Seismic surveys at sea to search for oil and gas deposits several kilometers below the ocean floor are complex and high tech operations that involve large assets in the form of vessels and equipment. Marine towed seismic is performed by generating pressurized airwaves from air guns towed behind the vessel. The pressure waves travel through the water column into the ocean floor. The echoes that are caught by a range of streamer cables, also towed behind the vessel, give detailed maps of the ocean floor geology.
Current seismic vessels comprise several advanced systems for monitoring and/or control of the operation, but these are essentially operating independently with a low degree of automation. The operation relies largely on the operator's experience and continuous judgement to maximize productive time and equipment lifetime, and to avoid costly and time-consuming failures.
This business is hard pressed on profitability, and is on a constant search for new and better ways to improve the operations. This project aims to increase the operational efficiency of seismic vessels by development of a real-time digital twin, providing users and systems aboard the vessel with a live digital representation of the state of the equipment during operations.
The present innovation will enable a giant leap with regards to optimizing, controlling and automating marine towed seismic operations, with huge potential for further development across a range of areas, both on-board and in-sea.
The development of the proposed digital twin is a challenging and significant step, encompassing systems of much larger scale and complexity than previous and current digital twin systems, and will be a game changer for the entire seismic industry when realized. Monitoring, diagnostics and prognostics of the system will be possible on a completely different level than before, and a vast array of possibilities for further automation is opened, all the way up to the control of the vessel.
Until the summer of 2019, the project was progressing generally according to plan. Partner CGG decided then to withdraw from the project, going into effect end of 2019. This withdrawal, in addition to strategic decisions and poor market prospects, resulted early spring 2020 in a decision by Project Owner to stop the remaining project. This was decided unanimously by all remaining partners in April 2020. As a consequence, there was a re-prioritization of the project tasks and deliverables towards project end. Summarized, the focus for the final year of the project was to finalize a fully functional demonstrator in the existing seismic training simulator at KM AS facilities in Aalesund. Linking the Seismic RTDT with the Seismic handling system (control system) in the simulator was key to demonstrate the benefits of the system.
Also, due to insufficient data, the work related to machine learning and data driven methods, were challenging to bring to completion. In particular the data driven methods suffered from lack of relevant data and sufficient verification possibilities, and it was decided to stop further work on the tasks after an initial study on a limited dataset from CGG.
The project was formally ended in June 2020.
Forbedret operasjonell effektivitet gjennom forbedret plattform for opplæring av mannskap og operatører. Forbedret kontroll gjennom økt og bedre monitorerings- og rådgivningsfunksjonalitet. Muligheter for økt automatisering av hele operasjonen fra manipulering av håndteringssystem til ideell seilingsrute og hastighet på skip. Summen er økt effektivitet, reduserte kostnader og utslipp, samt redusert belastning og slitasje på utstyr. Også forbedret kvalitet på innsamlet seismisk data og mer effektive surveyoperasjoner, noe som bedret resultatet til lavere kost for sluttkunder i olje og gassindustrien. Resultatet vil også ha overføringsverdi til andre marine industrier, for eksempel fiskeri og fortøyning. For eksempel er likheten mellom seismikkoperasjoer og tråling åpenbar. Kabelmodeller, maskinlæring og kontrollalgoritmer, grafisk brukergrensesnitt og simulering kan utvikles videre og anvendes i videre digitalisering av marine systemer og operasjoner.
The concept outlined in this proposal will increase the overall operational efficiency of seismic vessels through digitisation and automation. The cornerstone of the proposal is the development of a real-time digital twin (RTDT): an advanced hydrodynamic model and state estimator of all the in-sea seismic equipment, augmented with real-time data from the actual equipment, providing users and systems on-board the vessel with a live digital representation of the state of the equipment during operations.
Main R&D challenges:
-Identification, cleaning, and combination of available data sources for use in state estimator and machine learning models.
-Designing a robust state estimator for large-scale stiff systems.
-Combine various data streams with state estimation model outputs and suitable algorithms for equipment health monitoring and forecasting.
-Robust transition from an advanced mathematical model and a set of methods and algorithms to practical systems operating on a vessel performing complex operations using high value assets.
-Accurate and reliable monitoring of cable tension over a wide load range and dynamic response of large masses in relation to sensitive cables.
-Condition monitoring of the complex system of interconnected cables and ropes comprising the in-sea equipment.
1.Real-time visualisation of the state of the seismic system on-board and at remote operation centres.
2.Alarms, fault indications and notifications to the user.
3.Decision support systems providing advice on operation of winches and cable control devices.
4.Decision support systems providing advice on vessel speed and heading.
5.Equipment health monitoring systems
6.Improved crew training in on-shore simulator facilities.
7.Automatic control of winches in manoeuvring operations.
8.Automated sequences in launch and recovery operations.
9.Remote expert-in-the-loop during emergency operations.
10.Predictions of future state.
11.Data collection for offline use.