The present invention claims the benefits of European Patent Application No. 15290318.3, filed on Dec. 16, 2015, titled “Deconvolution of Electromagnetic Thickness Measurement,” the entire content of which is hereby incorporated by reference into the current application.
The present disclosure relates generally to improving data logging quality of electromagnetic downhole logging tools.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be help provide the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as an admission.
In well-logging via electromagnetic field testing, an electromagnetic logging tool is inserted into an interior diameter of a conductive tubular or casing joint (“casing”). A transmitter of the electromagnetic logging tool creates an electromagnetic field that interacts with the casing and varies depending on a metal thickness of the casing. One or more receivers of the electromagnetic logging tool may be used to measure and generate a data log illustrating variations in one or more resulting and returning electromagnetic fields. The metal thickness of the casing may be determined by analyzing the detected variations in the data log. An area of the casing that is determined to have less metal thickness may indicate a defect in the casing (e.g., due to corrosion). However, due to a physical design of the electromagnetic logging tool, the defect may appear more than once (as a “ghost”) on the data log.
Various refinements of the features noted above may exist in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. Again, the brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.
In a first embodiment, a system includes a plurality of nested casings disposed in a wellbore. The system also includes an electromagnetic logging tool disposed in one or more casings of the plurality of nested casings, wherein the electromagnetic logging tool includes one or more transmitters and one or more receivers. The one or more transmitters are configured to generate a magnetic field signal, wherein the magnetic field signal generates one or more eddy currents in the one or more casings, wherein the one or more eddy currents generate one or more returning magnetic field signals. The one or more receivers are configured to measure one or more characteristics of the one or more returning magnetic fields from the one or more of the casings. The system further includes a data processing system communicatively coupled to the electromagnetic logging tool, wherein the data processing system comprises one or more processors. The one or more processors are configured to obtain first data relating to a metal thickness of the one or more of the casings, wherein the data is obtained by moving the logging tool through the one or more casings. The one or more processors are also configured to obtain second data relating to a change in the metal thickness of the one or more casings. The one or more processors are further configured to determine the metal thickness of the casing by applying a deconvolution technique on the first data, using information extracted from the second data.
In a second embodiment, a tangible, non-transitory, machine-readable medium, includes machine-readable instructions to obtain a measurement relating to a first metal thickness of a casing by passing a transmitter and a receiver of an electromagnetic logging tool over a defect. The tangible, non-transitory, machine-readable medium, also includes machine-readable instructions to obtain a ghosting kernel of the electromagnetic logging tool including a response of the electromagnetic logging tool to a localized change in metal thickness from the first metal thickness of the casing to a second metal thickness, wherein the second thickness is different from the first metal thickness. The tangible, non-transitory, machine-readable medium, further includes machine-readable instructions to determine the metal thickness of the casing at the defect based on the measurement and the ghosting kernel of the electromagnetic logging tool.
In a third embodiment, a method includes obtaining, with a receiver of a logging tool, a measurement relating to a first metal thickness of a first casing by moving a logging tool through the first casing, wherein the first casing is coupled to a collar comprising a second metal thickness. The method also includes obtaining, with the receiver of the logging tool, a ghosting kernel of the logging tool based on the first casing and a localized change in metal thickness from the first metal thickness to the second metal thickness, wherein the ghosting kernel is a response of the logging tool to the localized change in metal thickness. The method further includes determining, with one or more processors of a data processing system, a third metal thickness of the first casing by deconvolving the measurement relating to the first metal thickness and the ghosting kernel of the logging tool, wherein the data processing system is coupled to the logging tool, and wherein the third metal thickness is different from the first metal thickness.
Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:
One or more specific embodiments of the present disclosure will be described below. These described embodiments are only examples of the presently disclosed techniques. Additionally, in an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.
Embodiments of the present disclosure relate to systems and methods for measuring metal thicknesses and detecting defects in tubulars or casing joints (“casing”) that may be nested or concentrically arranged of a wellbore using a downhole logging tool with a transmitter and at least one receiver to determine a metal thickness of at least an area of the casings. A defect may include a change (e.g., an increase or decrease) of metal thickness of a casing. In particular, the defect in the casing may appear more than once (as a “ghost”) on a data log of measurements of the logging tool due to a physical design of the logging tool. The embodiments of the present disclosure relate to removing the ghosting effects using deconvolution techniques.
With the foregoing in mind,
As seen in
The surface equipment 12 may carry out various well-logging operations to detect conditions of the casings 22. The well-logging operations may measure a metal thickness of the casing 22 by using the logging tool 26. The example of
The logging tool 26 may be deployed inside the wellbore 16 by the surface equipment 12, which may include a vehicle 30 and a deploying system such as a drilling rig 32. Data related to the casing 22 gathered by the logging tool 26 may be transmitted to the surface, and/or stored in the logging tool 26 for later processing and analysis. The vehicle 30 may be fitted with or may communicate with a computer and software to perform data collection and analysis. When the logging tool 26 provides measurements to the surface equipment 12 (e.g., through the cable 28), the surface equipment 12 may pass the measurements as electromagnetic casing evaluation data 36 to a data processing system 38 that includes one or more processors 40, one or more memory devices 42, storage 44, and/or a display 46. Further references to “the processor 40” are intended to include the one or more processors 40. In some embodiments, the processor 40 may include one or more microprocessors, one or more application specific processors (ASICs), one or more field programmable logic arrays (FPGAs), or any combination thereof. The processor 40 may execute instructions stored in the memory 42 and/or storage 44. As such, the memory 42 and/or the storage 44 of the data processing system 38 may be tangible, non-transitory, machine-readable media that store instructions executable by and data to be processed by the processor 40. The memory 42 and/or the storage 44 may be read-only memory (ROM), random-access memory (RAM), flash memory, an optical storage medium, a hard disk drive, and the like.
The display 46 may be any suitable electronic display that can display the data logs and/or other information relating to analyzing the electromagnetic casing evaluation data 36. In some embodiments, the data processing system 38 obtains the measurements from the logging tool 26 as raw data. In other embodiments, the measurements are processed or pre-processed by the logging tool 26 before sending the data to the data processing system 38. Processing of the measurements may incorporate using and/or obtaining other measurements, such as from ultrasonic, caliper, and/or other electromagnetic logging techniques to better constrain unknown parameters of the casing 22. Accordingly, in some embodiments, the data processing system 38 and/or the logging tool 26 may acquire additional information about the casing 22 or the wellbore 16 including a number of casings 22, nominal metal thicknesses of each casing 22, centering of the casing 22, ultrasonic properties of the casing 22, ambient temperature, caliper measurements, or other parameters that may be useful in analyzing metal thickness of the casing 22.
Turning now to
The logging tool 26 also may include one or more receivers (e.g., 62, 64, 66, 68). In the illustrated embodiment, the receivers 62, 64, 66, 68 are each located in a line along the logging tool 26. Each receiver 62, 64, 66, 68 is located some distance away from the transmitter 60. For example, the receiver 62 may be located a distance 70 from the transmitter 60, the receiver 64 may be located a distance 72 from the transmitter 60, the receiver 66 may be located a distance 74 from the transmitter 60, and the receiver 68 may located a distance 76 from the transmitter 60. In some embodiments, each distance 72, 74, 76 may be a multiple of the distance 70. For example, the distance 72 may be twice the distance 70, and distances 74, 76 may respectively be three and four times the distance 70. In some embodiments, the receivers 62, 64, 66, 68 may be located at distances of between 0 inches to 120 inches or more from the transmitter 60. For example, the receivers 62, 64, 66, 68 may be located 18, 36, 60, and 90 inches from the transmitter 60. The receivers 62, 64, 66, 68 may detect a strength and/or a phase of the returning magnetic field from the casing 22. These detected values may then be used to create a data log and determine a metal thickness of the casing 22 using suitable electromagnetic and/or field-testing analyses.
As illustrated, the receivers 62, 64, 66, 68 are axially located in the logging tool 26. In some embodiments, the receivers may also or alternatively be radially located or transversely located along an axis of the logging tool 26. In some embodiments, at least some of the receivers 62, 64, 66, 68 may be located azimuthally-adjacent to edges of the logging tool 26. The receivers 62, 64, 66, 68 may use any appropriate magnetic field detection technique, such as coiled-winding, Hall-effect sensor, giant magneto-resistive sensor, or any other magnetic field measuring instrument. In some embodiments, multiple receivers distributed azimuthally enable generating a two-dimensional image of properties (e.g., metal thickness) of the casing 22. There may be embodiments having multiple transmitter configurations where the windings are transverse or obliques as in a saddle coil arrangement which couple to the receivers or additional receiver windings.
Turning now to
With the foregoing in mind,
The logging tool 26 and/or the data processing system 38 may use the detected values to create a data log and determine a metal thickness of the casing(s) 22 using any suitable electromagnetic and/or field-testing analyses. In some embodiments, minimizing a norm of the difference (e.g., least-squares minimization) between the observed data and data from a numerical model yields best-fit parameters for the model of the casing 22. Various solution implementations, such as inversion, model-searching, simulated annealing, or other suitable techniques, may be used to interpret the data. Choosing a particular method to use relates to speed, complexity, and memory size for solving the minimization and may vary by implementation based on application specific choices.
In some embodiments, the data processing system 38 and/or the logging tool 26 may determine a change in metal thickness to the casing 22 as well as determine various properties of one or more casings 22. For example, additional information may be derived or used by the data processing system 38 and/or the logging tool 26 to determine metal thickness of the casing 22, such as thermal data, deviation survey results, caliper surveys of diameters, temperature, acoustic information, electromagnetic information (e.g., permeability and/or conductivity), or other suitable auxiliary information.
However, due to a physical design of the logging tool 26, the defect 48 may appear more than once (as a “ghost”) on the data log. In particular, as the logging tool 26 descends in the casing 22, the transmitter 60 may pass the defect 48 in the casing 22 and generate the magnetic field signal 92. Based on the returning magnetic field 94 detected at a receiver (e.g., 68) of the logging tool 26, the data log may show an change in the returning magnetic field signal strength and/or phase because of a change in metal thickness of the casing 22. In some embodiments, the change in the returning magnetic field signal strength and/or phase may be compared to a threshold to confirm an indication of the change in metal thickness of the casing 22. As the logging tool 26 continues to descend in the casing 22, the receiver 62 passes the defect 48. The transmitter 60 may again generate the magnetic field signal 92 as the transmitter 60 passes the defect 48. Again, based on the returning magnetic field 94 detected at the receiver (e.g., 68), the data log may show a change in the phase shift and/or the signal strength of the returning magnetic field 94 because of the change in metal thickness of the casing 22.
With the foregoing in mind,
Similarly, as the logging tool 26 continues descending in the casing 22, the logging tool 26 may pass a second defect in the casing 22. A third amplitude 130 in the plot 120 may correspond to the transmitter 60 passing the second defect and a fourth amplitude 132 in the plot 120 may correspond to the receiver 68 passing the second defect. As the logging tool 26 continues descending in the casing 22, the logging tool 26 may pass a third defect in the casing 22. A fifth amplitude 134 in the plot 120 may correspond to the transmitter 60 passing the third defect and a sixth amplitude 136 in the plot 120 may correspond to the receiver 68 passing the third defect. As the logging tool 26 continues descending in the casing 22, the logging tool 26 may pass a fourth defect in the casing 22. A seventh amplitude 138 in the plot 120 may correspond to the transmitter 60 passing the fourth defect and an eighth amplitude 140 in the plot 120 may correspond to the receiver 68 passing the fourth defect.
The plot 120, which may correspond to a data log of the logging tool 26, illustrates a ghosting effect based on the transmitter 60/receiver (e.g., 68) arrangement of the logging tool 26, wherein a defect (e.g., the first defect) is represented more than once in the data log. The ghosting effect is due to the defect appearing in the data log once (e.g., the first amplitude 126) when the transmitter 60 passes the defect, and again (e.g., the second amplitude 128) when the receiver passes the defect.
The present disclosure proposes using deconvolution techniques to remove the ghosting effect in the data log. Removing the ghosting effect may facilitate interpretation of the data log by enabling more accurate identification of defects in the casing 22 and limit detrimental impacts of predetermined changes in metal thickness of the casing 22 to the data log (e.g., through the use of the collar 24).
The measurements provided by the logging tool 26 (e.g., the data log) may be interpreted as a convolution between the actual metal thickness of the casing and an intrinsic response of the logging tool 26 to a localized change in metal thickness. The measurements provided by the logging tool 26 are denoted below as the measured data h, the actual metal thickness of the casing is denoted as the true metal thickness f and the intrinsic response of the logging tool 26 to the localized change in metal thickness is denoted as the ghosting kernel g. Advantageously, this approach may use a single receiver for deghosting. Furthermore, as high frequency noise may be damped by the disclosed technique, possible amplitudes present in the data log may not be amplified.
The convolution between the actual metal thickness of the casing and the ghosting kernel is a linear operation which may result in integrating a product of two functions. In particular, a first function is the ghosting kernel g and a second function is the actual metal thickness of the casing f. The result of the convolution is the measured data h:
h=g
f (Equation 1)
The convolution of these functions, which are indexed by depth, corresponds to a multiplication of the associated Fourier transforms. The previous formula may therefore be written:
h=ĝ×
The actual metal thickness of the casing f may be reconstructed by performing an inverse Fourier transform of the previous equation:
term on the left of the convolution operator may be described as the “deghosting” kernel.
To implement Equation 3, it may be useful to first determine the ghosting kernel g, i.e., the intrinsic response of the logging tool 26 to a localized predetermined change in metal thickness of the casing 22. For example, the localized predetermined change in metal thickness may be a single collar 24 on a casing string (which may change the metal thickness measured by the logging tool 26 from a metal thickness of the casing 22 to a metal thickness of the collar 24 coupled to the casing 22). In some embodiments, the collar 24 appears as additional metal thickness on the casing 22 and may measure approximately six inches long. Determining data related to the response of the logging tool 26 to the collar 24 may be achieved by extracting the data from a data log of the response of the logging tool 26. In some embodiments, the data related to the response of the logging tool 26 to the collar 24 may be achieved by other techniques, including modeling the data based on simulating the response of the logging tool 26, numerical simulation, experimental testing in a dedicated setup, selecting the data from a lookup table or database, or any combination thereof. The response of the logging tool 26 to the collar 24 may correspond to the ghosting kernel g, for which the Fourier transform may be computed.
With the foregoing in mind,
Turning now to
Turning now to
The logging tool may also obtain (block 164) a ghosting kernel g of the logging tool 26 based on the one or more casings 22 and a predetermined change in metal thickness of the one or more casings 22. For example, the predetermined change in metal thickness of the one or more casings 22 may be provided by using a collar 24 on a casing string (which may change the metal thickness measured by the logging tool 26 from a metal thickness of the one or more casings 22 to a metal thickness of the collar 24 coupled to the one or more casings 22). In some embodiments, the collar 24 appears as additional metal thickness on the one or more casings 22 and may measure approximately six inches long. Determining the ghosting kernel g of the logging tool 26 to the collar 24 may be achieved by modeling, simulation, numerical simulation, experimental testing in a dedicated setup, extracting the response from a data log, selection from a lookup table or database, or any combination thereof.
A processor, such as the processor 40 of the data processing system 38, may then determine (166) a metal thickness of the one or more casings f at the defect 48 by deconvolving a function of g and h. In particular, the metal thickness of the one or more casings f may be determined by using the Equations 1-3 as discussed above, such that Equation 2 is convolved with the “inverse” of the ghosting kernel, which is denoted as the deghosting kernel.
In some embodiments, because of noise in the measured data h, Equation 3 may not be applied directly. One standard method that may be applied to Equation 3 for deconvolution is called the Wiener Deconvolution. Alternate deconvolution techniques may also be used, such as the Richardson-Lucy deconvolution, the free deconvolution, etc. The Wiener Deconvolution relies on a priori knowledge of the ghosting kernel g and noise level in the measured data h. It can be written as follows:
In this relation, SNR is a signal to noise ratio of a system as a function of frequency. The signal to noise ratio SNR regularizes a response of the deghosting filter in frequency bands where the noise dominates the signal (e.g., the measured data h).
With the foregoing in mind,
Turning now to
The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.
Number | Date | Country | Kind |
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15290318.3 | Dec 2015 | EP | regional |