Having proven its capabilities in monitoring and detection leaks in oil and gas pipelines, PipeSense is now expanding its support to emerging CO2 applications and networks.

The company says it has successfully designed, implemented, and tested a new dynamic pressure analysis-based leak detection system on a South Texas pipeline transporting dense phase and supercritical carbon dioxide (sCO2) at approximately 2,000 psi (~140 bar). Deploying its team on-site for testing, PipeSense was responsible for a 12-mile stretch from a compressor station to a client’s handoff point within the state. 

Applying a Dynamic Pressure Analysis method, the leak detection system used two Field Processing Units equipped with high-sensitivity pressure sensors and positioned at each end of the pipeline segment. Installation commenced in Q3 2025, while tests continued throughout Q4 2025. PipeSense is planning for further testing and implementation within CO2 networks and infrastructure in the US and globally.  

Upon completion of the project, the system capability detects leak events within 2-3 minutes and with location precision down to below 20 ft (~6m). One false positive and zero downtime was observed over a 5-month period, with the system achieving sensitive, accurate detection and addressing key pipeline network challenges like such as speed of sound variability, unplanned compressor station shutdowns and changes between dense and supercritical phases. 

The system also demonstrated the efficacy of a Machine Learning-enhanced Dynamic Pressure Analysis system for leak detection in sCO2 pipelines. With US CO2 pipelines expected to grow 20x by 2050, the availability of PipeSense’s technology will help to enhance safety and minimise environmental risks from undetected leaks. 

Stuart Mitchell, President & CTO of PipeSense, commented, “Supercritical CO2 pipelines operate near the critical point, where fluid properties such as density and compressibility exhibit highly nonlinear and rapid variations. Standard Real-Time Transient Models and statistical models, which rely on simplified equations of state and linear assumptions, struggle to accurately predict pressure and flow transients during operational fluctuations. These modelling inaccuracies produce persistent discrepancies that can appear as false leak signatures when residuals or pressure anomalies are misinterpreted.  Our dynamic pressure analysis method offers a superior, mass-flow-independent alternative, paving the way for reliable monitoring in expanding CCS infrastructure.”