Automatic Geological Facies Analysis in Crust-Mantle Transition Zone
Authors:   SLB, Morelli Chiaki , Yang Shiduo ,   SLB, Maehara Yuki ,   SLB, Cai Huimin ,   SLB, Moe Kyaw ,   Japan Agency of Marine-Earth Science and Technology, Yamada Yasuhiro ,   Kyushu University, Matter Juerg ,   Univesity of Southhampton
Published: June 2024

The Oman Drilling Project was conducted as a part of the International Continental Scientific Drilling Program (ICDP) from 2017 to 2018, and several boreholes, including four across the crust-mantle transition zone, were drilled in the program (Matter et al., 2019; Kelemen et al., 2020; Takazawa, 2021). A full suite of slim and conventional wireline logs, as well as high-resolution electrical borehole image and geochemical spectroscopy logs (Ellis and Singer, 2007; Liu, 2017), were acquired near the coring borehole. Full core samples were acquired from the coring boreholes, and various core analyses were conducted manually with significant time and effort to create a detailed core description. Identification of geological facies is crucial to understanding the complicated crust-mantle transition zone. Being able to achieve this facies recognition from available logging data using an automated method would optimize the operation cost and analysis time. This would be useful for the future scientific ultradeep ocean drilling Mohole to Mantle (M2M) project (Umino, 2015; Moe et al., 2018) planned by the International Ocean Discovery Program (IODP). We propose an automatic geological facies analysis (FaciesSpect) method using borehole images and other petrophysical log data on the Oman Drilling Project. Among the available logging data, borehole image (resistivity type with dynamic color scaling) and two log curves (Fe and Ca) from geochemical spectroscopy log data were selected for the automatic facies analysis. Fifteen clusters were classified from the selected log data using the proposed approach. The cluster distribution trend was consistent with cuttings and core lithologies and indicated three major lithology zones: dunite, gabbro, and harzburgite. Two automatic facies analysis methods, class-based machine learning and heterogeneous rock analysis, were performed to compare the results with those of FaciesSpect. These are well-established methods that can validate the newer FaciesSpect method result. The class results of the three different methods were compared. They are matched at major lithology change boundaries. FaciesSpect class results calibrated by core data could be matched not only with core lithology changes but also with texture changes, such as massive, layered, severely altered, deformed, and fractured, due to the advantage of using borehole image data as one form of input. In this case study, we applied the automatic facies analysis to a complicated scientific drilling well in the crust-mantle transition zone for the first time. The result successfully identified different lithologies, such as dunite and harzburgite, which have a high potential for hydrogen generation and are important resources for emissions-free renewable energy. We validated that the FaciesSpect method is useful for rapidly understanding the overall lithology and texture trends such as fractured, deformed, and massive intervals. The FaciesSpect method can also save analysis time and provide a fit-for-purpose result for different objectives beyond petroleum evaluation in a way that does not rely on the individual interpreter’s experiences. The high-resolution borehole image and geochemical spectroscopy logs are crucial inputs for automatic facies analysis in this study.