A performance evaluation method for gas well live-line working machine slips

Abstract

This invention discloses a performance evaluation method for gas well live-line working machine slips, comprising the following steps: designing an accelerated life test for the slips based on slip performance testing methods; conducting accelerated life tests on the slips to obtain slip failure cycle count data under various stress conditions; selecting an acceleration model based on the accelerated stress type of the slips and the test object to describe the relationship between the slips' life characteristics and stress under mechanical stress; determining the upper and lower limit ranges of the acceleration model connection parameters between various stresses based on the test data, and obtaining survival functions using non-parametric prediction and inference methods respectively; and selecting the lower limit of the survival function as the slip reliability curve based on the actual use of the slips. This invention overcomes the uncertainties and solution difficulties inherent in traditional reliability evaluation methods when analyzing complex machinery with diverse failure modes.

Description

Technical Field

[0001] This invention relates to the field of live drilling and production technology, and in particular to a method for evaluating the performance of slips in a live drilling machine for gas wells. Background Technology

[0002] Slips in live drilling rigs are among the most critical components of oil and gas drilling and extraction equipment. They are widely used in petroleum engineering, mechanical engineering, and automation engineering, playing a vital role in various scenarios including onshore drilling, offshore oil and gas development, and unconventional oil and gas extraction. As the primary load-bearing and fixing component, the slips' main function is to firmly clamp and secure the drill pipe or tubing. During operation, they withstand complex loads such as tensile forces, shear forces, alternating loads, and the impact of high-pressure fluids downhole. Their main failure modes are fatigue fracture, excessive deformation, excessive wear, and the interaction between these modes. Under extreme working environments of high pressure, high temperature, and high corrosion, the material properties or structure of the slips may undergo irreversible changes. In complex downhole conditions, slips are subjected not only to mechanical loads from clamping and fixing, but also to scouring and corrosion from high-pressure fluids, as well as thermal stress caused by temperature changes. Therefore, slip failures are frequent, and their performance and lifespan directly affect the efficiency and safety of drilling operations. Furthermore, a failure can lead to serious blowouts or equipment damage, causing significant economic losses and safety hazards. Wear and deformation are widespread phenomena in mechanical engineering. Component failure caused by wear and deformation can result in substantial economic losses in oil extraction operations and even endanger the lives of workers.

[0003] As a core component of oil drilling equipment, slips are crucial for enabling live drilling operations and ensuring the secure fixation of the tubing string. The performance of slips directly impacts the safety and efficiency of the entire drilling operation. Slip failure has always been a major factor affecting the reliability of drilling equipment. During drilling operations, slips typically bear significant loads and experience extremely complex stresses, including clamping forces, downhole fluid pressure, mechanical vibration, and thermal stress caused by temperature changes. The complex and variable downhole conditions, drill pipe vibration, and fluid pressure fluctuations can all cause slip wear and deformation, leading to poor reliability and even fatigue fracture. With the advancements in petroleum engineering technology in recent years, slip design has made significant progress, making traditional life testing techniques for evaluation challenging. Live drilling machine slips exhibit diverse failure types, and traditional evaluation methods may contain significant errors in assessing reliability and lifespan under all failure types. Summary of the Invention

[0004] The purpose of this invention is to provide a performance evaluation method for gas well pressurized operation machine slips, in order to solve the problems of uncertainty and difficulty in solving problems that exist in traditional reliability evaluation methods when analyzing complex machinery with diverse failure modes.

[0005] This invention is achieved using the following technical solution: a method for evaluating the performance of slips in a gas well pressurized operation machine, comprising the following steps:

[0006] Design an accelerated life test for the Kevlar based on the Kevlar performance test method;

[0007] Accelerated life tests were conducted on the slip to obtain data on the number of slip failure cycles under various stress conditions.

[0008] The acceleration model is selected based on the type of accelerated stress of the kava and the test object to describe the relationship between the life characteristics of the kava under mechanical stress and the stress.

[0009] Based on the experimental data, the upper and lower limit ranges of the acceleration model connection parameters between various stresses were determined, and the survival function was obtained by using non-parametric prediction and inference methods respectively.

[0010] Based on the actual usage of Kava, the lower limit of the survival function is selected as the Kava reliability curve.

[0011] Furthermore, the slip performance testing method conforms to the drilling and workover slip standards, as well as the standards for live-line working machines used in oil and gas fields in the oil and gas industry.

[0012] Furthermore, the accelerated life test specifically refers to: under normal stress and accelerated stress, the slip clamps slide up and down the tubing until performance failure occurs, and the test is stopped. The number of cycles of up and down movement of the tubing during the accelerated life test is recorded as an observation sample.

[0013] Furthermore, the performance failure includes: excessive wear of the slip teeth or breakage of the slip seat weld.

[0014] Furthermore, the acceleration model is an inverse power-law model, which describes the relationship between the life characteristics of the Kava under mechanical stress and the stress.

[0015] Furthermore, the determination of the upper and lower limit ranges of the acceleration model connection parameters between various stresses specifically involves:

[0016] The relationship between the test failure data of the live-line working machine slips under various stresses is described by an inverse power law model, resulting in a transformation model for the failure data under each stress:

[0017] ;

[0018] In the formula, For normal working stress, To accelerate stress, The number of test cycles corresponding to accelerated stress. The following corresponds to the number of test cycles under normal stress, where m is the connection parameter;

[0019] The log-rank test is used to determine the imprecise interval of the connection parameter p between each accelerated stress life data and normal stress data, and the union of these intervals is taken as the final parameter interval.

[0020] Furthermore, the method of obtaining the survival function by using nonparametric prediction and inference methods is as follows: based on the boundary transformation accelerated lifetime data of the parameter interval, combined with the normal lifetime data, the upper and lower limit transformation data sets are obtained, and the data sets are respectively processed by the nonparametric prediction and inference algorithm to obtain the upper and lower limit survival function curves.

[0021] Furthermore, the upper and lower limit survival function curves are respectively:

[0022] ;

[0023] ;

[0024] In the formula, n is the number of failure time data points. This represents the lower bound of the survival function. This represents the upper bound of the survival function, where i represents the order of the data in the sample. These are the time points for the sample data.

[0025] The beneficial effects of this invention are as follows:

[0026] This invention combines normal operating life data and accelerated life data of the KAV (Kava) machine. It uses an inverse power-law model to analyze the relationship between the life data of the test objects under various stresses, employs a log-rank test to determine the inaccuracy range of the connection parameters in the inverse power-law model, transforms the life data based on the boundary of the connection parameter range, and uses a non-parametric predictive inference method to obtain the lower limit of the KAV's reliability. This overcomes the uncertainties and solution difficulties inherent in traditional reliability assessment methods when analyzing complex machinery with diverse failure modes.

[0027] This invention introduces accelerated life testing into the reliability assessment method for slips of live-line working machines. Through accelerated life testing, slip failure data can be obtained in a shorter time and converted into failure data under normal stress using an inverse power-law model, significantly shortening the duration of traditional reliability testing. Furthermore, by evaluating slip reliability using a non-parametric method, it eliminates the need to analyze various failure modes of the slips, relying solely on life data to assess reliability, thus improving computational efficiency and ultimately enhancing the efficiency of live-line working machine slip reliability assessment. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram illustrating the reliability analysis of the Kova of the present invention;

[0030] Figure 2 This is a flowchart of the present invention;

[0031] Figure 3 This is a schematic diagram of the upper and lower limit survival functions for nonparametric prediction inference in this invention. Detailed Implementation

[0032] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0033] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0034] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0035] See Figures 1 to 3 A method for evaluating the performance of slips in a gas well pressurized operation machine includes the following steps:

[0036] S1: Design accelerated life test according to the slip performance test method in SY / T 5049-2016 "Drilling and Workover Slips" and SY / T 6731-2014 "Live Working Machines for Oil and Gas Fields in the Petroleum and Natural Gas Industry";

[0037] S2: Accelerated life test was conducted on the slip to obtain data on the number of slip failure cycles under various stress conditions;

[0038] S3: Select the acceleration model based on the type of accelerated stress and the test object. The inverse power-law model describes the relationship between the life characteristics of the Kava under mechanical stress and the stress.

[0039] S4: Based on the experimental data from step S2, determine the upper and lower limit intervals of the inverse power law model connection parameters between each stress through the log-rank test, and obtain the union of these intervals;

[0040] S5: Based on the upper and lower limits of the connection parameter range in step S4, two sets of transformed data are obtained, and survival functions are obtained by using nonparametric prediction and inference methods respectively;

[0041] S6: Based on actual usage data and other field data, select the lower limit of the survival function as the reliability curve for Kava.

[0042] In this embodiment, step S1 specifically involves conducting an accelerated life test according to the performance testing methods for slips in the standards SY / T 5049-2016 "Drilling and Workover Slips" and SY / T6731-2014 "Live Working Machines for Oil and Gas Fields in the Petroleum and Natural Gas Industry". During the test, the slips clamp the test tubing, and a tensile (or compressive) force is applied to the tubing with its point of application and direction along the tubing axis. The tubing slides up and down repeatedly under the force until the slips exhibit insufficient clamping force, fatigue fracture, or other performance deficiencies, at which point the test is stopped, and the test data is recorded. Based on the slip nameplate and its normal operating environment, the normal stress and maximum stress of the test are determined, and the three quarter points between them are taken as the accelerated stress values.

[0043] In this embodiment, step S2 specifically involves: conducting an accelerated life test on the slips. The tubing is slid up and down under normal and accelerated stress until performance failure occurs, such as excessive wear of the slip teeth or weld fracture of the slip seat. The number of cycles of up-and-down movement of the tubing during the accelerated life test is recorded as an observation sample, where the normal operating stress is recorded as... Accelerated stress is denoted as , , The corresponding number of trial moves are denoted as follows: , , , The experimental data for step S2 are shown in Table 1:

[0044] Table 1 Experimental Data

[0045] .

[0046] In this embodiment, step S3 specifically involves: based on the accelerated stress type and main failure mode of the chuck, selecting the inverse power law model as the accelerated model to describe the relationship between the chuck's lifespan and stress under mechanical stress. The model is expressed as:

[0047] ;

[0048] in For normal working stress, To accelerate stress, The number of test cycles corresponding to accelerated stress. The following corresponds to the number of test cycles under normal stress, where m is the connection parameter, and its value determines the relationship between life under different stresses.

[0049] In this embodiment, step S4 specifically includes:

[0050] The log-rank test was used to determine the conversion relationship between lifetime data for each accelerated stress and normal stress. The log-rank test statistic Z is:

[0051] ;in and These represent the total number of failure samples under normal stress data and the total number of expected failure samples, respectively. Let Variance be the variance.

[0052] ;

[0053] ;

[0054] Where k is the number of failure time points in the two groups of samples; at the j-th failure time point, the number of failure samples under normal stress is... The number of surviving samples is The number of failure samples under accelerated stress is The number of surviving samples is The total number of failures in the two groups of samples is The total number of survivors is Accelerated stress sample data is the equivalent transformation of accelerated life test data using an inverse power-law model. By changing the connection parameter m, different accelerated stress sample data are obtained. The value of the statistic Z is obtained through a log-rank test, thus yielding the p-value. The p-value is a statistical indicator used in this embodiment to determine whether the transformed data and the normal stress test data follow the same distribution in a statistical sense. In this embodiment, a p-value greater than or equal to 0.05 is selected as the basis for considering the two sets of samples as having the same distribution, finally obtaining the interval of the connection parameter m. The log-rank test obtains the imprecise intervals of the connection parameter under each stress, and the union of these intervals is taken as the final parameter interval.

[0055] In this embodiment, step S5 specifically involves: obtaining upper and lower limit transition data sets based on the accelerated lifetime data obtained in step S4, using the parameter m interval boundary transition data, and combining it with normal lifetime data. Substituting these data sets into the nonparametric prediction inference formulas yields the upper and lower limit survival function curves.

[0056] Nonparametric prediction inference is based on the assumption that future observations have the same probability of falling among existing observations. That is, assuming there are n failure timestamps, these n data points divide the time axis into n+1 regions. The probability P of the next failure data point falling into each region is expressed as:

[0057] ;

[0058] Based on this assumption, the formula for the upper bound of the survival function inferred by nonparametric prediction is as follows:

[0059] ;

[0060] The formula for the lower limit of the survival function is:

[0061] .

[0062] In this embodiment, step S6 specifically involves: selecting the lower limit of the survival function as the reliability curve based on the field usage of the KAV, collecting failure data of the KAV in future production, updating the survival function curve, and obtaining the safety and reliability assessment result of the KAV at a given confidence level. Based on the given reliability requirements of the KAV, the upper limit of the survival function can be selected as a reference for the mandatory replacement time of the KAV.

[0063] This invention combines normal operating life data and accelerated life data of the KAV (Kava) machine. It uses an inverse power-law model to analyze the relationship between the life data of the test objects under various stresses, employs a log-rank test to determine the inaccuracy range of the connection parameters in the inverse power-law model, transforms the life data based on the boundary of the connection parameter range, and uses a non-parametric predictive inference method to obtain the lower limit of the KAV's reliability. This overcomes the uncertainties and solution difficulties inherent in traditional reliability assessment methods when analyzing complex machinery with diverse failure modes.

[0064] This invention introduces accelerated life testing into the reliability assessment method for slips of live-line working machines. Through accelerated life testing, slip failure data can be obtained in a shorter time and converted into failure data under normal stress using an inverse power-law model, significantly shortening the duration of traditional reliability testing. Furthermore, by evaluating slip reliability using a non-parametric method, it eliminates the need to analyze various failure modes of the slips, relying solely on life data to assess reliability, thus improving computational efficiency and ultimately enhancing the efficiency of live-line working machine slip reliability assessment.

[0065] For the foregoing embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0066] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Modifications and variations made by those skilled in the art without departing from the spirit and scope of the invention should be within the protection scope of the appended claims.

Claims

1. A method for evaluating the performance of slips in a gas well pressurized operation machine, characterized in that, Includes the following steps: Design an accelerated life test for the Kevlar based on the Kevlar performance test method; Accelerated life tests were conducted on the slip to obtain data on the number of slip failure cycles under various stress conditions. The acceleration model is selected based on the type of accelerated stress of the kava and the test object to describe the relationship between the life characteristics of the kava under mechanical stress and the stress. Based on the experimental data, the upper and lower limit ranges of the acceleration model connection parameters between various stresses were determined, and the survival function was obtained by using non-parametric prediction and inference methods respectively. Based on the actual usage of Kava, the lower limit of the survival function is selected as the Kava reliability curve; The acceleration model is an inverse power law model, which describes the relationship between the life characteristics of the Kava under mechanical stress and the stress. The specific steps for determining the upper and lower limit ranges of the acceleration model connection parameters between various stresses are as follows: The relationship between the test failure data of the live-line working machine slips under various stresses is described by an inverse power law model, resulting in a transformation model for the failure data under each stress: ； In the formula, For normal working stress, To accelerate stress, The number of test cycles corresponding to accelerated stress. The following corresponds to the number of test cycles under normal stress, where m is the connection parameter; The log-rank test is used to determine the imprecise interval of the connection parameter p between accelerated stress life data and normal stress data, and the union of these intervals is taken as the final parameter interval.

2. The performance evaluation method for gas well live-line working machine slips as described in claim 1, characterized in that, The slip performance test method conforms to the drilling and workover slip standards, as well as the standards for live-line working machines used in oil and gas fields in the oil and gas industry.

3. The performance evaluation method for gas well live-line working machine slips as described in claim 1, characterized in that, The accelerated life test specifically refers to: under normal stress and accelerated stress, the slip clamps slide the tubing up and down until performance failure occurs, and the test is stopped. The number of cycles of up and down movement of the tubing during the accelerated life test is recorded as an observation sample.

4. The performance evaluation method for gas well live-line working machine slips as described in claim 3, characterized in that, The performance failures include: excessive wear of the slip teeth or breakage of the slip seat weld.

5. The performance evaluation method for gas well live-line working machine slips as described in claim 1, characterized in that, The specific steps for obtaining the survival function using nonparametric prediction and inference methods are as follows: based on the boundary transition accelerated lifetime data of the parameter interval, and combined with the normal lifetime data, upper and lower limit transition data sets are obtained, and the data sets are respectively processed through nonparametric prediction and inference algorithms to obtain the upper and lower limit survival function curves.

6. The performance evaluation method for gas well live-line working machine slips as described in claim 5, characterized in that, The upper and lower limit survival function curves are respectively: ； ； In the formula, n is the number of failure time data points. This represents the lower bound of the survival function. This represents the upper bound of the survival function, where i represents the order of the data in the sample. These are the time points for the sample data.

Images