A machine learning approach for underwater gas leakage detection

Abstract

Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventually to the atmosphere. The early detection of such leaks is therefore tantamount to any underwater CCS project. In this work, we propose to use Passive Acoustic Monitoring (PAM) and a machine learning approach to design efficient detectors that can signal the presence of a leakage. We use data obtained from simulation experiments off the Brazilian shore, and show that the detection based on classification algorithms achieve good performance. We also propose a smoothing strategy based on Hidden Markov Models in order to incorporate previous knowledge about the probabilities of leakage occurrences.

Oil and Gas Pipelines Leakage Detection Approaches: A Systematic Review of Literature

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A Review on Analytic Anomaly Detection of Fluid in Pipeline Using Machine Learning

“…Petroleum products are normally transported via underground carbon steel pipes, which require regular inspections (Feng, Li, Lu, Liu, & Ma, 2017). Monitoring and prevention of pipeline leakages also help safeguard the geologic reservoir for storing greenhouse gases (Hubert & Padovese, 2019). As ascertained in the literature, the intensity of damage due to pipeline leakages is beyond repair expense, considering the amount of oil and gas lost (Jena & Neogi, 2019).…”

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“…ML models involve learning patterns in data that can inform decisionmaking based on predictions when new data are introduced (Wang et al, 2009). ML models have proved useful in various fields, such as medicine (Peng et al, 2021;Richens et al, 2020), engineering (Bevilacqua et al, 2010;Curiel-Ramirez et al, 2019;Elelu et al, 2023), finance (Emmanoulopoulos and Dimoska, 2022;Kumar et al, 2021), hydrology (Kratzert et al, 2018), and environment (Hubert and Padovese, 2019;Radford et al, 2016). ML models are vital for learning patterns in ecosystem and the climatic data and the utilization of these patterns for the early detection of HABs.…”