Exploiting image logs to reduce drilling hazards: an innovative Artificial Intelligence methodology applied in East Africa

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.