The interpretation of borehole images begins with the detection and classification of features—a time-consuming manual process subject to variations between interpreters. In seeking to automate the detection part for the most frequently picked features (which in circumferential images from clastic rock environments are sinusoids corresponding to planar or subplanar bedding surfaces and fractures), it is not necessary to pick all instances, but it is necessary to pick sufficient representative instances to satisfy the interpretation objective, accounting for a broad range of apparent dips, and allowing for the likelihood of fractures crossing bedding surfaces. A key challenge in this context is the minimization of false picks, as manual corrections would potentially negate the principal benefit of automation. A fast nonsubjective method is described for the detection of prominent discontinuities and the calculation of associated dip angles. It combines a gradient based approach for edge detection with a phase congruency method for validation, followed by a robust sinusoid detection technique. It has been evaluated on microresistivity images from wireline and logging-while-drilling tools, these images having a wide range of features with varying degrees of geologic complexity; the proportion of false positives in the case of noisy data is less than 5%, improving to better than 2% in the case of good-quality data. In contrast to manual picking, the method is fast and gives reproducible results. With potentially thousands of sinusoids in a single image, the method dramatically improves efficiency.
A pseudo-outcrop visualization is demonstrated for borehole and full-diameter rock core images to augment the ubiquitous unwrapped cylinder view and thereby to assist non-specialist interpreters. The pseudo-outcrop visualization is equivalent to a nonlinear projection of the image from borehole to earth frame of reference that creates a solid volume sliced longitudinally to reveal two or more faces in which the orientations of geological features indicate what is observed in the subsurface. A proxy for grain size is used to modulate the external dimensions of the plot to mimic profiles seen in real outcrops. The volume is created from a mixture of geological boundary elements and texture, the latter being the residue after the sum of boundary elements is subtracted from the original data. In the case of measurements from wireline microresistivity tools, whose circumferential coverage is substantially less
Images rendered from measurements made by wireline microresistivity imaging tools include longitudinal gaps whenever the well circumference exceeds the total width of the padmounted electrode arrays. The fraction of an image containing null data depends on tool design, and it is commonly approximately 30% for 261 mm (8.5 in.)-bit size wells increasing to approximately 50% in 311 mm (12.25 in.) wells. We use cues from the measured parts to infer information missing from the gaps; a method has been developed that simulates the process by decomposing the measured parts into their morphological components using sparse representations of multiscale multiorientation transforms, then recomposing the full-bore image assuming it to be efficiently represented by the transform's elemental bases. The approach was evaluated using real data sets with a variety of geologic features, including full-coverage images from small diameter wells artificially obscured to simulate images from larger diameter wells. For borehole images dominated by curvilinear features, reconstructions from artificially obscured images were visually indistinguishable from the original unobscured images for a broad range of coverage loss and for all apparent dip angles below near-vertical, regardless of degree of parallelism (or lack thereof). Successful reconstruction of nearvertical features (including those with complex boundaries such as breakouts) was more dependent on coverage loss, but in these cases, the results were consistent with judgments made by interpreters. Therefore, we found that inpainting provides a consistent starting point for reproducible quantitative geologic analysis, and it is an enabler for automated feature recognition.
Measurements of magnetic flux leakage are used extensively to map and monitor defects and corrosion in pipelines, well casings, and storage tanks. The application of this method enables locating defects and determining metal loss. The measurements have complex response characteristics, which must be understood to optimize measurement design and data processing choices. Until recently, the industry lacked an adequate first principles description of sensitivity to defect penetration and size, including the foundational cases of circular and elliptical defects. An inversion method has been developed and validated with independent synthetic and lab data. The data was obtained using an analytical forward model, finite element modeling, and laboratory data from different thickness and diameter pipes with known machined defects of different sizes and penetrations. The approach enables inverting field data to reconstruct 3D defect profiles, which helps to assess casing health and offers valuable information to the decision-maker for well integrity.
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