Intelligent geological drilling control system and method based on real-time rock debris analysis

Abstract

The invention relates to the technical field of geological drilling, and discloses an intelligent geological drilling control system and method based on real-time rock debris analysis, and the system comprises a data collection module which is used for obtaining all feature data of screened rock debris and real-time drilling data, a preprocessing module which is used for preprocessing all the obtained feature data, and a data analysis module which is used for analyzing the real-time drilling data. Analysis is carried out according to the preprocessed feature data of the screened rock debris and real-time drilling data, and a drilling strategy is intelligently generated according to an analysis result; the drilling strategy generated by the data analysis module is transmitted to the execution module based on 5G communication; and the execution module is used for executing the drilling strategy generated by the data analysis module. Rock debris multi-dimensional characteristic data in the drilling process and real-time data such as ground stress and fluid activity around a drill hole are collected, interference is eliminated through a preprocessing module, the format is unified, and then a first drilling index and a second drilling index are obtained through a data analysis module.

Description

Technical Field

[0001] This invention relates to the field of geological drilling technology, specifically to an intelligent geological drilling control system and method based on real-time cuttings analysis. Background Technology

[0002] After an earthquake, geological structures are prone to drastic changes, which may trigger secondary disasters such as landslides and ground collapses. At the same time, it will affect subsequent engineering planning such as building repair and road reconstruction. The main purpose of drilling work at this time is to explore the underground geological structure and assess geological stability. This will provide key information for the formulation of secondary disaster prevention and control plans, the selection of reconstruction sites in disaster-stricken areas, and the design of engineering safety protection measures. It is an important foundation for ensuring regional safety and recovery and reconstruction after an earthquake. In current post-earthquake drilling operations, the location of key underground geological structures relies heavily on the approximate scope of previous geological surveys and the experience of technical personnel. This lack of efficient and accurate structural identification and location methods results in drilling work often facing problems such as unclear targets and long operation cycles, leading to relatively low efficiency. Therefore, this invention proposes an intelligent geological drilling control system and method based on real-time cuttings analysis to address the shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent geological drilling control system and method based on real-time cuttings analysis, in order to overcome the shortcomings in the aforementioned background technology.

[0004] The objective of this invention can be achieved through the following technical solutions: The intelligent geological drilling control system based on real-time cuttings analysis includes the following modules: The data acquisition module is used to acquire various characteristic data of the screened rock cuttings and real-time drilling data; The preprocessing module preprocesses the acquired feature data. The data analysis module analyzes the various characteristic data of the pre-processed screened rock cuttings and real-time drilling data, and intelligently generates drilling strategies based on the analysis results. The signal transmission module transmits the drilling strategy generated by the data analysis module to the execution module based on 5G communication. The execution module is used to execute the drilling strategy generated by the data analysis module.

[0005] Preferably, the characteristic data of the screened rock cuttings include the reflectance characteristics under different wavelengths of light, the fluorescence intensity of the rock cuttings, and the color of the rock cuttings; the real-time drilling data includes real-time drilling depth data, borehole surrounding stress change data, and fluid activity data; the data analysis module operates as follows: The collection time period is divided according to the collection timestamp of each data item, and a time series is constructed based on the collection time period, the characteristic data of the screened rock cuttings, and the real-time drilling data. Multiple data curves are constructed based on the data in the time series. Data abrupt change points are obtained based on the constructed multiple data curves. Abnormal fluctuation areas in drilling are obtained based on the data abrupt change points. The obtained abnormal fluctuation areas in drilling are marked as key areas of concern for drilling. Based on the characteristic data of the screened rock cuttings in the key drilling area and the real-time drilling data, the first drilling index and the second drilling index are obtained respectively. Drilling strategies are intelligently generated based on the obtained first and second drilling indices.

[0006] Preferably, the method for obtaining the first drilling index is as follows: The characteristic variation coefficient of the rock cuttings in the key drilling area is obtained from the data curves of various characteristic data of the screened rock cuttings; The geostress anomaly and fluid anomaly values ​​of the key drilling area are obtained from the data curves of the real-time drilling data. The first drilling index is obtained based on the characteristic variation coefficient of rock cuttings, the stress variation amplitude, and the fluid anomaly value.

[0007] Preferably, the method for obtaining the second drilling index is as follows: Based on the geostress variation data in the real-time drilling data, obtain the geostress distribution difference coefficient for each potential drilling direction; Based on the fluid activity data in the real-time drilling data, obtain the fluid activity extension trend coefficient for each potential drilling direction; Based on the preprocessed rock cuttings characteristic data and real-time drilling depth data, the continuity index of rock cuttings characteristic changes in each potential drilling direction is obtained. The second drilling index is calculated based on the difference coefficient of geostress distribution, the activity extension trend coefficient, and the continuity index of cuttings characteristic changes in each potential drilling direction.

[0008] Preferably, the method for obtaining the key drilling focus area is as follows: By using the cuttings characteristic data and real-time drilling data from the data time series, the cuttings characteristic data curve and the real-time drilling data curve are obtained respectively. Based on the magnitude, frequency, and trend of the numerical changes in each data curve, the numerical points in the curve that exceed the preset normal fluctuation range are identified as data mutation points. Based on the real-time drilling depth data corresponding to the data mutation points, the depth range of each mutation point is obtained; Based on the rock cuttings characteristics, geostress changes, and fluid activity data within the depth range of each mutation point, depth areas with multiple types of data synchronization anomalies are identified, and these depth areas are marked as key drilling areas of interest.

[0009] Preferably, the intelligent generation process of the drilling strategy is as follows: Based on historical databases and current geological survey reports of the drilling area, a first drilling index threshold and a second drilling index threshold are generated. The first drilling index threshold includes a high threshold and a low threshold, and the second drilling index threshold includes an excellent threshold and a qualified threshold. When the first drilling index is higher than the high threshold of the first drilling index, the second drilling index is further compared with the optimal threshold of the second drilling index to obtain potential drilling directions where the second drilling index is higher than the optimal threshold of the second drilling index. The obtained potential drilling directions are determined as priority drilling directions. When the first drilling index is between the high threshold and the low threshold of the first drilling index, potential drilling directions with a second drilling index higher than the qualified threshold of the second drilling index are selected. The potential drilling directions with a second drilling index higher than the qualified threshold of the second drilling index are sorted according to the size of the second drilling index. The drilling direction ranked first is selected as the main drilling direction, and the other drilling directions are the backup drilling directions. When the first drilling index is lower than the first drilling index threshold, the second drilling index of each potential drilling direction is analyzed, and the direction with the highest second index and above the qualified threshold is selected as the drilling direction to be adjusted.

[0010] Preferably, the method for obtaining the potential drilling direction is as follows: Based on the geological survey report of the current drilling area, historical fault location records, and regional geological structure map, the preliminary directional range that can be extended is determined in conjunction with the current real-time borehole depth. Based on the stress variation data around the borehole in the real-time drilling data, the stress imbalance area and gradient change are analyzed, the preliminary direction range is optimized, and the candidate drilling direction is obtained. Based on real-time fluid activity data for each candidate drilling direction, select candidate drilling directions whose fluid activity extension trend conforms to fault characteristics. Based on the preprocessed cuttings characteristic data and real-time drilling depth data, the possibility of the extension of cuttings characteristics in the selected drilling direction is analyzed. The continuous index of cuttings characteristic changes is used to determine the potential drilling direction that meets the requirements.

[0011] The intelligent geological drilling control method based on real-time cuttings analysis, applicable to the aforementioned intelligent geological drilling control system based on real-time cuttings analysis, includes the following steps: Step 1: Acquire real-time drilling data and various characteristic data of the transported and screened rock cuttings through multiple types of sensors during the drilling process; Step 2: Preprocess the acquired real-time drilling data and various characteristic data of the cuttings; Step 3: Analyze the various characteristic data of the pre-processed screened rock cuttings and the real-time drilling data, and intelligently generate drilling strategies based on the analysis results. Step 4: Execute the generated drilling strategy.

[0012] The beneficial effects of this invention are: 1. This invention collects multi-dimensional characteristic data of rock cuttings and real-time data such as geostress and fluid activity around the borehole during the drilling process. After the preprocessing module eliminates interference and unifies the format, the data analysis module obtains a first drilling index and a second drilling index. The first drilling index is used to quantify the relative proximity of the area to the fault zone, and the second drilling index is used to screen potential drilling directions with higher efficiency in locating the fault zone. Finally, the index analysis results are combined to intelligently generate a drilling strategy adapted to the current geological conditions, realizing the accurate location of the fault zone after the earthquake and the scientific selection of the drilling direction, effectively avoiding the waste of resources and the reduction of earthquake service efficiency caused by blind drilling.

[0013] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a block diagram of the intelligent geological drilling control system based on real-time cuttings analysis of the present invention.

[0016] Figure 2 This is a flowchart illustrating the steps of the intelligent geological drilling control method based on real-time cuttings analysis of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, this invention is an intelligent geological drilling control system based on real-time rock cuttings analysis. The system mainly includes a data acquisition module, a preprocessing module, a data analysis module, a signal transmission module, and an execution module. After an earthquake, mountains often become loose and prone to landslides, collapses, or debris flows. Therefore, reinforcement works such as installing anchor cables or grouting are needed in the epicenter area, requiring drilling to locate fault zones. The system's data acquisition module is mainly used to acquire real-time drilling data and various characteristic data of broken rock cuttings during the drilling process. During drilling operations, a laser measuring instrument is used to acquire the reflection characteristics of collected, sieved rock cuttings under different wavelengths of light. Different minerals, such as quartz, feldspar, and clay, have different light absorption and reflection characteristics. This method allows for rapid and non-destructive identification of mineral composition in the rock cuttings. The data collection process includes analyzing the composition and elemental content of rock cuttings, as well as their fluorescence intensity changes and color characteristics. Rock colors such as red, black, and gray often represent different sedimentary environments, such as oxidizing or reducing environments. Through optical measurements, colors can be converted into specific RGB values, thus objectively reconstructing the history of the underground geological environment and ensuring comprehensive acquisition of rock cutting information that reflects the properties of the strata. Simultaneously, multiple sensors installed on the drilling equipment dynamically track and record depth changes during the drilling process. Furthermore, sensors deployed around the borehole continuously capture the dynamic fluctuations of geostress around the borehole and the activity of fluids inside the borehole, achieving comprehensive coverage of key dynamic data during the drilling process. All data are collected at a consistent cycle, such as three times per minute or once every twenty seconds.

[0019] The preprocessing module is used to process the acquired rock cuttings feature data and real-time drilling data, including dimensionless removal and format unification. For data such as the reflection characteristics, fluorescence intensity, and color of rock cuttings, which are easily affected by external environmental interference, noise and dimensionless removal are mainly performed. For depth, geostress changes, and fluid activity data in real-time drilling data, smoothing and format unification are performed to ensure that various types of data maintain consistency and coherence in the time and attribute dimensions. The preprocessing module adopts mature existing technologies, which will not be described in detail here.

[0020] The data analysis module is mainly used to analyze the various characteristic data of the pre-processed rock cuttings and real-time drilling data. Based on the analysis results, it intelligently generates drilling strategies. The rock cuttings' characteristic data includes reflection characteristics under different wavelengths of light, fluorescence intensity, and color. The real-time drilling data includes real-time drilling depth data, stress change data around the borehole, and fluid activity data. After pre-processing the data, the acquisition time periods are divided according to the acquisition timestamps of each data point. For example, if the first acquisition is real-time drilling depth data and the last acquisition is rock cutting color data, then the interval between acquiring real-time drilling depth data and acquiring rock cutting color data is the acquisition time period. After dividing the acquisition time periods, a time series is constructed based on the order of the acquisition time periods, the selected rock cuttings' characteristic data, and the real-time drilling data. By constructing the time series, the originally scattered rock cuttings information, depth information, stress information, and fluid information can be linked and integrated along the time dimension, avoiding data confusion and ensuring that each set of data can accurately correspond to a specific drilling stage, thereby accurately reflecting the geological conditions of that stage, especially for the dynamic changes that may occur in the geological environment after an earthquake.

[0021] Multiple data curves are constructed based on various data in the time series, including cuttings characteristic data and real-time drilling data. Cuttings characteristic data curves and real-time drilling data curves are obtained separately. A single data point can only reflect isolated information at a certain moment or depth. However, after plotting the data collected at different stages into data curves, the changing trends of various indicators with time or depth become visualized. For example, the increasing or decreasing trend of cuttings fluorescence intensity with drilling depth, the fluctuation of ground stress over time, etc., can be effectively identified based on the plotted data curves, which can effectively identify the normal range of data changes and the direction of abnormal fluctuations. The magnitude, frequency, and trend of the numerical changes of each data curve can be determined. Values ​​exceeding the preset normal fluctuation range in the curve are identified as data abrupt change points. Significant geological differences exist between the fault zone and surrounding normal strata after an earthquake. These differences directly lead to non-gradual abrupt changes in related data. For example, the color of rock debris changes gradually in normal strata, but may suddenly darken near the fault zone. Geostress fluctuates steadily in normal strata, but may experience sudden increases or decreases when encountering a fault zone. Fluid activity suddenly becomes more active near the fault zone. These data abrupt change points can be clearly seen in the plotted data curve. Based on these data abrupt change points, the preliminary location of a potential fault zone can be accurately determined. By analyzing rock cuttings characteristics, geostress changes, and fluid activity data within a specific depth range, we can identify depth areas with multiple synchronous anomalies. Individual data abrupt changes may be caused by accidental factors, such as temporary anomalies in geostress data due to brief equipment vibration or sudden changes in fluorescence intensity caused by a small amount of special rock cuttings. Such single anomalies do not necessarily indicate the existence of a fault zone. However, when the abrupt changes of multiple data curves converge at a certain depth or drilling stage, that is, when multiple types of data such as rock cuttings characteristics, geostress, and fluid activity show synchronous anomalies in this area, it indicates that the geological changes in this area are relatively uniform and more consistent with the overall geological disturbance characteristics caused by a fault zone. Such areas are marked as key areas of concern.

[0022] After identifying the key areas of interest, the first drilling index and the second drilling index are obtained based on the characteristic data of the rock cuttings screened out from the key areas of interest and the real-time drilling data.

[0023] The Drilling First Index represents the degree of anomalies in cuttings characteristics, geostress anomalies, and fluid activity anomalies within the drilling focus area. It is used to determine the relative proximity of the drilling area to the fault zone. The method for obtaining the index is as follows: The rock cuttings characteristic variation coefficient is obtained from the data curves of various characteristic data of the screened rock cuttings in the drilling focus area. The rock cuttings characteristic variation coefficient refers to the degree of difference between the reflection characteristics, fluorescence intensity, and color of the rock cuttings in the drilling focus area and the characteristics of the normal formation rock cuttings in the same area. The rock cuttings reflection signal, fluorescence detection results, and color observation information in the focus area are compared with the baseline of the normal formation rock cuttings characteristics one by one to determine the degree of deviation of the rock cuttings reflection signal, fluorescence detection results, and color observation information in the focus area from the baseline. The geostress anomaly and fluid anomaly values ​​of the key drilling area are obtained from the data curves of the real-time drilling data. The geostress anomaly value is the difference in the fluctuation of the geostress state in the key area compared with the geostress of the surrounding normal strata. The geostress anomaly value is obtained by calculating the difference between the geostress data and the benchmark value of the geostress data of the normal strata in this depth range. The fluid anomaly value is the degree of deviation of the flow rate, pressure, and composition of the fluid in the key area from the fluid state of the normal strata. The fluid activity data is obtained by comparing the acquired fluid activity data with the baseline of the normal strata fluid activity data. The first drilling index is obtained based on the characteristic anomaly coefficient of cuttings, the stress variation amplitude, and the fluid anomaly value, using the formula; in, The total variation coefficient of cuttings characteristics in the key drilling area is the focus. The characteristic variation coefficient of rock debris reflection, The coefficient of variation of fluorescence intensity in rock cuttings, The coefficient for color variation in rock fragments. To determine the geostress anomaly values ​​in the key drilling areas, Fluid anomalies in key drilling areas; pass By comprehensively considering variations in rock cutting reflectance, fluorescence, and color, we can avoid focusing solely on a single rock cutting indicator while ignoring other key characteristics. It reflects the relationship and balance between rock cuttings, stress, and fluid; the denominator To avoid calculation failure when the data on geostress changes or fluid activity is zero, the weight of the rock cutting characteristic anomaly coefficient is adjusted by geostress anomalies and fluid anomalies. This prevents the numerical value from being artificially inflated due to a single anomaly in the rock cutting characteristic anomaly coefficient. Furthermore, an exponential function amplifies the synergistic anomaly effect of geostress anomalies, fluid anomalies, and rock cutting characteristic anomaly coefficients. The more significant the total rock cutting anomaly and the more synchronously the geostress and fluid anomalies change, the more prominent the exponential term becomes, better reflecting the pattern of systemic geological disturbances caused by fault zones. (Logarithmic function...) It is used to smooth fluctuations in anomalies in ground stress and fluid, and to prevent the exponential from getting out of control due to a sudden increase in ground stress or a sudden increase in fluid activity.

[0024] For example, in a certain drilling operation, known parameters include the coefficient of variation of rock cuttings reflection characteristics. The coefficient of variation of fluorescence intensity in rock cuttings Color variation coefficient of rock fragments Variation in geostress Fluid activity .

[0025] but, The second drilling index represents the comprehensive fit between the differences in geostress distribution, the extension trend of fluid activity, and the continuity of cuttings characteristics in potential drilling directions. It is used to screen drilling directions with higher efficiency in locating fault zones. The method for obtaining it is as follows: Based on the ground stress change data in the real-time drilling data, the ground stress distribution difference coefficient for each potential drilling direction is obtained. The ground stress distribution difference coefficient is the difference between the ground stress data in other directions and the ground stress data monitored in real time in the current borehole. Based on the fluid activity data in the real-time drilling data, the fluid activity extension trend coefficient for each potential drilling direction is obtained. The fluid activity extension trend coefficient is the stability and extension capacity of fluid migration in the potential drilling direction. Based on the regional geological survey report and historical drilling data, the extension characteristics of the fault zone fluid are set as follows: the flow rate fluctuates stably by no more than ±3 units per drilling stage, the pressure gradually decreases by no more than ±2 units per drilling stage, and the content of the marker substance fluctuates stably by no more than ±0.3 units per drilling stage.

[0026] For example, in a potential drilling direction, fluid data is continuously monitored in five consecutive drilling stages: Stage 1: flow rate 25, pressure 47, marker substance content 0.32; Stage 2: flow rate 26, pressure 46, marker substance content 0.33; Stage 3: flow rate 25, pressure 45, marker substance content 0.32; Stage 4: flow rate 27, pressure 44, marker substance content 0.34; Stage 5: flow rate 26, pressure 43, marker substance content 0.33.

[0027] Comparing the typical characteristics of the fault zone fluid, it can be seen that the flow rate, pressure, and marker content of the five stages in this direction are all within the set extension characteristics of the fault zone fluid. The pressure fluctuation conforms to the gradual decay law, and the fluctuation of the marker content does not have any sudden change. They all completely match the extension characteristics of the fault zone fluid. According to the set weight coefficients: the flow rate stability weight is 0.3, the pressure gradual decay weight is 0.3, and the marker content stability ratio is 0.4. The full score of the matching degree of each dimension is 1 point. Then the total matching degree of this direction = 1×0.3+1×0.3+1×0.4. After substituting the values, it is the full score of 1 point. The basic coefficient = total matching degree × coefficient full score (5), that is, 1×5=5. Finally, the fluid activity extension trend coefficient of this potential drilling direction is calculated to be 5. If the flow rate, pressure, and marker content of the five stages in a direction are not within the set extension characteristics of the fault zone fluid, the matching degree will be deducted according to the specific values ​​that exceed or fall below the set extension characteristics. For example, if the flow rate in a certain stage in that direction fluctuates from 13 to 17, with a fluctuation range of 4 units, exceeding the set value by 1 unit, the matching degree will be deducted by 0.1, that is, the matching degree of this flow rate is 0.8. If the next stage fluctuates from 17 to 12, with a fluctuation range of 5 units, exceeding the set value by 2 units, then a further deduction of 0.2 will be made, that is, the matching degree of this flow rate is 0.7.

[0028] Based on the preprocessed rock cuttings characteristic data and real-time drilling depth data, the continuity index of rock cuttings characteristic changes in each potential drilling direction is obtained. The continuity index is the matching continuity of rock cuttings' reflectance characteristics, fluorescence intensity, color and other attributes with typical rock cuttings characteristics of the fault zone in the potential drilling direction. The preprocessed rock cuttings characteristic data of the potential drilling direction is compared with the typical rock cuttings characteristic database of the fault zone established through historical fault location cases and regional geological data to analyze the matching consistency between rock cuttings characteristics and typical characteristics, and thus obtain the continuity index. Based on historical fault cases and regional geological data, a baseline database of typical rock cutting characteristics for fault zones was established. The baseline range for reflectance spectral peak values ​​is 550–580 nm, fluorescence intensity is 80–90 nm, and color grayscale value is 30–40 nm. Lower grayscale values ​​represent deeper colors. The index range is set from 0 to 3, with higher values ​​indicating stronger continuity. The calculation uses a weighted average with reflectance characteristics weighted at 0.35, fluorescence intensity at 0.35, and color at 0.3. For a potential drilling direction, four sets of rock cutting samples were continuously collected and preprocessed. The data showed that the first set had a reflectance spectral peak of 560 nm, fluorescence intensity of 85 nm, and color grayscale value of 32 nm; the second set had a reflectance spectral peak of 570 nm, fluorescence intensity of 83 nm, and color grayscale value of 34 nm; the third set had a reflectance spectral peak of 565 nm, fluorescence intensity of 88 nm, and color grayscale value of 31 nm; and the fourth set had a reflectance spectral peak of 575 nm, fluorescence intensity of 86 nm, and color grayscale value of 33 nm. All data from the four sample groups fell within the baseline range of typical rock debris characteristics of the fault zone, with no deviations, achieving 100% consistency. After calculating the scores for each dimension according to weights, the final calculated continuity index of rock debris characteristics in this direction was 3, indicating a level that fully conforms to the continuity of rock debris characteristics in the fault zone. In another potential direction, if three of the four sample groups have reflectance spectral peaks within the baseline range (score 7.5), three have fluorescence intensities within the baseline range (score 7.5), and two have color grayscale values ​​within the baseline range (score 5), the weighted index would be 2.025, indicating a moderate level of continuity.

[0029] The second drilling index is calculated based on the difference coefficient of geostress distribution, the activity extension trend coefficient, and the continuity index of cuttings characteristic changes in each potential drilling direction. Potential drilling directions refer to the directions that may need to be drilled during the drilling process. Based on the geological survey report of the current drilling area, historical fault location records, and regional geological structure map, information such as the distribution range of the fault zone, the strike and dip angle of the strata are inferred. Based on the real-time depth of the current borehole, it is determined in which directions the fault zone may extend, thereby determining the preliminary direction range. Based on the stress change data around the borehole in the real-time drilling data, the uneven areas and gradient changes of the stress are analyzed, and the obtained preliminary direction range is optimized: since the fault zone usually extends along areas with weak stress or significant stress gradient changes, those directions with uniform stress distribution and no obvious gradient changes are excluded, and only the candidate drilling directions whose stress characteristics match the signs of fault extension are retained. Based on real-time fluid activity data for each candidate drilling direction, further screening of the candidate drilling directions is conducted. Since fault zones are often the main channels for underground fluid migration, their fluid activity will show a stable and regular extension trend. If the fluid activity of a candidate direction is chaotic or lacks obvious extension, it indicates that the direction is likely not distributed along the fault zone and will be eliminated. Finally, based on the pre-processed rock cuttings characteristic data and real-time drilling depth data, the extension possibility of rock cuttings characteristics in the candidate directions is analyzed: the reflection characteristics, fluorescence intensity, and color of rock cuttings directly reflect the material properties of the strata. If the difference between the rock cuttings characteristics of a candidate direction and the rock cuttings characteristics of typical strata in the fault zone gradually increases, it indicates that the direction may deviate from the fault zone; otherwise, it has continuity. Based on the continuity index of rock cuttings characteristic changes, directions that meet the continuity requirements are finally determined as potential drilling directions.

[0030] The second drilling index can be obtained using the following formula: in, The coefficient representing the difference in geostress distribution along the potential drilling direction. This represents the coefficient of fluid activity extension trend in the potential drilling direction. The total continuity index of cuttings characteristics in potential drilling directions. For the continuity index of rock debris reflection characteristics, For the continuity index of rock cutting fluorescence intensity, The color continuity index of rock fragments; pass By integrating detailed continuity indicators of rock cutting reflectance, fluorescence, and color, and avoiding the omission of key matching signals from a single rock cutting dimension, a fractional method is used. ,molecular The core geological advantages used in quantitative analysis, denominator The exponential function is used to ensure that the properties of the cuttings do not deviate from the matching. duped Increase and When the exponential term increases, the logarithmic function also increases significantly. Used for smoothing To prevent index anomalies caused by sudden increases in the coefficient of difference in ground stress distribution or the coefficient of extension trend of fluid activity; For example, during a drilling operation, known parameters include the stress distribution difference coefficient along the potential drilling direction. Fluid activity extension trend coefficient in potential drilling direction Continuity index of rock debris reflection characteristics The rock cutting reflection characteristics in this direction show a high degree of matching with the typical rock cutting reflection characteristics of the fault zone, good continuity, and a high continuity index for rock cutting fluorescence intensity. The continuity between the fluorescence response of rock fragments and the fluid contamination characteristics of the fault zone is prominent in this direction, with no obvious abrupt changes, and the color continuity index of the rock fragments is high. The rock fragments in this direction retain the dark brown color characteristic of the area near the fault zone, showing a strong continuity of difference from normal strata.

[0031] but, ; Subsequently, a drilling strategy is intelligently generated based on the obtained first drilling index and second drilling index.

[0032] Based on historical databases and current geological survey reports of the drilling area, drilling first index thresholds and drilling second index thresholds are generated. Drilling first index thresholds include high thresholds and low thresholds, and drilling second index thresholds include excellent thresholds and qualified thresholds. When the first drilling index is higher than the high threshold of the first drilling index, the second drilling index is further compared with the optimal threshold of the second drilling index to obtain potential drilling directions where the second drilling index is higher than the optimal threshold of the second drilling index. The obtained potential drilling directions are determined as priority drilling directions. When the first drilling index is between the high threshold and the low threshold of the first drilling index, potential drilling directions with a second drilling index higher than the qualified threshold of the second drilling index are selected. The potential drilling directions with a second drilling index higher than the qualified threshold of the second drilling index are sorted according to the size of the second drilling index. The drilling direction ranked first is selected as the main drilling direction, and the other drilling directions are the backup drilling directions. When the first drilling index is lower than the first drilling index threshold, the second drilling index of each potential drilling direction is analyzed, and the direction with the highest second index and above the qualified threshold is selected as the drilling direction to be adjusted.

[0033] The signal transmission module is used to transmit the drilling strategy generated by the data analysis module to the execution module via 5G communication signals, and the execution module executes the generated drilling strategy.

[0034] Please see Figure 2 The intelligent geological drilling control method based on real-time cuttings analysis is applicable to the aforementioned intelligent geological drilling control system based on real-time cuttings analysis, and includes the following steps: Step 1: Acquire real-time drilling data and various characteristic data of the transported and screened rock cuttings through multiple types of sensors during the drilling process; Step 2: Preprocess the acquired real-time drilling data and various characteristic data of the cuttings; Step 3: Analyze the various characteristic data of the pre-processed screened rock cuttings and the real-time drilling data, and intelligently generate drilling strategies based on the analysis results. Step 4: Execute the generated drilling strategy.

[0035] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. An intelligent geological drilling control system based on real-time cuttings analysis, characterized in that, Includes the following modules: The data acquisition module is used to acquire various characteristic data of the screened rock cuttings and real-time drilling data; The preprocessing module preprocesses the acquired feature data. The data analysis module analyzes the various characteristic data of the pre-processed screened rock cuttings and real-time drilling data, and intelligently generates drilling strategies based on the analysis results. The signal transmission module transmits the drilling strategy generated by the data analysis module to the execution module based on 5G communication. The execution module is used to execute the drilling strategy generated by the data analysis module.

2. The intelligent geological drilling control system based on real-time cuttings analysis according to claim 1, characterized in that, The characteristic data of the screened rock cuttings include the reflectance characteristics under different wavelengths of light, the fluorescence intensity of the rock cuttings, and the color of the rock cuttings. The real-time drilling data includes real-time drilling depth data, borehole stress change data, and fluid activity data. The data analysis module operates as follows: The collection time period is divided according to the collection timestamp of each data item, and a time series is constructed based on the collection time period, the characteristic data of the screened rock cuttings, and the real-time drilling data. Multiple data curves are constructed based on the data in the time series. Data abrupt change points are obtained based on the constructed multiple data curves. Abnormal fluctuation areas in drilling are obtained based on the data abrupt change points. The obtained abnormal fluctuation areas in drilling are marked as key areas of concern for drilling. Based on the characteristic data of the screened rock cuttings in the key drilling area and the real-time drilling data, the first drilling index and the second drilling index are obtained respectively. Drilling strategies are intelligently generated based on the obtained first and second drilling indices.

3. The intelligent geological drilling control system based on real-time cuttings analysis according to claim 2, characterized in that, The method for obtaining the first drilling index is as follows: The characteristic variation coefficient of the rock cuttings in the key drilling area is obtained from the data curves of various characteristic data of the screened rock cuttings; The geostress anomaly and fluid anomaly values ​​of the key drilling area are obtained from the data curves of the real-time drilling data. The first drilling index is obtained based on the characteristic variation coefficient of rock cuttings, the stress variation amplitude, and the fluid anomaly value.

4. The intelligent geological drilling control system based on real-time cuttings analysis according to claim 2, characterized in that, The method for obtaining the second drilling index is as follows: Based on the geostress variation data in the real-time drilling data, obtain the geostress distribution difference coefficient for each potential drilling direction; Based on the fluid activity data in the real-time drilling data, obtain the fluid activity extension trend coefficient for each potential drilling direction; Based on the preprocessed rock cuttings characteristic data and real-time drilling depth data, the continuity index of rock cuttings characteristic changes in each potential drilling direction is obtained. The second drilling index is calculated based on the difference coefficient of geostress distribution, the activity extension trend coefficient, and the continuity index of cuttings characteristic changes in each potential drilling direction.

5. The intelligent geological drilling control system based on real-time cuttings analysis according to claim 2, characterized in that, The method for identifying the key drilling areas is as follows: By using the cuttings characteristic data and real-time drilling data from the data time series, the cuttings characteristic data curve and the real-time drilling data curve are obtained respectively. Based on the magnitude, frequency, and trend of the numerical changes in each data curve, the numerical points in the curve that exceed the preset normal fluctuation range are identified as data mutation points. Based on the real-time drilling depth data corresponding to the data mutation points, the depth range of each mutation point is obtained; Based on the rock cuttings characteristics, geostress changes, and fluid activity data within the depth range of each mutation point, depth areas with multiple types of data synchronization anomalies are identified, and these depth areas are marked as key drilling areas of interest.

6. The intelligent geological drilling control system based on real-time cuttings analysis according to claim 2, characterized in that, The intelligent generation process of the drilling strategy is as follows: Based on historical databases and current geological survey reports of the drilling area, a first drilling index threshold and a second drilling index threshold are generated. The first drilling index threshold includes a high threshold and a low threshold, and the second drilling index threshold includes an excellent threshold and a qualified threshold. When the first drilling index is higher than the high threshold of the first drilling index, the second drilling index is further compared with the optimal threshold of the second drilling index to obtain potential drilling directions where the second drilling index is higher than the optimal threshold of the second drilling index. The obtained potential drilling directions are determined as priority drilling directions. When the first drilling index is between the high threshold and the low threshold of the first drilling index, potential drilling directions with a second drilling index higher than the qualified threshold of the second drilling index are selected. The potential drilling directions with a second drilling index higher than the qualified threshold of the second drilling index are sorted according to the size of the second drilling index. The drilling direction ranked first is selected as the main drilling direction, and the other drilling directions are the backup drilling directions. When the first drilling index is lower than the first drilling index threshold, the second drilling index of each potential drilling direction is analyzed, and the direction with the highest second index and above the qualified threshold is selected as the drilling direction to be adjusted.

7. The intelligent geological drilling control system based on real-time cuttings analysis according to claim 4, characterized in that, The method for obtaining the potential drilling direction is as follows: Based on the geological survey report of the current drilling area, historical fault location records, and regional geological structure map, the preliminary directional range that can be extended is determined in conjunction with the current real-time borehole depth. Based on the stress variation data around the borehole in the real-time drilling data, the stress imbalance area and gradient change are analyzed, the preliminary direction range is optimized, and the candidate drilling direction is obtained. Based on real-time fluid activity data for each candidate drilling direction, select candidate drilling directions whose fluid activity extension trend conforms to fault characteristics. Based on the preprocessed cuttings characteristic data and real-time drilling depth data, the possibility of the extension of cuttings characteristics in the selected drilling direction is analyzed. The continuous index of cuttings characteristic changes is used to determine the potential drilling direction that meets the requirements.

8. A smart geological drilling control method based on real-time cuttings analysis, applicable to the smart geological drilling control system based on real-time cuttings analysis as described in claims 1-7, characterized in that, Includes the following steps: Step 1: Acquire real-time drilling data and various characteristic data of the transported and screened rock cuttings through multiple types of sensors during the drilling process; Step 2: Preprocess the acquired real-time drilling data and various characteristic data of the cuttings; Step 3: Analyze the various characteristic data of the pre-processed screened rock cuttings and the real-time drilling data, and intelligently generate drilling strategies based on the analysis results. Step 4: Execute the generated drilling strategy.

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