Steel Lazy Wave Riser Optimization Using Artificial Intelligence Tool

Lal, Mayank; Sebastian, Abhilash; Wang, Feng; Lu, Xiaohua

doi:10.1115/omae2020-19308

Cited by 2 publications

(1 citation statement)

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“…A total number of 21,532 simulation models were established by changing six pipeline geometry and sea state variables, and then random sample data were generated by Latin hypercube sampling (LHS). In addition, some studies combine intelligent algorithms to better complete SCR engineering work [26][27][28][29][30]. However, these mentioned approaches tend to be influenced by the data source noise, and the high-dimensional characteristics of the data have not been taken into account.…”

Section: Introductionmentioning

confidence: 99%

A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

Liu

Mei

Wang

et al. 2023

JMSE

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As a new type of riser connecting offshore platforms and submarine pipelines, steel catenary risers (SCRs) are generally subject to waves and currents for a long time, thus it is significant to fully evaluate the SCR structure’s safety. Aiming at the damage identification of the SCR, the acceleration time series signals at multiple locations are taken as the damage characteristics. The damage characteristics include spatial information of the measurement point location and time information of the acquisition signal. Therefore, a convolutional neural network (CNN) is employed to obtain spatial information. Considering the variable period characteristics of the acceleration time series of the SCR, a gated recurrent unit (GRU) neural network is utilized to study these characteristics. However, neither a single CNN nor GRU model can simultaneously obtain temporal and spatial data information. Therefore, by combining a CNN with a GRU, the CNN-GRU model is established. Moreover, the hyperparameters of deep learning models have a significant influence on their performance. Therefore, particle swarm optimization (PSO) is applied to solve the hyperparameter optimization problem of the CNN-GRU. Thus, the PSO-CNN-GRU (PCG) model is established. Subsequently, an SCR damage identification method based on the PCG model is presented to predict the damage location and degree by SCR acceleration time series. By analyzing the SCR acceleration data, the prediction performances of the PCG model and the PSO optimization capacity are verified. The experimental results indicate that the identification result of the proposed PCG model is better than that of several existing models (CNN, GRU, and CNN-GRU).

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Section: Introductionmentioning

confidence: 99%

A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

Liu

Mei

Wang

et al. 2023

JMSE

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Introducing a Novel Shaped Steel Catenary Riser for Deepwater Applications

Sebastian

Lal

Rana

2023

Day 1 Mon, May 01, 2023

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Use of steel catenary and lazy wave risers has increased as oil and gas developments are happening in deeper waters or in parts of the world with no pipeline infrastructure utilizing Floating Production Storage and Offloading (FPSO) vessels as a host platform. A suitable Catenary form is selected based on the hang-off angle of the riser. Lazy-Wave form of the catenary riser is achieved by targeted placement of a custom configured buoyancy section. This includes the length to start of the buoyancy section, length of the buoyancy section, hang-off angle, and the buoyancy factor. The strength and fatigue performance of steel catenary and lazy wave risers are governed by its configuration parameters. Achieving these key performance drivers for an SCR or SLWR takes several rigorous analyses iterations throughout the design process. However, on many occasions a modified and optimized Shaped Steel Catenary Riser (SSCR) is necessary to satisfy the project technical, operational, and economic requirements. In certain instances, there is simply no solution achievable using a monolithic steel catenary or lazy wave riser. These include instances where an SCR may be technically feasible, however the top tension is too high or an SLWR simply doesn't have adequate operational fatigue. A shaped Steel-Titanium Riser (STIM Riser) allows to decouple the vessel motions sufficiently and reduces the hang-off tensions. The concept also provides good long-term and short-term fatigue due to environmental factors including wave and currents and operational factors such as slugging. In this paper a novel hybrid shaped riser is introduced. This riser system takes advantage of the modified catenary riser to achieve an optimized shaped SCR and combines it with the superior fatigue performance of Titanium. Titanium is abundant in the nature but currently difficult and expensive to extract at large scale. The industrial solution to produce Titanium at a greater scale is expected to improve in the near future. In that scenario, a riser system with Titanium applied to several critical areas will provide an attractive solution. This customized solution provides an attractive cost optimized configuration that meets all requirements.

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Steel Lazy Wave Riser Optimization Using Artificial Intelligence Tool

Cited by 2 publications

References 0 publications

A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

Introducing a Novel Shaped Steel Catenary Riser for Deepwater Applications

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