High-Resolution Acoustic Imaging for Submillimetric Casing Thickness Quantification and Advanced Effective-Area-Based Burst Pressure Analyses Free

High-resolution acoustic imaging provides operators with a comprehensive tool to evaluate all aspects of casing integrity in a single pass. This paper outlines the expanded development of the technology platform, such as the validation of the novel tool design and associated processing capabilities used to provide submillimetric casing thickness measurements. Furthermore, we describe the way the technology provides direct measurements of inner diameter (ID) and outer diameter (OD) wall loss defects in a fluid-agnostic, full-circumferential manner.

High-resolution acoustic imaging was originally introduced by Robinson et al. (2020), and it provides an order-of-magnitude shift in resolution while overcoming the fluid opacity challenges faced by legacy technologies. Solid-state arrays, state-of-the-art electronics, novel beam-forming imaging modes, onboard compression techniques, and advanced visualization software have enabled the capture and storage of submillimetric imaging data for up to 30,000 feet in a single pass. The initial development of the platform was optimized for the radial inspection of casing, providing deformation, ovality, ID wall loss, and textural differences for well integrity and perforation erosion analyses. Currently, the radial acoustic imaging platform has been used to image over 150,000 perforations and 6,000 frac plug set locations, and hundreds of integrity-based downhole inspections have been completed. The insight obtained from the step change in resolution has changed how operators to trial and assess new completion designs such as equipment selection and operational procedures. Insight from acoustic-based tools and findings from acoustic databases have been applied to understand frac plug failure root causes and improved plug performance (Wardynski et. al., 2021) to assess complex integrity challenges (Littleford et. al. 2021) and non-traversable obstructions for fishing and interventions (Littleford et. al. 2022).

The following sections outline the development, functional principles, laboratory validation, and field case studies of high-resolution acoustic imaging platforms for thickness evaluation. The tool configuration relies on an approach similar to that previously outlined by Robinson et al. (2020), and it is used to detect wall-loss defects on the inner and outer casing surfaces. The solid-state approach provides an alternative to legacy casing thickness assessment tools, including magnetic flux leakage (MFL) and single-element ultrasound-based tools that lack submillimetric resolution and full casing coverage. It is also possible to run both radial and thickness acoustic-based tools in tandem to obtain both ID and OD high-resolution casing assessments in a single pass. This can be advantageous for understanding complex geometric features and ID textural indications to improve holistic integrity datasets. In the dual-tool format, the pair generates 82 gigabits per second of front-end data, which is compressed and stored onboard the tools. After surfacing, a field technician uploaded the dataset to a cloud-based processing server that algorithmically processed the raw acoustic data. Afterward, the data and corresponding algorithmic casing penetration mapping were validated by analysts. This high-resolution dataset enables more advanced analysis methodologies such as effective area and RSTRENG burst-pressure failure assessments.

With this technology, operators can quantify corrosion rates, conduct pre-fracture burst pressure analyses, assess corrosion inhibitor programs, and prioritize remediation. As a casing material agnostic technology, it can measure the thickness of nonferrous and nonmagnetic permeable casing materials. Therefore, operators can inspect disposal wells that are fiber wrapped or assess titanium casing strings. Operators of gas storage and carbon sequestration assets may benefit from this technology by capturing defects at earlier stages, tracking corrosion growth rates, and increasing pressure ratings to increase gas storage capacity.

Two primary technologies have been deployed to measure casing thickness. The legacy technologies, their associated limitations, and comparisons with high-resolution acoustic imaging are outlined below.

Magnetic flux leakage (MFL) tools have been commonly used in the oil and gas industry for over 50 years. The tools saturate the casing steel with oriented magnetic fields and record the loss or reduction in the magnetic field imparted by the wall loss as the tool traverses the well. As defects interact with the magnetic field, they impede and cause leakage in the magnetic flux measured at the tool (Kamgang et. al., 2017). The magnetic flux loss was calibrated against known defects, and the results were output as a percentage of the nominal wall. This is considered an inferred measurement technology; defect size and penetration are inferred based on the magnitude of flux leakage compared with lab-generated, ideal-scenario calibration datasets. Generally, the lab-generated reference defects are of simplified geometry and may not be representative of field data.

Magnetic flux leakage tools use discriminator sensors to induce a near-field response on the inner surface of the casing. Applying the discriminator response relative to the response of the larger saturated flux signal, one can interpret if the metal loss is present on the ID or OD of the casing. In the case of concurrent wall loss events on the ID and OD, the ability to differentiate both wall loss events may be limited. Even though the technology is highly robust and proven, it struggles when it comes to complex corrosion, such as local deep pits in widespread corrosion, where the background flux changes make it difficult to isolate a local pit's location and depth. Similarly, MFL tools struggle with gradual wall-loss events, where the change in flux is minor and difficult to detect. In addition, MFL tools may struggle to detect splits, cracks, and weld seam damage that do not have sufficient metal removed to cause a detectable flux change.

In summary, the fundamental limitations associated with MFL-based inspections are as follows:

• Indirect or inferred measurement principles result in substantial analyses- and algorithm-based interpretations of calibration datasets.

• The indirect measurement principles lack the ability to provide visual information. Typical outputs are raw sensor readings measured in terms of the percentage of flux leakage.

• Relatively large magnets are required which limit the number of sensors and resolution of MFL tools.

• Changes in casing geometry or complex widespread corrosion results in unstable flux signals and can limit the detection and sizing ability of local defect in these regions.

• Gradual wall loss events often do not cause large enough signal changes to be detectable.

• Reliability of discriminators for internal and external defect assessments. In the event of both ID and OD wall losses, the tools can struggle to differentiate between the scenarios.

• Thresholds for a minimum quantity of material lost result in the inability to detect splits in casings or welds.

• Magnetic material compatibility is required to permit the saturation of magnetic fields.

• Generally, a large tool ID is required to minimize standoff, and, it can limit the ability to traverse regions of casing damage or deformation.

Two primary types of ultrasound-based tools have historically been deployed in oil and gas wells. The tools can be categorized based on the frequency of the transmitted waveforms.

Low-frequency tools emit a wide band in the frequency range of approximately 200–700 kHz. A particular frequency within the selected range causes a resonant acoustic response in the steel casing. The resonant frequency domain is used to calculate the casing thickness. Additionally, the acoustic impedance is derived from acoustic resonance. Furthermore, the impedance represents the opposition of the casing to the resonance, which indicates the quality of the cement bond on the backside of the casing. A larger dampening effect can be correlated with a higher-quality cement bond. A typical spatial resolution for this methodology was approximately 1.2 inches in the axial direction and 10° in the circumferential or azimuthal dimension (A. J. Hayman et al, 1995). The low spatial resolution and azimuthal coverage associated with the rotating head make this approach poorly suited for providing a high-resolution assessment of the casing.

High-frequency ultrasound tools operate in the megahertz frequency transmission range and emit a single center-band frequency. The tools offer a direct measurement principle based on a known speed of sound in fluids and steel, and they transmit a pulse and record various echoes received at the transducer. The precise time at which the reflected signal is received back at the tool is used to locate the surface, and the time of flight between surface echoes can be used to calculate the casing thickness based on the known speed of sound in the material. The thickness resolution of the tools is partly determined by the wavelength. Higher-frequency signals have a shorter wavelength, thereby providing a more granular thickness measurement.

In summary, the fundamental limitations of rotating single-element ultrasound tools are as follows:

• Limited resolution due to low-frequency and wide-beam signal transmission. Generally, the technology can only detect large casing damage.

• Lack of ability to provide circumferential coverage due to rotating head; a helical coverage pattern is generated by the mechanical rotation of the imaging head.

• The limited resolution provides a very coarse depiction of the casing, and localized defects may not be detected and quantified.

• Imaging heads are specific to casing size, and multiple runs and imaging head swaps may be required to image wells with multiple casing diameters.

High-resolution acoustic imaging technology initially optimized for radial inspection of the casing has been adapted for the identification and quantification of submillimetric casing thickness loss. The technology is highly data-intensive and is feasible due to recent advancements in high-density arrays, electronic packaging, data compression techniques, onboard data storage, and cloud-based processing. Signal transmission and acquisition occur without moving components. Up to 512 high-frequency elements were embedded around the circumference of the tool. This approach used proprietary electronics, software, and firmware onboard the tool string to energize the banks of individual elements sequentially. These banks are virtually spun around the solid-state array, eliminating the requirement of a rotating sub. As aforementioned by Robinson et al. (2020), the transducers were controlled electronically via proprietary firmware and could be adjusted automatically to casing conditions, sizes, and orientation. All beam focusing, steering, and mode determinations were performed electronically. Furthermore, the acoustic lens retained the same OD as the remainder of the tool string, and there is no requirement to change the imaging heads to comply with multiple casing sizes. A high-resolution acoustic tool string is shown in Figure 1.

The solid-state nature of the imaging head implies that the imaging process can occur at much faster speeds and can capture the full circumference of the casing compared to a physically rotating head that only captures a helical imaging window. Axial resolutions of 0.5 mm can be achieved at by imaging from multiple apertures, as shown in Figure 2. This novel approach can obtain submillimetric axial and sub-degree azimuthal resolutions while providing direct measurements of wall loss defects. A high-frequency signal can be tailored for a given material, logging speed, or casing thickness. The configuration of the acoustic system was performed completely through software; the number of active apertures, elements active per aperture, and many other parameters can be adjusted in real time either from the surface or on memory. The solution provided a highly flexible and compatible system for high-resolution inspection of casings of various diameters, materials, and fluid properties in a single pass.

Simulations can be used to depict the fundamental principles of a solid-state acoustic probe. As shown in Figure 3, the process by which the casing thickness results are generated can be realized using sequential images. A high-frequency wave was emitted from the transducer (frame 1), and the wave traveled through the fluid medium towards the casing (frame 2). Energy is reflected back immediately after hitting the inner wall of the casing, and some is absorbed by the material, where it travels to the outer casing wall (frame 3). The first received signal represented the ID wall profile, followed by any indication of wall loss, and finally, the complete signal of the OD of the casing. Because the time between the ID, defect, and OD waves were recorded at the probe, and the speed of sound was known in the fluid and casing material, the information could be used to map the surfaces and ultimately obtain the remaining wall at the defect location. This methodology directly measures the ID wall and OD back wall in the time domain.

A large-scale experimental evaluation process was conducted to validate and quantify the thickness measurement capabilities of a high-resolution acoustic imaging platform. A phased approach was applied by iteratively adding layers of complexity to experimental samples. The samples were machined with high-precision five-axis CNC machines, and the dimensions were validated with industrial computed tomography (CT) scans. Both corroded and non-corroded samples were included. This study enclosed findings from 24 samples scanned with varying imaging parameters for 88 scan datasets, and this enabled the development of novel and optimized imaging modes, acoustic settings, machine-learning algorithms for wall loss detection, QC analysis tools, and operational procedures for various casing sizes, materials, and defect types. An automated multi-axis laboratory test stand traveling in the x-, y-, and z-directions was used to replicate the in-well scanning process. The following section outlines each type of sample used during the various phases of the evaluation and presents the findings.

To establish a performance baseline, samples with machined surfaces and no localized defects were assessed. The samples permitted the evaluation of the bulk wall thickness measurement capabilities and accuracy. Various machined samples with varying wall thicknesses are manufactured, as shown in Figure 4.

Each set of acoustic lab scan results was compared with the as-designed sample measurements. The results were assessed for accuracy and the wall loss detection threshold was determined. A summary of the results from the four samples with commonplace casing diameters and wall thicknesses is presented in Table 1. The thicknesses of the samples ranged from 9.10 mm (0.36 in) to 0.864 mm (0.034 in). The absolute error was calculated from all wall thickness measurements above the minimum detectable thickness.

The average error of the absolute wall thickness was 0.123 mm (0.005 in), and the average detection threshold was 1.749 mm (0.069 in).

Samples with round defects were designed and manufactured to mimic the discrete areas of wall loss, corrosion, or pinholes. The samples shown in Figure 5 used flat-bottomed circular defects of varying penetration and diameters milled into the steel casing. The sample casing diameters varied from 4.5 to 9.625 in.

A representative real-corrosion surface finish was created across the samples by applying an accelerated corrosion process using a saline bath and a 12-V energy source. To prevent corrosion of portions of the sample, masking was applied, which resulted in a highly insightful sample bearing a matrix of wall-loss regions with a representative corroded surface and clean machined surfaces, as shown in Figure 6. The sample was corroded on the ID and selectively corroded on the OD. The sample comprised flat-bottomed pockets with diameters ranging from 25.400 mm (1 in) to 6.350 mm (0.250 in) with remaining wall depths varying from 2.337 mm (0.092 in) to 7.976 mm (0.314 in).

This sample presented a challenge because the thickness from the as-manufactured design was altered. To gather a representative ground truth assessment of the corroded sample, the casing sample was measured using a CT scan. The results from the CT and high-resolution acoustic scans are presented in Figure 7. As observed in the CT results, the penetrations across all the defects were consistent. Furthermore, the defects were highly circular and surface corrosion bands were present. The CT results were ideal for the defect sample, where the changes in the wall were sudden and significant. The CT results were processed into a comparable dataset from raw 16-bit data with an axial resolution of 0.7 mm. The acoustic results were processed by machine learning-based surface detection and wall thickness processing algorithms, and afterward manually quality controlled by analysts.

Several qualitative conclusions regarding the acoustic platform performance were made after comparing the thickness measurements from the CT scan and high-resolution acoustic imaging technology. The acoustic imaging platform can detect and measure even minor OD surface corrosion wall loss from the unmasked surfaces, and this is denoted by a strong match in the banded corrosion surfaces. The presence of corrosion on the inner and unmasked outer surfaces has little impact on the ability of the high-resolution acoustic imaging tool to resolve the wall thickness. The thickness resolution of the platform was comparable to that of the CT scan, however, the gradient of the wall loss was smoother and more consistent with the CT imagery. This difference was expected and was due to the location of the tools relative to the sample. While the X-ray source of the CT tool was located outside the sample, the acoustic tool was inserted inside the casing sample with its energy traveling through a solid medium (steel) to the outer surfaces.

A quantitative comparison of the minimum wall captured by the two technologies is presented in Figure 8. In the comparative results, seven rows of wall loss defects, represent the wall loss defects that fall within the tool depth specification. The sharp corners of the manufactured defects could act as point reflectors, thereby reducing the signal quality at the boundaries. The 90° corners were not typical of in-field corrosion events and were considered for future testing and validation sample design. The processing of the CT dataset yielded a +/−0.7 mm error, which represented a surface detection error of one pixel in either direction. For reference, the as-machined penetration (pre-corrosion process) for the deepest row of defects was specified as 2.337 mm (+/-0.127 mm) or 2.210–2.464 mm.

In addition to the minimum comparison, the mean and maximum wall results are generated from the 2D results and compared, as shown in Figure 9 and Figure 10.

The absolute average difference between the mean wall thickness profiles was calculated as 0.160 mm. Similarly, the absolute average difference between the maximum wall thickness profiles was calculated as 0.167 mm.

In summary, the performance of the acoustic and CT tools was highly analogous, and it highlighted the resolution benefits of the solid-state approach, generating representative data in the presence of corrosion.

A near-surface casing sample was investigated to assess the performance of high-resolution acoustic imaging technology over severe corrosion and pitting. The sample was used to assess the performance of high-resolution acoustic imaging to detect the minimum remaining wall as compared to other methodologies. The depth of the deepest wall loss defect is used as a key input for decision making regarding the remediation of these wells exposed to these severe corrosion conditions. The casing sample experienced severe corrosion in the field due to its near-surface location and exposure to standing water and oxygen. The sample of the scanned casing was 7-in in diameter, 3.8 ft (1.15 m) in length, and 0.317-in (8.05 mm) in wall thickness. Furthermore, a reference dataset was generated by lab-grade CT. A visual comparison of the results is shown in Figure 11 where the high-resolution acoustic imaging (middle) and CT results (right) are overlaid on top of the textured photo-scan (left).

The sample had several distinct features for assessing the performance of the acoustic-based imaging platform. The topmost third sample was clear of any regions of wall loss and close to nominal; over this region, the CT- and acoustic-based assessments were in strong agreement. In the middle third of the sample, pitting was present and detected in both datasets. In the bottom third of the sample, a severe corrosion region was present in the soil line of the exposed casing. In this region, key features such as local high spots and widespread thin regions were accurately represented in the acoustic imaging dataset as compared to the CT results. This assessment was used to evaluate and improve the algorithms applied to the raw signal data for the generation of wall thickness mapping. A summary of the minimum wall thickness measurements for the given sample captured using several approaches and technologies is presented in Table 2.

Quantitatively, we observed that the CT and high-resolution acoustic wall losses were extremely similar with only submillimetric differences between the deepest wall loss calls. The in-ditch technician applied ultrasound probe yielded a much greater 3.9 mm at a region located uphole approximately 20 cm and similarly, the other internal log resulted in an even greater 5.27 mm minimum wall. In this case the application of different technologies would have resulted in different remediation decisions and residual risk outcomes based on these different measurements. The agreement between the reference CT data and the high-resolution acoustic imaging data confirms the suitability of this technology for the assessment of severely corroded and pitted casing. Conversely, the lack of agreement of the reference CT dataset and the other-in hole technology as well as the in-ditch handheld reinforce the large variability of these low-resolution methodologies. In conclusion, high-resolution acoustic imaging provides a similar capability to that offered by lab-grade CT, however, it can be conducted in-hole and at field temperature and pressure for logs up to 30,000 feet in length.

Substantial prior work has been completed to define equations used to predict the failure of welded line pipes used for pipelines as well as for oil country tubular goods (OCTG) and their threaded connections. The following section provides a brief overview and compares the relevant standards and technical bulletins from both the OCTG and pipeline sectors, which are used to define burst capacity calculations. A few similarities and differences were observed between the failure modes and equations derived for each. While onshore pipelines were primarily exposed to hoop stresses due to internal pressure, OCTG was subjected to combined loads comprising internal and external pressures as well as axial loads. Additionally, the root causes of defects share some similarities and differences, while corrosion and cracks can be present in both types of piping systems; drill wear is a consideration assessed for OCTG alone. OCTG performance is managed through American Petroleum Institute API-5C3 and its corresponding technical reports and bulletins (API, 1994). Initially, API-5C3 employed Barlow's formula (outlined below) to determine the OCTG burst pressure at 87.5% of the nominal wall thickness (accounting for the maximum wall thickness manufacturing tolerance). This elastic burst capacity equation was further refined (Klever and Stewart, 1998) by applying an ultimate limit state approach through von Mises and Tresca yield criteria, which accounted for the survivability of modern OCTG in the inelastic region of the stress-strain curve. The equations account for the inelastic material changes that occur such as strain hardening. The equations apply a logarithmic strain ratio to model the material response at very large strains. This approach was used to assess the impact of localized defects, such as drill string wear, which concentrated forces and are expected to experience large strains. The ultimate limit state approach was further used to assess combined loads and has been used to define equations for collapse under combined loads (Klever and Tamano, 2004) and to assess necking, wrinkling, and burst capacity under combined loading (Klever, 2010).

The pipeline integrity industry manages corrosion-based defects based on the manual for determining the remaining strength of corroded pipelines or the B31G standard (ASME, 2012). The pipeline sector has benefited from the near-surface location of assets that have allowed for the validation of tool assessments and failure testing of line pipes with defects. The feedback loop for the assessment of defects and inspection tools has led to the rapid development of assessment methods for determining the remaining strength of corroded piping. While the ultimate limit state approach is more suited for complex downhole loading and accounts for inelastic behavior before true burst failure, the pipeline industry elastic failure models offer operators a rapid and practical approach to assessing and comparing the impact of localized corrosion events on burst pressure. Regardless of the approach used to assess the burst capacity and failure state, the inputs used to complete the calculations rely on the resolution and accuracy of the measurement platforms used to quantify defects, such as corrosion and drill wear. The ability to quantify the remaining wall and apply various assessments is often limited by the ability to generate a high-resolution depiction of wall loss, casing connections, and deformation of the casing. This limitation is due to the restricted resolution and data fidelity offered by legacy downhole technologies. Until recently, the resolution offered by downhole inspection technologies has not materially advanced due to the difficulty of developing advanced imaging technologies for high-temperature and high-pressure wellbore environments.

In the following section, we demonstrate how high-resolution acoustic imaging in thickness applications can provide highly precise measurements for quantifying the minimum burst pressure of each casing joint by leveraging pipeline-based elastic failure burst assessment models. This approach is well suited to assess longstanding OCTG operating in corrosive environments, which requires validation and continued assessment of internal pressure capabilities.

Burst capacity analyses rely on measurements gathered using inspection tools to calculate the failure pressure associated with a defect in a pipe joint. The calculation determines the internal pressure that a pipe can withstand, given the wall loss dimensions, pipe strength, and pipe size. The calculations presented below are elastic equations that include only internal pressure loading, which offers a rapid and practical assessment methodology for operators. Combined loads and inelastic behavior can be considered for assessments that are more refined. However, a greater understanding of inelastic material behavior is required to do so. Five commonly applied elastic methodologies used to calculate the burst pressure of piping with wall loss are outlined below, in order of complexity. Figure 12 depicts various methodologies for computing the metal-loss area. The red-dashed section shows the cross-sectional wall loss of a single defect. For each methodology, the approximated area of metal loss is shown in blue. The region bounded in orange represents the original cross-section for each methodology. Moving from left to right, the approximated wall loss ratio becomes more accurate and less conservative.

1. Barlow: This calculation relies only on the maximum penetration of metal loss for a given defect. It is the least accurate and most conservative approximation because it assumes that the maximum defect penetration applies across the entire pipe joint. API 5C3 (API, 2018) relies on Barlow's formula to quantify the burst pressure of the casing by applying the manufacturing tolerance for the wall thickness to account for 87.5% of the nominal thickness.

   $PF=[2 SFtD]$

   (1)

   P_F = Failure pressure or burst capacity

   S_F = Estimated failure stress

   t = Nominal wall thickness

   D = Nominal outer diameter (OD)

2. B31G: This calculation considers defect length and maximum penetration. These methods were defined by the American Society of Mechanical Engineers (ASME) in the manual for determining the remaining strength of corroded pipelines (ASME, 2012). B31G provides a parabolic approximation of defects of a given length and maximum penetration. The flow stress is used to define the estimated failure stress, which is defined as a function of the specified minimum yield strength (SMYS) of the pipe material.

   $Sflow=1.1∗SMYS$

   (2)

   There are considerations for this calculation at temperatures >250 °F. The flow stress does not exceed the specified minimum tensile strength (SMTS) under ambient conditions. For more details on the applications, refer to ASME B31G. In addition to the flow stress, z was calculated, which is a function of the length of the wall loss relative to the diameter and nominal thickness.

   $z=L2/Dt$

   (3)

   L = Length of metal loss

   If z ≤ 20:

   $SF=Sflow[1−23(dt)1−23(dt)/M]$

   (4)

   If z ≥ 20 :

   $SF=Sflow[1−(dt)]$

   (5)

   where M₁ is the Folias factor, which accounts for the stress concentration effects due to the geometry of the wall loss.

   $M1=(1+0.8z)1/2$

   (6)

   Afterward, the calculated failure stress (S_F) was inputted into Equation (1) to determine the burst capacity.

3. Modified B31G: This calculation applies a variation in B31G and uses a rectangular approximation of a defect of a given length and 85% of its maximum defect penetration. It is like B31G. However, it relies on a more accurate Folias factor, defined as M₂(Kiefner and Vieth, 1989), which is dependent on the value of z determined using Equation (3).

   $SF=Sflow[1−0.85(dt)1−0.85(dt)/M]$

   (7)

   If z ≤ 50:

   $M2=(1+0.6275z−0.003375z2)1/2$

   (8)

   If z ≥ 50:

   $M2=(0.032z+3.3)$

   (9)

   The calculated failure stress (S_F) was then inputted into Equation (1) to determine the burst capacity.

4. Effective Area: This calculation considers a wall loss profile over the entire length of a given defect, as opposed to only the maximum penetration. The effective area method requires high-resolution tools to capture more measurements within a given defect. The effective area compresses the 3D representation into a 2D cross-sectional wall loss profile using a technique referred to as "river bottom pathing." The river bottom path followed the deepest route of metal loss through the defect, and each point was transposed or aligned to generate a single wall loss profile representing the defect. The effective area approach is less pessimistic; it does not assume uniform wall loss across the defect and considers the variation in wall loss across a complex defect, where S_Fis calculated as follows:

   $SF=Sflow[1−(AA0)1−(AA0)/M2]$

   (10)

   A = Local area of metal loss in the longitudinal plane over riverbottom profile

   A₀ = Lt = Local area of original in place metal in the longitudinal plane

   Afterward, the calculated failure stress (S_F) was inputted into Equation (1) to determine the burst capacity.

5. RSTRENG: This software/calculation is similar to the effective area calculation as it relies on the same river bottom profile and Equation (10). However, this calculation iteratively assesses the defect to determine the limiting burst pressure for any possible continuous subregion of the wall loss within the defect (Kiefner et. al, 1995). This approach is computationally intensive and is packaged in software. The software divides the defect into continuous sub-lengths and iteratively determines the limiting burst pressure for all continuous permutations of wall loss across the river-bottom profile of the defect. This calculation is most impactful when defects are long, non-uniform, and/or comprise multiple interacting wall loss events.

High-resolution, three-dimensional reconstructions of wall-loss regions can be generated owing to the submillimetric resolution and direct-measurement capabilities of the novel acoustic imaging platform. A dimensionally accurate topographical map of a wall-loss region was generated with each tool pass. Afterward, this dataset could be analyzed using more accurate methodologies, thereby providing operators with a more refined quantification of burst pressure. The axial resolution of the acoustic platform enabled the measurement of the entire profile of the wall loss throughout the defect. Without the high-resolution wall-loss maps, the effective area or RSTRENG methodologies could not be accurately deployed.

A cross-sectional profile must be generated by condensing 3D data into a single wall loss profile before conducting an advanced burst-pressure calculation. The deepest path along the defect was traced in a top-down view. The corresponding max penetration points through the defect were aligned to generate the cross-sectional profile of the maximum wall loss across the defect. This cross-sectional profile was referred to as the "river bottom" and represented the deepest contour path for any defect or series of defects combined by the interaction rule. Figure 13 shows how the river bottom path is transposed from a planar to a cross-sectional view. In effect, the river bottom profile represents a single wall loss profile, capturing the deepest wall loss over which the internal pressure loading acts.

The river bottom profile amalgamates both ID and OD wall losses. Burst pressure calculations do not discriminate between ID and OD wall loss; the ID and OD wall losses are summed to generate the overall profile of wall loss. The subsequent effective area and RSTRENG calculations used the river bottom profile to calculate the ratio of the remaining wall to the original wall over the defect length.

The effective area and RSTRENG burst pressure methodologies are particularly noteworthy when defects are nonuniform, distributed, or complex. The greater the nonuniformity or complexity, the greater the benefit of using an advanced burst pressure methodology. Examples include complex interacting pits that are non-uniform and span long regions, elongated milling damage, mechanical damage combined with OD corrosion, microbially influenced corrosion, and severe corrosion.

The following section demonstrates the estimation error of the Modified B31G burst pressure for complex areas of wall loss compared to an effective area-based calculation. Figures 14 and 15 show examples of uniform and varying penetration, respectively. In Figure 14, the defect has a uniform wall-loss river bottom profile, where the penetration has a low variance across the defect length. The burst pressure calculations for the defect were 8,718 psi with Modified B31G and 8,950 psi with RSTRENG; the two methodologies were in close agreement. The defect was well approximated by a rectangle and showed little variation in penetration across its length, and this justified why the RSTRENG limiting subregion (shown as the hashed shaded region) extended for nearly the entire length of the defect.

In Figure 14, the captured defect has a non-uniform wall loss profile across its length. The burst pressure calculations were 9,486 psi with Modified B31G and 14,169 psi with RSTRENG. The large difference between these calculations was due to the non-uniform wall loss, which was substantially overestimated by the Modified B31G evaluation. The iterative RSTRENG approach excluded approximately 40% of the defect length because it did not contribute to the limiting continuous region of wall loss.

This divergence in burst pressure estimation phenomena was commonplace where discrete deep pits were in widespread wall loss, where the river bottom profile was poorly approximated by Modified B31G. The effective area and RSTRENG methodologies could more accurately estimate the burst pressure in the scenarios by accounting for the variation in the wall loss profile. Furthermore, the visual analysis enabled by the resolution of the acoustic platform enabled operators to identify the root cause of the defect (identifying if the wall loss was mechanical, general corrosion, pinhole corrosion, or a combination thereof) as well as enable defect tracking over subsequent inspections.

This section presents an example of qualitative and quantitative defect analysis completed after the inspection of a 5.5-in casing string using high-resolution acoustic imaging. Throughout the well, substantial internal pitting is present, and numerous regions are captured, one of which is shown in Figure 16. Numerous small pitting events and local continuous deeper wall loss events were observed in the images shown below. A 3D rendering corresponding to the damage was provided, as shown below, which represented a similar recurring pattern throughout the well with pitting and wall loss in many joints and wall loss immediately above the casing connections. Each joint was processed using the Modified B31G and effective area burst pressure calculations. Owing to the widespread nature of pitting, the interaction rule sensitivity was altered, and its impact on the burst pressure calculation was analyzed. The interaction rule expanded the perimeter around each wall loss location in the axial and circumferential directions, which was typically a multiple of the wall thickness, and common values applied were 1.5, 3.0, and 6.0 times the nominal wall thickness.

An example of the limiting effective area defect used to calculate the burst pressure is shown in Figure 17 which shows the defect corresponding to the 40% penetration event in the given joint and its corresponding river bottom profile. The white perimeter surrounding the plane view of the defect represents an interaction perimeter of three times the wall thickness.

Burst pressure analysis was conducted for both 1.5 and 3.0 the wall thickness interaction rules and the results from the limiting defect for each run are summarized in Table 3.

The first observation was the variation in burst pressure between methodologies, which iteratively assessed the limiting region. With such severe and widespread damage, the different methodologies (effective area and Modified B31G) resulted in different limiting defects, as shown in Figure 18. As expected, the limiting defect footprints were larger when determined with a larger interaction rule, as small pitting events were added to the overall defect for assessment. However, the impact of the additional pits was much larger for the Modified B31G calculation because the small pits resulted in an overestimation of the wall loss (extending 85% of wall loss through additional defect length). As a result of the minor pit defects, an additional small wall loss area was included in the calculation, increasing the effective area-based calculation very minimally.

High-resolution acoustic imaging has been adapted to measure casing thickness. The system relies on a solid-state high-density acoustic array comprising up to 512 elements encircled around the OD of the imaging head. High-speed electronics and proprietary software enable higher-resolution datasets and advanced defect-based assessment. The solid-state approach provides many advantages over legacy casing integrity inspection offerings, which are summarized below.

• The electronic system control enables multiple imaging apertures at any given time. This results in a platform capable of a submillimeter axial resolution down to 0.5 mm.

• Acoustic-based imaging provides direct measurements of the casing wall based on the known speed of sound of the material. The resulting minimum measurable steel casing wall thickness was 1.749 mm (0.069-in) with an error of +/-0.123 mm (0.005-in).

• The resolution and coverage within defects enable more reliable and consistent defect tracking over time. This provides more accurate corrosion growth rate estimates and more informed risk-based decision making.

• High-resolution direct measurement technology provides a detailed spatial understanding of defects by capturing a definitive, full-circumferential wall loss profile over the defect length. The axial resolution ensures more data points across the defect profile, resulting in a more representative penetration mapping.

• The captured 3D defect datasets enable more accurate and less conservative failure analysis methodologies to be applied to quantify the burst pressure. These advanced effective area-based calculations rely on the capture of the defect profile and topography and not just a single maximum defect penetration recording.

• The high-resolution platform enables true-to-form defect imagery, allowing for run-to-run tracking and the management of defects and their root causes.

• The high-frequency and adaptable nature of the technology combined with advanced processing techniques means that thickness can be measured in a wide range of ferrous and non-ferrous metals.

• Solid-state arrays contain no rotating components to ensure high mechanical reliability, a narrow tool diameter capable of navigating restricted well regions and removing the requirement of swapping imaging heads for different casing sizes.

• The high-density acoustic array was entirely controlled by software. This allows for the real-time adjustment and correction of various casing sizes and thicknesses. This implies that the platform can image a casing string comprising multiple sizes, thicknesses, and materials in a single logging pass.

• Radial and thickness acoustic imaging tools are compatible; when run in tandem, they provide highly comprehensive ID measurements, textural mapping, and wall loss measurements for a given casing string.

• The high-resolution acoustic imaging platform enables defect interaction rules and distances to be varied to quantify the reduced rating owing to interacting defects.

Nomenclature
Abbreviations:Expansion 
• ASME =

  American Society of Mechanical Engineers

 
• CT =

  Computed tomography

 
• ID =

  Inner Diameter

 
• MFL =

  Magnetic flux leakage

 
• OCTG =

  Oil country tubular goods

 
• OD =

  Outer Diameter

 
• RSTRENG =

  Modified criterion for evaluating the remaining strength of a corroded pipe

 
• SMYS =

  Specified minimum yield strength

 
• STL =

  Stereolithography computer file used for representation of three-dimensional surfaces