Drilling speed regulation-based drilling debris dynamic real-time sampling control method and system

Abstract

The application discloses a kind of based on drilling speed regulation's rock debris dynamic real-time sampling control method and system, comprising: in response to drilling speed regulation instruction, according to drilling engineering parameter dynamic adjustment sampling strategy, obtain fresh rock debris sample from drilling fluid outlet;Fresh rock debris sample is pretreated, and the multidimensional real-time analysis data of rock debris is analyzed by multi-station cooperation;Based on drilling engineering parameter and multidimensional real-time analysis data, the depth of rock debris is dynamically corrected, and the real-time depth of correction is obtained;According to the change characteristics of the real-time depth of correction, dynamically adjust the sampling parameter of subsequent sampling period, and the rock debris section satisfying the preset condition is executed encryption sampling;The rock debris sample and multidimensional real-time analysis data obtained are bound with the real-time depth of correction after correction, and based on the comparison between analysis result and expected target, the sampling strategy is optimized.The application realizes the improvement of rock debris depth homing precision and while drilling geological evaluation data quality.

Description

Technical Field

[0001] This application relates to the fields of oil and gas drilling and geological exploration technology, and in particular to a method and system for dynamic real-time sampling control of cuttings based on drilling speed regulation. Background Technology

[0002] In drilling engineering, cuttings are the most direct and economical physical data for obtaining information about subsurface formations. By collecting and analyzing cuttings from the returned drilling fluid, downhole lithology can be determined in real time, oil and gas shows can be identified, and drilling trajectory adjustments can be guided. However, current cuttings sampling methods mostly use fixed time intervals or fixed depth intervals. When drilling speed is frequently adjusted due to formation changes or engineering needs, fixed-interval sampling often leads to misalignment between the depth of the cuttings sample and the actual formation, especially when encountering abrupt lithological transitions, which can easily result in the loss of crucial geological information.

[0003] Secondly, the time it takes for cuttings to return to the surface from the well bottom is affected by multiple factors, including drilling fluid flow rate, rheology, and wellbore conditions, leading to discrepancies between theoretical calculations and actual return times. Traditional methods often rely on manual experience to estimate the time of return or on timed calibration using tracers, making it difficult to achieve continuous and accurate depth positioning. Furthermore, the analysis of cuttings samples is often conducted offline, with data from different dimensions such as images, elements, and minerals acquired in a scattered manner. This results in poor data synchronization, hindering the formation of a comprehensive assessment of lithological changes, leading to delayed formation interface identification and insufficient depth correction accuracy. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a dynamic real-time sampling control method and system for cuttings based on drilling speed regulation. This method can dynamically adjust the sampling strategy according to changes in drilling conditions, achieve precise depth positioning of cuttings samples, and improve the representativeness and accuracy of cuttings sampling through collaborative processing and feedback optimization of multi-dimensional real-time analysis data, thus providing reliable data support for geological evaluation while drilling.

[0005] In some embodiments of this application, a method for dynamic real-time sampling control of cuttings based on drilling speed regulation is provided, including: In response to drilling speed control commands, the sampling strategy is dynamically adjusted according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet; The fresh rock cuttings samples were pretreated, and multi-dimensional real-time analysis data of the rock cuttings were collected synchronously through multi-station collaborative analysis. Based on the drilling engineering parameters and the multi-dimensional real-time analysis data, the depth of cuttings repositioning is dynamically corrected to obtain the corrected real-time repositioning depth. Based on the changes in the corrected real-time repositioning depth, the sampling parameters of subsequent sampling cycles are dynamically adjusted, and intensive sampling is performed on rock debris sections that meet the preset conditions. The acquired rock cuttings samples and multi-dimensional real-time analysis data are bound to the corrected real-time repositioning depth, and the sampling strategy is optimized based on the comparison between the current analysis results and the expected target.

[0006] In some embodiments of this application, when responding to a drilling speed control command and dynamically adjusting the sampling strategy according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet, the following steps are included: Based on the drilling engineering parameters, the theoretical cuttings return time under the current working conditions is calculated, and the initial sampling frequency and sampling quantity are set based on the theoretical cuttings return time; Fresh cuttings samples were obtained from the drilling fluid outlet based on the initial sampling frequency and sampling volume. The system receives multi-dimensional analysis data of the fresh rock cuttings samples in real time. When a sudden change in lithological characteristics is detected, the sampling frequency and sampling amount of subsequent sampling actions are adjusted to capture key rock cuttings samples before and after the change. The drilling engineering parameters include at least real-time drilling time, current well depth, and drilling fluid discharge rate.

[0007] In some embodiments of this application, when preprocessing the fresh rock cuttings sample and simultaneously collecting multi-dimensional real-time analysis data of the rock cuttings through multi-station collaborative analysis, the process includes: The fresh rock fragments sample was sequentially washed, dried and sieved to obtain a pretreated rock fragments sample. The pre-treated rock cuttings samples were divided into coarse-grained samples and fine-grained samples according to their particle size, and then sent to the corresponding stations of the rotary multi-station analysis device. The rotary multi-station analysis device drives the rock cutting sample through the optical imaging station, elemental analysis station and mineral analysis station in sequence, and simultaneously collects high-definition image data, elemental spectral data and mineral microstructure data of the rock cutting, as the multi-dimensional real-time analysis data.

[0008] In some embodiments of this application, when dynamically correcting the depth repositioning of cuttings based on the drilling engineering parameters and the multi-dimensional real-time analysis data to obtain the corrected real-time repositioning depth, the following steps are included: Based on the drilling engineering parameters, calculate the theoretical delay time for cuttings to return from the bottom of the well to the drilling fluid outlet, and combine this with the current well depth to obtain the theoretical delay depth. Real-time acquisition of the multi-dimensional real-time analysis data to identify abrupt changes in lithological characteristics; Using the lithological abrupt change points as formation interface markers, the theoretical late well depth is corrected to obtain the corrected real-time repositioning depth corresponding to the actual formation depth.

[0009] In some embodiments of this application, identifying abrupt changes in lithological characteristics based on the real-time acquisition of the multi-dimensional real-time analysis data includes: When a single lithological feature abrupt change point is identified within a continuous depth range, the abrupt change point is used as a formation interface marker to perform single-point correction on the theoretical late well depth; When multiple lithological feature abrupt changes are identified within a continuous depth range, a piecewise linear correction method is used to divide the continuous depth range into several sub-ranges based on the distribution location and feature significance of each abrupt change point. A linear mapping relationship between depth and time is established in each sub-range to comprehensively correct the theoretical late well depth. The theoretical late arrival depth is the theoretical depth at which cuttings return from the bottom of the well to the drilling fluid outlet, calculated based on drilling engineering parameters.

[0010] In some embodiments of this application, the lithological abrupt change point is used as a formation interface marker to correct the theoretical late well depth, obtaining a corrected real-time repositioning depth corresponding to the actual formation depth, including: Calculate the confidence level of each lithological feature abrupt change point, wherein the confidence level is determined by at least the range of elemental content change, the rate of mineral component transformation, and the degree of difference in image texture; High correction weights are assigned to mutation points with confidence levels higher than the first threshold, which serve as the master control points for deep correction. Mutation points with confidence levels below the second threshold are assigned low correction weights and are used only as correction references; Based on the confidence level and weight of each mutation point, the theoretical late well depth is weighted and corrected to obtain the corrected real-time return depth.

[0011] In some embodiments of this application, when dynamically adjusting the sampling parameters of subsequent sampling cycles based on the change characteristics of the corrected real-time repositioning depth, and performing intensified sampling on rock debris sections that meet preset conditions, the method includes: The rate of change of the corrected real-time positioning depth is monitored in real time. When the rate of change exceeds a preset threshold, it is determined that the current drilling has entered a lithological abrupt change section. Based on the magnitude of the rate of change, the sampling frequency and sample size of subsequent sampling periods are dynamically adjusted. The greater the rate of change, the higher the sampling frequency and the larger the sample size. Densified sampling was performed on the rock fragments before and after the lithological abrupt change section to obtain complete rock fragment samples on both sides of the abrupt change interface.

[0012] In some embodiments of this application, when binding the acquired rock cuttings samples and multi-dimensional real-time analysis data with the corrected real-time repositioning depth, and optimizing the sampling strategy based on the comparison between the current analysis results and the expected target, the following steps are included: Each acquired cuttings sample and its corresponding multi-dimensional real-time analysis data are bound to the corrected real-time positioning depth to generate a drilling cuttings information database containing the correspondence between samples, data and depth. The multi-dimensional real-time analysis data obtained in the current sampling cycle is compared with the preset expected geological profile to evaluate the representativeness and accuracy of the current sampling strategy and generate evaluation indicators. When the evaluation index is lower than the preset standard, it is determined that the current sampling strategy has a deviation, and the control parameters of the sampling strategy are automatically adjusted. Based on the adjusted control parameters, adjust the sampling actions for subsequent sampling cycles.

[0013] In some embodiments of this application, when the evaluation index is lower than a preset standard, it is determined that the current sampling strategy has a deviation, and the control parameters of the sampling strategy are automatically adjusted, including: When the accuracy of lithology identification is low, adjust the initial sampling frequency and correct the calculation benchmark for theoretical cuttings return time. When the interface capture is not complete enough, adjust the correction weight of depth correction to increase the influence of lithological feature abrupt change points in depth relocation. When the element content matching degree deviation is large, adjust the interpolation method for multi-mutation point correction; When the sample quality compliance rate is low, adjust the trigger threshold for encrypted sampling and lower the threshold for judging lithological abrupt change sections.

[0014] In some embodiments of this application, the dynamic real-time sampling control system for cuttings based on drilling speed regulation includes: The sampling control module is used to respond to drilling speed control commands and dynamically adjust the sampling strategy according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet. The preprocessing and multi-station analysis module is used to preprocess the fresh rock cuttings sample and simultaneously collect multi-dimensional real-time analysis data of the rock cuttings through multi-station collaborative analysis. The depth correction module is used to dynamically correct the depth of cuttings repositioning based on the drilling engineering parameters and the multi-dimensional real-time analysis data, so as to obtain the corrected real-time repositioning depth. The dynamic adjustment module is used to dynamically adjust the sampling parameters of subsequent sampling cycles based on the change characteristics of the corrected real-time repositioning depth, and to perform intensive sampling on rock debris sections that meet preset conditions. The binding optimization module is used to bind the acquired rock cuttings samples and multi-dimensional real-time analysis data with the corrected real-time repositioning depth, and to optimize the sampling strategy based on the comparison between the current analysis results and the expected target.

[0015] The present application discloses a method and system for dynamic real-time sampling control of cuttings based on drilling speed regulation. Compared with the prior art, its advantages are as follows: By dynamically adjusting the sampling strategy in response to drilling speed control commands, and combining drilling engineering parameters to calculate the theoretical cuttings return time in real time and adaptively adjust the sampling frequency and sample volume, rapid response to complex working conditions was achieved, significantly improving the capture efficiency and representativeness of fresh cuttings samples. A rotary multi-station collaborative analysis device was used to simultaneously acquire high-definition images, elemental spectra, and mineral microstructure data of the cuttings, enabling real-time fusion of multi-dimensional information and intelligent identification of lithological abrupt change points, providing reliable formation interface markers for depth repositioning. Based on lithological abrupt change points, the theoretical delayed well depth was dynamically corrected, and through confidence-weighted piecewise linear correction, the theoretical delayed well depth was significantly improved. This improved the accuracy of cuttings depth repositioning, eliminating depth deviations caused by changes in drilling parameters and wellbore conditions. Sampling parameters were dynamically adjusted based on the corrected real-time repositioning depth characteristics, and intensive sampling was automatically performed in lithological transition zones, ensuring complete capture of key geological interfaces and achieving a balance between sampling efficiency and accuracy. A drilling-while-drilling cuttings information database was generated by binding cuttings samples, multi-dimensional real-time analysis data, and corrected depths. Furthermore, the control parameters of the sampling strategy were automatically optimized by comparing with expected geological profiles in real time, forming a closed-loop adaptive mechanism from sampling, analysis, correction to optimization. This effectively improved the intelligence level and data quality of drilling-while-drilling geological analysis. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the dynamic real-time sampling control method for cuttings based on drilling speed regulation in the embodiments of this application. Detailed Implementation

[0017] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0018] like Figure 1 As shown, some embodiments of this application provide a method for dynamic real-time sampling control of cuttings based on drilling speed regulation, including: In response to drilling speed control commands, the sampling strategy is dynamically adjusted according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet; The fresh rock cuttings samples were pretreated, and multi-dimensional real-time analysis data of the rock cuttings were collected synchronously through multi-station collaborative analysis. Based on the drilling engineering parameters and the multi-dimensional real-time analysis data, the depth of cuttings repositioning is dynamically corrected to obtain the corrected real-time repositioning depth. Based on the changes in the corrected real-time repositioning depth, the sampling parameters of subsequent sampling cycles are dynamically adjusted, and intensive sampling is performed on rock debris sections that meet the preset conditions. The acquired rock cuttings samples and multi-dimensional real-time analysis data are bound to the corrected real-time repositioning depth, and the sampling strategy is optimized based on the comparison between the current analysis results and the expected target.

[0019] In some embodiments of this application, when responding to a drilling speed control command and dynamically adjusting the sampling strategy according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet, the following steps are included: Based on the drilling engineering parameters, the theoretical cuttings return time under the current working conditions is calculated, and the initial sampling frequency and sampling quantity are set based on the theoretical cuttings return time; Fresh cuttings samples were obtained from the drilling fluid outlet based on the initial sampling frequency and sampling volume. The system receives multi-dimensional analysis data of the fresh rock cuttings samples in real time. When a sudden change in lithological characteristics is detected, the sampling frequency and sampling amount of subsequent sampling actions are adjusted to capture key rock cuttings samples before and after the change. The drilling engineering parameters include at least real-time drilling time, current well depth, and drilling fluid discharge rate.

[0020] In this embodiment, current drilling parameters are collected, including at least real-time drilling time, current well depth, and drilling fluid discharge rate. Based on these parameters, the theoretical cuttings return time under the current working conditions is calculated. The basic calculation formula is:

[0021] in, The cross-sectional area of ​​the wellbore annulus. This represents the well depth at the previous measurement point. The actual return time is affected by drilling fluid rheology, wellbore conditions, and cuttings particle size distribution, resulting in deviations from the theoretical value. This embodiment introduces a dynamic correction factor. This factor is calculated using an empirical model based on real-time measurements of drilling fluid density, funnel viscosity, and annular return velocity.

[0022] Correction factor The values ​​are dynamically updated through a pre-established database. The model is trained using historical drilling data and can output accurate correction coefficients based on current drilling fluid properties and wellbore conditions. This is based on the corrected theoretical cuttings return time. and current drilling time The system automatically sets the initial sampling frequency. and single sample size The design goal of the sampling frequency is to ensure that at least a certain number of samples are collected per unit drilling depth to cover that depth range. Let the target sampling density be... Samples / meter, then the sampling interval time It should meet the following requirements:

[0023] To avoid sample accumulation due to excessively frequent sampling, a minimum sampling interval should be set. Initial sampling frequency Single sample size The minimum sample mass required for multi-site analysis is determined based on the cuttings generation rate, and is typically set to an adjustable value of 50-200 grams. For example, in conventional sandstone formations, Set the sample size to 100 grams; if the current drilling time is extremely low and the amount of cuttings produced is large, the sample size can be increased appropriately to ensure representativeness, and vice versa.

[0024] While sampling according to the initial parameters, the system receives multi-dimensional real-time analysis data from the multi-station analysis module, including but not limited to: high-definition image data: extracting color histograms, texture features, particle morphology, etc.; elemental spectral data: relative content and rate of change of major elements; mineral microstructure data: mineral types and semi-quantitative content identified by X-ray diffraction or Raman spectroscopy.

[0025] To objectively and quantitatively detect lithological abrupt changes, this embodiment employs a sliding window analysis of variance (ANOVA). A sliding window ANOVA with a length of [missing value] is defined. A sliding window is used to calculate the mean and variance of the data within the window for each feature parameter. When the feature value of a new sample deviates from the window mean by more than a preset multiple, it is initially identified as a candidate mutation point. Simultaneously, multi-dimensional data is used for comprehensive verification: if multiple features show significant changes simultaneously, the mutation confidence level is increased. Mutation determination indicators are defined. :

[0026] in, These represent the current changes in elemental content, image texture features, and mineral composition, respectively. This represents the historical maximum variation of the corresponding feature. These are the weighting coefficients. When Exceeding the preset threshold At that time, the system determined it to be a sudden change in lithological characteristics. Once a lithological change is detected, the system immediately initiates dynamic adjustment of sampling parameters: adjusting the sampling interval... Shortened to the original interval times, of which The encryption coefficient is determined based on the mutation strength. linear change , As a regulating factor. For example, if the original sampling interval is 10 minutes, the mutation intensity... The encryption coefficient The new interval is approximately 7 minutes. The sampling amount... Increase to the original amount times, Also related to mutation intensity, generally taken as This ensures that sufficient samples are collected near the interface for subsequent detailed analysis. The system continuously monitors the subsequent samples. Value, when After a certain number of consecutive samplings below the threshold, the sampling parameters are restored to the initial parameters, thereby completely capturing the key rock fragments on both sides of the abrupt change interface.

[0027] In some embodiments of this application, when preprocessing the fresh rock cuttings sample and simultaneously collecting multi-dimensional real-time analysis data of the rock cuttings through multi-station collaborative analysis, the process includes: The fresh rock fragments sample was sequentially washed, dried and sieved to obtain a pretreated rock fragments sample. The pre-treated rock cuttings samples were divided into coarse-grained samples and fine-grained samples according to their particle size, and then sent to the corresponding stations of the rotary multi-station analysis device. The rotary multi-station analysis device drives the rock cutting sample through the optical imaging station, elemental analysis station and mineral analysis station in sequence, and simultaneously collects high-definition image data, elemental spectral data and mineral microstructure data of the rock cutting, as the multi-dimensional real-time analysis data.

[0028] In this embodiment, the pretreatment process includes: placing the collected fresh rock cuttings in an ultrasonic cleaning device, using a specific cleaning agent to remove drilling fluid, oil, rock cutting powder, and soluble salts adhering to the surface of the rock cuttings. The cleaning time and intensity are automatically adjusted according to the degree of cementation of the rock cuttings and the type of contaminants to avoid over-cleaning that could lead to rock cutting breakage. The cleaned rock cuttings sample is then placed in a constant temperature drying oven and dried at a set temperature until the sample weight is constant. Drying removes free water and some bound water from the rock cuttings, avoiding interference from moisture in subsequent spectral analysis, and also facilitates accurate weighing and sieving. The dried rock cuttings sample is then sieved using an automatic vibrating sieve machine. The sieve aperture is set according to the analytical requirements; in this embodiment, a two-stage sieve is used: first, excessively large particles or drill cuttings are removed through a 2mm sieve, and then the sample is separated into coarse and fine particles through a 1mm or 0.5mm sieve. The sieve process is carried out in a closed environment to prevent dust contamination and sample loss.

[0029] Separating by grain size is because rock fragments of different sizes possess different information-carrying characteristics. Coarse-grained samples retain relatively complete macroscopic features such as rock structure, bedding, bioclastic material, pores, and fractures, allowing for intuitive identification of rock types, sedimentary structures, and hydrocarbon indications through high-definition images. Fine-grained samples exhibit better compositional homogeneity, are more representative, and are easier to compress or prepare, making them suitable for X-ray fluorescence elemental analysis and X-ray diffraction mineral analysis, providing more stable and accurate geochemical and mineralogical data. Based on this understanding, this embodiment uses an automatic sample divider to send pre-treated rock fragment samples to their respective workstations in a rotary multi-station analysis device: coarse-grained samples are sent to a dedicated optical imaging sample cup, while fine-grained samples are sent to a shared sample cup for elemental and mineral analysis, ensuring the specificity and effectiveness of each workstation's analysis.

[0030] In this embodiment, a rotary multi-station analysis device is used. This device mainly includes a circular turntable, a drive motor, an angle encoder, sample stations, an analytical instrument module, and a control system. N sample stations are evenly distributed along the circumference of the turntable, each capable of holding one sample cup. Three analytical stations are sequentially arranged below the turntable: an optical imaging station, an elemental analysis station, and a mineral analysis station.

[0031] In this embodiment, pre-treated coarse and fine samples are loaded into corresponding sample cups via an automatic sample loading device. The control system records the position number of each sample cup and its corresponding sample type, sampling time, and initial depth. A drive motor rotates the turntable by stepping, rotating one station angle at a time and stopping precisely with an angle encoder, ensuring each sample cup arrives at its respective analysis station sequentially. When the sample cup reaches the optical imaging station, a high-resolution camera and microscope system are triggered to acquire high-definition images of the sample under a standard light source. The image data includes color, grayscale, and multispectral images, and the system automatically extracts color features, texture features, and particle morphology parameters. When the sample cup reaches the elemental analysis station, an X-ray fluorescence spectrometer probe is triggered to irradiate the sample and detect characteristic X-rays, obtaining elemental types and content data. For fine samples, XRF analysis can obtain stable quantitative results; for coarse samples, multi-point scanning averaging can be selected to reduce the influence of heterogeneity. When the sample cup arrives at the mineral analysis station, a laser Raman spectrometer or a miniature X-ray diffractometer is triggered to acquire the molecular vibrational spectrum or diffraction pattern of the sample. By comparing it with a standard mineral database, the main mineral components and their relative contents are identified. While the first sample is being imaged at the optical station, the second sample is being analyzed at the elemental station, the third sample is being analyzed at the mineral station, and so on. The control system uses a unified time reference and sample ID to link and store the image data, elemental data, and mineral data of the same sample, achieving data-level synchronization.

[0032] In this embodiment, to ensure the accuracy of data synchronization, buffer stations are set up before and after stations with longer analysis times to allow samples to wait and avoid turntable jamming due to differences in analysis time. The analysis time of each station is monitored in real time. When the analysis time of a station exceeds a preset range, the system automatically adjusts the turntable dwell time or the timing of subsequent sample injections to ensure that all samples obtain complete data after completing one cycle. Each analysis data point is recorded with a precise timestamp and turntable position information for easy data fusion and traceability.

[0033] Through the aforementioned multi-station collaborative analysis, this embodiment simultaneously collects the following three types of data as multi-dimensional real-time analysis data: High-resolution image data, including macroscopic photographs, microscopic magnified images, color space parameters, and texture feature vectors of rock fragment samples. This data is used for preliminary lithology identification, sedimentary structure analysis, hydrocarbon detection, and grain size distribution statistics. Elemental spectral data, including the intensity or content values ​​of major rock-forming elements and characteristic trace elements. This data is used for rock type identification, sedimentary environment analysis, stratigraphic correlation, and resource evaluation. Mineral microstructure data, including mineral types, semi-quantitative content, and crystal structure parameters. This data is used for diagenetic studies, reservoir sensitivity analysis, and stratigraphic division and correlation.

[0034] In some embodiments of this application, when dynamically correcting the depth repositioning of cuttings based on the drilling engineering parameters and the multi-dimensional real-time analysis data to obtain the corrected real-time repositioning depth, the following steps are included: Based on the drilling engineering parameters, calculate the theoretical delay time for cuttings to return from the bottom of the well to the drilling fluid outlet, and combine this with the current well depth to obtain the theoretical delay depth. Real-time acquisition of the multi-dimensional real-time analysis data to identify abrupt changes in lithological characteristics; Using the lithological abrupt change points as formation interface markers, the theoretical late well depth is corrected to obtain the corrected real-time repositioning depth corresponding to the actual formation depth.

[0035] In this embodiment, during drilling, after cuttings are generated at the bottom of the well, they need a certain amount of time to circulate back to the surface outlet with the drilling fluid. This time is called the delay time. Theoretical delay time. The calculation is based on drilling engineering parameters, mainly including the current well depth. Drilling fluid displacement , wellbore diameter and drill pipe outer diameter The basic calculation formula is:

[0036] In actual drilling operations, the cuttings carrying efficiency of the drilling fluid is affected by factors such as its rheological properties, the particle size and density of the cuttings, and wellbore conditions, leading to discrepancies between theoretical calculations and actual arrival times. Therefore, this embodiment introduces a dynamic correction factor. This factor is dynamically output based on real-time monitored drilling fluid performance parameters and wellbore trajectory data, through a pre-trained artificial neural network model:

[0037] Among them, the correction factor The value typically ranges from 0.8 to 1.5, determined by calibration using historical field data. For example, when drilling fluid viscosity increases, the cuttings carrying capacity improves, and the cuttings return velocity accelerates. The value is less than 1; conversely, when the wellbore enlarges, the annular volume increases. The value is greater than 1.

[0038] Theory is late and well is deep Refers to the current moment The drill bit depth corresponding to the rock cuttings returned to the surface. The method for determining this is as follows:

[0039] That is, the current rock cuttings are being returned. Since the drilling occurred before the bottom of the well, its depth should be traced back to the drill bit position at that moment. In practical applications, drilling depth is recorded continuously, and the system obtains the depth through depth-time curve interpolation. The precise depth value at any given time. This theoretically delayed well depth, as the initial positioning result, is output and temporarily stored in real time, awaiting subsequent correction. Due to the influence of various factors, the theoretically delayed well depth often deviates from the actual formation depth. To eliminate this deviation, it is necessary to utilize the characteristics of the formation itself as correction markers. This embodiment identifies abrupt changes in lithological characteristics by acquiring multi-dimensional real-time analysis data, serving as potential formation interface markers.

[0040] Pre-processed rock cuttings samples were simultaneously acquired using a rotary multi-station analysis device, generating three types of data: high-resolution image data including color histograms, texture features, and grain morphology parameters; elemental spectral data including normalized contents of major elements and ratios of characteristic elements; and mineral microstructure data including semi-quantitative contents of major minerals and characteristic mineral assemblages. These data, on a sample-by-sample basis, formed a multi-dimensional feature vector. ,in For sample serial number, Let be the feature dimension.

[0041] In this embodiment, to achieve objective and accurate identification of mutation points, a sliding window analysis of variance method combined with multi-dimensional data fusion technology is used. The specific steps are as follows: A length of... The sliding window, with the current sample Centered on, take the front and back sides Each sample constitutes an analysis window. Each feature within the window... Calculate its mean and standard deviation Defines the amount of change in this feature for the current sample. for:

[0042] in The constant is small to avoid division by zero. Since changes in a single feature may be caused by noise, this embodiment uses a weighted fusion method to calculate the comprehensive mutation index. :

[0043] Among them, weight The sensitivity of each feature to lithological variations is reflected through principal component analysis or expert experience. For example, the Ca content varies significantly at the interface between carbonate and clastic rocks, and is therefore assigned a higher weight; image brightness may be affected by illumination, and is therefore assigned a lower weight. A threshold for abrupt changes is set. .when When a sample is identified as a candidate mutation site, it is determined that the current sample is a candidate mutation site. Meanwhile, to prevent misjudgments caused by consecutive mutations, it is stipulated that there must be at least a time interval between adjacent mutation sites. Each sample is examined. For candidate abrupt changes, the direction of change in each dimension of the data is further checked to see if it conforms to geological patterns. For example, if the elemental analysis shows a sudden increase in Ca, but calcite is not detected in the minerals, there may be a misjudgment, and the confidence level should be lowered or the sample removed. Through the above algorithm, the system can output the location and confidence level of lithological feature abrupt changes in real time. The identified high-confidence abrupt change points are considered to reflect the true stratigraphic interfaces and can be used as anchor points for depth correction. For time... The system records the corresponding theoretical late well depth for each identified mutation point. Simultaneously, based on prior geological knowledge or regional stratigraphic correlation, the system can determine the actual stratigraphic depth represented by this abrupt change point. For example, a set of "Wangjiagang limestone" is prevalent in a certain area, with its top boundary depth of 2000m in the adjacent well. When the analysis data of this well identifies a combination of features such as a sudden increase in Ca element, the appearance of calcite, and the gray-white image, it can be determined that the point of change is the top boundary of Wangjiagang limestone, and its true depth should be 2000m.

[0044] The theoretical delayed well depth is corrected based on one or more abrupt change points. This embodiment supports multiple correction modes: when there is only one reliable abrupt change point, a global translation correction is used. The depth deviation is calculated. Then the corrected depth of all samples is When multiple abrupt change points exist, piecewise linear interpolation is used for correction. Assume there are... There are 1 mutation point, and the corresponding theoretical depth is 1. The actual depth is Then the interval between adjacent mutation points Within, establish a linear mapping relationship:

[0045] For the interval before the first mutation point, extrapolation or translation is used; for the interval after the last mutation point, the slope of the last interval is used for extrapolation. This method can correct for time-varying systematic errors, such as gradually changing lateness. When the confidence level of the mutation point... When different depths are found, a weighted fusion method can be used. The theoretical late well depth is used as the predicted value, and the actual depth corresponding to the abrupt change point is used as the observed value. The observation noise covariance is set according to the confidence level, and the depth estimate is dynamically updated.

[0046] In some embodiments of this application, identifying abrupt changes in lithological characteristics based on the real-time acquisition of the multi-dimensional real-time analysis data includes: When a single lithological feature abrupt change point is identified within a continuous depth range, the abrupt change point is used as a formation interface marker to perform single-point correction on the theoretical late well depth; When multiple lithological feature abrupt changes are identified within a continuous depth range, a piecewise linear correction method is used to divide the continuous depth range into several sub-ranges based on the distribution location and feature significance of each abrupt change point. A linear mapping relationship between depth and time is established in each sub-range to comprehensively correct the theoretical late well depth. The theoretical late arrival depth is the theoretical depth at which cuttings return from the bottom of the well to the drilling fluid outlet, calculated based on drilling engineering parameters.

[0047] In this embodiment, before performing depth correction, the confidence level of each identified lithological feature abrupt change point is first assessed. The confidence level is determined by a combination of factors, including the magnitude of the abrupt change, consistency of multi-dimensional data, and geological rationality, and is normalized to the 0-1 range. Abrupt change points with a confidence level higher than 0.8 are used as control points, those with a confidence level lower than 0.4 are considered unreliable points and do not participate in the correction, and those in between are used as reference points.

[0048] When only one high-confidence lithological feature abrupt change point is identified within a continuous depth range, the system employs a single-point correction mode. In this mode, the deviation between the theoretical late-arrival depth of the abrupt change point and the actual formation depth is calculated. This deviation is then added to the theoretical late-arrival depths of all samples within the entire range to achieve overall translational correction. When multiple high-confidence lithological feature abrupt changes are identified within a continuous depth range, the system employs a piecewise linear correction mode. In this case, the entire depth range is divided into several sub-ranges, using the theoretical late-arrival depth of each abrupt change point as the boundary. Within each sub-range, a linear mapping relationship is established using the theoretical depths and actual depths of the two abrupt change points at both ends.

[0049] For cases where the confidence levels of abrupt change points are not entirely the same, the system employs a weighted correction strategy. Only the correction curve is forced to pass through the high-confidence master control point, while the reference point with medium confidence is used as an attraction point when constructing the linear mapping. This ensures that the correction curve passes through the master control point while approaching the reference point as closely as possible, thereby improving the robustness of the correction. After correction, each rock cutting sample obtains its corresponding real-time corrected depth, which is stored along with the original theoretical depth, the information on the abrupt change points used, and the correction parameters. The system continuously monitors newly identified abrupt change points and, when necessary, verifies or re-optimizes the correction results for existing intervals to ensure that the accuracy of depth repositioning continuously improves with data accumulation.

[0050] In some embodiments of this application, the lithological abrupt change point is used as a formation interface marker to correct the theoretical late well depth, obtaining a corrected real-time repositioning depth corresponding to the actual formation depth, including: Calculate the confidence level of each lithological feature abrupt change point, wherein the confidence level is determined by at least the range of elemental content change, the rate of mineral component transformation, and the degree of difference in image texture; High correction weights are assigned to mutation points with confidence levels higher than the first threshold, which serve as the master control points for deep correction. Mutation points with confidence levels below the second threshold are assigned low correction weights and are used only as correction references; Based on the confidence level and weight of each mutation point, the theoretical late well depth is weighted and corrected to obtain the corrected real-time return depth.

[0051] In this embodiment, the confidence score calculation includes three quantitative assessments: the magnitude of elemental content change, which calculates the rate of change in the content of major rock-forming elements such as silicon, calcium, aluminum, and iron at the abrupt change point; a larger magnitude of change indicates a more drastic lithological transformation; the rate of mineral composition transformation, which analyzes the rate of change in mineral types and contents before and after the abrupt change point, and evaluates it through mineral content gradients or mineral assemblage change indices; and the degree of image texture difference, which uses high-resolution image data to extract texture features and calculates the distance between feature vectors; a larger image difference indicates a more significant change in rock structure. The above three dimensions of indicators are normalized and then weighted and fused to obtain a comprehensive confidence score. The weighting coefficients can be determined based on the characteristic sensitivity of different strata through expert experience or machine learning methods.

[0052] Based on the calculated confidence levels, this embodiment classifies lithological feature abrupt change points into three levels and assigns different correction weights. Abrupt change points with confidence levels above the first threshold are designated as control points. These points exhibit consistent and significant multi-dimensional characteristics, serving as reliable bases for depth correction. They are assigned high correction weights and act as mandatory constraint points, meaning the corrected depth must pass through the actual formation depths corresponding to these points. Abrupt change points with confidence levels between the first and second thresholds are designated as reference points. These points have some reference value but are less reliable than control points. They are assigned lower correction weights and act as attraction points, guiding the correction curve towards them but not forcing it to pass through. Abrupt change points with confidence levels below the second threshold are considered unreliable points and do not participate in depth correction to avoid introducing erroneous information.

[0053] In the weighted correction process, this embodiment employs a weighted least squares fitting method to construct the correction function. The goal of this optimization problem is to make the output of the correction function at abrupt change points as close as possible to the true depth, with the fitting error calculated using confidence weights. Points with high confidence have larger fitting error weights, while points with low confidence are allowed some deviation. For the master control points, equality constraints are set to ensure that the correction curve strictly passes through them; for the reference points, the confidence level is directly used as a weighting coefficient to exert an attractive effect in the correction. Depending on the distribution of abrupt change points, the correction function can be selected from forms such as translation correction, piecewise linear correction, or spline interpolation, and the optimal correction parameters are solved within the weighted least squares framework.

[0054] In some embodiments of this application, when dynamically adjusting the sampling parameters of subsequent sampling cycles based on the change characteristics of the corrected real-time repositioning depth, and performing intensified sampling on rock debris sections that meet preset conditions, the method includes: The rate of change of the corrected real-time positioning depth is monitored in real time. When the rate of change exceeds a preset threshold, it is determined that the current drilling has entered a lithological abrupt change section. Based on the magnitude of the rate of change, the sampling frequency and sample size of subsequent sampling periods are dynamically adjusted. The greater the rate of change, the higher the sampling frequency and the larger the sample size. Densified sampling was performed on the rock fragments before and after the lithological abrupt change section to obtain complete rock fragment samples on both sides of the abrupt change interface.

[0055] In this embodiment, during drilling, the system continuously receives the corrected real-time positioning depth output by the depth correction module and associates it with time or sample number to form a depth sequence. To quantitatively characterize the depth variation features, this embodiment introduces a depth change rate index, defined as the rate at which the corrected real-time positioning depth changes with drilling time; its physical meaning is equivalent to the current drilling speed. The depth change rate is calculated by dividing the depth difference of consecutive samples by the time interval and smoothed using a moving average method to eliminate random noise interference and extract a stable trend.

[0056] When the lithology of the formation changes, the drilling speed usually changes accordingly, manifesting as significant fluctuations in the depth change rate. The system monitors the smoothed depth change rate in real time and compares it with a preset threshold. The threshold is set based on the statistical characteristics of historical data: when the depth change rate exceeds the normal fluctuation range, it is determined that the current drilling has entered a lithological abrupt change section. Specifically, a base threshold is set as the upper limit of the normal drilling speed, while a change rate increment threshold is set. When the depth change rate is significantly higher or lower than the normal range, it indicates that the drilling has entered soft or hard formations, respectively, triggering the lithological abrupt change section determination.

[0057] Once the system determines that it has entered a lithological abrupt change zone, it immediately initiates a dynamic adjustment mechanism for sampling parameters. The core principle of the adjustment is that the greater the rate of change, the more drastic the lithological transition, requiring a higher sampling density to capture detailed geological information. The sampling frequency is adjusted by proportionally reducing the conventional sampling interval; the greater the deviation in the rate of change, the smaller the sampling interval. Similarly, the sampling quantity is adjusted by proportionally increasing the conventional sampling quantity; the greater the deviation in the rate of change, the greater the increase in sampling quantity. The adjusted sampling parameters take effect immediately and are applied to subsequent sampling actions until the system determines that it has exited the lithological abrupt change zone.

[0058] To ensure complete capture of rock debris samples on both sides of the abrupt change interface, this embodiment extends the encrypted sampling range to a distance before and after the interface. When the depth change rate is first detected to exceed the threshold, the system automatically backtracks to several samples before the detection point and performs additional analysis on the already collected samples. Once the depth change rate returns to normal for several consecutive samples, encrypted sampling continues for a period of time before gradually resuming normal parameters. Through this forward and backward extension mechanism, encrypted sampling covers a continuous interval from before the abrupt change to after the abrupt change, ensuring that samples from the transition zone on both sides of the interface are completely collected.

[0059] In some embodiments of this application, when binding the acquired rock cuttings samples and multi-dimensional real-time analysis data with the corrected real-time repositioning depth, and optimizing the sampling strategy based on the comparison between the current analysis results and the expected target, the following steps are included: Each acquired cuttings sample and its corresponding multi-dimensional real-time analysis data are bound to the corrected real-time positioning depth to generate a drilling cuttings information database containing the correspondence between samples, data and depth. The multi-dimensional real-time analysis data obtained in the current sampling cycle is compared with the preset expected geological profile to evaluate the representativeness and accuracy of the current sampling strategy and generate evaluation indicators. When the evaluation index is lower than the preset standard, it is determined that the current sampling strategy has a deviation, and the control parameters of the sampling strategy are automatically adjusted. Based on the adjusted control parameters, adjust the sampling actions for subsequent sampling cycles.

[0060] In some embodiments of this application, when the evaluation index is lower than a preset standard, it is determined that the current sampling strategy has a deviation, and the control parameters of the sampling strategy are automatically adjusted, including: When the accuracy of lithology identification is low, adjust the initial sampling frequency and correct the calculation benchmark for theoretical cuttings return time. When the interface capture is not complete enough, adjust the correction weight of depth correction to increase the influence of lithological feature abrupt change points in depth relocation. When the element content matching degree deviation is large, adjust the interpolation method for multi-mutation point correction; When the sample quality compliance rate is low, adjust the trigger threshold for encrypted sampling and lower the threshold for judging lithological abrupt change sections.

[0061] In this embodiment, during drilling, the system strictly binds each acquired cuttings sample and its corresponding multi-dimensional real-time analysis data with the dynamically corrected real-time positioning depth, generating a structured drilling cuttings information database. This database uses depth as the primary key, and each record includes a unique sample identifier, sampling time, corrected depth, high-resolution image data, elemental spectral data, mineral microstructure data, sampling parameters, and associated lithological abrupt change information.

[0062] The system compares and analyzes the multi-dimensional real-time analysis data acquired during the current sampling cycle with a preset expected geological profile. The expected geological profile, derived from adjacent well data or regional geological research, stores standard lithology, typical elemental content ranges, and characteristic mineral assemblages for each stratum, using depth as the coordinate system. The comparative evaluation is conducted from four dimensions: lithology identification accuracy (calculating the matching degree by matching the currently identified lithological sequence with the expected profile point-by-point); interface capture integrity (statistically analyzing the matching between currently identified lithological abrupt change points and expected stratigraphic interfaces); elemental content matching degree (calculating the deviation between measured elemental contents and expected standard ranges); and sample quality compliance rate (statistically calculating the proportion of qualified samples that meet the requirements for subsequent analysis). Quantitative indicators are calculated for each of these four dimensions, and a weighted fusion is used to obtain a comprehensive evaluation index.

[0063] When the comprehensive evaluation index falls below the preset standard, the system determines that the current sampling strategy has a deviation and automatically activates the control parameter adjustment mechanism. Based on different deviation types, the system adopts targeted adjustment strategies. When the lithology identification accuracy is low, it indicates that the current sampling density may be insufficient to capture the true changes in the strata, or that there is a systematic deviation in sample depth repositioning. In this case, the system adjusts the initial sampling frequency, increasing the basic sampling frequency by a certain proportion to increase the number of samples per unit depth; simultaneously, it corrects the calculation benchmark for theoretical cuttings return time, recalibrates the correction factor in the late arrival time calculation model, and introduces the most recently identified reliable abrupt change point as a reference to improve the accuracy of depth repositioning. When the interface capture integrity is insufficient, it indicates that key strata interfaces have not been fully captured or the depth correction mechanism has not functioned effectively. In this case, the system adjusts the correction weight of depth correction to increase the influence of lithological characteristic abrupt change points in depth repositioning. For example, it appropriately lowers the confidence threshold of abrupt change points to allow more potential interfaces to participate in the correction, or it increases the weight coefficient of reference points to enhance their attraction to the correction curve. When the element content matching degree deviation is large, it indicates that the sample representativeness is insufficient or that there is a systematic error at the analysis station. At this point, the system adjusts the interpolation method for multi-mutation point correction. When the existing piecewise linear correction fails to eliminate the bias, it switches to a higher-order interpolation method such as spline interpolation or polynomial fitting, so that the correction curve can more flexibly adapt to the nonlinear changes in depth error. At the same time, it optimizes the sample pretreatment parameters, adjusts the washing time, drying temperature or sieving particle size, and ensures that the sample state is more suitable for elemental analysis.

[0064] When the sample quality compliance rate is low, it indicates that the sampling parameters are not compatible with the current operating conditions or there is a problem with the preprocessing stage. At this time, the system adjusts the trigger threshold for intensified sampling, appropriately lowers the threshold for judging lithological abrupt change sections, and enables the system to start intensified sampling earlier to ensure sufficient sample volume in key well sections; at the same time, it optimizes the sampling volume control, dynamically adjusting the base values ​​of conventional sampling volume and intensified sampling volume according to the current cuttings generation rate.

[0065] After the above adjustments are completed, the system will apply the updated control parameters to the sampling actions in subsequent sampling cycles, and all adjustment records will be stored in the drilling cuttings information database. The system will continuously monitor the changes in the adjusted evaluation indicators. If the indicators gradually improve, the current parameters will be maintained; if no improvement is observed, iterative optimization will continue until the sampling strategy achieves the expected goals.

[0066] In some embodiments of this application, the dynamic real-time sampling control system for cuttings based on drilling speed regulation includes: The sampling control module is used to respond to drilling speed control commands and dynamically adjust the sampling strategy according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet. The preprocessing and multi-station analysis module is used to preprocess the fresh rock cuttings sample and simultaneously collect multi-dimensional real-time analysis data of the rock cuttings through multi-station collaborative analysis. The depth correction module is used to dynamically correct the depth of cuttings repositioning based on the drilling engineering parameters and the multi-dimensional real-time analysis data, so as to obtain the corrected real-time repositioning depth. The dynamic adjustment module is used to dynamically adjust the sampling parameters of subsequent sampling cycles based on the change characteristics of the corrected real-time repositioning depth, and to perform intensive sampling on rock debris sections that meet preset conditions. The binding optimization module is used to bind the acquired rock cuttings samples and multi-dimensional real-time analysis data with the corrected real-time repositioning depth, and to optimize the sampling strategy based on the comparison between the current analysis results and the expected target.

[0067] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for dynamic real-time sampling control of cuttings based on drilling speed regulation, characterized in that, include: In response to drilling speed control commands, the sampling strategy is dynamically adjusted according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet; The fresh rock cuttings samples were pretreated, and multi-dimensional real-time analysis data of the rock cuttings were collected synchronously through multi-station collaborative analysis. Based on the drilling engineering parameters and the multi-dimensional real-time analysis data, the depth of cuttings repositioning is dynamically corrected to obtain the corrected real-time repositioning depth. Based on the changes in the corrected real-time repositioning depth, the sampling parameters of subsequent sampling cycles are dynamically adjusted, and intensive sampling is performed on rock debris sections that meet the preset conditions. The acquired rock cuttings samples and multi-dimensional real-time analysis data are bound to the corrected real-time repositioning depth, and the sampling strategy is optimized based on the comparison between the current analysis results and the expected target.

2. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 1, characterized in that, When responding to drilling speed control commands and dynamically adjusting the sampling strategy based on drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet, this includes: Based on the drilling engineering parameters, the theoretical cuttings return time under the current working conditions is calculated, and the initial sampling frequency and sampling quantity are set based on the theoretical cuttings return time; Fresh cuttings samples were obtained from the drilling fluid outlet based on the initial sampling frequency and sampling volume. The system receives multi-dimensional analysis data of the fresh rock cuttings samples in real time. When a sudden change in lithological characteristics is detected, the sampling frequency and sampling amount of subsequent sampling actions are adjusted to capture key rock cuttings samples before and after the change. The drilling engineering parameters include at least real-time drilling time, current well depth, and drilling fluid discharge rate.

3. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 2, characterized in that, When preprocessing the fresh rock cuttings sample and simultaneously acquiring multi-dimensional real-time analysis data of the rock cuttings through multi-station collaborative analysis, the process includes: The fresh rock fragments sample was sequentially washed, dried and sieved to obtain a pretreated rock fragments sample. The pre-treated rock cuttings samples were divided into coarse-grained samples and fine-grained samples according to their particle size, and then sent to the corresponding stations of the rotary multi-station analysis device. The rotary multi-station analysis device drives the rock cutting sample through the optical imaging station, elemental analysis station and mineral analysis station in sequence, and simultaneously collects high-definition image data, elemental spectral data and mineral microstructure data of the rock cutting, as the multi-dimensional real-time analysis data.

4. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 3, characterized in that, Based on the drilling engineering parameters and the multi-dimensional real-time analysis data, the depth repositioning of cuttings is dynamically corrected to obtain the corrected real-time repositioning depth, including: Based on the drilling engineering parameters, calculate the theoretical delay time for cuttings to return from the bottom of the well to the drilling fluid outlet, and combine this with the current well depth to obtain the theoretical delay depth. Real-time acquisition of the multi-dimensional real-time analysis data to identify abrupt changes in lithological characteristics; Using the lithological abrupt change points as formation interface markers, the theoretical late well depth is corrected to obtain the corrected real-time repositioning depth corresponding to the actual formation depth.

5. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 4, characterized in that, Based on the real-time acquisition of the multi-dimensional real-time analysis data, abrupt changes in lithological characteristics are identified, including: When a single lithological feature abrupt change point is identified within a continuous depth range, the abrupt change point is used as a formation interface marker to perform single-point correction on the theoretical late well depth; When multiple lithological feature abrupt changes are identified within a continuous depth range, a piecewise linear correction method is used to divide the continuous depth range into several sub-ranges based on the distribution location and feature significance of each abrupt change point. A linear mapping relationship between depth and time is established in each sub-range to comprehensively correct the theoretical late well depth. The theoretical late arrival depth is the theoretical depth at which cuttings return from the bottom of the well to the drilling fluid outlet, calculated based on drilling engineering parameters.

6. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 5, characterized in that, Using the aforementioned lithological abrupt change points as formation interface markers, the theoretical late-arrival well depth is corrected to obtain the corrected real-time repositioning depth corresponding to the actual formation depth, including: Calculate the confidence level of each lithological feature abrupt change point, wherein the confidence level is determined by at least the range of elemental content change, the rate of mineral component transformation, and the degree of difference in image texture; High correction weights are assigned to mutation points with confidence levels higher than the first threshold, which serve as the master control points for deep correction. Mutation points with confidence levels below the second threshold are assigned low correction weights and are used only as correction references; Based on the confidence level and weight of each mutation point, the theoretical late well depth is weighted and corrected to obtain the corrected real-time return depth.

7. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 1, characterized in that, Based on the changes in the corrected real-time repositioning depth, the sampling parameters for subsequent sampling cycles are dynamically adjusted, and when performing intensified sampling on rock cuttings sections that meet preset conditions, the following steps are taken: The rate of change of the corrected real-time positioning depth is monitored in real time. When the rate of change exceeds a preset threshold, it is determined that the current drilling has entered a lithological abrupt change section. Based on the magnitude of the rate of change, the sampling frequency and sample size of subsequent sampling periods are dynamically adjusted. The greater the rate of change, the higher the sampling frequency and the larger the sample size. Densified sampling was performed on the rock fragments before and after the lithological abrupt change section to obtain complete rock fragment samples on both sides of the abrupt change interface.

8. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 1, characterized in that, When binding the acquired rock cuttings samples and multi-dimensional real-time analysis data with the corrected real-time repositioning depth, and optimizing the sampling strategy based on the comparison between the current analysis results and the expected target, the following steps are taken: Each acquired cuttings sample and its corresponding multi-dimensional real-time analysis data are bound to the corrected real-time positioning depth to generate a drilling cuttings information database containing the correspondence between samples, data and depth. The multi-dimensional real-time analysis data obtained in the current sampling cycle is compared with the preset expected geological profile to evaluate the representativeness and accuracy of the current sampling strategy and generate evaluation indicators. When the evaluation index is lower than the preset standard, it is determined that the current sampling strategy has a deviation, and the control parameters of the sampling strategy are automatically adjusted. Based on the adjusted control parameters, adjust the sampling actions for subsequent sampling cycles.

9. The method for dynamic real-time sampling control of cuttings based on drilling speed regulation as described in claim 8, characterized in that, When the evaluation index is lower than the preset standard, it is determined that the current sampling strategy has a deviation, and the control parameters of the sampling strategy are automatically adjusted, including: When the accuracy of lithology identification is low, adjust the initial sampling frequency and correct the calculation benchmark for theoretical cuttings return time. When the interface capture is not complete enough, adjust the correction weight of depth correction to increase the influence of lithological feature abrupt change points in depth relocation. When the element content matching degree deviation is large, adjust the interpolation method for multi-mutation point correction; When the sample quality compliance rate is low, adjust the trigger threshold for encrypted sampling and lower the threshold for judging lithological abrupt change sections.

10. A dynamic real-time sampling control system for cuttings based on drilling speed regulation, characterized in that, include: The sampling control module is used to respond to drilling speed control commands and dynamically adjust the sampling strategy according to drilling engineering parameters to obtain fresh cuttings samples from the drilling fluid outlet. The preprocessing and multi-station analysis module is used to preprocess the fresh rock cuttings sample and simultaneously collect multi-dimensional real-time analysis data of the rock cuttings through multi-station collaborative analysis. The depth correction module is used to dynamically correct the depth of cuttings repositioning based on the drilling engineering parameters and the multi-dimensional real-time analysis data, so as to obtain the corrected real-time repositioning depth. The dynamic adjustment module is used to dynamically adjust the sampling parameters of subsequent sampling cycles based on the change characteristics of the corrected real-time repositioning depth, and to perform intensive sampling on rock debris sections that meet preset conditions. The binding optimization module is used to bind the acquired rock cuttings samples and multi-dimensional real-time analysis data with the corrected real-time repositioning depth, and to optimize the sampling strategy based on the comparison between the current analysis results and the expected target.

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