X-ray synchronous analysis system and method for lithology and physical property of oil and gas reservoir

Abstract

The invention relates to the technical field of X-ray analysis instrument software, in particular to an X-ray synchronous analysis system and method for lithology and physical properties of an oil and gas reservoir, and the system comprises an X-ray source module, a signal acquisition module, an environment control module, a data analysis module, a sample preparation optimization module and a hardware cooperation module. X-ray signals with different energies are generated through time sequence switching of a Cu target material and a Mo target material, fluorescence and diffraction signal acquisition channels are alternately activated in a time-sharing mode, and element characteristic energy spectrums and diffraction angle data are separated by adopting a multi-channel analyzer and a Bragg monochromator; the environment control module adjusts the vacuum degree and the temperature according to the morphological parameters of the sample and optimizes signal acquisition conditions; and the hardware cooperation module realizes time sequence synchronization of target material switching, signal separation and environment regulation and control. According to the invention, collaborative operation of XRF and XRD detection is realized, the problems of low efficiency, data splitting and hardware redundancy of a split instrument are solved, and a high-precision integrated solution is provided for oil and gas reservoir analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of X-ray analysis instruments, and in particular to an X-ray synchronous analysis system and method for the lithology and physical properties of oil and gas reservoirs. Background Art

[0002] The modular, simultaneous X-ray fluorescence (XRF) and X-ray diffraction (XRD) instrument is a comprehensive analytical device designed based on two complementary physical principles. XRF technology analyzes the elemental identity and content of a sample by detecting the characteristic fluorescence spectrum produced by X-ray excitation. Its core principle relies on the specific energy released by atomic inner-shell electron transitions. XRD technology, on the other hand, measures the diffraction pattern produced by X-rays in crystalline samples, deconstructing the lattice parameters and phase composition of the material based on Bragg's law, thus revealing microstructural information.

[0003] Existing X-ray fluorescence spectroscopy (XRF) and X-ray diffraction analysis (XRD) technologies, due to the use of separate instruments, have the pain points of low analysis efficiency, poor data coordination and redundant hardware resources. The fundamental reason is that independent systems require repeated sampling and equipment switching, which increases time costs and introduces errors due to inconsistent sample states. Elemental content and phase structure data are difficult to establish a real-time correlation model due to the separation of acquisition time sequences. At the same time, the repeated configuration of hardware such as X-ray sources and sample stages pushes up equipment costs. For example, in geological rock chip analysis, the existing method requires the use of XRF to determine elemental composition and XRD to identify mineral phases. This not only prolongs the detection cycle, but also cannot directly correlate the iron content with the hematite / magnetite mineral phase ratio, limiting the accuracy of the interpretation of the mineral genesis mechanism. Summary of the Invention

[0004] In response to the shortcomings of existing technologies, the present invention provides an X-ray synchronous analysis system and method for the lithology and physical properties of oil and gas reservoirs. The present invention solves the problems of low analysis efficiency due to independent operation of split XRF and XRD instruments, the lack of real-time correlation between element and phase data, and the waste of resources caused by repeated hardware configuration.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] In a first aspect, the present invention provides an X-ray synchronous analysis system for lithology and physical properties of oil and gas reservoirs, comprising:

[0007] An X-ray source module is used to generate X-ray signals of different energies by sequentially switching between Cu and Mo targets. The X-ray signals include fluorescence signals and diffraction signals generated by exciting the sample;

[0008] a signal acquisition module, configured to receive the fluorescence signal and diffraction signal generated by the X-ray source module, alternately activate acquisition channels in a time-sharing mode, and separate the elemental characteristic energy spectrum in the fluorescence signal from the angular data in the diffraction signal using a multi-channel analyzer and a Bragg monochromator;

[0009] An environmental control module, configured to adjust the vacuum and temperature parameters of the sample chamber according to the excitation energy parameters of the X-ray source module and the acquisition mode of the signal acquisition module;

[0010] A data analysis module is used to input the element characteristic energy spectrum into the XRF element library and the diffraction angle data into the XRD diffraction database, extract features through a convolutional neural network, and generate a quantitative relationship matrix between element content and phase composition;

[0011] a sample preparation optimization module, configured to output adjustment instructions for sample preparation time and surface roughness parameters to a sample preparation device based on the element distribution uniformity index in the quantitative relationship matrix;

[0012] A report generation module is used to integrate the element content, phase composition and reservoir physical property parameters, and generate a comprehensive report including a three-dimensional mineral distribution map and reservoir evaluation index through a data fusion algorithm;

[0013] The hardware collaboration module is used to send target material switching instructions to the X-ray source module, send time-sharing mode switching instructions to the signal acquisition module, and send parameter adjustment instructions to the environmental control module.

[0014] Furthermore, in the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the X-ray source module includes:

[0015] The X-ray source module includes a timing switching unit for Cu target and Mo target, and the timing switching unit adjusts the X-ray energy by receiving a high-voltage power supply adjustment instruction sent by the hardware coordination module;

[0016] The signal acquisition module includes a multi-channel analyzer and a Bragg monochromator. The multi-channel analyzer receives the target material switching completion signal sent by the timing switching unit and separates the low-energy signal in the fluorescence energy spectrum. The Bragg monochromator receives the target material switching completion signal and filters the high-energy noise in the diffraction signal.

[0017] The hardware coordination module generates a synchronization instruction according to the vacuum degree parameter transmitted by the environmental control module and the target material switching state of the timing switching unit, and the synchronization instruction triggers the energy spectrum separation operation of the multi-channel analyzer and the Bragg monochromator.

[0018] Furthermore, in the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the signal acquisition module is configured as follows:

[0019] Time-sharing mode, alternately activating the fluorescence signal acquisition channel and the diffraction signal acquisition channel. The switching trigger condition of the time-sharing mode is that the activation state of the Cu target or Mo target of the X-ray source module matches the excitation signal type corresponding to the time-sharing mode;

[0020] Energy filtering mode, which separates the mixed signal based on the real-time energy thresholds of the XRF element library and the XRD diffraction database output by the data analysis module, and uses an adaptive filtering algorithm to eliminate cross-interference between the fluorescence signal and the diffraction signal. The noise characteristic parameters of the adaptive filtering algorithm are dynamically updated by the temperature control data output by the temperature adjustment unit of the environmental control module;

[0021] The switching instructions between the time-sharing mode and the energy filtering mode are generated by the hardware collaboration module based on the excitation energy parameters of the X-ray source module and the vacuum parameters of the environmental control module, and the activation state of the switching instruction trigger signal acquisition channel is synchronized with the input signal type of the data analysis module.

[0022] Furthermore, in the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the environmental control module includes:

[0023] A vacuum and atmospheric environment switching device, used to switch the chamber pressure parameters according to the sample morphology parameters output by the sample preparation optimization module, wherein the sample morphology parameters are generated by the sample preparation optimization module according to the element distribution uniformity index and the surface roughness parameter;

[0024] a temperature regulating unit, which adjusts the power of the heater and the cooler by receiving the noise interference index output by the multi-channel analyzer in the signal acquisition module, so as to maintain the temperature in the sample chamber within the target temperature range calculated by the data analysis module based on the XRD diffraction database;

[0025] The status information of the vacuum and atmospheric environment switching device is transmitted to the hardware coordination module in real time. The hardware coordination module generates an excitation power adjustment instruction according to the status information and the current target material type of the X-ray source module and sends it to the X-ray source module.

[0026] Furthermore, in the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the sample preparation optimization module includes:

[0027] A genetic algorithm unit is used to generate an optimal parameter combination of sample preparation time, particle size distribution and surface roughness based on the element distribution uniformity index output by the data analysis module and the diffraction peak signal-to-noise ratio of the signal acquisition module as optimization targets;

[0028] a parameter feedback unit, configured to transmit the optimal parameter combination to the ball mill and the cutting device, and receive the deviation value calculated by the report generation module according to the reservoir evaluation index to update the iteration weight of the genetic algorithm;

[0029] The parameter feedback unit is connected to the data analysis module, and dynamically adjusts the sample preparation quality control threshold according to the element distribution uniformity index and the temperature control error value output by the environmental control module. The adjusted threshold is fed back to the sample preparation optimization module to regenerate the parameter combination.

[0030] In a second aspect, the present invention provides an X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties, which is applied to the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties, comprising:

[0031] Step 1: Generate X-ray signals of different energies by sequentially switching between a Cu target and a Mo target, wherein the X-ray signals include a fluorescence signal and a diffraction signal generated by exciting the sample;

[0032] Step 2: receiving the fluorescence signal and the diffraction signal in a time-sharing manner, separating the elemental characteristic energy spectrum in the fluorescence signal and the angle data in the diffraction signal by alternately activating acquisition channels and using a multi-channel analyzer and a Bragg monochromator;

[0033] Step 3, adjusting the vacuum parameter and temperature parameter of the sample chamber according to the excitation energy parameter and the acquisition mode;

[0034] Step 4: input the element characteristic energy spectrum into the XRF element library and the diffraction angle data into the XRD diffraction database, extract the features through a convolutional neural network and generate a quantitative relationship matrix between element content and phase composition;

[0035] Step 5: generating adjustment instructions for sample preparation time and surface roughness parameters according to the element distribution uniformity index in the quantitative relationship matrix and sending the instructions to the sample preparation equipment;

[0036] Step 6: Integrate the element content, phase composition and reservoir physical property parameters, and generate a comprehensive report including a three-dimensional mineral distribution map and reservoir evaluation index through data fusion algorithm.

[0037] Furthermore, in the X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to the present invention, step 1 comprises:

[0038] Receive high-voltage power supply adjustment instructions and adjust the X-ray energy by sequentially switching the Cu target and the Mo target;

[0039] After the target material is switched, a switching completion signal is sent to the multi-channel analyzer and the Bragg monochromator to separate the low-energy signal in the fluorescence energy spectrum and filter the high-energy noise in the diffraction signal;

[0040] A synchronization instruction is generated according to the vacuum parameter of the sample chamber and the target material switching state to trigger the low-energy signal separation and high-energy noise filtering operations.

[0041] Furthermore, in the X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention, step 2 includes:

[0042] When the Cu target or Mo target is activated, switch to the corresponding fluorescence signal or diffraction signal collection channel;

[0043] Mixed signals are separated based on real-time energy thresholds from the XRF element library and the XRD diffraction database, and cross-interference between signals is eliminated using an adaptive filtering algorithm whose noise characteristic parameters are dynamically updated based on temperature control data.

[0044] A mode switching instruction is generated based on the excitation energy parameter and the sample chamber vacuum parameter, so that the activation state of the signal acquisition channel is synchronized with the input signal type.

[0045] Furthermore, in the X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to the present invention, step 3 comprises:

[0046] Switching between vacuum and atmospheric environments according to sample morphology parameters generated based on element distribution uniformity index and surface roughness parameters;

[0047] The power of the heater and cooler is adjusted by the noise interference index to maintain the sample chamber temperature within the target temperature range calculated according to the XRD diffraction database;

[0048] The vacuum environment status information is associated with the current target material type, and an excitation power adjustment instruction is generated to match the X-ray energy requirement.

[0049] Furthermore, in the X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention, step 5 includes:

[0050] Taking the element distribution uniformity index and the diffraction peak signal-to-noise ratio as optimization targets, the optimal parameter combination of sample preparation time, particle size distribution and surface roughness was generated through genetic algorithm.

[0051] The optimal parameter combination is sent to the ball mill and the cutting device, and the iterative weight of the genetic algorithm is updated according to the reservoir evaluation index deviation value;

[0052] The sample preparation quality control threshold is dynamically adjusted according to the temperature control error value, and the adjusted threshold is fed back to the parameter generation process to re-optimize the parameter combination.

[0053] Beneficial effects of the present invention:

[0054] The beneficial effects of the present invention are that the synchronous operation of XRF and XRD detection is realized through the time-sequential switching and time-sharing signal acquisition mode of Cu target and Mo target, reducing the time loss of repeated sampling and equipment switching of split instruments; combining with the environmental control module to dynamically adjust the vacuum degree and temperature parameters of the sample chamber, optimizing the low-energy fluorescence signal acquisition efficiency and the signal-to-noise ratio of the high-energy diffraction signal, and improving the accuracy of the detection data; constructing a quantitative relationship matrix between element content and phase composition through a convolutional neural network, integrating reservoir physical property parameters to generate a three-dimensional mineral distribution model, and solving the problem of element and phase data separation; based on the closed-loop optimization mechanism of sample preparation parameters of the genetic algorithm, the sample preparation process is dynamically adjusted to reduce the need for hardware repeated calibration due to sample heterogeneity, and ultimately achieving the comprehensive technical effects of improved analysis efficiency, enhanced data relevance and optimized hardware resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0056] Figure 1 This is a flow chart of a system and method for synchronous X-ray analysis of lithology and physical properties of oil and gas reservoirs provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0058] In a first aspect, the present invention provides an X-ray synchronous analysis system for lithology and physical properties of oil and gas reservoirs, comprising:

[0059] An X-ray source module, configured to generate X-ray signals of different energies by sequentially switching between a Cu target and a Mo target. The X-ray signals include fluorescence signals and diffraction signals generated by exciting the sample;

[0060] a signal acquisition module, configured to receive the fluorescence signal and diffraction signal generated by the X-ray source module, alternately activate acquisition channels in a time-sharing mode, and separate the elemental characteristic energy spectrum in the fluorescence signal from the angular data in the diffraction signal using a multi-channel analyzer and a Bragg monochromator;

[0061] An environmental control module, configured to adjust the vacuum and temperature parameters of the sample chamber according to the excitation energy parameters of the X-ray source module and the acquisition mode of the signal acquisition module;

[0062] A data analysis module is used to input the element characteristic energy spectrum into the XRF element library and the diffraction angle data into the XRD diffraction database, extract features through a convolutional neural network, and generate a quantitative relationship matrix between element content and phase composition;

[0063] a sample preparation optimization module, configured to output adjustment instructions for sample preparation time and surface roughness parameters to a sample preparation device based on the element distribution uniformity index in the quantitative relationship matrix;

[0064] A report generation module is used to integrate the element content, phase composition and reservoir physical property parameters, and generate a comprehensive report including a three-dimensional mineral distribution map and reservoir evaluation index through a data fusion algorithm;

[0065] The hardware collaboration module is used to send target material switching instructions to the X-ray source module, send time-sharing mode switching instructions to the signal acquisition module, and send parameter adjustment instructions to the environmental control module.

[0066] The present invention provides an X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties, and the specific implementation of its technical solution is as follows:

[0067] The X-ray source module generates X-ray signals of different energies by sequentially switching the Cu target and the Mo target. The sequential switching is driven by the high-voltage power supply adjustment instruction sent by the hardware collaboration module, and the alternating excitation of the Cu target and the Mo target is controlled by adjusting the voltage and current parameters of the high-voltage power supply. The X-ray energy range generated by the Cu target is 8-20keV, which is suitable for exciting the fluorescence signal of light elements (such as Na and Mg) in the sample; the X-ray energy of the Mo target is 17-40keV, which is used to excite heavy elements (such as Fe and Zn) and generate high-energy diffraction signals. After completing the target switching, the sequential switching unit sends a switching completion signal to the signal acquisition module to trigger the subsequent signal separation operation.

[0068] The signal acquisition module uses a time-sharing mode to alternately activate the fluorescence signal acquisition channel and the diffraction signal acquisition channel. When the X-ray source module activates the Cu target, the fluorescence signal acquisition channel is turned on, and the multi-channel analyzer receives the low-energy fluorescence signal and extracts the elemental characteristic energy spectrum through energy spectrum separation technology. When switching to the Mo target, the diffraction signal acquisition channel is activated, and the Bragg monochromator filters high-energy noise and collects diffraction angle data. The switching trigger conditions of the time-sharing mode are strictly synchronized with the target activation state of the X-ray source module to ensure that the acquisition channel matches the excitation signal type. The multi-channel analyzer and Bragg monochromator are time-aligned through the synchronization instructions of the hardware collaboration module to avoid signal cross-interference.

[0069] The environmental control module dynamically adjusts the vacuum and temperature of the sample chamber according to the excitation energy parameters of the X-ray source module. For powder samples, the vacuum and atmospheric environment switching device starts the vacuum mode and the pressure in the chamber is maintained at 10 -3 The sample chamber is maintained at a pressure below 20°C (Pa) to reduce air absorption of low-energy fluorescence signals. Bulk samples are switched to atmospheric pressure. The temperature control unit, based on the noise interference indicator output by the signal acquisition module, adjusts the power of the heater and cooler in real time to maintain a stable sample chamber temperature between 20°C and 40°C. This temperature range is calculated by the data analysis module based on the crystal thermal expansion coefficients in the XRD diffraction database to match the diffraction peak stability requirements of different minerals.

[0070] The data analysis module inputs the elemental energy spectrum of the fluorescence signal into the XRF element library for matching. Using a convolutional neural network, it extracts the energy spectrum peaks, identifies the element types, and calculates their contents. Simultaneously, the diffraction angle data is input into the XRD diffraction database, where the Bragg equation is used to analyze the interplanar spacings. The mineral composition is then determined using a library of standard phase atlases. The convolutional neural network employs a multi-layer convolutional architecture, with training data derived from combined XRF and XRD test results of known mineral samples. It outputs a quantitative relationship matrix between elemental content and phase composition, reflecting the spatial correlation between elemental distribution and mineral phases.

[0071] The sample preparation optimization module utilizes a genetic algorithm to generate the optimal parameter combination for sample preparation time, particle size distribution, and surface roughness based on the element distribution uniformity index in the quantitative relationship matrix. This genetic algorithm iteratively optimizes sample preparation parameters using the element distribution dispersion and the diffraction peak signal-to-noise ratio as fitness functions. The parameter feedback unit transmits the optimized parameters to the ball mill and cutting equipment to adjust the sample preparation process. Simultaneously, it receives the reservoir evaluation index deviation value calculated by the report generation module and dynamically updates the iterative weights of the genetic algorithm to improve parameter optimization efficiency. The sample preparation quality control threshold is dynamically adjusted based on the temperature control error value output by the environmental control module to ensure environmental adaptability during the sample preparation process.

[0072] The report generation module integrates elemental content, mineral phase composition, reservoir permeability, and porosity parameters to generate a three-dimensional mineral distribution map and reservoir evaluation index using a data fusion algorithm. This data fusion algorithm uses principal component analysis to reduce the dimensionality of multi-source data and combines it with a kriging interpolation algorithm to construct a three-dimensional mineral distribution model. The final output is a comprehensive report that includes mineral abundance, reservoir physical parameters, and comprehensive evaluation indicators.

[0073] The hardware coordination module coordinates the operational timing of each module. Sending a target switching command to the X-ray source module simultaneously triggers the signal acquisition module to switch to a time-sharing mode. Based on the vacuum parameters and temperature data transmitted by the environmental control module, the module adjusts the X-ray source's excitation power and signal acquisition sensitivity. The synchronization command generation logic is based on status feedback signals from each module, forming a closed-loop control process that ensures stable system operation in dynamic environments.

[0074] Through the coordinated implementation of the above technical solutions, the system achieves synchronization and data correlation between XRF and XRD detection, solves the problems of low efficiency, data fragmentation and hardware redundancy of split instruments, and provides a high-precision, integrated solution for oil and gas reservoir lithology and physical property analysis.

[0075] Specifically, the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the X-ray source module includes:

[0076] The X-ray source module includes a timing switching unit for Cu target and Mo target, and the timing switching unit adjusts the X-ray energy by receiving a high-voltage power supply adjustment instruction sent by the hardware coordination module;

[0077] The signal acquisition module includes a multi-channel analyzer and a Bragg monochromator. The multi-channel analyzer receives the target material switching completion signal sent by the timing switching unit and separates the low-energy signal in the fluorescence energy spectrum. The Bragg monochromator receives the target material switching completion signal and filters the high-energy noise in the diffraction signal.

[0078] The hardware coordination module generates a synchronization instruction according to the vacuum degree parameter transmitted by the environmental control module and the target material switching state of the timing switching unit, and the synchronization instruction triggers the energy spectrum separation operation of the multi-channel analyzer and the Bragg monochromator.

[0079] In the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the technical solution of the X-ray source module is specifically implemented as follows:

[0080] The timing switching unit dynamically adjusts the working status of the Cu and Mo targets by receiving high-voltage power supply adjustment instructions sent by the hardware collaboration module. The high-voltage power supply adjustment instructions include voltage and current parameter adjustment strategies. When the Cu target is activated, the high-voltage power supply outputs 8-12kV to generate medium- and low-energy X-rays suitable for light element excitation. When switching to the Mo target, the voltage is increased to 25-30kV to generate high-energy X-rays suitable for heavy element excitation and diffraction analysis. During the target switching process, the timing switching unit completes the target position alternation through a mechanical transmission device and sends a target switching completion signal containing a timestamp to the signal acquisition module after the switching is completed.

[0081] The signal acquisition module's multichannel analyzer and Bragg monochromator perform signal activation separation based on target switching. The multichannel analyzer uses pulse height analysis to separate low-energy signals (<20keV) from the fluorescence signal excited by the Cu target, extracting the characteristic energy spectrum through energy threshold screening. The Bragg monochromator performs Bragg angle filtering on the diffraction signal excited by the Mo target, eliminating scattering noise while retaining diffraction lines from specific crystal planes. The activation timing of the multichannel analyzer and Bragg monochromator is strictly aligned via synchronization instructions from the hardware collaboration module, ensuring that the occupied time periods of the fluorescence signal acquisition channel and the diffraction signal acquisition channel do not overlap.

[0082] The hardware coordination module generates synchronization instructions based on the vacuum parameters and target switching status transmitted by the environmental control module. -3 When the target switching state is abnormal (e.g., timeout or stuck), the synchronization command is delayed and sent to the signal acquisition module until the vacuum environment stabilizes. If the target switching state is abnormal (e.g., switching timeout or stuck), the hardware coordination module terminates the current command and triggers the fault feedback mechanism. The synchronization command contains the trigger time window and energy range parameters for the energy spectrum separation operation. The multichannel analyzer and Bragg monochromator activate the corresponding filtering algorithm based on the time window parameters in the command, achieving coordinated control of signal separation and noise suppression.

[0083] In this technical solution, the target switching completion signal and the synchronization instruction generation logic form a closed-loop control. After sending the target switching completion signal, the timing switching unit continuously monitors the output stability of the X-ray source. If the monitored energy fluctuation exceeds the allowable range, a recalibration request is sent to the hardware coordination module, triggering a secondary adjustment of the high-voltage power supply parameters. After completing signal separation, the multi-channel analyzer and Bragg monochromator transmit the energy spectrum and angle data with time stamps to the data analysis module, ensuring that the signal source and acquisition conditions can be traced in the subsequent data processing stage.

[0084] Through the above implementation, the X-ray source module, signal acquisition module and hardware collaboration module form a collaborative mechanism with strict timing synchronization and dynamic parameter adaptation, which solves the problems of signal crosstalk and energy matching in the time-sharing excitation mode and provides a hardware foundation for the synchronous detection of elements and phases.

[0085] Specifically, in the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the signal acquisition module is configured as follows:

[0086] Time-sharing mode, alternately activating the fluorescence signal acquisition channel and the diffraction signal acquisition channel. The switching trigger condition of the time-sharing mode is that the activation state of the Cu target or Mo target of the X-ray source module matches the excitation signal type corresponding to the time-sharing mode;

[0087] Energy filtering mode, which separates the mixed signal based on the real-time energy thresholds of the XRF element library and the XRD diffraction database output by the data analysis module, and uses an adaptive filtering algorithm to eliminate cross-interference between the fluorescence signal and the diffraction signal. The noise characteristic parameters of the adaptive filtering algorithm are dynamically updated by the temperature control data output by the temperature adjustment unit of the environmental control module;

[0088] The switching instructions between the time-sharing mode and the energy filtering mode are generated by the hardware collaboration module based on the excitation energy parameters of the X-ray source module and the vacuum parameters of the environmental control module, and the activation state of the switching instruction trigger signal acquisition channel is synchronized with the input signal type of the data analysis module.

[0089] In the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the specific implementation of the signal acquisition module is as follows:

[0090] The time-sharing mode achieves signal separation by alternately activating the fluorescence signal acquisition channel and the diffraction signal acquisition channel. When the X-ray source module activates the Cu target, the fluorescence signal acquisition channel starts to collect the low-energy fluorescence signal excited by the Cu target; when switching to the Mo target, the diffraction signal acquisition channel is turned on to collect the high-energy diffraction signal generated by the Mo target. The switching trigger condition of the time-sharing mode is strictly bound to the target activation state, that is, when the Cu target is activated, only the fluorescence channel is allowed to work, and when the Mo target is activated, only the diffraction channel is allowed to run, avoiding mismatch between signal type and acquisition channel. The trigger condition is realized through real-time monitoring of the hardware collaboration module. When it is detected that the target current parameter reaches the preset threshold, a channel switching instruction is sent to the signal acquisition module.

[0091] The energy filtering mode separates the mixed signals based on the real-time energy thresholds of the XRF element library and the XRD diffraction database output by the data analysis module. The XRF element library provides the energy range of the characteristic energy spectrum of each element, and the XRD diffraction database defines the Bragg angle range of different phases. The energy threshold is dynamically adjusted based on the element and phase type of the current detection target. The adaptive filtering algorithm analyzes the frequency characteristics of the fluorescence signal and the diffraction signal, establishes a noise model and generates filtering parameters to eliminate cross-interference between signals. The noise characteristic parameters are dynamically updated by the temperature control data output by the temperature adjustment unit of the environmental control module. When the temperature fluctuation of the sample chamber exceeds ±1°C, the adaptive filtering algorithm recalculates the thermal noise coefficient and adjusts the cutoff frequency and gain parameters of the filter.

[0092] The switching instructions between the time-sharing mode and the energy filtering mode are generated by the hardware coordination module according to the excitation energy parameters of the X-ray source module and the vacuum parameters of the environmental control module. When the excitation energy parameter is lower than 20keV, the time-sharing mode is activated first to match the low-energy fluorescence signal acquisition requirements; when the energy parameter is higher than 20keV and the vacuum degree is lower than 10 -2 When the signal is Pa, it switches to energy filtering mode to deal with the scattering noise in the high-energy diffraction signal. The switching instruction contains the target mode identifier and energy range parameters. The signal acquisition module activates the corresponding channel according to the identifier in the instruction and adjusts the filtering parameters of the multi-channel analyzer and Bragg monochromator.

[0093] The hardware coordination module transmits switching instructions to the signal acquisition module via a synchronization bus, triggering the activation state of the acquisition channel to synchronize with the input signal type of the data analysis module. Upon receiving a signal, the data analysis module verifies the consistency of the signal type tag with the current analysis task. If the tag does not match, the data is discarded and a new acquisition is requested. This synchronization mechanism is achieved through timestamp alignment. After completing data separation, the signal acquisition module attaches the acquisition time, energy range, and environmental parameter tags to each energy spectrum and angle data point, allowing the data analysis module to construct a time series correlation model.

[0094] In the above implementation, the dynamic switching between time-sharing mode and energy filtering mode relies on the coordinated control of multiple source parameters. When generating instructions, the hardware coordination module integrates X-ray energy, vacuum level, and temperature data to assess signal quality and select the optimal acquisition mode. The signal acquisition module adjusts the hardware configuration based on the instructions to ensure that the separated signals meet subsequent processing requirements. By dynamically updating environmental and algorithmic parameters, the system can adapt to varying sample states and detection conditions, improving the accuracy and consistency of elemental and phase data.

[0095] Specifically, in the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the environmental control module includes:

[0096] A vacuum and atmospheric environment switching device, used to switch the chamber pressure parameters according to the sample morphology parameters output by the sample preparation optimization module, wherein the sample morphology parameters are generated by the sample preparation optimization module according to the element distribution uniformity index and the surface roughness parameter;

[0097] a temperature regulating unit, which adjusts the power of the heater and the cooler by receiving the noise interference index output by the multi-channel analyzer in the signal acquisition module, so as to maintain the temperature in the sample chamber within the target temperature range calculated by the data analysis module based on the XRD diffraction database;

[0098] The status information of the vacuum and atmospheric environment switching device is transmitted to the hardware coordination module in real time. The hardware coordination module generates an excitation power adjustment instruction according to the status information and the current target material type of the X-ray source module and sends it to the X-ray source module.

[0099] In the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the technical solution of the environmental control module is specifically implemented as follows:

[0100] The vacuum and atmospheric environment switching device switches the chamber pressure parameters according to the sample morphology parameters output by the sample preparation optimization module. The sample morphology parameters are generated by the sample preparation optimization module based on the element distribution uniformity index and the surface roughness parameter. When the element distribution uniformity index is lower than the set threshold or the surface roughness exceeds the allowable range, the sample is judged to be in powder form and the vacuum mode is triggered; if the surface roughness meets the preset standard and the element distribution uniformity meets the standard, it is judged to be a block sample and the sample is switched to the atmospheric environment mode. In the vacuum mode, the pressure in the chamber is adjusted to 10 -3 Pa, reducing the absorption of low-energy fluorescence signals by air molecules; in atmospheric mode, the pressure relief valve opens and the pressure in the chamber returns to normal pressure, avoiding deformation of bulk samples caused by vacuum negative pressure.

[0101] The temperature control unit dynamically adjusts the power of the heater and cooler by receiving the noise interference index output by the multi-channel analyzer in the signal acquisition module. The noise interference index reflects the level of thermal noise during the signal acquisition process. When the index exceeds the threshold, the temperature control unit activates the cooler to lower the temperature in the chamber; if the index is lower than the threshold, the heater is activated to increase the temperature. The target temperature range is dynamically calculated by the data analysis module based on the mineral thermal expansion coefficient in the XRD diffraction database. For example, the temperature range corresponding to quartz minerals is 25-35°C, and that for calcite is 30-40°C, to match the diffraction peak stability requirements of different phases. The temperature sensor monitors the temperature fluctuations in the chamber in real time and transmits feedback data to the temperature control unit to form a closed-loop control.

[0102] The status information of the vacuum and atmospheric environment switching device is transmitted to the hardware collaboration module in real time through the communication interface. The status information includes the current pressure value, the working status of the vacuum pump, and the opening and closing status of the pressure relief valve. The hardware collaboration module generates an excitation power adjustment instruction in combination with the current target material type of the X-ray source module: when using a Cu target material and in vacuum mode, the high-voltage power supply power is reduced to 8-12kV to reduce the energy loss of low-energy X-rays; if switched to a Mo target material and in atmospheric mode, the power is increased to 25-30kV to enhance the penetration of high-energy X-rays. The excitation power adjustment instruction is transmitted to the timing switching unit of the X-ray source module via a digital signal, triggering the update of the high-voltage power supply parameters, and at the same time sending an energy range synchronization instruction to the signal acquisition module to match the filtering parameters of the multi-channel analyzer and the Bragg monochromator with the current X-ray energy.

[0103] In the above implementation, the environmental control module forms a data linkage with the sample preparation optimization module, the signal acquisition module, and the hardware collaboration module. Sample morphology parameters, as the core basis for environmental switching, directly affect the vacuum pressure and temperature control strategies. The dynamic correlation between the noise interference index and the target temperature range ensures the thermal stability of the signal acquisition process. The coordinated control of the vacuum state and the target material type optimizes the adaptability of the X-ray energy to the sample environment. Through real-time parameter interaction and closed-loop feedback of instructions between multiple modules, the system can maintain optimal analysis conditions under different sample morphologies and detection requirements, improving the accuracy and repeatability of elemental and phase data.

[0104] Specifically, the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the sample preparation optimization module includes:

[0105] A genetic algorithm unit is used to generate an optimal parameter combination of sample preparation time, particle size distribution and surface roughness based on the element distribution uniformity index output by the data analysis module and the diffraction peak signal-to-noise ratio of the signal acquisition module as optimization targets;

[0106] a parameter feedback unit, configured to transmit the optimal parameter combination to the ball mill and the cutting device, and receive the deviation value calculated by the report generation module according to the reservoir evaluation index to update the iteration weight of the genetic algorithm;

[0107] The parameter feedback unit is connected to the data analysis module, and dynamically adjusts the sample preparation quality control threshold according to the element distribution uniformity index and the temperature control error value output by the environmental control module. The adjusted threshold is fed back to the sample preparation optimization module to regenerate the parameter combination.

[0108] In the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention, the technical solution of the sample preparation optimization module is specifically implemented as follows:

[0109] The genetic algorithm unit constructs a fitness function based on the element distribution uniformity index output by the data analysis module and the diffraction peak signal-to-noise ratio of the signal acquisition module. The element distribution uniformity index is calculated by statistically analyzing the discreteness of the element content on the sample surface. The lower the discreteness, the higher the score. The diffraction peak signal-to-noise ratio is determined by comparing the ratio of the diffraction peak intensity to the background noise. The higher the ratio, the better the score. The genetic algorithm uses sample preparation time, particle size distribution, and surface roughness as chromosome variables, and iteratively generates parameter combinations through selection, crossover, and mutation operations, and finally outputs the optimal parameter combination with the highest fitness score. During each round of iteration, the algorithm retains the top 10% of individuals in the previous generation population, and generates new parameters through random perturbations to explore the solution space and avoid the local optimal solution trap.

[0110] The parameter feedback unit transmits the optimal parameter combination to the ball mill and cutting equipment, controls the rotation speed and grinding time of the ball mill to adjust the sample particle size distribution, and sets the feed speed and pressure parameters of the cutting equipment to optimize the surface roughness. The parameter transmission adopts the industrial bus protocol, and after the equipment is executed, the actual sample preparation data is fed back to the parameter feedback unit in real time. The parameter feedback unit receives the reservoir evaluation index deviation value calculated by the report generation module. The deviation value is obtained by comparing the difference between the measured reservoir physical property parameters and the theoretical model, and is used to update the iterative weight of the genetic algorithm. When the deviation value is higher than the preset threshold, the algorithm increases the weight of the surface roughness parameter and prioritizes optimizing the sample surface quality; if the deviation value is mainly due to abnormal element distribution, the weight ratio of the element uniformity index is increased.

[0111] The parameter feedback unit is connected to the data analysis module and dynamically adjusts the sample preparation quality control threshold based on the real-time trend of the element distribution uniformity index and the temperature control error value output by the environmental control module. If the temperature control error value exceeds ±2°C, the parameter feedback unit reduces the allowable deviation range of the particle size distribution, requiring the ball mill to improve grinding precision. If the fluctuation range of the element distribution uniformity index increases, the surface roughness quality control threshold is tightened, triggering the cutting equipment to perform secondary trimming. The adjusted threshold is fed back to the sample preparation optimization module via the data interface. The genetic algorithm unit regenerates the parameter combination based on the updated threshold, forming a closed-loop optimization mechanism.

[0112] In the above-mentioned implementation, the sample preparation optimization module achieves dynamic parameter adjustment through the linkage of multi-source data. The core optimization objective of the genetic algorithm unit is directly linked to the sample test results. The parameter feedback unit converts the deviation between the equipment execution data and the theoretical model into a basis for algorithm weight adjustment. The dynamic correction of the quality control threshold relies on real-time monitoring data of environmental parameters and element distribution. Through the synergistic effect of iterative optimization and threshold feedback, the system can adapt to the sample preparation requirements of different minerals, improve the standardization of sample preparation, and provide more consistent analytical samples for subsequent X-ray testing.

[0113] Second, see Figure 1 The present invention provides an X-ray synchronous analysis method for the lithology and physical properties of oil and gas reservoirs, which is applied to the X-ray synchronous analysis system for the lithology and physical properties of oil and gas reservoirs, comprising:

[0114] Step 1: Generate X-ray signals of different energies by sequentially switching between a Cu target and a Mo target, wherein the X-ray signals include a fluorescence signal and a diffraction signal generated by exciting the sample;

[0115] Step 2: receiving the fluorescence signal and the diffraction signal in a time-sharing manner, separating the elemental characteristic energy spectrum in the fluorescence signal and the angle data in the diffraction signal by alternately activating acquisition channels and using a multi-channel analyzer and a Bragg monochromator;

[0116] Step 3, adjusting the vacuum parameter and temperature parameter of the sample chamber according to the excitation energy parameter and the acquisition mode;

[0117] Step 4: input the element characteristic energy spectrum into the XRF element library and the diffraction angle data into the XRD diffraction database, extract the features through a convolutional neural network and generate a quantitative relationship matrix between element content and phase composition;

[0118] Step 5: generating adjustment instructions for sample preparation time and surface roughness parameters according to the element distribution uniformity index in the quantitative relationship matrix and sending the instructions to the sample preparation device;

[0119] Step 6: Integrate the element content, phase composition and reservoir physical property parameters, and generate a comprehensive report including a three-dimensional mineral distribution map and reservoir evaluation index through data fusion algorithm.

[0120] The X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention is specifically implemented as follows:

[0121] In step 1, X-ray signals of varying energies are generated by sequentially switching between the Cu and Mo targets. The hardware coordination module sends a target switching command to the X-ray source module, controlling the voltage and current parameters of the high-voltage power supply. When the Cu target is activated, the output voltage is adjusted to 8-12 kV to generate X-rays in the 8-20 keV range, stimulating fluorescence signals from light elements. When switching to the Mo target, the voltage is increased to 25-30 kV to produce high-energy X-rays in the 17-40 keV range, which are used to stimulate heavy elements and diffraction signals. After target switching is complete, the sequential switching unit sends a switching completion signal to the signal acquisition module, triggering initialization of the multichannel analyzer and Bragg monochromator.

[0122] In step 2, fluorescence and diffraction signals are received in a time-sharing manner. When the Cu target is detected to be in an activated state, the signal acquisition module activates the fluorescence signal acquisition channel. The multi-channel analyzer uses pulse height analysis technology to separate fluorescence signals with energies below 20keV and extract the characteristic energy spectrum. When the Mo target is switched to an activated state, the diffraction signal acquisition channel is activated. The Bragg monochromator eliminates scattering noise through angle filtering, retaining the diffraction angle data of specific crystal planes. The switching logic of the time-sharing mode is synchronized in real time with the target current parameters. The hardware coordination module aligns the activation periods of the acquisition channels through timestamps to prevent signal overlap.

[0123] In step 3, the sample chamber environmental parameters are adjusted according to the excitation energy parameters and the acquisition mode. When the low-energy X-rays of the Cu target are detected, the environmental control module starts the vacuum mode and reduces the pressure in the chamber to 10 -3 The temperature is kept below Pa to reduce air absorption of the fluorescence signal. When using high-energy X-rays from Mo targets, the system switches to atmospheric mode and maintains a normal pressure environment. The temperature control unit receives noise interference indicators from the signal acquisition module and dynamically adjusts the power of the heater and cooler to maintain the chamber temperature within the target range calculated by the data analysis module. The target temperature range is dynamically set based on the thermal expansion characteristics of the mineral phases in the XRD diffraction database, for example, 25-30°C for clay minerals and 30-35°C for carbonate minerals.

[0124] In step 4, the elemental characteristic energy spectrum is input into the XRF element library for matching, and the element type and content are identified using a convolutional neural network. The convolutional neural network uses a multi-level convolution kernel to extract the peak shape characteristics of the energy spectrum. The training data comes from the XRF test results of known mineral samples. The diffraction angle data is input into the XRD diffraction database, and the interplanar spacing is analyzed based on the Bragg equation to match the mineral composition in the phase standard atlas library. The neural network outputs a quantitative relationship matrix between element content and phase composition. Each cell in the matrix associates the element distribution and mineral phase ratio in a specific area, reflecting the spatial correlation of the lithologic structure.

[0125] In step 5, sample preparation parameter adjustment instructions are generated based on the element distribution uniformity index in the quantitative relationship matrix. The genetic algorithm unit uses the element distribution dispersion and the diffraction peak signal-to-noise ratio as optimization targets to iteratively generate parameter combinations for sample preparation time, particle size distribution, and surface roughness. The parameter feedback unit sends the optimal parameters to the ball mill and cutting equipment via the industrial bus to control the grinding time and cutting pressure. At the same time, it receives the reservoir evaluation index deviation value calculated by the report generation module and dynamically adjusts the weight distribution of the genetic algorithm. If the deviation value is mainly caused by uneven element distribution, the algorithm increases the optimization weight of the surface roughness parameter; if the deviation is caused by diffraction peak noise, the sample preparation time and particle size distribution are prioritized.

[0126] In step 6, a comprehensive report is generated using a data fusion algorithm that integrates elemental content, mineral phase composition, and reservoir permeability and porosity parameters. This algorithm uses principal component analysis to reduce the dimensionality of multi-source data and extract key characteristic parameters. This algorithm then uses a kriging interpolation algorithm to construct a three-dimensional mineral distribution model. The data for each node in the model includes mineral abundance, pore structure, and physical property evaluation indices. The report generation module outputs a visual report containing a two-dimensional profile, three-dimensional spatial distribution, and a comprehensive reservoir evaluation index. The evaluation index is derived through a weighted calculation of mineral content, pore connectivity, and permeability parameters.

[0127] In the above steps, the hardware coordination module coordinates the timing logic of each link through synchronization instructions. The target switching signal triggers the activation of the acquisition channel, and environmental parameter adjustment and signal separation operations are carried out simultaneously. Data analysis results are fed back to the sample preparation optimization module in real time, forming a closed-loop process of detection-analysis-optimization. Through time-sharing control, dynamic parameter adaptation, and multi-source data fusion, the system realizes the coordination of XRF and XRD detection, improving the efficiency and accuracy of oil and gas reservoir lithology and physical property analysis.

[0128] Specifically, the X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to the present invention comprises the following steps:

[0129] Receive high-voltage power supply adjustment instructions and adjust the X-ray energy by sequentially switching the Cu target and the Mo target;

[0130] After the target material is switched, a switching completion signal is sent to the multi-channel analyzer and the Bragg monochromator to separate the low-energy signal in the fluorescence energy spectrum and filter the high-energy noise in the diffraction signal;

[0131] A synchronization instruction is generated according to the vacuum parameter of the sample chamber and the target material switching state to trigger the low-energy signal separation and high-energy noise filtering operations.

[0132] The X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention is specifically implemented as follows:

[0133] In step 1, X-ray signals of varying energies are generated by sequentially switching between the Cu and Mo targets. The hardware coordination module sends a target switching command to the X-ray source module, controlling the voltage and current parameters of the high-voltage power supply. When the Cu target is activated, the output voltage is adjusted to 8-12 kV to generate X-rays in the 8-20 keV range, stimulating fluorescence signals from light elements. When switching to the Mo target, the voltage is increased to 25-30 kV to produce high-energy X-rays in the 17-40 keV range, which are used to stimulate heavy elements and diffraction signals. After target switching is complete, the sequential switching unit sends a switching completion signal to the signal acquisition module, triggering initialization of the multichannel analyzer and Bragg monochromator.

[0134] In step 2, fluorescence and diffraction signals are received in a time-sharing manner. When the Cu target is detected to be in an activated state, the signal acquisition module activates the fluorescence signal acquisition channel. The multi-channel analyzer uses pulse height analysis technology to separate fluorescence signals with energies below 20keV and extract the characteristic energy spectrum. When the Mo target is switched to an activated state, the diffraction signal acquisition channel is activated. The Bragg monochromator eliminates scattering noise through angle filtering, retaining the diffraction angle data of specific crystal planes. The switching logic of the time-sharing mode is synchronized in real time with the target current parameters. The hardware coordination module aligns the activation periods of the acquisition channels through timestamps to prevent signal overlap.

[0135] In step 3, the sample chamber environmental parameters are adjusted according to the excitation energy parameters and the acquisition mode. When the low-energy X-rays of the Cu target are detected, the environmental control module starts the vacuum mode and reduces the pressure in the chamber to 10 -3 The temperature is kept below Pa to reduce air absorption of the fluorescence signal. When using high-energy X-rays from Mo targets, the system switches to atmospheric mode and maintains a normal pressure environment. The temperature control unit receives noise interference indicators from the signal acquisition module and dynamically adjusts the power of the heater and cooler to maintain the chamber temperature within the target range calculated by the data analysis module. The target temperature range is dynamically set based on the thermal expansion characteristics of the mineral phases in the XRD diffraction database, for example, 25-30°C for clay minerals and 30-35°C for carbonate minerals.

[0136] In step 4, the elemental characteristic energy spectrum is input into the XRF element library for matching, and the element type and content are identified using a convolutional neural network. The convolutional neural network uses a multi-level convolution kernel to extract the peak shape characteristics of the energy spectrum. The training data comes from the XRF test results of known mineral samples. The diffraction angle data is input into the XRD diffraction database, and the interplanar spacing is analyzed based on the Bragg equation to match the mineral composition in the phase standard atlas library. The neural network outputs a quantitative relationship matrix between element content and phase composition. Each cell in the matrix associates the element distribution and mineral phase ratio in a specific area, reflecting the spatial correlation of the lithologic structure.

[0137] In step 5, sample preparation parameter adjustment instructions are generated based on the element distribution uniformity index in the quantitative relationship matrix. The genetic algorithm unit uses the element distribution dispersion and the diffraction peak signal-to-noise ratio as optimization targets to iteratively generate parameter combinations for sample preparation time, particle size distribution, and surface roughness. The parameter feedback unit sends the optimal parameters to the ball mill and cutting equipment via the industrial bus to control the grinding time and cutting pressure. At the same time, it receives the reservoir evaluation index deviation value calculated by the report generation module and dynamically adjusts the weight distribution of the genetic algorithm. If the deviation value is mainly caused by uneven element distribution, the algorithm increases the optimization weight of the surface roughness parameter; if the deviation is caused by diffraction peak noise, the sample preparation time and particle size distribution are prioritized.

[0138] In step 6, a comprehensive report is generated using a data fusion algorithm that integrates elemental content, mineral phase composition, and reservoir permeability and porosity parameters. This algorithm uses principal component analysis to reduce the dimensionality of multi-source data and extract key characteristic parameters. This algorithm then uses a kriging interpolation algorithm to construct a three-dimensional mineral distribution model. The data for each node in the model includes mineral abundance, pore structure, and physical property evaluation indices. The report generation module outputs a visual report containing a two-dimensional profile, three-dimensional spatial distribution, and a comprehensive reservoir evaluation index. The evaluation index is derived through a weighted calculation of mineral content, pore connectivity, and permeability parameters.

[0139] In the above steps, the hardware coordination module coordinates the timing logic of each link through synchronization instructions. The target switching signal triggers the activation of the acquisition channel, and environmental parameter adjustment and signal separation operations are carried out simultaneously. Data analysis results are fed back to the sample preparation optimization module in real time, forming a closed-loop process of detection-analysis-optimization. Through time-sharing control, dynamic parameter adaptation, and multi-source data fusion, the system realizes the coordination of XRF and XRD detection, improving the efficiency and accuracy of oil and gas reservoir lithology and physical property analysis.

[0140] Specifically, in the X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to the present invention, step 2 comprises:

[0141] When the Cu target or Mo target is activated, switch to the corresponding fluorescence signal or diffraction signal collection channel;

[0142] Mixed signals are separated based on real-time energy thresholds from the XRF element library and the XRD diffraction database, and cross-interference between signals is eliminated using an adaptive filtering algorithm whose noise characteristic parameters are dynamically updated based on temperature control data.

[0143] A mode switching instruction is generated based on the excitation energy parameter and the sample chamber vacuum parameter, so that the activation state of the signal acquisition channel is synchronized with the input signal type.

[0144] In the X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention, the specific implementation of step 2 is as follows:

[0145] When the Cu target of the X-ray source module is activated, the hardware coordination module sends a channel switching instruction to the signal acquisition module, shutting down the diffraction signal acquisition channel and activating the fluorescence signal acquisition channel. When the Mo target is activated, the fluorescence channel is shut down and the diffraction channel is activated. The channel switching instruction is synchronized with the rising edge of the target current parameter. By monitoring the transition signal of the target drive current in real time, the activation state is determined and the channel switching operation is triggered. The fluorescence signal acquisition channel uses a multichannel analyzer to perform energy spectrum separation on low-energy signals, while the diffraction channel uses a Bragg monochromator to filter out scattered noise at non-target angles, achieving physical isolation of signal types.

[0146] The XRF element library and XRD diffraction database provide real-time energy thresholds for signal separation. The XRF element library dynamically sets energy threshold ranges based on the element being detected. For example, the Kα line energy for iron is 6.4 keV, and for calcium, 3.69 keV. The multichannel analyzer then filters the characteristic energy spectrum based on the thresholds. The XRD diffraction database calculates the Bragg angle range based on the interplanar spacing of the mineral phases, and the Bragg monochromator adjusts the angle filter window to allow only signals at the target diffraction angle to pass. The adaptive filtering algorithm analyzes the frequency domain characteristics of the mixed signal, identifies the low-frequency components of the fluorescence signal and the high-frequency noise of the diffraction signal, and generates band-stop filter parameters to suppress cross-interference. The algorithm's noise characteristic parameters are dynamically updated by the temperature sensor data from the environmental control module. When the chamber temperature fluctuates by more than ±1°C, the frequency domain distribution of the thermal noise is recalculated and the filter parameters are updated.

[0147] The hardware coordination module generates a mode switching instruction based on the excitation energy parameters of the X-ray source module and the vacuum parameters of the sample chamber. -3 Pa, the time-sharing mode is preferred to avoid low-energy signal attenuation; if the energy is higher than 20keV and the vacuum degree is higher than 10 -2 Pa, it switches to energy filtering mode to account for scattered noise in the atmospheric environment. This instruction is transmitted to the signal acquisition module via a synchronous bus, triggering a parameter reset for the multichannel analyzer and Bragg monochromator. After completing data separation, the signal acquisition module adds energy range, acquisition time, and environmental parameter labels to each energy spectrum and angle data. This allows the data analysis module to verify the consistency of the signal type with the input data. If the label does not match the current analysis task, the data is discarded and the acquisition process is triggered again.

[0148] In the above-mentioned implementation, the switching logic of the signal acquisition channel and the energy threshold separation mechanism form a multi-level collaborative control. The hardware collaboration module dynamically selects the optimal acquisition mode based on real-time environmental parameters and excitation energy, and the signal separation algorithm achieves adaptive adjustment through temperature control data and database thresholds. The hardware parameters of the multi-channel analyzer and Bragg monochromator are deeply linked with the filtering algorithm to ensure the precise execution of the signal processing flow under different modes. Through labeled data management and exception handling mechanisms, the system maintains data integrity and analysis reliability under complex working conditions, providing high-quality input for the subsequent generation of quantitative relationship matrices.

[0149] Specifically, in the X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to the present invention, step 3 comprises:

[0150] Switching between vacuum and atmospheric environments according to sample morphology parameters generated based on element distribution uniformity index and surface roughness parameters;

[0151] The power of the heater and cooler is adjusted by the noise interference index to maintain the sample chamber temperature within the target temperature range calculated according to the XRD diffraction database;

[0152] The vacuum environment status information is associated with the current target material type, and an excitation power adjustment instruction is generated to match the X-ray energy requirement.

[0153] In the X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention, the specific implementation of step 3 is as follows:

[0154] The sample morphology parameters are generated by the sample preparation optimization module based on the element distribution uniformity index and surface roughness parameters. The element distribution uniformity index is calculated by statistically analyzing the standard deviation of the element content in each area of ​​the sample surface. When the standard deviation is lower than the set threshold, it is judged to be uniformly distributed. The surface roughness parameter is measured by the arithmetic mean deviation of the sample surface profile using a laser scanner. When the deviation exceeds the preset range, it is judged to be too rough. When the sample morphology parameters indicate powder morphology, the environmental control module starts the vacuum mode and adjusts the pressure in the chamber to 10 -3 Pa to reduce the absorption of low-energy fluorescence signals by air; if the parameters indicate a bulk sample, switch to atmospheric mode and maintain a normal pressure environment to avoid deformation of the sample due to vacuum negative pressure.

[0155] The temperature control unit receives the noise interference index output by the multi-channel analyzer in the signal acquisition module and dynamically adjusts the power of the heater and cooler. The noise interference index is obtained by analyzing the amplitude fluctuation of the background noise of the fluorescence signal. When the index exceeds the threshold, the temperature control unit starts the cooler to lower the temperature in the chamber; if the index is lower than the threshold, the heater is activated to increase the temperature. The target temperature range is dynamically calculated by the data analysis module based on the thermal expansion characteristics of the mineral phases in the XRD diffraction database. For example, quartz minerals correspond to 25-30°C and calcite minerals to 30-35°C, in order to match the diffraction peak stability of different phases at specific temperatures. The temperature sensor monitors the temperature in the chamber in real time and feeds the data back to the temperature control unit to form a closed-loop control circuit to control temperature fluctuations within the range of ±1°C.

[0156] The status information of the vacuum and atmospheric environment switching device is transmitted to the hardware collaboration module through the communication interface. The status information includes the current pressure value, the operating status of the vacuum pump and the position of the pressure relief valve. The hardware collaboration module generates an excitation power adjustment instruction in combination with the current target material type of the X-ray source module: when using a Cu target material and in vacuum mode, the high-voltage power supply output power is reduced to 8-12kV to reduce the energy loss of low-energy X-rays in a vacuum environment; if switched to a Mo target material and in atmospheric mode, the power is increased to 25-30kV to enhance the penetration ability of high-energy X-rays into bulk samples. The instruction is transmitted to the timing switching unit of the X-ray source module via a digital signal, the high-voltage power supply parameters are synchronously adjusted, and an energy range synchronization signal is sent to the signal acquisition module to match the filtering parameters of the multi-channel analyzer and the Bragg monochromator with the current X-ray energy.

[0157] In the above-mentioned implementation, the environmental control module forms a data linkage with the sample preparation optimization module, the signal acquisition module, and the hardware collaboration module. Sample morphology parameters, as the core basis for environmental switching, directly influence the vacuum pressure control strategy. The dynamic correlation between the noise interference index and the target temperature range ensures the thermal stability of the signal acquisition process. The coordinated control of the vacuum state and the target material type optimizes the adaptability of the X-ray energy to the sample environment. Through real-time parameter interaction and closed-loop feedback of instructions between multiple modules, the system can maintain optimal analysis conditions under different sample morphologies and detection requirements, improving the accuracy and repeatability of elemental and phase data.

[0158] Specifically, in the X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to the present invention, step 5 comprises:

[0159] Taking the element distribution uniformity index and the diffraction peak signal-to-noise ratio as optimization targets, the optimal parameter combination of sample preparation time, particle size distribution and surface roughness was generated through genetic algorithm.

[0160] The optimal parameter combination is sent to the ball mill and the cutting device, and the iterative weight of the genetic algorithm is updated according to the reservoir evaluation index deviation value;

[0161] The sample preparation quality control threshold is dynamically adjusted according to the temperature control error value, and the adjusted threshold is fed back to the parameter generation process to re-optimize the parameter combination.

[0162] In the X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties of the present invention, the specific implementation of step 5 is as follows:

[0163] The genetic algorithm unit uses the element distribution uniformity index output by the data analysis module and the diffraction peak signal-to-noise ratio of the signal acquisition module as the core parameters of the fitness function. The element distribution uniformity index is calculated by statistically analyzing the discreteness of the element content in each area of ​​the sample surface. The lower the discreteness, the higher the scoring weight; the diffraction peak signal-to-noise ratio is determined based on the ratio of the diffraction peak intensity of the target mineral phase to the background noise. The larger the ratio, the higher the scoring priority. The genetic algorithm uses sample preparation time, particle size distribution and surface roughness as optimization variables, adopts a tournament selection strategy to screen high-fitness individuals from the population, and generates offspring parameter combinations through single-point crossover and Gaussian mutation operations. During the iteration process, the top 10% of individuals in fitness in each generation are retained, and the solution space is explored through random perturbations, and the optimal parameter combination that meets the preset scoring threshold is finally output.

[0164] The parameter feedback unit transmits the optimal parameter combination to the ball mill and cutting equipment through the industrial bus protocol. The ball mill adjusts the grinding speed and duration according to the particle size distribution parameters to control the sample particle size within the target range; the cutting equipment sets the feed speed and pressure value according to the surface roughness parameters to optimize the surface flatness of the sample. After the equipment is executed, the actual sample preparation data is fed back to the parameter feedback unit in real time. The unit receives the reservoir evaluation index deviation value calculated by the report generation module. The deviation value is obtained by comparing the difference between the measured reservoir physical properties (such as permeability and porosity) and the theoretical model. When the deviation value is higher than the set threshold, the genetic algorithm unit increases the weight coefficient of the surface roughness parameter to prioritize the optimization of the sample surface quality; if the deviation is mainly caused by uneven element distribution, the weight ratio of the element uniformity index is increased, and the iteration priority of the sample preparation time parameter is strengthened.

[0165] The parameter feedback unit is connected to the data analysis module and dynamically adjusts the sample preparation quality control threshold based on real-time monitoring data from the elemental distribution uniformity index and the temperature control error value output by the environmental control module. When the temperature control error value exceeds ±2°C, the allowable deviation range of the particle size distribution is narrowed, triggering the ball mill to improve grinding accuracy. If the fluctuation range of the elemental distribution uniformity index increases, the surface roughness quality control threshold is tightened, requiring the cutting equipment to perform a secondary trimming operation. The adjusted threshold is fed back to the sample preparation optimization module via a data interface. The genetic algorithm unit regenerates parameter combinations based on the updated threshold value, forming a closed-loop "analysis-optimization-feedback" control mechanism.

[0166] In the above-mentioned implementation, the sample preparation optimization module achieves dynamic parameter adjustment through multi-source data linkage. The genetic algorithm's optimization objectives are directly linked to the test results. The parameter feedback unit converts equipment execution deviations into a basis for algorithm weight adjustment. Dynamic correction of quality control thresholds relies on real-time monitoring data of environmental parameters and elemental distribution. Through the synergistic effect of iterative optimization and threshold feedback, the system can adapt to the sample preparation requirements of different minerals, improve the standardization of sample preparation, and provide more consistent analytical samples for X-ray detection, thereby enhancing the reliability of subsequent elemental and phase analysis.

[0167] The specific implementation of the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties of the present invention is as follows:

[0168] The X-ray source module generates X-ray signals of different energies by sequentially switching between Cu and Mo targets. The sequential switching unit receives the high-voltage power supply adjustment instructions sent by the hardware collaboration module and dynamically adjusts the working parameters of the Cu and Mo targets: when the Cu target is activated, the high-voltage power supply outputs a voltage of 8-12kV to generate low-energy X-rays in the range of 8-20keV, which are used to excite light element fluorescence signals; when switching to the Mo target, the voltage is increased to 25-30kV to generate high-energy X-rays of 17-40keV, which are suitable for heavy element excitation and diffraction signal acquisition. During the target switching process, the mechanical transmission device completes the alternation of the target position. After the switching is completed, a switching completion signal containing a timestamp is sent to the signal acquisition module to trigger the subsequent signal separation process.

[0169] The signal acquisition module uses a time-sharing mode to alternately activate the fluorescence and diffraction signal acquisition channels. When the Cu target is activated, the fluorescence signal acquisition channel is started. The multi-channel analyzer uses pulse height analysis technology to separate fluorescence signals with energy below 20keV and extract the elemental characteristic energy spectrum. When the Mo target is activated, the diffraction channel is turned on. The Bragg monochromator filters the scattered noise through the preset Bragg angle, retaining the diffraction angle data of the target crystal surface. The switching trigger condition of the time-sharing mode is synchronized with the rising edge of the target current parameter. The hardware collaboration module aligns the acquisition time period through timestamps to prevent signal overlap. For mixed signal scenarios, the energy filtering mode dynamically adjusts the separation parameters according to the real-time thresholds of the XRF element library and the XRD diffraction database. The adaptive filtering algorithm combines temperature control data to update the noise model and suppress cross-interference.

[0170] The environmental control module adjusts the environment in the chamber according to the sample morphology parameters output by the sample preparation optimization module. When the element distribution uniformity index is lower than the threshold or the surface roughness exceeds the allowable range, the sample is judged to be in powder form, the vacuum mode is activated, and the pressure in the chamber is reduced to 10 -3The vacuum state is associated with the target type, and the hardware coordination module adjusts the excitation power of the X-ray source: the Cu target uses a low-power mode in a vacuum environment, and the Mo target uses an increased power in an atmospheric environment to enhance penetration.

[0171] The data analysis module uses a convolutional neural network to extract peak shape features of the fluorescence energy spectrum and matches them with the XRF element library to identify element types and contents. Diffraction angle data is analyzed using the Bragg equation to determine interplanar spacing, and the mineral composition is determined in conjunction with a standard phase atlas library. A quantitative relationship matrix between element content and phase composition is output, reflecting the spatial correlation between element distribution and mineral phases. The sample preparation optimization module uses a genetic algorithm to iteratively generate the optimal parameter combination for sample preparation time, particle size distribution, and surface roughness based on the element distribution uniformity index in the matrix. The parameter feedback unit controls the ball mill and cutting equipment to perform optimization operations. The reservoir evaluation index deviation value updates the algorithm weight in real time, and the temperature control error value dynamically adjusts the quality control threshold, forming a closed-loop optimization mechanism.

[0172] The report generation module integrates elemental content, mineral phases, and reservoir permeability parameters, employs principal component analysis to reduce the dimensionality of multi-source data, and combines it with the Kriging interpolation algorithm to construct a three-dimensional mineral distribution model. The module then outputs a visual report that includes mineral abundance, pore structure, and comprehensive evaluation indicators. Through command synchronization and data linkage within the hardware collaboration module, the system achieves time-series collaboration between XRF and XRD detection, dynamic adaptation of environmental parameters, and efficient resource utilization. This solves the issues of duplicate sampling, data fragmentation, and hardware redundancy associated with separate instruments, providing an integrated solution for oil and gas reservoir analysis.

[0173] The present invention solves the problems caused by the independent operation of split XRF and XRD instruments through the following technical solutions:

[0174] The present invention dynamically adjusts the excitation energy of the Cu and Mo targets through the timing switching unit of the X-ray source module, alternately activating the fluorescence and diffraction signal acquisition channels in a time-sharing mode to achieve synchronized operation of XRF and XRD detection. The hardware coordination module generates synchronization instructions based on the target switching state and vacuum parameters, triggering the multi-channel analyzer and Bragg monochromator of the signal acquisition module to separate the signals, reducing the time loss of repeated sampling and equipment switching. The environmental control module dynamically adjusts the vacuum and temperature conditions according to the sample morphology parameters, avoiding detection interruptions caused by environmental adaptation delays, thereby improving analysis efficiency.

[0175] The data analysis module of the present invention uses a convolutional neural network to extract the spatial correlation between fluorescence spectral characteristics and diffraction angle data, generating a quantitative relationship matrix between element content and mineral phase composition. This matrix maps the element distribution uniformity index and the diffraction peak signal-to-noise ratio into a single data model. Combined with reservoir physical properties, a data fusion algorithm is used to construct a three-dimensional mineral distribution map. The report generation module integrates multi-source data and outputs a comprehensive report containing an element-matter correlation model and reservoir evaluation index, enabling real-time collaborative analysis of test results.

[0176] The system eliminates the drawbacks of independent hardware duplication in separate instruments by sharing the X-ray source module, signal acquisition channels, and sample chamber environmental control unit. The hardware collaboration module dynamically adjusts X-ray power and acquisition mode based on excitation energy, vacuum level, and temperature parameters to optimize resource utilization. The sample preparation optimization module iteratively generates sample preparation parameters using a genetic algorithm and controls the ball mill and cutting equipment through a parameter feedback unit, reducing hardware recalibration due to inconsistent sample preparation and overall resource consumption.

[0177] The above technical solution solves the efficiency bottleneck, data fragmentation and resource waste problems of split instruments through time switching, data collaborative modeling and hardware resource sharing.

Claims

1. An X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties, characterized by: include: An X-ray source module is used to generate X-ray signals of different energies by sequentially switching between Cu and Mo targets. The X-ray signals include fluorescence signals and diffraction signals generated by exciting the sample; a signal acquisition module, configured to receive the fluorescence signal and diffraction signal generated by the X-ray source module, alternately activate acquisition channels in a time-sharing mode, and separate the elemental characteristic energy spectrum in the fluorescence signal from the angular data in the diffraction signal using a multi-channel analyzer and a Bragg monochromator; An environmental control module, configured to adjust the vacuum and temperature parameters of the sample chamber according to the excitation energy parameters of the X-ray source module and the acquisition mode of the signal acquisition module; A data analysis module is used to input the element characteristic energy spectrum into the XRF element library and the diffraction angle data into the XRD diffraction database, extract features through a convolutional neural network, and generate a quantitative relationship matrix between element content and phase composition; a sample preparation optimization module, configured to output adjustment instructions for sample preparation time and surface roughness parameters to a sample preparation device based on the element distribution uniformity index in the quantitative relationship matrix; A report generation module is used to integrate the element content, phase composition and reservoir physical property parameters, and generate a comprehensive report including a three-dimensional mineral distribution map and reservoir evaluation index through a data fusion algorithm; The hardware collaboration module is used to send target material switching instructions to the X-ray source module, send time-sharing mode switching instructions to the signal acquisition module, and send parameter adjustment instructions to the environmental control module.

2. The X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties according to claim 1 is characterized in that: The X-ray source module includes: The X-ray source module includes a timing switching unit for Cu target and Mo target, and the timing switching unit adjusts the X-ray energy by receiving a high-voltage power supply adjustment instruction sent by the hardware coordination module; The signal acquisition module includes a multi-channel analyzer and a Bragg monochromator. The multi-channel analyzer receives the target material switching completion signal sent by the timing switching unit and separates the low-energy signal in the fluorescence energy spectrum. The Bragg monochromator receives the target material switching completion signal and filters the high-energy noise in the diffraction signal. The hardware coordination module generates a synchronization instruction according to the vacuum degree parameter transmitted by the environmental control module and the target material switching state of the timing switching unit, and the synchronization instruction triggers the energy spectrum separation operation of the multi-channel analyzer and the Bragg monochromator.

3. The X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties according to claim 1, characterized in that: The signal acquisition module is configured as follows: Time-sharing mode, alternately activating the fluorescence signal acquisition channel and the diffraction signal acquisition channel. The switching trigger condition of the time-sharing mode is that the activation state of the Cu target or Mo target of the X-ray source module matches the excitation signal type corresponding to the time-sharing mode; Energy filtering mode, which separates the mixed signal based on the real-time energy thresholds of the XRF element library and the XRD diffraction database output by the data analysis module, and uses an adaptive filtering algorithm to eliminate cross-interference between the fluorescence signal and the diffraction signal. The noise characteristic parameters of the adaptive filtering algorithm are dynamically updated by the temperature control data output by the temperature adjustment unit of the environmental control module; The switching instructions between the time-sharing mode and the energy filtering mode are generated by the hardware collaboration module based on the excitation energy parameters of the X-ray source module and the vacuum parameters of the environmental control module, and the activation state of the switching instruction trigger signal acquisition channel is synchronized with the input signal type of the data analysis module.

4. The X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties according to claim 1, characterized in that: The environmental control module includes: A vacuum and atmospheric environment switching device, used to switch the chamber pressure parameters according to the sample morphology parameters output by the sample preparation optimization module, wherein the sample morphology parameters are generated by the sample preparation optimization module according to the element distribution uniformity index and the surface roughness parameter; a temperature regulating unit, which adjusts the power of the heater and the cooler by receiving the noise interference index output by the multi-channel analyzer in the signal acquisition module, so as to maintain the temperature in the sample chamber within the target temperature range calculated by the data analysis module based on the XRD diffraction database; The status information of the vacuum and atmospheric environment switching device is transmitted to the hardware coordination module in real time. The hardware coordination module generates an excitation power adjustment instruction according to the status information and the current target material type of the X-ray source module and sends it to the X-ray source module.

5. The X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties according to claim 1, characterized in that: The sample preparation optimization module includes: A genetic algorithm unit is used to generate an optimal parameter combination of sample preparation time, particle size distribution and surface roughness based on the element distribution uniformity index output by the data analysis module and the diffraction peak signal-to-noise ratio of the signal acquisition module as optimization targets; a parameter feedback unit, configured to transmit the optimal parameter combination to the ball mill and the cutting device, and receive the deviation value calculated by the report generation module according to the reservoir evaluation index to update the iteration weight of the genetic algorithm; The parameter feedback unit is connected to the data analysis module, and dynamically adjusts the sample preparation quality control threshold according to the element distribution uniformity index and the temperature control error value output by the environmental control module. The adjusted threshold is fed back to the sample preparation optimization module to regenerate the parameter combination.

6. An X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties, applied to the X-ray synchronous analysis system for oil and gas reservoir lithology and physical properties according to any one of claims 1 to 5, characterized in that: include: Step 1: Generate X-ray signals of different energies by sequentially switching between a Cu target and a Mo target, wherein the X-ray signals include a fluorescence signal and a diffraction signal generated by exciting the sample; Step 2: receiving the fluorescence signal and the diffraction signal in a time-sharing manner, separating the elemental characteristic energy spectrum in the fluorescence signal and the angle data in the diffraction signal by alternately activating acquisition channels and using a multi-channel analyzer and a Bragg monochromator; Step 3, adjusting the vacuum parameter and temperature parameter of the sample chamber according to the excitation energy parameter and the acquisition mode; Step 4: input the element characteristic energy spectrum into the XRF element library and the diffraction angle data into the XRD diffraction database, extract the features through a convolutional neural network and generate a quantitative relationship matrix between element content and phase composition; Step 5: generating adjustment instructions for sample preparation time and surface roughness parameters according to the element distribution uniformity index in the quantitative relationship matrix and sending the instructions to the sample preparation equipment; Step 6: Integrate the element content, phase composition and reservoir physical property parameters, and generate a comprehensive report including a three-dimensional mineral distribution map and reservoir evaluation index through data fusion algorithm.

7. The X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to claim 6, characterized in that: The step 1 comprises: Receive high-voltage power supply adjustment instructions and adjust the X-ray energy by sequentially switching the Cu target and the Mo target; After the target material is switched, a switching completion signal is sent to the multi-channel analyzer and the Bragg monochromator to separate the low-energy signal in the fluorescence energy spectrum and filter the high-energy noise in the diffraction signal; A synchronization instruction is generated according to the vacuum parameter of the sample chamber and the target material switching state to trigger the low-energy signal separation and high-energy noise filtering operations.

8. The X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to claim 6, characterized in that: The step 2 includes: When the Cu target or Mo target is activated, switch to the corresponding fluorescence signal or diffraction signal collection channel; Mixed signals are separated based on real-time energy thresholds from the XRF element library and the XRD diffraction database, and cross-interference between signals is eliminated using an adaptive filtering algorithm whose noise characteristic parameters are dynamically updated based on temperature control data. A mode switching instruction is generated based on the excitation energy parameter and the sample chamber vacuum parameter, so that the activation state of the signal acquisition channel is synchronized with the input signal type.

9. The X-ray synchronous analysis method for oil and gas reservoir lithology and physical properties according to claim 6, characterized in that: The step 3 comprises: Switching between vacuum and atmospheric environments according to sample morphology parameters generated based on element distribution uniformity index and surface roughness parameters; The power of the heater and cooler is adjusted by the noise interference index to maintain the sample chamber temperature within the target temperature range calculated according to the XRD diffraction database; The vacuum environment status information is associated with the current target material type, and an excitation power adjustment instruction is generated to match the X-ray energy requirement.

10. The X-ray synchronous analysis method for lithology and physical properties of oil and gas reservoirs according to claim 6, characterized in that: The step 5 comprises: Taking the element distribution uniformity index and the diffraction peak signal-to-noise ratio as optimization targets, the optimal parameter combination of sample preparation time, particle size distribution and surface roughness was generated through genetic algorithm. The optimal parameter combination is sent to the ball mill and the cutting device, and the iterative weight of the genetic algorithm is updated according to the reservoir evaluation index deviation value; The sample preparation quality control threshold is dynamically adjusted according to the temperature control error value, and the adjusted threshold is fed back to the parameter generation process to re-optimize the parameter combination.

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