Offshore oil and gas production platforms are complex and hazardous process facilities which are usually attended by a permanent human crew to run the daily operations. In recent years, the oil and gas industry has demonstrated strong commitment to change this traditional operations approach and move towards inherently safer philosophy in offshore facilities design and operations, i.e. removing human crew from the facility and operating it remotely from a safe location over extended periods. This paper aims to demonstrate the readiness of robotics technologies coupled with digitalization technologies in process control and facility automation in transforming offshore oil and gas production platforms into smart unmanned installations. This paper is focused on the application of smart robotics, with highly dexterous capabilities and equipped with multiple sensing instruments, in maintaining an offshore oil and gas production facility in full operation without a permanent human crew, and with planned visits to the platform at 12-week intervals, in a case study. The robots are developed to be remotely operated from an onshore control center and/or may be programmed to function autonomously for routine missions on the offshore facility.

This study presents two tension leg platform (TLP) conceptual designs to account for different environmental conditions including water depths in the Asia Pacific region. The TLPs are designed for similar functional requirements to support relatively small topsides for dry tree systems. Hull global motions are calculated using proprietary and commercially available simulation tools. For one of the two TLPs, tendon springing and ringing responses are calculated using advanced numerical methods, i.e. Computational Fluid Dynamics (CFD) with additional user codes based on Euler Overlay Method (EOM) and a newly developed fully coupled hull and mooring analysis procedure. This study shows that TLPs can be designed for different environmental conditions and they can potentially be scaled down to cater for smaller wellbays or to use tender assisted drilling (TAD) on the platform. The CFD methodology presented for tendon springing and ringing analyses can be used to complement model-scale laboratory testing which has been the only means to obtain these high frequency responses, until the present time.

Use of robotics for subsea field developments became common practice during the last few decades. The development of unmanned aerial vehicles in conjunction with Internet of Things (IoT), Artificial Intelligence (AI), Cloud and Edge computing, the fast-evolving technologies, are opening new horizons for construction monitoring and supervision and facility inspection, maintenance and repair activities. This paper presents the results of TechnipFMC's latest investigations and tests to develop the use of advanced Unmanned Autonomous Systems (UAS's) to leverage the operational performance of some of its activities.

Floating platform offshore operations, such as load transfer from a supply boat and hydrocarbon offloading operations, are often limited by sea conditions. Access to sea condition information from a nearby wave/environmental monitoring buoy is not always available or there may be delays in data transmission. In the absence of reliable and accurate data from a nearby wave buoy, the motions of a floating platform/vessel may be used to estimate the sea conditions. This paper presents a method in using Artificial Neural Network (ANN) models to map the motions of a floating vessel to the wave elevations and to eventually estimate the significant wave heights of the sea the floating vessel is in. The ANN models can be trained using either: (1) measured data in the form of measured vessel motions and data from a nearby wave buoy, or (2) simulated data, i.e. vessel motions computed using numerical simulations. In this paper, demonstration of the ANN method uses simulated data under various sea conditions. The trained ANN models are tested for sea conditions that are not part of the training data, and the ANN predictions are found to be very close to the results calculated using numerical simulations. This is an important step to show that the trained ANN models have learned the presented information and can generalize the knowledge. The methodology presented in this paper may be used to establish an ANN model for estimation of the significant wave heights based on the measured motions of a floating vessel.

This paper presents the methodology of simulating green water events of a spread-moored FPSO platform using a CFD-based Numerical Wave Basin (NWB) with an optimized CFD model. Previous work done on the assessment of the motions of the spread-moored FPSO platform in irregular waves has demonstrated excellent agreement between CFD calculation and model test measurement (Baquet et al., 2019). This study further investigates the CFD methodology to efficiently simulate highly non-linear/breaking waves to extend the NWB applications to include prediction of green water occurrence on FPSO platforms. In this study, a CFD model is developed to enable efficient and direct simulation of green water events identified from global motion analysis of the FPSO platform. Smaller time-steps are used, and mesh refinement is applied at the regions which are potentially subjected to green water to ensure more accurate prediction of the green water elevations could be achieved compared to the global motion analysis model. The computing resources required to run the CFD model are maintained below the practical limits for use during typical FPSO project engineering phase. CFD model simulation results for green water events of the FPSO platform are compared against available model test data and good agreement is observed. This paper demonstrates an application of CFD-based NWB for green water prediction for a spread-moored FPSO platform based on an optimized CFD model which is developed to enable fast simulation of green water events. To evaluate the susceptibility of a FPSO platform to green water, large numbers of green water event simulations will be required to obtain reliable statistical data.