Method, device and equipment for analyzing and rating vibration condition of drill string based on real-time logging data and medium

Abstract

The invention discloses a method, device, equipment and medium for analyzing and rating the vibration condition of a drill string based on real-time logging data, and the method comprises the steps: obtaining real-time continuous logging parameters based on the feature representation of the vibration condition, including time, well depth, drilling speed, drilling pressure, torque, rotating speed, hook load and mechanical specific energy; performing data processing on the real-time continuous logging parameters to establish a continuous time window; establishing a torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model; and based on the established torsional vibration analysis hierarchical model and the axial vibration analysis hierarchical model, analyzing data in each continuous time window to realize abnormal vibration screening and rating. Therefore, the method can realize preliminary marking and grading of torsional vibration and axial vibration of the drilling column at the bottom of the marine extended reach well, and can be widely applied to marine drilling and completion operation.

Description

TECHNICAL FIELD

[0001] The application relates to a method, device, equipment and medium for analyzing and rating drilling string vibration based on real-time mud logging data, and relates to the field of offshore drilling and completion. BACKGROUND

[0002] In marine oil and gas exploration and development operations, drilling string abnormal vibration is one of the main factors affecting the efficiency of drilling operations. Extended reach drilling technology is widely used in marine drilling operations because it can develop remote oil fields with fewer platforms. As the build-up rate increases and the horizontal section extends, the axial extension of the drilling string increases, the equivalent stiffness decreases, and complex vibration becomes more frequent. Frequent vibration not only reduces the efficiency of the operation, but also easily causes damage to the drilling string and fatigue failure of the drill pipe, shortening the service life of the drilling tool. However, the current operation site mainly relies on manual experience to determine whether the drilling string is in abnormal vibration, which has low accuracy and poor interpretation ability, and individual abnormal situations are easily missed. The manual marking result has a certain subjectivity. In addition, due to the high risk and high cost of marine drilling, downhole monitoring devices are usually not installed in every well, and the cost of obtaining the bottom three-axis acceleration is high, and the further analysis method is not mature.

[0003] Machine learning methods, as emerging technologies, have been applied in the field of vibration recognition, but most of them have poor generalization ability and are rarely used in actual operations, so the reliability is low. Therefore, in general, there is currently a lack of simple and effective methods for identifying and rating complex vibration at the bottom of the well. Complex vibration at the bottom of the well is divided into three categories according to the vibration mode: axial vibration, torsional vibration and lateral vibration. In drilling operations, one type of vibration often causes other vibrations to occur, and the main feature of lateral vibration is the change in the three-axis acceleration of the bottom hole string, while the surface data shows no obvious change. Therefore, the present application mainly analyzes and judges the other two types of vibration, namely torsional and axial vibration, using mud logging data.

[0004] In summary, in the traditional torsional and axial vibration analysis field of marine extended reach wells, there are the following problems: the manual judgment method is mainly based on experience, and the result has a certain subjectivity and poor interpretation ability; due to the three-high particularity of marine drilling, the cost of downhole monitoring devices is high, the analysis method is complex, and the evaluation is difficult; new algorithms lack practical application in the field, have low accuracy and poor generalization ability. SUMMARY

[0005] The present application aims to at least solve one of the problems in the prior art. To this end, in order to solve the above problems, the present application aims to provide a method, device, equipment and medium for analyzing and rating drilling string vibration based on real-time mud logging data, which can realize preliminary marking and grading of torsional and axial vibration of the bottom hole string of marine extended reach wells.

[0006] To achieve the above object, the present application adopts the technical scheme of: In a first aspect, the present application provides a method for analyzing and rating the vibration condition of a drill string based on real-time mud logging data, comprising: Based on the characteristic performance of the vibration condition, real-time continuous mud logging parameters are obtained, including time, well depth, drilling speed, drilling pressure, torque, rotation speed, hook load and mechanical specific energy; The real-time continuous mud logging parameters are subjected to data processing to establish a continuous time window; A torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model are established; Based on the established torsional vibration analysis hierarchical model and axial vibration analysis hierarchical model, the data in each continuous time window is analyzed to realize abnormal vibration screening and rating.

[0007] In some possible implementation manners, the real-time continuous mud logging parameters are subjected to data processing to establish a continuous time window, which comprises a data preprocessing layer, a basis layer, a window expansion layer and a data output layer, wherein: The data preprocessing layer is used for collecting the continuous mud logging parameters and replacing the abnormal points of the collected continuous data set; The basis layer comprises a torsional vibration preliminary marking model and an axial vibration preliminary marking model, the torsional vibration preliminary marking model calculates the drilling speed drop ratio in the window to screen out the local window segment meeting the preset condition as output data, and the axial vibration preliminary marking model calculates the mechanical specific energy range and drilling speed dispersion in the window to screen out the local window segment meeting the preset condition as output data; The window expansion layer is used for expanding the size of the marked window by defining forward and backward expansion data; The data output layer merges the overlapping windows and outputs a new continuous window.

[0008] In some possible implementation manners, the torsional vibration and axial vibration analysis hierarchical model is used for analyzing the torsional vibration of the drill string at the bottom of the well, comprising an input layer, a processing layer, an index layer, a criterion layer and an output layer, wherein: The input layer: according to the different vibration conditions, the input data comprises the continuous window segment after preliminary screening , the continuous segment , the previous segment data , the overall continuous data

[0009] The processing layer: is used for removing the abnormal values of the data segment of the input layer, and processing the data segment of the input layer by using the filter smoothing function and the Welch frequency domain function according to the required vibration condition judgment; Index layer: calculate relevant data indicators, including torque variance, dispersion degree, difference sum of squares, mechanical specific energy variance, and local extreme proportion of the above three parts of data; Criteria layer: set composite judgment conditions and judgment criteria, the composite judgment conditions include basic conditions and accessory conditions, wherein, according to the set judgment criteria, the basic conditions and accessory conditions data of the input window are judged, and the continuous window meeting the conditions is marked as torsional vibration.

[0010] In some possible embodiments, when judging by mechanical specific energy, the following cases are included: Composite judgment condition 1: Basic condition: the mechanical specific energy variance of the current window is greater than the product of the set threshold value and the global mechanical specific energy variance; Additional condition: in this case, other judgment conditions are judged by the current window indicators and global indicators, and if the current window torque variance is greater than the product of the global torque variance and the corresponding threshold value, or the torque variance after the current window self-defined filter function processing is greater than the product of the global torque variance and the corresponding threshold value, the torsional vibration condition is met. Composite judgment condition 2: Basic condition: the mechanical specific energy variance of the current window is greater than the product of the set threshold value and the mechanical specific energy variance of the previous section; Additional condition: in this case, the current window and the previous window section indicators are used for judgment, and the torque difference sum of squares of the current window is greater than the torque difference sum of squares of the previous window section and the mechanical specific energy local extreme proportion is greater than the set value; or the torque dispersion degree of the current window is greater than the torque dispersion degree of the previous window and the mechanical specific energy local extreme proportion is greater than the set value. Using the above two composite judgment conditions, if the basic condition is met, as long as any one of the additional conditions is met, the corresponding window is marked as torsional vibration.

[0011] In some possible embodiments, an axial vibration analysis hierarchical model is established for analyzing the axial vibration of the drill string at the well bottom, including an input layer, a processing layer, an index layer, a criteria layer, and an output layer, wherein: Input layer: obtain the current window data set and the current window previous section data set; Data processing layer: discard the abnormal data points in the window by IQR to input the index layer, and define the Welch frequency domain processing function to perform relevant operations on the data after IQR removal of abnormal values as another part of the output to represent the frequency characteristics of the window internal parameters: Index layer: calculate relevant data indicators, including torque variance, large hook load variance, average speed, large hook load total energy, and weight on bit variance of the above two parts of data; The criterion layer sets a composite judgment condition and a judgment criterion. The composite judgment condition includes a basic condition and an accessory condition. According to the set judgment criterion, the basic condition and the accessory condition data of the input window are judged. The continuous window meeting the condition is marked as having axial vibration.

[0012] In some possible implementation manners, when the torque variance is taken as an index for judgment, the following cases are included: The basic judgment condition is that the current window torque variance is greater than the previous torque variance: The accessory condition is that the current window large-hook load variance is greater than the previous large-hook load variance, and the current window drilling speed average is less than the previous window drilling speed average; or, The absolute value of the difference between the current window large-hook load total energy and the previous large-hook load total energy is greater than a set value, and the current window drilling speed average is less than the previous window drilling speed average; or, The current window drilling pressure variance is less than the previous drilling pressure variance, and the current window drilling speed average is less than the previous drilling speed average; If the basic judgment condition is met and any of the above accessory conditions is met on this basis, the corresponding window is marked as having axial vibration.

[0013] In some possible implementation manners, abnormal vibration screening and grading are implemented. Specifically, a mechanical specific energy coefficient is set, the mechanical specific energy coefficients are arranged in descending order, and 15% and 50% are set to divide the vibration levels into severe vibration, medium vibration and slight vibration.

[0014] In a second aspect, the present application further provides a device for analyzing and grading the vibration condition of a drill string based on real-time mud logging data, comprising: A parameter acquisition unit configured to acquire real-time continuous mud logging parameters based on the characteristic performance of the vibration condition, including time, well depth, drilling speed, drilling pressure, torque, rotating speed, large-hook load and mechanical specific energy; A data processing unit configured to perform data processing on the real-time continuous mud logging parameters to establish a continuous time window; A model establishing unit configured to establish a torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model; An analysis and grading unit configured to analyze the data in each continuous time window based on the established torsional vibration analysis hierarchical model and axial vibration analysis hierarchical model, to realize abnormal vibration screening and grading.

[0015] In a third aspect, the present application further provides an electronic device, comprising at least one processor and a memory connected with the processor in communication; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor to enable the processor to execute the method.

[0016] Fourthly, the present invention also provides a computer-readable storage medium for storing one or more programs, wherein the one or more programs include computer instructions for causing a computer to perform the method.

[0017] Because the present invention adopts the above technical solution, it has the following characteristics: 1. In response to the high cost of installing detection equipment and the complexity of experimental simulations in offshore drilling and completion operations, this invention utilizes readily available real-time logging parameters and, through data processing and other operations, defines indicators that conform to objective conditions for judgment. Theoretically, it satisfies the relevant characteristic performance when complex vibrations occur, thereby reducing the impact of data acquisition errors.

[0018] 2. Compared with acquiring all data at once for overall analysis, this invention effectively reduces interference problems at different times and well sections by defining a sliding window, thereby improving the locality and timeliness of data analysis.

[0019] 3. Compared with using sensors to acquire downhole triaxial acceleration data, this invention constructs a continuous sliding window and combines filtering functions and Welch function methods to perform spectral analysis on surface parameters. It explores and simulates an analysis approach without directly acquiring vibration signals, reduces dependence on downhole monitoring equipment, and simulates signal analysis and processing from another perspective.

[0020] 4. This invention defines composite judgment conditions under different conditions to judge and evaluate torsional vibration and axial vibration. By setting reasonable thresholds and judgment mechanisms, it effectively reduces the error caused by individual judgment indicators and improves the objectivity of vibration state identification.

[0021] 5. This invention, through data processing, establishes a continuous time window and uses filtering functions and Welch spectrum analysis methods to simulate signal analysis, thereby calculating the relevant parameter indices; then, by constructing composite judgment conditions and judgment criteria, a vibration level assessment standard is realized, marking the intervals where torsional vibration and axial vibration occur, and classifying them into levels, ultimately achieving preliminary marking and classification of torsional vibration and axial vibration of the drill string at the bottom of marine large-displacement wells.

[0022] In summary, this invention can be widely applied to offshore drilling and completion operations. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 Overall flow framework for bottom hole torsional and axial vibration tagging and rating in embodiments of the present application.

[0024] Figure 2 Architecture for data processing and initial data screening in embodiments of the present application.

[0025] Figure 3 Hierarchical architecture for bottom hole torsional vibration tagging in embodiments of the present application.

[0026] Figure 4 Hierarchical architecture for bottom hole axial vibration tagging in embodiments of the present application.

[0027] Figure 5 Torsional vibration tagging and rating intervals in embodiments of the present application.

[0028] Figure 6 Axial vibration tagging and rating intervals in embodiments of the present application.

[0029] Figure 7 Field manual torsional vibration tagging intervals in embodiments of the present application.

[0030] Figure 8 Field manual axial vibration tagging intervals in embodiments of the present application. DETAILED DESCRIPTION

[0031] It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and "has" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order

[0032] Although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can be only used to distinguish one element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates otherwise, terms such as "first", "second" and the like used herein do not imply a sequence or an order, but rather are used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of example embodiments.

[0033] For ease of description, spatial relative terms can be used herein to describe the relationship of one element or feature to another element or feature as shown in the drawings, such as "inner", "outer", "inside", "outside", "lower", "upper", etc. Such spatial relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the drawings.

[0034] In the field of traditional torsional and axial vibration analysis of ocean large displacement well operation, there are problems of expensive cost of bottom hole monitoring device, complex analysis method, difficult evaluation, lack of practical application of new algorithm, low accuracy, and poor generalization ability. The method, device, equipment and medium for analyzing and rating the vibration condition of a drill string based on real-time mud logging data provided by the present application preliminarily identify and rate the bottom hole torsional vibration and axial vibration condition of the ocean large displacement well, including: based on the characteristic performance of the vibration condition, acquiring real-time continuous mud logging parameters, including: time, depth, drilling speed, drilling pressure, torque, rotation speed, large hook load and mechanical specific energy; performing data processing on the real-time continuous mud logging parameters to establish a continuous time window; establishing a torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model; based on the established torsional vibration analysis hierarchical model and axial vibration analysis hierarchical model, analyzing the data in each continuous time window to realize abnormal vibration screening and rating. Therefore, the present application can realize early identification and evaluation of the complex vibration condition of the ocean drilling downhole, and improve drilling efficiency and safety.

[0035] Exemplary embodiments of the present application will be described in greater detail below, with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0036] Example 1: As Figure 1As shown, the method for analyzing and rating the vibration condition of the drill string based on real-time mud logging data provided by the embodiment comprises: S1, acquiring real-time continuous mud logging parameters.

[0037] In this embodiment, the real-time continuous mud logging parameters are acquired in combination with the characteristic performance of the vibration condition, and the parameters for evaluation are preliminarily screened. The preliminarily screened parameters for evaluation include the time (TIME), the well depth (DBTM), the drilling speed (ROP), the drilling pressure (WOB), the torque (TQA), the rotating speed (RPM), the hook load (HKLA), and the mechanical specific energy (MSE) under the condition of 1hz.

[0038] S2, data processing and initial window screening.

[0039] As shown in the embodiment, Figure 2 the data processing and initial window screening include a data preprocessing layer, a basis layer, a window expansion layer, and a data output layer.

[0040] Further, the data preprocessing layer includes data collection and data processing, wherein: Data collection: The continuous data required for processing is acquired, and the adjacent interval of the continuous data is required to be 1s, and the frequency is 1hz, including the time (TIME), the well depth (DBTM), the drilling speed (ROP), the drilling pressure (WOB), the torque (TQA), the rotating speed (RPM), the hook load (HKLA), and the mechanical specific energy (MSE).

[0041] Data processing: The four-quartile range method (IQR) is adopted to process all the continuous data, and the local median value replacement method is adopted for the abnormal points identified by the IQR, that is, the median of the most 15 adjacent normal data points before and after the abnormal point is replaced, and the number of adjacent points can be selected according to the actual needs, and the calculation formula of the IQR method is:

[0042] Specifically, the method for determining whether a data point is an abnormal point is as follows: The data is sorted in ascending order, wherein, is the value at the 25th percentile, is the value at the 75th percentile. IQR is the four-quartile range, reflecting the dispersion degree of 50% of the data, and are the lower limit and the upper limit, respectively. The data points lower than and higher than are abnormal points.

[0043] Further, the basis layer comprises a torsional vibration preliminary marking model based on the change of drilling speed and an axial vibration preliminary marking model based on the change of mechanical specific energy and the fluctuation of drilling speed, wherein: The torsional vibration preliminary marking model screens out a local window segment with a drop ratio greater than 50% as output data by calculating the drop ratio of ROP in a window (calculating the adjacent ROP difference in the window, if the calculated difference is negative, it is marked as a drop point, otherwise it is marked as a rise point, and the drop ratio of ROP is the ratio of the total number of drop points to the total number of drop points and rise points); the initial sliding window can be set to 30, for example, which is not limited thereto, and the number of initial sliding windows can be set according to actual needs. Among them, according to the characteristics of the collected data, the sliding window is defined as a continuous analysis interval of a fixed number of drilling data, for example, when the initial sliding window is set to 30, the first window is the corresponding drilling data of sequence 1 to 30, the second window is the corresponding drilling data of sequence 2 to 31, the third window is the corresponding drilling data of sequence 3 to 32, and so on, and the nth window is the corresponding drilling data of sequence n to n+29; since the input data is 1hz, i.e. 1s interval continuous drilling data, it can be considered that the window is a time window.

[0044] The axial vibration preliminary marking model selects window data with a large MSE range of all data, and further calculates the ROP dispersion in these continuous segments. The local window segment with a dispersion greater than a set threshold (set to 0.02) is selected as output data.

[0045] Further, the window expansion layer is used to expand the size of the marked window by defining forward and backward expansion data. For example, for torsional vibration, after data preprocessing, the initial window size is set to 30; since the window is a continuous sliding window, it can traverse all data to determine the relationship between the current data and the previous data (used to calculate the drop ratio of ROP), and after the first step of processing, the local continuous segments that meet the conditions are marked. Assuming that the two marked continuous segments are sequence 41 to sequence 66 and sequence 75 to sequence 114, and the forward and backward expansion data is set to 15, then after expansion, the two continuous sequences are expanded to sequence 26 to sequence 81 and sequence 60 to sequence 129.

[0046] Further, the data output layer merges the overlapping windows and outputs new continuous windows. For example, the first data is sequence 26 to 81, and the second data is 60 to 129. At this time, the two data have obvious overlap, i.e. there is overlap between sequence 60 and sequence 81, so after merging the overlapping windows, the new data segment is sequence 26 to sequence 129, which is output as new continuous window data.

[0047] S3. Establish a hierarchical model for the analysis of torsional vibration and axial vibration.

[0048] In this embodiment, a hierarchical model for torsional vibration and axial vibration analysis is established, including: S31. Establish a hierarchical model for torsional vibration analysis.

[0049] In this embodiment, as Figure 3 As shown, the torsional vibration analysis hierarchical model is used to analyze the torsional vibration of the drill string at the bottom of the well. It includes an input layer, a processing layer, an index layer, a criterion layer, and an output layer. The specific implementation method is as follows: S311, Input Layer: Depending on the vibration conditions, the input data includes continuous window segments after initial screening. Continuous segments Previous data Overall continuous data (The overall data is the entire segment of the initial input). It is assumed that the segment is ultimately labeled after the preceding steps. For sequences 178 to 257, the first part here... This refers to the unlabeled raw data, i.e., the input data, sequences 147 to 177 (initial window size 30). Detailed explanations are provided for some cases: Case 1: Assumption The segment is sequence 15 to 44. Since the window size is 30, expanding forward will result in negative values. Therefore, the previous segment... Sequences 1 to 14; Case 2: Assume two consecutive segments , Sequences 50 to 70 and 75 to 100 are respectively; at this point, the second segment... After forward expansion The sequences remain 44 to 74, although some data are in the already labeled sequences. However, this does not affect the final result calculation, because comparing the previous and subsequent segments is only to determine the change of the current segment in the overall data relative to the previous continuous time period.

[0050] In summary, taking a filtered continuous data segment as an example, the input data mainly consists of three parts: the filtered continuous time period data, the previous data segment in the overall structure of the current data segment, and the overall data segment.

[0051] S312, Processing Layer: Used to perform IQR outlier removal on the data segments of the input layer, and to process the data segments of the input layer using filtering smoothing functions and Welch frequency domain functions according to the required vibration conditions. Specifically, IQR processing is performed on the three parts of data in S311 respectively, and outliers within the window are directly discarded.

[0052] It should be noted that for the current input data part, for example, define a 6-order low-pass function to change the TQA screening, so that it meets the characteristics of low frequency in a certain form, where the low-pass function is as follows: (1); In the formula, indicates the normalized cutoff frequency, indicates the cutoff frequency, set to 0.1hz, indicates the Nyquist frequency, which is half of the sampling frequency, 0.5hz, is the transfer function, which mainly describes the frequency characteristics through inverse Laplace transform, is the filter order, set to = 6, is a zero-phase filter, which eliminates delay by forward and reverse filtering, and outputs the amplitude and phase of the signal, w is a general angular frequency variable, which is used to describe the attenuation characteristics of the analog filter for signals of different frequencies ，b is a polynomial coefficient vector in the numerator of the digital filter transfer function, corresponding to the "weight of the input signal delay term" in the filter formula. a is a polynomial coefficient vector in the denominator of the digital filter transfer function, corresponding to the "weight of the output signal delay term" in the filter formula.

[0053] Further, the digital filter function is as follows:

[0054] In the formula, indicates the weight of the current value of the input signal, indicates the weight of the 1-step delayed value of the input signal, indicates the weight of the n-step delayed value of the input signal. The 1 in the denominator indicates the weight of the current value, indicates the weight of the 1-step delayed value of the input signal, indicates the weight of the n-step delayed value of the input signal.

[0055] S313, indicator layer: calculate relevant data indicators respectively, including torque variance, dispersion degree, difference sum of squares, mechanical specific energy variance and local extreme proportion of the above three parts of data, etc. The specific formula is: (2) (3) (4) (5) Wherein, the formula (2) to (5) are respectively the mean, variance, standard deviation and dispersion degree of the continuous segment in the current window, is the mean, is the variance, is the standard deviation, is the dispersion degree. Wherein, is the current time step of the time window, is the value corresponding to the time step i, is the total length of the time window.

[0056] (6) Wherein, the formula (6) shows the calculation formula of the local extreme value ratio in each group of windows, is the selected data length for calculation in the window, is the value input by the current calculation length, is the local mean of the current calculation length, is the local standard deviation of the current calculation length, is the set threshold value, is a judgment function, when the conditions in the function are met, the output is 1, otherwise, the output is 0.

[0057] (7) Wherein, the formula (7) shows the calculation formula of the difference square sum in each group of windows. The difference square sum is one of the energy indicators for evaluating the signal fluctuation strength. In the torsional vibration case judgment, it is mainly used to calculate the fluctuation energy size of TQA, as one of the TQA change amplitudes of adjacent points in the simulation window, is the current window length, is used to represent the first-order difference of adjacent time points.

[0058] S314, criterion layer: In this embodiment, the criterion layer is provided with a judgment criterion and a composite judgment condition. For the ground condition of torsional vibration, the torque is mostly low-frequency high-amplitude fluctuation. When setting the corresponding judgment criterion, this embodiment only takes the mechanical specific energy as the basis for judgment, and expands other limiting conditions, including the following cases: (1) Composite judgment condition 1: Basic condition: the variance of the current window mechanical specific energy is greater than the product of the set threshold value and the global mechanical specific energy variance, wherein the set threshold value is 1.45, and this is only an example, not limited thereto.

[0059] Additional condition: in this case, other judgment conditions are judged by current window indicators and global indicators. In order to meet the torsional vibration condition, the current window torque variance needs to be greater than the product of the global torque variance and the corresponding threshold value; or the torque variance after the current window self-defined filter function processing is greater than the product of the global torque variance and the corresponding threshold value.

[0060] (2) Composite judgment condition 2: Basic condition: the current window mechanical specific energy variance is greater than the product of the set threshold value and the previous mechanical specific energy variance.

[0061] Additional condition: in this case, the current window and the previous window segment indicators are judged. The current window torque difference square sum is greater than the previous window segment torque difference square sum, and the mechanical specific energy local extremum proportion is greater than 25%; or the current window torque dispersion degree is greater than the previous window torque dispersion degree, and the mechanical specific energy local extremum proportion is greater than 25%.

[0062] Using the above two composite judgment conditions, if the basic condition is met, as long as any one of the additional conditions is met, the corresponding window is marked as torsional vibration.

[0063] S32, axial vibration analysis hierarchical model.

[0064] In this embodiment, as shown in Figure 4 , the axial vibration analysis hierarchical model includes an input layer, a processing layer, an indicator layer, a criterion layer and an output layer, and the specific implementation process is as follows: S321, input layer.

[0065] In this embodiment, unlike torsional vibration judgment, in the input layer of axial vibration analysis data, the global data is discarded, and only the current window data set and the previous segment data set of the current window are used as the input layer.

[0066] S322, data processing layer.

[0067] In this embodiment, in the data processing layer, the input parameters are subjected to IQR to directly discard the abnormal data points in the window as the input of the indicator layer, and the Welch frequency domain processing function is defined to perform related operations on the data after IQR removal of abnormal values as another part of the output, to represent the frequency characteristics of the window internal parameters. Among them, the Welch frequency domain energy function is as follows: (8) Among them, is the main function, the output is the energy value of each point in the window, K is the number of windows after preliminary screening, f sFor the sampling frequency, for the present application, the sampling frequency is designated as 1, M is the average energy of each window function, N is the number of data points in the window, respectively the Kth data signal and hann the window function calculation formula, is the defined adjacent segment overlap point, is the imaginary unit.

[0068] S323, index layer.

[0069] In this embodiment, in addition to the conventional variance, standard deviation, etc. of the torsional vibration case, the torque variance, the hook load variance, the average rotating speed, the total energy of the hook load, and the WOB variance of the two parts of data are included. The axial vibration preliminary screening range is to screen high MSE value range and ROP fluctuation range.

[0070] Further, the parameter fluctuation (range) formula is shown in formula (9): (9) In the formula, is the parameter x fluctuation value (range), and are the maximum and minimum values of the parameter x in the current calculation window, respectively.

[0071] In addition, the parameter energy value in the corresponding window also needs to be simulated: (10) In which, as shown in formula (10), it is a window frequency domain feature calculation method for measuring the energy size. is the dominant frequency of the parameter in the window, is the corresponding frequency component; are the dominant frequency energy peak value, the dominant frequency total energy, the first energy value in the window, respectively.

[0072] S324, criterion layer.

[0073] In this embodiment, for the judgment criterion of axial vibration, it must meet the following basic judgment conditions, and other additional conditions are extended on the basis of the judgment conditions: Basic judgment condition: the torque change variance of the current window is greater than the torque change variance of the previous segment: On this basis, the following any additional conditions also need to be met: The hook load variance of the current window is greater than the hook load variance of the previous segment, and the average drilling speed of the current window is less than the average drilling speed of the previous window; or, The absolute value of the difference between the total energy of the current window large hook load and the total energy of the previous window large hook load is greater than a set value (set to 0.01), and the average drilling speed of the current window is less than the average drilling speed of the previous window; or, The variance of the current window drilling pressure is less than the variance of the previous window drilling pressure, and the average drilling speed of the current window is less than the average drilling speed of the previous window.

[0074] If the basic condition is met, as long as any one of the additional conditions is met, it is marked that the corresponding window has axial vibration.

[0075] S4, abnormal vibration level division.

[0076] In this embodiment, the abnormal vibration rating method arranges the mechanical specific energy coefficients in descending order by setting the mechanical specific energy coefficients, and divides the vibration levels into three levels of severe, medium and slight by setting the 15% and 50% positions, wherein the calculation formula of the mechanical specific energy coefficient is: (11).

[0077] Further, the torsional vibration classification: for the screened torsional vibration continuous group, first calculate the mechanical specific energy mean of all data sets, and calculate the mechanical specific energy coefficient; then, arrange the classification coefficients of each section group in descending order, take the first 15% as the severe vibration condition, 15% to 50% as the medium vibration condition, and the rest as the slight vibration condition.

[0078] Further, the axial vibration classification: for the screened axial vibration continuous group, calculate the mechanical specific energy mean of all data sets, and calculate the mechanical specific energy coefficient and arrange them in descending order, the first 15% is the severe vibration condition, 15% to 50% is the medium vibration condition, and the rest is the slight vibration condition.

[0079] The specific application of the drilling string vibration condition analysis and rating method based on real-time mud logging data provided by the present application will be described in detail below through specific embodiments.

[0080] This embodiment takes a large displacement well in the South China Sea as an example, which is continuously drilled for 2h operation, and the target is to preliminarily screen the intervals of torsional and axial vibration occurring in this time period. Some parameters are shown in Table 1: Table 1 Partial parameter diagram

[0081] Based on the above obtained parameters, the drilling string vibration condition analysis and rating method based on real-time mud logging data provided by the present application comprises: S1, according to the required index to be calculated, the required parameters of the time period are obtained, wherein the required parameters include time, depth, drilling speed, drilling pressure, torque, rotation speed, large hook load and mechanical specific energy.

[0082] S2, after data processing, using continuous time window, preliminary screening index, marking the preliminary range group and window expansion.

[0083] In this embodiment, the data processing method is IQR processing, and the median replacement method is used for abnormal values, and the range of adjacent values is 15.

[0084] In this embodiment, the torsional vibration preliminary marking continuous window is set to 30, and the screening index is mainly based on the reduction of drilling speed; the axial vibration preliminary marking continuous window is set to 30, and the screening index is mainly based on the high mechanical specific energy and high drilling speed fluctuation, and the expansion window is set to 15.

[0085] S3, based on the torsional vibration and axial vibration analysis hierarchical model, the window data required for input is calculated and evaluated, including data processing layer, index calculation layer, judgment criterion layer and output layer.

[0086] Data processing layer: IQR processing is performed on the input window data, and abnormal values are directly discarded for next calculation. According to different vibration conditions, low-pass filter function and Welch frequency energy function are used to further calculate related data.

[0087] Index layer: according to different vibration conditions, the parameters after data processing are used for calculation. The related indexes to be calculated are shown in the following table: Table 2 Calculation index

[0088] Judgment criterion layer: according to the judgment criterion of different conditions shown in the following formula, the input window data is judged, and the continuous window meeting the conditions is marked, wherein formula (12) is the composite discrimination condition 1 of the bottom torsional vibration, formula (13) is the composite discrimination condition 2 of the bottom torsional vibration, and formula (14) is the composite judgment condition of the bottom axial vibration.

[0089] (12) In the formula, and respectively represent the mechanical specific energy variance of the current window and the global overall data mechanical specific energy variance; and respectively represent the torque variance of the current window and the global overall data torque variance; and respectively represent the torque variance after self-defined low-pass filter function processing of the current window and the torque variance after low-pass filter function processing of the global overall data; is a set threshold, which is set to 1.45 in this embodiment.

[0090] As long as any one of the right conditions is met, the left condition is met.

[0091] (13) In the formula, And The mechanical specific energy variance of the current window and the mechanical specific energy variance of the previous data ; And The torque difference square sum of the current window and the torque difference square sum of the previous data ; The mechanical specific energy local extremum proportion of the current window, the threshold value is set to 0.25, which can be selected according to actual conditions; And The torque dispersion of the current window and the torque dispersion of the previous data. As long as any one of the right conditions is met, the left condition is met.

[0092] (14) In the formula, And The torque change variance of the current window segment and the torque change variance of the previous segment ; And The large hook load change variance of the current window segment and the large hook load change variance of the previous segment; And The average drilling speed of the current window segment and the average drilling speed of the previous segment; And The total energy of the large hook load of the current window segment and the total energy of the large hook load of the previous segment, and the threshold value is set to 0.01, which can be adjusted according to actual conditions; And The WOB variance of the current window segment and the WOB variance of the previous segment. As long as any one of the right conditions is met, the left condition is met.

[0093] Output layer: output the continuous window data after judgment, which is the window judged to have related vibration, and use the mechanical specific energy coefficient to evaluate.

[0094] After processing, the final situation is shown in Figures 5 to 8 After comparing with the field marking situation, the successful marking proportion of torsional vibration is 85.38%, the successful marking proportion of axial vibration is 78.91%, and the marking area level is basically close, which is suitable for assisting field workers to preliminarily mark and evaluate.

[0095] Embodiment two: the above embodiment one provides a method for analyzing and rating the drilling string vibration condition based on real-time logging data, and correspondingly, the present embodiment provides a device for analyzing and rating the drilling string vibration condition based on real-time logging data. The device provided by the present embodiment can implement the method for analyzing and rating the drilling string vibration condition based on real-time logging data of embodiment one. The device can be realized by software, hardware or a combination of software and hardware. For the convenience of description, the device is described as various units in the function. Of course, in the implementation, the functions of the units can be realized in the same or multiple software and / or hardware. For example, the device can include integrated or separate functional modules or functional units to perform the corresponding steps in the method of embodiment one. Since the device of the present embodiment is basically similar to the method embodiment, the description process of the present embodiment is relatively simple, and the related parts can be referred to the part of the description of embodiment one. The device for analyzing and rating the drilling string vibration condition based on real-time logging data provided by the present application is only illustrative.

[0096] Specifically, the device for analyzing and rating the drilling string vibration condition based on real-time logging data provided by the present application comprises: The parameter acquisition unit is configured to acquire real-time continuous logging parameters including time, depth, drilling speed, drilling pressure, torque, rotating speed, large hook load and mechanical specific energy based on the characteristic performance of the vibration condition. The data processing unit is configured to perform data processing on the real-time continuous logging parameters to establish a continuous time window. The model establishing unit is configured to establish a torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model. The analysis and rating unit is configured to analyze the data in each continuous time window based on the established torsional vibration analysis hierarchical model and axial vibration analysis hierarchical model to realize abnormal vibration screening and rating.

[0097] Embodiment three: the present embodiment provides an electronic device corresponding to the method for analyzing and rating the drilling string vibration condition based on real-time logging data provided by the present embodiment one. The electronic device can be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment one.

[0098] The electronic device comprises a processor, a memory, a communication interface and a bus. The processor, the memory and the communication interface are connected through the bus to complete the communication among each other. The memory stores a computer program which can run on the processor. When the processor runs the computer program, it executes the method of embodiment one, and the implementation principle and technical effects are similar to those of embodiment one, which will not be described here.

[0099] In a preferred embodiment, the logic instructions in the memory described above can be realized in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an optical disc, and various media that can store program codes.

[0100] In a preferred embodiment, the processor can be a central processing unit (CPU), a digital signal processor (DSP), and various types of general-purpose processors, which are not limited here.

[0101] Embodiment four: the embodiment provides a computer readable storage medium storing one or more programs, and the one or more programs include computer instructions, which, when executed by a computer, cause the computer to execute the method provided in the above embodiment one.

[0102] In a preferred embodiment, the computer readable storage medium can be a tangible device that maintains and stores the instructions for execution by a processor, such as, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer readable storage medium stores computer program instructions, which cause the computer to execute the method provided in the above embodiment one.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (apparatus), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0106] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. In the description of the present specification, the description referring to the terms "one preferred embodiment", "further", "specifically", "in the present embodiment", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present specification. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0107] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing and rating the vibration condition of a drill string based on real-time mud logging data, characterized in that, The method comprises the following steps: Based on the characteristics of the vibration situation, real-time continuous logging parameters are obtained, including time, well depth, drilling speed, drilling pressure, torque, rotation speed, large hook load and mechanical specific energy; Data processing is performed on the real-time continuous logging parameters to establish a continuous time window; A torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model are established; Based on the established torsional vibration analysis hierarchical model and axial vibration analysis hierarchical model, data in each continuous time window is analyzed to realize abnormal vibration screening and grading.

2. The method for analyzing and rating the vibration condition of the drill string based on real-time mud logging data according to claim 1, characterized in that, The data processing on the real-time continuous logging parameters to establish a continuous time window comprises a data preprocessing layer, a basis layer, a window expansion layer and a data output layer, wherein: The data preprocessing layer is used for collecting continuous logging parameters and replacing abnormal points of the collected continuous data set; The basis layer comprises a torsional vibration preliminary marking model and an axial vibration preliminary marking model, the torsional vibration preliminary marking model filters out a local window segment meeting a preset condition as output data by calculating a drilling speed drop ratio in the window, and the axial vibration preliminary marking model filters out a local window segment meeting a preset condition as output data by calculating a mechanical specific energy range difference and a drilling speed dispersion degree in the window; The window expansion layer is used for expanding the size of the marked window by defining forward and backward expansion data; The data output layer merges overlapping windows and outputs a new continuous window.

3. The method for analyzing and rating the vibration condition of the drill string based on real-time mud logging data according to claim 2, characterized in that, The torsional vibration and axial vibration analysis hierarchical model is used for analyzing the torsional vibration of the bottom hole assembly, comprising an input layer, a processing layer, an index layer, a criterion layer and an output layer, wherein: Input layer: The input data includes the continuous window segment after preliminary screening according to different vibration conditions , the continuous segment the previous segment of data the overall continuous data The processing layer is used for removing abnormal values of the data segment of the input layer, and processing the data segment of the input layer by using a filter smoothing function and a Welch frequency domain function according to the required vibration situation; The index layer calculates related data indexes, including torque variance, dispersion degree, difference sum of squares, mechanical specific energy variance and local extreme value proportion of the above three parts of data; The criterion layer sets a composite judgment condition and a judgment criterion, the composite judgment condition comprises a basic condition and an additional condition, wherein, according to the set judgment criterion, the basic condition and the additional condition data of the input window are judged, and the continuous window meeting the condition is marked as having torsional vibration.

4. The method for analyzing and rating the vibration condition of the drill string based on real-time mud logging data according to claim 1, characterized in that, When the mechanical specific energy is used as an index for judgment, the following conditions are included: Composite judgment condition 1: Basic condition: the current window mechanical specific energy variance is greater than the product of the set threshold value and the global mechanical specific energy variance; Additional condition: in this case, other judgment conditions are judged by the current window index and the global index, if the current window torque variance needs to meet the torsional vibration condition, the current window torque variance needs to be greater than the product of the global torque variance and the corresponding threshold value; or the torque variance of the current window after processing by the self-defined filter function is greater than the product of the global torque variance and the corresponding threshold value; Composite judgment condition 2: Basic condition: the current window mechanical specific energy variance is greater than the product of the set threshold value and the previous segment mechanical specific energy variance; Additional condition: in this case, the current window and the previous window segment index are judged, the current window torque difference square sum is greater than the previous window segment torque difference square sum, and the mechanical specific energy local extreme value proportion is greater than the set value; or the current window torque dispersion degree is greater than the previous window torque dispersion degree, and the mechanical specific energy local extreme value proportion is greater than the set value; Adopting the above two kinds of composite judgment conditions, if the basic condition is met, as long as any one of the additional conditions is met, it is marked that the corresponding window has occurred torsional vibration.

5. The method for analyzing and rating the vibration condition of the drill string based on real-time mud logging data according to claim 1, characterized in that, An axial vibration analysis hierarchical model is established for analyzing the axial vibration of the drill string at the well bottom, including an input layer, a processing layer, an index layer, a criterion layer and an output layer, wherein: The input layer: obtains the current window data set and the previous segment data set of the current window; The data processing layer: the input parameters are subjected to IQR to directly discard the abnormal data points in the window as the input of the index layer, and a Welch frequency domain processing function is defined to perform related operations on the data after IQR removal of abnormal values as another part of the output, to represent the frequency characteristics of the parameters in the window: The index layer: calculate related data indexes, including torque variance, hook load variance, average speed, hook load total energy and weight on bit variance of the above two parts of data; The criterion layer: set composite judgment conditions and judgment criteria, the composite judgment conditions include basic conditions and additional conditions, wherein, according to the set judgment criteria, the basic conditions and additional condition data of the input window are judged, and the continuous window that meets the conditions is marked as having occurred axial vibration.

6. The method for analyzing and rating the vibration condition of the drill string based on real-time mud logging data according to claim 5, characterized in that, When the torque variance is used as the index for judgment, the following conditions are included: Basic judgment condition: the current window torque variance is greater than the previous segment torque variance: Additional condition: the current window hook load variance is greater than the previous segment hook load variance, and the current window average drilling speed is less than the previous window average drilling speed; or, The absolute value of the difference between the current window hook load total energy and the previous segment hook load total energy is greater than the set value, and the current window average drilling speed is less than the previous window average drilling speed; or, The current window weight on bit variance is less than the previous segment weight on bit variance, and the current window average drilling speed is less than the previous segment average drilling speed; If the basic judgment condition is met and any of the above additional conditions is met on this basis, it is marked that the corresponding window has occurred axial vibration.

7. The method for analyzing and rating the vibration condition of a drill string based on real-time mud logging data according to claim 1, characterized in that, Abnormal vibration screening and grading are realized, specifically: by setting the mechanical specific energy coefficient, arranging the mechanical specific energy coefficient from large to small, and setting the 15% position and the 50% position to divide the vibration level into severe vibration, medium vibration and slight vibration.

8. A device for analyzing and rating the vibration condition of a drill string based on real-time mud logging data, characterized in that, Including: The parameter acquisition unit is configured to acquire real-time continuous logging parameters based on the characteristic performance of the vibration occurrence, including: time, depth, drilling speed, weight on bit, torque, rotational speed, hook load and mechanical specific energy; The data processing unit is configured to process the real-time continuous logging parameters to establish a continuous time window; The model establishment unit is configured to establish a torsional vibration analysis hierarchical model and an axial vibration analysis hierarchical model; The analysis and rating unit is configured to analyze data in each continuous time window based on the established torsional vibration analysis hierarchical model and the axial vibration analysis hierarchical model, so as to realize abnormal vibration screening and rating.

9. An electronic device, comprising: Comprise: At least one processor; And the memory connected with the processor in communication;Wherein, the memory has instructions executable by the processor, the instructions are executed by the processor to enable the processor to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions for: The one or more programs include computer instructions for causing a computer to execute the method according to any one of claims 1-7. The one or more programs include computer instructions for causing a computer to execute the method according to any one of claims 1-7.

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