thresholds for intentional incidents is becoming lower and increases the need for evaluations and greater security around vulnerable infrastructure, such as oil and gas installations and airports.
Experiences that can be transferred to other areas are as follows:
• safety levels have become extremely high as a result of the gradual introduction of automation. The number of personnel has been reduced, but pilots have been given a key role in dealing with unexpected and complex events.
• prioritisation of HF
• well-developed infrastructure with control centres staffed 24 hours a day, 7 days a week
• systematic data reporting
• comprehensive regulations prioritising thorough testing and certification
• systematic training and learning following accidents.
3.3.1 Offline models
Drilling is a complex operation where the use of models is widespread. Models are used to simulate all or certain aspects of the equipment and process. These models can be used offline for testing equipment and processes, for planning and training purposes ahead of an operation or before the next stage in an operation. Examples of use include the selection of drilling mud properties or the drawing up of plans concerning how a well should be drilled.
One challenge with the use of models is that they simplify reality. A model will never be able to fully reflect reality, and it is difficult to ensure that all parameters are taken into account. Even for accurate and complex models with good parameter adaptation, changes in operating conditions and the actual conditions existing during the drilling process itself will lead to inaccuracies in the model.
Such adaptations are often not adequately taken into account during use. There will also always be a trade-off between the complexity of the model on the one hand, and the requirements regarding performance on the other. It is therefore important to involve users with a detailed knowledge of the processes that are to be modelled throughout the development process, and to ensure that the
models are tested before being taken into use. This should be addressed through user-centred development [T2].
3.3.2 Data infrastructure and quality assurance
Utilising automated drilling solutions will place greater demands on the use of sensors, data quality, reliability and data communication (Godhavn, 2011). Data is often stored by parties which offer a range of services, such as directional drilling, mud and fluidhandling, cementing, downhole tool handling and sometimes other services such as MPD (managed pressurised drilling) and circulation systems. This presents challenges regarding joint access to, and the quality assurance of, data, which has also led operators to set up initiatives to address the problems using integrated platforms.
Sensors are supplied by different suppliers, and the data from these sensors must undergo quality assurance and be compiled in an appropriate manner so that trust in the data can be built up. It must also be ensured that data characteristics such as integrity, completeness, consistency, availability, punctuality, etc. are safeguarded to ensure that systems are operated safely (Data Safety Guidance Version 3.2, 2020). Delays in time and space between tools in the well and models represent one of the most challenging aspects of automated drilling, according to (Sugiura 2015). With low
bandwidth, using models in real time (e.g. for transferring data from the drill bit up to the surface) is challenging. Developments are now under way aimed at introducing and commercialising “wired pipe”, which will provide real-time data from the well during drilling, Equinor (2018). This could solve some of the challenges associated with time delay and low bandwidth.
The introduction of technologies such as 5G can present greater opportunities for data collection from many components, which can provide more support for monitoring, optimisation and remote operation. As the world becomes digitalised and is made “smarter", greater demands are placed on data infrastructure. Large quantities of data lead to a need for efficient access and acceptable response times. Data must be distributed and shared not only between the installation and any onshore operations centres, but also between a complex ecosystem of contributing engineering companies, equipment and drilling mud suppliers, drilling and maintenance firms and consultants.
Data sharing presents new challenges relating to ensuring data security both between the parties
involved and between OT and IT systems. This sharing requires safety analyses to have been carried out and good systems and procedures to be established which address security and safety between all the parties involved in the ecosystem as described. This integration of systems must be handled through technical, organisational and HF measures as outlined in [T3c].
3.3.3 Handling of machinery
Drilling involves the use of heavy machinery for handling pipes and tubular components.
Developments have come a long way with regards the robotisation of drill floor, which now have both semi-automated and fully automated systems for pipe handling. Several robots are often used in such systems, e.g. drill floor robots for moving pipes and equipment around on deck, pipe handlers to assemble pipes, iron roughnecks for threaded connections and elevators for raising and lowering pipes. The equipment that is handled is heavy and large forces are involved at times, and remote control and automation enable the need for personnel to be present on drill floor to be largely eliminated, which in turn helps to improve safety. However, there are still certain complex operations where manual work is required, such as repairs and the moving of smaller components.
Exacting demands are imposed on the synchronisation of machinery, in terms of both speed and placement, and the opening and closing of end tools, and this can be a challenge to predict and program for every possible situation. According to Flemisch (2012), it is important that automation is focussed on achieving the most effective balance between humans and machines. In such an environment involving the use of heavy machinery, there will be much to gain from physically removing humans from the process, given that remote control can be handled safely and efficiently.
With a greater degree of remote control, more control takes place via screens rather than physical monitoring. In some cases, plans are being drawn up to relocate the drillers cabin away from the drill floor. This makes it important to have systems in place which provide a good overview – and ensure that operators can maintain an overview of the process when looking only at monitors. With a higher level of automation, as outlined with the LOA, drillers may be given a role which involves more monitoring than active management. (I.e. strategic monitoring and the confirmation of
sequences of operations.) This can present challenges linked to sense of ownership and knowledge of the process. In the event of an incident where the system fails and a driller has to intervene, either in order to correct errors or to start the process manually, it can be problematic if the driller does not possess the necessary competence or understand what is required. This will particularly be a
problem in the case of systems which have worked well over a prolonged period of time, with the result that operators come to trust the system so blindly that they do not see any need to understand the underlying processes or have any training in doing the job manually. It is therefore important that users are involved in development [T2], and that the systems are designed to address the need for meaningful human control [T3].
3.3.4 Higher level of automation
The subsequent discussion and description of the example projects (Cases 1 and 2 in the section on
"Collated experiences") illustrates how remote control and automation of certain aspects of the drilling process are being used. This gives an indication of what key parties see as being realistic in the relatively short term. A detailed analysis of whether fully automated drilling operations are possible from a technical, safety and economic perspective falls outside the scope of this report. We do not see any specific technical obstacles to achieving this, but increasing complexity and
vulnerability will present challenges that require ongoing learning, and users/operators must be
included. Sufficient time must be set aside, and a step-by-step process must be followed towards this end. We also believe it will be necessary to both further develop and upgrade hardware, algorithms, sensors and data transfer systems. The details will depend on the situation, but we anticipate that automation will require more accurate and reliable measurements of what is happening down the well, particularly as regards more challenging wells, with a combination of better data quality and better algorithms for handling the remaining deficiencies in data quality. We have therefore proposed further work in this area, [V7].
3.3.5 Automated drilling
Offline models can be used in real time during operation, with a direct link to the control systems that are controlling or monitoring the drilling operation. One example is automated directional drilling, where non-vertical wells are drilled. This involves drilling along a planned trajectory towards a specific destination. This entails the use of solutions that involve advanced models, where angles are computed and sent down to a local regulator, which uses feedback from sensors to
achieve the desired trajectoy (Matheus and Naganathan, 2010). There are also many other examples of technologies which contribute to automated drilling, including the automatic optimisation of drilling parameters such as Weight On Bit (WOB), Rate Of Penetration (ROP) and vibrations which interfere with the drilling process (Nystad, 2020). Other systems focus on avoiding jerky
fluctuations in torque and torsional drill string vibrations based on measurements of drill string rotation and torque (Kyllingstad, 2010). Automatic MPD (managed pressurised drilling) and DG (dual gradient drilling) are systems that enable faster and more accurate regulation of the bottom hole pressure (Godhavn, 2011). Drilltronics (Florence and Iversen, 2010) and eControl
(Rommetveit et. al., 2010) are examples of commercially available products, where advanced computer models are used to monitor and, in some cases, control drilling equipment. In addition to assisting drillers with decision-making, many tasks can be performed automatically, e.g. passive protection of barriers and safeguards concerning limitations for wells, equipment and process, as well as the active management of machinery.
As mentioned previously, autonomous systems are intelligent facilities that can perform tasks without human intervention. The system then knows its "abilities" and its "state". The system can choose between a set of alternative actions and perform them in accordance with the applicable rules. In order for this to be possible, the system relies on access to a realistic model of the current state of the process and its own behaviour in an interaction with the outside world, often referred to as a "Digital twin" (Rosen, 2015). The phrase "Digital twin" was made widely known by NASA (NASA, 2012) and was defined as follows:
In the case of drilling systems, more data concerning the physical characteristics will be connected, in addition to the advanced mathematical models that are used. Such a real-time system will provide a better understanding of what is happening in the process at all times, which will both contribute to earlier and more reliable detection of hazardous events and enable the operation to be controlled more optimally in order to achieve the goal more quickly. Earlier and more reliable detection stems from the fact that the system itself helps out with the interpretation and understanding of
measurements, and that automated operations are performed in exactly the same way each time a sequence is repeated, thus making it much easier to detect non-conformities.
One development which has implications for the use of models is that this can contribute to the relocation of functions to onshore operations centres. This requires systems on the rig to be
sufficiently robust and remotely controlled to render the presence of experts on the rig unnecessary.
In this regard, operations centres at a number of physical locations may also be used, with some operators being responsible for modelling and parameterisation, others for planning, and others still for operation. This will lead to changes in roles and responsibilities for the parties involved in the event of new technology being introduced, and this could impact on the quality of the processes.
A digital twin is a dynamic concept that often increases in complexity throughout its life-cycle. The models must have as much robustness and flexibility as the system that is modelled. It must for example be developed as the operational system is developed. It must have the ability to deal with errors in input data and adapt to real observations. Like a real system, it can lead to a lack of insight and understanding of the system, which in turn can lead to errors and incidents, particularly in situations where an autonomous system has to be overridden. It is a known issue that automated systems are inferior at dealing with unexpected events compared with experienced operators. With a high degree of automation, human operators receive less training and gain less experience in
operating without the automation. These considerations require a step-by-step, robust
implementation process, where it is ensured at every step that the automatic systems can cope with unexpected situations satisfactorily, [T2].
3.3.6 Automated drilling mud handling
The drilling mud system consists of equipment on board which contributes to the storage, mixing, circulation and treatment of drilling mud. Drilling mud management has traditionally been a manual process involving two to four people, with some assistance from machinery. With a manual process, operators are exposed to chemicals and other hazards, and the precision of the mixing process is often poorer than in the case of automated systems. Tests on automated solutions, including tests performed at Valhall WIP (Water Injection Platform), have shown promising results in terms of improved working environment for the operators involved, efficient mixing, reduced emissions to the environment and cost savings. Valhall WIP is a ‘category 3’ drilling mud handling system, where the entire mud handling process is automated with minimal human monitoring. By way of comparison, category 1 is an entirely manual system, while category 2 is monitored from a control room or an operator’s station (Gunnerod et al., 2009).
3.3.7 Automated well control
The aim of well control is to maintain an overview and control over unexpected inflows of hydrocarbons into the well, which in the worst case could result in high pressures and blowouts.
Loss of well control can cause catastrophic damage to equipment and serious injuries to personnel.
Well control includes the monitoring of uncontrolled inflow to the well bore the well and
procedures for preventing and managing such incidents (Godhavn et al., 2011). By comparing flow and pressure measurements with calculations from hydraulic models of the well without inflows or drilling mud loss, such incidents can be detected at an earlier stage compared with detection based entirely on drilling pit level and mud return flow measurements. This approach enables action to be taken sooner, potentially reducing the consequences. Detection can be achieved through the direct
observation of trends of actual measurements against values predicted by models. This can be done by drillers, support personnel or automatically via detection software based on the model.
These are complex systems that can fail if HF is not taken into account during development (Ciavarelli, 2016). Overconfidence that systems can deal with any situation can also lead to problems. In the event of an incident where systems drop out or do not function as intended, the system may not be able to provide the necessary support, and personnel may lack the training needed to take over and operate the systems manually. The development of such systems therefore requires us to prioritise human-centred design when such systems are introduced, via user-centred development [T2] and ensure that principles such as "meaningful human control" are applied, [T3].