Automation of the Drilling System: What has been done, what is being done, and why it is important
•Download as PPTX, PDF•
2 likes•490 views
Drilling systems automation is the real-time reliance on digital technology in creating a wellbore. It encompasses downhole tools and systems, surface drilling equipment, remote monitoring and the use of models and simulations while drilling. While its scope is large, its potential benefits are impressive, among them: fewer workers exposed to rig-floor hazards, the ability to realize repeatable performance drilling, and lower drilling risk. While drilling systems automation includes new drilling technology, it is most importantly a collaborative infrastructure for performance drilling. In 2008, a small group of engineers and scientists attending an SPE conference noted that automation was becoming a key topic in drilling and they formed a technical section to investigate it further. By 2015, the group reached a membership of sixteen hundred as the technology rapidly gaining acceptance. Why so much interest? The benefits and promises of an automated approach to drilling address the safety and fundamental economics of drilling. What will it take? Among the answers are an open collaborative digital environment at the wellsite, an openness of mind to digital technologies, and modified or new business practices. What are the barriers? The primary barrier is a lack of understanding and a fear of automation. When will it happen? It is happening now. Digital technologies are transforming the infrastructure of the drilling industry. Drilling systems automation uses this infrastructure to deliver safety and performance, and address cost.
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Automation of the Drilling System: What has been done, what is being done, and why it is important
Similar to Automation of the Drilling System: What has been done, what is being done, and why it is important (20)
More from Society of Petroleum Engineers
More from Society of Petroleum Engineers (20)
Recently uploaded
Recently uploaded (20)
Automation of the Drilling System: What has been done, what is being done, and why it is important
- 1. Primary funding is provided by The SPE Foundation through member donations and a contribution from Offshore Europe The Society is grateful to those companies that allow their professionals to serve as lecturers Additional support provided by AIME Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl
- 2. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 2 John D. Macpherson Automation of the Drilling System: What has been done, what is being done, and why it is important
- 3. Presentation Outline • Definitions and background • The drivers for automation • Technical and Business Challenges • SPE Drilling Systems Automation TS • A brief look into automation systems • Conclusions • Q&A 3
- 4. Drilling Systems Automation (DSA) • Systems Automation: Involves the control of the drilling process by automatic means, ultimately reducing human intervention to a minimum. • Scope: Includes all components downhole, on surface and remote to the drilling rig that are used in real-time to drill the wellbore. • Digital Backbone: Employs control systems and information technologies 4
- 5. The process of creating a borehole with systems and sub-systems that are computer controlled, leading to reduced human intervention What is Drilling Systems Automation? 5
- 6. Levels Monitor Advise Select/Decide Implement Manual Control H H H H Action Support H C H H H C Batch Processing H C H H C Shared Control H C H C H H C Decision Support H C H C H C Blended Decisions H C H C H C C Rigid System H C C H C Auto Decisions H C H C C C Supervisory Ctrl H C C C C Full Automation C C C C 1 Levels of Computerization Functions H – Human C - Computer Ref: Endsley and Kaber, 1999, Ergonomics, Vol. 42 6 2 3 4 5 6 7 8 9 10
- 7. Control Systems on Offshore Rigs Dynamic Positioning Power ManagementThruster Control BOP Control Hydraulic Systems Drillship: Pacific Drilling, Pacific Santa Ana Active Heave Drawworks 7
- 8. Monitor L2 • Wellsite Monitoring Systems • Remote Data Centers • Smart Alarms Advise L3-L4 • Drilling Dynamics Diagnostic Systems • Directional Drilling Advisors Control L5-L7 • Auto-Driller • Stick-Slip Surface Control • MPD Control Systems Autonomous L8-L10 • MWD Rotary Steerable Systems • LWD Formation Samplers Drilling Systems Automation The current level of automation in Drilling Surface Digital Networks MWD Network 10101011100100110001 Source: Macpherson etal, 2013, SPE 166263 8
- 9. Drilling Systems Automation • The drilling industry has equipment that is highly automated (level 8+) • Automation of drilling systems and processes is quite rudimentary (level 3) • Why? • What are the business drivers? 9
- 10. • Difficult profiles, bottomhole pressure margins • Mitigate risk and control costs Well Complexity • Real-time measurements, complex operations • Manage, interpret, act on large volumes of data Data Overload • 35 to 40% of deep-water drilling costs • Minimized by responding predictably to events NPT Significant • Drill many similar profile wells per field • Repetitively drill to plan with minimal risk and cost Well Manufacturing DSA Drivers 10 Source: Baker Hughes
- 11. • Difficult profiles, bottomhole pressure margins • Mitigate risk and control costs Well Complexity • Real-time measurements, complex operations • Manage, interpret, act on large volumes of data Data Overload • 35 to 40% of deep-water drilling costs • Minimized by responding predictably to events NPT Significant • Drill many similar profile wells per field • Repetitively drill to plan with minimal risk and cost Well Manufacturing DSA Drivers 11 Don’t buckle the pipe Don’t exceed the maximum RPM of the bit Stay on target Don’t exceed maximum weight on the bit Drill efficiently Avoid lateral vibrations Avoid stick-slip vibrations Don’t exceed make-up torque of the drill string Don’t stall the downhole motor Don’t stall the top drive Drill faster Don’t exceed the maximum RPM of the top drive Connect a new pipe joint fast and safely Condition the hole Go back to bottom fast and safely Clean the hole Drill this section without tripping; i.e. with one bit Time is money Safety first Source: Mark Anderson, Shell
- 12. • Difficult profiles, bottomhole pressure margins • Mitigate risk and control costs Well Complexity • Real-time measurements, complex operations • Manage, interpret, act on large volumes of data Data Overload • 35 to 40% of deep-water drilling costs • Minimized by responding predictably to events NPT Significant • Drill many similar profile wells per field • Repetitively drill to plan with minimal risk and cost Well Manufacturing DSA Drivers 12
- 13. • Difficult profiles, bottomhole pressure margins • Mitigate risk and control costs Well Complexity • Real-time measurements, complex operations • Manage, interpret, act on large volumes of data Data Overload • 35 to 40% of deep-water drilling costs • Minimized by responding predictably to events NPT Significant • Drill many similar profile wells per field • Repetitively drill to plan with minimal risk and cost Well Manufacturing DSA Drivers 13Source: SPE 178875, 2016, Livingston etal, Team Approach to Horizontal Drilling Optimization in the Marcellus Delivers Record Setting Performance
- 14. • Operations starved for expertise • Will make available scarce expert resources Expert Resources • Skilled employees exiting the industry • Transfer knowledge from skilled to new employees Knowledge Transfer • Driver is to make the work environment a safer place • Reduce the number of people in “red zones.” HS & E DSA Drivers 14 Well Complexity Data Overload NPT Significant Well Manufacturing
- 15. Technology and Business • There are significant business drivers behind drilling systems automation • But to make DSA happen we have to communicate the technology to the business 15
- 16. 16 Technology: DSA is Challenging Necessity of domain expertise in technology enablers Taskuncertainty Source: UT Austin RAPID Consortium
- 17. DSA Decision Making and Control Framework • Well Design, Budget, Data Repository, • Data mining & analytics Enterprise • Wellsite & Remote Operations • Monitoring, Modeling, Simulation Operations • Process Control Systems • Onsite Interpretation & Control Execution • Machine Control Systems • Instrumentation & Measurement Machine Control • Physical Drilling Process • No Intelligent Sensing Well Construction 17 0 1 2 3 4 Modified from ISA 95 for the Drilling Process Source: de Wardt etal, 2015, SPE 173010 WellsiteRemote OPC UA Deterministic Protocol WITSML Non-deterministic Protocol
- 18. DSATS Rig Control System 18 rilling ystems utomation echnical ection Operators, Service Companies, Drilling Contractors, Equipment Suppliers, …
- 19. DSATS Communications Guidelines • OPC UA protocol for automation communications • Interface to proprietary or other standards • Machine independent software • data-to-information • control algorithms • Standardized method for real-time drilling data • Simplified device control architecture for drilling rigs • Standardized units, security, rig-information-model 19 Next Step: Data
- 20. DSA Decision Making and Control Framework • Well Design, Budget, Data Repository, • Data mining & analytics Enterprise • Wellsite & Remote Operations • Monitoring, Modeling, Simulation Operations • Process Control Systems • Onsite Interpretation & Control Execution • Machine Control Systems • Instrumentation & Measurement Machine Control • Physical Drilling Process • No Intelligent Sensing Well Construction 20 0 1 2 3 4 Data Mining Analytics Subsurface Predictions Swab Surge ROP Machine Control Data flow between levels of the framework (NO SILOS)
- 21. Data in Systems Automation • Without data there is no automation • “First comes data” (Lord Kelvin) • Who owns the data? – Separation of Data Ownership, Guardianship and Confidentiality – Open System versus current Closed System – Data Model embedded in the Business Model 21
- 22. 22 Sensors & IMS Communications Machines Equipment Control Systems Simulation & Modeling Human Factors Certification & Standards DataAggregator telemetry, latency accuracy, precision Completeness Logic Proximity Accuracy Conversion Criticality Availability Data, Derived Data, System State Calibration Validity data Instrumentation and Measurement System (IMS) -centric view of drilling systems automation, showing relationship with other challenges 2 3 4 5 6 7 8 Systems Architecture 1
- 23. Real-Time Data Flow and Value 23 0 1 2 3 4 Well Construction Machine Control Execution Operations Enterprise REAL-TIME PROCESS SENSOR DATA IN STREAM ANALYTICS ENTERPRISE MODELING / PLANNING ACTIVE ADVISORY SYSTEMS PERFORMANCE VISUALIZATION MONITOR/CONTROL Source: DSATS Luncheon Presentation, 2015, SPE Digital Energy Conference
- 24. Data Flow: Shared Autonomy HUMAN COMPUTER Source: Iversen et al., 2016 SPE-181047-MS 0 Well Construction 1 Machine Control 2 Execution
- 25. A BRIEF LOOK INTO SYSTEMS 25 1. Concept: Drilling Control System 2. Implementation: Real-Time Data Modeling 3. Implementation: Wired Pipe and DSA Drilling Process E(t) H Drilling Control System drilling equipment C(t) controls advice Adcs(tm) Driller “delayed” action Ad(tmd- d) MWD telemetry Rd(tT-T) decimated and delayed downhole responses Rd(td) downhole measurement decimated surface responses Rs (ts) surface tools Cdcs(tm) command Rd(t) downhole responses Rs(t) surface response s
- 26. Drilling Control System 26 Drilling Process E(t) H Drilling Control System drilling equipment C(t) controls advice Adcs(tm) Driller “delayed” action Ad(tmd- d) MWD telemetry Rd(tT-T) decimated and delayed downhole responses Rd(td) downhole measurement decimated surface responses Rs (ts) surface tools Cdcs(tm) command Rd(t) downhole responses Rs(t) surface response s Source: Dashevskiy etal, 2003, AADE-03-NTCE-10 1
- 27. Drilling Control System 27 time (min) 240 180 120 60 ROP (ft/hr) Source: Dashevskiy etal, 2003, AADE-03-NTCE-10 End of training set 1
- 28. Source: Chmela et al, 2014, SPE 168018 28 Model more accurate over time Model data and sensor data deviate indicating a problem Modeling for DSA Use of mechanical, hydraulic and thermal models, in real-time, to derive “virtual sensor data” 2
- 29. Modeling for DSA • Example, models predict safe “pull-window” during tripping • Set-points and operational constraints for safe, efficient operations • Staged introduction: monitoring, shadow system, then control 29Source: Chmela et al, 2014, SPE 168018 2
- 30. • Automated Tripping System 30 Modeling for DSA ECD ECD DEPTH(m) DEPTH(m) CSG SHOE DEPTH CSG SHOE DEPTH BITDEPTH BITDEPTH Pore Pressure Pore Pressure Manual System Automated System • System automatically adjusts running speed and acceleration to avoid swab Source: Chmela, etal. 2014, SPE 168018 2
- 31. MWD Interface Sub Wired Pipe Control System Automation Applications • ROP Optimization • Dynamics • Directional • Geosteering • … Downhole Data • DWOB • DTOB • Bending • Pressure • Survey • FE Data • … Downlink Commands Wired pipe and DSA3 57kbps Low Latency
- 32. Application of wired pipe, high-speed downhole data and closed-loop drilling automation in the Bakken: • Drilling Efficiency: Real-time information improved awareness of drilling efficiency • Standardizing connection practices reduces cumulative effects of back-to-bottom lateral vibrations • Automation system shows promise to manage/mitigate downhole drilling issues • Recommended to further develop drilling automation technology 3 Wired pipe and DSA Source: Trichel et al, 2016, SPE 178870
- 33. Wired pipe and DSA Source: Iversen et al, 2016, SPE 181047 3 On-Bottom Days, 8-3/4” Section With Automation, On-Bottom Days, 8-3/4” Section 0 6 0 6 FeetMD
- 34. Summary • Drilling Systems Automation (DSA) – The process of creating a borehole with systems and sub-systems, – That are computer controlled, and which – Lead to reduced human intervention • Impact is across the entire organization and drilling process: Drill Bit to Enterprise 34
- 35. Conclusions While drilling equipment is highly automated, the drilling system/process is poorly automated. Drilling systems automation is technically challenging, requires a holistic approach, and is rewarding with significant business drivers: Performance, Safety and Cost 35
- 36. Further Reading • SPE 166263, “Drilling Systems Automation: Current State, Initiatives and Potential Impact”, SPE ATCE, October 2013 • SPE 173010, “Drilling Systems Automation Roadmap - The Means to Accelerate Adoption”, SPE/IADC Drilling Conference, London, March 2015 36
- 37. Drilling Systems Automation Sources of Information 37 connect.spe.org/dsats/home/ connect.spe.org/dsaroadmap/home/ www.iadc.org/dsaroadmap www.iadc.org/advanced-rig-technology- committee/#access
- 38. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 38 Your Feedback is Important Enter your section in the DL Evaluation Contest by completing the evaluation form for this presentation Visit SPE.org/dl
Editor's Notes
- Before I start let me acknowledge the role played by the SPE Foundation and my employer in making this presentation possible. We also wish to acknowledge the support of the American Institute of Mining, Metallurgical and Petroleum Engineers.
- I will be talking on drilling systems automation – what has been done and what is being done in the industry. But most important I wish to communicate why it is important. If you come away willing to read the literature that I give at the end then I will have succeeded. If you come away and join one of the industry collaboration groups and start to contribute then I will be truly happy. I am extremely gracious to the Daqing section of the SPE for inviting me to present, and giving me the opportunity to visit this great country.
- Current state-of-the-art is that we have isolated sites of automation: downhole in the MWD tools, on the surface at the rigsite, and remote in the data center. Each of these is discrete and is at a different level of automation. The objective of “Drilling Systems Automation” is to bridge these islands of drilling automation by creating an “automated drilling system” which delivers many productivity and safety benefits to all oilfield companies. There are many groups working to create the correct environment for drilling systems automation. But more about all this later, and let’s work on a few definitions first.
- Endsley and Kaber define levels of automation by function: monitoring, advice generation, selecting (decision making) and implementation (control). H corresponds to Human involvement in the function, while C corresponds to computer involvement. Note that each function, for example monitoring, is present over a wide range in levels of automation. However monitoring is the predominant computer function at lower levels of automation, and the progression is monitoring to advice to decision to control with increasing levels of automation.
- If we look at an offshore floating rig we see that there are many automated components. Each of these is quite sophisticated – the level of automation is high. But these are isolated components. In drilling systems automation we are interested in systems automation, which is the automation of the drilling process.
- The state of the art in drilling automation is actually quite impressive. For human and computer monitoring, both wellsite-based and remote systems are readily available, are quite advanced, and are being work on daily. For computer based advisory systems, a great deal of effort has been expended in interpreting data downhole and transmitting compressed diagnostics to surface. Directional drilling advisors (geosteering for example) are often executed remotely or at the wellsite, and control systems (auto-drillers are one example) are present at the wellsite. However, the highest degree of automation are downhole autonomous or semi-autonomous drilling systems, such as rotary steerable systems, which compare favorably with anything developed in any industry, especially considering their operating environment (high pressure, high temperature, high vibration). The high degree of downhole automation is needed because there is (has been) a digital restriction between downhole and surface. With high bandwidth telemetry such as high-speed mud pulse and wired pipe, this barrier may be overcome in the near future.
- So to summarize, why are we so rudimentary in drilling systems automation? We have already seen one reason, restriction of the bandwidth available for digital communication. However, technically that barrier can be overcome, so it is worthwhile looking at the business drivers: are there business drivers for drilling systems automation?
- The first driver is well complexity. The complexity of wells that we can drill has increased tremendously over the last couple of decades. This example shows a well drilled to tap reserves that lie directly under a drilling location. This profile is only possible with advanced steering technologies and sophisticated analysis and control systems. Systems automation makes feasible the control of such complex profiles by mitigating risk (such as steering risk and wellbore collision) while allowing operators to control and predict cost.
- Another driver is well overload. We ask our drilling personnel to monitor and respond to an abundance of information, while maintaining focus on safety. Systems automation allows for monitoring and responding to many data and information sources, reducing the real-time data load on the driller, and allowing him to handle those which are more critical. As a side note this is “shared autonomy” – the computer-based systems are aides to the driller. It is technically a challenging area to develop. It is far simpler to develop systems that are wholly computer controlled, which are termed autonomous systems.
- On to the third driver, which in reduction in Non-Productive Time (NPT). Much has been said about NPT, but basically systems automation allow us to address NPT by being repetitive and predictable. Each task is repeated in the same way, each time, while maintaining the safety of the well bore and physical equipment. For example, the tripping speed is maximized by taking into consideration the swab and surge pressures in the wellbore and the fracture and pore pressure gradients; pumps are brought up at the correct rate to break mud gel strength without generating excessive pressure surges.
- Another business driver is well manufacturing – the repetitive drilling of many wellbores with the same or similar profiles. This is the practice in drilling wells in the US Land environment in shale plays (“unconventional plays”).
- Another three drivers. Systems automation constructs (the digital backbone) make it possible to bring expert resources no matter where they are located in the world to solve drilling issues. In the mining industry these resources are termed “centers of excellence” to differentiate them from remote monitoring centers. Systems automation also helps transfer knowledge, or develop a repository of knowledge, as skilled employees leave the industry. The mode of knowledge capture varies from physics-based models and their use while drilling a well, through electronic procedures, to data-driven analytics techniques that can be applied in real-time, as the well is drilled, such as deep learning. Finally in this list (which is not exhaustive), and most important, is HS&E. Drilling systems automation directly moves remove people from red zones. The red zone can be the rig floor or it can be the wellsite itself.
- So there are many significant business drivers behind drilling systems automation – why then has it not already happened? The issue is that we have to communicate the technology to the business - we have to let the business see what is possible and how to implement it and how to innovate and compete in the drilling automation space. And we have to realize that drilling systems automation is not technically trivial. It helps to place drilling systems automation in context with other systems automation products in other industries.
- This slide is from Dr Mitch Pryor of the University of Texas at Austin, who graciously allowed me to use it. On the horizontal axis is the necessity of the automated components to have domain knowledge. On the vertical axis is a measure of task uncertainty – how difficult is it to automate the task. The first view is of robots in a car assembly line. The robots do not need to have knowledge of what they are assembling, and there is little uncertainty in the task – a hole must be drilled at a price location in metal. The second view is of robots in use in an operating room. While task uncertainty is high the robots have no knowledge they are operating on a human – indeed there is a bank of human “robo-surgeons” controlling the robots The third view is of mining and other systems – here domain expertise in robots becomes more critical. For example in mining the shot holes are drilled by a completely autonomous drilling rig which can manufacture repetitively against plan, and can monitor loading of the holes depending on lithology. There is also a large digital backbone for logistics in moving the ore from the workface to processing plant. The fourth view is of a robot for handling nuclear materials. Here it is important that the robot know what it is handling – if it is handling a “hot” item then it may not be able to move it through a barrier. The last view is drilling systems automation. The task is technically complex, one of the most complex around. We drill holes in the ground, often with very limited information, and attempt to control the progress from very far away, similar to being on the end of a piece of spaghetti. This task complexity needs to be communicated to the business so that expectations are managed – but the same task complexity opens a tremendous opportunity for collaboration and competition, and we will now take a look at how collaboration is developing standards that allow drilling systems automation to be realized.
- The first collaborative construct we will look at is the “Drilling Systems Automation Decision Making and Control Framework”. This is modified from ISA 95, which is the automation standard for manufacturing (and other) industries. The lowest level represents the physical process – it has no intelligent sensing. The overlying layers extend from the wellsite into remote systems to the enterprise. Connectivity between these layers relies on a digital protocol and language. These have been selected as OPC UA for deterministic high-speed communication, and WITSML, the industry standard for communicating data in a non-deterministic fashion. The mapping between these two protocols is currently underway, with the semantic model for WITSML 2.0 being merged or placed within the semantic model for OPC UA. Once this is finished, it will be possible to move seamlessly between the two protocols depending on need.
- DSATS stands for drilling systems automation technical section, which is an SPE body of about 1,600 individuals who are collaborating on the guidelines required for systems automation of the drilling process. One of their first moves was to develop the communication layout for control of a drilling rig, so that service companies, equipment providers, and others, could all “play” within the automation space.
- The early main deliverable from DSATS was the recommendation to use OPC UA as the secure digital protocol, in line with other industries. OPC UA has existing bridges or adapters to other standards, and use of the protocol allows us to develop machine independent software, and to standardize on various aspects. Once the protocol is in place, the next development step is to focus on DATA
- The goal is to allow data to flow bi-directionally between levels in the DSA Decision Making and Control Framework. This links machines with models and controls in real-time.
- Data is the most important element in drilling systems automation, and it must be reliable. There are data issues that the industry is working through, such as data ownership. Contracts typically place data ownership with the operator (he who pays for it owns it). However, ownership also means responsibility for data quality, and by paying for it one does not ensure data quality, so it is likely that data providers and compilers need to take responsibility or ownership of data. At the moment there is confusion in contracts between the terms ownership and confidentiality that needs to be resolved in a collaborative environment.
- Shown here is a data-centric view of drilling systems automation. This is actually a product of the drilling systems automation roadmap (DSA-R) that is being developed as an Joint Industry Project (JIP). The main components of drilling systems automation are shown numbered. The sensors and instrumentation and measurement systems (IMS) is expanded to give an idea of the criticality of data in the DSA construct. Basically sensors and IMS relate to machines and equipment, communications, and certification and standards. The product of data drives control systems and simulation and modeling. If you like, data is the blood of automation. The rules shown (completeness, logic, proximity, and so on) were developed primarily by Dr Eric Cayeux to describe the needs of data in an automation environment. They govern, for example, the level of automation that can be achieved by a weight-on-bit measurement at the end of the deadline, in a load pin, in a surface sub, or at the drill bit.
- This slide is to reinforce the value of data in a systems engineering construct, which is essentially a nested series of feedback loops. So data gathered is monitored and feedback to control performance. In a higher loop data may by analyzed as it flows, and then feedback through advisory systems to the control task and achieve an even better level of performance. This series of nested feedback loops adds value to the drilling process. Without flow of data there is no incremental value achieved and the system is constrained by silos within and between levels
- Don’t expect to be able to read this slide. This is from a recent paper by Dr Fionn Iversen of International Research Institute of Stavanger and it shows what happens at level zero in the physical process, but more importantly the shared autonomy between human and computer systems at higher levels. Please read the reference (not just because I am a co-author) but because it illustrates the overall concept of what we are achieving in the drilling industry, and how “human-factors” play with computer systems in this “shared autonomy”
- In the early 2000’s there was research being performed on drilling control systems. In particular the use of neural networks to function as drilling control systems. This construct shows the various data flow paths and the role latency and decimation of data play in the construct, depending on if the data is from surface or downhole, and if the system is used as an advisor or to directly control the equipment.
- The results with this concept were excellent, as shown here. It was possible to predict, for example, rate-of-penetration based on training sets of data. The prediction being done here is ahead of the drill-bit – in other words what the next value of ROP will be. Unfortunately, the down side is that such accurate prediction depends on data quality – how well does the training set define the control space. This level of data quality is difficult to achieve as drilling plans are tightly focused on offset recommended parameters. However, this approach does show the viability for neural nets and analytics. Similar techniques, on a new scale, are being practiced in “deep learning” methods by IBM and others in other industries.
- So turning away from data – driven systems, such as analytics and neural nets, anther approach has been the use of physics-based models. In this case, the models are calibrated in real-time, and used to predict the likely value of a measurement – this prediction is termed a virtual sensor. Any deviation between the predicted and measured value may indicate an issue, which is then handled by an alarm, advising the operator, taking control, and so on.
- This example shows the use of the physics-based model to predict a safe hookload window, deviation outside it results in an alarm - but by feedback it also places a control on the amount of pull permitted. The horizontal axis is hookload and block position, while the vertical axis is time. This is active modification of constraints in automation. The rig can pull as fast as it is physically capable of, but the automation system modifies the constraints to prevent the operator from damaging the wellbore or the rig itself.
- This is an example of constraints in pipe running speed being modified to prevent damage to the wellbore, namely by keeping the running speed slow so that the downhole pressure does not drop and allow an influx of fluids. The horizontal axis is pressure scaled in terms of mud weight (ECD – Equivalent Circulating Density), while the vertical axis is depth.
- Sources of further information
- Sources of information
- Please be sure to go to this site and complete the evaluation form.
- This is the plane control loop. The three control loops are now automated in onboard computers. Manuscript: The flight trajectory is usually pre-designed by the airline company or the pilot. During the flight, the pilot modifies the target flight trajectory according to the real-time flight state, which includes the plane position and the wind velocity, etc. Based on the preset trajectory, the pilot figures out the plane’s target attitude, which is the work of the flight trajectory control loop. Should the plane roll to perform a coordinated turn? Or should it pitch the nose up for a climb? The planned plane attitude is realized by the actions of the plane’s rudders, and the plane attitude control loop calculates the needed rudder action. The rudder action commands are then executed by the motors and actuators, which makes the rudder control loop. The rudder actions directly change the aerodynamic shape of the plane and changes the aerodynamic load on the plane. The onboard sensors measure the plane’s position and attitude, and report to the pilot. All these three control loops can be realized in a set of onboard computers to replace the pilot and take over the plane control. The pilots in modern commercial flights can just sit and supervise the autopilot do its job
- Here we can decompose the drilling system. In the third loop, the top drive and drawworks control are now automated. The first two loops are under development and illustrate active R&D areas in drilling systems automation.
- A final example is from managed pressure drilling (MPD). In this example, drilled offshore Myanmar, wired pipe was used. It is capable of about 57,000 bits per second, so it can be used in control systems. In this case pressure was measured by an MWD (measurement while drilling) tool at the bottom of the BHA (bottom hole assembly). The objective was to drill a shallow gas sand with a floating rig. In order to do this it was necessary to maintain bottomhole pressure within a 50 psi window. This could only be done by using a surface backpressure system calibrated with real-time downhole measurements.
- This is a sketch of the systems used in this operation. The point being made here is that there are about 7 companies involved in this one operation, which was executed in 2007. They shared data and developed the system to where this challenging project could be accomplished.
- The x-axis is time, while the vertical axis are various pressures in the system. The one that is most important is the bottomhole pressure that had to be maintained within a 50 psi window, The well was successfully drilled after about 6 weeks training and system development. This is a highly complex system; the latencies, models, data, etc. all had to be known and communicated among parties in real-time. This one example shows what can be achieved by collaboration – at this time (2007) standards were few; but we now have the infrastructure to make this commonplace.