Well Integrity Evaluation Using Pseudo Attributes

BACKGROUND

For oil and gas exploration and production, a network of wells, installations and other conduits may be established by connecting sections of metal pipe together. For example, a well installation may be completed, in part, by lowering multiple sections of metal pipe (i.e., a casing string) into a wellbore, and cementing the casing string in place. In some well installations, multiple casing strings are employed (e.g., a concentric multi-string arrangement) to allow for different operations related to well completion, production, or enhanced oil recovery (EOR) options.

Well installations, which utilize metal casing strings, tubulars, and/or the like may be subject to corrosion. Efforts to mitigate corrosion include use of corrosion-resistant alloys, coatings, treatments, and corrosion transfer, among others. Corrosion is often monitored to determine when and if sections of pipe should be removed, replaced, and/or remediated. For downhole casing strings, various types of corrosion monitoring tools are available. One type of corrosion monitoring tool uses electromagnetic (EM) fields to estimate pipe thickness or other corrosion indicators. As an example, an EM logging tool may collect data on pipe thickness to produce an EM log. The EM log data may be interpreted to determine the condition of production and intermediate casing strings, tubing, collars, filters, packers, and perforations.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings illustrate certain aspects of some examples of the present disclosure and should not be used to limit or define the disclosure.

FIG. 1 illustrates an example of an EM logging tool disposed in a wellbore;

FIG. 2 illustrates an example of arbitrary defects within multiple pipes;

FIG. 3A illustrates an example of an EM logging tool traversing a wellbore;

FIG. 3B illustrates another example of an EM logging tool traversing a wellbore;

FIG. 3C illustrates another example of an EM logging tool traversing a wellbore;

FIG. 3D illustrates another example of an EM logging tool traversing a wellbore;

FIG. 3E illustrates another example of an EM logging tool traversing a wellbore;

FIG. 4 illustrates an example of a well plan;

FIG. 5 is an example of a workflow for a radial 1-D inversion in dual completion wells;

FIG. 6A illustrates the bar plot of misfit of the cost function and the bias from total thickness;

FIG. 6B illustrates how different performance metrics change with different regularization parameters with DDO type 1;

FIG. 6C illustrates the difference between model-based attributes and pseudo attributes;

FIG. 6D illustrates the cross-covariance between individual pipe metal loss;

FIG. 7 illustrates the metal loss on different pipes with/without regularization and total metal loss;

FIG. 8 illustrates the metal loss with uncertainty estimation;

FIG. 9 illustrates an example information handling system;

FIG. 10 illustrates another example information handling system; and

FIG. 11 illustrates an example of one arrangement of resources in a computing network.

DETAILED DESCRIPTION

This disclosure may generally relate to systems and methods for deriving pseudo thicknesses of individual pipes directly from the raw data using a linear or non-linear mapping of the raw data. Currently, EM tools may be utilized to detect anomalies on multiple nested tubulars. However, it is a challenge to perform the inversion and evaluate the integrity of the pipes accurately and in a short amount of time. The greater the number of nested pipes increases the likelihood that when tool measurements are inverted for individual pipe thicknesses, inherent instabilities may be created, such as non-uniqueness. Using available prior knowledge may regularize the inversion. However, a bottleneck exists in the application of utilizing prior knowledge to regularize the inversion in that it utilizes extensive processing to extract prior knowledge and optimize the regularization parameters. The methods and systems discussed above overcome these challenges by using a pseudo thickness to infer prior knowledge on the metal loss profile and help stabilize the inversion. This allows the methods and systems described below to evaluate the integrity of one or more nested well pipes, which current systems and methods cannot do.

EM logging tools may measure eddy currents to determine metal loss and use magnetic cores with one or more coils to detect defects in multiple concentric pipes. The EM logging tools may use pulse eddy current (time-domain) and may employ multiple (long, short, and transversal) coils to evaluate multiple types of defects in multiple pipes. It should be noted that the techniques utilized in time-domain may be utilized in frequency-domain measurements. In examples, EM logging tools may operate on a conveyance. Additionally, EM logging tools may include an independent power supply and may store the acquired data on memory.

Monitoring the condition of the production and intermediate casing strings is crucial in oil and gas field operations. EM eddy current (EC) techniques have been successfully used in inspection of these components. EM EC techniques consist of two broad categories: frequency-domain EC techniques and time-domain EC techniques. In both techniques, one or more transmitters are excited with an excitation signal, and the signals from the pipes are received and recorded for interpretation. The magnitude of a received signal is typically proportional to the amount of metal that is present in the inspection location. For example, less signal magnitude is typically an indication of more metal, and more signal magnitude is an indication of less metal. This relationship may allow for measurements of metal loss, which typically is due to an anomaly related to the pipe such as corrosion or buckling.

FIG. 1 illustrates an operating environment for an EM logging tool 100 as disclosed herein in accordance with some embodiments. EM logging tool 100 may comprise a transmitter 102 and/or a receiver 104. In examples, transmitters 102 and receivers 104 may be coil antennas. Furthermore, transmitter 102 and receiver 104 may be separated by a space between about 0.1 inches (0.254 cm) to about 200 inches (508 cm). In examples, EM logging tool 100 may be an induction tool that may operate with continuous wave execution of at least one frequency. This may be performed with any number of transmitters 102 and/or any number of receivers 104, which may be disposed on EM logging tool 100. In additional examples, transmitter 102 may function and/or operate as a receiver 104 or vice versa. EM logging tool 100 may be operatively coupled to a conveyance 106 (e.g., wireline, slickline, coiled tubing, pipe, downhole tractor, and/or the like) which may provide mechanical suspension, as well as electrical connectivity, for EM logging tool 100. Conveyance 106 and EM logging tool 100 may extend within casing string 108 to a desired depth within the wellbore 110. Conveyance 106, which may include one or more electrical conductors, may exit wellhead 112, may pass around pulley 114, may engage odometer 116, and may be reeled onto winch 118, which may be employed to raise and lower the tool assembly in the wellbore 110. Signals recorded by EM logging tool 100 may be stored on memory and then processed by display and storage unit 120 after recovery of EM logging tool 100 from wellbore 110. Alternatively, signals recorded by EM logging tool 100 may be conducted to display and storage unit 120 by way of conveyance 106. Display and storage unit 120 may process the signals, and the information contained therein may be displayed for an operator to observe and stored for future processing and reference. It should be noted that an operator may include an individual, group of individuals, or organization, such as a service company. Alternatively, signals may be processed downhole prior to receipt by display and storage unit 120 or both downhole and at surface 122, for example, by display and storage unit 120. Display and storage unit 120 may also contain an apparatus for supplying control signals and power to EM logging tool 100. Typical casing string 108 may extend from wellhead 112 at or above ground level to a selected depth within a wellbore 110. Casing string 108 may comprise a plurality of joints 130 or segments of casing string 108, each joint 130 being connected to the adjacent segments by a collar 132. There may be any number of layers in casing string 108. For example, a first casing 134 and a second casing 136. It should be noted that there may be any number of casing layers.

FIG. 1 also illustrates a typical pipe string 138, which may be positioned inside of casing string 108 extending part of the distance down wellbore 110. Pipe string 138 may be production tubing, tubing string, casing string, or other pipe disposed within casing string 108. Pipe string 138 may comprise concentric pipes. It should be noted that concentric pipes may be connected by collars 132. EM logging tool 100 may be dimensioned so that it may be lowered into the wellbore 110 through pipe string 138, thus avoiding the difficulty and expense associated with pulling pipe string 138 out of wellbore 110.

In logging systems, such as, for example, logging systems utilizing the EM logging tool 100, a digital telemetry system may be employed, wherein an electrical circuit may be used to both supply power to EM logging tool 100 and to transfer data between display and storage unit 120 and EM logging tool 100. A DC voltage may be provided to EM logging tool 100 by a power supply located above ground level, and data may be coupled to the DC power conductor by a baseband current pulse system. Alternatively, EM logging tool 100 may be powered by batteries located within the downhole tool assembly, and/or the data provided by EM logging tool 100 may be stored within the downhole tool assembly, rather than transmitted to the surface during logging (corrosion detection).

During operations, transmitter 102 may broadcast electromagnetic fields into subterranean formation 142. It should be noted that broadcasting electromagnetic fields may also be referred to as transmitting electromagnetic fields. The electromagnetic fields from transmitter 102 may be referred to as a primary electromagnetic field. The primary electromagnetic fields may produce Eddy currents in casing string 108 and pipe string 138. These Eddy currents, in turn, produce secondary electromagnetic fields that may be sensed and/or measured with the primary electromagnetic fields by receivers 104. Characterization of casing string 108 and pipe string 138, including determination of pipe attributes, may be performed by measuring and processing these electromagnetic fields. Pipe attributes may comprise, but are not limited to, pipe thickness, pipe conductivity, and/or pipe permeability.

As illustrated, receivers 104 may be positioned on the EM logging tool 100 at selected distances (e.g., axial spacing) away from transmitters 102. The axial spacing of receivers 104 from transmitters 102 may vary, for example, from about 0 inches (0 cm) to about 40 inches (101.6 cm) or more. It should be understood that the configuration of EM logging tool 100 shown on FIG. 1 is merely illustrative and other configurations of EM logging tool 100 may be used with the present techniques. A spacing of 0 inches (0 cm) may be achieved by collocating coils with different diameters. While FIG. 1 shows only a single array of receivers 104, there may be multiple sensor arrays where the distance between transmitter 102 and receivers 104 in each of the sensor arrays may vary. In addition, EM logging tool 100 may include more than one transmitter 102 and more or less than six receivers 104. In addition, transmitter 102 may be a coil implemented for transmission of magnetic field while also measuring EM fields, in some instances. Where multiple transmitters 102 are used, their operation may be multiplexed or time multiplexed. For example, a single transmitter 102 may broadcast, for example, a multi-frequency signal or a broadband signal. While not shown, EM logging tool 100 may include a transmitter 102 and receiver 104 that are in the form of coils or solenoids coaxially positioned within a downhole tubular (e.g., casing string 108) and separated along the tool axis. Alternatively, EM logging tool 100 may include a transmitter 102 and receiver 104 that are in the form of coils or solenoids coaxially positioned within a downhole tubular (e.g., casing string 108) and collocated along the tool axis.

Broadcasting of EM fields by the transmitter 102 and the sensing and/or measuring of secondary electromagnetic fields by receivers 104 may be controlled by display and storage unit 120, which may include an information handling system 144. As illustrated, the information handling system 144 may be a component of the display and storage unit 120. Alternatively, the information handling system 144 may be a component of EM logging tool 100. An information handling system 144 may include any instrumentality or aggregate of instrumentalities operable to compute, estimate, classify, process, transmit, broadcast, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling system 144 may be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price.

Information handling system 144 may include a processing unit 146 (e.g., microprocessor, central processing unit, etc.) that may process EM log data by executing software or instructions obtained from a local non-transitory computer readable media 148 (e.g., optical disks, magnetic disks). The non-transitory computer readable media 148 may store software or instructions of the methods described herein. Non-transitory computer readable media 148 may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time. Non-transitory computer readable media 148 may include, for example, storage media such as a direct access storage device (e.g., a hard disk drive or floppy disk drive), a sequential access storage device (e.g., a tape disk drive), compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), and/or flash memory; as well as communications media such wires, optical fibers, microwaves, radio waves, and other electromagnetic and/or optical carriers; and/or any combination of the foregoing. Information handling system 144 may also include input device(s) 150 (e.g., keyboard, mouse, touchpad, etc.) and output device(s) 152 (e.g., monitor, printer, etc.). The input device(s) 150 and output device(s) 152 provide a user interface that enables an operator to interact with EM logging tool 100 and/or software executed by processing unit 146. For example, information handling system 144 may enable an operator to select analysis options, view collected log data, view analysis results, and/or perform other tasks.

EM logging tool 100 may use any suitable EM technique based on Eddy current (“EC”) for inspection of concentric pipes (e.g., casing string 108 and pipe string 138). EC techniques may be particularly suited for characterization of a multi-string arrangement in which concentric pipes are used. EC techniques may include, but are not limited to, frequency-domain EC techniques and time-domain EC techniques.

In frequency domain EC techniques, transmitter 102 of EM logging tool 100 may be fed by a continuous sinusoidal signal, producing primary magnetic fields that illuminate the concentric pipes (e.g., casing string 108 and pipe string 138). The primary electromagnetic fields produce Eddy currents in the concentric pipes. These Eddy currents, in turn, produce secondary electromagnetic fields that may be sensed and/or measured with the primary electromagnetic fields by receivers 104. Characterization of the concentric pipes may be performed by measuring and processing these electromagnetic fields.

In time domain EC techniques, which may also be referred to as pulsed EC (“PEC”), transmitter 102 may be fed by a pulse. Transient primary electromagnetic fields may be produced due to the transition of the pulse from “off” to “on” state or from “on” to “off” state (more common). These transient electromagnetic fields produce EC in the concentric pipes (e.g., casing string 108 and pipe string 138). The EC, in turn, produces secondary electromagnetic fields that may be sensed and/or measured by receivers 104 placed at some distance on the EM logging tool 100 from transmitter 102, as shown on FIG. 1. Alternatively, the secondary electromagnetic fields may be sensed and/or measured by a co-located receiver (not shown) or with transmitter 102 itself.

It should be understood that while casing string 108 is illustrated as a single casing string, there may be multiple layers of concentric pipes disposed in the section of wellbore 110 with casing string 108. EM log data may be obtained in two or more sections of wellbore 110 with multiple layers of concentric pipes. For example, EM logging tool 100 may make a first measurement of pipe string 138 comprising any suitable number of joints 130 connected by collars 132. Measurements may be taken in the time-domain and/or frequency range. EM logging tool 100 may make a second measurement in a casing string 108 of first casing 134, wherein first casing 134 comprises any suitable number of pipes connected by collars 132. Measurements may be taken in the time-domain and/or frequency domain. These measurements may be repeated any number of times and for second casing 136 and/or any additional layers of casing string 108. In this disclosure, as discussed further below, methods may be utilized to determine the location of any number of collars 132 in casing string 108 and/or pipe string 138. Determining the location of collars 132 in the frequency domain and/or time domain may allow for accurate processing of recorded data in determining properties of casing string 108 and/or pipe string 138 such as corrosion. As mentioned above, measurements may be taken in the frequency domain and/or the time domain.

Monitoring the condition of pipe string 138 and casing string 108 may be performed on information handling system 144 in oil and gas field operations. Information handling system 144 may be utilized with Electromagnetic (EM) Eddy Current (EC) techniques to inspect pipe string 138 and casing string 108. EM EC techniques may include frequency-domain EC techniques and time-domain EC techniques. In time-domain and frequency-domain techniques, one or more transmitters 102 may be excited with an excitation signal which broadcast an electromagnetic field and receiver 104 may sense and/or measure the reflected excitation signal, a secondary electromagnetic field, for interpretation. The received signal is proportional to the amount of metal that is around transmitter 102 and receiver 104. For example, less signal magnitude is typically an indication of more metal, and more signal magnitude is an indication of less metal. This relationship may be utilized to determine metal loss, which may be due to an abnormality related to the pipe such as corrosion or buckling.

FIG. 2 shows EM logging tool 100 disposed in pipe string 138 which may be surrounded by a plurality of nested pipes (i.e., first casing 134 and second casing 136) and an illustration of anomalies 200 disposed within the plurality of nested pipes in accordance with some embodiments. As EM logging tool 100 moves across pipe string 138 and casing string 108, one or more transmitters 102 may be excited, and a signal (mutual impedance between 102 transmitter and receiver 104) at one or more receivers 104, may be recorded.

Due to eddy current physics and electromagnetic attenuation, pipe string 138 and/or casing string 108 may generate an electrical signal that is in the opposite polarity to the incident signal and results in a reduction in the received signal. Typically, more metal volume translates to more lost signal. As a result, by inspecting the signal gains, it is possible to identify zones with metal loss (such as corrosion). In order to distinguish signals that originate from anomalies at different pipes of a multiple nested pipe configuration, multiple transmitter-receiver spacing, and frequencies may be utilized. For example, short spaced transmitters 102 and receivers 104 may be sensitive to first casing 134, while longer spaced transmitters 102 and receivers 104 may be sensitive to second casing 136 and/or deeper (3rd, 4th, etc.) pipes. By analyzing the signal levels at these different channels with inversion methods, it is possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and conductivity may also be estimated by inversion methods. It should be noted that inversion methods may include model-based inversion which may include forward modeling, misfit inversions, cost function inversion, and/or the like. However, there may be factors that complicate interpretation of losses. For example, deep pipe signals may be significantly lower than other signals. Double dip indications appear for long spaced transmitters 102 and receivers 104. Spatial spread of long spaced transmitter-receiver signals for a collar 132 may be long (up to 6 feet). Due to these complications, methods may need to be used to accurately inspect pipe features.

FIGS. 3A-3E illustrate an electromagnetic inspection and detection of anomalies 200 (i.e., defects) or collars 132 (e.g., Referring to FIG. 2) in accordance with some embodiments. As illustrated, EM logging tool 100 may be disposed in pipe string 138, by a conveyance, which may comprise any number of concentric pipes. As EM logging tool 100 traverses across pipe 300, one or more transmitters 102 may be excited, and a signal (mutual impedance between transmitter 102 and receiver 104) at one or more receivers 104, may be recorded. Due to eddy currents and electromagnetic attenuation, pipe 300 may generate an electrical signal that is in the opposite polarity to the incident signal and results in a reduction in a received signal. Thus, more metal volume translates to greater signal lost. As a result, by inspecting the signal gains, it may be possible to identify zones with metal loss (such as corrosion). Similarly, by inspecting the signal loss, it may be possible to identify metal gain such as due to presence of a casing collar 132 (e.g., Referring to FIG. 1) where two pipes meet with a threaded connection. In order to distinguish signals from different pipes in a multiple concentric pipe configuration, multiple transmitter-receiver spacing, and frequencies may be used. For example, short spaced transmitters 102 and receivers 104 may be sensitive to pipe string 138, while long spaced transmitters 102 and receivers 104 may be sensitive to deeper pipes (i.e., first casing 134, second casing 136, etc.). By analyzing the signal levels at these different channels through a process of inversion, it may be possible to relate a certain received signal set to a certain set of metal loss or gain at each pipe. In examples, there may be factors that complicate the interpretation and/or identification of collars 132 and/or anomalies 200 (e.g., defects).

For example, due to eddy current physics and electromagnetic attenuation, pipes disposed in pipe string 138 (e.g., referring to FIG. 1 and FIG. 2) may generate an electrical signal that may be in the opposite polarity to the incident signal and results in a reduction in the received signal. Generally, as metal volume increases the signal loss may increase. As a result, by inspecting the signal gains, it may be possible to identify zones with metal loss (such as corrosion). In order to distinguish signals that originate from anomalies 200 (e.g., defects) at different pipes of a multiple nested pipe configuration, multiple transmitter-receiver spacing, and frequencies may be used. For example, short spaced transmitters 102 and receivers 104 may be sensitive to first pipe string 138 (e.g., referring to FIG. 2), while long spaced transmitters 102 and receivers 104 may be sensitive to deeper (2nd, 3rd, etc.) pipes (i.e., first casing 134 and second casing 136).

Analyzing the signal levels at different channels with an inversion scheme, it may be possible to relate a certain received signal to a certain metal loss or gain at each pipe. In addition to loss of metal, other pipe properties such as magnetic permeability and electrical conductivity may also be estimated by inversion. There may be several factors that complicate interpretation of losses: (1) deep pipe signals may be significantly lower than other signals; (2) double dip indications appear for long spaced transmitters 102 and receivers 104; (3) Spatial spread of long spaced transmitter-receiver signal for a collar 132 may be long (up to 6 feet); (4) To accurately estimate of individual pipe thickness, the material properties of the pipes (such as magnetic permeability and electrical conductivity) may need to be known with fair accuracy; (5) inversion may be a non-unique process, which means that multiple solutions to the same problem may be obtained and a solution which may be most physically reasonable may be chosen. Due to these complications, an advanced algorithm or workflow may be used to accurately inspect pipe features, for example when more than two pipes may be present in pipe string 138.

During logging operations as EM logging tool 100 traverses across pipe 300 (e.g., Referring to FIG. 3), an EM log of the received signals may be produced and analyzed. The EM log may be calibrated prior to running inversion to account for the deviations between measurement and simulation (forward model). The deviations may arise from several factors, including the nonlinear behavior of the magnetic core, magnetization of pipes, mandrel effect, and inaccurate well plans. Multiplicative coefficients and constant factors may be applied, either together or individually, to the measured EM log for this calibration.

FIG. 4 illustrates an example of a well plan 400 in accordance with some embodiments. Depending on the design of well plan 400, well construction may have between two and four main components. These components include conductor, surface, intermediate and production casings. After completion of the well, tubing may be inserted to pump hydrocarbon products. In this example, well plan 400 may comprise pipe string 138, first casing 134, second casing 136, a conductor casing 402, and wherein cement may be disposed in annulus 404 between each casing. However, it should be noted that well plan 400 may include any number of pipes, casings, tubulars, and/or the link. Well plan 400 is not limited or bound by the four pipes that are displayed in FIG. 4. When EM logging tool 100 is used to monitor the pipe condition a log may be produced. The log may be affected by many factors, for instance, the pipe electrical properties and pipe geometry including number, size, and shape. Additionally, the number of casings may be one of the most influential factors that may impact the signal level. The signal level change may provide information to interpret the EM log for better pipe inspection. The nominal signals corresponding to each zone with known pipe configuration may be extracted to analyze the characteristics of individual pipe properties.

FIG. 5 illustrates workflow 500 for evaluating the integrity of one or more nested well pipes. In examples, workflow 500 may be performed at least in part on information handling system 144 (e.g., referring to FIG. 1). Workflow 500 may be processed downhole, at the surface, at least partially downhole and at least partially at the surface, and/or remotely begin with block 502. In block 502, a raw electromagnetic log may be acquired. This may be performed during measurement operations described above. For example, EM logging tool 100 may be disposed within wellbore 110 without removing casing string 108 (e.g., referring to FIG. 1) to obtain multi-frequency and multi-spacing logs. As discussed above, EM logging tool 100 comprises one or more transmitters 102 and one or more receivers 104 (e.g., referring to FIG. 1) that may be utilized to acquire measurements in frequency or time domains, as described above. Eddy current techniques may allow for multiple pipes characterizations. Different depths of penetration and vertical resolutions may be achieved via two or more receivers 104 at the same axial location and two or more transmitters 102 placed at various distances from receivers 102. Transmitters 102 that may be placed at a shorter distance from receivers 104, may measure the response due to the inner pipes of casing string 108 with better axial and vertical resolution. Transmitters 102 that are at longer distances from receivers 104 may measure responses of outer pipes within casing string 108 but with degraded resolution. Thus, the electromagnetic log may be processed to invert all casing information with one run.

With continued reference to block 502, a multi-frequency and/or multi-spacing log may be obtained from measurement operations. EM logging tool 100 comprises one or more electromagnetic transmitters 102 and one or more electromagnetic receivers 104 and may acquire measurements in frequency or time domains. Eddy current techniques may allow for multiple pipes characterizations. Different depths of penetration and vertical resolutions may be achieved via several receivers at various axial locations and several transmitters placed at various distances from the receivers 104. The transmitters 102 that are placed at a shorter distance from the receivers, may measure the response due to the inner pipes with better resolution. The transmitters 102 that are at longer distances from the receivers 104 may measure responses of outer pipes but with degraded resolution. Thus, the electromagnetic log may be processed to invert all casing information with one run. The product of block 502 may be raw measurements that may be in a multi-frequency and/or multi-spaced log.

In block 504, a first database of pipe attributes may be built. In examples, several samples of pipe attributes are selected for each pipe. A first database of multifrequency and multi-spacing tool responses is generated based on different combinations of pipe attributes. In examples, the structure of individual pipes for a given well may be compiled into the first database. Further, the well diagram and the estimations of material properties, such as the magnetic permeability and electrical conductivity (from measurements in block 502) may be utilized in the first database of responses or different percentages of metal loss on each one of the pipes. Additionally, the first database may be used to train a model, to be discussed in detail below.

In block 506, pseudo attributes of one or more pipes comprising at least the tubing and the casing string may be computed. In examples, pseudo attributes of the pipes may be computed utilizing one or more raw measurements in a multi-frequency and/or multi-spaced log from the first database or one or more measurements from blocks 502 and/or 504. To illustrate, regression/machine learning models may be trained to estimate each one of the pseudo attributes from predefined sets of raw measurements. In one example, the set of raw measurements may be based on the sensitivity of the response of EM logging tool 100 to the different combinations of pipe attributes. In another example, the set of raw measurements may be defined on a principal component analysis of the covariance matrix of the tool response to the different combinations of pipe attributes. During the process of computing pseudo attributes, an optimization algorithm may be used to minimize a cost function. Noise may be added to the cost function to regularize the optimization with the cost function. The quality of the pseudo attributes of a given pipe at a given depth is determined based on the values of the pseudo attributes of the inner pipes at that depth. For example, the pseudo metal loss on a given pipe is deemed unreliable at a given depth if the pseudo metal loss of any of the inner pipes at that depth exceeds an adjustable threshold. In examples, an adjustable threshold may be 50%-10%, 10%-1%, or 1%-0.01%. The quality of the pseudo attributes may be found utilizing a computer model. In examples, a machine learning (ML) model, such as a regression model, may be utilized. A model may be a regression model or any ML model. Each of these models may be trained utilizing at least the first database. Regression comprises linear or non-linear models and ML models may comprise an artificial neural network, a convolutional neural network, a deep neural network, support vector machine, decision tree, random forest, and/or the like. All of these models may be performed or computed on different information handling systems 144 (e.g., referring to FIG. 11), with different data agents 1102.

With continued reference to FIG. 5, in block 508, a second database of pipe attributes may be built. In examples, a second database may comprise denser sampling around pseudo attributes. In examples, a second database may be created from the first database by adding raw measurements to sample concentrations around the pseudo attribute values. The second database may be used to run fast, brute-force inversions with a plurality of different regularization parameters to determine the optimal value of regularization parameters to be used in the model-based inversion. In further examples, the second database may be configured to yield an estimation for the pipe attribute (i.e., metal loss or thickness) of each pipe. Some estimations of pipe attributes may be closer than others. Thus, for regions with accurate estimations, where an accurate estimation is above an adjustable threshold. This region may be saved and merged with the first database.

In block 510, regularization parameters for a model-based inversion may be determined from the second and/or first database determined in blocks 508 or 504 respectively. In examples, regularization parameters may comprise regularization parameters of the ML model, channel weights, and/or any parameters for determining pseudo attributes of one or more pipes. In examples, second database from block 508 may be used to run fast, brute-force inversions with a plurality of different regularization parameters to determine a value of regularization parameters based one or more performance metrics. The performance metrics may comprise cross-covariance between individual pipe thicknesses or metal loss, misfit of the cost function, difference between model-based attributes and pseudo attributes. The acceptable ranges for the performance metrics are determined through statistical analysis of an ensemble of well data. For a given measurement set of well data, the performance metrics may be compared against the acceptable ranges to detect outliers. Regularization parameters of the model-based inversion may be optimized to minimize outliers.

In block 512, raw data from block 502 may be processed in model-based inversion with the regularization parameters determined in block 510. In examples, processing raw data may comprise running a model-based inversion or a supervised machine learning model to estimate model-based attributes of the pipes from the raw data. Further, a cost function may be minimized by using gradient descent methods or a brute-force search from the first database. Additionally, the regularization parameters from block 510 may be utilized in a model-based inversion. As a result, attributes of the pipes may be computed.

In block 514, an uncertainty estimation may be performed on the attributes from block 510. In examples, variance in the output between different input parameters such as different channel weights may be determined. Further, different frequencies or different spacings may measure the same pipe with different sensors and add at different frequencies to process any subset of channels may find presence of noise and some calibration errors. Further, uncertainty estimation may be performed with a Monte Carlo algorithm to estimate pseudo attributes or model-based attributes for different realizations of measurement weights or random noise. Histograms of the estimated attributes are computed, and uncertainty bands are outlined.

In block 516, quality control may be performed. In examples, the quality control may be performed by comparing attributes of the pipes from block 510 to pseudo attributes of the pipes from block 506. In examples, this may be performed by using gradient descent or brute-force search. The comparison may utilize a mean and variance across multiple attributes of the pipes from block 510 and pseudo attributes of the pipes from block 506. A high correlation between the model-based attributes and the pipe attributes would indicate high quality inversion.

FIG. 6A illustrates the bar plot of misfit of the cost function from block 512 and the bias from total thickness. Data driven optimization (DDO) type is used to describe the way of corrosion progression. Corrosion may occur from outside pipes to inside pipes, from inside pipes to outside pipes, or from intermediate pipe to two-sided pipes, etc. DDO aggressiveness level is used to describe the strength of constraint applied in the algorithm. In examples, three values may be defined for different ML models, from DDO type 0 to DDO type 2. DDO type 0 means no DDO is applied. DDO type 1 means corrosion happens from outside pipe to inside pipe. DOO type 2 means corrosion happens from inside pipe to outside pipe. DDO type 0 may be applied as reference. The DDO type in the example is chosen as type 1. The y-axis shows the value of misfit of the cost function and the bias from the total thickness. Total thickness (TT) Bias may be the bias of the total thickness. Therefore, the prior knowledge type is chosen as type 1, which is outside-in, the corrosion happens from outside to inside and there will be more metal loss on the outer pipes than in the inner pipes. FIG. 6B illustrates how different performance metrics change with different regularization parameters with DDO type 1. For example, the performance metrics become a constant when the regularization parameter is larger than a certain value. FIG. 6C illustrates the difference between model-based attributes and pseudo attributes. Herein, the x-axis is the DDO aggressiveness level, which may describe the strength of constraint applied in the algorithm. In examples, define the DDO aggressiveness level from 0 to 10. y axis shows the value of different error performance metrics. The difference is the smallest when the regularization parameter is 5. Therefore, the regularization parameter may be selected to be 5. FIG. 6D illustrates the cross-covariance between individual pipe metal loss.

FIG. 7 illustrates the metal loss on different pipes with/without regularization and total metal loss as calculated as a product of block 512. Pipe 1 indicates the inner most pipe surrounded by pipe 2, pipe 3, and pipe 4-the outermost pipe. In examples, pseudo metal loss 702, masked pseudo metal loss 704, model-based metal loss 706 may be depicted. Pseudo metal loss 702 may be determined in block 506 (e.g., referring to FIG. 5). Masked pseudo metal loss 704 may be determined in block 506, a mask is also constructed to mask out unreliable pseudo metal loss. The masked pseudo metal loss may be achieved by applying the mask to pseudo metal loss. Model-based metal loss 706 may be determined by the minimum value of cost function from block 510. The metal loss which generates the minimum value of cost function will be the output. The first row shows the metal loss with regularization and the second row shows the metal loss without regularization. Regularization may be determined in block 512. The first to fourth columns show the metal loss on pipes 1,2,3,4, respectively. The fifth column shows the total metal loss. Because the prior knowledge applied is type 1: outside in, it shows more metal loss on the outer pipes than inner pipes. It may also be seen that after applying regularization, the difference between model-based metal loss and pseudo metal loss is reduced compared to the case without regularization.

FIG. 8 illustrates the metal loss with uncertainty estimation, the product of block 514. The uncertainty estimation 802 may be plotted. Four levels of uncertainty are computed and plotted. The average uncertainty is shown in the subtitles of each plot. If uncertainty in the first and second rows is compared, it may be illustrated that uncertainty is greatly reduced after regularization is applied.

As described above, workflows 500 (e.g., referring to FIG. 5) may utilize multi-frequency and multi-spacing logs acquired by using one or more transmitters 102 and one or more receivers 104 (e.g., referring to FIG. 1) disposed on EM logging tool 100. Workflow 500 may be performed on information handling system 144. Further, one or more transmitters 102 and one or more receivers 104 may be communicably coupled to information handling system 144. In examples, information handling system 144 may be disposed downhole on or near EM logging tool 100, at surface 122, at least in part on EM logging tool and at least in part at surface, or in remote embodiments.

FIG. 9 illustrates an example information handling system 144 which may be employed to perform various steps, methods, and techniques disclosed herein. Persons of ordinary skill in the art will readily appreciate that other system examples are possible. As illustrated, information handling system 144 includes a processing unit (CPU or processor) 902 and a system bus 904 that couples various system components including system memory 906 such as read only memory (ROM) 908 and random-access memory (RAM) 910 to processor 902. Processors disclosed herein may all be forms of this processor 902. Information handling system 144 may include a cache 912 of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 902. Information handling system 144 copies data from memory 906 and/or storage device 914 to cache 912 for quick access by processor 902. In this way, cache 912 provides a performance boost that avoids processor 902 delays while waiting for data. These and other modules may control or be configured to control processor 902 to perform various operations or actions. Another system memory 906 may be available for use as well. Memory 906 may include multiple different types of memory with different performance characteristics. It may be appreciated that the disclosure may operate on information handling system 144 with more than one processor 902 or on a group or cluster of computing devices networked together to provide greater processing capability. Processor 902 may include any general-purpose processor and a hardware module or software module, such as first module 916, second module 918, and third module 920 stored in storage device 914, configured to control processor 902 as well as a special-purpose processor where software instructions are incorporated into processor 902. Processor 902 may be a self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. Processor 902 may include multiple processors, such as a system having multiple, physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, processor 902 may include multiple distributed processors located in multiple separate computing devices but working together such as via a communications network. Multiple processors or processor cores may share resources such as memory 906 or cache 912 or may operate using independent resources. Processor 902 may include one or more state machines, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA (FPGA).

Each individual component discussed above may be coupled to system bus 904, which may connect each and every individual component to each other. System bus 904 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in ROM 908 or the like, may provide the basic routine that helps to transfer information between elements within information handling system 144, such as during start-up. Information handling system 144 further includes storage devices 914 or computer-readable storage media such as a hard disk drive, a magnetic disk drive, an optical disk drive, tape drive, solid-state drive, RAM drive, removable storage devices, a redundant array of inexpensive disks (RAID), hybrid storage device, or the like. Storage device 914 may include software modules 916, 918, and 920 for controlling processor 902. Information handling system 144 may include other hardware or software modules. Storage device 914 is connected to the system bus 904 by a drive interface. The drives and the associated computer-readable storage devices provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for information handling system 144. In one aspect, a hardware module that performs a particular function includes the software component stored in a tangible computer-readable storage device in connection with the necessary hardware components, such as processor 902, system bus 904, and so forth, to carry out a particular function. In another aspect, the system may use a processor and computer-readable storage device to store instructions which, when executed by the processor, cause the processor to perform operations, a method or other specific actions. The basic components and appropriate variations may be modified depending on the type of device, such as whether information handling system 144 is a small, handheld computing device, a desktop computer, or a computer server. When processor 902 executes instructions to perform “operations”, processor 902 may perform the operations directly and/or facilitate, direct, or cooperate with another device or component to perform the operations.

As illustrated, information handling system 144 employs storage device 914, which may be a hard disk or other types of computer-readable storage devices which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memories (RAMs) 910, read only memory (ROM) 908, a cable containing a bit stream and the like, may also be used in the exemplary operating environment. Tangible computer-readable storage media, computer-readable storage devices, or computer-readable memory devices, expressly exclude media such as transitory waves, energy, carrier signals, electromagnetic waves, and signals per sr.

To enable user interaction with information handling system 144, an input device 922 represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. Additionally, input device 922 may take in data that was measured and/or acquired from EM logging tool 100, discussed above. An output device 924 may also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with information handling system 144. Communications interface 926 generally governs and manages the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic hardware depicted may easily be substituted for improved hardware or firmware arrangements as they are developed.

As illustrated, each individual component described above is depicted and disclosed as individual functional blocks. The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software and hardware, such as a processor 902, that is purpose-built to operate as an equivalent to software executing on a general-purpose processor. For example, the functions of one or more processors presented in FIG. 9 may be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may include microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM) 908 for storing software performing the operations described below, and random-access memory (RAM) 910 for storing results. Very large-scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general-purpose DSP circuit, may also be provided.

The logical operations of the various methods, described below, are implemented as: (1) a sequence of computer implemented steps, operations, or procedures running on a programmable circuit within a general use computer, (2) a sequence of computer implemented steps, operations, or procedures running on a specific-use programmable circuit; and/or (3) interconnected machine modules or program engines within the programmable circuits. Information handling system 144 may practice all or part of the recited methods, may be a part of the recited systems, and/or may operate according to instructions in the recited tangible computer-readable storage devices. Such logical operations may be implemented as modules configured to control processor 902 to perform particular functions according to the programming of software modules 916, 918, and 920.

In examples, one or more parts of the example information handling system 144, up to and including the entire information handling system 144, may be virtualized. For example, a virtual processor may be a software object that executes according to a particular instruction set, even when a physical processor of the same type as the virtual processor is unavailable. A virtualization layer or a virtual “host” may enable virtualized components of one or more different computing devices or device types by translating virtualized operations to actual operations. Ultimately however, virtualized hardware of every type is implemented or executed by some underlying physical hardware. Thus, a virtualization computer layer may operate on top of a physical computer layer. The virtualization computer layer may include one or more virtual machines, an overlay network, a hypervisor, virtual switching, and any other virtualization application.

FIG. 10 illustrates an example information handling system 144 having a chipset architecture that may be used in executing the described method and generating and displaying a graphical user interface (GUI). Information handling system 144 is an example of computer hardware, software, and firmware that may be used to implement the disclosed technology. Information handling system 144 may include a processor 902, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 902 may communicate with a chipset 1000 that may control input to and output from processor 902. In this example, chipset 1000 outputs information to output device 924, such as a display, and may read and write information to storage device 914, which may include, for example, magnetic media, and solid-state media. Chipset 1000 may also read data from and write data to RAM 910. Bridge 1002 for interfacing with a variety of user interface components 1004 may be provided for interfacing with chipset 1000. User interface components 1004 may include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to information handling system 144 may come from any of a variety of sources, machine generated and/or human generated.

Chipset 1000 may also interface with one or more communication interfaces 926 that may have different physical interfaces. Such communication interfaces may include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein may include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 902 analyzing data stored in storage device 914 or RAM 910. Further, information handling system 144 receives inputs from a user via user interface components 1004 and executes appropriate functions, such as browsing functions by interpreting these inputs using processor 902.

In examples, information handling system 144 may also include tangible and/or non-transitory computer-readable storage devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices may be any available device that may be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which may be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network, or another communications connection (either hardwired, wireless, or combination thereof), to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable storage devices.

Computer-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

In additional examples, methods may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Examples may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

FIG. 11 illustrates an example of one arrangement of resources in a computing network 1100 that may employ the processes and techniques described herein, although many others are of course possible. As noted above, an information handling system 144, as part of their function, may utilize data, which includes files, directories, metadata (e.g., access control list (ACLS) creation/edit dates associated with the data, etc.), and other data objects. The data on the information handling system 144 is typically a primary copy (e.g., a production copy). During a copy, backup, archive or other storage operation, information handling system 144 may send a copy of some data objects (or some components thereof) to a secondary storage computing device 1104 by utilizing one or more data agents 1102.

A data agent 1102 may be a desktop application, website application, or any software-based application that is run on information handling system 144. As illustrated, information handling system 144 may be disposed at any rig site (e.g., referring to FIG. 1) or repair and manufacturing center. Data agent 1102 may communicate with a secondary storage computing device 1104 using communication protocol 1108 in a wired or wireless system. Communication protocol 1108 may function and operate as an input to a website application. In the website application, field data related to pre-and post-operations, generated DTCs, notes, and the like may be uploaded. Additionally, information handling system 144 may utilize communication protocol 1108 to access processed measurements, operations with similar DTCs, troubleshooting findings, historical run data, and/or the like. This information is accessed from secondary storage computing device 1104 by data agent 1102, which is loaded on information handling system 144.

Secondary storage computing device 1104 may operate and function to create secondary copies of primary data objects (or some components thereof) in various cloud storage sites 1106A-N. Additionally, secondary storage computing device 1104 may run determinative algorithms on data uploaded from one or more information handling systems 144, discussed further below. Communications between the secondary storage computing devices 1104 and cloud storage sites 1106A-N may utilize REST protocols (Representational state transfer interfaces) that satisfy basic C/R/U/D semantics (Create/Read/Update/Delete semantics), or other hypertext transfer protocol (“HTTP”)-based or file-transfer protocol (“FTP”)-based protocols (e.g., Simple Object Access Protocol).

In conjunction with creating secondary copies in cloud storage sites 1106A-N, the secondary storage computing device 1104 may also perform local content indexing and/or local object-level, sub-object-level or block-level deduplication when performing storage operations involving various cloud storage sites 1106A-N. Cloud storage sites 1106A-N may further record and maintain DTC code logs for each downhole operation or run, map DTC codes, store repair and maintenance data, store operational data, and/or provide outputs from determinative algorithms that are fun at cloud storage sites 1106A-N.

Improvements discussed above may comprise quickly processing the measurement to obtain pseudo attributes as prior knowledge. Prior knowledge may provide the corrosion progression either outside-in or inside-out. It may also provide accurate pipe attribute estimation for certain pipes, not for all pipes, typically inner pipes. But systems and methods may integrate prior knowledge to model-based inversion to have accurate estimation for all pipes. According to the prior information, the model-based inversion may be faster as well.

Statement 1: A method may comprise disposing an electromagnetic (EM) logging tool into a pipe string configured to perform measurements at one or more depths, creating a log from the measurements at one or more depths taken by the EM logging tool in the pipe string, computing one or more pseudo attributes of one or more pipes with the log, and determining a remedial operation based at least on the one or more pseudo attributes.

Statement 2: The method of statement 1, wherein the pseudo attributes of the one or more pipes are pipe thickness, metal loss, magnetic permeability, or electrical permeability of the one or more pipes.

Statement 3: The method of any previous statement 1 or 2, wherein computing the one or more pseudo attributes comprises building a regression model to estimate at least one pseudo attribute from the one or more pseudo attributes.

Statement 4: The method of any previous statements 1-3, wherein computing the one or more pseudo attributes comprises training a machine learning model to estimate at least one pseudo attribute from the one or more pseudo attributes.

Statement 5: The method of any previous statements 1-4, further comprising generating a first database with at least one or more pipe attributes.

Statement 6: The method of statement 5, further comprising computing one or more model-based attributes with a model-based inversion.

Statement 7: The method of statement 6, wherein one or more pseudo attributes are used to extract prior knowledge on a direction of metal loss progression.

Statement 8: The method of statement 7, wherein the prior knowledge and the one or more pseudo attributes are used to determine one or more regularization parameters.

Statement 9: The method of statement 8, wherein the model-based inversion comprises an optimization algorithm of one or more regularization parameters of the model-based inversion.

Statement 10: The method of statements 6 or 7, wherein the model-based inversion comprises a cost function using gradient descent methods or a brute-force search of the database.

Statement 11: The method of statement 10, wherein the cost function is defined in terms of the one or more pseudo attributes.

Statement 12: The method of any previous statements 6, 7, or 10, further comprising performing a quality control on the one or more model-based attributes, wherein the quality of the one or more model-based attributes at a measurement depth is determined based on the one or more pseudo attributes at the measurement depth.

Statement 13: The method of statement 12, wherein a Monte Carlo algorithm is used to estimate the one or more pseudo attributes or the one or more model-based attributes for different regularization parameters of measurement weights or random noise.

Statement 14: The method of any previous statements 6, 7, 10 or 12, further comprising comparing the model-based attributes to one or more pseudo attributes to form a comparison.

Statement 15: The method of statement 14, wherein the comparison comprises a cross-covariance between individual pipe thicknesses or metal loss, misfit of a cost function, difference between model-based attributes and pseudo attributes.

Statement 16: The method of statement 15, wherein acceptable ranges for the comparison are determined through statistical analysis.

Statement 17: The method of statement 16, further comprising adjusting regularization parameters of the model-based inversion if the comparison is outside of an acceptable range.

Statement 18: A system may comprise an electromagnetic (EM) logging tool into a pipe string configured to perform measurements at one or more depths and an information handling system. The information handling system may be configured to create a log from the measurements at one or more depths taken by the EM logging tool in the pipe string, compute one or more pseudo attributes of one or more pipes with the log, and determine a remedial operation based at least on the one or more pseudo attributes.

Statement 19: The system of statement 18, wherein the EM logging tool operates in time-domain or frequency domain.

Statement 20: The system of statement 18, wherein the EM logging tool comprises at least one transmitter coil and at least one receiver coil.

The preceding description provides various examples of the systems and methods of use disclosed herein which may contain different method steps and alternative combinations of components. It should be understood that, although individual examples may be discussed herein, the present disclosure covers all combinations of the disclosed examples, including, the different component combinations, method step combinations, and properties of the system. It should be understood that the compositions and methods are described in terms of “comprising,” “containing,” or “including” various components or steps, the compositions and methods may also “consist essentially of” or “consist of” the various components and steps. Moreover, the indefinite articles “a” or “an,” as used in the claims, are defined herein to mean one or more than one of the elements that it introduces.

For the sake of brevity, only certain ranges are explicitly disclosed herein. However, ranges from any lower limit may be combined with any upper limit to recite a range not explicitly recited, as well as, ranges from any lower limit may be combined with any other lower limit to recite a range not explicitly recited, in the same way, ranges from any upper limit may be combined with any other upper limit to recite a range not explicitly recited. Additionally, whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any included range falling within the range are specifically disclosed. In particular, every range of values (of the form, “from about a to about b,” or, equivalently, “from approximately a to b,” or, equivalently, “from approximately a-b”) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values even if not explicitly recited. Thus, every point or individual value may serve as its own lower or upper limit combined with any other point or individual value or any other lower or upper limit, to recite a range not explicitly recited.

Therefore, the present examples are well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular examples disclosed above are illustrative only and may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Although individual examples are discussed, the disclosure covers all combinations of all of the examples. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. It is therefore evident that the particular illustrative examples disclosed above may be altered or modified and all such variations are considered within the scope and spirit of those examples. If there is any conflict in the usages of a word or term in this specification and one or more patent(s) or other documents that may be incorporated herein by reference, the definitions that are consistent with this specification should be adopted.

Well Integrity Evaluation Using Pseudo Attributes

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