Using Flowline Digital Twin to Plan for Inspections and Remediation

Abstract In-field strain or motion monitoring with physical sensors provides a direct indication of structural fatigue and strength performance although not always utilized or practical for every system. LLOG has implemented cloud-based automated structural digital twins for several production flowlines to assess and monitor thermal fatigue damage and stress/walking cycles. This paper demonstrates how the predictions of the digital twin were used to optimize inspections and remediation activities and improve life extension readiness. A machine learning model is used to reduce conservatism and computational time in processing flowline structural response from thermal cycles using field measured temperature data. The flowline stress and displacement response, as obtained from finite element analysis (FEA), is used to train and develop the model. The flowline structural response can be obtained at critical locations in near realtime and are available for immediate visualization for monitoring. Structural response results presented included 1) flowline stresses due to lateral buckling, axial walking, and soil-pipe interaction, 2) accumulated fatigue damage, and 3) flowline walking. The potential for flowline walking was identified in 2021 for one flowline loop and an engineering assessment was completed to determine walking rates. The wellsite had been in service since 2015 and has had a phased approach to well expansion throughout its service life. The additional wells resulted in an increased walking potential and the digital twin was implemented to track the structural behavior. Based on the walking trends predicted by the digital twin over a two (2) year period of time, the risk based integrity management plan recommended a positional LiDAR survey to confirm operational changes. The survey data confirmed that excessive walking had been experienced and remediation was required. Prior to the digital twin implementation, a lack of known structural response in service can result in qualitative inspection planning, conservative fatigue predictions, and reduced asset utilization. The survey data shows strong correlation with digital twin walking rates and allows planning of remediation with high confidence and before an urgent remediation may be required. The flowline digital twin implemented provides a digital replica of the physical system with an accessible user interface to process field data and visualize results in real/semi real-time and may be used as an operational decision tool for integrity management. Although, it does not replace physical measurements that can be obtained via positional survey, a benchmarked/calibrated digital twin allows for reliable prediction of in-service response and optimized planning of inspection campaigns.