Method for combination of super-torque and super-long rotor screw drill

Abstract

The application discloses a kind of super-large torque and super-long rotor screw drill combination synergistic method, specifically related to equipment monitoring technical field;Through the construction stratum multi-parameter feature model including acoustic travel time, density, lithology distribution and the like information, and real-time acquisition screw drill under different working conditions torque, vibration, drilling speed and the like feedback data, form response matrix SQ, on this basis, carry out torque adaptability, force transmission efficiency and vibration stability analysis, establish comprehensive evaluation function H and determine combination matching performance, and then structure parameters and working condition parameters are intelligently optimized iteration, the method realizes that screw drill and target stratum are efficiently matched, improve drilling stability and construction efficiency, significantly reduce the engineering risk caused by stratum misjudgment.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, specifically to a method for enhancing the efficiency of a combination of ultra-high torque and ultra-long rotor screw drill bit. Background Technology

[0002] The synergistic effect of combining ultra-high torque with ultra-long rotor screw drills refers to the use of a power system with ultra-high output torque in conjunction with a screw drill with an ultra-long rotor to improve drilling efficiency and stability. This combination can provide stronger power output under high load conditions, while extending the effective contact length of the drill string, improving pressure on the drill bit and wellbore trajectory control, thereby achieving faster drilling speeds, higher mechanical drilling rates, and better downhole performance.

[0003] The existing technology has the following shortcomings: Existing techniques for assessing formation drilling difficulty by inverting uniaxial compressive strength (UCS) from laboratory rock samples or well logging data suffer from misjudgments of UCS due to rock heterogeneity and microscale fractures. This is particularly problematic in complex formations such as shale gas wells, where experimental rock samples often lack representativeness, and well logging data is severely affected by natural fractures, leading to significant UCS inversion biases. This can result in high-risk engineering accidents such as drill string mismatch, drill pressure control failure, and incorrect wellbore stability predictions. Summary of the Invention

[0004] The purpose of this invention is to provide a combined efficiency enhancement method for ultra-high torque and ultra-long rotor screw drills. By constructing a multi-parameter characteristic model of the formation and a drill response feedback matrix SQ, intelligent matching and adaptive optimization between the screw drill and complex formations are achieved, overcoming problems such as UCS misjudgment, drill selection mismatch and drilling instability caused by rock heterogeneity and micro-fractures.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for enhancing the combined efficiency of ultra-high torque and ultra-long rotor screw drill bits, comprising: Acquire formation parameter data for the target well section, including sonic transit time, density, lithology distribution, well diameter variation, and pressure gradient, and construct a multi-parameter geological feature model of the well section; Torque, rotational speed, axial load, displacement, and vibration feedback data of different screw drill bits were collected during the drilling process in the field. A drill bit response feedback matrix SQ={A, M, F} was constructed, where SQ includes the drill bit structural parameter vector A=(A1, A2, ..., A...). m ), m is the number of dimensions of the screw structure parameters, and the working condition loading parameter vector M = (M1, M2, ..., M... n ), where n is the number of actual control parameters, and the drilling feedback vector is F = (F1, F2, ..., F...). k ), where k is the dimension of the drilling response data; Based on SQ data, a multidimensional error analysis was performed on the torque adaptability, force transmission efficiency and vibration stability between the selected screw drill bit and the target formation model. An evaluation function H is established to characterize the matching effectiveness of the drill string assembly in the target well section, and to determine whether the synergistic effect conditions of the assembly are met. If H < H0, then optimize and adjust the screw structure parameter A or the dynamic parameter M, and re-iterate the test; If H≥H0, the current combination parameters and the recommended configuration under the corresponding working conditions will be output, and drill selection and operation optimization suggestions will be generated.

[0006] Preferably, the multi-parameter geological feature model for constructing the well section includes: Collect logging data for the well section, including sonic transit time, density, natural gamma, resistivity, well diameter, and porosity parameters; Preprocessing of well logging data includes data cleaning, normalization, interpolation repair, and standardization of sampling intervals; Construct the input feature vector X = {DT, RHOB, GR, RT, CAL, NPHI, DPHI}, treating each depth point as a sample; Based on core experimental UCS labels or empirical model pseudo-labels, train nonlinear regression models, including random forest regression, support vector machine or gradient boosting tree; The trained model is applied to the target well section to achieve continuous prediction of UCS curves and output hard and soft layer division and layer classification labels.

[0007] Preferably, constructing the drill string response feedback matrix SQ includes: The following drilling data were collected synchronously with the ground sensing system through measurement while drilling: torque, rotational speed, drilling pressure, drilling speed, pump pressure, and axial and radial vibration spectrum. Define the drill string structure parameter vector A = (A1, A2, ..., A... m This includes rotor length, number of stages, stator inner diameter, bushing material type, pitch ratio, and helix angle; Define the load condition parameter vector M = (M1, M2, ..., M... n This includes rotational speed, drilling pressure, mud flow rate, drilling fluid density and viscosity; Define the drilling feedback vector F = (F1, F2, ..., F... k This includes torque fluctuation value, vibration RMS value, WOB real-time value, ROP real-time value, pump pressure fluctuation and displacement response; After normalizing A, M, and F, they are combined into a multidimensional feedback matrix SQ.

[0008] Preferably, the torque adaptability analysis specifically includes: calculating the theoretical torque demand value T. thThe theoretical drilling torque required is calculated from the UCS formation strength prediction model: T th =k1·UCS·D 2 In the formula, k1 is an empirical constant, UCS is the uniaxial compressive strength of the formation, and D is the drill bit diameter. Extract the real-time torque sequence T(t) under a certain working condition from the feedback vector F of SQ, and calculate the mean T. act The expression is: ; Calculate the torque adaptability error E T The expression is: ; Force transmission efficiency analysis specifically includes: defining the drilling pressure W applied to the ground. set The measured downhole WOB is W act Calculate the drilling pressure transmission efficiency E A1 The expression is: Extract the mechanical drilling rate (ROP) from the feedback vector F of the SQ (Screen Quantity) and construct the footage efficiency (E) per unit WOB (Wide Operating Body). A2 The expression is: Overall computing power transmission efficiency E A The expression is: In the formula, α is the weighting coefficient. To maximize advance efficiency; Extracting the vibration spectrum energy value RMS specifically includes: extracting the vibration frequency spectrum V(f) from F, and calculating the RMS value of a certain frequency band, expressed as: ; q represents the frequency spectrum, v i Set a stability upper limit threshold RMS for the spectral amplitude value. max Define the vibration stability value E. V The expression is: ; By combining dimensions, a drill string assembly matching performance evaluation function H is established: H = w1·(1-E) T )+w2·E A +w3·E V In the formula, w1, w2, and w3 are weighting coefficients, and H ∈ [0, 1].

[0009] Preferably, a target matching efficiency threshold H0 is set as the criterion for determining whether the combined synergy is satisfied: If H≥H0: the current screw assembly is considered to have good matching performance in the formation well section; If H < H0: It is considered that there is a mismatch between the structure and the operating conditions in the current combination, and optimization and adjustment should be carried out.

[0010] Preferably, if the evaluation function H < H0, then structural parameter optimization is performed: Increasing the screw stage number z increases the output torque T. output=z·T0; satisfying T output ≥T th ; where: T output T0 represents the output torque of the screw, T0 is the single-stage torque, and z is the number of screw stages. Increase rotor length L to improve length-to-diameter ratio Satisfying R L ≥6.0; Adjusting the stator inner diameter D and the rubber sleeve material optimizes the meshing interference δ=D. stator -D rotor Target: 0.1≤δ≤0.3mm; D rotor This indicates the outer diameter of the rotor in a screw drill, which is the maximum cross-sectional dimension of the rotor when it is meshing with the stator.

[0011] Preferably, if the evaluation function H < H0, the optimization of operating parameters is also included: Reduce the speed (RPM) and increase the pump pressure (P) to improve low-speed torque conversion efficiency. Increase the weight on drill bit (WOB) to improve the efficiency per unit weight on drill bit. ; Adjusting the mud flow rate Q and density ρ enhances bottom hole cooling, cuttings carrying capacity, and pressure balance.

[0012] The beneficial effects of this invention are: 1. This invention achieves intelligent matching and adaptive optimization between screw drills and complex formations by constructing a multi-parameter formation feature model and a drill string response feedback matrix SQ. This overcomes problems in existing technologies such as UCS misjudgment, drill string mismatch, and drilling instability caused by rock heterogeneity and microfractures. Machine learning methods are used to predict UCS from logging data, and real-time feedback data such as drilling pressure, torque, and vibration are integrated. The combined effectiveness is quantitatively judged through an evaluation function H, which not only improves the scientific nature of parameter analysis but also significantly enhances the system's adaptability and responsiveness to different well sections.

[0013] 2. This invention achieves closed-loop optimization adjustment of drill string structural parameters and operating parameters by setting a matching efficiency threshold H0. During the iterative process, it continuously approaches the optimal configuration, thereby improving drilling efficiency and reducing vibration risk and tool damage probability. This method is applicable to geologically complex areas such as shale gas, coalbed methane, and tight oil and gas fields, and possesses high stability, high accuracy, and field applicability. Attached Figure Description

[0014] Figure 1 Mind map of the combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1, please refer to Figure 1 As shown, the present invention provides a method for enhancing the efficiency of a drill bit with ultra-high torque and ultra-long rotor screw, comprising: Acquire formation parameter data for the target well section, including sonic transit time, density, lithology distribution, well diameter variation, and pressure gradient, and construct a multi-parameter geological feature model of the well section; Torque, rotational speed, axial load, displacement, and vibration feedback data of different screw drill bits were collected during the drilling process in the field. A drill bit response feedback matrix SQ={A, M, F} was constructed, where SQ includes the drill bit structural parameter vector A=(A1, A2, ..., A...). m ), m is the number of dimensions of the screw structure parameters, and the working condition loading parameter vector M = (M1, M2, ..., M... n ), where n is the number of actual control parameters, and the drilling feedback vector is F = (F1, F2, ..., F...). k ), where k is the dimension of the drilling response data; Based on SQ data, a multidimensional error analysis was performed on the torque adaptability, force transmission efficiency and vibration stability between the selected screw drill bit and the target formation model. An evaluation function H is established to characterize the matching effectiveness of the drill string assembly in the target well section, and to determine whether the synergistic effect conditions of the assembly are met. If H < H0, then optimize and adjust the screw structure parameter A or the dynamic parameter M, and re-iterate the test; If H≥H0, the current combination parameters and the recommended configuration under the corresponding working conditions will be output, and drill selection and operation optimization suggestions will be generated.

[0017] Based on the wellbore structure design and geological prediction data, the target well section that requires screw drill matching optimization is selected, which is usually a hard formation, a soft-hard alternating formation, or a section with abnormal well diameter.

[0018] Collect raw logging data within the well section, including but not limited to: sonic transit time (DT), density (RHOB), natural gamma (GR), caliber (CAL), resistivity (RT), neutron / density porosity (NPHI, DPHI), and formation pressure-related data (mud specific gravity, wellhead pressure, etc.).

[0019] Data preprocessing includes data cleaning to remove outliers and gaps; normalization and standardization of various curves to unify sampling intervals; and repair of missing data using interpolation, local weighted regression, and other methods.

[0020] Feature parameters are constructed and combined to form the input feature vector X, with each depth point corresponding to one sample. The input format is as follows: X={DT, RHOB, GR, CAL, RT, NPHI, DPHI}. Uniaxial compressive strength (UCS) results from partial core physical experimental data are obtained and used as training labels in the supervised learning model. If experimental data is unavailable, pseudo-labels can be generated using empirical formulas or regional models for weak supervision.

[0021] Based on the characteristics and degree of nonlinearity of the data, select appropriate machine learning algorithms, such as Random Forest Regression, Support Vector Regression (SVR), Gradient Boosting Tree (XGBoost), or Multilayer Perceptron Neural Network (MLP).

[0022] Divide the sample set into a training set and a validation set (e.g., 80% / 20%); use cross-validation to determine the model hyperparameters; fit the nonlinear relationship between the input feature X and the UCS, and output the prediction model UCS=f(X), where f is the model function.

[0023] Using mean squared error (MSE) and coefficient of determination R 2 The predictive performance is evaluated using various indicators; the performance of multiple models is compared, and the model with the smallest error and the strongest stability is selected.

[0024] The trained model is applied to the logging data of the entire well section to predict the UCS point by point, generating continuous UCS curves. By combining the UCS curves with the original logging curves, the well section is divided into hard and soft layers, faults are identified, and anomalous sections are marked.

[0025] The output includes: the UCS prediction value at each depth point, the corresponding multi-parameter logging characteristics, and the layer classification label (e.g., hard layer, weak cross-section, etc.).

[0026] The geological feature model is used as the input interface to the drill string response quality matrix (SQ) analysis module, serving as the basic geological input for subsequent screw assembly efficiency matching and optimization judgment.

[0027] Integrating measurement while drilling (MWD) and / or logging while drilling (LWD) systems into the drill string assembly allows for the real-time acquisition of key physical parameters, ensuring accurate and continuous transmission of downhole data to the surface.

[0028] It works in conjunction with ground sensor systems (such as torque meters, pump pressure recorders, vibration monitors, etc.) to acquire control signals and feedback signals from the drilling rig.

[0029] The following key drilling data were collected simultaneously for subsequent analysis: Torque (T), rotational speed (RPM), weight on bit (WOB), rate of penetration (ROP), axial and lateral vibration spectra, mud pump pressure (Ppump), and bit displacement response, etc.

[0030] Define the drill string response feedback matrix SQ={A, M, F}, where: A=(A1, A2, ..., A m ): Drill tool structural parameter vector, where m is the number of dimensions of the screw structural parameters, which may include, but are not limited to: rotor length, number of rotor stator stages, stator inner diameter, bushing material type, pitch ratio, and helix angle, etc.

[0031] M = (M1, M2, ..., M) n ): The working condition loading parameter vector, where n is the number of field-controllable working condition variables, which may include: surface rotation speed (RPM), applied drilling pressure (WOB set value), mud flow rate (Q), drilling fluid density and viscosity, and screw feed rate, etc.

[0032] F = (F1, F2, ..., F k ): Drilling feedback vector, where k is the number of dimensions of the drilling response data, mainly including: real-time torque fluctuation value (ΔT), vibration spectrum (frequency-amplitude data), real-time WOB (comparison of measured value vs. set value), real-time ROP, mud pump pressure fluctuation value, and displacement fluctuation and force-displacement response coupling data.

[0033] Timestamp alignment was performed on multi-channel data to remove outliers and missing data segments. Time-domain, frequency-domain, and statistical features, such as mean torque, vibration RMS value, ROP change rate, and pump pressure cycle amplitude, were extracted to construct a unified-dimensional feedback vector F. All features in A, M, and F were subjected to interval scaling (e.g., normalized to [0,1]) to enhance the model's versatility.

[0034] SQ sample set construction: one SQ sample point corresponds to each drilling condition. A training dataset is constructed for multidimensional response pattern analysis. The SQ sample set is used as input for subsequent multidimensional error analysis, UCS matching evaluation, optimal screw combination selection, and condition recommendation.

[0035] The output results include: the torque response of each structural parameter A under different geological conditions, the risk analysis of drilling efficiency (ROP) coupled with vibration, and structural improvement suggestions for abnormal feedback. Integrated with the geological model, a unified structure-geology-feedback database is formed, providing data support for screw selection and parameter optimization.

[0036] Using the drill string response feedback matrix SQ={A, M, F}, the torque adaptability, force transmission efficiency and vibration stability of the screw drill string in the target formation model are quantitatively analyzed. Finally, a combined efficiency judgment function H is established for optimization judgment.

[0037] Torque adaptability analysis specifically includes: calculating the theoretical torque demand value T. th The theoretical drilling torque required is calculated from the UCS formation strength prediction model: T th =k1·UCS·D 2 In the formula, k1 is an empirical constant, which is related to the drill bit type and rock type; UCS is the uniaxial compressive strength of the formation (MPa); and D is the drill bit diameter (m); as shown in the table below:

[0038] Extract the real-time torque sequence T(t) under a certain working condition from the feedback vector F of SQ, and calculate the mean T. act The expression is: ; Calculate the torque adaptability error E T The expression is: ;like (Set a threshold) to determine if torque adaptability is good. See the table below:

[0039] Force transmission efficiency analysis specifically includes: defining the drilling pressure W applied to the ground. set The measured downhole WOB is W act Calculate the drilling pressure transmission efficiency E A1 The expression is: Extract the mechanical drilling rate (ROP) from the feedback vector F of the SQ (Screen Quantity) and construct the footage efficiency (E) per unit WOB (Wide Operating Body). A2 The expression is: Overall computing power transmission efficiency E A The expression is: In the formula, α is the weighting coefficient (usually set to 0.6 to 0.8). To achieve maximum advance efficiency, see the table below:

[0040] Extracting the vibration spectrum energy value RMS specifically includes: extracting the vibration frequency spectrum V(f) from F, and calculating the RMS value of a certain frequency band, expressed as: ; q represents the frequency spectrum, v i Set a stability upper limit threshold RMS for the spectral amplitude value. max Define the vibration stability value E. V The expression is: If E VA value less than 0 indicates that the vibration exceeds the allowable limit, requiring structural optimization. See the table below:

[0041] Combining the above three dimensions, we establish a drill string assembly matching performance evaluation function H: H = w1·(1-E) T )+w2·E A +w3·E V In the formula, w1, w2, and w3 are weight coefficients, and their sum is 1 (e.g., 0.3, 0.4, 0.3). H ∈ [0,1]: the larger the value, the better the combination; as shown in the table below:

[0042] Set a target matching performance threshold H0 (e.g., 0.75) as the criterion for determining whether the combined synergy is satisfied: If H≥H0: the current screw assembly is considered to have good matching performance in this formation well section; If H < H0: It is considered that there is a mismatch between the structure and the operating conditions in the current combination, and optimization and adjustment should be carried out.

[0043] In this invention, when the performance evaluation function value H of the drill string assembly in the target well section is less than a set threshold H0 (e.g., 0.75), it indicates that the current structural parameters and operating parameters are not sufficiently matched with the formation conditions, and combined efficiency improvement needs to be achieved through parameter optimization and iterative updates. The optimization process includes joint adjustment of the structural parameter vector A and the operating condition loading parameter vector M, and the determination is made through the real-time response of the feedback matrix SQ.

[0044] Optimization of structural parameter A includes L: the number of lifting screw stages z. The number of lifting stages helps to improve pressure drop and torque output, and its approximate expression is: T output =z·T0, ΔP=z·ΔP0; where: T output ΔP is the screw output torque, and ΔP is the screw output voltage drop; T0 and ΔP0 are the single-stage torque and single-stage voltage drop, respectively; z is the number of screw stages.

[0045] The optimization objective is: T output ≥T th And ΔP≤ΔP max That is, the output torque meets the requirements of the formation, and the total pressure drop does not exceed the pump pressure limit.

[0046] Extend the rotor length L to increase the rotor's length-to-diameter ratio R. L Improve axial stability: The target is R. L ≥6.0, to avoid increased vibration; L rotor(Rotor length, unit: meters, m) refers to the total length of the helical rotor in a screw drill bit from the inlet to the outlet. A longer rotor results in a greater meshing length, which is beneficial for stable power output and reduced drill bit vibration. D stator (Stator inner diameter, unit: meters, m) is the inner diameter of the stator, which is also the diameter of the channel in which the rotor is nested. It reflects the lateral dimension of the drill bit and affects the meshing tightness, pressure drop capacity, and rigidity.

[0047] Optimize the stator inner diameter D and material, and optimize the meshing interference δ between the stator and rotor. The expression is: δ=D stator -D rotor Target: 0.1≤δ≤0.3mm; D rotor This indicates the outer diameter of the rotor in the screw drill bit, i.e., the maximum cross-sectional dimension of the rotor when it meshes with the stator. The unit is usually millimeters (mm) or meters (m). Select the appropriate bushing material based on the formation temperature and abrasion intensity (e.g., upgrading from NBR to HNBR).

[0048] Optimize the rotational speed (RPM) and pump pressure (P), and express the energy output efficiency: In other words, appropriately increasing pump pressure and decreasing speed can enhance the low-speed, high-torque effect, which is beneficial for hard formations.

[0049] Increasing the weight on drill bit (WOB) and calculating the effect of WOB on drilling efficiency on the relationship between WOB and mechanical drilling rate (E) ROP The expression is: Increasing WOB can increase drill bit penetration depth and improve ROP, but it must be controlled within the limits of the equipment.

[0050] Adjusting mud parameters, such as flow rate Q and density ρ, improves cuttings carrying and cooling at the bottom of the well; and increases formation pressure balance, which helps stabilize the wellbore.

[0051] The optimized parameter combination is input into the field system, the drilling feedback vector F is collected, a new drill string response feedback matrix is ​​formed, and the combined effectiveness is recalculated using a multidimensional evaluation function: If H≥H0: This indicates that the combined parameters are well matched, and the configuration result and recommended operating condition window are output. If H < H0: Return to the structure and operating condition optimization step and continue iterating until H reaches the threshold.

[0052] Output recommended configuration and operation suggestions. If H≥H0, perform the following operations: including structural parameter vector A, load condition parameter vector M, and matching performance H value.

[0053] Generate an operation optimization suggestion table, providing recommended operating condition windows, such as: recommended rotation speed range: 120–160 rpm; recommended drilling pressure range: 8–12 t; recommended pump pressure / flow rate combination; suitable well depth / well section range; archive this configuration to the parameter optimization database for subsequent experience transfer and model training updates for similar well sections.

[0054] In this embodiment, a multi-parameter evaluation system integrating formation characteristics and drill string response is constructed. A formation mechanics model is established by collecting logging data. Combined with the screw drill string's structural parameters and operating condition feedback, a drill string response matrix SQ is constructed. An evaluation function H is introduced to comprehensively assess torque adaptability, force transmission efficiency, and vibration stability. When H is below a set threshold, structural parameters (such as screw stage number, rotor length, and stator structure) and operating condition parameters (such as rotational speed, WOB, and mud flow rate) are automatically optimized. Feedback is iteratively calculated until the combined efficiency enhancement conditions are met, thereby achieving efficient matching and stable operation between the drill string and the formation, significantly improving the performance of the screw drill string in complex well sections.

[0055] Example 2: This example selects a horizontal shale gas well section (well depth: 2650–3120m, wellbore diameter φ216mm). This section is a typical area of ​​alternating soft and hard layers and well-developed fractures, often experiencing problems such as low drilling efficiency and high drill string vibration. The goal is to improve drilling performance through parameter optimization using the combined efficiency enhancement method described in this invention.

[0056] Well logging curve acquisition: Obtain raw curves: DT (sonic transit time), RHOB (density), GR (gamma), RT (resistivity), CAL (well diameter), NPHI / DPHI (porosity), with a resolution of 0.125m.

[0057] UCS Predictive Modeling: Using a Random Forest Regression Model (R) 2 =0.84, MSE=6.72) Predict the UCS curve; UCS range: 32–112MPa, with alternating soft and hard layers, and an average variation frequency of once every 12m; Stratigraphic division: Seven hard layer (UCS>80MPa) sections, four soft layer sections, and three interlayer sections were divided; and two sections with high well diameter variation and pressure anomaly were marked.

[0058] Drill string assembly configuration and data acquisition: Original drill string structural parameters (Group A): Rotor length L = 1.2m; Number of screw stages z = 5; Stator inner diameter D = 65mm; Sleeve material: NBR. Operating parameters (Group M): Surface rotation speed RPM = 160; Drilling pressure WOB = 7.5t; Mud flow rate Q = 28L / s; Density ρ = 1.35g / cm³. 3The collected feedback parameters (group F) are as follows: real-time average torque T_avg = 4500 Nm, fluctuation range ±22%; ROP (mechanical drilling rate) = 5.8 m / h; vibration RMS = 8.7; and the bottom hole displacement response is unstable.

[0059] Initial matching performance calculation: Torque adaptability error ε_T=0.26; Force transmission efficiency η=0.58; Vibration stability S=-0.14; Comprehensive evaluation function H=0.61 (lower than H0=0.75).

[0060] Optimization strategy execution: Increase the number of screw stages to z=7; extend the rotor to L=1.6m (L / D ratio increased to 6.15); change the stator material to HNBR to improve heat resistance; reduce the speed to RPM=130 and increase the pump pressure to P=10MPa; increase WOB to 9.8 t and adjust the mud flow rate to 30 L / s.

[0061] Optimized feedback data: T_avg=4800Nm, fluctuation reduced to ±13%; ROP increased to 9.6 m / h; vibration RMS decreased to 5.1; displacement response continuity significantly improved.

[0062] Final matching performance analysis: Torque error ε_T=0.12; Force transmission efficiency η=0.79; Vibration stability S=+0.07; Comprehensive matching evaluation function H=0.83 (>H0, meets optimization requirements).

[0063] By optimizing both structural and operational parameters using the method of this invention, the matching of the screw assembly in the target well section is significantly enhanced, drilling efficiency is increased by 65%, and vibration risk is reduced by more than 40%. This method has good adaptability to formation changes, effectively reducing the risk of misjudgment in selection and improving the safety and economy of drilling operations.

[0064] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0065] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0066] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this document generally indicates an "or" relationship between the preceding and following related objects, but it may also indicate an "and / or" relationship; please refer to the context for specific understanding. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for enhancing the combined efficiency of ultra-high torque and ultra-long rotor screw drill bits, characterized in that: include: Acquire formation parameter data for the target well section, including sonic transit time, density, lithology distribution, well diameter variation, and pressure gradient, and construct a multi-parameter geological feature model of the well section; Torque, rotational speed, axial load, displacement, and vibration feedback data of different screw drill bits were collected during the drilling process in the field. A drill bit response feedback matrix SQ={A, M, F} was constructed, where SQ includes the drill bit structural parameter vector A=(A1, A2, ..., A...). m ), m is the number of dimensions of the screw structure parameters, and the working condition loading parameter vector M = (M1, M2, ..., M... n ), where n is the number of actual control parameters, and the drilling feedback vector is F = (F1, F2, ..., F...). k ), where k is the dimension of the drilling response data; Based on SQ data, a multidimensional error analysis was performed on the torque adaptability, force transmission efficiency and vibration stability between the selected screw drill bit and the target formation model. An evaluation function H is established to characterize the matching effectiveness of the drill string assembly in the target well section, and to determine whether the synergistic effect conditions of the assembly are met. If H < H0, then optimize and adjust the screw structure parameter A or the dynamic parameter M, and re-iterate the test; If H≥H0, the current combination parameters and the recommended configuration under the corresponding working conditions will be output, and drill selection and operation optimization suggestions will be generated.

2. The combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit according to claim 1, characterized in that: The construction of a multi-parameter geological feature model of the well section includes: Collect logging data for the well section, including sonic transit time, density, natural gamma, resistivity, well diameter, and porosity parameters; Preprocessing of well logging data includes data cleaning, normalization, interpolation repair, and standardization of sampling intervals; Construct the input feature vector X = {DT, RHOB, GR, RT, CAL, NPHI, DPHI}, treating each depth point as a sample; Based on core experimental UCS labels or empirical model pseudo-labels, train nonlinear regression models, including random forest regression, support vector machine or gradient boosting tree; The trained model is applied to the target well section to achieve continuous prediction of UCS curves and output hard and soft layer division and layer classification labels.

3. The combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit according to claim 1, characterized in that: Constructing the drill string response feedback matrix SQ includes: The following drilling data were collected synchronously with the ground sensing system through measurement while drilling: torque, rotational speed, drilling pressure, drilling speed, pump pressure, and axial and radial vibration spectrum. Define the drill string structure parameter vector A = (A1, A2, ..., A... m This includes rotor length, number of stages, stator inner diameter, bushing material type, pitch ratio, and helix angle; Define the load condition parameter vector M = (M1, M2, ..., M... n This includes rotational speed, drilling pressure, mud flow rate, drilling fluid density and viscosity; Define the drilling feedback vector F = (F1, F2, ..., F... k This includes torque fluctuation value, vibration RMS value, WOB real-time value, ROP real-time value, pump pressure fluctuation and displacement response; After normalizing A, M, and F, they are combined into a multidimensional feedback matrix SQ.

4. The combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit according to claim 3, characterized in that: Torque adaptability analysis specifically includes: calculating the theoretical torque demand value T. th The theoretical drilling torque required is calculated from the UCS formation strength prediction model: T th =k1·UCS·D 2 In the formula, k1 is an empirical constant, UCS is the uniaxial compressive strength of the formation, and D is the drill bit diameter. Extract the real-time torque sequence T(t) under a certain working condition from the feedback vector F of SQ, and calculate the mean T. act The expression is: ; Calculate the torque adaptability error E T The expression is: ; Force transmission efficiency analysis specifically includes: defining the drilling pressure W applied to the ground. set The measured downhole WOB is W act Calculate the drilling pressure transmission efficiency E A1 The expression is: Extract the mechanical drilling rate (ROP) from the feedback vector F of the SQ (Screen Quantity) and construct the footage efficiency (E) per unit WOB (Wide Operating Body). A2 The expression is: Overall computing power transmission efficiency E A The expression is: In the formula, α is the weighting coefficient. To maximize advance efficiency; Extracting the vibration spectrum energy value RMS specifically includes: extracting the vibration frequency spectrum V(f) from F, and calculating the RMS value of a certain frequency band, expressed as: ; q represents the frequency spectrum, v i Set a stability upper limit threshold RMS for the spectral amplitude value. max Define the vibration stability value E. V The expression is: ; By combining dimensions, a drill string assembly matching performance evaluation function H is established: H = w1·(1-E) T )+w2·E A +w3·E V In the formula, w1, w2, and w3 are weighting coefficients, and H ∈ [0, 1].

5. The combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit according to claim 4, characterized in that: Set a target matching performance threshold H0 as the criterion for determining whether the combined synergy is satisfied: If H≥H0: the current screw assembly is considered to have good matching performance in the formation well section; If H < H0: It is considered that there is a mismatch between the structure and the operating conditions in the current combination, and optimization and adjustment should be carried out.

6. The combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit according to claim 5, characterized in that: If the evaluation function H < H0, then perform structural parameter optimization: Increasing the screw stage number z increases the output torque T. output =z·T0; satisfying T output ≥T th ; where: T output T0 represents the output torque of the screw, T0 is the single-stage torque, and z is the number of screw stages. Increase rotor length L to improve length-to-diameter ratio Satisfying R L ≥6.0; Adjusting the stator inner diameter D and the rubber sleeve material optimizes the meshing interference δ=D. stator -D rotor Target: 0.1≤δ≤0.3mm; D rotor This indicates the outer diameter of the rotor in a screw drill, which is the maximum cross-sectional dimension of the rotor when it is meshing with the stator.

7. The combined efficiency enhancement method of ultra-high torque and ultra-long rotor screw drill bit according to claim 6, characterized in that: If the evaluation function H < H0, it also includes the optimization of operating parameters: Reduce the speed (RPM) and increase the pump pressure (P) to improve low-speed torque conversion efficiency. ; Increasing the weight on drill bit (WOB) improves the efficiency per unit of WOB. ; Adjusting the mud flow rate Q and density ρ enhances bottom hole cooling, cuttings carrying capacity, and pressure balance.

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