The Role of Soil Structure Interaction in the Fragility Assessment of HP/HT Unburied Subsea Pipelines

Subsea high pressure/high temperature (HP/HT) pipelines may be significantly affected by the effects of soil structure interaction (SSI) when subjected to earthquakes. Numerical simulations are herein applied to assess the role of soil deformability on the seismic vulnerability of an unburied pipeline. Overcoming most of the contributions existing in the literature, this paper proposes a comprehensive 3D model of the system (soil + pipeline) by performing OpenSees that allows the representation of non-linear mechanisms of the soil and may realistically assess the induced damage caused by the mutual interaction of buckling and seismic loads. Analytical fragility curves are herein derived to evaluate the role of soil structure interaction in the assessment of the vulnerability of a benchmark HP/HT unburied subsea pipeline. The probability of exceeding selected limit states was based on the definition of credited failure criteria.

Technical–Economic Feasibility Analysis of Subsea Shuttle Tanker

This paper presents the technical and economic feasibility analysis of the subsea shuttle tanker (SST). The SST is proposed as an alternative to subsea pipelines and surface tankers with the primary purpose of transporting CO2 autonomously underwater from onshore facilities to subsea wells for direct injection at marginal subsea fields. In contrast to highly weather-dependent surface tanker operations, the SST can operate in any condition underwater. The technical–economic analysis is performed in two steps. First, the SST’s technical feasibility is evaluated by investigating designs with lower and higher capacities. The purpose is to observe the appearance of technical limits (if present) when the SST is scaled down or up in size. Second, an economic analysis is performed using the well-reviewed cost models from the publicly available Zero Emissions Platform (ZEP) and Maritime Un-manned Navigation through Intelligence in Networks (MUNIN) D9.3 reports. The scenarios considered are CO2 transport volumes of 1 to 20 million tons per annum (mtpa) with transport distances of 180 km to 1500 km in which the cost per ton of CO2 is compared between offshore pipelines, crewed/autonomous tanker ships, and SST. The results show that SSTs with cargo capacities 10,569 m3, 23,239 m3, and 40,730 m3 are technically feasible. Furthermore, the SSTs are competitive for short and intermediate distances of 180–750 km and smaller CO2 volumes of 1–2.5 mtpa. Lastly, it is mentioned that the SST design used the DNVGL Rules for Classification for Naval Vessels, Part 4 Sub-surface ships, Chapter 1 Submarine, DNVGL-RU-NAVAL-Pt4Ch1, which is primarily catered towards military submarine design. It is expected that a dedicated structural design code that is optimized for the SST would reduce the structural weight and corresponding capital expenditure (CAPEX).

20 YEARS DECOMMISSIONING EXPERIENCE - EVOLUTION OF INNOVATIVE SOLUTIONS

Decommissioning is an increasing market sector which has been gradually gathering momentum particularly in the North Sea. The forecast expenditure for removing existing platforms increases with time. This comes together with the increasing complexity of the decommissioning operation. Experience and expertise in this field are key for success. Saipem have been contracted to perform a number of ‘removals’ ranging from subsea templates, subsea pipelines, seabed debris clearance, jacket structures and topside modules. This paper provides an account of our experience gained over the last 20 years performing decommissioning activities. The paper presents the evolution of the techniques developed and focuses in particular on the Lift and Tow method developed after 2004 for a number of subsea applications. Problems always materialise post contract award due to inadequate data. The paper gives a detailed description of the Lift and Tow method along with various innovative techniques developed for this method, ranging from lifting operations supported by motion forecasting through to personnel access onto the structures.

CFD Investigation on Hydrodynamic Resistance of a Novel Subsea Shuttle Tanker

The Subsea Shuttle Tanker (SST) was proposed by Equinor as an alternative to subsea pipelines and surface tankers for the transportation of liquid carbon dioxide (CO2) from existing offshore/land facilities to marginal subsea fields. In contrast to highly weather-dependent surface tanker operations, the SST can operate in any condition underwater. Low resistance is paramount to achieving maximum range. In this paper, the resistance of the SST at an operating forward speed of 6 knots (3.09 m/s) and subject to an incoming current velocity of 1 m/s is computed using Computational Fluid Dynamics (CFD). The Delayed Detached Eddy Simulation (DDES) method is used. This method combines features of Reynolds-Averaged Navier–Stokes Simulation (RANS) in the attached boundary layer parts at the near-wall regions, and Large Eddy Simulation (LES) at the unsteady, separated regions near to the propeller. The force required to overcome forward resistance is calculated to be 222 kN and agrees well with experimental measurements available in the open literature. The corresponding power consumption is calculated to be 927 kW, highlighting the high efficiency of the SST. The method presented in this paper is general and can be used for resistance optimization studies of any underwater vessel.

Artificial Intelligence for the Online Prediction of the Cool-down Time in a Subsea Pipeline After an Unplanned Shutdown

Hydrates formation in subsea pipelines is one of the main reliability concerns for flow assurance engineers. A fast and reliable assessment of the Cool-Down Time (CDT), the period between a shut-down event and possible hydrates formation in the asset, is of key importance for the safety of operations. Existing methods for the CDT prediction are highly dependent on the use of very complex physics-based models that demand large computational time, which hinders their usage in an online environment. Therefore, this work presents a novel methodology for the development of surrogate models that predict, in a fast and accurate way, the CDT in subsea pipelines after unplanned shutdowns. The proposed methodology is, innovatively, tailored on the basis of reliability perspective, by treating the CDT as a risk index, where a critic CDT threshold (i.e. the minimum time needed by the operator to preserve the line from hydrates formation) is considered to distinguish the simulation outputs into high-risk and low-risk domains. The methodology relies on the development of a hybrid Machine Learning (ML) based model using datasets generated through complex physics-based model’ simulations. The hybrid ML-based model consists of a Support Vector Machine (SVM) classifier that assigns a risk level (high or low) to the measured operating condition of the asset, and two Artificial Neural Networks (ANNs) for predicting the CDT at the high-risk (low CDT) or the low-risk (high CDT) operating conditions previously assigned by the classifier. The effectiveness of the proposed methodology is validated by its application to a case study involving a pipeline in an offshore western African asset, modelled by a transient physics-based commercial software. The results show outperformance of the capabilities of the proposed hybrid ML-based model (i.e., SVM + 2 ANNs) compared to the classical approach (i.e. modelling the entire system with one global ANN) in terms of enhancing the prediction of the CDT during the high-risk conditions of the asset. This behaviour is confirmed applying the novel methodology to training datasets of different size. In fact, the high-risk Normalized Root Mean Square Error (NRMSE) is reduced on average of 15% compared to the NRMSE of a global ANN model. Moreover, it’s shown that high-risk CDT are better predicted by the hybrid model even if the critic CDT, which divides the simulation outputs in high-risk and low-risk values (i.e. the minimum time needed by the operator to preserve the line from hydrates formation), changes. The enhancement, in this case, is on average of 14.6%. Eventually, results show how the novel methodology cuts down by more than one hundred seventy-eight times the computational times for online CDT predictions compared to the physics-based model.

A simple machine learning based framework for processing the inline inspection data of subsea pipelines

This paper presents a simple machine learning based framework for diagnosing the inline inspection data (ILI) of subsea pipelines. ILI data are obtained by intelligent pigging devices operating along subsea pipelines. The wall thickness (WT) and standoff distance (SO) are collected by the sensors installed on the pigging, which are normally in the format of 2D arrays. There are many uncertainties for the ILI data collected from the offshore survey. An attempt was made to apply the machine learning method to diagnose the uncertainties. A convolutional neural network (CNN) is used, the ILI data are discretized and processed in 64x64 grid size. Fabricated training datasets were made for training the machine learning model since the ground truth information (actual corroded wall thickness) is hardly known in this case. The trained model was successfully. It is demonstrated that certain corrosion patterns have been recognized by the trained model. Comparisons were performed between the new method and traditional methods with case studies on real ILI data. The validity of the methodology was discussed.

Subsea FRP pipeline performance in external pressure: Failure and external pressure-induced buckling

The Subsea pipelines are subjected to high hoop stresses due to the hydrostatic pressure. When the internal pressure of the liquid has a higher value than the external hydrostatic one, there is a balance of the tensile and compressive stresses. However, during the offshore installation, the subsea pipelines are empty and the compression due to the hydrostatic pressure of the sea is predominant. High compressive stresses in FRP pipelines can cause failure or buckling. In the present work, an investigation of the external pressure-induced failure and buckling is carried out. Analytical formulae and results are provided and discussed.