

[0052] Preferably, the rock cuttings fluorescent image automatic recognition module specifically includes:

[0053] An acquisition module, used for receiving the fluorescent image of the rock cutting sample slice, and acquiring the color chromatogram of the rock cutting sample slice according to the fluorescent image of the rock cutting sample slice;

[0054] Extracting color type and fluorescence pixel intensity data of rock cutting sample slices, and transmitting the acquired data to the analysis module;

[0055] An analysis module, used for analyzing the color of the rock cutting area and the characteristics of the fluorescent pixels according to the color chromatogram of the rock cutting sample slice;

[0056] The recognition module is used to extract the boundaries between particles in the rock cuttings from the fluorescent image, obtain the oil-containing component analysis of the rock cuttings fluorescent image, and automatically identify the oil content of the rock cuttings based on the analysis results of the oil-containing components combined with the color characteristics.

[0057] Preferably, the analysis of the brittleness of the rock specifically includes:

[0058] The rock brittleness analysis module is used to process the fluorescence images of rock cuttings thin sections to obtain the hardness, Young's modulus and crack characteristics of the rock;

[0059] Non-dimensionalizing hardness, Young's modulus and fracture toughness to generate non-dimensionalized hardness, non-dimensionalized Young's modulus and non-dimensionalized fracture toughness;

[0060] Generate rock brittle parameters based on non-dimensionalized hardness, non-dimensionalized Young's modulus, non-dimensionalized fracture toughness and crack characteristics;

[0061] Generate rock brittleness analysis results based on brittleness parameters.

[0062] Preferably, the rock brittleness analysis module specifically includes:

[0063] A processing module is used to process the fluorescent image of the rock cutting sample slice to obtain the hardness, Young's modulus and crack characteristics of the rock;

[0064] Fracture toughness calculation module, used to calculate the fracture toughness of rock according to the fracture characteristics and Young's modulus of rock;

[0065] The brittleness parameter generation module is used to analyze the rock brittleness and generate the rock brittleness parameters according to the hardness, Young's modulus and fracture toughness of the rock.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention performs high-definition imaging of rock cuttings sample slices to facilitate the extraction of characteristic data of the rock cuttings, and then automatically analyzes the oil content of the rock cuttings based on the extracted characteristic data of the rock cuttings, can quickly determine the oil content of the rock cuttings, and improves work efficiency. By observing the crack characteristics in the rock cuttings sample slices, the brittleness of the rock can be evaluated without sending it to the laboratory for analysis. The process is simple and time-saving, which meets the requirements of drilling for the oil content of the rock cuttings and the brittleness of the rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a fast laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings of the present invention;

[0069] Figure 2 The present invention is a flow chart of oil content analysis of the fast laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In order to solve the problem that the existing patents collect samples of rock cuttings and then send them to the laboratory for analysis, the process is complicated and time-consuming, and it is difficult to meet the needs of rapid drilling. Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0072] The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings includes the following steps:

[0073] S1: Collect rock cuttings samples from the well site, and quickly freeze and package them through a sample rapid freezing device to make thin slices. The freezing slice making method can ensure that the original shape of the well site rock cuttings will not be destroyed, thereby ensuring that the results of oil analysis and rock brittleness analysis of the rock cuttings samples are more accurate.

[0074] S2: The rock cuttings samples made into thin slices are processed by a portable confocal microscope system to convert the rock cuttings sample slices into fluorescent images. The converted images are clearer, and the feature extraction of the rock cuttings sample slices is more accurate, which facilitates the analysis of the oiliness of the rock cuttings and the brittleness of the rock, making the analysis data more accurate.

[0075] S3: Analyze the color and intensity attributes of each pixel in the fluorescent image. By analyzing and counting the ratio of fluorescent pixels of different colors and intensities, identify the fluorescence level and content of the rock cuttings, and automatically judge the oil content of the rock cuttings. Analyze the color and pixels of the fluorescent image, and automatically identify the oil content of the rock cuttings based on the analysis results. There is no need to send the cuttings to the laboratory for analysis, which saves analysis time and improves efficiency.

[0076] S4: The crack characteristics of rock cuttings are analyzed using a portable confocal microscope system. The brittleness of the rock is evaluated based on the crack characteristics obtained by the analysis. The characteristics of the fluorescent image of the rock cutting sample slices are extracted to analyze the crack characteristics of the rock cuttings, thereby realizing the analysis of the rock brittleness. This method is time-saving and easy to operate, meeting the needs of rapid drilling.

[0077] The rock cuttings samples were quickly frozen and packaged, including:

[0078] Put the rock samples collected on site into a sample rapid freezing device, and the rock samples collected on site are core samples or rock cuttings samples collected on site;

[0079] The rock samples are quickly frozen in a sample rapid freezing device, using liquid nitrogen for external cooling and propane for internal cooling to produce rock cutting sample slices.

[0080] Specifically, the collected rock cuttings samples are quickly frozen and packaged through a sample rapid freezing device, and the well site rock cuttings samples are quickly made into thin slices suitable for microscopic observation, effectively preserving the original state and oil content information of the rock cuttings, ensuring the accuracy of the analysis results of the oil content of the rock cuttings and the brittleness of the rock.

[0081] Portable confocal microscope system, including:

[0082] The light source module is a vertical cavity surface emitting laser array, which is used to generate multiple lights, and each light is provided with a corresponding spectrum;

[0083] An optical signal receiving device is used to perform multi-point simultaneous detection on the rock cuttings sample slices, control the light source module during multi-point detection to achieve the purpose of making them emit light one by one, and transmit the detection results to the industrial control computer;

[0084] Industrial control computer, used to process the signal received by the optical signal receiving device, and also has D / A output, and obtains the maximum point of measured light intensity to achieve confocal measurement;

[0085] The beam expansion and collimation lens is used to expand and collimate the light beam output by the light source module so that the light beam can be transmitted within the range of the system optical components;

[0086] a beam splitter for directing the reflected and scattered beams of light from the thin slice of rock sample to be analyzed into a long-tube microscope objective;

[0087] A long-tube microscope objective lens is used to converge the output light beam of the light-emitting module onto the rock chip sample slice and to perform tomographic scanning on the rock chip sample slice;

[0088] The imaging module is used to convert the thin-section tomographic image of the rock cuttings sample scanned by the long-tube microscope objective into a fluorescent image for display.

[0089] Specifically, the portable confocal microscope system is used to convert rock cuttings sample slices into fluorescent images, which is suitable for rapid deployment at the well site. It can achieve high-definition microscopic imaging of rock cuttings and facilitate the extraction of features in rock cuttings sample slices. The oil content and rock brittleness of the rock cuttings can be analyzed based on the features of the rock cuttings sample slices. The crack features generated in the rock cuttings observed using the confocal microscope can be used to evaluate the brittleness of the rock, providing a reference for drilling and fracturing design. The system is simple to operate, takes less time, and is more efficient.

[0090] Specifically, the light source module is controlled during multi-point detection, including:

[0091] Adjust the intensity, direction and frequency of the light source module in real time according to detection requirements;

[0092] Extracting intensity information corresponding to the intensity of the light adjusted each time;

[0093] Extracting direction information corresponding to the direction of light adjusted each time;

[0094] Extracting frequency information corresponding to the frequency of light adjusted each time;

[0095] Obtaining an intensity adjustment parameter, a direction adjustment parameter, and a frequency adjustment parameter using intensity information corresponding to the intensity of the light, direction information corresponding to the direction of the light, and frequency information corresponding to the frequency of the light;

[0096] The intensity adjustment parameter, the direction adjustment parameter and the frequency adjustment parameter are used to obtain the adjustment operation evaluation parameter of the light source module, wherein the adjustment operation evaluation parameter is obtained by the following formula:

[0097]

[0098] Wherein, Q represents the adjustment operation evaluation parameter; Q0 represents the preset reference parameter value; B represents the intensity adjustment parameter; Ψ represents the direction adjustment parameter; F represents the frequency adjustment parameter;

[0099] When the adjustment operation evaluation parameter is lower than a preset parameter threshold, an adjustment operation abnormality alarm is issued.

[0100] The technical effect of the above technical solution is that the system can adjust the intensity, direction and frequency of the light of the light source module in real time according to the detection requirements. This real-time dynamic adjustment ensures that the light source module can quickly adapt to different detection environments and requirements, and improves the flexibility and adaptability of the system. The system can extract information about the intensity, direction and frequency of the light after each adjustment, and obtain corresponding adjustment parameters (intensity adjustment parameters, direction adjustment parameters and frequency adjustment parameters) based on this information. This helps the system understand the current working status of the light source module and provides data support for subsequent evaluation and optimization.

[0101] By using the intensity adjustment parameter, direction adjustment parameter and frequency adjustment parameter, the system can obtain the adjustment operation evaluation parameter of the light source module. This evaluation parameter comprehensively considers multiple adjustment dimensions of the light source module and can fully reflect the performance of its adjustment operation. When the adjustment operation evaluation parameter is lower than the preset parameter threshold, the system can trigger an adjustment operation abnormality alarm. This abnormal alarm mechanism can promptly discover and report possible problems with the light source module, which helps users or system administrators take timely measures to repair or adjust it to avoid affecting the normal operation of the detection system. Through the above-mentioned real-time adjustment, data collection and analysis, adjustment operation evaluation and abnormal alarm mechanism, this technical solution can significantly improve the reliability and stability of the light source module in the multi-point detection system, ensuring that the system can continuously and accurately provide detection services.

[0102] In summary, this technical solution effectively improves the reliability and stability of the multi-point detection system by real-time controlling multiple parameters of the light source module, collecting and analyzing relevant information, and providing an evaluation and optimization mechanism, and has important practical application value.

[0103] Specifically, obtaining the intensity adjustment parameter, the direction adjustment parameter, and the frequency adjustment parameter using the intensity information corresponding to the intensity of the light, the direction information corresponding to the direction of the light, and the frequency information corresponding to the frequency of the light includes:

[0104] The intensity information corresponding to the intensity of the light adjusted each time is retrieved, and the intensity adjustment parameter is obtained according to the intensity information corresponding to the intensity of the light adjusted each time, wherein the intensity adjustment parameter is obtained by the following formula:

[0105]

[0106] Wherein, B represents the intensity adjustment parameter; b represents the first evaluation coefficient; n represents the number of times the light intensity is adjusted; B i Indicates the intensity value corresponding to the actual adjustment of the intensity of the i-th light when the adjustment is completed; B mi Indicates the target intensity value corresponding to the intensity adjustment of the i-th light; E b Indicates the preset maximum allowable strength error value; exp indicates the exponential function operation with e as the base;

[0107] The direction information corresponding to the direction of the light adjusted each time is retrieved, and the direction adjustment parameter is obtained according to the direction information corresponding to the direction of the light adjusted each time, wherein the direction adjustment parameter is obtained by the following formula:

[0108]

[0109] Where, Ψ represents the direction adjustment parameter; P brepresents the overall change rate of the amplitude of the intensity adjustment parameter; ω represents the second evaluation coefficient; m represents the number of light direction adjustments; α i Indicates the direction deviation angle value when the actual adjustment corresponding to the i-th direction adjustment is completed; α mi represents the target angle value corresponding to the i-th direction adjustment; E α Indicates the preset maximum allowable direction angle error value; exp indicates the exponential function operation with e as the base;

[0110] The frequency information corresponding to the frequency of the light adjusted each time is retrieved, and the frequency adjustment parameter is obtained according to the frequency information corresponding to the frequency of the light adjusted each time, wherein the frequency adjustment parameter is obtained by the following formula:

[0111]

[0112] Wherein, F represents the frequency adjustment parameter; b represents the first evaluation coefficient; ω represents the second evaluation coefficient; k represents the frequency adjustment times of the light; f i represents the frequency value when the actual adjustment corresponding to the ith frequency adjustment is completed; f mi Indicates the target frequency value corresponding to the i-th frequency adjustment; E f Indicates the preset maximum allowable frequency error value; exp indicates the exponential function operation with e as the base.

[0113] The technical effect of the above technical solution is: through specific formula calculation, the technical solution can quantitatively evaluate the adjustment performance of the light source module in terms of intensity, direction and frequency. This quantitative evaluation method makes the evaluation results more objective and comparable, which helps users or system administrators understand the adjustment performance of the light source module. Using exponential function calculations to evaluate the adjustment effect can more accurately reflect minor changes in the adjustment process. When there is a difference between the actual adjustment value and the target value, the exponential function can amplify the difference, making the adjustment effect more obvious, which is convenient for users or system administrators to quickly find problems and make adjustments.

[0114] When calculating the adjustment parameters, preset maximum allowable error values ​​(Eb, Eα, Ef) are introduced. These error tolerances enable the system to tolerate adjustment errors within a certain range, thereby improving the stability and reliability of the system. When the error between the actual adjustment value and the target value exceeds these tolerances, the adjustment parameters will increase accordingly, thereby reminding the user or system administrator to make further adjustments. When calculating the adjustment parameters, this technical solution comprehensively considers information in three aspects: intensity, direction, and frequency. This comprehensive consideration makes the evaluation results more comprehensive and accurate, and can better reflect the overall adjustment performance of the light source module. When the adjustment operation evaluation parameters calculated using these adjustment parameters are lower than the preset parameter threshold, the system will trigger an abnormal alarm. This abnormal alarm mechanism can promptly detect and report possible problems with the light source module to avoid affecting the normal operation of the detection system.

[0115] In summary, the above technical solution significantly improves the adjustment performance of the light source module in the multi-point detection system and the reliability of the system by quantitatively evaluating the adjustment performance, accurately feeding back the adjustment effect, introducing error tolerance, comprehensively considering multiple factors, and providing an abnormal alarm mechanism. This technical solution has important practical application value and can provide effective technical support and guarantee for users or system administrators.

[0116] Analysis of the oil content of the cuttings, including

[0117] Receiving the fluorescence image of the rock cuttings sample slice through the rock cuttings fluorescence image automatic recognition module, and obtaining the color chromatogram of the rock cuttings sample slice according to the fluorescence image of the rock cuttings sample slice;

[0118] The color of the rock cuttings area and the characteristics of the fluorescent pixels are analyzed according to the color chromatogram of the rock cuttings sample slice to obtain the oil-containing components of the rock cuttings.

[0119] A clustering algorithm is used to perform hierarchical training on the color of the rock cuttings area and the features of the fluorescent pixels to generate a training model. The features of the fluorescent pixels include luminous intensity, wavelength, color, brightness and saturation for color classification. Different area points are marked as light oil, medium oil and heavy oil, and the color waiting signs of the categories are recorded.

[0120] By extracting the boundaries between the particles in the cuttings from the fluorescent image and then identifying the cuttings targets in the cuttings based on the texture characteristics of the cuttings and mudstone, the problem of being unable to accurately identify the cuttings under fluorescent images is solved. The cuttings targets in the cuttings can be obtained more completely, thereby ensuring the accuracy of the oil content analysis of the cuttings.

[0121] The cuttings targets are classified by training the model to obtain the oil-containing component analysis of the cuttings fluorescence image. The oil content of the cuttings is analyzed based on the analysis results of the oil-containing components combined with the color characteristics.

[0122] The boundaries between particles of rock fragments are extracted from the fluorescence image, including:

[0123] The particles in the rock cuttings image are divided into a plurality of closed regions, the pixel points in the plurality of closed regions are determined, and the flow direction of each pixel point pointing to the nearest edge of the pixel point is determined;

[0124] Then the fluorescence image of the rock cutting sample slice is transformed into color space from RGB space to Lab space, and then the edge flow vector is constructed at a single scale.

[0125] Calculate the edge flow vector of the current point and the probability of finding an edge in each direction, determine the point vector, traverse the complete fluorescent image, and draw an edge flow vector map. The opposite direction of the edge flow vector in the vector map is the location of the target edge in the image.

[0126] The rock cuttings fluorescence image automatic recognition module includes:

[0127] An acquisition module, used for receiving the fluorescent image of the rock cutting sample slice, and acquiring the color chromatogram of the rock cutting sample slice according to the fluorescent image of the rock cutting sample slice;

[0128] Extracting color type and fluorescence pixel intensity data of rock cutting sample slices, and transmitting the acquired data to the analysis module;

[0129] An analysis module, used for analyzing the color of the rock cutting area and the characteristics of the fluorescent pixels according to the color chromatogram of the rock cutting sample slice;

[0130] The recognition module is used to extract the boundaries between particles in the rock cuttings from the fluorescent image, obtain the oil-containing component analysis of the rock cuttings fluorescent image, and automatically identify the oil content of the rock cuttings based on the analysis results of the oil-containing components combined with the color characteristics.

[0131] Specifically, a portable confocal microscope system is used to scan the prepared rock cuttings sample slices, capture fluorescent images, and automatically analyze the oil content of the rock cuttings by analyzing the fluorescent characteristics of the rock cuttings sample slices, quickly and accurately providing oil and gas display information, making the analysis of the oil content of the rock cuttings simpler and faster, ensuring the accuracy of the analysis results of the oil content of the rock cuttings while improving the efficiency of the analysis.

[0132] Analyze the brittleness of rocks, including

[0133] The rock brittleness analysis module is used to process the fluorescence images of rock cuttings thin sections to obtain the hardness, Young's modulus and crack characteristics of the rock;

[0134] Non-dimensionalizing hardness, Young's modulus and fracture toughness to generate non-dimensionalized hardness, non-dimensionalized Young's modulus and non-dimensionalized fracture toughness;

[0135] Generate rock brittle parameters based on dimensionless hardness, dimensionless Young's modulus, dimensionless fracture toughness and crack characteristics. The crack characteristics include crack length, width, density and other parameters.

[0136] According to the cracks in the fluorescent image of the rock cuttings thin section, the crack trace skeleton image is obtained, and the ratio parameter between the size of the crack and the actual size is obtained according to the crack trace skeleton image to obtain the length and width of the crack. According to the crack trace skeleton image and the ratio parameter, the surface crack density, surface crack strength and surface crack intersection density are determined to obtain the density of the cracks. According to the length, width and density of the cracks, more data can be combined when analyzing the brittleness of the rock, thereby ensuring the accuracy of the analysis results.

[0137] The rock brittleness analysis results are generated according to the brittleness parameters, which improves the accuracy and reliability of the rock brittleness analysis results.

[0138] Rock brittleness analysis module, including

[0139] A processing module is used to process the fluorescent image of the rock cutting sample slice to obtain the hardness, Young's modulus and crack characteristics of the rock;

[0140] Fracture toughness calculation module, used to calculate the fracture toughness of rock according to the fracture characteristics and Young's modulus of rock;

[0141] The brittleness parameter generation module is used to analyze the rock brittleness and generate the rock brittleness parameters according to the hardness, Young's modulus and fracture toughness of the rock.

[0142] Specifically, a portable confocal microscope system is used to observe the crack characteristics in rock cuttings sample slices, observe and analyze the crack characteristics in rock cuttings sample slices, and generate rock brittleness parameters based on the dimensionless hardness, dimensionless Young's modulus, dimensionless fracture toughness and crack characteristics to generate rock brittleness analysis results. There is no need to send the samples to a laboratory for analysis, and the process is simple and time-saving, which meets the drilling requirements for the oil content of rock cuttings and the brittleness of rocks and improves the accuracy of rock brittleness analysis.

[0143] In summary, the rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings of the present invention places the collected cuttings samples into a sample rapid freezing device for rapid freezing and packaging, and then quickly makes them into thin slices, so that the prepared cuttings sample slices can be scanned conveniently using a portable confocal microscope system, and high-definition imaging of the cuttings sample slices can be achieved by capturing the fluorescent image of the cuttings sample slices, and then the characteristic data of the cuttings can be extracted conveniently according to the fluorescent image of the cuttings sample slices, and then the oil content of the cuttings can be automatically analyzed according to the extracted characteristic data of the cuttings, and the oil content of the cuttings can be quickly judged, thereby improving work efficiency. The portable confocal microscope system is also used to observe the crack characteristics in the cuttings sample slices to evaluate the rock brittleness without sending them to the laboratory for analysis, and the process is simple and time-saving, which meets the drilling requirements for the oil content and rock brittleness of the cuttings.

[0144] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0145] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fast laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings, characterized by: The following steps are involved: S1: Collect rock cuttings samples from the well site, and quickly freeze and package the collected rock cuttings samples through a sample rapid freezing device to make thin slices; S2: Process the thin-sectioned rock cuttings samples using a portable confocal microscope system to convert the rock cuttings thin sections into fluorescent images; S3: Analyze the color and intensity attributes of each pixel in the fluorescent image, analyze and count the ratios of fluorescent pixels of different colors and intensities, identify the fluorescence level and content of the cuttings, and automatically judge the oil content of the cuttings; S4: The fracture characteristics of the rock cuttings were analyzed using a portable confocal microscope, and the brittleness of the rock was evaluated based on the fracture characteristics obtained from the analysis.

2. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 1, characterized in that: The rapid freezing and packaging of the rock cuttings samples specifically includes: Put the rock samples collected on site into a sample rapid freezing device, and the rock samples collected on site are core samples or rock cuttings samples collected on site; The rock samples are quickly frozen in a sample rapid freezing device, using liquid nitrogen for external cooling and propane for internal cooling to produce rock cutting sample slices.

3. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 1, characterized in that: The portable confocal microscopy system specifically comprises: The light source module is a vertical cavity surface emitting laser array, which is used to generate multiple lights, and each light is provided with a corresponding spectrum; An optical signal receiving device is used to perform multi-point simultaneous detection on the rock cutting sample slice, control the light source module during multi-point detection, and transmit the detection results to the industrial control computer; An industrial control computer is used to process the signal received by the optical signal receiving device and obtain the maximum point of the measured light intensity; A beam expansion and collimation lens, used for expanding and collimating the light beam output by the light source module; a beam splitter for directing the reflected and scattered beams of light from the thin slice of rock sample to be analyzed into a long-tube microscope objective; A long-tube microscope objective lens is used to converge the output light beam of the light-emitting module onto the rock chip sample slice and to perform tomographic scanning on the rock chip sample slice; The imaging module is used to convert the thin-section tomographic image of the rock cuttings sample scanned by the long-tube microscope objective into a fluorescent image for display.

4. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 3, characterized in that: Control the light source module during multi-point detection, including: Adjust the intensity, direction and frequency of the light source module in real time according to detection requirements; Extracting intensity information corresponding to the intensity of the light adjusted each time; Extracting direction information corresponding to the direction of light adjusted each time; Extracting frequency information corresponding to the frequency of light adjusted each time; Obtaining an intensity adjustment parameter, a direction adjustment parameter, and a frequency adjustment parameter using intensity information corresponding to the intensity of the light, direction information corresponding to the direction of the light, and frequency information corresponding to the frequency of the light; The intensity adjustment parameter, the direction adjustment parameter and the frequency adjustment parameter are used to obtain the adjustment operation evaluation parameter of the light source module, wherein the adjustment operation evaluation parameter is obtained by the following formula: Wherein, Q represents the adjustment operation evaluation parameter; Q0 represents the preset reference parameter value; B represents the intensity adjustment parameter; Ψ represents the direction adjustment parameter; F represents the frequency adjustment parameter; When the adjustment operation evaluation parameter is lower than a preset parameter threshold, an adjustment operation abnormality alarm is issued.

5. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 4, characterized in that: Obtaining an intensity adjustment parameter, a direction adjustment parameter, and a frequency adjustment parameter using the intensity information corresponding to the intensity of the light, the direction information corresponding to the direction of the light, and the frequency information corresponding to the frequency of the light, including: The intensity information corresponding to the intensity of the light adjusted each time is retrieved, and the intensity adjustment parameter is obtained according to the intensity information corresponding to the intensity of the light adjusted each time, wherein the intensity adjustment parameter is obtained by the following formula: Wherein, B represents the intensity adjustment parameter; b represents the first evaluation coefficient; n represents the number of times the light intensity is adjusted; B i Indicates the intensity value corresponding to the actual adjustment of the intensity of the i-th light when the adjustment is completed; B mi Indicates the target intensity value corresponding to the intensity adjustment of the i-th light; E b Indicates the preset maximum allowable strength error value; exp indicates the exponential function operation with e as the base; The direction information corresponding to the direction of the light adjusted each time is retrieved, and the direction adjustment parameter is obtained according to the direction information corresponding to the direction of the light adjusted each time, wherein the direction adjustment parameter is obtained by the following formula: Where, Ψ represents the direction adjustment parameter; P b represents the overall change rate of the amplitude of the intensity adjustment parameter; ω represents the second evaluation coefficient; m represents the number of light direction adjustments; α i Indicates the direction deviation angle value when the actual adjustment corresponding to the i-th direction adjustment is completed; α mi represents the target angle value corresponding to the i-th direction adjustment; E α Indicates the preset maximum allowable direction angle error value; exp indicates the exponential function operation with e as the base; The frequency information corresponding to the frequency of the light adjusted each time is retrieved, and the frequency adjustment parameter is obtained according to the frequency information corresponding to the frequency of the light adjusted each time, wherein the frequency adjustment parameter is obtained by the following formula: Where F represents the frequency adjustment parameter; k represents the number of times the frequency of light is adjusted; f i represents the frequency value when the actual adjustment corresponding to the ith frequency adjustment is completed; f mi Indicates the target frequency value corresponding to the i-th frequency adjustment; E f Indicates the preset maximum allowable frequency error value; exp indicates the exponential function operation with e as the base.

6. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 1, characterized in that: Analysis of the oil content of the cuttings, including Receiving the fluorescence image of the rock cuttings sample slice through the rock cuttings fluorescence image automatic recognition module, and obtaining the color chromatogram of the rock cuttings sample slice according to the fluorescence image of the rock cuttings sample slice; The color of the rock chip area and the characteristics of the fluorescent pixels were analyzed based on the color chromatogram of the rock chip sample thin section; A clustering algorithm is used to perform hierarchical training on the color of the rock cuttings area and the features of the fluorescent pixels to generate a training model, marking points in different areas as light oil, medium oil and heavy oil, and recording the color waiting features of the categories; By extracting the boundaries between the particles of the rock cuttings from the fluorescent image, the rock cuttings targets in the rock cuttings are identified based on the texture characteristics of the rock cuttings and mudstone. The cuttings targets are classified by training the model to obtain the oil-containing component analysis of the cuttings fluorescence image. The oil content of the cuttings is analyzed based on the analysis results of the oil-containing components combined with the color characteristics.

7. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 6, characterized in that: The boundaries between particles of rock fragments are extracted from the fluorescence image, including: The particles in the rock cuttings image are divided into a plurality of closed regions, the pixel points in the plurality of closed regions are determined, and the flow direction of each pixel point pointing to the nearest edge of the pixel point is determined; Then the fluorescence image of the rock cutting sample slice is transformed into color space from RGB space to Lab space, and then the edge flow vector is constructed at a single scale; Calculate the edge flow vector of the current point and the probability of finding an edge in each direction, determine the point vector, traverse the complete fluorescent image, and draw an edge flow vector map. The opposite direction of the edge flow vector in the vector map is the location of the target edge in the image.

8. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 6, characterized in that: The rock cuttings fluorescence image automatic recognition module specifically includes: An acquisition module, used for receiving the fluorescent image of the rock cutting sample slice, and acquiring the color chromatogram of the rock cutting sample slice according to the fluorescent image of the rock cutting sample slice; Extracting color type and fluorescence pixel intensity data of rock cutting sample slices, and transmitting the acquired data to the analysis module; An analysis module, used for analyzing the color of the rock cutting area and the characteristics of the fluorescent pixels according to the color chromatogram of the rock cutting sample slice; The recognition module is used to extract the boundaries between particles in the rock cuttings from the fluorescent image, obtain the oil-containing component analysis of the rock cuttings fluorescent image, and automatically identify the oil content of the rock cuttings based on the analysis results of the oil-containing components combined with the color characteristics.

9. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 1, characterized in that: Analyze the brittleness of rocks, including The rock brittleness analysis module is used to process the fluorescence images of rock cuttings thin sections to obtain the hardness, Young's modulus and crack characteristics of the rock; Non-dimensionalizing hardness, Young's modulus and fracture toughness to generate non-dimensionalized hardness, non-dimensionalized Young's modulus and non-dimensionalized fracture toughness; Generate rock brittle parameters based on non-dimensionalized hardness, non-dimensionalized Young's modulus, non-dimensionalized fracture toughness and crack characteristics; Generate rock brittleness analysis results based on brittleness parameters.

10. The rapid laser scanning confocal image analysis method for oil content and rock brittleness of well site cuttings according to claim 9, characterized in that: The rock brittleness analysis module specifically includes A processing module is used to process the fluorescent image of the rock cutting sample slice to obtain the hardness, Young's modulus and crack characteristics of the rock; Fracture toughness calculation module, used to calculate the fracture toughness of rock according to the fracture characteristics and Young's modulus of rock; The brittleness parameter generation module is used to analyze the rock brittleness and generate the rock brittleness parameters according to the hardness, Young's modulus and fracture toughness of the rock.

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