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Image logs are crucial for petrophysical and structural characterization of drilled formations. Logging while drilling (LWD) image logs allow near real-time characterization of formations during drilling. Interpretation time required by humans can become critical, as structural interpretation provides the most crucial information for decision-making steps with respect to drilling operations. An example of this occurred in a deepwater system of reservoirs in East Africa. During the drilling of some wells, drilling was interrupted by unexpected borehole stability issues observed in a mass transport deposit. Those problems can detrimentally affect operations by impacting the efficiency of drilling. Therefore, a procedure for the automation of LWD data analysis was developed aimed at reducing drilling risk associated with borehole stability. A workflow is proposed based on an innovative assortment of AI methodologies for automatic real-time interpretation of LWD image log, applied to density image logs. The goal is to mimic manual human dip picking, combining computer vision, numerical series analysis and machine learning. Computer vision techniques are applied to detect the main density contrasts, representing the most likely surfaces. Sharp contrasts in the image are then investigated by dynamic time warping for similarity analysis. Finally, the geological plane is defined as a regression plane passing through the most similar contrasts, and a surface confidence criterion is defined based on the homogeneity of contrast along the curve suggested by the regression. Feature identification by a geologist was used as benchmark for comparison and metrics. Accuracy is referred to the difference between apparent dip and azimuth of geological surfaces coming from human and automated workflows, following the idea that two similar geometric results would eventually lead to equally similar geological considerations, and thus to similar operational measures. In general, a prediction accuracy of 60 per cent for the tested wells is observed, considering the total set of surfaces coming from the model. Nevertheless, the results suggest a positive correlation between the apparent dip of the discontinuities and the model error and a general less confidence for the surfaces with greater dip. By isolating the subset of low dipping features generated by the model, that is up to 35°, the accuracy appears to increase up to more than 70 per cent, suggesting that correlation is more difficult for high dipping geological planes. This class of curves also shows a general lower confidence and much of this latter class of surfaces are more likely to be found in the depth interval in which borehole stability issues occurred. [ABSTRACT FROM AUTHOR]

We evaluate the applicability and the effectiveness of integrated GPR attribute analysis to image the internal sedimentary features of the Piscinas Dunes, SW Sardinia, Italy. The main objective is to explore the limits of GPR techniques to study sediment-bodies geometry and to provide a non-invasive high-resolution characterization of the different subsurface domains of dune architecture. On such purpose, we exploit the high-quality Piscinas dataset to extract and test different attributes of the GPR trace. Composite displays of multiattributes related to amplitude, frequency, similarity and textural features are displayed with overlays and RGBmixed models.Amulti-attribute comparative analysis is used to characterize different radar facies to better understand the characteristics of internal reflection patterns. The results demonstrate that the proposed integrated GPR attribute analysis can provide enhanced information about the spatial distribution of sediment bodies, allowing an enhanced and more constrained data interpretation. [ABSTRACT FROM AUTHOR]

We evaluate the applicability and the effectiveness of GPR attribute analysis for high-resolution glacier imaging and characterization, testing this approach on 4-D GPR multifrequency data collected in a small glacier in the Eastern Alps, by repeating the acquisition along the same profiles in four different periods of the year 2013. The main objectives are to image and characterize the glacier's inner structure and to quantitatively monitor the seasonal thawing of near-surface frozen materials (snow/firn). A multiattribute approach is used to characterize the subsurface through different attribute categories, including instantaneous and textural attributes considering not only amplitude-, phase- and frequency-related attributes, but also other more complex and integrated parameters. We combine information from more than one attribute into a single image with composite displays, using overlays or mixed displays. The results demonstrate that the developed GPR attribute analysis can provide significant improvements in the discrimination of GPR signals, and obtain enhanced and more constrained data interpretations. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the applicability and the effectiveness of ground-penetrating radar (GPR) to identify a thin burnt soil layer, buried more than 2 m below the topographic surface at the Liangzhu Site, in Southeastern China. The site was chosen for its relatively challenging conditions of GPR techniques due to electrical conductivity and to the presence of peach tree roots that produced scattering. We completed the data acquisition by using 100 and 200 MHz antennas in TE and TM broadside and cross-polarized configurations. In the data processing and interpretation phase, we used GPR attribute analysis, including instantaneous phase and geometrical attributes. Validation analysis ground-truthing performed after the geophysical surveys, validated the GPR imaging, confirmed the electrical conductivity and relative dielectric permittivity (RDP) measurements performed at different depths, and allowed a reliable quantitative correlation between GPR results and subsurface physical properties. The research demonstrates that multiple antenna configurations in GPR data acquisition combined with attribute analysis can enhance the ability to characterize prehistoric archaeological remains even in complex subsurface conditions. [ABSTRACT FROM AUTHOR]