A new predictive model developed by researchers at Texas A&M University could revolutionize the safety of oil rigs and other offshore installations by forecasting underwater landslides before they occur. This innovative approach, detailed by experts at the university, combines advanced site characterization data with sophisticated Bayesian modeling, offering a critical tool to protect vital energy infrastructure from one of the most destructive marine geohazards.

Underwater landslides, massive seafloor movements capable of shifting vast amounts of sediment, pose a significant threat to subsea infrastructure and can even trigger tsunamis. These formidable events can be set off by a variety of factors like earthquakes, severe storms, and even human activity, leading to severe disruptions in the operations of critical energy components like pipelines, anchors, risers, and cables. The economic implications for offshore companies are substantial, as uncertainties surrounding the resilience of subsea designs against such geohazards often result in considerable losses.

Led by Dr. Zenon Medina-Cetina, an associate professor of civil and environmental engineering at Texas A&M, the research team has proposed a comprehensive method centered on the meticulous collection and integration of site characterization data. Dr. Medina-Cetina emphasized the paramount importance of this integrated approach: “One of the main events threatening onshore and offshore facilities is landslides: They can completely wipe out all these installations.” He further stressed that the correct sequencing of information from multiple disciplines is absolutely crucial for accurately assessing the probability of landslide development at any given location and time.

The process involves gathering insights from a diverse group of specialists, including geophysicists, geologists, geomatics specialists, and geotechnical engineers. This multidisciplinary data is then integrated through a carefully sequenced process, ensuring that each layer of information builds logically upon the preceding one. This structured methodology allows the team’s Bayesian model to continuously refine its predictions. The result is an improvement in the accuracy and confidence with which potential landslide zones can be identified, transforming them from active threats to predictable risks.

Dr. Medina-Cetina’s work is deeply rooted in ensuring the resilience of offshore structures like oil rigs. “My job is to make sure that under any geo-hazardous conditions, these offshore structures are going to be safe and are going to remain where they were designed to be,” he affirmed. He highlighted that while the input from various specialists is undeniably essential for understanding the seafloor, the order in which site characterization is carried out is equally critical.