In various production sectors, the search for methane leaks is becoming an increasingly pressing commitment both to avoid the emission of an important greenhouse gas and to allow greater fuel recovery. In this context, the oil and gas sector is at the forefront of the search for adequate technological solutions. The EU Regulation on methane emissions reduction in the energy sector, adopted on May 2024, aims to reduce the emissions of methane into the atmosphere and to minimize leaks coming from fossil energy companies operating in the EU. The main rules introduced by the regulation include improved measurement, reporting and verification of energy sector methane emissions in order to help understand the exact locations and volumes of methane emitted, allowing a shift from estimates to direct measurements. The regulation requires the use of increasingly standardized measurement methods and among these also includes site-level measurement using sensors mounted on Unmanned Aerial Systems (UAS). In recent years, there has been an attempt to use drone-mounted sensors more extensively for rapid methane leak detection and quantification of the flow associated with them. However, the use of this technology, despite the many advantages it offers, still poses multiple challenges in the search for a balanced configuration of the detection and measurement system, adequate for the specific sensor used. In this case the search for methane leaks was carried out using a TDLAS sensor mounted on a drone. Once the survey has been carried out, data obtained feed the algorithms necessary for estimating the methane flow using the mass balance approach. Aim of the present work is the testing of various algorithms in the background measurement phases and in the actual detection phase, integrated with each other in order to constitute a single balanced set-up for the estimation of the flow emitted. Another fundamental step is the choice of the right mathematical integral to be used for the best representation of the flow calculation in the presence of intermittent data and not continuous functions.
Detection of methane leaks via drone in the oil & gas sector: optimization of the flow estimation method
lucianna canana;giuseppe tassielli
2025-01-01
Abstract
In various production sectors, the search for methane leaks is becoming an increasingly pressing commitment both to avoid the emission of an important greenhouse gas and to allow greater fuel recovery. In this context, the oil and gas sector is at the forefront of the search for adequate technological solutions. The EU Regulation on methane emissions reduction in the energy sector, adopted on May 2024, aims to reduce the emissions of methane into the atmosphere and to minimize leaks coming from fossil energy companies operating in the EU. The main rules introduced by the regulation include improved measurement, reporting and verification of energy sector methane emissions in order to help understand the exact locations and volumes of methane emitted, allowing a shift from estimates to direct measurements. The regulation requires the use of increasingly standardized measurement methods and among these also includes site-level measurement using sensors mounted on Unmanned Aerial Systems (UAS). In recent years, there has been an attempt to use drone-mounted sensors more extensively for rapid methane leak detection and quantification of the flow associated with them. However, the use of this technology, despite the many advantages it offers, still poses multiple challenges in the search for a balanced configuration of the detection and measurement system, adequate for the specific sensor used. In this case the search for methane leaks was carried out using a TDLAS sensor mounted on a drone. Once the survey has been carried out, data obtained feed the algorithms necessary for estimating the methane flow using the mass balance approach. Aim of the present work is the testing of various algorithms in the background measurement phases and in the actual detection phase, integrated with each other in order to constitute a single balanced set-up for the estimation of the flow emitted. Another fundamental step is the choice of the right mathematical integral to be used for the best representation of the flow calculation in the presence of intermittent data and not continuous functions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
