Elevate your jack-up operations by gap-bridging monitoring – a case study

Barstool type structural model (Orcaflex) of a jack-up, extreme value distribution and spectrum

In this previous article, I presented my personal view on monitoring of offshore operations. This follow-up article presents a case study of a jack-up involved in turbine installation at a wind farm, showcasing the advantages of implementing gap-bridging monitoring to optimize operations. 

The case – a wind turbine installation project 

A large four-legged jack-up is to perform wind turbine installation operations in the North Sea through a nine-month campaign, starting in May. The jack-up will stay on each of the 100 locations for three days, installing the monopile, nacelle, hub and blades of a 12 MW wind turbine. She has components on board for three turbines, so she will attend three locations before sailing back to port. The soil conditions are relatively homogeneous throughout the wind farm, consisting of clay of medium stiffness. 

A model of a jack-up performing wind turbine installation operations as used for the site-specific assessment

The soil conditions are determined based upon in-situ measurements performed on the soil, such as cone penetration tests (CPT) and laboratory tests performed on samples. Due to the variability of soil composition, there is always a high level of uncertainty with respect to its strength and stiffness.  

For each location, a leg penetration analysis (LPA) is performed to determine minimum and maximum expected penetrations of the legs of the jack-up. A single site-specific assessment (SSA) in accordance with ISO-19905-1 is performed for the entire wind farm, taking into account upper-bound and lower-bound soil conditions.  

Penetration resistance curve for all-clay soil, showing Lower Bound (LB) and Upper Bound (UB) resistance

An SSA is an assessment of the suitability of a jack-up in elevated condition for storm survival and for performing planned operations at a specific site. A well-performed SSA includes optimizations, such as minimizing the required preload. 

The SSA for this case addresses the planned crane operations, stating the maximum allowable wave height, wind speed and current speed. The minimum required preload is governed by this case, not by the storm survival case. 

The uncertainties – damping and foundation fixity 

The SSA is performed using a set of engineering models. Engineering models represent reality, but always with some level of uncertainty on every parameter. For each parameter with a significant level of uncertainty, care is taken to err on the right (safe) side. Therefore, large uncertainties lead to (overly) conservative assessments, which in turn leads to suboptimal operations. 

In engineering, it is crucial to accurately assess the level of uncertainty associated with the parameters used in the models. Aspects that are well understood tend to carry a low level of uncertainty, while those aspects that are challenging to model tend to carry a high level of uncertainty and therefore have a significant influence on the outcome of the assessment.  

Unfortunately, in many engineering projects, the amount of time and attention spent on individual aspects of the model tends to be inversely proportional to the level of understanding of an aspect. In other words, engineers tend to spend most of their time creating intricate models of aspects for which they are properly equipped, while disregarding aspects with high uncertainty because they lack the necessary tools to address them. This is where the importance of gap-bridging monitoring becomes evident, as it serves as a tool to address these otherwise overlooked uncertainties. 

The uncertainties in the structural and hydrodynamic models of the jack-up as used for performing the SSA are relatively low, compared to those with respect to 1) damping and 2) foundation fixity. These two topics will therefore get most of our attention. 

Damping on a jack-up in elevated condition consists of three parts, being structural damping, hydrodynamic damping and foundation damping. Damping occurs at component level, but for the performance of the model applying an overall damping percentage is sufficient. Damping can have a significant influence on the dynamic response of the jack-up model. 

Foundation fixity is represented by the rotational stiffness of the foundation in the model. It can have a significant influence on the static response as well as the dynamic response of the jack-up model. It has a major influence on the resonance period of the first natural modes. 

For this case study, the damping is conservatively set at the lower bound of the uncertainty range, at 5%. The foundation fixity is chosen within the range of uncertainty for the clay soil, so that the resulting resonance period of the surge-mode of the jack-up matches the wave reinforcement period. 

The assessment models now conservatively represent a worst-case scenario. With the limited data available, this is the only proper choice. This leads to a higher-than-necessary preload requirement – costing valuable offshore time – and to sub–optimal workability.  

Let’s see how we can enhance our information position to improve this situation. 

Enhancing our information position – a monitoring campaign 

A monitoring campaign could be performed to reduce the uncertainties in the assessment models. It should be designed to enable analysis of the foundation fixity and damping percentage based upon measurements. 

Two components are required to do so: 

  • The simulation model of the jack-up in elevated condition as used for the SSA, including the well-understood hydrodynamic loading and structural response models 
  • Field data from the jack-up, including wave data, lateral motion measurements of the hull and operational logs 
Barstool type structural model (Calypso) of a jack-up 

The wave data should be suitable for direct application to the simulation model. Metocean bureaus that provide daily environmental forecasts base the published wave parameters such as significant wave height upon 2D wave spectra which are highly suitable for this purpose. More on 2D wave spectra in the next article. 

Applying this wave data to the simulation model will lead to simulated accelerations of the hull of the jack-up model. These can be compared directly to the as-measured accelerations of the jack-up hull, obtained using a simple Motion Reference Unit (MRU). This comparison can be used to calibrate both the foundation fixity and the damping in the calculation model.  

The monitoring campaign thus brings together the desktop reality of analysis through models and the on-board reality of actual jack-up behavior through measurements, focusing on the largest uncertainties in the models. This is consistent with the philosophy of gap-bridging monitoring

A monitoring campaign should bridge the gap between the on-board engineering reality and the desktop engineering reality. 

Model calibration 

Comparison between measured and simulated hull accelerations should be made firstly for extreme values. Both should follow a similar extreme value distribution. In most cases, the signals will be Gaussian, in which case a spectral comparison can be made as well. 

If sufficient measurement data is available, it becomes possible to calibrate the fixity and damping of the simulation model to align with the measured values. Firstly, the fixity is calibrated so that the natural periods are correctly represented. Secondly, the damping is calibrated so that the magnitude of the dynamic response is correctly represented. 

To ensure that the model is the best representation of the jack-up at all times, detailed logs of the on-board reality, such as the (crane) operations and the variable deck load should be made available. 

The validity of the calibration should be verified through more comparison without further calibration. This should confirm the robustness and wider applicability of the findings for similar locations. 

Barstool type structural model (Orcaflex) of a jack-up, extreme value distribution and spectrum 
Using Results to Enhance Operations 

The initial locations visited by the jack-up can serve as the basis for conducting the study. Detailed knowledge of the fixity and damping (both mainly properties of the soil at the site) can be used for other locations with similar soil characteristics to determine the actual required preload and allowable conditions through a revision of the SSA. Indeed, knowledge gathered during summer operations can be capitalized upon in the more challenging seasons. 

In this case, the fixity is found to be on the low side of the range, moving the resonance period away from wave reinforcement in surge mode. The damping is shown to be somewhat higher, at 6%. Using these parameters for a revision of the SSA leads to a lower preload requirement and higher workability, thus directly reducing cost and duration of operations. 

Admittedly, the case study as presented here may be considered somewhat aspirational. Not because of the outcome, which is realistic, but because of the objective. The monitoring campaigns which I have been involved in were all challenging projects, where a jack-up was being pushed to its limits. Monitoring was used as enabling technology rather than a tool for optimizing operations as presented here. 

However, this article aims to demonstrate that a focused gap-bridging monitoring campaign can be both cost-effective and non-invasive. Every smartphone is equipped with an MRU, so why not every offshore unit? Detailed wave data is available at the metocean bureaus that provide us with our daily forecasts. My next article will present in detail how the wave data and motion measurements can be acquired and processed. 

At Calypso, we have not only embraced the concept of gap-bridging monitoring but also developed, integrated, and largely automated the analysis process necessary to reduce the uncertainties outlined in this article. A gap-bridging monitoring campaign can be conducted cost-effectively, efficiently, and without imposing a significant burden on the team on board.