Drilling stratum identification and adaptive parameter adjustment method and system

Abstract

The invention provides a drilling formation identification and adaptive parameter adjustment method and system, and the method comprises the steps: converting multi-source sensing data into structured formation feature representation through constructing a formation response mode library, and achieving the precise identification of formation interface distribution; a parameter response area distribution analysis and missing information reconstruction technology is adopted, recessive stratum characteristics are deeply excavated, and the internal law of stratum changes is revealed; an adjusting point optimization selection and enhanced drilling path planning mechanism is introduced, and an efficient drilling control network is constructed; potential field distribution is constructed through parameter change features, and coupling correlation between the parameter features and spatial positions is established; intelligent configuration and self-adaptive adjustment of drilling parameters are realized by combining harmonic force analysis and execution scheme generation; and finally, a complete drilling stratum identification and parameter control system is formed, an accurate and efficient technical solution can be provided for drilling operation in a complex stratum environment, and the drilling efficiency and the operation quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drilling engineering, and in particular to a drilling stratum identification and adaptive parameter adjustment method and system. Background Art

[0002] As a core technology for geological exploration, resource development, and engineering construction, drilling operations require precise identification and adaptive control in complex geological environments, which have always been key challenges in the industry's technological development. Modern drilling projects often face technical challenges such as complex and variable geological structures, frequent changes in lithology, and difficulty in optimizing drilling parameters in real time. These challenges directly impact drilling efficiency, cost control, and operational safety.

[0003] Traditional drilling parameter adjustment methods rely primarily on operator experience and simple resistance feedback control, lacking a deep understanding of formation characteristics and systematic analysis. Existing technologies are unable to effectively process the complex information from multi-source sensor data, struggle to identify implicit formation characteristics, and ultimately fail to achieve intelligent, adaptive adjustment of drilling parameters. Especially in conditions with complex formation interfaces and drastic impedance variations, traditional methods often lead to low drilling efficiency, increased equipment wear, and unstable drilling quality.

[0004] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0005] The present invention provides a drilling stratum identification and adaptive parameter adjustment method and system, which aims to achieve accurate identification of complex stratum structures through composite processing of multi-source sensor data and construction of a stratum response pattern library; reveal the inherent variation law of the stratum through parameter response regional distribution analysis and implicit stratum feature reconstruction; establish a coupling association mechanism between drilling parameters and stratum characteristics through eddy current parameter sequence conversion and potential field distribution construction; and ultimately achieve intelligent generation of drilling execution plans and adaptive adjustment of parameters.

[0006] A first aspect of the present invention provides a method for drilling formation identification and adaptive parameter adjustment, comprising the following steps: Collecting multi-source sensor data during the drilling process, the multi-source sensor data including vibration signals and resistance data, and performing composite processing on the multi-source sensor data to construct a formation response pattern library; performing parameter response regional distribution analysis based on the formation response pattern library to generate a regional distribution map, extracting parameter response blank areas from the regional distribution map, reconstructing missing information in the parameter response blank areas to obtain implicit formation characteristics, and establishing a candidate set of adjustment points based on the regional distribution map and the implicit formation characteristics; Comprehensively evaluating the candidate set of adjustment points to obtain a parameter range and an adjustment potential value of each candidate adjustment point, determining a core drilling adjustment point based on the parameter range and the adjustment potential value, and extracting a high-impedance core area and a low-impedance transition area from the core drilling adjustment point; Planning an enhanced drilling path for the high-impedance core area, formulating a progressive adjustment direction for the low-impedance transition area, constructing a conflicting intersection between the enhanced drilling path and the progressive adjustment direction, performing a harmonic force analysis on the conflicting intersection to determine a primary adjustment path, and obtaining an auxiliary adjustment flow based on the primary adjustment path; Converting the auxiliary regulating flow into an eddy current parameter sequence, converting the main regulating path into a drilling parameter regulating sequence, and generating a parameter variation feature based on a fusion process of the eddy current parameter sequence and the drilling parameter regulating sequence; A drilling potential field distribution is constructed based on the parameter variation characteristics, dispersed stress is collected from the drilling potential field distribution to generate concentrated stress data, and the concentrated stress data is used to generate a final parameter combination.

[0007] A second aspect of the present invention provides a drilling formation identification and adaptive parameter adjustment system, comprising: A data acquisition module is used to collect multi-source sensor data during the drilling process, wherein the multi-source sensor data includes vibration signals and resistance data, and compositely process the multi-source sensor data to construct a formation response pattern library; a regional analysis module configured to analyze the regional distribution of parameter responses based on the formation response pattern library to generate a regional distribution map, extract parameter response blank areas from the regional distribution map, reconstruct missing information in the parameter response blank areas to obtain implicit formation characteristics, and establish a candidate set of adjustment points based on the regional distribution map and the implicit formation characteristics; a core identification module configured to comprehensively evaluate the candidate set of adjustment points to obtain a parameter range and an adjustment potential value of each candidate adjustment point, determine a drilling core adjustment point based on the parameter range and the adjustment potential value, and extract a high-impedance core area and a low-impedance transition area from the drilling core adjustment point; a path planning module for planning an enhanced drilling path for the high-impedance core area, formulating a progressive adjustment direction for the low-impedance transition area, constructing a conflicting intersection between the enhanced drilling path and the progressive adjustment direction, performing a harmonic force analysis on the conflicting intersection to determine a primary adjustment path, and obtaining an auxiliary adjustment flow based on the primary adjustment path; a parameter processing module, configured to convert the auxiliary regulating flow into an eddy current parameter sequence, convert the main regulating path into a drilling parameter regulating sequence, and generate a parameter variation feature by performing a fusion process based on the eddy current parameter sequence and the drilling parameter regulating sequence; A parameter generation module is used to construct a drilling potential field distribution based on the parameter change characteristics, collect dispersed stress from the drilling potential field distribution to generate concentrated stress data, and use the concentrated stress data to generate a final parameter combination.

[0008] The beneficial effects of the present invention are reflected in the following aspects: First, a formation response pattern library is constructed through the composite processing of multi-source sensor data, integrating vibration signals and resistance data to form a complete formation feature identification system. This technology can accurately identify the distribution of formation interfaces and distinguish different types of formations, such as loose formations, dense formations, and hard rock formations, providing a reliable geological basis for drilling parameter adjustment. Furthermore, by analyzing the regional distribution of parameter responses and reconstructing implicit formation features, it can identify features of blank areas that are difficult to detect using traditional methods, thereby expanding the coverage and accuracy of formation identification. Second, a mechanism for demarcating high-impedance core areas and low-impedance transition areas is established, enabling the formulation of differentiated drilling strategies for different regions. By strengthening drilling path planning and formulating progressive adjustment directions, a systematic drilling control scheme is formed. Combined with the analysis of the harmonic force at conflicting intersections, conflicts between different adjustment strategies can be effectively resolved, ensuring the continuity and stability of the drilling process and improving the adaptability and control accuracy of drilling in complex formation environments. Finally, intelligent configuration and adaptive adjustment mechanisms for drilling parameters are implemented. By transforming eddy current parameter sequences and constructing potential field distributions, a correlation between parameter variation characteristics and spatial position is established. A drilling execution plan is generated based on the matching correlation matrix, automatically adjusting key parameters such as drilling power, feed rate, rotational torque, and axial pressure based on real-time formation changes. This adaptive control mechanism reduces the need for manual intervention, improves drilling efficiency, and reduces equipment wear and operational risks.

[0009] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings herein illustrate specific examples of the technical solutions described in the present invention, and together with the specific implementation methods constitute a part of the specification, and are used to explain the technical solutions, principles and effects of the present invention.

[0011] Unless otherwise specified, the same reference numerals in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference numerals.

[0012] Figure 1 It is a flow chart of a drilling formation identification and adaptive parameter adjustment method of the present invention; Figure 2 It is a structural block diagram of a drilling formation identification and adaptive parameter adjustment system of the present invention. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0015] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0016] The technical solutions of the embodiments of this application are introduced below.

[0017] like Figure 1 As shown, an embodiment of the present invention provides a method for drilling formation identification and adaptive parameter adjustment, including the following steps S110 to S160: Step S110 , collecting multi-source sensor data during the drilling process, the multi-source sensor data including vibration signals and resistance data, and performing composite processing on the multi-source sensor data to construct a formation response pattern library.

[0018] Specifically, multi-source sensor data is collected through a multi-type sensor array deployed at key locations on the drilling equipment. Vibration signals are collected using a combination of triaxial accelerometers and velocity sensors. The accelerometers are located at the top, middle, and near the drill bit of the drill pipe, with a sampling frequency of 2kHz and a measurement range of ±50g, capturing high-frequency impact vibrations during drilling. The velocity sensor, with a sampling frequency of 1kHz, is installed at the connection between the main shaft and the drill pipe, measuring overall vibration velocity changes in the drill pipe. Resistance data is collected using a torque sensor and an axial force sensor. The torque sensor has a range of 0-5000 N·m and an accuracy of 0.1%, monitoring rotational resistance changes in real time during drilling. The axial force sensor has a range of 0-200 kN and a sampling frequency of 500 Hz, measuring the axial feed resistance of the drill bit. The data acquisition system uses a 16-bit A / D converter to ensure signal accuracy, and data transmission utilizes the CAN bus protocol for strong anti-interference capabilities. The vibration signal data format is triaxial, corresponding to the vibration responses in the X, Y, and Z axes. The resistance data consists of torque and axial force sequences, with a uniform sampling interval of 0.5ms. The raw data is low-pass filtered to remove high-frequency noise, with a cutoff frequency of 500 Hz and an 8th-order Butterworth filter. The synchronous acquisition mechanism for multi-source sensor data is achieved using a GPS clock reference, with a time synchronization accuracy of 1 μs.

[0019] In some embodiments, the composite processing of the multi-source sensing data to construct a formation response pattern library includes: generating a formation response spectrum by applying frequency excitation to the vibration signal; performing frequency band decomposition on the formation response spectrum to identify formation interface distribution; constructing a formation response envelope based on the formation interface distribution and the resistance data; and establishing a formation response pattern library based on the formation response envelope.

[0020] The formation response spectrum is generated by applying frequency excitation to the vibration signal. A linear frequency sweep signal is used as the excitation source, with a sweep range of 10-1000 Hz and a sweep period of 2 seconds. The excitation signal expression is s(t)=A·sin(2π(f0+kt)t), where A is the excitation amplitude, f0 is the starting frequency of 10 Hz, and k is the sweep rate. The excitation signal is applied to the formation interface via the drill bit vibration exciter, while the drill pipe vibration response signal is simultaneously acquired. The formation response spectrum is generated using transfer function analysis. The frequency domain transfer function between the excitation signal and the response signal is calculated as H(ω)=Y(ω) / X(ω), where X(ω) is the excitation signal spectrum and Y(ω) is the vibration response spectrum extracted from the characteristic parameter set. The transfer function calculation uses the Welch power spectrum estimation method, with a Hanning window selected as the window function, a 50% overlap, and a frequency resolution of 0.5 Hz. The formation response spectrum consists of two components: the amplitude spectrum and the phase spectrum. The amplitude spectrum reflects the formation's attenuation characteristics for vibrations of different frequencies, while the phase spectrum reflects the propagation delay characteristics. The spectrum smoothing process uses a 1 / 3 octave smoothing algorithm to eliminate random fluctuations in the spectrum. The response spectrum data format is a frequency-amplitude correspondence relationship, with 2000 frequency points covering the entire excitation frequency range.

[0021] The formation response spectrum is decomposed into frequency bands to identify the distribution of different formation interfaces. The Empirical Mode Decomposition (EMD) algorithm is used to decompose the composite response spectrum into multiple intrinsic mode function (IMF) components. The EMD decomposition process is achieved through iterative screening: first, the local maximum and minimum points in the spectrum are identified, the upper and lower envelopes are constructed, the average envelope is calculated, and the average envelope is subtracted from the original spectrum to obtain the first IMF component. This process is repeated until the residual component becomes a monotonic function. The decomposition result is expressed as , of which the IMF i is the i-th intrinsic mode function, r(ω) is the residual term, and m is the number of IMF components. Stratum interface identification is achieved based on the frequency characteristics of different IMF components: high-frequency IMF components (500-1000 Hz) correspond to interfaces in shallow, loose strata, medium-frequency components (100-500 Hz) correspond to interfaces in moderately dense strata, and low-frequency components (10-100 Hz) correspond to interfaces in deep, hard rock strata. Interface distribution characteristics are quantitatively characterized by the energy distribution of each IMF component, with the energy contribution reflecting the thickness and density characteristics of the corresponding stratum type. Interface depth is located through phase spectrum analysis. The interface depth d = v·τ / 2 is calculated based on the propagation delay time of different frequency components, where v is the wave velocity in the stratum and τ is the propagation delay time. The interface distribution results are presented as a depth-to-stratum type relationship, containing information about the main stratum interfaces.

[0022] The formation response envelope is constructed based on the formation interface distribution and resistance data. The resistance interpolation method constrained by the formation interface is adopted, and the interface depth information identified in the fourth section is used to segment the torque sequence and axial force sequence collected in the first section according to the interface depth. For each formation section, according to the depth interval division in the interface distribution, the torque time series and axial force time series within the corresponding depth range are extracted from the original resistance data for analysis and processing. The resistance envelope is composed of an upper envelope and a lower envelope. The upper envelope connects the resistance peak points in each depth section, and the lower envelope connects the valley points. The envelope fitting adopts the cubic spline interpolation algorithm to ensure the continuity and smoothness of the envelope. Formation response envelope expression: upper envelope , lower envelope , where T(d) is the torque value at depth d extracted from the original torque sequence, F(d) is the axial force value at depth d extracted from the original axial force sequence, and α and β are the weighting coefficients for torque and axial force, respectively, determined based on the formation type identified in the interface distribution: α = 0.8, β = 1.2 for loose formations, α = 1.0, β = 1.0 for dense formations, and α = 1.2, β = 0.8 for hard rock formations. The response envelope width W(d) = Rupper(d) - Rlower(d) reflects the severity of the formation resistance change; a wide bandwidth indicates strong formation heterogeneity. Envelope characteristic parameters include key indicators such as average resistance, resistance gradient, and envelope area.

[0023] Based on the constructed formation response envelopes, a database of categorized formation response patterns was established. A relational database architecture was used, with formation type and depth range as the primary indexes. The database contains four core tables: the formation type table stores standard characteristic parameters for loose, dense, and hard rock formations; the depth distribution table records the distribution patterns of each formation type at different depths; the response envelope table stores the upper and lower envelope values ​​and characteristic parameters for each depth point; and the pattern matching table establishes the correspondence between envelope characteristics and formation types. Standard patterns in the pattern library were established through statistical analysis: clustering analysis was performed on the response envelopes of similar formations, and typical envelope shapes were extracted as standard patterns. The clustering algorithm used was the K-means method, with the number of clusters k=3 corresponding to the three categories of loose, dense, and hard rock formations. The distance metric used was the dynamic time warping (DTW) algorithm, with a bandwidth constraint set to 10% of the sequence length, suitable for calculating similarity between envelope sequences of varying lengths. Each standard pattern includes a pattern identifier, formation type label, depth range, characteristic envelope sequence, and statistical characteristic parameters. The pattern library contains a variety of standard patterns that cover the response characteristics of common formation types.

[0024] Step S120: Analyze the regional distribution of parameter responses based on the formation response pattern library to generate a regional distribution map, extract parameter response blank areas from the regional distribution map, reconstruct missing information in the parameter response blank areas to obtain implicit formation characteristics, and establish an alternative set of adjustment points based on the regional distribution map and the implicit formation characteristics.

[0025] Specifically, the drilling area was first spatially discretized into a 100m×100m grid, forming a two-dimensional analysis grid. For each grid node, a matching standard pattern was retrieved from the formation response pattern library. The matching criteria were based on three dimensions: formation type, depth range, and response envelope similarity. The pattern matching algorithm employed a minimum distance classifier to calculate the Euclidean distance between the measured response and each standard pattern in the pattern library, selecting the pattern with the smallest distance as the matching result. For grid nodes without direct drilling data, spatial interpolation was performed using the Kriging interpolation method, with a weighting function determined based on spatial distance and geological continuity assumptions. A regional distribution map was constructed using a hierarchical coloring scheme, with different formation types represented by different colors: loose formations in yellow, dense formations in green, and hard formations in red. The color depth reflects the response intensity. The map resolution was set to 1m×1m, covering the entire drilling area. The parameter response intensity was quantified using a comprehensive score ranging from 0 to 100, taking into account factors such as the resistance envelope area, spectral energy distribution, and interface clarity. The atlas data structure is stored in a raster format, with each pixel containing attribute information such as coordinate location, formation type, response intensity, and matching confidence. Regional distribution maps are acquired through pattern library matching and spatial analysis techniques.

[0026] Parameter response blank areas are extracted from the regional distribution map. A method combining threshold segmentation and connectivity analysis is used. First, the response intensity threshold is set to 20 (out of 100), and pixels below the threshold are marked as potential blank areas. Connectivity analysis uses an 8-neighborhood connectivity algorithm to merge adjacent low-response pixels into connected areas. Fragmented areas with an area of ​​less than 25 square meters are filtered out to avoid noise interference. The geometric features of the blank areas are extracted, including parameters such as area, perimeter, shape factor, and orientation angle. The shape factor is defined as 4π×area / perimeter. ² , used to quantify the degree of regularity of an area. Blank area classification is based on shape and distribution characteristics: strip-shaped blank areas typically correspond to geological faults or weak interlayers, elliptical blank areas may indicate caves or soft soil areas, and irregular blank areas often indicate complex geological structures. Each blank area is assigned a unique identifier that records information such as its boundary coordinate sequence, geometric parameters, and surrounding stratigraphic background. B-spline curve fitting is used to smooth the boundaries of blank areas to eliminate boundary jagged effects.

[0027] In some embodiments, the reconstructing missing information of the parameter response blank area to obtain implicit formation characteristics includes: obtaining boundary characteristics of the parameter response blank area; inferring internal response laws of the parameter response blank area based on the boundary characteristics; performing data interpolation processing according to the response law to generate compensation response data; and reconstructing implicit formation characteristics based on the compensation response data.

[0028] For each blank area boundary coordinate sequence, the boundary features are calculated in advance, and a method combining curvature analysis and gradient calculation is used. The boundary curvature calculation adopts the three-point method. For any point P on the boundary i , using the adjacent point P i-1 and P i+1 Calculate the local curvature κi=|det(P i-1 -P i ,P i+1 -P i )| / |P i+1 -P i-1 |³, the magnitude of the curvature reflects the degree of curvature of the boundary. The response gradient is calculated by calculating the difference in response intensity between the inside and outside of the boundary, with the gradient direction indicating increasing response intensity. A boundary feature vector consists of four components: location coordinates, curvature value, gradient magnitude, and gradient direction, forming a complete description of the boundary. Boundary segmentation is implemented based on curvature variations, dividing continuous boundaries into three categories: high curvature segments, low curvature segments, and turning segments. Different boundary segment types have different geological significance. High curvature segments may correspond to abrupt stratigraphic changes, low curvature segments represent relatively gentle transitions, and turning segments often indicate structural control points. Statistical analysis of boundary characteristics includes parameters such as mean curvature, maximum gradient, and principal direction angle. These parameters are used to characterize the boundary complexity and anisotropy of the blank area. Boundary feature data is organized by segment, each of which contains information such as start and end locations, characteristic parameters, and geological interpretation.

[0029] The internal response law is inferred using the field theory method under boundary constraints, which regards the blank area as a two-dimensional potential field, and the boundary conditions are determined by the boundary characteristics. The potential field equation uses the Laplace equation Description, where φ is the response potential function, and the boundary condition is φ|boundary = f(boundary characteristics). The boundary response intensity is estimated based on gradient information: for a boundary point, its response intensity is equal to the response intensity of the adjacent non-blank area minus the product of the gradient magnitude and the distance. The internal response distribution is assumed to follow a distance decay law, where the boundary influence decreases with distance. The decay function takes the exponential form r(d) = r0 · exp(-d / λ), where r0 is the boundary response intensity, d is the distance to the boundary, and λ is the decay constant. The decay constant λ is determined based on the geological type of the blank area: λ = 10 m for fault-type blank areas, λ = 5 m for cave-type blank areas, and λ = 15 m for soft soil blank areas. A linear weighting method is used to superimpose multiple boundary effects, with weights inversely proportional to distance. The anisotropy of the response distribution is achieved by modulating the main direction angle, with slower decay along the main direction and faster decay perpendicular to the direction. The internal response law is expressed in the form of a mathematical model, which includes three core elements: boundary constraints, distance decay, and anisotropy.

[0030] The data is interpolated through the response law to generate the compensation response data. The radial basis function method based on physical constraints is used to combine the inferred mathematical model with the measured boundary data for calculation. The boundary constraint value is obtained by extracting the average value of the response intensity within 3-5 meters outside the boundary of the blank area in the nearest non-blank area in the regional distribution map to ensure the reliability of the boundary conditions. The radial basis function selects the multi-quadratic function φ(r)=√(r²+c²), where r is the spatial distance and c is the shape parameter. The interpolation function expression is , where wi is the weight coefficient and P(x,y) is the polynomial term. The weight coefficient is determined by solving a system of linear equations using boundary constraints. The boundary response intensity extracted from the regional distribution map is used as a known value, combined with the inferred response distribution law as an internal constraint. This ensures that the interpolation function satisfies the measured response value at the boundary and follows the inferred distribution law internally. Interpolation accuracy is controlled by grid refinement. The initial interpolation grid is 5m×5m, and is adaptively refined to 1m×1m for areas with large gradients. The compensated response data format is consistent with the original regional distribution map, containing information such as location coordinates, response intensity, and confidence level.

[0031] Implicit stratigraphic characteristics are reconstructed based on compensated response data. First, feature extraction is performed on the compensated response data, including multiple dimensions such as local response intensity, spatial gradient, second-order derivative, and local variance, to form a feature vector for each grid point. Cluster analysis utilizes the K-means algorithm, with the number of clusters, K, adaptively determined based on the complexity of the blank area, typically ranging from 3 to 7 categories. The clustering results correspond to different implicit stratigraphic units, each with similar response characteristics. Implicit stratigraphic characteristic parameters include average response intensity, response variability, spatial distribution pattern, and correlation with surrounding strata. Stratigraphic unit boundaries are identified through spatial connectivity analysis of the clustering results, forming the geometric outline of the implicit stratigraphic unit. The geological interpretation of implicit characteristics is based on comparative analysis of the response characteristics with known stratigraphic types: areas with low response intensity and low variability may correspond to homogeneous soft soil layers, while areas with high response intensity and high variability may contain hidden bedrock uplifts.

[0032] A set of candidate adjustment points is established based on the regional distribution map and implicit stratigraphic characteristics. The preliminary screening of candidate points is carried out in the regional distribution map, and locations with larger response intensity gradients are selected as potential adjustment points. The gradient threshold is set to 10 units / meter. The integration of implicit stratigraphic characteristics is achieved through weighted superposition. The weight of implicit characteristics is determined according to the reconstruction credibility. The weight of high-credibility areas is 0.8, the medium-credibility area is 0.5, and the low-credibility area is 0.2. The evaluation indicators of adjustment points include three dimensions: stratigraphic representativeness, spatial distribution uniformity, and technical operability. Stratigraphic representativeness is quantified by the diversity of stratigraphic types around the adjustment point, and the diversity index adopts Shannon entropy H=-Σp i log(p i ) calculation, where p i is the area proportion of the i-th stratigraphic type. Spatial distribution uniformity was assessed using nearest neighbor distance analysis to avoid excessive clustering or dispersion of adjustment points. Technical operability considered engineering factors such as terrain conditions, transportation convenience, and equipment accessibility. A genetic algorithm was used to optimize the candidate set. The objective function was to maximize the comprehensive evaluation index. Constraints included a limit on the number of adjustment points and minimum spacing requirements. Through a comprehensive analysis of regional distribution maps and implicit stratigraphic characteristics, a candidate set of adjustment points was successfully established.

[0033] In step S130, a comprehensive evaluation is performed on the candidate set of adjustment points to obtain the parameter range and adjustment potential value of each candidate adjustment point, and the drilling core adjustment point is determined based on the parameter range and adjustment potential value, and the high-impedance core area and low-impedance transition area are extracted from the drilling core adjustment point.

[0034] Specifically, a systematic and comprehensive analysis and calculation of candidate adjustment points was conducted using a set of candidate adjustment points. The influence radius calculation method was used to determine the parameter's range of action. With each candidate adjustment point as the center, its influence radius was calculated based on the formation propagation characteristics. The influence radius calculation formula is R = k√(P / ρ), where k is the formation coefficient, P is the formation representativeness evaluation score of the adjustment point, and ρ is the surrounding formation density. The formation coefficient is determined based on the formation context of the adjustment point: k = 1.2 for loose formations, k = 1.0 for dense formations, and k = 0.8 for hard formations. Influence domain overlap analysis was performed by calculating the intersection area of ​​the influence circles of adjacent adjustment points. Adjustment points with an overlap exceeding 30% exhibit mutual interference. The effective influence area metric was used to quantify the parameter's range of action. The net influence area, after deducting the overlap, was defined as the actual range of action for that adjustment point. The adjustment potential value was derived through a multi-factor fusion process, taking into account factors such as formation complexity, response variation, and construction difficulty. Stratum complexity is quantified using the Shannon entropy of the stratigraphic types surrounding the adjustment point. The magnitude of the response is characterized by the standard deviation of the local response intensity. Construction difficulty is calculated based on a comprehensive calculation of engineering parameters such as terrain slope, rock hardness, and equipment accessibility. The adjustment potential value is calculated as V = w1 × C + w2 × R + w3 × (1-D), where C represents stratigraphic complexity, R represents the magnitude of the response, and D represents the construction difficulty. Weighting coefficients w1 = 0.4, w2 = 0.4, and w3 = 0.2 are used. For each candidate adjustment point, comprehensive calculation results are recorded, including its coordinate location, range of action, potential value, and overlap of influence domains.

[0035] Core drilling adjustment points are determined based on the parameter range and adjustment potential. Core adjustment point selection adheres to the principles of maximum coverage and minimum redundancy. While ensuring full coverage of the range, the combination of adjustment points with the highest adjustment potential is selected. The optimization problem is modeled as an integer programming problem, with the objective of maximizing the sum of the adjustment point potentials. Constraints include regional coverage requirements and a limit on the number of adjustment points. A genetic algorithm is used for the solution, with a population size of 100, a crossover probability of 0.8, a mutation probability of 0.1, and 500 iterations. The fitness function is designed as a weighted combination of the objective function value and the constraint violation degree to ensure solution feasibility. Niching techniques are used to maintain solution diversity and prevent premature convergence. The resulting core adjustment point screening typically includes 8-12 adjustment points, ensuring both sufficient representativeness and ease of project implementation. Each core adjustment point retains its original coordinate location, stratigraphic background, and evaluation score, while additional identifiers such as core grade and coverage responsibility area are added.

[0036] In some embodiments, extracting the high-impedance core area and the low-impedance transition area from the drilling core adjustment point includes: performing impedance gradient operation on the drilling core adjustment point to obtain an impedance change rate distribution; separating a high-gradient component and a low-gradient component from the impedance change rate distribution; performing a ratio analysis between the high-gradient component and the low-gradient component to obtain an impedance distribution characteristic; and dividing the high-impedance core area and the low-impedance transition area according to the impedance distribution characteristic.

[0037] The impedance gradient calculation is performed on the core adjustment points of the drilling to obtain the impedance change rate distribution. First, the response intensity value of each core adjustment point is extracted from the regional distribution map. The response intensity is converted into an impedance value based on the linear conversion relationship between the response intensity and the formation impedance. The conversion coefficient is determined according to the formation type, and the benchmark impedance value is set using the regional average value. Using the finite difference method, for the adjustment point i, its x-direction gradient is , the gradient in the y direction is , where Z i is the impedance value at adjustment point i, and Δx and Δy are the spatial spacings. The gradient amplitude is calculated using the vector synthesis method, and the gradient direction angle is calculated using the inverse tangent function. To obtain a continuous gradient distribution field, the discrete gradient values ​​of the adjustment points are spatially interpolated using the inverse distance weighted method. The resolution of the gradient distribution field is set to 10m×10m, covering the influence area of ​​all core adjustment points. A mean filter is used for gradient field smoothing to eliminate high-frequency noise during the calculation process. Gradient statistical features include parameters such as the maximum gradient value, the average gradient value, and the gradient variance. The spatial pattern of the impedance change rate distribution is visualized using a gradient rose diagram, which shows the directional characteristics of the gradient.

[0038] Frequency domain analysis is used to separate high- and low-gradient components from the acquired impedance change rate distribution. First, a two-dimensional fast Fourier transform (FFT) is performed on the gradient distribution to obtain a frequency domain representation. Spectral analysis shows that high-gradient components are primarily distributed in the high-frequency band, corresponding to localized, dramatic changes, while low-gradient components are concentrated in the low-frequency band, reflecting regional, slow changes. The separation threshold is determined through spectral energy analysis, with the frequency at which the cumulative energy reaches 80% being used as the high- and low-frequency demarcation point. High-gradient components are extracted using a high-pass filter, while low-gradient components are extracted using a low-pass filter. The filtered frequency domain signal is transformed back into the spatial domain using an inverse FFT to separate high- and low-gradient components. Component separation effectiveness is assessed using reconstruction error; the correlation coefficient between the reconstructed signal and the original gradient field should be greater than 0.95. High-gradient components typically correspond to geological structural boundaries such as faults and joints, while low-gradient components reflect the overall trend of change across different stratigraphic units. The separated components maintain the original spatial resolution and coordinate system.

[0039] The impedance distribution characteristics are determined by analyzing the ratios of the separated high-gradient and low-gradient components. A point-by-point calculation is performed for each location in space, calculating the gradient ratio R(x,y) = G_high(x,y) / G_low(x,y). The low-gradient component is constrained to be nonzero during the ratio calculation. When the low-gradient component is too small, a smoothing surrogate is used to avoid division-by-zero errors. Statistical analysis of the ratio field, including characteristic parameters such as mean, standard deviation, skewness, and kurtosis, characterizes the overall impedance distribution. The spatial distribution pattern of the ratio is visualized using contour plots. High-ratio regions indicate dramatic local variations with moderate background variations, while low-ratio regions indicate dominant global variations. Impedance distribution characteristics are extracted using pattern recognition methods, classifying the ratio field into five levels based on the numerical range: extremely high ratio, high ratio, medium ratio, low ratio, and extremely low ratio. Each level corresponds to different geological significance and impedance characteristics. Extremely high ratio regions typically correspond to abrupt lithologic transitions, while extremely low ratio regions indicate relatively uniform lithologic characteristics.

[0040] Based on the impedance distribution characteristics, the region is divided into a high-impedance core area and a low-impedance transition area. This regional division utilizes a multi-criteria decision-making approach, comprehensively considering factors such as the absolute impedance value, gradient ratio, and spatial connectivity. The high-impedance core area is identified by an impedance value greater than the overall mean plus one standard deviation and a moderate gradient ratio, indicating high impedance and relatively uniform internal impedance. Impedance peak centers within the core area are extracted using a combination of local extreme value search and cluster analysis. First, local extreme value detection is performed within the core area to identify local impedance maxima as candidate peak points. K-means clustering is then performed on these candidate points. The number of clusters is adaptively determined based on the core area, with one cluster center set for every 1000 square meters. The center of the clustering result is the impedance peak center, and each peak center is recorded with its precise coordinates, peak impedance value, influence radius, and other attribute parameters. The low-impedance transition area is identified by an impedance value below the overall mean and a high gradient ratio, indicating low but drastically varying impedance, serving as a transition connecting different regions. Through the peak center extraction and area division algorithm, the peak center coordinates of each high-impedance core area and the distribution of the low-impedance transition area are obtained.

[0041] Step S140: Plan an enhanced drilling path for the high-impedance core area, formulate a progressive adjustment direction for the low-impedance transition area, construct a conflict intersection between the enhanced drilling path and the progressive adjustment direction, perform a harmonic force analysis on the conflict intersection to establish a primary adjustment path, and obtain an auxiliary adjustment flow based on the primary adjustment path.

[0042] Based on high-impedance core areas, an enhanced drilling path is planned using a core area connectivity optimization method. The impedance peak center of each high-impedance core area is used as a mandatory control point, and primary and secondary control points are determined by impedance ranking. The path optimization objective function comprehensively considers path length, average impedance value, and path curvature, and a genetic algorithm is used to solve the optimal path. Constraints include that the path must pass through all major core areas, turning angles must not exceed 45 degrees, and the length of each segment must be within the range of 50-200 meters. Enhanced drilling parameters are dynamically adjusted based on the impedance characteristics of the core areas traversed by the path. The power adjustment factor is calculated as P_factor=(Z_local / Z_max)^0.8, where Z_local is the local impedance value of the path point and Z_max is the maximum impedance value of all core areas. The drilling speed and rotational torque are adjusted segmentally in proportion to the impedance value. Each path segment records technical attributes such as start and end coordinates, segment length, average impedance, and power adjustment factor. Path geometric accuracy is ensured through B-spline curve fitting to ensure that the drilling equipment guidance requirements are met.

[0043] Formulate a progressive adjustment direction for the low-impedance transition zone. The adjustment direction is determined based on the main axis direction of the transition zone, and the main axis is calculated by the ellipse fitting method. The ellipse fitting adopts the least squares method, with the boundary points of the transition zone as the fitting data, and the direction of the major axis of the ellipse is the main progressive direction. The angular accuracy of the adjustment direction is controlled within the range of ±5 degrees to ensure the directional stability of the drilling process. The progressive strategy adopts a step-by-step adjustment mode, dividing the transition zone into multiple adjustment segments according to the impedance gradient, and the length of each segment is determined according to the impedance change rate. A shorter adjustment segment (10-20 meters) is set in areas with high impedance change rates, and the adjustment segment (30-50 meters) can be appropriately extended in areas with low change rates. The parameter adjustment amplitude is inversely proportional to the impedance gradient. Areas with large gradients use small and frequent adjustments, and areas with small gradients can be adjusted in large amplitudes. The adjustment parameters include drilling speed, rotational torque, axial pressure, etc. The adjustment formula is: , where ΔP is the parameter adjustment amount, α is the adjustment coefficient, The local impedance gradient. Directional control utilizes a real-time feedback mechanism, monitoring changes in drilling resistance to determine deviation from the intended direction. Directional correction is initiated if the deviation exceeds 10 degrees. Each adjustment segment records attributes such as the starting coordinates, adjustment direction, adjustment parameters, and segment length.

[0044] Construct the conflict intersection point of the enhanced drilling path and the progressive adjustment direction. Using the parametric equation solving method, the enhanced path segment is expressed as P(t)=P1+t(P2 - P1), and the progressive path segment is expressed as Q(s)=Q0 + s×D(s), where Q0 is the starting point coordinate of the progressive path, and D(s) is the direction vector function. The intersection point calculation is achieved by solving the equation set P(t)=Q(s), and the discriminant method is used to judge the existence of the solution. The classification of the intersection point is based on the intersection angle: the acute intersection (θ < 60°) is defined as a strong conflict point, the right-angle intersection (60° ≤ θ ≤ 120°) is a medium conflict point, and the obtuse intersection (θ > 120°) is a weak conflict point. Each conflict intersection point records attributes such as the intersection point coordinates, intersection angle, path segment identifiers involved, and conflict intensity level. The conflict point density analysis is realized through the nearest neighbor distance statistics, and the areas with too high density need to be optimized and adjusted for the path. The influence domain of the intersection point is defined by a circular area centered on the intersection point, and the influence radius is determined according to the conflict intensity: 30 meters for strong conflict points, 20 meters for medium conflict points, and 10 meters for weak conflict points. The influence domain overlap analysis identifies the complex conflict areas that need to be focused on for processing.

[0045] In some embodiments, the harmonic force analysis of the conflict intersection point to establish the main adjustment path includes: detecting the path conflict intensity at the conflict intersection point; performing a force balance analysis based on the path conflict intensity to obtain balance parameters; generating a harmonic force vector according to the balance parameters; and establishing the main adjustment path using the harmonic force vector.

[0046] Detect the path conflict intensity at the conflict intersection point. The conflict intensity detection comprehensively considers factors such as the intersection angle, path priority, and local formation conditions to establish an evaluation model. The intersection angle factor is quantified by the degree of deviation from the right angle, and the calculation formula is A_factor = |sin(2θ)|, where θ is the intersection angle, and this factor reaches the maximum value of 1.0 at 45 degrees and 135 degrees. The path priority factor is determined based on the technical importance difference between the enhanced drilling path and the progressive adjustment direction. The average value of the power adjustment coefficient of the enhanced path is used as its weight base number, and the average value of the parameter adjustment amount of the progressive path is used as its weight base number. The priority factor P_factor is calculated by the ratio of the weight base numbers of the two paths. The formation condition factor is evaluated by the impedance gradient and formation complexity at the intersection point. The greater the gradient and the more complex the formation, the higher the difficulty of conflict handling. The comprehensive conflict intensity calculation formula is I = A_factor×P_factor×G_factor, where G_factor is the formation condition factor. The conflict intensity grading standard is: I > 0.8 is a strong conflict, 0.5 < I ≤ 0.8 is a medium conflict, and I ≤ 0.5 is a weak conflict. Strong conflict points need to adopt special harmonic treatment strategies, medium conflict points are subjected to conventional harmonization, and weak conflict points can be ignored.

[0047] The balance parameters are obtained by force balance analysis based on the path conflict intensity. The numerical value of the corresponding intersection position is extracted from the power adjustment coefficient P_factor recorded in each section of the enhanced drilling path as the basis for calculating the driving force, and the numerical value of the corresponding position is extracted from the parameter adjustment amount ΔP recorded in each adjustment section of the progressive adjustment direction as the basis for calculating the adjustment force. The drilling path is regarded as a force system, and the driving force generated by the enhanced drilling path and the adjustment force generated by the progressive adjustment direction form a force at the intersection. The magnitude of the driving force is proportional to the power adjustment coefficient of the enhanced path at the intersection. The driving force calculation formula is F_drive=k1×P_factor, where k1 is the force conversion coefficient. The magnitude of the adjustment force is related to the parameter adjustment amount of the progressive path at the intersection. The adjustment force formula is F_adjust=k2×ΔP, where k2 is the adjustment force coefficient. The force balance condition requires the minimization of the resultant force, and the balance equation is , where α is the angle between the two forces. The equilibrium parameters include the force amplitude ratio , force direction angle β, and resultant force magnitude F_resultant. The optimal equilibrium state corresponds to the minimum resultant force and is obtained by solving the constrained optimization problem using the Lagrange multiplier method. The stability of the equilibrium parameters is evaluated through sensitivity analysis, and the impact of parameter changes on the resultant force is quantified as a sensitivity coefficient.

[0048] Based on the acquired balance parameter values, a vector synthesis method is used to generate the harmonic force vector of the conflicting intersection. The force vector is represented in polar coordinates, with the driving force vector being F_d(r1,θ1) and the regulating force vector being F_a(r2,θ2), where r is the magnitude of the force and θ is the direction angle of the force. Vector synthesis is achieved through component synthesis, and the magnitude and direction angle of the harmonic force vector are obtained through geometric calculations. Normalization of the harmonic force vector ensures comparability between different intersections, and the normalization coefficient is the maximum force value of each intersection. The harmonic effect is evaluated by measuring the deviation of the angle between the force vector and the original conflict direction. The smaller the deviation angle, the better the harmonic effect. Special case handling includes: using algebraic sum when the two forces are collinear, using the Pythagorean theorem when the two forces are perpendicular, and determining the direction of the dominant force when the two forces are in opposite directions. The stability of the harmonic force vector is verified through time series analysis to ensure directional stability during dynamic drilling.

[0049] The generated harmonic force vector is used to establish the primary adjustment path for drilling operations. The path adjustment strategy is graded based on the magnitude of the harmonic force: large harmonic forces (|F| > 0.8F_max) result in significant path deviations, medium harmonic forces (0.4F_max < |F| ≤ 0.8F_max) result in moderate adjustments, and small harmonic forces (|F| ≤ 0.4F_max) employ a fine-tuning strategy. Path deviations are calculated using a vector projection method, with the deviation distance proportional to the harmonic force magnitude and the deviation direction aligned with the harmonic force vector. The primary adjustment path is formed by connecting the adjusted path control points. Path smoothing uses cubic spline interpolation to ensure continuity. Path quality assessment metrics include path length, curvature change, and deviation from the original path. Path coordination at multiple intersections is achieved through global optimization, using a genetic algorithm to solve a multi-objective optimization problem. The objective function consists of minimizing total path length and conflict levels. Path segment identification retains the existing enhanced and progressive segment attributes, while also adding harmonic segment identification. The geometric accuracy of the main adjustment path is controlled by the control point density and interpolation order to ensure that the guidance accuracy requirements of the drilling equipment are met.

[0050] Auxiliary regulating flow is obtained based on the main regulating path. Using potential flow theory, the main regulating path is regarded as a streamline and auxiliary flow field is established around it. The flow field is established based on the Laplace equation , where φ is the velocity potential function, and the boundary conditions are determined by the primary regulating path. Streamline calculations use the orthogonal relationship between the stream function ψ and the potential function, with the streamline equation being dψ = 0. The intensity of the auxiliary flow is proportional to the importance of the primary path. The auxiliary flow intensity coefficient generated in the enhanced section is 1.0, in the progressive section it is 0.7, and in the harmonized section it is 0.5. The flow field is meshed using a structured grid, with the mesh density increasing near the primary path and becoming sparser in areas away from the path. The direction of the auxiliary regulating flow is determined by the streamline tangent, and the flow velocity is calculated using the gradient of the potential function. The convergence of the flow field is verified through residual analysis to ensure the stability of the numerical calculations. The range of the auxiliary flow is determined by streamline tracing, with tracing termination conditions being when the flow velocity drops below a threshold or reaches the boundary of the computational domain. Each auxiliary streamline records properties such as the starting coordinates, flow direction, velocity distribution, and range of influence. The distribution of the auxiliary regulating flow is ultimately determined based on the geometric characteristics of the primary regulating path and potential flow theory analysis.

[0051] Step S150: convert the auxiliary regulating flow into an eddy current parameter sequence, convert the main regulating path into a drilling parameter regulating sequence, and generate parameter variation characteristics based on a fusion process of the eddy current parameter sequence and the drilling parameter regulating sequence.

[0052] In some embodiments, converting the auxiliary regulating flow into a vortex parameter sequence includes: using the auxiliary regulating flow to identify flow rhythm characteristics; distinguishing the main evolution path from the auxiliary evolution path through the flow rhythm characteristics; establishing a vortex generation benchmark based on the main evolution path, and using the vortex generation benchmark to divide the auxiliary evolution path into stages to determine the vortex formation stage; and using the vortex formation stage to construct a vortex parameter sequence.

[0053] Auxiliary regulated flow is used to identify flow rhythmic characteristics. This is achieved by setting time sampling points along the drilling path. At fixed intervals (set to 10 seconds), the velocity vector at the corresponding location is extracted from the auxiliary regulated flow vector field, generating time series data for the flow velocity. Flow rhythmic characteristics are identified based on streamline tracing and velocity field analysis of these time series. Rhythmic patterns are identified by calculating the curvature changes of streamlines and the temporal evolution of velocity gradients. Rhythmic characteristic parameters include time-domain features such as pulsation frequency, amplitude variation, and phase relationship. Pulsation frequency is obtained through power spectrum analysis of the velocity time series at each sampling point, with the dominant pulsation frequency corresponding to the peak position of the spectral density. Amplitude variation is obtained by extracting the envelope of the velocity time series, calculated using the Hilbert transform method. Phase relationship analysis is used to analyze the synchronization of velocity pulsations at different spatial locations. Regions with a phase difference less than π / 4 are considered to exhibit synchronized rhythmic characteristics. Rhythmic intensity is quantified by the ratio of pulsation energy to average flow energy; a larger ratio indicates a stronger rhythm. Spatial rhythmic patterns are visualized using isorhythm plots to identify the spatial distribution of the rhythmic patterns. Rhythmic data are organized in the form of spatiotemporal sequences, including information such as time tags, spatial coordinates, flow velocity vectors, and rhythmic parameters.

[0054] Flow rhythmic characteristics are used to distinguish between primary and secondary evolutionary paths. Path differentiation is based on rhythmic intensity and spatial continuity criteria. Streamlines with rhythmic intensity greater than a threshold and spatial continuity are defined as primary evolutionary paths. Primary path identification utilizes a connectivity analysis algorithm to spatially cluster regions of high rhythmic intensity, with the largest connected region corresponding to the primary path. Secondary evolutionary paths are streamlines other than the primary path, typically exhibiting weaker rhythmic intensity or discontinuous spatial distribution. Path importance is assessed using flow weights, with the primary path carrying at least 60% of the total flow. Path stability analysis assesses the temporal persistence of different paths, with stable paths existing for at least 80% of the total observation time. Interactions between primary and secondary paths are identified through streamline intersection analysis, with flow regime transitions potentially occurring at intersections. Path classification results are stored as hierarchical vector data, with primary paths labeled level 1 and secondary paths labeled level 2, level 3, and so on, based on their importance. Each path records attributes such as streamline coordinates, rhythmic parameters, importance level, and stability index.

[0055] The vortex generation benchmark is established based on the dominant flow path and the vortex formation stage is determined. The streamline coordinates of the dominant flow path are extracted from the level-1 marked path to obtain the node coordinate sequence and connection relationship that constitute the dominant path. The rhythmic parameters are extracted from the spatiotemporal sequence data to extract the characteristic values ​​of the pulsation frequency, amplitude change and rhythmic intensity at the corresponding position of the dominant path. The vortex generation benchmark is determined by the vorticity distribution on the dominant path. The vorticity value is calculated using the flow field velocity data. The vorticity is defined as ,in is the gradient operator, v is the velocity vector, and ω is the vorticity vector. The vorticity value of each point on the dominant path is calculated by the finite difference method. The vorticity calculation formula in the two-dimensional case is , where vx and vy are the components of the velocity in the x and y directions respectively, Represents the partial derivative operator. The baseline vorticity value is taken as the threshold of the strongest 25% of all calculated results, serving as the vortex formation threshold. The baseline establishment includes two dimensions: a spatial baseline and an intensity baseline. The spatial baseline defines the effective region for vortex formation, determined by analyzing the high-curvature segments of the streamline coordinates of the dominant path. The intensity baseline specifies the minimum vorticity requirement for vortex formation. The stages of the auxiliary evolution paths are divided based on the relative relationship between the identified auxiliary paths and the dominant path. Each auxiliary path is divided into three stages based on the strength of its interaction with the dominant path. The first stage is the approach stage, in which the auxiliary path gradually approaches the dominant path and the interaction gradually increases. The second stage is the interaction stage, in which the two paths are closest and the vorticity exchange is most active. The third stage is the separation stage, in which the auxiliary path moves away from the dominant path and the interaction weakens. The stage boundaries are determined by the change points of the inter-path distance and the vorticity gradient, with the distance extreme point and the gradient zero point serving as the stage demarcation line. The vortex formation stage refers to the period in the second stage when the vorticity exceeds the baseline threshold, indicating that the auxiliary path meets the conditions for vortex formation. Characteristic parameters such as the start and end time, average vorticity, and interaction strength are recorded for each stage.

[0056] The vortex formation stage is used to construct the vortex parameter sequence. The vortex calculation formula is applied to the obtained flow velocity time series. Obtained, the two-dimensional vorticity is calculated using the finite difference method: , where vx and vy are the x and y components of the velocity, respectively. 、 represents the partial derivative with respect to spatial coordinates. The vortex parameter sequence is constructed using the vortex formation phase as the time window, extracting characteristic vortex parameters within this time period. The sequence parameters include four core elements: vortex center position trajectory, vorticity intensity change, vortex scale evolution, and energy transfer characteristics. The vortex center trajectory is obtained by tracing the spatial path of the vorticity extreme point in the calculated vorticity field, and the trajectory data is represented as a coordinate time series. The vorticity intensity change directly uses the calculated vorticity modulus |ω| to record the intensity evolution of the vortex from generation to dissipation, with the change pattern typically showing an initial increase followed by a decrease. The vortex scale evolution is described by the temporal variation of the area enclosed by the isovorticity lines. Contour lines are drawn based on the calculated vorticity distribution and the enclosed area is calculated, reflecting the dynamic changes in the vortex influence range. The energy transfer characteristics are calculated using the time derivative of the vortex kinetic energy. The kinetic energy is calculated based on the flow velocity data. Positive values ​​indicate energy input, while negative values ​​indicate energy dissipation. The vortex parameter sequence data structure uses a multidimensional array to support efficient time series analysis.

[0057] The main control path is converted into a drilling parameter adjustment sequence. Based on the formation type and impedance characteristics of each section of the main control path, the corresponding drilling power, feed rate, rotary torque, and axial pressure parameters are configured according to drilling process standards: low power and high speed are used for loose formations, medium power and medium speed are used for dense formations, and high power and low speed are used for hard formations. Drilling parameters include four core control quantities: drilling power, feed rate, rotary torque, and axial pressure. Parameter values ​​are directly extracted from the node attributes of the main control path. Sequencing arranges parameters according to the path's progression, forming a time series of adjustment instructions. Parameter interpolation is used to control parameter transitions between points, using cubic spline interpolation to ensure smooth parameter changes. The sampling interval of the adjustment sequence is set to 1 meter, corresponding to the parameter update frequency during drilling. Parameter normalization maps parameters of different dimensions to a unified numerical range [0, 1] to facilitate subsequent fusion analysis. Sequence segmentation retains the original path's attributes of reinforcement, progressive, and harmonization segments, while each segment adopts a different parameter adjustment strategy. The data format of the drilling parameter adjustment sequence adopts the form of time-parameter pairs, where the time label corresponds to the drilling mileage and the parameter vector contains the set values ​​of the four control quantities.

[0058] In some embodiments, the generating of parameter change characteristics by fusion processing based on the eddy current parameter sequence and the drilling parameter adjustment sequence includes: identifying the main frequency components through the eddy current parameter sequence; performing cross-modulation processing using the main frequency components and the drilling parameter adjustment sequence to form a modulation spectrum; determining parameter fusion key points based on the modulation spectrum; and forming parameter change characteristics through the parameter fusion key points.

[0059] The main frequency components are identified through the vortex parameter sequence. The spectrum of the vortex center position trajectory reflects the periodic characteristics of vortex movement, and the main frequency corresponds to the main oscillation mode of the vortex. The spectrum of the vortex intensity change reveals the time scale characteristics of vortex strengthening and attenuation. The low-frequency component corresponds to the slow energy accumulation process, and the high-frequency component reflects the rapid vortex pulsation. The spectrum of the vortex scale evolution describes the change law of the vortex geometric size, and the main frequency is usually related to the life cycle of the vortex. The spectrum of the energy transfer characteristics characterizes the energy exchange pattern between the vortex and the surrounding flow field, and the peak frequency corresponds to the most active energy transfer process. The main frequency components are identified by the peak of the power spectrum density, and the first three frequencies with the largest amplitude in the power spectrum are selected as the main components. The stability of the frequency components is evaluated by the consistency of the spectrum in different time windows. The frequencies with a consistency coefficient greater than 0.8 are considered to be stable main components.

[0060] Cross-modulation processing is performed using the primary frequency components and the drilling parameter adjustment sequence. Multiplication modulation is employed, and the modulation signal expression is s(t) = A(t) × cos(2πfct + φ), where A(t) is the amplitude envelope of the drilling parameters, fc is the primary eddy current frequency, and φ is the initial phase. The four drilling parameters are modulated with the primary eddy current frequency components, forming four modulation signals. The modulation depth is controlled by the ratio of the parameter variation amplitude to the average value. Excessive modulation depth results in overmodulation distortion, while too low a modulation depth results in a weak modulation effect. The modulation spectrum is obtained by frequency-domain transforming the modulated signal and consists of the carrier frequency and its sideband components. The distribution of the sideband components reflects the modulation effect of the drilling parameters on the eddy current dynamics; larger sideband amplitudes indicate stronger modulation. Multi-carrier modulation considers multiple primary eddy current frequencies and employs frequency division multiplexing to avoid inter-carrier interference. The bandwidth of the modulation spectrum is defined by the 99% power bandwidth to ensure that the primary spectral components are fully preserved.

[0061] Based on the resulting modulation spectrum, spectral analysis methods are used to identify key control points for parameter fusion. Spectral power density is assessed, and frequencies with power density greater than two standard deviations from the mean are defined as energy concentration points. Frequency response sensitivity is calculated using the derivative of the modulation depth with respect to frequency variation; frequencies with large absolute values ​​of the derivative indicate a sensitive response. Key point screening utilizes a dual threshold criterion, with frequency points that meet both energy and sensitivity requirements being selected as key points. The spatial distribution of key points is optimized through cluster analysis to avoid excessive clustering or dispersion. Attribute parameters such as frequency position, power density, sensitivity coefficient, and impact range are recorded for each key point. The stability of key points is verified through time-varying analysis, ensuring that stable key points maintain similar attribute characteristics across different time windows. The number of key points is determined using the information entropy maximization criterion, ensuring sufficient information while avoiding redundancy.

[0062] Parameter variation features are generated by fusing the eddy current parameter sequence and the drilling parameter adjustment sequence. Time-frequency analysis is performed on key points to generate parameter variation features, extracting dynamic features such as amplitude change, phase change, and frequency shift at key frequency points. The amplitude variation feature reflects the modulation effect of drilling parameters on eddy current intensity. An increase in amplitude indicates enhanced eddy current activity due to parameter adjustment, while a decrease in amplitude indicates an inhibitory effect. The phase variation feature describes the temporal relationship between drilling parameters and eddy current dynamics. A phase advance indicates predictive parameter adjustment, while a phase lag indicates a delayed response. The frequency shift feature reflects the nonlinear response of the system. An upward frequency shift generally corresponds to enhanced system excitation, while a downward frequency shift corresponds to weakened excitation. Feature parameters are normalized to ensure comparability between different feature types, with the normalization range set to [-1, 1]. The temporal resolution of the parameter variation feature matches the drilling control cycle, ensuring real-time feature updates. Feature vector construction combines the feature parameters of multiple key points into a high-dimensional vector, where the vector dimension is equal to the number of key points multiplied by the number of feature types. Parameter variation features are ultimately generated by extracting and analyzing the time-frequency features of the key points of parameter fusion.

[0063] Step S160 : constructing a drilling potential field distribution based on the parameter variation characteristics, collecting dispersed stress from the drilling potential field distribution to generate concentrated stress data, and using the concentrated stress data to generate a final parameter combination.

[0064] Specifically, the drilling potential field distribution is constructed based on the parameter change characteristics. The parameter change characteristics are converted into a spatial potential field distribution through potential energy conversion. The dynamic characteristics of the characteristic vector, such as amplitude change, phase change, and frequency offset, are used as potential energy items to construct the potential field intensity distribution. The potential field intensity is calculated using the weighted superposition method, and the intensity function expression is Φ(x,y)=Σ iwi ×F i (x,y), where Φ is the potential field strength, wi is the feature weight, F i is the spatial distribution function of the i-th characteristic component. The weight assignment is determined based on the variance contribution of each characteristic component, and characteristic components with large variances receive higher weights. The spatial resolution of the potential field is set to a 2m×2m grid, covering the entire drilling operation area. The potential field gradient is calculated using the central difference format, which represents the spatial variation direction and rate of change of the potential field intensity. Potential field contour drawing is used to visualize the potential field distribution pattern. Areas with dense contour lines correspond to areas with large potential field gradients. The potential field data is stored in a raster format, and each grid cell contains attribute information such as the potential field intensity value, gradient size, and gradient direction.

[0065] In some embodiments, collecting dispersed stress from the drilling potential field distribution to generate concentrated stress data includes: identifying stress-intensive areas in the drilling potential field distribution; performing stress extraction on the stress-intensive areas to obtain dispersed stress values; and performing spatial convergence processing on the dispersed stress values ​​to form concentrated stress data.

[0066] In the constructed drilling potential field distribution grid data, based on the correspondence between the potential field and the stress field, the potential field intensity gradient is converted into an equivalent stress gradient to identify stress-dense areas. Based on the spatial distribution characteristics of the potential field intensity gradient, areas with gradients exceeding a threshold are defined as potential stress-dense areas. The threshold is set at the potential field gradient mean plus two standard deviations to ensure the identification of significant stress concentrations. Spatial clustering analysis merges adjacent high-gradient grids into continuous dense areas. The clustering algorithm uses a region growing method, with seed points selected as local gradient maxima. Region boundaries are defined by isogradient lines, and the boundary gradient value is set at 70% of the threshold to ensure region integrity. Geometric characteristics of stress-dense areas include regional area, perimeter, shape factor, and principal axis orientation. Region classification is based on average gradient values: Level 1 dense areas have an average gradient greater than three times the threshold, Level 2 dense areas have an average gradient between 1.5 and 3 times the threshold, and Level 3 dense areas have an average gradient between 1 and 1.5 times the threshold. Each dense area is assigned a unique identifier, which records attributes such as its geometric boundary, gradient statistics, and classification level. The coordinates of the region centroid are calculated by area-weighted averaging and used as the representative location of the region.

[0067] A detailed stress field analysis is performed on the identified stress-intensive areas to extract the dispersed stress distribution values ​​within the area. Stress extraction is based on the calculation of the second-order derivative of the potential field, and a local coordinate system is established in each dense area for refined analysis. The mesh encryption process in the area subdivides the original 2m×2m grid into a 0.5m×0.5m high-resolution grid to improve the accuracy of stress calculation. The stress tensor components are calculated using the central difference method, and the normal stress and shear stress components are obtained through the spatial second-order derivative of the potential field value. The principal stress calculation is obtained by solving the eigenvalue of the stress tensor, and the magnitude of the principal stress is The stress direction is determined by the eigenvector, and the principal stress angle is calculated using the inverse tangent function. The dispersed stress values ​​are stored in tensor form, with each grid point recording three stress components and two principal stress values. Stress statistical analysis includes statistics such as the maximum, minimum, mean, and standard deviation of stress within the region. The stress distribution pattern is visualized using stress contours and isostress lines.

[0068] The extracted scattered stress values ​​are spatially aggregated, and concentrated stress data representing the characteristics of the entire dense area are formed through stress integration and weight distribution methods. Spatial aggregation uses the area weighted integration method, and the concentrated stress value calculation formula is: , where σ(x,y) is the stress distribution function in the region, and the integration domain is the boundary of the dense region. The discretization calculation converts the surface integral into a grid summation form, taking into account the stress value and area weight of each grid. The weight distribution takes into account the influence of stress gradient, and the stress value in the high gradient area obtains a greater weight. The convergence of the principal stress direction adopts the vector average method, considering the stress magnitude in each direction as a weight factor. The convergence result includes comprehensive characteristics such as the numerical magnitude of concentrated stress, principal stress direction, and stress concentration coefficient. The position of the stress centroid is determined by stress moment calculation, taking into account the weighted average of stress magnitude and position. The validity of the concentrated stress data is verified by stress conservation, and the total stress integral before and after convergence should be consistent. The data format is stored in point elements, and each concentrated stress point contains attributes such as position coordinates, stress magnitude, direction angle, and concentration coefficient.

[0069] In some embodiments, generating a final parameter combination using the concentrated stress data includes: transferring the concentrated stress data to the drilling core adjustment point to form a stress concentration area; deducing a drilling force transmission path using the stress concentration area; generating a drilling execution plan based on coupling and matching the drilling force transmission path with the parameter variation characteristics; and generating a final parameter combination based on the drilling execution plan. The final parameter combination is generated based on the concentrated stress data.

[0070] Concentrated stress data is transferred to the core adjustment point in the drilling process to form a stress concentration area. The coordinates of the previously determined core adjustment point are used as the stress transfer target, taking into account material parameters such as the elastic modulus and Poisson's ratio of the formation medium. The transfer path follows the shortest distance principle, calculating the straight-line distance from the center of mass of each stress concentrator to the nearest core adjustment point. The adjustment point with the shortest distance is used as the stress transfer target. The stress attenuation model considers the effect of transfer distance, using an exponential attenuation function: σ(r) = σ0 × exp(-r / λ), where σ0 is the initial stress value, r is the transfer distance, and λ is the attenuation length constant. The attenuation length is determined based on the formation type: λ = 50 m for loose formations, λ = 80 m for dense formations, and λ = 120 m for hard formations. Stress superposition considers the transfer of multiple stress concentrators to the same adjustment point, and the resultant stress is calculated using the principle of vector superposition. The stress concentration area is defined as a circular region centered at the core adjustment point and bounded by the effective stress influence radius. The influence radius R = -λ × ln(0.1) ≈ 2.3λ ​​is determined by the distance at which the stress decays to 10% of the initial value. The geometric and mechanical properties of each stress concentration area are recorded, including the center coordinates, concentrated stress value, influence radius, and stress direction.

[0071] Drilling force transmission paths are deduced using stress concentration areas. Based on stress streamline tracing theory, stress concentration areas are treated as force sources and the force propagation paths through the formation are traced. The stress streamline equations are described using the Lagrangian formula: dx / σx=dy / σy=ds, where σx and σy are stress components and ds is the arc length of the streamline. Path tracing utilizes the fourth-order Runge-Kutta numerical integration method with a 1-meter integration step to ensure accurate path tracing. The variation in force transmission intensity along the path is calculated using a stress decay model, with the intensity gradually decaying with transmission distance. Path branch identification addresses the convergence and divergence of stress streamlines, with convergence points corresponding to stress concentration locations and divergence points corresponding to stress release areas. Primary transmission paths are defined as paths with a bearing stress exceeding 50% of the average value, while secondary paths are paths with a bearing stress below the average value. Path connectivity analysis ensures that effective force transmission channels exist in all stress concentration areas. Transmission path data is stored as polyline features, with each path recording attributes such as the coordinates of the start and end points, path length, transmission intensity, and path grade. The path network topology is described by graph theory, where nodes represent stress concentration areas and edges represent transfer paths.

[0072] A drilling execution plan is generated by coupling and matching the drilling force transmission path with parameter variation characteristics. First, spatial location information is identified based on the drilling force transmission path. Key locations, such as node coordinates, path intersections, and force convergence points, are extracted from the transmission path data. Node coordinates are directly obtained from the start and end points of the path elements. Path intersections are identified using a geometric intersection detection algorithm to identify the spatial intersections between different transmission paths. Force convergence points are determined by calculating the force line density at each spatial location. Location importance is assessed based on connectivity and force transmission strength, and the influence range of spatial locations is determined using Voronoi diagram partitioning. Next, spatial location information is used to perform clustering to determine core node locations. An improved K-means algorithm is used, with the number of clusters K determined by the elbow rule to be 3-8. The distance metric is weighted Euclidean distance, with weights determined by location importance scores. Cluster centers are initially selected from the K locations with the highest importance scores. During the iterative process, cluster centers move toward the importance-weighted centroid xc = Σi(wi × xi) / Σiwi. Convergence is criterion: the center position change is less than 1 meter and meets the condition for three consecutive iterations. Then, a matching correlation matrix was established based on the core node locations and parameter variation characteristics. The parameter variation characteristics were mapped to the spatial grid of the operating area through spatial interpolation, resulting in an m×n-dimensional correlation matrix (m is the number of core nodes, n is the number of feature components). The matrix element M(i,j) = α × R_spatial(i,j) + β × R_feature(i,j). The spatial correlation R_spatial = exp(-d² / 2σ²). Feature similarity R_feature was calculated using the eigenvector correlation coefficient, with a correlation strength threshold set at 0.5. Finally, a drilling execution plan was formed by matching the correlation matrix. The matrix row vectors were analyzed to identify the characteristic response patterns of each core node, and the column vectors were used to identify the spatial influence patterns of the feature components. Four strategies, namely power regulation, speed control, torque regulation, and pressure control, were formulated based on node-feature pairs with high correlation strength (>0.5). Strategy parameters were determined by quantitative mapping of the correlation strength. The execution sequence was arranged based on the spatial order of the core nodes and the feature time series. Ultimately, a complete drilling execution plan was formed, including control strategy types, adjustment parameters, and execution sequence.

[0073] The final parameter combination is generated based on the drilling execution plan. The control strategy in the execution plan is converted into specific equipment operating parameters. Core parameters include four key control variables: drilling power, feed rate, rotary torque, and axial pressure. Parameter values ​​are extracted and digitized from the execution plan's strategy module. Parameter boundaries are determined based on equipment specifications: drilling power range 50-500 kW, feed rate range 0.1-5.0 m / min, rotary torque range 1000-8000 N·m, and axial pressure range 10-200 kN. Formation identification is implemented through a parameter-driven drilling process, leveraging real-time signals such as resistance feedback, vibration response, and feed resistance during drilling to identify formation type and characteristics. The identification algorithm employs a pattern matching approach, comparing measured signals with a library of formation response patterns. The pattern with the highest match corresponds to the current formation type. An adaptive parameter adjustment mechanism is implemented based on the formation identification results and the execution plan's feedback control strategy. If the identified formation type differs from the expected one, the corresponding parameter adjustment strategy is automatically invoked. The adjustment strategy includes two modes: gradual and step adjustment. Gradual adjustment is suitable for slowly changing formations, with the adjustment amplitude controlled within ±10% of the current value. Step adjustment is suitable for sudden formation changes, with the adjustment amplitude reaching ±30%. Real-time parameter adjustment is ensured by a fast response mechanism, with the time from signal detection to parameter adjustment completion controlled within 2 seconds, ultimately completing drilling formation identification and adaptive parameter adjustment.

[0074] In order to implement the drilling formation identification and adaptive parameter adjustment method corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The following is a block diagram of a drilling formation identification and adaptive parameter adjustment system 200 provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The drilling formation identification and adaptive parameter adjustment system 200 provided in an embodiment of the present application includes: The data acquisition module 201 is used to collect multi-source sensor data during the drilling process, wherein the multi-source sensor data includes vibration signals and resistance data, and compositely process the multi-source sensor data to construct a formation response pattern library; A regional analysis module 202 is configured to analyze the regional distribution of parameter responses based on the formation response pattern library to generate a regional distribution map, extract parameter response blank areas from the regional distribution map, reconstruct missing information in the parameter response blank areas to obtain implicit formation characteristics, and establish a candidate set of adjustment points based on the regional distribution map and the implicit formation characteristics; A core identification module 203 is configured to comprehensively evaluate the candidate set of adjustment points to obtain a parameter range and an adjustment potential value of each candidate adjustment point, determine a drilling core adjustment point based on the parameter range and the adjustment potential value, and extract a high-impedance core area and a low-impedance transition area from the drilling core adjustment point; A path planning module 204 is configured to plan an enhanced drilling path for the high-impedance core area, formulate a progressive adjustment direction for the low-impedance transition area, construct a conflicting intersection between the enhanced drilling path and the progressive adjustment direction, perform a harmonic force analysis on the conflicting intersection to determine a primary adjustment path, and obtain an auxiliary adjustment flow based on the primary adjustment path; a parameter processing module 205 for converting the auxiliary regulating flow into an eddy current parameter sequence, converting the main regulating path into a drilling parameter regulating sequence, and generating a parameter variation feature by fusing the eddy current parameter sequence and the drilling parameter regulating sequence; The parameter generation module 206 is configured to construct a drilling potential field distribution based on the parameter variation characteristics, collect dispersed stress from the drilling potential field distribution to generate concentrated stress data, and generate a final parameter combination using the concentrated stress data.

[0075] The aforementioned drilling formation identification and adaptive parameter adjustment system 200 can implement the drilling formation identification and adaptive parameter adjustment method of the aforementioned method embodiment. The optional options in the aforementioned method embodiment also apply to this embodiment and are not described in detail here. The remaining contents of the present application embodiment can be referenced to the contents of the aforementioned method embodiment and are not further described in this embodiment.

[0076] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0077] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. A drilling formation identification and adaptive parameter adjustment method, characterized in that: include: Collecting multi-source sensor data during the drilling process, the multi-source sensor data including vibration signals and resistance data, and performing composite processing on the multi-source sensor data to construct a formation response pattern library; performing parameter response regional distribution analysis based on the formation response pattern library to generate a regional distribution map, extracting parameter response blank areas from the regional distribution map, reconstructing missing information in the parameter response blank areas to obtain implicit formation characteristics, and establishing a candidate set of adjustment points based on the regional distribution map and the implicit formation characteristics; Comprehensively evaluating the candidate set of adjustment points to obtain a parameter range and an adjustment potential value of each candidate adjustment point, determining a drilling core adjustment point based on the parameter range and the adjustment potential value, and extracting a high-impedance core area and a low-impedance transition area from the drilling core adjustment point; Planning an enhanced drilling path for the high-impedance core area, formulating a progressive adjustment direction for the low-impedance transition area, constructing a conflicting intersection between the enhanced drilling path and the progressive adjustment direction, performing a harmonic force analysis on the conflicting intersection to determine a primary adjustment path, and obtaining an auxiliary adjustment flow based on the primary adjustment path; converting the auxiliary regulating flow into an eddy current parameter sequence, converting the main regulating path into a drilling parameter regulating sequence, and generating a parameter variation feature based on a fusion process of the eddy current parameter sequence and the drilling parameter regulating sequence; A drilling potential field distribution is constructed based on the parameter variation characteristics, dispersed stress is collected from the drilling potential field distribution to generate concentrated stress data, and the concentrated stress data is used to generate a final parameter combination.

2. The method according to claim 1, characterized in that The composite processing of the multi-source sensing data to construct a formation response pattern library includes: generating a formation response spectrum by applying frequency excitation to the vibration signal; performing frequency band decomposition on the formation response spectrum to identify formation interface distribution; constructing a formation response envelope based on the formation interface distribution and the resistance data; A formation response pattern library is established based on the formation response envelope.

3. The method according to claim 1, characterized in that The step of reconstructing missing information in the parameter response blank area to obtain implicit formation characteristics includes: Obtaining boundary features of the parameter response blank area; Inferring the internal response law of the parameter response blank area based on the boundary characteristics; Performing data interpolation processing according to the response rule to generate compensation response data; Implicit formation characteristics are reconstructed based on the compensated response data.

4. The method according to claim 1, wherein The extracting of the high-impedance core area and the low-impedance transition area from the drilling core adjustment point comprises: Performing impedance gradient calculation on the drilling core adjustment point to obtain impedance change rate distribution; separating a high gradient component and a low gradient component from the impedance change rate distribution; Performing ratio analysis on the high gradient component and the low gradient component to obtain impedance distribution characteristics; A high-impedance core region and a low-impedance transition region are divided according to the impedance distribution characteristics.

5. The method according to claim 1, wherein The said analysis of the reconciliation force of the conflict intersection to establish the main adjustment path includes: detecting path conflict intensity at the conflict intersection; Performing force balance analysis based on the path conflict strength to obtain balance parameters; generating a harmonic force vector according to the balance parameter; The main adjustment path is established using the harmonic force vector.

6. The method according to claim 1, wherein The step of converting the auxiliary regulating flow into a vortex parameter sequence comprises: Using the auxiliary regulating flow to identify flow rhythm characteristics; Distinguishing the main evolution path from the auxiliary evolution path by the flow rhythm characteristics; Establishing a vortex generation benchmark based on the main evolution path, dividing the auxiliary evolution path into stages using the vortex generation benchmark, and determining the vortex formation stage; The vortex formation stage is used to construct a vortex parameter sequence.

7. The method according to claim 1, characterized in that The generating of parameter change characteristics by performing fusion processing based on the eddy current parameter sequence and the drilling parameter adjustment sequence includes: identifying main frequency components through the eddy current parameter sequence; cross-modulation processing is performed using the main frequency component and the drilling parameter adjustment sequence to form a modulation spectrum; Determining a parameter fusion key point according to the modulation spectrum; The parameter variation features are formed by fusing the key points with the parameters.

8. The method according to claim 1, characterized in that The step of collecting dispersed stress from the drilling potential field distribution to generate concentrated stress data includes: identifying stress-intensive regions in the drilling potential field distribution; Performing stress extraction on the stress-intensive area to obtain a dispersed stress value; The dispersed stress values ​​are spatially aggregated to form concentrated stress data.

9. The method according to claim 1, characterized in that The generating a final parameter combination by using the concentrated stress data includes: transferring the concentrated stress data to the drilling core adjustment point to form a stress concentration area; Deducing a drilling force transmission path using the stress concentration area; Generate a drilling execution plan based on coupling matching between the drilling force transmission path and the parameter change characteristics; A final parameter combination is generated based on the drilling execution plan.

10. A drilling formation identification and adaptive parameter adjustment system, characterized in that: include: A data acquisition module is used to collect multi-source sensor data during the drilling process, wherein the multi-source sensor data includes vibration signals and resistance data, and compositely process the multi-source sensor data to construct a formation response pattern library; a regional analysis module configured to analyze the regional distribution of parameter responses based on the formation response pattern library to generate a regional distribution map, extract parameter response blank areas from the regional distribution map, reconstruct missing information in the parameter response blank areas to obtain implicit formation characteristics, and establish a candidate set of adjustment points based on the regional distribution map and the implicit formation characteristics; a core identification module configured to comprehensively evaluate the candidate set of adjustment points to obtain a parameter range and an adjustment potential value of each candidate adjustment point, determine a drilling core adjustment point based on the parameter range and the adjustment potential value, and extract a high-impedance core area and a low-impedance transition area from the drilling core adjustment point; a path planning module for planning an enhanced drilling path for the high-impedance core area, formulating a progressive adjustment direction for the low-impedance transition area, constructing a conflicting intersection between the enhanced drilling path and the progressive adjustment direction, performing a harmonic force analysis on the conflicting intersection to determine a primary adjustment path, and obtaining an auxiliary adjustment flow based on the primary adjustment path; a parameter processing module, configured to convert the auxiliary regulating flow into an eddy current parameter sequence, convert the main regulating path into a drilling parameter regulating sequence, and generate a parameter variation feature by performing a fusion process based on the eddy current parameter sequence and the drilling parameter regulating sequence; A parameter generation module is used to construct a drilling potential field distribution based on the parameter change characteristics, collect dispersed stress from the drilling potential field distribution to generate concentrated stress data, and use the concentrated stress data to generate a final parameter combination.

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