Rapid laser scanning confocal image analysis method for wellsite cuttings oiliness and rock friability

Abstract

The application discloses a well site debris oiliness and rock brittleness rapid laser scanning confocal image analysis method and relates to the field of oil geological exploration and development.The well site debris oiliness and rock brittleness rapid laser scanning confocal image analysis method comprises the following steps: S1, rapidly freezing and packaging the collected well site debris sample and making a slice; S2, converting the debris sample slice into a fluorescence image; S3, automatically identifying the oiliness of the debris; and S4, evaluating the brittleness of the rock according to the fracture characteristics.The application solves the problems that the existing patent has a complex process and a relatively long time in analyzing the debris and is difficult to meet the needs of rapid drilling, and the application automatically analyzes the oiliness of the debris according to the characteristic data of the extracted debris, can rapidly judge the oiliness of the debris, improves the work efficiency, realizes the evaluation of the brittleness of the rock, does not need to be sent to a laboratory for analysis, has a simple process and a relatively short time.

Description

Technical Field

[0001] This invention relates to the field of petroleum geological exploration and development technology, specifically to a rapid laser scanning confocal image analysis method for oil-bearing rock cuttings and rock brittleness at well sites. Background Technology

[0002] Oilfield logging cuttings analysis equipment refers to instruments and equipment used to analyze cuttings obtained during logging to understand the properties, composition, and mineral composition of rocks. It can perform microscopic observation, photography, classification, and counting of the obtained cuttings. Users can obtain information such as mineral composition and grain size distribution in oil-bearing and gas-bearing rocks through the cuttings analysis equipment, providing valuable reference data for exploration and development. Before using the cuttings analysis equipment, the cuttings samples must be cut and ground to prepare specimens suitable for observation.

[0003] Chinese Patent Publication No. CN103927547B discloses a method for rapid analysis of rock cuttings fluorescence images, comprising the following steps: 1) training for oil component identification based on RGB three-dimensional color space to obtain a clustering file; 2) performing oil component analysis on the rock cuttings fluorescence image to be analyzed based on the clustering file obtained in step 1), specifically including the following steps: 2.1) extracting color features from the input rock cuttings fluorescence image to be analyzed and generating a feature vector array; 2.2) analyzing each feature vector in the feature vector array; 2.3) until the analysis of all feature vectors in the vector array is completed, calculating the oil components contained in the rock cuttings fluorescence image and the proportion of each component. This patent's rock cuttings fluorescence hierarchical clustering method based on color difference calculation in a non-uniform color space is accurate and fast in oil component identification and rock cuttings fluorescence image analysis, and can meet the needs of intelligent logging by geological personnel on offshore platforms.

[0004] The aforementioned patents involve collecting rock cuttings samples and sending them to a laboratory for analysis, a process that is complex and time-consuming, making it difficult to meet the needs of rapid drilling. Therefore, it does not meet the existing requirements. In response, we propose a rapid laser scanning confocal image analysis method for oil-bearing properties and rock brittleness in well site rock cuttings. Summary of the Invention

[0005] The purpose of this invention is to provide a rapid laser scanning confocal image analysis method for oil content and rock brittleness in well cuttings. By extracting characteristic data of the cuttings, the oil content of the cuttings is automatically analyzed based on the extracted characteristic data, which can quickly determine the oil content of the cuttings, improve work efficiency, and enable the assessment of rock brittleness without sending them to a laboratory for analysis. The process is simple and time-saving, meeting the requirements for drilling to determine the oil content and rock brittleness of cuttings, and solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness, comprising the following steps:

[0007] S1: Collect well site cuttings samples, and use a sample rapid freezing device to rapidly freeze and encapsulate the collected well site cuttings samples, and make them into thin slices;

[0008] S2: Process the prepared rock debris samples using a portable confocal microscope system to convert the rock debris sample slices into fluorescence images;

[0009] S3: Analyze the color and intensity attributes of each pixel in the fluorescence image. By analyzing and statistically analyzing the proportion of fluorescent pixels of different colors and intensities, identify the fluorescence level and content of rock cuttings and automatically determine the oil content of rock cuttings.

[0010] S4: The crack characteristics of rock fragments obtained by analyzing the crack characteristics obtained by the portable confocal microscopy system are used to assess the brittleness of the rock.

[0011] Preferably, the rapid freezing and encapsulation of the rock fragment samples specifically includes:

[0012] The rock samples collected on site were placed into a rapid freezing device. The rock samples collected on site were either core samples or rock fragments.

[0013] Rock samples are rapidly frozen using a sample rapid freezing device, with liquid nitrogen used for external cooling and propane used for internal cooling, to produce thin sections of rock debris.

[0014] Preferably, the portable confocal microscope system specifically includes:

[0015] The light source module is a vertical cavity surface-emitting laser array used to generate multiple beams of light, and each beam of light is configured with a corresponding spectrum.

[0016] An optical signal receiving device is used to simultaneously detect multiple points on thin sections of rock cuttings samples. During multi-point detection, the device controls the light source module and transmits the detection results to an industrial control computer.

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

[0018] A beam-expanding and collimating lens is used to expand and collimate the beam output from the light source module.

[0019] A beam splitter is used to reflect the reflected and scattered light beams from a thin section of rock cutting sample to be analyzed into a long-tube microscope objective.

[0020] The long-tube microscope objective is used to focus the output beam of the light-emitting module onto a thin section of rock cutting sample and to perform tomographic scanning on the thin section of rock cutting sample;

[0021] The imaging module is used to convert the tomographic images of rock debris samples scanned by the long-tube microscope objective into fluorescence images for display.

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

[0023] The intensity, direction, and frequency of the light from the light source module are adjusted in real time according to the detection requirements.

[0024] Extract the intensity information corresponding to each adjustment of light intensity;

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

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

[0027] Intensity adjustment parameters, direction adjustment parameters, and frequency adjustment parameters are obtained using the intensity information corresponding to the light intensity, the direction information corresponding to the light direction, and the frequency information corresponding to the light frequency.

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

[0029]

[0030] Where Q represents the adjusted operation evaluation parameter; Q0 represents the preset benchmark parameter value; B represents the intensity adjustment parameter; Ψ represents the direction adjustment parameter; and F represents the frequency adjustment parameter.

[0031] When the adjustment operation evaluation parameter is lower than the preset parameter threshold, an adjustment operation abnormality alarm will be triggered.

[0032] Preferably, the intensity adjustment parameter, direction adjustment parameter, and frequency adjustment parameter are obtained using the intensity information corresponding to the light intensity, the direction information corresponding to the light direction, and the frequency information corresponding to the light frequency, including:

[0033] The intensity information corresponding to each adjustment of light intensity is retrieved, and the intensity adjustment parameter is obtained based on the intensity information corresponding to each adjustment of light intensity. The intensity adjustment parameter is obtained using the following formula:

[0034]

[0035] Where B represents the intensity adjustment parameter; b represents the first evaluation coefficient; n represents the number of light intensity adjustments; Bi B represents the actual intensity value at the end of the i-th light intensity adjustment; mi E represents the target intensity value corresponding to the i-th light intensity adjustment; b This represents the preset maximum allowable strength error value; exp represents the exponential function operation with base e.

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

[0037]

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

[0039] The frequency information corresponding to the frequency of the light adjusted each time is retrieved, and the frequency adjustment parameters are obtained based on the frequency information corresponding to the frequency of the light adjusted each time. The frequency adjustment parameters are obtained by the following formula:

[0040]

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

[0042] Preferably, the analysis of the oil content of the rock fragments specifically includes...

[0043] The automatic identification module for rock cutting fluorescence images receives fluorescence images of rock cutting sample sections and obtains color chromatograms of rock cutting sample sections based on the fluorescence images.

[0044] The color of the rock debris region and the characteristics of the fluorescent pixels were analyzed based on the color chromatogram of the rock debris sample thin section.

[0045] A clustering algorithm was used to perform hierarchical training on the color and fluorescent pixel features of the rock debris area to generate a training model. Different area points were labeled as light oil, medium oil and heavy oil, and the color and other features of the categories were recorded at the same time.

[0046] By extracting the boundaries of particles between rock fragments from the fluorescence image, and then identifying the rock fragment targets in the rock fragments based on the texture features of the rock fragments and mudstone.

[0047] By training a model to classify rock cutting targets, we can obtain an analysis of the oil-bearing components in the fluorescence images of rock cuttings. Based on the analysis results of the oil-bearing components and color features, we can analyze the oil content of the rock cuttings.

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

[0049] The particles in the rock debris image are segmented into multiple closed regions, the pixels within the multiple closed regions are identified, and the flow direction of each pixel pointing to the nearest edge of that pixel is determined.

[0050] Then, the fluorescence images of the rock debris sample sections were converted from RGB space to Lab space, and edge flow vectors were constructed at a single scale.

[0051] Calculate the edge flow vector of the current point and the probability of finding the edge in each direction, determine the vector of the point, and after traversing the complete fluorescence image, draw the edge flow vector diagram. The location of the target edge in the image is where the edge flow vector is opposite in direction in the vector diagram.

[0052] Preferably, the automatic identification module for rock cutting fluorescence images specifically includes:

[0053] The acquisition module is used to receive fluorescence images of rock cutting sample sections and acquire color chromatograms of rock cutting sample sections based on the fluorescence images of the rock cutting sample sections;

[0054] The color types and fluorescence pixel intensity data of the rock cutting sample thin sections are extracted, and the acquired data is transmitted to the analysis module;

[0055] The analysis module is used to analyze the color of rock debris regions and the characteristics of fluorescent pixels based on the color chromatograms of thin sections of rock debris samples;

[0056] The identification module is used to extract the boundaries between particles in the fluorescence image of rock cuttings, obtain the oil-bearing components analysis of the fluorescence image of rock cuttings, and automatically identify the oil content of rock cuttings based on the analysis results of oil-bearing components and color features.

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

[0058] The fluorescence images of thin sections of rock fragments were processed using the rock brittleness analysis module to obtain the rock's hardness, Young's modulus, and fracture characteristics.

[0059] The hardness, Young's modulus and fracture toughness are made dimensionless to generate dimensionless hardness, dimensionless Young's modulus and dimensionless fracture toughness.

[0060] The brittle parameters of the rock are generated based on the dimensionless hardness, dimensionless Young's modulus, dimensionless fracture toughness, and fracture characteristics.

[0061] The rock brittleness analysis results are generated based on the brittleness parameters.

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

[0063] The processing module is used to process the fluorescence images of thin sections of rock cuttings to obtain the rock's hardness, Young's modulus, and fracture characteristics.

[0064] The fracture toughness calculation module is used to calculate the fracture toughness of rocks based on the fracture characteristics and Young's modulus.

[0065] The brittleness parameter generation module is used to analyze the brittleness of rocks based on their hardness, Young's modulus, and fracture toughness, and to generate brittleness parameters for the rocks.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] This invention enables high-resolution imaging of thin sections of rock cuttings, facilitating the extraction of characteristic data from the rock cuttings. This allows for automatic analysis of the oil content of the rock cuttings based on this extracted data, rapidly determining their oil content and improving work efficiency. Furthermore, by observing the crack characteristics in the rock cuttings sample sections, the brittleness of the rock can be assessed without sending them to a laboratory for analysis. The process is simple and time-efficient, meeting the drilling requirements for assessing the oil content and brittleness of rock cuttings. Attached Figure Description

[0068] Figure 1 This is a flowchart of the rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to the present invention;

[0069] Figure 2 This is a flowchart illustrating the oil content analysis process of the rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] To address the issue that existing patents involve collecting rock cuttings samples and sending them to a laboratory for analysis, a process that is complex and time-consuming, and thus unsuitable for the demands of rapid drilling, please refer to [link to relevant documentation]. Figures 1-2 This embodiment provides the following technical solution:

[0072] A rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness includes the following steps:

[0073] S1: Collect well site cuttings samples and rapidly freeze and encapsulate them using a sample rapid freezing device to prepare thin sections. This method of cryopreservation ensures that the original morphology of the well site cuttings is not destroyed, thereby guaranteeing more accurate results when performing oil content analysis and rock brittleness analysis on the cuttings samples.

[0074] S2: The rock cutting samples prepared into thin sections are processed using a portable confocal microscope system. The rock cutting sample thin sections are converted into fluorescence images, making the converted images clearer. This makes feature extraction of the rock cutting sample thin sections more accurate, facilitating the analysis of rock cutting oil and rock brittleness, and making the analysis data more accurate.

[0075] S3: Analyzes the color and intensity attributes of each pixel in the fluorescence image. By analyzing and statistically analyzing the proportion of fluorescent pixels of different colors and intensities, it identifies the fluorescence level and content of rock cuttings and automatically determines the oil content of rock cuttings. It analyzes the color and pixels of the fluorescence image and automatically identifies the oil content of rock cuttings based on the analysis results. It eliminates the need to send the rock cuttings to the laboratory for analysis, saving analysis time and improving efficiency.

[0076] S4: The crack characteristics of rock cuttings obtained by analyzing the crack characteristics obtained by the portable confocal microscopy system are used to assess the brittleness of the rock. The crack characteristics of the rock cuttings are analyzed by extracting the characteristics of the fluorescence images of thin sections of rock cuttings, thereby realizing the analysis of rock brittleness. The process is quick and simple, meeting the needs of rapid drilling.

[0077] Rapid freezing and encapsulation of rock cutting samples specifically includes:

[0078] The rock samples collected on site were placed into a rapid freezing device. The rock samples collected on site were either core samples or rock fragments.

[0079] Rock samples are rapidly frozen using a sample rapid freezing device, with liquid nitrogen used for external cooling and propane used for internal cooling, to produce thin sections of rock debris.

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

[0081] Portable confocal microscopy systems, specifically including:

[0082] The light source module is a vertical cavity surface-emitting laser array used to generate multiple beams of light, and each beam of light is configured with a corresponding spectrum.

[0083] An optical signal receiving device is used to simultaneously detect multiple points on a thin section of rock cuttings sample. During multi-point detection, the light source module is controlled to make each point emit light, and the detection results are transmitted to an industrial control computer.

[0084] An industrial control computer is used to process the signals received by the optical signal receiving device. It also has D / A output and obtains the point of maximum light intensity to achieve confocal measurement.

[0085] A beam-expanding and collimating lens is used to expand and collimate the beam output from the light source module, so that the beam can be transmitted within the range of the system's optical elements;

[0086] A beam splitter is used to reflect the reflected and scattered light beams from a thin section of rock cutting sample to be analyzed into a long-tube microscope objective.

[0087] The long-tube microscope objective is used to focus the output beam of the light-emitting module onto a thin section of rock cutting sample and to perform tomographic scanning on the thin section of rock cutting sample;

[0088] The imaging module is used to convert the tomographic images of rock debris samples scanned by the long-tube microscope objective into fluorescence images for display.

[0089] Specifically, by using a portable confocal microscopy system to convert thin sections of rock cuttings into fluorescence images, it is suitable for rapid deployment at well sites. It can achieve high-resolution microscopic imaging of rock cuttings, making it easy to extract features from thin sections of rock cuttings. This allows for the analysis of oil content and rock brittleness based on the features of the thin sections of rock cuttings. By utilizing the fracture characteristics of rock cuttings observed by confocal microscopy, the brittleness of the rock can be assessed, providing a reference for drilling and fracturing design. The operation is simple, time-saving, and more efficient.

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

[0091] The intensity, direction, and frequency of the light from the light source module are adjusted in real time according to the detection requirements.

[0092] Extract the intensity information corresponding to each adjustment of light intensity;

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

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

[0095] Intensity adjustment parameters, direction adjustment parameters, and frequency adjustment parameters are obtained using the intensity information corresponding to the light intensity, the direction information corresponding to the light direction, and the frequency information corresponding to the light frequency.

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

[0097]

[0098] Where Q represents the adjusted operation evaluation parameter; Q0 represents the preset benchmark parameter value; B represents the intensity adjustment parameter; Ψ represents the direction adjustment parameter; and F represents the frequency adjustment parameter.

[0099] When the adjustment operation evaluation parameter is lower than the preset parameter threshold, an adjustment operation abnormality alarm will be triggered.

[0100] The technical advantages of the above solution are as follows: the system can adjust the intensity, direction, and frequency of the light from the light source module in real time according to detection requirements. This real-time dynamic adjustment ensures that the light source module can quickly adapt to different detection environments and needs, improving the system's flexibility and adaptability. The system can extract information on the intensity, direction, and frequency of the light after each adjustment and obtain the corresponding adjustment parameters (intensity adjustment parameters, direction adjustment parameters, and frequency adjustment parameters) based on this information. This helps the system understand the current operating status of the light source module and provides data support for subsequent evaluation and optimization.

[0101] By utilizing intensity, direction, and frequency adjustment parameters, the system can acquire evaluation parameters for the light source module's adjustment operation. These evaluation parameters comprehensively consider multiple adjustment dimensions of the light source module, fully reflecting its adjustment performance. When the evaluation parameters fall below preset thresholds, the system triggers an alarm for abnormal operation. This alarm mechanism promptly detects and reports potential problems with the light source module, helping users or system administrators to take timely corrective or adjustment measures to avoid affecting the normal operation of the detection system. Through the aforementioned real-time adjustment, data collection and analysis, adjustment operation evaluation, and alarm mechanism, this technical solution significantly improves the reliability and stability of the light source module in a multi-point detection system, ensuring the system can continuously and accurately provide detection services.

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

[0103] Specifically, intensity adjustment parameters, direction adjustment parameters, and frequency adjustment parameters are obtained using the intensity information corresponding to the light intensity, the direction information corresponding to the light direction, and the frequency information corresponding to the light frequency, including:

[0104] The intensity information corresponding to each adjustment of light intensity is retrieved, and the intensity adjustment parameter is obtained based on the intensity information corresponding to each adjustment of light intensity. The intensity adjustment parameter is obtained using the following formula:

[0105]

[0106] Where B represents the intensity adjustment parameter; b represents the first evaluation coefficient; n represents the number of light intensity adjustments; B i B represents the actual intensity value at the end of the i-th light intensity adjustment; mi E represents the target intensity value corresponding to the i-th light intensity adjustment; b This represents the preset maximum allowable strength error value; exp represents the exponential function operation with base e.

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

[0108]

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

[0110] The frequency information corresponding to the frequency of the light adjusted each time is retrieved, and the frequency adjustment parameters are obtained based on the frequency information corresponding to the frequency of the light adjusted each time. The frequency adjustment parameters are obtained by the following formula:

[0111]

[0112] Where F represents the frequency adjustment parameter; b represents the first evaluation coefficient; ω represents the second evaluation coefficient; k represents the number of light frequency adjustments; f i f represents the actual frequency value at the time the i-th frequency adjustment is completed; mi E represents the target frequency value corresponding to the i-th frequency adjustment; f This represents the preset maximum allowable frequency error value; exp represents the exponential function operation with base e.

[0113] The technical benefits of the above solution are as follows: Through specific formula calculations, this 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, helping 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 subtle changes during the adjustment process. When there is a difference between the actual adjustment value and the target value, the exponential function can amplify this difference, making the adjustment effect more obvious and facilitating users or system administrators to quickly identify problems and make adjustments.

[0114] When calculating the adjustment parameters, preset maximum permissible error values ​​(Eb, Eα, Ef) are introduced. These error tolerances allow the system to tolerate adjustment errors within a certain range, improving system stability and reliability. When the error between the actual adjusted value and the target value exceeds these tolerances, the adjustment parameters will increase accordingly, thus alerting the user or system administrator to make further adjustments. This technical solution comprehensively considers information from three aspects—intensity, direction, and frequency—when calculating the adjustment parameters. This comprehensive approach makes the evaluation results more comprehensive and accurate, better reflecting 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 thresholds, the system will trigger an abnormal alarm. This abnormal alarm mechanism can promptly detect and report potential problems with the light source module, avoiding impact on the normal operation of the detection system.

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

[0116] The oil content of rock cuttings was analyzed, specifically including...

[0117] The automatic identification module for rock cutting fluorescence images receives fluorescence images of rock cutting sample sections and obtains color chromatograms of rock cutting sample sections based on the fluorescence images.

[0118] The oil-bearing components of rock fragments can be obtained by analyzing the color chromatograms of thin sections of rock fragment samples and the characteristics of fluorescent pixels in the rock fragment regions.

[0119] Clustering algorithms were used to train the color and fluorescent pixel features of the rock debris area in a hierarchical manner to generate a training model. The fluorescent pixel features, including luminescence intensity, wavelength, color, brightness and saturation, were used for color classification. Different areas were labeled as light oil, medium oil and heavy oil, and the color and other features of the categories were recorded.

[0120] By extracting the boundaries of particles between rock fragments from fluorescence images, and then identifying rock fragment targets within the rock fragments based on the texture features of rock fragments and mudstone, the drawback of not being able to accurately identify rock fragments under fluorescence images is solved. This allows for a more complete acquisition of rock fragment targets within the rock fragments, thereby ensuring the accuracy of rock fragment oil content analysis.

[0121] By training a model to classify rock cutting targets, we can obtain an analysis of the oil-bearing components in the fluorescence images of rock cuttings. Based on the analysis results of the oil-bearing components and color features, we can analyze the oil content of the rock cuttings.

[0122] Boundary extraction of particles between rock debris was performed on fluorescence images, specifically including:

[0123] The particles in the rock debris image are segmented into multiple closed regions, the pixels within the multiple closed regions are identified, and the flow direction of each pixel pointing to the nearest edge of that pixel is determined.

[0124] Then, the fluorescence images of the rock debris sample sections were converted from RGB space to Lab space, and edge flow vectors were constructed at a single scale.

[0125] Calculate the edge flow vector of the current point and the probability of finding the edge in each direction, determine the vector of the point, and after traversing the complete fluorescence image, draw the edge flow vector diagram. The location of the target edge in the image is where the edge flow vector is opposite in direction in the vector diagram.

[0126] The automatic identification module for rock cuttings fluorescence images specifically includes:

[0127] The acquisition module is used to receive fluorescence images of rock cutting sample sections and acquire color chromatograms of rock cutting sample sections based on the fluorescence images of the rock cutting sample sections;

[0128] The color types and fluorescence pixel intensity data of the rock cutting sample thin sections are extracted, and the acquired data is transmitted to the analysis module;

[0129] The analysis module is used to analyze the color of rock debris regions and the characteristics of fluorescent pixels based on the color chromatograms of thin sections of rock debris samples;

[0130] The identification module is used to extract the boundaries between particles in the fluorescence image of rock cuttings, obtain the oil-bearing components analysis of the fluorescence image of rock cuttings, and automatically identify the oil content of rock cuttings based on the analysis results of oil-bearing components and color features.

[0131] Specifically, a portable confocal microscope system is used to scan the prepared rock cutting sample sections to capture fluorescence images. By analyzing the fluorescence characteristics of the rock cutting sample sections, the oil content of the rock cuttings is automatically analyzed, providing rapid and accurate oil and gas display information. This makes the analysis of the oil content of rock cuttings simpler and faster, ensuring the accuracy of the analysis results while improving the efficiency of the analysis.

[0132] The brittleness of the rock was analyzed, specifically including...

[0133] The fluorescence images of thin sections of rock fragments were processed using the rock brittleness analysis module to obtain the rock's hardness, Young's modulus, and fracture characteristics.

[0134] The hardness, Young's modulus and fracture toughness are made dimensionless to generate dimensionless hardness, dimensionless Young's modulus and dimensionless fracture toughness.

[0135] Brittle parameters of rocks are generated based on dimensionless hardness, dimensionless Young's modulus, dimensionless fracture toughness, and fracture characteristics. Fracture characteristics include parameters such as fracture length, width, and density.

[0136] Based on the fluorescence images of rock fragment samples, fracture trace skeleton images are obtained. The ratio between the fracture size and the actual size is then calculated from the fracture trace skeleton image to determine the fracture length and width. Based on the fracture trace skeleton image and the ratio parameters, the surface fracture density, surface fracture intensity, and surface fracture intersection density are determined to obtain the fracture density. By combining the fracture length, width, and density, more data can be incorporated into the analysis of rock brittleness, ensuring the accuracy of the analysis results.

[0137] The generation of rock brittleness analysis results based on brittleness parameters improves the accuracy and reliability of rock brittleness analysis.

[0138] The rock brittleness analysis module specifically includes...

[0139] The processing module is used to process the fluorescence images of thin sections of rock cuttings to obtain the rock's hardness, Young's modulus, and fracture characteristics.

[0140] The fracture toughness calculation module is used to calculate the fracture toughness of rocks based on the fracture characteristics and Young's modulus.

[0141] The brittleness parameter generation module is used to analyze the brittleness of rocks based on their hardness, Young's modulus, and fracture toughness, and to generate brittleness parameters for the rocks.

[0142] Specifically, by using a portable confocal microscopy system to observe the fracture characteristics in thin sections of rock cuttings, the fracture characteristics in the rock cuttings samples are observed and analyzed. Based on the dimensionless hardness, dimensionless Young's modulus, dimensionless fracture toughness, and fracture characteristics, the brittleness parameters of the rock are generated to produce rock brittleness analysis results. There is no need to send the samples to the laboratory for analysis. The process is simple and time-saving, which meets the drilling needs 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 in well cuttings of this invention involves placing the collected cuttings samples into a rapid freezing device for rapid freezing and encapsulation, and then quickly preparing them into thin sections. This allows for easy scanning of the prepared cuttings sample sections using a portable confocal microscope system. By capturing the fluorescence image of the cuttings sample sections, high-resolution imaging of the cuttings samples can be achieved. This facilitates the extraction of characteristic data from the fluorescence image of the cuttings sample sections, enabling automatic analysis of the oil content of the cuttings based on the extracted characteristic data. This rapid assessment of the oil content of the cuttings improves work efficiency. Similarly, the portable confocal microscope system is used to observe the fracture characteristics in the cuttings sample sections, enabling the assessment of rock brittleness without the need for laboratory analysis. The process is simple and time-efficient, meeting the drilling needs for assessing the oil content and rock brittleness of cuttings.

[0144] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

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

Claims

1. A rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness, characterized in that; Includes the following steps: S1: Collect well site cuttings samples, and use a sample rapid freezing device to rapidly freeze and encapsulate the collected well site cuttings samples, and make them into thin slices; S2: Process the prepared rock debris samples using a portable confocal microscope system to convert the rock debris sample slices into fluorescence images; S3: Analyze the color and intensity attributes of each pixel in the fluorescence image. By analyzing and statistically analyzing the proportion of fluorescent pixels of different colors and intensities, identify the fluorescence level and content of rock cuttings and automatically determine the oil content of rock cuttings. S4: The crack characteristics of rock fragments obtained by analyzing the crack characteristics obtained by the portable confocal microscopy system are used to assess the brittleness of the rock. The portable confocal microscopy system specifically includes: The light source module is a vertical cavity surface-emitting laser array used to generate multiple beams of light, and each beam of light is configured with a corresponding spectrum. An optical signal receiving device is used to simultaneously detect multiple points on thin sections of rock cuttings samples. During multi-point detection, the device controls the light source module and transmits the detection results to an industrial control computer. An industrial control computer is used to process the signals received by the optical signal receiving device and obtain the point of maximum measured light intensity. A beam-expanding and collimating lens is used to expand and collimate the beam output from the light source module. A beam splitter is used to reflect the reflected and scattered light beams from a thin section of rock cutting sample to be analyzed into a long-tube microscope objective. The long-tube microscope objective is used to focus the output beam of the light-emitting module onto a thin section of rock cutting sample and to perform tomographic scanning on the thin section of rock cutting sample; The imaging module is used to convert the tomographic images of rock cutting samples scanned by the long-tube microscope objective into fluorescence images for display. During multi-point detection, the light source module is controlled, including: adjusting the intensity, direction, and frequency of the light from the light source module in real time according to the detection requirements; extracting the intensity information corresponding to each adjustment of the light intensity; extracting the direction information corresponding to each adjustment of the light direction; and extracting the frequency information corresponding to each adjustment of the light frequency. Intensity adjustment parameters, direction adjustment parameters, and frequency adjustment parameters are obtained using the intensity information corresponding to the light intensity, the direction information corresponding to the light direction, and the frequency information corresponding to the light frequency. The intensity adjustment parameter, direction adjustment parameter, and frequency adjustment parameter are used to obtain the adjustment operation evaluation parameters of the light source module, wherein the adjustment operation evaluation parameters are obtained by the following formula: Where Q represents the adjusted operation evaluation parameter; Q0 represents the preset benchmark parameter value; B represents the intensity adjustment parameter; Ψ represents the direction adjustment parameter; and F represents the frequency adjustment parameter. When the adjustment operation evaluation parameter is lower than the preset parameter threshold, an adjustment operation abnormality alarm is triggered; The intensity adjustment parameter, direction adjustment parameter, and frequency adjustment parameter are obtained using the intensity information corresponding to the light intensity, the direction information corresponding to the light direction, and the frequency information corresponding to the light frequency, including: The intensity information corresponding to each adjustment of light intensity is retrieved, and the intensity adjustment parameter is obtained based on the intensity information corresponding to each adjustment of light intensity. The intensity adjustment parameter is obtained using the following formula: Where B represents the intensity adjustment parameter; b represents the first evaluation coefficient; n represents the number of light intensity adjustments; B i B represents the actual intensity value at the end of the i-th light intensity adjustment; mi E represents the target intensity value corresponding to the i-th light intensity adjustment; b This represents the preset maximum allowable strength error value; exp represents the exponential function operation with base e. The direction information corresponding to the direction of light adjusted each time is retrieved, and the direction adjustment parameter is obtained based on the direction information corresponding to the direction of light adjusted each time. The direction adjustment parameter is obtained by the following formula: Where Ψ represents the direction adjustment parameter; P b The amplitude of the intensity adjustment parameter represents the overall rate of change; ω represents the second evaluation coefficient; m represents the number of times the light direction is adjusted; α i This represents the actual directional offset angle value when the i-th directional adjustment is completed; α mi E represents the target angle value corresponding to the i-th direction adjustment; α This indicates the preset maximum allowable direction angle error value; exp represents the exponential function operation with base e. The frequency information corresponding to the frequency of the light adjusted each time is retrieved, and the frequency adjustment parameters are obtained based on the frequency information corresponding to the frequency of the light adjusted each time. The frequency adjustment parameters are obtained by the following formula: Where F represents the frequency adjustment parameter; k represents the number of times the light frequency is adjusted; f i f represents the actual frequency value at the time the i-th frequency adjustment is completed; mi E represents the target frequency value corresponding to the i-th frequency adjustment; f This represents the preset maximum allowable frequency error value; exp represents the exponential function operation with base e.

2. The rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to claim 1, characterized in that: The rapid freezing and encapsulation of rock fragment samples specifically includes: The rock samples collected on site were placed into a rapid freezing device. The rock samples collected on site were either core samples or rock fragments. Rock samples are rapidly frozen using a sample rapid freezing device, with liquid nitrogen used for external cooling and propane used for internal cooling, to produce thin sections of rock debris.

3. The rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to claim 1, characterized in that: The oil content of rock cuttings was analyzed, specifically including... The automatic identification module for rock cutting fluorescence images receives fluorescence images of rock cutting sample sections and obtains color chromatograms of rock cutting sample sections based on the fluorescence images. The color and fluorescent pixel characteristics of the rock debris regions were analyzed based on the color chromatograms of thin sections of rock debris samples. A clustering algorithm was used to perform hierarchical training on the color and fluorescent pixel features of the rock debris area to generate a training model. Different area points were labeled as light oil, medium oil and heavy oil, and the color and other features of the categories were recorded at the same time. By extracting the boundaries of particles between rock fragments from fluorescence images, and then identifying rock fragment targets within the rock fragments based on the texture features of the rock fragments and mudstone; By training a model to classify rock cutting targets, we can obtain an analysis of the oil-bearing components in the fluorescence images of rock cuttings. Based on the analysis results of the oil-bearing components and color features, we can analyze the oil content of the rock cuttings.

4. The rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to claim 3, characterized in that: Boundary extraction of particles between rock debris was performed on fluorescence images, specifically including: The particles in the rock debris image are segmented into multiple closed regions, the pixels within the multiple closed regions are identified, and the flow direction of each pixel pointing to the nearest edge of that pixel is determined. Then, the fluorescence images of the rock debris sample thin sections were transformed in color space from RGB to Lab, and edge flow vectors were constructed at a single scale. Calculate the edge flow vector of the current point and the probability of finding the edge in each direction, determine the vector of the point, and after traversing the complete fluorescence image, draw the edge flow vector diagram. The location of the target edge in the image is where the edge flow vector is opposite in direction in the vector diagram.

5. The rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to claim 3, characterized in that: The automatic identification module for rock cutting fluorescence images specifically includes: The acquisition module is used to receive fluorescence images of rock cutting sample sections and acquire color chromatograms of rock cutting sample sections based on the fluorescence images of the rock cutting sample sections; The color types and fluorescence pixel intensity data of the rock cutting sample thin sections are extracted, and the acquired data is transmitted to the analysis module; The analysis module is used to analyze the color of rock debris regions and the characteristics of fluorescent pixels based on the color chromatograms of thin sections of rock debris samples; The identification module is used to extract the boundaries between particles in the fluorescence image of rock cuttings, obtain the oil-bearing components analysis of the fluorescence image of rock cuttings, and automatically identify the oil content of rock cuttings based on the analysis results of oil-bearing components and color features.

6. The rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to claim 1, characterized in that: The brittleness of the rock was analyzed, specifically including... The fluorescence images of thin sections of rock fragments were processed using the rock brittleness analysis module to obtain the rock's hardness, Young's modulus, and fracture characteristics. The hardness, Young's modulus and fracture toughness are made dimensionless to generate dimensionless hardness, dimensionless Young's modulus and dimensionless fracture toughness. The brittle parameters of the rock are generated based on the dimensionless hardness, dimensionless Young's modulus, dimensionless fracture toughness, and fracture characteristics. The rock brittleness analysis results are generated based on the brittleness parameters.

7. The rapid laser scanning confocal image analysis method for oil-bearing properties of well cuttings and rock brittleness according to claim 6, characterized in that: The rock brittleness analysis module specifically includes: The processing module is used to process the fluorescence images of thin sections of rock cuttings to obtain the rock's hardness, Young's modulus, and fracture characteristics. The fracture toughness calculation module is used to calculate the fracture toughness of rocks based on the fracture characteristics and Young's modulus. The brittleness parameter generation module is used to analyze the brittleness of rocks based on their hardness, Young's modulus, and fracture toughness, and to generate brittleness parameters for the rocks.

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