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    • 专利摘要:
    The present invention relates to methods for detecting pipe characteristics, such as detecting a defect in downhole tubular members and estimating the overall thickness of downhole tubular members, using of a technique using the Foucault current in far field. A fault detection method may include placing a defect detection tool in a wellbore, the defect detection tool comprising an emitter and a plurality of receivers; recording measurements for a plurality of channels, each channel corresponding to a particular frequency and a particular receiver; using previously calculated estimation curves corresponding to the plurality of channels at a plurality of defective candidates to obtain thicknesses corresponding to the plurality of channels at each defective candidate; and evaluating the variations of the thicknesses by calculating the standard deviations between the thicknesses obtained for the plurality of channels at the level of each defective candidate; the use of a minimum variation, the minimum variation comprising a minimum standard deviation for determining the plurality of defective candidates; and calculating an overall thickness change using overall thickness estimates for the plurality of defective candidates.
    公开号:FR3058453A1
    申请号:FR1759338
    申请日:2017-10-05
    公开日:2018-05-11
    发明作者:Reza Khalaj Amineh；Luis Emilio San Martin；Burkay Donderici
    申请人:Halliburton Energy Services Inc；
    IPC主号:
    专利说明:

    © Publication number: 3,058,453 (to be used only for reproduction orders) © National registration number: 17 59338 ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY
    COURBEVOIE © Int Cl 8 : E 21 B 47/117 (2017.01), E 21 B 12/02
    A1 PATENT APPLICATION
    ©) Date of filing: 05.10.17. © Applicant (s): HALLIBURTON ENERGY SERVICES, © Priority: 06.11.16 IB WOUS2016060750. INC. - US. @ Inventor (s): AMINEH REZA KHALAJ, SAN MAR- TIN LUIS EMILIO and DONDERICI BURKAY. (43) Date of public availability of the request: 11.05.18 Bulletin 18/19. ©) List of documents cited in the report preliminary research: The latter was not established on the date of publication of the request. (© References to other national documents © Holder (s): HALLIBURTON ENERGY SERVICES, related: INC .. ©) Extension request (s): (© Agent (s): GEVERS & ORES Société anonyme.
    DETECTION OF THE CHARACTERISTICS OF A PIPE WITH A FOUCAULT CURRENT IN A FAR FIELD.
    FR 3,058,453 - A1 _ The present invention relates to methods for detecting the characteristics of a pipe, such as detecting a defect in tubular downhole elements and estimating the overall thickness of tubular elements downhole, using a technique using far-field eddy current. A fault detection method may include placing a fault detection tool in a wellbore, the fault detection tool comprising a transmitter and a plurality of receivers; recording measurements for a plurality of channels, each channel corresponding to a particular frequency and to a particular receiver; the use of estimation curves previously calculated corresponding to the plurality of channels at the level of a plurality of defective candidates to obtain thicknesses corresponding to the plurality of channels at the level of each defective candidate; and evaluating thickness variations by calculating standard deviations between the thicknesses obtained for the plurality of channels at each defective candidate; using a minimum variation, the minimum variation including a minimum standard deviation to determine the plurality of defective candidates; and calculating a change in overall thickness using estimates of overall thickness for the plurality of defective candidates.
    -105
    -100, fai 04
    i
    DETECTION OF THE CHARACTERISTICS OF A PIPE WITH A CURRENT
    FROM FOUCAULT IN A FAR FIELD
    BACKGROUND OF THE INVENTION For the exploration and production of oil and gas, a network of wells, facilities and other conduits can be established by connecting together sections of metal pipe. For example, a well installation can be achieved, in part, by introducing multiple sections of metal pipe (i.e., a casing train) into a borehole, and by cementing the train casing in place. In some well installations, multiple casing trains are used (for example, a concentric arrangement of several trains) to allow for different operations relating to the completion, production or enhanced oil recovery (EOR) options associated with a well.
    Corrosion of metal pipes is a permanent problem. In order to reduce corrosion, corrosion resistant alloys, coatings, treatments and corrosion transfer are used, among others. In addition, efforts to improve corrosion monitoring are ongoing. For downhole casing trains, various types of corrosion monitoring tools are available. One type of corrosion detection tool uses electromagnetic (EM) fields to estimate the thickness of a pipe or other corrosion indicators. For example, an EM logging tool can collect EM logging data, where EM logging data can be interpreted to correlate a level of flux loss or EM induction with corrosion. When multiple casing trains are used together, the proper management of corrosion detection operations by EM logging tools and the interpretation of data can be complex.
    BRIEF DESCRIPTION OF THE FIGURES These drawings illustrate certain aspects of certain examples of this disclosure, and should not be used to limit or define the disclosure.
    Figure 1 is a schematic illustration of an operating environment for a fault detection tool.
    Figure 2 is a schematic illustration of the mutual impedance phase between the transmitter and the receiver with respect to the overall thickness of the pipes.
    Figure 3 is a schematic illustration of a configuration of a fault detection tool comprising a transmitter and a receiver which can be used to probe four concentric pipes.
    Figure 4 is a schematic illustration of a simulated differential phase with respect to the change in overall thickness of pipes.
    Figure 5 is a schematic illustration of a process diagram summarizing the steps of detecting a single defective pipe.
    Figure 6 is a schematic illustration of a fault detection tool with multiple receivers for evaluating multiple pipes.
    Figure 7 is a schematic illustration of a process diagram summarizing the steps for detecting multiple defective pipes.
    DETAILED DESCRIPTION This disclosure may relate, in general, to methods of detecting the characteristics of a pipe, such as detecting a defect in downhole tubular elements and estimating the overall thickness of downhole tubular elements, using a current technique
    Far-field eddy ("RFEC").
    The disclosed approaches can have the following advantages: (i) in addition to estimating the overall thickness of multiple pipes, the disclosed approaches can detect which pipe (s) is (are) defective; (ii) by combining the results from multiple receivers and at multiple frequencies, the evaluation process can be more robust to noise; (iii) this approach can still be significantly faster than conventional optimization-based inversion approaches in which the anticipated model must be evaluated many times; (iv) characterizing multiple pipes with better resolution and accuracy (for thickness estimation) can provide a more precise assessment of these components and ultimately have a significant positive impact on the production process.
    Monitoring the condition of the production casing and possibly multiple casing trains may be desirable during operations on the oil and gas fields. Electromagnetic (“EM”) techniques may be common to inspect these components. EM techniques can fall into two main categories: (1) techniques based on loss of magnetic flux ("MFL") and (2) techniques based on eddy current ("EC"). Although MFL techniques may be more appropriate for inspecting a single pipe, EC techniques can be used to characterize multiple pipes. EC techniques can themselves be divided into two categories, EC techniques in the frequency domain and EC techniques in the time domain.
    In EC techniques in the frequency domain, a transmitter (for example, a coil) can be powered by a continuous sinusoidal signal, to produce primary fields that can irradiate the pipes. Primary fields can produce eddy currents in pipes. These eddy currents can in turn produce secondary fields which can be detected with the primary fields in the receiving coils which can be placed at a certain distance from the transmitter. The characterization of the pipes can be carried out by measuring and treating these fields.
    In time domain EC techniques (also called pulsed EC (PEC)), the transmitter can be powered by a pulse. As in the case of the frequency domain technique, transient primary fields may be produced due to the transition of the pulse from a "disabled" state to an "activated" state or from an "activated" state to a "disabled" state (more common). These transient fields can produce eddy currents in the pipes. Eddy currents can then produce secondary magnetic fields that can be measured by a separate take-up coil away from the transmitter, by a separate coil co-located with the transmitter, or by the same coil as that used as the transmitter.
    In the EC frequency range, as mentioned above, the excitation frequency can be adjusted so that the multiple reflections in the wall of the pipe are insignificant and the spacing between the coils can be large enough to that the contribution to mutual impedance from the dominant (but evanescent) waveguide mode may be small compared to the contribution to mutual impedance from the branch cut component, the RFEC effect can be observed . In an RFEC regime, the mutual impedance between the transmitter and the receiver (for example, a coil) can be very sensitive to the thickness of the wall of a pipe. To be more precise, the phase of the impedance can vary as follows (1) and the magnitude of the impedance presents the dependence: exp | _-2Gyùiuer / 2) r] (2) where ω is the angular frequency of the excitation source, μ is the magnetic permeability of the pipe, σ is the electrical conductivity of the pipe, and t is
    the thickness of the pipe. Using the following classic definition of skin effect depth for metals:
    ωμσ (3) the impedance phase can vary as follows:
    „T φ” 2— δ
    (4) and the magnitude of the impedance presents the dependence: exp [-2 / 7t>] (5) [0007] In RFEC, the estimated quantity can be the overall thickness of the metal. Therefore, for multiple pipes, the estimated parameter can be the overall thickness or the sum of pipe thicknesses.
    Figure 1 illustrates an operating environment for a fault detection tool 10 as disclosed in this document. The detection tool 100 may include a transmitter 102 (for example, a coil) and receivers 104 (for example, a coil). The fault detection tool 100 can be operatively coupled to a transport line 106 (e.g., a wired line or a smooth cable) which can provide electrical connectivity, as well as mechanical suspension, for the tool 100. The transport line 106 and the fault detection tool 100 can extend inside a casing train 108 to a desired depth within the wellbore 109. The line transport 106, which may include one or more electrical conductors, may exit from the wellhead 110, may pass around the pulley 112, may come into contact with Pedometer 114, and may be wound on a winch 116, which may be used to raise or lower the tool assembly in the wellbore 109. The electrical signals coming from the transport line 106 can be routed from the winch 116 to a display and storage unit 118 where the signals can be processed, and l The information it contains can be displayed to an operator for observation and stored for future processing and serve as a reference. The display and storage unit 118 may also contain an apparatus for supplying control signals and energy to the downhole tool assembly, the downhole tool assembly possibly including a detection tool 100.
    A conventional casing train 108 can extend from the wellhead 110 at ground level or above to a selected depth within a wellbore 109. The casing train 108 can comprise a plurality of junctions or segments of casing, each segment being connected to the adjacent segments by a threaded collar.
    Figure 1 also illustrates a conventional production tube 120, which can be positioned inside the casing train 108 by extending downward over part of the distance from the wellbore 109. A gasket sealing 122 can generally seal the lower end of the annular tube-tubing space and can fix the lower end of the production tube 120 to the tubing. The fault detection tool 100 can be dimensioned so as to be lowered into the wellbore 109 through the tube, thereby avoiding the difficulties and expenses associated with removing the tube from the wellbore 109.
    In logging systems, such as, for example, logging systems using the fault detection tool 100, a digital telemetry system can be used, in which rm electrical circuit is used both to provide power to the fault detection tool 100 and to transfer data between the display and storage unit 118 and the fault detection tool 100. DC voltage can be supplied to the fault detection tool 100 by a power supply located above ground level, and the data can be coupled to the DC power conductor by a baseband current pulse system. Alternatively, the fault detection tool 100 can be powered by batteries located in the downhole tool assembly, and / or the data provided by the detection tool 100 can be stored in the tool assembly bottom of the well, rather than transmitted to the surface during logging (fault detection).
    The transmission of electromagnetic fields by the transmitter 102 and the recording of signals by the receivers 104 can be controlled by an information manipulation system. The transmitter 102 and receivers 104 may include coils.
    The systems and methods of this disclosure can be implemented, at least in part, with an information manipulation system 124. An information manipulation system 124 can include any instrumentality or n any aggregate of instrumentalities allowing to calculate, estimate, classify, process, transmit, receive, find, produce, switch, store, display, manifest, detect, d '' record, reproduce, manipulate or use any form of information, intelligence or data for commercial, scientific, control or other purposes. For example, an information manipulation system 124 may be a personal computer, a network storage device, or any other suitable device, and may vary in size, shape, performance, functionality, and of price. The information handling system 124 may include a random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or a hardware or software control logic, a ROM, and / or other types of non-volatile memory. Additional components of the information manipulation system 124 may include one or more disk drives, one or more network ports for communicating with external devices, as well as various input and output (I / O) devices, such as '' keyboard, mouse and video display. The information handling system 124 may also include one or more buses for transmitting communications between the various hardware components.
    Alternatively, the systems and methods of this disclosure can be implemented, at least in part, with non-transient computer readable media. Non-transient computer readable media may include any instrumentality or aggregation of instrumentalities which may retain data and / or instructions for a period of time. Non-transient computer readable media may include, for example, but not limited to, storage media such as a direct access storage device (for example, a hard disk drive or a floppy drive), sequential access storage device (e.g., tape drive), compact disc, CD-ROM, DVD, RAM, ROM, erasable and electrically programmable read-only memory (EEPROM) and / or memory flash; as well as communication media such as wires, optical fibers, microwaves, radio waves, and other electromagnetic and / or optical carriers; and / or any combination of the above.
    The fault detection tool 100 can be used to excite the transmitters 102. The transmitters 102 can transmit electromagnetic signals in an underground formation. The electromagnetic signals can be received and measured by the receivers 104 and processed by the information manipulation system 124 to determine the parameters of a pipe, such as for example the thickness of a pipe and the faulty pipes. The quasi-linear variation of the phase of mutual impedance with the overall thickness of a metal can be used to perform a rapid inversion to estimate the overall thickness of multiple pipes. To this end, for any given set of pipe dimensions, material properties and tool configuration, this linear variation can be constructed quickly and used to quickly estimate the overall thickness of the pipes. In order to establish this linear variation, two simulations can be performed. A simulation with the nominal section of the pipes (overall thickness t „) and a second simulation with a change in overall thickness for the pipes so that this change in overall thickness (At = can be greater than any possible change of overall thickness for the configurations tested. By having the simulated phases φ η and <p m corresponding to the overall thicknesses t „and t m , a line can be established, as shown in FIG. 2 between the points (t n , ψη ) and (t m , (p m ) · This line can be used for the inversion of any other measured phase to obtain the overall thickness of the pipes for any unknown defective section. 2 shows that a measured phase of the defective section cp s can be inverted to obtain the overall thickness t s when this linear approximation is used Figure 2 illustrates the establishment of the estimation line necessary for the inversion based on the hypo RFEC theses between two points (t n , φη) and (t m , ψπι) corresponding to the maximum possible global thicknesses (nominal) and minimum of the pipes. Any unknown overall thickness t s can then be estimated from this line taking into account the phase measured at the level of the defective section (p s ) A practical method for the inversion can consist in using the differential phase instead of the absolute phase to construct the estimation line described above. In this approach, the differential phase can be the phase difference measured at the nominal section (non-defective section) of the pipes and the defective section .
    As described above, conventionally, in RFEC techniques, the estimated quantity can be the overall thickness of multiple pipes. During a conventional pipe inspection by RFEC, it can be assumed that the same amount of thickness change on various pipes can produce the same amount of phase shift for mutual impedance. However, in reality there is a slight difference between the phase shifts obtained due to the same thickness changes on the various pipes. For example, Figure 3 illustrates the configuration of a fault detection tool 100 comprising a transmitter 102 and a receiver 104 which can be used for the logging of four concentric pipes: the pipe 300 which can be positioned in the pipe 302 which may be positioned in pipe 304 which may be positioned in pipe 306. Without limitation, the number of turns for the coils of the transmitter 102 and receiver 104 may vary from about 100 to about 50,000 turns. Without limitation, the length of the coils can vary from approximately 1 inch, or 2.54 centimeters (cm), to approximately 20 inches, or 50.8 cm. Without limitation, the spacing between transmitter 102 and receiver 104 can vary from approximately 5 inches, or 12.7 cm, to approximately 80 inches, or approximately 2.03 meters (m). Table 1 presents the parameters of pipes 300, 302, 304 and 306. Figure 4 illustrates the simulated differential phase with respect to the changes in overall thickness of pipes 300, 302, 304 and 306 when each time one of pipes 300, 302, 304 and 306 is defective, that is to say each time the thickness of one of the pipes 300, 302, 304 and 306 changes. Such a difference in the responses of the four plots can be exploited for the detection of the defective pipe using an inversion based on the RFEC. It can be observed in Figure 3 that the variation of the differential phase compared to the change in overall thickness can present differences when the defect is on the different pipes. Therefore, such differences can be used to distinguish the faulty pipe (s).
    Pipe 300 Pipe 302 Pipe 304 Pipe 306 DE (inch) 5 (either12.7 cm) 9 + 5/8 (i.e.about24.45 cm) 13 + 3/8 (i.e.about 34 cm) 18 + 5/8 (i.e.about 47.3 cm) Thicknessnominal(thumb) 0.4 (i.e.about 1 cm) 0.4 (approximately1 cm) 0.4 (i.e.about 1 cm) 0.4 (approximately1 cm)
    Table 1: Dimensions of pipes 300, 302, 304 and 306.
    Detection of a single defective pipe.
    In the following, an example of a technique is used to detect the defective pipe (in addition to estimating the overall thickness of the pipes) in a process for inspecting multiple pipes (pipes 1 to N p ) with an inversion based on the RFEC.
    In a multiple pipe configuration (pipes 1 to N p ), the k-th pipe may be defective. In order to detect this defective pipe, N p separate RFEC estimation lines, L] to Ln p can be constructed like that shown in FIG. 2, assuming each time that the fault is on one of pipes 1 to N p . Then, for any configuration tested, these estimation lines can be used to reverse the measured differential phase to obtain the overall thickness of the pipes 300, 302,
    304 and 306 (shown in Figure 3). This can provide the overall thickness estimates 1 to Tnp which may be slightly different from each other due to the slight differences in the estimation lines L to Lv p . If a single receiver is used (for example, the receiver 104 shown in Figure 3) and the measurement is made at a single frequency, it may not be possible to distinguish the defective pipe and use the estimation line corresponding to obtain the most accurate estimate of the overall thickness of the pipes 300, 302, 304 and 306. However, measurements with multiple receivers (for example, at least two receivers 104 shown in FIG. 1) at RXnt and / or at multiple frequencies at (for example, at least two frequencies), and for the measurement of each receiver RXi at each frequency f which can be called "channels," the corresponding estimation lines Lf to Lu p îJ (to produce them, each time it can be assumed that the defect can be found on one of the pipes 300, 302, 304 and 306) can be used to provide the estimates of overall thickness Tf to Tnp îj . Therefore, the number of overall thickness estimates is / N p . In order to detect the defective pipe, it can be assumed that, for example, if the pipe k is defective, the estimates of overall thickness T '/ For i = l, ..., N r andj = Ϊ,., ., Ν / have the smallest variations (are the most consistent results), while the other estimates of overall thickness based on the assumption that any other pipe k 'is defective (Tk'N for i = ï, ..., N r and j = Ϊ,.,., Ν /) may be more varied and less consistent. Consequently, when the consistency of the results is compared with an appropriate parameter, such as the standard deviation, when a pipe k is defective, the lowest standard deviation can be obtained for the TV estimates (for i = N r and j = Ϊ,.,., Ν /), while for any other assumption, TV (for i = Ϊ,.,., Ν, -, / = 1, ···, N /, and k 'jk), the standard deviations may be higher. This can lead to the detection of the pipe k as being the defective pipe and to the average or weighted average Tk iJ , for i = Ϊ,.,., Ν · and j = 1, ... Nf, as being the result the more precise estimation of the overall thickness. The two smaller standard deviations can be used as a quality factor.
    Figure 5 summarizes the steps disclosed above. The frame 500 can provide measurements for all the channels (at the level of the receivers RX to RX ^ r at frequencies f to / v /). The frame 502 can use the estimation lines E „ ij previously calculated and stored in a bank for all the candidates (there N p ), and for all the channels. Each candidate can include a tubular element (for example, a pipe) analyzed. Frame 504 can use the estimation lines to obtain Tf. The frame 506 can provide the estimation lines Ef to obtain T / · '. Box 508 can provide the estimation lines L Z) / À to obtain Τ η ρ '· ί Box 510 can calculate a variation parameter ίο for values T],! / To obtain STDi. The frame 512 can calculate a variation parameter for the values T f to obtain STD ^. Box 514 can calculate a variation parameter for fifi values to obtain STD „ P. Frame 516 can provide Min (STD ^ for k '= 1, ..., N p ) = STDt- Frame 518 can show that the set k of pipes is defective. Overall thickness estimate = weighted average fi for i = 1, ..., N r J = 1, ..., N /.
    Detection of multiple defective pipes.
    As an extension of the previous description concerning an example of a technique for the detection of a single defective pipe, an example of a technique for detecting multiple defective pipes (in addition to the estimation of the overall thickness pipes) in a multiple pipe inspection process (pipes 1 to N p ) with an inversion based on the RFEC will now be described. It can be assumed that in a multiple pipe configuration (pipes 1 to N p ), N d of the pipes are defective. In order to detect these defective pipes, several RFEC estimation lines with the number of estimation lines M being equal to the combination N of N p pipes can be constructed, each time assuming that the fault is on a different set of N pipes among N p pipes. In mathematical terms, the relation between M, N d , and N p can be written:
    (6) In other words, a possibility of M set of defective pipes which in each set m (m = l, ..., M), N pipes among N p pipes can be defective with one of the N ^ d thickness change distributions among the N d pipes during the construction of the corresponding estimation lines, can be considered.
    Then, for any configuration tested, these estimation lines can be used to reverse the measured differential phase to obtain the overall thickness of the pipes 300, 302, 304 and 306 (shown in Figure 3). This can provide the overall thickness estimates 1 to Tm which may be slightly different from each other due to the slight differences in the estimation lines Zi to Lm. If a single receiver 104 is used (shown in Figure 3) and the measurement is made at a single frequency, it may not be possible to distinguish the defective pipe and use the corresponding estimation line to obtain the most accurate estimate of the overall thickness of pipes 300, 302, 304 and 306. However, measurements with multiple receivers RXi to RXnt and / or at multiple frequencies fi to fNf, and for the measurement of each receiver RXi to each frequency f which can be called "channel" can be used. The corresponding estimation lines Lfl to Lm îj (to produce them, each time it can be assumed that the defect is on Nd pipes among Np pipes with a distribution profile of thickness change among N / d cases) can be used to provide the overall thickness estimates T fl to Trfl. Therefore, the number of overall thickness estimates is NrN / M. In order to detect defective pipes, for example, it can be assumed that the w-th set of pipes is actually defective (m can be any number between 1 and M), the overall thickness estimates Trfl for Qtj = , ..., Nf have the smallest variations (are the most consistent results), while the other estimates of overall thickness based on the assumption that any other set of pipes 'is defective (Tni 1 ' · 1 for i = Ι ,,. ,, Λζ. And j = have higher variations, therefore, when the variation of the results is compared with an appropriate parameter, such as the standard deviation, when a set m of pipes is defective, the lowest standard deviation can be obtained for estimates Tfl '' (for i = l, ..., N r and j = while for any other assumption, Tfl'i ( for z = 1, ..., N r , j = 1, ..., Nf, and '#), the standard deviations can be higher, which can lead to the detection of a set m of pipe x as being the defective pipes and with the average or weighted average Τ „Ρ, for i = 1, .. ,, N r and y = 1 ,. ..Nf, as being the most precise result of the estimation of the overall thickness.
    General approach in the detection of multiple defective pipes.
    What is disclosed above may represent approaches for detecting a single defective pipe or multiple defective pipes by assuming to be interested in a number of defective pipes (the number of defective pipes was assumed to be known).
    A more general technique in which the number of defective pipes may be unknown is described below. In this example technique, the number of defective pipes can be estimated in addition to detecting the defective pipes themselves and an accurate estimate of the overall thickness of the pipes can be provided.
    It can be assumed that in a multiple pipe configuration (pipes 1 to N p ), the number of pipes and the defective pipes are unknown. To solve this problem, several REEC estimation lines with the number of estimation lines M being equal to the sum of "-combination of N p pipes for" going from 1 to N p , times Nf where N, is the number thickness change levels assumed for each defective pipe can be constructed. In mathematical terms, the relation between M, n, and N p can be written:
    (Ό [0030] In other words, there may be a possibility of M set of defective pipes which in each set mn (n = l, ..., N p ) pipes among N p pipes are defective with one of the Nf thickness change distributions among the n pipes during the construction of the corresponding estimation lines.
    Figure 7 summarizes the steps disclosed above. The frame 700 can provide measurements for all the channels (at the level of the receivers RX to RX Nr at frequencies f to / "/). The frame 702 can use the estimation lines Lf J previously calculated and stored in a bank for all the candidates (there M), and for all the channels. Frame 704 can use the estimation lines Lf to obtain Tf. The frame 706 can provide the estimation lines Lf to obtain Tf. Frame 708 can provide the estimation lines L „, lJ to obtain Tm ' J. Frame 710 can calculate a variation parameter for the values T fl to obtain STDi. Box 712 can calculate a variation parameter for the values to obtain STD ^. Box 714 can calculate a variation parameter for the Tffl values to obtain STDM. Frame 716 can supply Min (STDi ', for Æ' = 1, ..., M) = STDi. Frame 718 can show that the set k of pipes is defective. Overall thickness estimate = weighted average T fl for i = l, ..., N r j = The rest of the process can be similar to the technique for detecting multiple defective pipes described above in which measurements at multiple receivers RX at RXnt and / or at multiple frequencies f at / "/ are used and for the measurement of each receiver RXi at each frequency fi, which can be called" channel ". The corresponding estimation lines Lf to Lm 1 '(to produce them, each time it is assumed that the defect is on n pipes among N p pipes with a thickness change distribution profile among N £ cases) can be used to provide the overall thickness estimates T fi to TtfL Therefore, the number of overall thickness estimates is N r NfM. In order to detect defective pipes, it can be assumed for example that the ninth set of pipes is actually defective (m can be any number between 1 and M), the estimates of overall thickness Tnflpovi i = l, ..., A r and j = have the smallest variations (are the most consistent results), while the other estimates of overall thickness based on the assumption that any other set of pipes is faulty (T m M for i = and j = have higher variations. Therefore, when the variation of the results is compared with an appropriate parameter, such as the standard deviation, when a set m of pipes is defective, the the lowest standard deviation can be obtained for the estimates T m U (for i = l, ..., N r and j = while for any other assumption, Tm '^ (for i = f .. ., N r , j = Ι,.,.,. Νβ and m'fm), the standard deviations can be higher. This can lead to the detection of a set m of pipes as é both the defective pipes and the mean or weighted average TmD, for i = l, ... Nr and y = λ,.,. Νβ as being the most precise result of the estimation of the overall thickness.
    Detection of the class of defective pipes.
    Although a general approach by which an arbitrary number of defective pipes can be detected has been presented above, the signal-to-noise ratio in the phase measurements may not be high enough to differentiate the adjacent pipes in a multiple pipe inspection scenario. In other words, the precision of the phase measurements may not be sufficient to allow the use of a slight difference between the estimation lines for adjacent pipes and to distinguish which of them is defective.
    Here, there is disclosed a more general technique in which the pipes 300, 302, 304 and 306 (shown in Figure 3) can be classified into groups, starting with the innermost pipes and ending with the pipes the more external. Each group can include a number of adjacent pipes. In this approach, the group of defective pipes can be estimated, but not each individual pipe. In addition to detecting the group of defective pipes, an accurate estimate of the overall thickness of pipes 300, 302, 304 and 306 can be provided.
    It can be assumed that in a multiple pipe configuration (pipes 1 to N p ), the number of pipes and the defective pipes are unknown. To solve this problem, pipes 300, 302, 304 and 306 can be classified in groups M as described above for example: pipes 1 to n are in group 1, pipes W] + i to "2 are in group 2, and so on. Then, several RFEC estimation lines can be constructed with the number of estimation lines being equal to the number of groups M. To construct each estimation line, the thicknesses of the pipes in the corresponding group can be modified. The rest of the method may be similar to the technique for detecting multiple defective pipes described above in which measurements at multiple receivers RX to RXnt and / or at multiple frequencies f to fNf are used and for the measurement of each receiver RXî at each frequency fi, the corresponding estimation lines Lfi J to Lm îJ (to produce them, each time it is assumed that the fault is on n pipes among Np pipes) can be used to provide the estimates of overall thickness T7 to Tm u . Therefore, the number of overall thickness estimates is NrNfM. In order to detect the defective pipe group, it can be assumed, for example, that the m-th pipe group is actually defective (m can be any number between 1 and M), the overall thickness estimates Tm + for i = i, ..., N r and y = 1, ... A / have the smallest variations (are the most consistent results), while the other estimates of overall thickness based on the assumption that any other group of pipes is faulty to me (TmH for i = N r and j = ï, ..., Nfi can be more varied and less coherent. Therefore, when the consistency of the results is compared with a appropriate parameter, such as standard deviation, when a group m of pipes is faulty, the lowest standard deviation can be obtained for estimates T m l '> (for / = 1, ..., N r ety = l, .. ,, Nfi while for any other assumption, r m >'7 (for /=1,...,7Ç, and m'# zn), the standard deviations can be higher. This can lead to the detection of a n group m of pipes as being the defective pipes and at the mean or weighted average Tm 1 ', for i = l, ..., N r and as being the most precise result of the estimation of the overall thickness.
    Therefore, methods are provided for detecting the characteristics of a pipe, such as detecting a defect in downhole tubular elements and estimating the overall thickness of downhole tubular elements wells, using a technique using far-field eddy current ("RFEC"). The methods can also be implemented using an information manipulation system. The systems and methods may include any of the various features of the systems and methods disclosed in this document, including one or more of the following statements.
    Statement 1: a fault detection method comprising: placing a fault detection tool in a wellbore, the fault detection tool comprising a transmitter and a plurality of receivers; recording measurements for a plurality of channels, each channel corresponding to a particular frequency and to a particular receiver; the use of estimation curves previously calculated corresponding to the plurality of channels at the level of a plurality of defective candidates to obtain thicknesses corresponding to the plurality of channels at the level of each defective candidate; and assessing variations in thicknesses by calculating standard deviations between the thicknesses obtained for the plurality of channels at the level of each defective candidate; the use of a minimum variation, the minimum variation including a minimum standard deviation to determine the plurality of defective candidates; and calculating a change in overall thickness using estimates of overall thickness for the plurality of defective candidates.
    Item 2: The defect detection method according to item 1, in which the calculation of a change in overall thickness comprises the calculation of a weighted average of the estimates of change in overall thickness for the defective candidates with measurements taken from at least two receivers or at least two frequencies.
    Item 3: The fault detection method according to item 1 or item 2, in which the range for the different frequencies is from about 0.5 Hz to about 10 Hz.
    Statement 4: the defect detection method according to any preceding statement, in which the estimation curves are based on the maximum and minimum overall thicknesses of the plurality of defective candidates.
    Item 5: The fault detection method according to any preceding statement, in which at least two receivers and the transmitter include coils.
    Statement 6: The fault detection method according to statement 5, in which the coils comprise approximately 100 turns to approximately 50,000 turns.
    Statement 7: The fault detection method according to Statement 5, in which a length of the coils is about 1 inch, or 2.54 cm, to 20 inches, or 50.8 cm.
    Statement 8: The fault detection method according to any preceding statement, in which a spacing between at least two receivers and the transmitter is approximately 5 inches, or 12.7 cm, to 80 inches, or approximately 2.03 m.
    Statement 9: the fault detection method according to any preceding statement, in which the estimation curves are based on differential phases.
    Statement 10: The fault detection method according to any preceding statement, in which the smallest standard deviation is used as a quality factor.
    Statement 11: a fault detection method comprising:
    placing a pipe fault detection tool in a wellbore, wherein the pipe fault detection tool comprises a transmitter and a plurality of receivers;
    the classification of a set of candidates into groups; recording measurements for a plurality of channels; the use of estimation lines to obtain the thicknesses of the groups; calculating thickness variations to obtain standard deviations; and the use of a minimum standard deviation to determine a faulty group.
    Item 12: The fault detection method according to item 11, in which a number of the estimation lines is equal to a number of the groups.
    Statement 13: The fault detection method according to statement 11 or statement 12, in which the classification includes the grouping of the most internal pipes to the most external pipes.
    Statement 14: The method of fault detection according to any one of statements 11 to 13, in which the range for the different frequencies is from approximately 0.5 Hz to approximately 2 Hz.
    Statement 15: the defect detection method according to any one of statements 11 to 14, in which the estimation lines are based on the maximum and minimum overall thicknesses of the groups.
    Item 16: The fault detection method according to any one of items 11 to 15, in which at least two receivers and one transmitter include coils.
    Statement 17: The defect detection method according to statement 16, in which the coils comprise approximately 100 to approximately 50,000 turns.
    Statement 18: The defect detection method according to Statement 16, wherein a length of the coils is from about 1 inch, or 2.54 cm, to about 20 inches, or 50.8 cm.
    Statement 19: The fault detection method according to any one of statements 11 to 18, in which a spacing between at least two receivers and the transmitter is approximately 5 inches, or 12.7 cm, at about 80 inches, or about 2.03 m.
    Statement 20: The fault detection method according to any one of the statements 11 to 19, in which the smallest standard deviation is used as a quality factor.
    The foregoing description provides various examples of the systems and methods of use disclosed in this document which may contain different process steps and alternative combinations of components. It should be understood that, although individual examples may be presented in this document, this disclosure covers all combinations of the examples disclosed, including, but not limited to, the various combinations of components, the combinations of stages of process, and properties of the system.
    Therefore, the present examples are well suited to achieve the ends and advantages mentioned, as well as those which are inherent to them. The particular examples disclosed above are illustrative only, and may be modified and practiced in different but equivalent ways evident to a specialist in the field and who benefits from these teachings. Although individual examples are discussed, the disclosure covers all combinations of all examples. In addition, there is no limitation to the construction or design details described herein, other than those described in the claims below. In addition, the terms in the claims have their clear and ordinary meaning, unless explicitly stated otherwise clearly defined by the patent owner.
    权利要求:
    Claims (17)
    [1" id="c-fr-0001]
    The claims relate to the following:
    1. A fault detection method comprising:
    placing a fault detection tool (100) in a wellbore (109), wherein the fault detection tool comprises a transmitter (102) and a plurality of receivers (104);
    recording measurements for a plurality of channels, in which each channel corresponds to a particular frequency and to a particular receiver;
    the use of estimation curves previously calculated corresponding to the plurality of channels at the level of a plurality of defective candidates to obtain thicknesses corresponding to the plurality of channels at the level of each defective candidate; and evaluating variations in thicknesses by calculating standard deviations between the thicknesses obtained for the plurality of channels at the level of each defective candidate; the use of a minimum variation, wherein the minimum variation includes a minimum standard deviation to determine the plurality of defective candidates; and calculating a change in overall thickness using estimates of overall thickness for the plurality of defective candidates.
    [2" id="c-fr-0002]
    The defect detection method according to claim 1, wherein calculating an overall thickness change comprises calculating a weighted average of the overall thickness change estimates for the defective candidates with measurements taken from at least two receivers (104) or at least two frequencies.
    [3" id="c-fr-0003]
    3. A fault detection method according to claim 2, wherein the range for the different frequencies is 0.5 Hz to 10 Hz.
    [4" id="c-fr-0004]
    4. A defect detection method according to claim 1, in which the estimation curves are based on the maximum and minimum overall thicknesses of the plurality of defective candidates.
    [5" id="c-fr-0005]
    5. A fault detection method according to claim 1, in which at least two receivers (104) and the transmitter (102) comprise coils.
    [6" id="c-fr-0006]
    6. A fault detection method according to claim 5, in which the coils comprise from 100 turns to 50,000 turns.
    [7" id="c-fr-0007]
    The defect detection method according to claim 5, wherein a length of the coils is 1 inch (2.54 cm) to 20 inches (50.8 cm).
    [8" id="c-fr-0008]
    8. A fault detection method according to claim 1, wherein a spacing between at least two receivers (104) and the transmitter (102) is 5 inches (12.7 cm) to 80 inches (2.032 m).
    [9" id="c-fr-0009]
    9. A fault detection method according to claim 1, in which the estimation curves are based on differential phases.
    [10" id="c-fr-0010]
    10. A fault detection method according to claim 1, in which the smallest standard deviation is used as a quality factor.
    [11" id="c-fr-0011]
    11. Fault detection method comprising:
    placing a pipe fault detection tool (100) in a wellbore (109), wherein the pipe fault detection tool includes a transmitter (102) and a plurality of receivers (104);
    the classification of a set of candidates into groups; recording measurements for a plurality of channels; the use of estimation lines to obtain the thicknesses of the groups;
    calculating thickness variations to obtain standard deviations; and
    The use of a minimum standard deviation to determine a faulty group.
    [12" id="c-fr-0012]
    12. The fault detection method according to claim 11, wherein the number of estimation lines is equal to the number of groups.
    [13" id="c-fr-0013]
    13. A fault detection method according to claim 11, in which the classification comprises the grouping of the innermost pipes to the outermost pipes.
    [14" id="c-fr-0014]
    14. A fault detection method according to claim 11, wherein the range for the different frequencies is from 0.5 Hz to 2 Hz.
    15. The defect detection method according to claim 11, in which the estimation lines are based on the maximum and minimum overall thicknesses of the groups.
    16. A fault detection method according to claim 11, wherein
    At least two receivers (104) and one transmitter (102) include coils.
    17. A fault detection method according to claim 16, in which the coils comprise from 100 to 50,000 turns.
    [15" id="c-fr-0015]
    18. A fault detection method according to claim 16, wherein a length of the coils is from 1 inch (2.54 cm) to 20 inches (50.8 cm).
    [16" id="c-fr-0016]
    19. A fault detection method according to claim 11, in which a spacing between at least two receivers (104) and the transmitter (102) is 5 inches
    [17" id="c-fr-0017]
    20 (12.7 cm) to 80 inches (2.032 m).
    20. A fault detection method according to claim 11, wherein the smallest standard deviation is used as a quality factor.
    1/6
    2/6
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    同族专利:
    公开号 | 公开日
    GB2567785A|2019-04-24|
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    BR112019006116A2|2019-06-18|
    WO2018084862A1|2018-05-11|
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    法律状态:
    2018-09-28| PLFP| Fee payment|Year of fee payment: 2 |
    2019-10-30| PLFP| Fee payment|Year of fee payment: 3 |
    2020-07-24| RX| Complete rejection|Effective date: 20200617 |
    优先权:
    申请号 | 申请日 | 专利标题
    IBWOUS2016060750|2016-11-06|
    PCT/US2016/060750|WO2018084862A1|2016-11-06|2016-11-06|Detection of pipe characteristics with a remote field eddy current|
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