7 tips for a successful offshore operation

This article is the final in a series of four articles about workability and monitoring of offshore operations. It showcases a full monitoring campaign together with live determination of workability.

The series follows the arrangement of Wagner’s ring cycle. The first article sets the stage in a lighthearted manner, whereas the second and third present the two major players. In this fourth article, all will come together in a splendid climax.

The first article provides a somewhat tongue-in-cheek introduction to monitoring campaigns and the things that typically go wrong. The second article introduces gap-bridging monitoring. According to this concept, the purpose of a monitoring campaign must be to bridge the gap between the on-board and the desktop engineering reality. The third articlepresents the workability of offshore operations. Workability expressed in terms of sea state parameters, such as allowable significant wave height, comes with limitations (no pun intended). It was argued that the wave height experienced offshore is very much different from the wave height used in desktop engineering studies.

Tip 01 – it is not the waves that restrict workability, it is the physical limitations to the offshore spread

The elephant in the room of the third article is that wave height or sea state is seldom a direct limitation on the offshore operation. Instead, workability is primarily limited by real, physical restrictions. For instance, the stresses in the steel should not exceed the yield stress. Or, no green water is allowed on deck. Or, vessel accelerations should be limited for crew comfort level. A great many restrictions may apply to an offshore operation.

This article is about these real physical limitations of offshore operations and how they can be directly applied to determine workability objectively.

Unfortunately, for most offshore operations, there is no such thing available as a yield stress forecast, or a green water forecast, or an acceleration forecast. These operations will have to make do with the only forecast available: the weather, current and wave forecast. The wave forecast is typically expressed in terms of sea state parameters, such as wave height and period. Therefore, engineers venture to connect the physical restrictions, such as vessel accelerations, to sea state parameters through simulation models. The drawbacks and limitations of this approach were the topic of the third article.

Flow diagram for offshore decision-making based upon allowable wave parameters
Example of allowable wave height for going-on-location of a jack-up
Tip 02 – Ask for a workability forecast, not a wave forecast

We can actually create forecasts of the restricted items, such as a structural stress forecast or an acceleration forecast. Doing so eliminates the need for expressing workability in terms of sea state parameters. These forecasts can be used to objectively determine workability based upon the real, physical restrictions as opposed to the engineering construct of allowable sea state parameters.

We revisit the case study of the soft-pinned phase of a jack-up going on location. In this condition, the hull is subjected to hydrodynamic actions while the motions are restricted by the legs and seabed. The governing physical criterion for workability in this condition is the stress in the part of the legs that interfaces with the hull.

Simulation models are used in engineering studies to determine workability. The model includes:

  • Hydrodynamic actions (wave force, dependent upon incoming wave energy per direction and period)
  • Hydrodynamic reactions (added mass and radiation damping)
  • Structural response (stiffness)
  • Mass

The simulation model is governed by the equation of motions, which ensures dynamic equilibrium.

Weight, buoyancy and environmental actions are imposed upon the model. The simulation model is used to find the responses of interest. In this case, the responses of interest are the bending moment and axial load in the leg. These, together with the cross-section of the leg, yield the actual stresses in the steel.

Model of a jack-up in soft-pinned condition

Most models used in our industry are either linear or linearizable. This means that a factor, dependent upon wave direction and period, can be determined to relate incoming wave energy to the required output, such as stress or accelerations. This factor is called a transfer function. A transfer function should be seen as a dense form of the model.

The transfer function is dependent upon wave direction and frequency. It has the same dimensions as the 2D wave spectra, which were introduced in depth in the third article. It can thus be readily applied to the 2D wave spectra. This way, a forecast of any physical parameter, such as stress and acceleration, can be performed if forecasted 2D wave spectra are available. But are they?

Tip 03 – Any wave forecast is based on 2D wave spectra. Ask your forecast provider for this additional data for free!

Most of us are (all too) familiar with environmental forecasts used offshore. Long lists of numbers and graphs, providing upcoming wave, current and weather for coming days. These forecasts are based on intricate models of the sea and air. Similar to a FEM model, the sea and air are discretized into small areas. Within each area, conditions are uniformly defined. To keep track of the waves, which move from one area to the next, the wave energy for every direction and period is to be kept. Oceanographers, apart from brilliant scientists, are also great bookkeepers.

People cannot think in terms of large arrays of data. Therefore, wave data is condensed to sea state parameters, such as significant wave height and period. Typically, multiple wave systems such as local wind sea and swell are identified and assigned individual parameters. Even though only wave parameters are presented in the environmental forecast, that does not mean that the underlying data is not available. Since all forecasted wave parameters are based upon a 2D wave spectrum, the 2D wave spectrum is readily available to anyone who is paying for a forecast.

A polar plot presenting the 2D wave spectrum of a real-world sea state
Tip 04 – Ask your local engineering firm for a workability forecast

With the condensed model and the forecasted 2D wave spectra available, creating a workability forecast is straightforward. All physically limiting items, such as steel stress, accelerations, freeboard, etc. can be forecasted and compared to the allowable values, the restriction. If all requirements are met, the sea state is workable.

The forecast can be performed on-shore, in real-time as the wave forecast comes it. It is presented similarly to the wave forecast and shared through e-mail at the same frequency as the weather forecast.

Calypso offers this service for jack-up operations. They create the required models, establish applicable limitations and perform the forecast.

The on-board system of MO4  takes this to the next level. It enables the user to provide loading conditions and heading. It performs the forecast of restrictions live on board, for which Calypso can supply the required applicable limitations and condensed jack-up model.

Flow diagram for offshore decision-making based upon forecast of applicable restrictions
Tip 05 – Check your forecast – measure that which is easiest to measure

The behavior of an offshore operation can be measured through many means. Measurements from strain gauges and load cells can often be revealing for single events, such as a leg impact of a jack-up going on location. However, these are generally less suitable for analysis of longer operations. Waves can be measured directly or indirectly by radar. The most advanced systems provide a measurement that can be used to verify and calibrate the forecasted 2D wave spectra.

The one measurement that is suitable for most types of operations, is most cost-effective, is easiest to apply and most straightforward to analyze is… the motion response measurement.

A Motion Reference Unit (MRU) can be used for measuring quickly varying motions in six degrees of freedom of the offshore unit and the load. MRUs measure accelerations in translation and velocities in rotation. These can be converted to position by integration over time, minding drift. Since gravity is an acceleration, MRUs know where is up and where is down.

It has been argued through this series of articles that every offshore operation should be equipped with at least one MRU. Dozens of MRUs are available on board, right in the pockets of crew members. Every smartphone has an MRU and to many it is somewhat preposterous that not every object offshore is equipped with one.

Calypso, and many others, provide stand-alone MRUs with storage capacity and an internet connection for communicating measurements. The price for most projects is less than $100,- per day.

Tip 06 – Measure only that which you can also model or simulate

This one should go without saying but unfortunately tends to go wrong often. The purpose of a monitoring campaign is to bridge the gap between the on-board reality, which to some extent can be measured, and the desktop engineering reality. In this fourth article, we will finally have to define this desktop reality. Many people think of coffee machines and pocket protectors when hearing this term. 

What people on board think the desktop engineering reality looks like

However, what is meant here by the desktop engineering reality, is the modeled version of the on-board reality. Desktop reality is defined by the models it uses. It is the digital twin of the on-board reality if you will. To bridge the gap means to improve the models. Therefore, only those things that are an outcome of the model, such as accelerations, should be measured. Such measurements can be used to improve the models.

Conversely – before even thinking of a monitoring campaign, a model of the offshore operation should be available. Trying to increase workability, for instance of the going-on-location of a jack-up, through measurements only, cannot be done. A gap-bridging monitoring campaign starts by questioning the uncertainties in the model, as was detailed in the second article.

What the desktop engineering reality really looks like (barstool model of a jack-up)
Tip 07 – Get buy-in for your workability or monitoring project

It has been established at several places in this series of articles that a gap-bridging monitoring campaign is non-invasive and cost-effective. It is also shown that often there is a significant collateral benefit – a monitoring campaign typically includes a four-times-a-day workability forecast. Yet, sometimes it can be difficult to get buy-in from all stakeholders.

Think of what is most valuable to your company. Do you wish to have an optimal, objectively established workability forecast? Or do you wish to ensure optimal workability while increasing operational safety? Let your engineering service provider know and they will be happy to provide you with a proposal with the right focus.

At Calypso, we are always ready to support you in bridging the gap between on-board and desktop reality. We do so through monitoring campaigns, workability forecasts and, most importantly, by always keeping the end-user on-board in mind when writing our reports, such as site-specific assessments (SSA) and leg penetration analysis (LPA). Reach out to us to elevate your jack-up operations.

Workability of offshore operations – the gap between on-board and desktop interpretations

This previous article presented a case study for a gap-bridging monitoring campaign to optimize workability for elevated operations of a jack-up. The concept of gap-bridging monitoring was developed to bridge the gap between the on-board engineering reality and the desktop engineering reality. 

This article focuses on the gap between the on-board reality and the desktop reality when it comes to workability and decision making. Workability is typically expressed in terms of allowable sea state parameters, such as restrictions upon allowable wave height for a specific wave period. Wave height, wave period and wave direction are examples of sea state parameters. On-board decision making is typically done by comparing the forecasted sea state parameters to the allowable sea state parameters. 

It will be demonstrated that these sea state parameters, when used in the desktop reality, have a very different meaning from how they are used on board. Workability, expressed in allowable sea state parameters, is determined in the desktop engineering reality and is applied in the on-board engineering reality. This article is written to explain this gap, to enable engineers to mind the gap. A follow-up article will present an alternative method of defining workability, to bridge the gap

The case study from this previous article is elaborated upon. A large four-legged jack-up is performing wind turbine installation operations. This involves two or three rig moves every week.  

The rig move is a weather-restricted operation. Going-on-location, where the jack-up goes from free-floating condition (as a vessel) to elevated condition (as a bottom-founded structure), is typically the most onerous stage of the operation and can have a significant impact on the duration of the offshore campaign, especially in more challenging seasons. 

For this case study, we focus on the part of the going-on-location operation where the jack-up is in soft-pinned condition. In this condition, the hull is subjected to hydrodynamic actions while the motions are restricted by the legs and seabed.  The governing physical criterion for workability in this condition is the maximum allowable bending moment in the legs. An environment is workable if the expected bending moments are below this limit. 

Model of a jack-up in soft-pinned condition
Model of a jack-up in soft-pinned condition
Introducing sea state restrictions 

Most offshore operations are weather-restricted, which means that the operation may be performed if the waves and weather are below a certain threshold or restriction. On-board decision making is done by considering environmental parameters from a forecast, such as wind speed and wave height, and comparing these to allowable parameters. These allowable parameters, or weather restrictions, are determined through desktop engineering studies. 

The restriction on the sea state is most important. A sea state is the combination of all waves that are present at the location. The restriction is typically expressed as a maximum allowable wave height, which depends on the wave period and, possibly, on the wave direction. 

This sea state restriction should be determined specifically for the site/area where the jack-up goes on location, as part of the site-specific assessment for installation (SSA-I). This restriction is determined using a site-specific model of the jack-up, taking into account water depth, the loading condition of the vessel, and the soil type that makes up the seabed. However, this restriction is in most cases copied from the design conditions provided in the operating manual of the jack-up, which are based upon a conservative generalized model. 

The simulation model is used, in the desktop engineering reality, to perform a big batch of simulations for a range of sea state parameters. Typically, significant wave height, zero-crossing period and wave direction are variably applied. Using sea state parameters to simulate the offshore situation seems great, because thinking about a sea state in terms of these parameters is done on board as well. The on-board engineering reality seems to be applied in the desktop engineering reality through the use of the very same sea state parameters.  

Sea state parameters used on board and at the desktop 

The term significant wave height (Hs) has many origins. One of those is in human nature. Ask ten people to estimate the wave height of a wind sea just by watching the water. The average will tend to be close to the engineering definitions of Hs. One engineering definition is the average of the highest one third of the observed waves, which can be measured. The most used engineering definition is a measure of the total amount of energy held in a sea state. 

Zero-crossing period (Tz) is the period of the combined waves. Wave direction is the main direction from which the waves come. Both Tz and direction have several engineering definitions which are not elaborated upon here. 

These sea state parameters are good coffee-machine / smoking room discussion material – at least, for some of us – both at the office and on board. However, the gap between the on-board reality and the desktop reality on this topic is significant and often overlooked. 

When discussing wave parameters on board, these are based upon the sea state as a whole, in all its complexity. Terms like significant wave height are a handle to grasp and think about a more intricate reality. Typically, wave heights are assigned to several components of the sea state, or wave systems, each with a different period and direction. Locally generated wind sea and incoming swell components are distinguished. Used on board, the wave parameters are a boiled-down version of the current or forecasted reality. 

Contrarily, in the desktop engineering reality, the wave parameters are a starting point, a premise. One cannot apply a wave parameter to a simulation model. Only a sea state can be applied to a simulation model. Wave parameters therefore have to be converted to a sea state, where wave energy is assigned directionality and period. To do so, major assumptions are made as will be shown in the following paragraph. 

The world upside-down: Creating a sea state based upon parameters 

In the most simple engineering studies, a regular, long-crested wave is used. This is a simple assumption, but typically a bad representation of reality. There is no variability in crest height and period. In engineering terms, all wave energy is applied at a single period and in a single direction. For this example, a wave height of 2 m is applied at a direction of 275 degrees and a period of 6 s. 

This can be visualized in a polar plot. The direction is shown by the azimuth angle and the period is shown by the radius, from 3 to 15 seconds. Both the direction and period are considered from the center looking radially outward. This simple regular wave case is thus represented by a red dot on this otherwise blue polar plot, which is called a 2D wave spectrum. 

Every wave, or wave component, has a period and a direction. It can therefore always be shown in a 2D wave spectrum. The color indicates the wave energy density, which is a measure for wave height. 

When this sea state is applied to the simulation model of the jack-up in soft-pinned condition, it is found that structural strength is exceeded and therefore this situation is not workable. The simulation model was found to resonate with wave loading from this direction and period. 

Polar plot indicating wave energy per direction (azimuth) and period (radius - increasing radially outward from center). All wave energy in one dot – a regular wave at 275 deg direction and 6 s period
Polar plot indicating wave energy per direction (azimuth) and period (radius – increasing radially outward from center). All wave energy in one dot – a regular wave at 275 deg direction and 6 s period 

In somewhat more advanced engineering studies, the wave energy is distributed over a range of periods. A wave energy distribution is called a spectrum. In this case, the JONSWAP spectrum is used. The spectrum has a zero-crossing period of 6 seconds and is applied along a 275 degree direction. This is a somewhat better representation of reality, but a more complex assumption. The wave period and crest height are variable, leading to an irregular, but long crested sea state. This case is represented by a radial line in the 2D spectrum, the width of the line being a reflection of the resolution of the grid, not of directional spreading. 

The simulation model resonates to this sea state and, as for the regular wave, is not workable. 

A polar plot presenting a JONSWAP wave spectrum with 6 second zero-crossing period along a 275 degree direction
A polar plot presenting a JONSWAP wave spectrum with 6 second zero-crossing period along a 275 degree direction 

At a next level of complexity, the assumed spectrum may have directional spreading, typically applied as a cosine to the power n, where n is an even number.  Now the waves are short-crested in addition to being irregular. The line on the polar plot has a wider thickness. 

The simulation model indicates that this sea state, with the wave energy now distributed over period and direction, is almost workable. The allowable significant wave height for this direction and period is 1.8 m. 

A polar plot indicating a JONSWAP wave spectrum with 6 second zero-crossing period along a 275 degree direction with directional spreading
A polar plot indicating a JONSWAP wave spectrum with 6 second zero-crossing period along a 275 degree direction with directional spreading 

It can be observed from the above that the road to a better representation of a generalized reality is paved with assumptions. It only leads to sea states that look somewhat realistic and prevent typical simulation errors such as exaggerated resonance, but in no way are a proper representation of any actual sea state. Actual sea states, as known from the forecast, are typically multi-modal. This means that there are several components including locally generated wind sea and incoming swell. 

A real-world sea state 

Let’s now consider an example of a real-world sea state, presented as a 2D wave spectrum. This one is obtained from the actual forecast/hindcast model that is used to generate the daily wave forecast as used on board. Note that the sea state parameters as presented in the daily forecast are based upon detailed forecasted 2D wave spectra – a boiled down version of reality. 

The wave direction is 275 deg and zero-crossing period is 6 s, just like for the assumed sea states above. The total wave energy, expressed as significant wave height is much higher than in the assumed sea states, at 2.85 m. Clearly, based upon an engineering study that uses assumed sea states, this real-world sea state would not be workable. The significant wave height is higher than allowable.  

It can be seen that part of the real-world sea state is a scattered swell, coming from 345 deg around a period of 11 s. The simulation model does not respond strongly to that. The locally generated wind sea part, around a period of 6 s is distributed over a wide direction ranging from 180 degrees to 275 degrees. 

This is very different from the assumed sea states above. The wave energy is not concentrated closely around the peak period and main direction, but much more scattered. 

This real-world sea state is applied to the simulation model. It is found that this sea state, with a significant wave height of 2.85 m, is in fact workable. 

A polar plot presenting the 2D wave spectrum of a real-world sea state
A polar plot presenting the 2D wave spectrum of a real-world sea state 
Applying the engineering study on board 

The on-board decision making is done by comparing forecasted wave parameters (a boiled down version of a real sea state) to allowable wave parameters (a premise to an assumed sea state). 

Flow diagram for on board decision making for workability of offshore operations based upon forecast and engineering study
Flow diagram for on-board decision making based upon forecast and engineering study 

It is shown that directly applying a real-world sea state to the simulation model is a more objective method for determining the workability. This approach will be elaborated upon in the following article. Calypso implements this approach in workability studies for jack-up operations. The MO4 system makes a similar approach available for decision making directly on board. 

This article argues that workability expressed as allowable sea state parameters is a mere indication, a guideline. Objectivity is lost when a go/no-go decision is made merely by comparing the forecasted significant wave height to the allowable significant wave height, at centimeter accuracy. To say the least, workability expressed as allowable sea state parameters is no more objective or accurate than the subjective judgement of an experienced captain. 

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. 

Monitoring of offshore operations – A tongue-in-cheek introduction about what can go wrong and what to do about it

Most engineers active in the offshore energy business have, at some point in their career, sat in on a HAZID meeting where a critical risk had to be mitigated. One of the attendees (typically a manager) suggests that a monitoring campaign should be carried out. A wonderful idea! 

Most stakeholders tend to agree, because they see only benefits. Monitoring does not affect operations, costs are limited as compared to typical day rates (let alone the consequences of the risk it intends to mitigate) and everybody looks like they are taking responsibility. 

What people outside our industry think HAZID meetings look like 

A monitoring campaign is devised to measure and check an offshore operation, for instance a complex heavy lift. It could include the following: 

  • Motion Reference Units (MRU) for measuring quickly varying motions in six degrees of freedom of the offshore unit and the load. MRUs measure accelerations in translation and velocities in rotation. These can be converted to position by integration over time, minding drift. Since gravity is an acceleration, MRUs know where is up and where is down. Every smartphone has an MRU and to many it is somewhat preposterous that not every object offshore is equipped with one. 
  • High-precision GPS for measuring longer term motions in the horizontal plane (position and orientation). These systems are typically used for positioning of mobile offshore units. They are relatively expensive and are continuously operated by a specialist and his back-to-back. 
  • Wave rider buoy or downward looking wave radar. These frequently used systems measure the instantaneous water surface level at great accuracy at a specific location. Processing to identify the wave energy density distribution over distinct frequencies is commonplace, but over distinct directions is hard and often impossible. 
  • Wind sensor / anemometer. Simple and cheap measurement device for wind speed at a specific location. Measurement quality is highly dependent upon location and vulnerable to shielding effects. 
  • Current measurement device (either propeller or acoustic based). These devices measure current at a specific location either at a specific depth or for the entire water column. When hung of from a vessel, the vessel presence will severely influence the measurement. 
  • Strain gauges. Contrary to popular belief, these devices do not measure stress (let alone force) but accurately measure local strains. Many strain gauges, at often hard to reach places, in addition to detailed knowledge and assumptions about a structure are required to translate strain measurements to cross-sectional stress and force. 
Devices typically used for offshore monitoring campaigns

One often overlooked form of measurement is a calibrated environmental now-cast. Now-cast is similar to environmental forecast and provides detailed wind, wave and current information. The now-cast is based on a detailed model of the sea and weather and is continuously calibrated based upon real-time (mostly satellite-based) measurement. The now-cast, more than any measurement, aligns greatly with the desktop engineering reality as well as the on-board engineering reality due to the similarity with the environmental forecast. Much more about this in an upcoming article. 

Now, back to how most offshore monitoring campaigns come into existence. 

Engineers enjoy performing monitoring campaigns because something is to be learned about the on-board reality, which is greatly different from the desktop reality. HSE officers like them because they provide a sense of control over risks. Managers like them because they provide big data and a sense of ownership. Offshore unit owners like them, because they feel that somehow the acquired data can be capitalized upon for future projects.  


And thus, during already stressful mobilization, an uncoordinated pack of measurement specialists boards the offshore unit and starts planting devices, pulling wires and fighting with the chief electrician over whether the right ports are opened on the firewall or not. 

The offshore operations commence, and measurement starts interfering (‘Can we perform this lift if we don’t receive signal from our wave rider buoy?’, ‘The measurements and forecast disagree, should we use the most onerous?’, or, more existentially: ‘If my current position cannot be accurately measured, am I really here?’). 

Usable measurement results are expected once the operation is done and over. Reports are provided from the various parties performing measurements. Desk studies trying to tie all the data together into firm, usable conclusions tend to struggle because some crucial element is unclear (‘Why is there a sudden list occurring here?’, ‘Why do the motion measurements seem to conflict?’, ‘The MRU knows where is up, by why did no one report whether the x-axis aligns with forward or starboard?’). Engineers may be pressed to declare that, based on the measurements, the operation was even more safe than anticipated. 

None of the expensively acquired data can be used to capitalize upon for a next similar operation, because without a firm understanding of underlying physics, minor differences cannot be accounted for. The operation was successful, so everyone will be happy at the end, but the success of the monitoring campaign was nothing more than a very expensive ticking the box. 

What went wrong? Are monitoring campaigns worthless exercises? What can be done to improve this? Did it occur to you, reader, that at the start of this article, the contents of a monitoring campaign were introduced as a mere list of measurement devices? 

Measurements alone will lead to data only. A measurement campaign should start with questioning which engineering uncertainties are to be minimized through measurement. The answer to that question allows an experienced engineer, with a strong basis in both desktop reality and on-board reality, to devise a scope of work for analysis to be performed. One of the inputs into the analysis is well-specified measurement data. The analysis should lead to a firm grasp on the physics behind the actual behavior of the offshore spread. The monitoring campaign should bridge the gap between the on-board engineering reality and the desktop engineering reality. 

This first article introduced the philosophy of ‘gap-bridging monitoring’. In my next article I will present this philosophy in more detail and apply it to a typical jack-up performing a recurring operation. 

Calypso presents: The Calypso LPA application

The LPA application allows the user to perform a Leg Penetration Analysis for any jack-up platform. It makes available the powerful LPA module of the Calypso app for usage through a web-interface following many requests we received since the launch of Calypso.

Some of the included features are:

  • Infinite number of layers
  • Complex interaction between layers
  • Inclusion of back-fill and in-fill
  • Sophisticated spudcan geometry

The input and results are summarized in a crisp one-page LPA report. The application enables geotechnical engineers to easily perform jack-up leg penetration analyses. For jack-up owners and operators, it provides an easy-to-use tool for assessing penetration before performing an SSA.

The P-delta effect on jack-up platforms as a stiffness matrix

Introduction to the P-delta effect

The purpose of a jack-up platform is to support a deck and mission equipment, e.g. a crane or derrick, above the water level. This mission equipment, together with the hull and variable deck load, makes up most of the weight of the platform. The weight is the P-part of the P-delta effect. The hull will move laterally due to environmental loads on the hull and legs. This lateral movement is the delta-part of the P-delta effect.

Schematic view of P-delta effect and visual output from Calypso

When the weight P is at an offset delta, this introduces an extra contribution to the overturning moment of the platform, in turn leading to another (smaller) contribution to the offset. Therefore, it is said that the P-delta effect is a secondary effect. In this definition, the extra offset will lead to another extra offset, ad infinitum. This is how some numerical solvers include geometric non-linearities such as the P-delta effect, requiring a number of iterations.

The P-delta effect as stiffness reduction of a beam

Another way of thinking about – and implementing of – the P-delta effect is as a stiffness matrix. This is introduced in E. Wilsons excellent book, http://www.edwilson.org/BOOK-Wilson/11-PDE~1.pdf .

As a simple example, think of a rod in tension. As most people are aware, a rod or rope in tension, has a restoring force against transverse displacement of its ends e.g. has a lateral stiffness. If a rod is in compression, this stiffness is negative, reflected in buckling behavior.

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The forces Fi and Fj due to displacements vi and vj can be expressed as the following matrix equation:

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If T is negative (compression P), the stiffness matrix is negative, thus giving a stiffness reduction.

Applying a negative stiffness of P/L on the hull is a simplified method proposed in ISO 19905-1, where P is the elevated weight and L the free leg length. However, the implicit assumption that the legs together behave like a rod may be an oversimplification.

In Calypso each leg is modeled as a stack of beams connecting intermediate nodes. A beam not only has end forces, but also end moments. If we assume that bending of the beam due to end-force is parabolic and cubic due to end-moment (valid for Euler beams – close enough for Timoshenko beams as used in Calypso), the following equation applies for the stiffness due to the P-delta effect on a beam:

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Where Mi, Mj are end moments and φi and φj are end rotations. If T is set to be the axial load in the beam, this is a good reflection of the P-delta effect. This matrix can be added to the beam stiffness matrix, so that the system can be solved in one go. Based on the top-left item in the matrix, it can be concluded that if a simplified approach is used, a better negative spring stiffness would be 36T/30L.

The P-delta effect on jack-up platforms

Until this point in this article, the P-delta effect was considered for a single beam-column supporting a weight. A jack-up is best modelled as a bar-stool – a mass supported by a multitude of beam-columns. When a jack-up is subjected to a horizontal force, the following things happen:

  • The hull will be offset from its initial position
  •  All legs will sustain a roughly equal bending moment
  • The leeward leg will have an increased axial load whereas the windward leg will have a decreased axial load due to the overturning moment

So, for a jack-up leg, the P (axial load) is not just given by weight, but also by the effects from environmental actions.

The ISO standard for site-specific assessments of jack-ups (ISO 19905-1) distinguishes between a P-Δ effect (capital Delta) and a P-δ effect (small delta). The former pertains to the influence on overall jack-up behavior such as offset of the hull and overturning moment, whereas the latter considers the element level e.g. for the local chord beam-column check, which is outside of the scope of this article.

The overall behavior of a jack-up is not (much) influenced by the effects of variant in axial load between the legs. The stiffness of the leeward leg reduces, but the stiffness of the windward leg increases, netting to a similar overall horizontal stiffness of the platform as a whole.

However, the stiffness reduction of the leeward leg will lead to a moment redistribution to the stiffer windward leg. This effect is not captured in the definition in the ISO standard. It is sort-of intermediate between P-Delta (capital) and P-delta.

In order to capture the effect of axial load variation, one iteration is required. In the first run the stiffness matrices for the P-delta effect are set based upon weight alone and the environmental load is applied. In the iteration, the stiffness matrix due to the P-delta effect is adapted per element to account for the axial load found in the first run. Since the axial load is not further influenced by this, one iteration is sufficient.

The P-delta effect in dynamic simulations

Typically, time domain dynamic simulations of jack-up behavior are performed to calculate the dynamic amplification factor. This DAF can be determined based on overall platform behavior, for which the P-delta effect need not be implemented iteratively if a stiffness matrix approach is used. Furthermore, the foundation model for this type of simulation is typically a linear spring, the damping is applied as viscous damping and all other elements also have linear behavior.

From the facts stated above, it can be concluded that the jack-up model for dynamic simulation can be written as a linear model, if the P-delta effect is included as a stiffness matrix in each element, with P based on the platform and leg weight.

The wave forcing is non-gaussian, so a fully statistical frequency domain approach is not evident. However, the static and dynamic behavior being linear does open up possibilities to a more statistical approach of determining a DAF, based on a shorter time trace of wave forces.


The stiffness matrix implementation of the P-delta effect is the model of choice in Calypso. It allows for a simple and effective approach. This paper presents a comparison between several methods and shows that correctly implementing the P-delta effect can have a significant influence on the leg moments.