Cutting element online detection method, device, equipment and storage medium

Abstract

The application provides a kind of cutting element online detection method, device, equipment and storage medium, related to geological exploration and real-time analysis technical field.The method comprises: obtaining the cutting sample to be detected by sampler.Utilizing the micro laser-induced breakdown spectroscopy system integrated in the sampler, the cutting sample is laser excited to generate plasma spectrum;By wide-spectrum focusing lens, the plasma spectrum is focused to double-waveband fiber spectrometer, and the spectral data of first preset waveband and second preset waveband are synchronously collected.The spectral data is analyzed by spectral line, the content of cutting element is identified, and the cutting identification result is output.The method is used to greatly shorten the laboratory analysis cycle and can guide the on-site decision-making in real time according to the analysis result.

Description

Technical Field

[0001] This application relates to the field of geological exploration and real-time analysis technology, and more specifically, to a method, apparatus, equipment, and storage medium for online detection of rock fragment elements. Background Technology

[0002] Currently, in drilling engineering and geological exploration, real-time acquisition of formation lithology information is crucial for optimizing drilling parameters, accurately assessing reservoirs, and implementing geological steering. At present, on-site lithology identification mainly relies on manual cuttings logging, which is highly subjective, has low resolution, and is significantly lagging. While laboratory analytical methods (such as X-ray fluorescence spectroscopy and inductively coupled plasma mass spectrometry) offer high precision, they require complex sample preparation and transportation, and the analysis cycle can take several hours to several days, failing to meet the needs of real-time on-site decision-making.

[0003] However, existing technologies still suffer from long laboratory analysis cycles and cannot guide on-site decision-making. Summary of the Invention

[0004] The purpose of this application is to provide an online detection method, apparatus, device, and storage medium for rock cuttings elements, which solves the above-mentioned problems existing in the prior art. It can greatly shorten the laboratory analysis cycle and guide on-site decision-making in real time based on the analysis results, realizing rapid, synchronous, and high-precision online detection of multiple elements in rock cuttings and automatic lithology identification.

[0005] Firstly, a method for online detection of rock cutting elements is provided, which may include: The rock cuttings sample to be tested is obtained using a sampler; The rock cutting sample is laser-excited using a miniature laser-induced breakdown spectroscopy system integrated in the sampler to generate a plasma spectrum. The plasma spectrum is then focused onto a dual-band fiber optic spectrometer using a wide-spectrum focusing lens, and spectral data from the first and second preset bands are acquired simultaneously. The spectral data is analyzed to identify the elemental content of rock fragments, and the rock fragment identification results are output.

[0006] Secondly, an online detection device for rock cuttings elements is provided, the device comprising: The acquisition module is used to acquire rock cutting samples to be tested through a sampler; The focusing module is used to use a miniature laser-induced breakdown spectroscopy system integrated in the sampler to laser-excite the rock cutting sample and generate a plasma spectrum; the plasma spectrum is focused onto a dual-band fiber optic spectrometer through a broadband focusing lens, and spectral data of the first preset band and the second preset band are acquired simultaneously. The identification module is used to perform spectral line analysis on the spectral data, identify the rock fragment element content, and output the lithology identification result.

[0007] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0008] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0009] This application provides an online detection method, apparatus, device, and storage medium for rock fragment elements. The method involves acquiring a rock fragment sample to be tested using a sampler. A miniature laser-induced breakdown spectroscopy system integrated into the sampler is used to excite the rock fragment sample with a laser, generating a plasma spectrum. This plasma spectrum is then focused onto a dual-band fiber optic spectrometer using a broadband focusing lens, and spectral data from a first preset band and a second preset band are simultaneously acquired. Spectral line analysis is performed on the spectral data to identify the rock fragment element content, and the rock fragment identification result is output. In this scheme, a miniature LIBS system is integrated into the sampler to rapidly scan the extracted rock cuttings for key elements such as Mg, Ca, and Fe. A dual-band fiber optic spectrometer (detecting Mg and Al in the ultraviolet band of 200-400nm and Ca, Fe, Si, K, and Na in the visible band of 400-800nm) is used, paired with a wide-spectrum focusing lens (transmittance ≥90%, covering 200-800nm). A single laser pulse can simultaneously acquire the characteristic spectral lines of more than 7 elements. Laser-induced breakdown spectroscopy technology eliminates the need for complex sample preparation and enables rapid multi-element analysis. Therefore, the use of "wide-spectrum lenses and dual-band spectrometers" can overcome the limitations of traditional micro LIBS which can only detect 3-5 elements, enabling simultaneous and rapid detection of multiple elements such as Mg, Ca, Fe, Si, Al, K, and Na in rock cuttings. This enhances the dimensionality of formation lithology identification, providing an efficient tool for rapid lithology identification, especially for complex formations such as interbedded carbonate and clastic rocks, as well as unconventional oil and gas exploration. It improves drilling efficiency and reservoir evaluation accuracy, greatly shortens the laboratory analysis cycle, and can guide on-site decision-making in real time based on the analysis results, achieving rapid, synchronous, and high-precision online detection and automatic lithology identification of multiple elements in rock cuttings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of an online detection method for rock cuttings elements provided in an embodiment of this application; Figure 2 A schematic flowchart of an online detection method for rock cuttings elements provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an online rock cuttings element detection device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] Currently, in drilling engineering and geological exploration, real-time acquisition of formation lithology information is crucial for optimizing drilling parameters, accurately assessing reservoirs, and implementing geological steering. At present, on-site lithology identification mainly relies on manual cuttings logging, which is highly subjective, has low resolution, and is significantly lagging. While laboratory analytical methods (such as X-ray fluorescence spectroscopy and inductively coupled plasma mass spectrometry) offer high precision, they require complex sample preparation and transportation, and the analysis cycle can take several hours to several days, failing to meet the needs of real-time on-site decision-making.

[0014] In one example, existing micro LIBS systems used in the field are considered promising tools for field analysis due to their advantages of not requiring complex sample preparation and enabling rapid multi-element analysis. However, they still suffer from problems such as long laboratory analysis cycles and inability to guide field decision-making.

[0015] Existing technologies also have the following drawbacks: Insufficient elemental dimension detection: Due to the limitations of the spectrometer's detection range and optical system design, it can usually only stably detect 3-5 elements (such as Ca, Fe, Si), which is difficult to support the fine identification of complex lithologies (such as interbedded carbonate rocks and clastic rocks).

[0016] Severe spectral interference and low accuracy: The coexistence of multiple elements in rock debris samples leads to severe overlap of characteristic spectral lines, resulting in large quantitative errors in traditional peak height or peak area integration methods, and a lack of effective on-site correction methods.

[0017] Poor system stability: The on-site environment (temperature, vibration, dust) can easily cause optical system drift and laser energy fluctuations. The lack of embedded automatic calibration function makes it difficult to guarantee the long-term reliability of the test results.

[0018] Therefore, developing an in-situ LIBS system capable of simultaneous high-precision detection of multiple elements, with intelligent spectral line analysis and online self-calibration capabilities, is key to solving the bottleneck of real-time lithology analysis in the field.

[0019] The online detection method for rock cuttings elements provided in this application embodiment can be applied to electronic devices, terminal devices, online detection devices or equipment for rock cuttings elements, or other devices or equipment capable of executing this embodiment, and there are no limitations on this application. In this embodiment, the execution subject is described as an electronic device.

[0020] This method is particularly suitable for online detection of rock fragment elements and fine lithological classification of complex strata (such as interbedded carbonate rocks and clastic rocks, volcanic rocks, etc.).

[0021] The terminal can be a user equipment (UE) such as a mobile phone, smartphone, laptop computer, digital broadcast receiver, personal digital assistant (PDA), or tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), or mobile terminal. This terminal has the ability to communicate with one or more core networks via a radio access network (RAN).

[0022] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0023] Figure 1 This is a schematic flowchart illustrating an online detection method for rock cuttings elements provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S101: Obtain the rock cutting sample to be tested using a sampler.

[0024] For example, a sampler is used to acquire rock cuttings samples to be tested. For instance, a robotic arm or pneumatic conveying device integrated next to a drilling vibrating screen is used to acquire fresh rock cuttings samples at regular intervals and in quantitative quantities, and then the fresh rock cuttings samples are transported to the sample focusing position in the testing chamber. Optionally, a cleaning air knife is included to blow away dust and mud film from the sample surface.

[0025] Step S102: Using a miniature laser-induced breakdown spectroscopy system integrated in the sampler, the rock cutting sample is laser-excited to generate a plasma spectrum; the plasma spectrum is focused onto a dual-band fiber optic spectrometer through a broadband focusing lens, and spectral data of the first and second preset bands are acquired simultaneously.

[0026] For example, a miniaturized LIBS excitation unit, i.e., a miniature laser-induced breakdown spectroscopy system, is integrated above the detection chamber. This unit emits a high-energy pulsed laser (e.g., an Nd:YAG laser, 1064 nm wavelength, pulse energy 10-100 mJ, pulse width 5-10 ns, etc., without limitation) towards the rock cutting sample, generating a plasma spectrum. The plasma spectrum is focused onto a dual-band fiber optic spectrometer via a broadband focusing lens, and then focused onto the surface of the rock cutting sample by the dual-band fiber optic spectrometer, generating a high-temperature, high-energy plasma, while simultaneously acquiring spectral data in the first and second preset wavelength bands.

[0027] Optionally, the continuous spectrum and characteristic spectral lines of the plasma emission are collected by a coaxial broadband focusing lens system. This lens system consists of ultraviolet-visible coated lenses with a transmittance of ≥90% and a spectral coverage of 200-800 nm, ensuring efficient transmission of optical signals from the ultraviolet to the visible light band.

[0028] The collected optical signal is coupled to a dual-channel fiber optic spectrometer via optical fiber. This spectrometer is equipped with two independent detection channels: First channel (ultraviolet channel): The spectral range covers the first preset wavelength band, preferably 200-400 nm, and is used to detect elements with strong characteristic emission lines in this wavelength band, such as magnesium (Mg) and aluminum (Al).

[0029] The second channel (visible channel) covers a second preset wavelength band, preferably 400-800 nm, and is used to detect elements with strong characteristic emission lines in this wavelength band, such as calcium (Ca), iron (Fe), silicon (Si), potassium (K), sodium (Na), etc.

[0030] Both channels are triggered by the same laser pulse, achieving strictly synchronized acquisition, thus allowing a single laser pulse to acquire complete spectral data covering more than seven key elements. The above values ​​are merely examples and are not intended to be limiting.

[0031] Step S103: Analyze the spectral data to identify the elemental content of rock fragments and output the rock fragment identification results.

[0032] For example, spectral data is analyzed to identify the element content of rock fragments. Based on the preset mapping relationship between element content and lithology association database, the identified element content is matched to the target lithology category, and the rock fragment identification result is output according to the target lithology category.

[0033] The method provided in this application involves acquiring rock fragment samples for testing using a sampler. A miniature laser-induced breakdown spectroscopy (LIBS) system integrated into the sampler is used to excite the rock fragment sample with a laser, generating a plasma spectrum. This plasma spectrum is then focused onto a dual-band fiber optic spectrometer using a wide-spectrum focusing lens, simultaneously acquiring spectral data from the first and second preset bands. The spectral data is analyzed to identify the elemental content of the rock fragments, and the identification results are output. In this scheme, a miniature LIBS system is integrated into the sampler for rapid scanning of key elements such as Mg, Ca, and Fe in the extracted rock fragments. A dual-band fiber optic spectrometer (detecting Mg and Al in the ultraviolet band 200-400nm and Ca, Fe, Si, K, and Na in the visible band 400-800nm), coupled with a wide-spectrum focusing lens (transmittance ≥90%, covering 200-800nm), allows for the simultaneous acquisition of characteristic spectral lines of more than seven elements in a single laser pulse. Laser-induced breakdown spectroscopy technology eliminates the need for complex sample preparation and enables rapid multi-element analysis. Therefore, the use of "wide-spectrum lenses and dual-band spectrometers" can overcome the limitations of traditional micro LIBS which can only detect 3-5 elements, enabling simultaneous and rapid detection of multiple elements such as Mg, Ca, Fe, Si, Al, K, and Na in rock cuttings. This enhances the dimensionality of formation lithology identification, providing an efficient tool for rapid lithology identification, especially for complex formations such as interbedded carbonate and clastic rocks, as well as unconventional oil and gas exploration. It improves drilling efficiency and reservoir evaluation accuracy, greatly shortens the laboratory analysis cycle, and can guide on-site decision-making in real time based on the analysis results, achieving rapid, synchronous, and high-precision online detection and automatic lithology identification of multiple elements in rock cuttings.

[0034] Figure 2 A flowchart illustrating an online detection method for rock cuttings elements provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method is described in detail below, and the method includes: Step S201: Obtain the rock cutting sample to be tested using a sampler.

[0035] For example, this step is described in step S101, and will not be repeated here.

[0036] Step S202: Using a miniature laser-induced breakdown spectroscopy system integrated in the sampler, the rock cutting sample is laser-excited to generate a plasma spectrum; the plasma spectrum is focused onto a dual-band fiber optic spectrometer through a broadband focusing lens, and spectral data of the first preset band and the second preset band are acquired simultaneously.

[0037] In one example, the first preset band is used to detect the characteristic spectral lines of Mg and Al elements; the second preset band is used to detect the characteristic spectral lines of Ca, Fe, Si, K, and Na elements.

[0038] In one example, “focusing the plasma spectrum onto a dual-band fiber optic spectrometer via a wide-spectrum focusing lens” includes: collecting the plasma spectrum via a wide-spectrum focusing lens and transmitting it to the dual-band fiber optic spectrometer, and acquiring the spectral data output by the dual-band fiber optic spectrometer.

[0039] For example, a miniature laser-induced breakdown spectroscopy system integrated in the sampler is used to laser-excite the rock cutting sample to generate a plasma spectrum; the plasma spectrum is collected by a broadband focusing lens and transmitted to a dual-band fiber optic spectrometer to obtain the spectral data output by the dual-band fiber optic spectrometer, and spectral data of the first preset band and the second preset band are acquired simultaneously.

[0040] Optionally, the first preset wavelength band is used to detect the characteristic spectral lines of Mg and Al; the second preset wavelength band is used to detect the characteristic spectral lines of Ca, Fe, Si, K, and Na. The first preset wavelength band is 200-400 nm, and the second preset wavelength band is 400-800 nm. These wavelength band data are merely examples and are not intended to be limiting.

[0041] Step S203: Analyze the spectral data to identify the elemental content of rock fragments.

[0042] In one example, S203 includes: performing spectral analysis on overlapping spectral lines in spectral data based on a spectral line overlap correction algorithm to identify the elemental content of rock fragments; wherein, spectral line analysis includes spectral line fitting separation and characteristic peak intensity normalization.

[0043] For example, the acquired raw dual-band spectral data is first preprocessed, including dark noise subtraction, spectral smoothing, and continuous background correction. Then, based on the spectral line overlap correction algorithm, the overlapping spectral lines in the spectral data are analyzed to identify the rock debris element content. The spectral line analysis includes spectral line fitting separation and characteristic peak intensity normalization.

[0044] Optionally, the core multi-element spectral line overlap correction algorithm is implemented as follows: For the preprocessed spectral data, regions where spectral lines may overlap are identified (e.g., Ca II 396.847 nm and Al I 396.152 nm). A spectral line fitting method based on a physical model (such as multi-peak Voigt or Gauss-Lorentz mixture model fitting) is used to analytically separate the overlapping peaks. After separation, the net peak area of ​​the characteristic spectral lines of each element is extracted and the intensity is normalized to eliminate the influence of matrix effects and excitation condition fluctuations.

[0045] Based on the pre-established calibration curves (concentration-intensity relationship) of each element, the normalized characteristic peak intensity is converted into element content or relative percentage content.

[0046] Preferably, the spectral line overlap correction algorithm specifically includes sub-steps such as spectral line database matching, overlapping peak region identification, multi-peak fitting separation, and goodness-of-fit evaluation.

[0047] Therefore, based on the wide-spectrum focusing lens, a spectral line analysis algorithm is developed for the data acquired by the dual-band fiber optic spectrometer: a multi-element spectral line overlap correction algorithm is developed. For overlapping spectral lines, the spectral line fitting separation and characteristic peak intensity normalization techniques are used to achieve accurate identification of overlapping spectral lines. The multi-element spectral line overlap correction algorithm improves the accuracy and reliability of the analysis and effectively solves the problem of spectral line interference in complex matrices.

[0048] Step S204: Output the rock cuttings identification results.

[0049] In one example, S204 includes: matching the identified element content to the target lithology category based on the preset mapping relationship between element content and lithology association database, and outputting the rock cuttings identification result according to the target lithology category.

[0050] For example, the obtained multi-element content data is input into a pre-stored element content and lithology association database, which contains preset mapping relationships between element content and lithology association databases. This database is generated based on the spectral data and element content of a large number of standard samples of known lithologies, trained using machine learning algorithms (such as support vector machines, random forests, or artificial neural networks). The electronic device automatically matches and outputs the most probable target lithology category (such as "limestone," "sandstone," "mudstone," "granite," etc.) and the confidence level of the target lithology category. Based on the target lithology category, it outputs the rock cuttings identification results, which are displayed in real time and transmitted to the geological guidance system.

[0051] Step S205: After reaching the preset number of tests, the detection module is automatically calibrated by calling the built-in standard samples with known element content through the calibration module; the calibration module includes a miniature laser-induced breakdown spectroscopy system, a wide-spectrum focusing lens and a dual-band fiber optic spectrometer.

[0052] In one example, the standard sample includes at least one of the following: CaCO3, Fe2O3.

[0053] For example, the detection module is periodically calibrated online automatically. Specifically, to maintain the long-term analytical accuracy of the electronic device, a periodic automatic calibration program is set up. The electronic device has a built-in multi-position standard sample tray containing standard substances with known precise elemental contents (such as high-purity CaCO3, Fe2O3, SiO2, etc. tablets). After each set number of detections (e.g., preferably 50), the electronic device controls the sample tray to rotate automatically, moving the standard sample to the detection position and executing a complete detection process. The measured values ​​are compared with the standard reference values, the correction factor is calculated, and the quantitative analysis model parameters are automatically updated to ensure that the relative detection error of all elements remains below the error threshold.

[0054] Optionally, the calibration module includes a miniature laser-induced breakdown spectroscopy system, a broadband focusing lens, a dual-band fiber optic spectrometer, and a spectral overlap correction algorithm. Optionally, standard samples include CaCO3 and Fe2O3 with a purity of not less than 99.9%.

[0055] Therefore, the added standard sample calibration module has built-in rock cutting standard samples with known content (such as...). With CaCO3 purity of 99.9% and Fe2O3 purity of 99.9%, the system automatically calls upon standard samples for calibration every 50 tests, ensuring that the relative error of multi-element detection is less than 5%, thus improving detection accuracy. Combined with high-frequency embedded automatic calibration, it can automatically compensate for environmental drift, ensuring long-term analytical accuracy (relative error <5%) and meeting the requirements of quantitative analysis.

[0056] The method provided in this application involves acquiring a rock cutting sample to be tested using a sampler. A miniature laser-induced breakdown spectroscopy system integrated into the sampler is used to excite the rock cutting sample with a laser, generating a plasma spectrum. This plasma spectrum is then focused onto a dual-band fiber optic spectrometer using a wide-spectrum focusing lens, and spectral data from a first preset band and a second preset band are simultaneously acquired. Spectral line analysis is performed on the spectral data to identify the elemental content of the rock cuttings. The rock cutting identification result is output. After a preset number of tests are completed, a calibration module automatically calibrates the detection module by calling a built-in standard sample with known elemental content. The calibration module includes a miniature laser-induced breakdown spectroscopy system, a wide-spectrum focusing lens, and a dual-band fiber optic spectrometer. Therefore, the use of "wide-spectrum lenses and dual-band spectrometers" can overcome the limitations of traditional micro LIBS which can only detect 3-5 elements, enabling simultaneous and rapid detection of multiple elements such as Mg, Ca, Fe, Si, Al, K, and Na in rock cuttings. This enhances the dimensionality of formation lithology identification, providing an efficient tool for rapid lithology identification, especially for complex formations such as interbedded carbonate and clastic rocks, as well as unconventional oil and gas exploration. It improves drilling efficiency and reservoir evaluation accuracy, greatly shortens the laboratory analysis cycle, and can guide on-site decision-making in real time based on the analysis results, achieving rapid, synchronous, and high-precision online detection and automatic lithology identification of multiple elements in rock cuttings.

[0057] In one embodiment, an online detection method for rock cuttings elements provided in this application is described.

[0058] Exemplarily, this embodiment is applied to an electronic device, which includes: Sampling port and pretreatment chamber: connected to the vibrating screen diversion pipe, with built-in cyclone separator and micro crusher, to perform preliminary cleaning and particle size uniformization of rock cuttings.

[0059] The main analytical chamber is equipped with a miniature Nd:YAG laser (pulse energy 80mJ, repetition rate 10Hz) and a focusing objective lens mounted on the top. A coaxial collecting optical path is mounted on the side, including a 100mm focal length, 25.4mm diameter ultraviolet fused silica lens (coated with a 200-800nm ​​antireflection coating) and a 400μm core diameter high-UV transmission fiber. The other end of the fiber is connected to a dual-channel spectrometer (model such as AvaSpec-ULS2048CL-EVO).

[0060] Standard sample turntable: Located next to the sample stage in the main analysis chamber, it is a six-position automatic turntable, with three positions containing CaCO3, Fe2O3, and granite standard sample pellets.

[0061] Control and processing chassis: Contains an industrial computer, laser power supply, spectrometer controller, and motion control card. The computer runs self-developed "online analysis software."

[0062] Results display and communication unit: Touch screen display, which displays spectrum, element content and lithology results in real time, and uploads data to the logging room or geological guidance system via Modbus TCP protocol.

[0063] In one embodiment, a method for online detection of rock cuttings elements provided by this application is described. In a complete application cycle: (1) System initialization and calibration: After startup, the software controls the turntable to move the CaCO3 standard sample to the focal point, continuously excites it 10 times, collects the average spectrum, establishes the initial calibration curve of Ca element and stores the system baseline.

[0064] (2) Rock cuttings sample analysis cycle: a. The robotic arm takes about 0.5 grams of rock fragments from the pretreatment chamber and places them on the sample stage.

[0065] b. Laser triggering, single-pulse excitation. The spectrometer acquires data synchronously through dual channels, and the dual-band spectrum is displayed in real time on the software interface.

[0066] c. Software automatically invokes algorithm flow: - Preprocessing: Subtract the spectrometer dark current and perform Savitzky-Golay smoothing.

[0067] - Overlapping Peak Analysis: Overlapping peaks are identified near 396nm. The algorithm calls the stored Voigt peak shape parameters to fit and separate CaII at 396.847nm and AlI at 396.152nm, iteratively calculating until the fitting residual is minimized, and accurately calculating their respective integral areas.

[0068] - Quantitative calculation: Using the pre-calibrated "intensity-content" curves of each element, the net intensity of spectral lines such as Mg 279.5nm, Ca 396.8nm, Si 288.1nm, Al 396.1nm, and Fe 438.3nm is converted into content.

[0069] d. Lithology identification: The obtained data vectors, such as [Ca]=32.5%, [Si]=15.2%, [Al]=8.7%, ... are input into the trained random forest lithology classification model. The model quickly calculates and outputs the result: "Muddy limestone, confidence level 87%".

[0070] e. The results are updated on the screen and sent over the network.

[0071] (3) Automatic calibration trigger: The software's internal counter records the number of samples. When the cumulative number reaches 50, the calibration process is automatically inserted before the next sample analysis: CaCO3 and Fe2O3 standard samples are analyzed in sequence, and the current measurement deviations of Ca and Fe are calculated to be +2.1% and -1.8%, respectively. The software automatically fine-tunes the slope of the calibration curves of Ca and Fe, completes the calibration, and resets the counter.

[0072] In one embodiment, the application effect of this application in complex strata is as follows: This application was applied in an ultra-deep well within an Ordovician carbonate formation. This formation contained multiple thin layers of mudstone and siliceous bands, which were difficult to distinguish using conventional logging methods. This application continuously analyzed the returned cuttings and, by monitoring the subtle and rapid changes in the contents of Ca, Si, and Al, successfully identified multiple mudstone interlayers and siliceous bands with thicknesses of only 0.3-0.8 meters. Electronic equipment provided real-time warnings of lithological changes, providing crucial information for drilling engineers to adjust drilling pressure and drilling fluid performance, avoiding the risk of wellbore instability at weak interlayers. Subsequent logging data confirmed the accuracy of the identification results.

[0073] In one embodiment, this application provides an in-situ laser-induced breakdown spectroscopy (LIBS) elemental online detection module for implementing the above-described online detection method for rock cuttings elements, comprising: Automatic sampling and injection unit: including robotic arm, conveyor belt or flow path system, used to acquire and transfer cuttings from drilling fluid circulation system to the analysis site.

[0074] Miniature laser-induced breakdown spectroscopy excitation unit: integrated within the analysis chamber, including a pulsed laser, a beam expander and focusing system, a CCD camera for observation, and an illumination source.

[0075] The broadband collection and spectroscopic detection unit includes the aforementioned broadband focusing lens system, transmission optical fiber, and the dual-channel fiber optic spectrometer. This unit is arranged coaxially with or at a certain angle to the excitation unit's optical path to efficiently collect plasma signals.

[0076] Data processing and lithology identification unit: This is an industrial computer or embedded industrial control computer with a built-in data acquisition card, running dedicated analysis software. The software integrates spectral preprocessing, spectral line overlap correction, quantitative calculation, and intelligent lithology identification algorithms, and stores elemental spectral line databases and lithology correlation databases.

[0077] Embedded automatic calibration unit: includes a standard sample tray, a precision rotation or translation drive mechanism and its controller. The standard sample tray is placed in the analysis chamber and can be switched between the detection position and the detection position without affecting normal detection.

[0078] Sealed protection and cleaning unit: The module housing has a dustproof and explosion-proof design, and the analysis chamber is equipped with a self-cleaning device for the viewing window (such as a rotating scraper or airflow curtain) to adapt to harsh field environments.

[0079] Therefore, the beneficial effects of the embodiments of this application are as follows: Breakthrough in multi-element synchronous detection capability: The innovative hardware architecture of "wide-spectrum lens junction and dual-band spectrometer" can cover the ultraviolet to visible light range with a single excitation, and simultaneously capture the characteristic spectral lines of more than 7 key elements such as Mg, Al, Ca, Fe, Si, K and Na, which greatly enriches the elemental fingerprint dimension of lithology identification.

[0080] True in-situ online and real-time performance: The modules are highly integrated and automated, seamlessly connected with the drilling process. From sampling to lithology results output, it can be completed within 2-5 minutes, realizing a fundamental transformation from "offline lag analysis" to "online real-time guidance".

[0081] Strong environmental adaptability and intelligence: The overall module has dustproof, shockproof design and self-cleaning function; combined with a lithology identification database based on machine learning, it reduces the dependence on operator experience and the identification results are objective and consistent.

[0082] Corresponding to the above method, embodiments of this application also provide an online detection device for rock cuttings elements, such as... Figure 3 As shown, the device includes: Acquisition module 41 is used to acquire rock cuttings samples to be tested through a sampler; The focusing module 42 is used to use a micro laser-induced breakdown spectroscopy system integrated in the sampler to laser excite the rock cutting sample and generate a plasma spectrum; the plasma spectrum is focused onto a dual-band fiber optic spectrometer through a broadband focusing lens, and spectral data of the first preset band and the second preset band are acquired simultaneously. The identification module 43 is used to perform spectral line analysis on the spectral data, identify the rock fragment element content, and output the rock fragment identification result.

[0083] The functions of each functional unit of the online rock cuttings element detection device provided in the above embodiments of this application can be implemented through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the online rock cuttings element detection device provided in the embodiments of this application will not be repeated here.

[0084] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0085] Memory 530 is used to store computer programs; The processor 510 performs the above steps when executing the program stored in the memory 530.

[0086] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0087] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0088] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0089] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0090] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0091] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the above-described methods for online detection of rock cutting elements.

[0092] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the above-described methods for online detection of rock cuttings elements.

[0093] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0098] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for online detection of rock fragment elements, characterized in that, The method includes: The rock cuttings sample to be tested is obtained using a sampler; The rock cutting sample is laser-excited using a miniature laser-induced breakdown spectroscopy system integrated in the sampler to generate a plasma spectrum. The plasma spectrum is then focused onto a dual-band fiber optic spectrometer using a wide-spectrum focusing lens, and spectral data from the first and second preset bands are acquired simultaneously. The spectral data is analyzed to identify the elemental content of rock fragments, and the rock fragment identification results are output.

2. The online detection method for rock cutting elements according to claim 1, characterized in that, The first preset band is used to detect the characteristic spectral lines of Mg and Al elements; The second preset band is used to detect the characteristic spectral lines of Ca, Fe, Si, K, and Na elements.

3. The method as described in claim 1, characterized in that, Spectral line analysis was performed on the spectral data to identify the elemental content of rock fragments, including: Based on the spectral overlap correction algorithm, spectral analysis is performed on the overlapping spectral lines in the spectral data to identify the elemental content of rock fragments; wherein, the spectral analysis includes spectral line fitting separation and characteristic peak intensity normalization.

4. The method as described in claim 1, characterized in that, Output the rock cuttings identification results, including: Based on the preset mapping relationship between element content and lithology association database, the identified element content is matched to the target lithology category, and the rock cutting identification result is output according to the target lithology category.

5. The method as described in claim 1, characterized in that, Focusing the plasma spectrum onto a dual-band fiber optic spectrometer using a broadband focusing lens includes: The plasma spectrum is collected via a broadband focusing lens and transmitted to a dual-band fiber optic spectrometer to obtain spectral data output by the dual-band fiber optic spectrometer.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: After reaching the preset number of tests, the detection module is automatically calibrated by calling the built-in standard samples with known element content through the calibration module; the calibration module includes the miniature laser-induced breakdown spectroscopy system, the broadband focusing lens and the dual-band fiber optic spectrometer.

7. The method as described in claim 6, characterized in that, The standard sample includes at least one of the following: CaCO3, Fe2O3.

8. An online detection device for rock cuttings elements, characterized in that, The device includes: The acquisition module is used to acquire rock cutting samples to be tested through a sampler; The focusing module is used to use a miniature laser-induced breakdown spectroscopy system integrated in the sampler to laser-excite the rock cutting sample and generate a plasma spectrum; the plasma spectrum is focused onto a dual-band fiber optic spectrometer through a broadband focusing lens, and spectral data of the first preset band and the second preset band are acquired simultaneously. The identification module is used to perform spectral line analysis on the spectral data, identify the elemental content of rock fragments, and output the rock fragment identification results.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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