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Collaborating Authors
eDrilling
Drilling Parameter Optimization in Real-Time
Mal, Anwesha (eDrilling) | Oedegaard, Sven Inge (eDrilling) | Helgeland, Stig (eDrilling) | Navabi, Josef (eDrilling) | Skogestad, Jan Ole (Sintef) | Bjoekevoll, Knut Steinar (Sintef) | Lien, Morten (Equinor) | Weltzin, Tore (Equinor) | Rudshaug, Bjørn (Equinor)
Abstract There is an increased demand for contactless and/or low touch activities as well as a requirement for most product delivery and services to be such. This paper aims to demonstrate how drilling parameter optimisation in real-time provides a drilling team with an Edge based solution that can continuously improve performance and avoid problems without the need of subject matter experts. Results from testing the Edge-System on multiple wells from several operators are presented related to auto-calibration of real-time prediction models and for optimization of drilling parameters. This system based on cloud technology with Model based reasoning in Artificial Intelligence (AI) is able to give real-time and forward advice for operational parameters, ref SPE-204074-MS The key enabler for such system is "automatic" auto-calibration of models which is used for multiple forward-looking and what-if calculations to find optimal drilling parameters within the well envelope ahead. A simplified configuration has been made so that the rig-team can operate and maintain the system without the need for subject matter experts. "Automatic" Auto-calibration at stable conditions and/or during ramping conditions removes the need for subject matter experts. Such a system helps reduce operational costs as the rig-team can operate the system without the need of back-office support. Comparison will be made to document operational improvements.
Drilling Parameter Optimization in Real-Time
Oedegaard, Sven Inge (eDrilling) | Helgeland, Stig (eDrilling) | Mayani, Maryam Gholami (eDrilling) | Trebler, Andrey (eDrilling) | Bjorkevoll, Knut Steinar (Sintef Industry) | Skogestad, Jan Ole (Sintef Industry) | Lien, Morten (Equinor) | Weltzin, Tore (Equinor) | Rudshaug, Bjørn (Equinor)
Abstract The objective of this paper is to demonstrate how drilling parameter optimization in real-time provides a drilling team with an Edge-system that can continuously improve performance and avoid problems without the need for subject-matter experts. An Edge-system based on cloud technology with Model based reasoning in Artificial Intelligence (AI) is made to give real-time and forward advice for operational parameters, see (Lahlou et al, 2021) for description. The key enabler for such system is "automatic" auto-calibration of models to be used for multiple forward-looking and what-if to find optimal drilling parameters within the well envelope ahead. A simplified configuration has been made so that the rig-team can operate and maintain the system without the need for subject matter experts. "Automatic" Auto-calibration at stable conditions and/or during ramping conditions removes the need for such experts. Results from testing of the Edge-system on multiple wells from several operators will be presented both related to automatic auto-calibration of real-time prediction models and for optimization of drilling parameters. As expected, a major challenge has been to design a calibration algorithm that improves accuracy of calculations without being kicked out by any data quality issues, and without masking upcoming actual anomalies like kicks, losses and issues related to hole cleaning. This challenge has been approached by using a combination of time-delayed robust calibration methods and testing on a comprehensive set of data from diverse operations.
Prediction of Stuck Pipe Incidents Using Models Powered by Deep Learning and Machine Learning
Mal, Anwesha (eDrilling) | Ødegård, Sven Inge (eDrilling) | Helgeland, Stig (eDrilling) | Zulkhifly Sinaga, Samuel (eDrilling) | Svendsen, Morten (eDrilling)
Abstract One of the most common issues faced in the Drilling industry is a Stuck Pipe situation. Stuck Pipes lead to huge losses in cost, energy and productive time. The objective of this paper is to predict stuck pipe incidents prior to their occurrence by harnessing the power of machine learning and deep learning models. Stuck incidents are some of the most difficult and challenging situations. The proposed method uses a two-step model and available historical data from prior drilling operations to predict the occurrence of such an incident well in advance so that it can be avoided. The first step of the model performs time series predictions using a Recurrent Neural Network (RNN) with Walk Forward Validation. The second part of the model uses a Random Forest Classifier to classify the predictions from the previous step and determine if a stuck situation is likely. The classification model is pre-trained using historical drilling operational data collected from old wells. It is possible to determine how far back the model should look at the data and how far ahead it should predict. For the purpose of this paper, the model looks back 5000 timesteps and predicted 3000 timesteps ahead. One timestep is 2 seconds in this case. This model would be able to reduce nonproductive time, and help make drilling operations cleaner, faster and greener. The biggest benefit of such a model is that it can learn on the go and would not require manual intervention. Also, the model can upgrade and modify itself to changes in the drilling operations.
Drilling Advisory for Automatic Drilling Control
Lahlou, Kenza (eDrilling) | Oedegaard, Sven Inge (eDrilling) | Svendsen, Morten (eDrilling) | Weltzin, Tore (Equinor) | Bjørkevoll, Knut Steinar (Sintef) | Rudshaug, Bjørn (Equinor)
Abstract This paper describes a system being developed for providing an optimized real-time decision support with automatic forward-looking and what-if simulations. It will address the challenge of achieving automation, better performance, and avoidance of non-productive time (NPT) in drilling operations. It will additionally address the demanding human support currently required in the entire decision support workflow. The approach includes utilization of Model based reasoning in Artificial Intelligence (AI) with a Digital Twin combined with Machine Learning (ML) and advanced 3D visualization which is a key enabler for operation alerts and optimization. Multiple forward-looking and what-if simulations will also be run in real-time to find optimal parameters for flow, rotation and running speed. A Diagnostic module will detect abnormalities and trigger safeguards. Auto-configuration and auto-calibration will be the key elements for Drilling Advisory system and deployment without the need for back-office support. The personnel involved in the operation (drilling contractor, service provider and operator) will be able to quickly provide the necessary operational input and then the system will be auto-calibrated during the operation. Results will be an Advisory Tool providing the operation with an optimal flow, rotation speed and running speed during Drilling, Tripping, Casing/liner/screen running and cement operations in two applications areas: In front of the driller as an Advisory tool for rigs with legacy drilling control systems not capable of receiving automated instructions. Base for providing direct commands and safeguards to rigs with control systems capable of receiving automated commands of optimal flow, rotation speed and running speed.
Optimizing Drilling Wells and Increasing the Operation Efficiency Using Digital Twin Technology
Mayani, Maryam Gholami (eDrilling) | Baybolov, Timur (GasPromNeft-NTC) | Rommetveit, Rolv (eDrilling) | Ødegaard, Sven Inge (eDrilling) | Koryabkin, Vitaly (GasPromNeft-NTC) | Lakhtionov, Sergey (GasPromNeft-NTC)
Abstract As part of the digital transformation, the oil and gas sector should move beyond the traditional way of drilling, towards utilizing new and more efficient technologies. The objective of this paper is to show how a digital twin based on the virtual model of a drilling well can be used to optimize the operation and improve operational performance. Utilizing digital twins in drilling is a more advanced and cost-effective method to plan, monitor and operate well construction than the traditional method. A Digital twin in drilling is to use advanced down hole data and advanced modeling of the physical drilling system based on thermo-hydraulic and mechanical models during the lifecycle of well construction. It provides several benefits to the operation and improves drilling performance. Various drilling models interact during the whole drilling life cycle. During operations, real-time data from wells are used in combination with modeled data from a digital twin. This can realize early detection of anomalies and offer early diagnostic messages to avoid problems before they fully develop. It helps to reduce non-productive time and increase safety. The Digital Twin technology can be used during the whole drilling operation. Several drilling case studies will be presented using the digital twin to provide real-time ECD control during tripping in and out of the well, as well as back-reaming procedure on some oil fields. Automatic pickup hook load roadmap plotting including; lift, slack and rotation off bottom (ROB), for some oilfields are discussed. The results of the simulation are presented in both 2D and 3D visualization format. By using the digital twin, challenges and risks during the operation have been identified. Automated diagnostic alarms have detected and prevented hazardous incidents ahead of the time. Digital Twin technology will play an important role in the automation process of drilling. The technology will provide automatic quality control and calibration of drilling data, automated forward looking, automated diagnostics and decision support and eventually automatic optimization of the drilling process in real-time. This will be achieved by linking the Digital Twin to the rig control system.
- Europe > Norway (0.29)
- North America > United States > Texas (0.28)
- Asia (0.28)
Dynamic RT Modelling and 3D Visualization of Critical Safety Parameters on Drillfloor During Drilling, Running Liners & Screens and Cementing of Challenging Offshore Wells
Rommetveit, Rolv (eDrilling, now with RSR Holding) | Nabavi, Josef (eDrilling) | Ødegård, Sven Inge (eDrilling) | Gholami Mayani, Maryam (eDrilling) | Syltøy, Svein (Equinor) | Lauve, Marit (Equinor) | Jordal, Ole Henrik (Equinor) | Kolltveit, Tore (Equinor)
Abstract As part of the digitalization and utilization of Automated Monitoring during drilling operations, real-time dynamic modelling of downhole combined with 3D dynamic visualization have been implemented on the drillfloor in offshore rigs. The objectives have been to give the driller instant feedback on the ECD and other effects of the operations and allow for a safer and smoother operation within the limits of the well. The basic elements of this technology are A digital twin of the well with all relevant data and properties included. A set of integrated transient models (hydraulics, surge & swab, displacement, mechanical friction). These models are driven by the RT data from operation and compute critical safety parameters which are presented for the driller. A diagnostic module analyzing differences between measured and modelled parameters and trends. A 3D Virtual Well which visualize the downhole well, the risk matrix, the diagnostics and messages as well as the ECDs at critical positions in the well. The 3D System has been utilized during drilling of several very challenging ERD and Multi-Lateral wells on two platforms in the North Sea. The system was also used during tripping in and out of the well, and during running of casing and liners. During these operations there is no PWD data available, and the modelled ECD values proved especially useful. The Trip-risk log from the Geologist was included in the 3D View during these operations, and the Driller could then see on the 3D when a risk was coming up. This paper will present the experiences from using the dynamic 3D & modelling system on several wells. The feedback from the Drillers and Drilling manager have been positive, and the results are very promising.
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Statfjord Group (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Lunde Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Lista Formation (0.99)
- (17 more...)
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Measurement while drilling (0.88)
- Well Drilling > Pressure Management > Well control (0.68)
- Information Technology > Visualization (0.43)
- Information Technology > Architecture > Real Time Systems (0.35)
Automatic Realtime Monitoring of Drilling Using Digital Twin Technologies Enhance Safety and Reduce Costs
Rommetveit, Rolv (eDrilling) | Gholami Mayani, Maryam (eDrilling) | Nabavi, Josef (eDrilling) | Helgeland, Stig (eDrilling) | Hammer, Raymond (Equinor) | Råen, Jostein (Equinor)
Abstract As part of the digital transformation in oil and gas industry, well construction move toward new efficient methods using digital twins of the wells. This paper will highlight how the drilling operations are monitored, how a digital twin of the well is utilized and how learnings are implemented for future wells. A Digital Twin is a digital copy of assets, systems and processes. A Digital Twin in drilling is an exact digital replica of the physical well during the whole drilling life cycle. Its functionality is based on advanced hydraulic and dynamic models processing in real time. By utilizing real-time data from the well, it enables automatic analysis of data and monitoring of the drilling operation and offer early diagnostic messages to detect early signs of problems or incidents. In the current study various actual operational cases will be presented related to different wells. This includes using digital twin during drilling under challenging circumstances such as conditions when using MPD techniques. Also, various diagnostic messages which gave early signs of problems during running in the hole, pulling out of the hole and drilling will be presented. High restrictions were detected using comparisons of real-time values and transient modelling results. These will be discussed. Different real cases have been studied. Combining digital RT modelled and real-time measured data in combination with predictive diagnostic messages will improve the decision making and result in less non-productive time and more optimal drilling operations.
- Europe > United Kingdom > North Sea > Central North Sea > Ness Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 037 > Drake Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Statfjord Group (0.99)
- (12 more...)
Advanced Real-Time Monitoring Provides Early Detection and Prevention of Costly Well Problems
Mayani, Maryam Gholami (eDrilling) | Rommetveit, Rolv (eDrilling) | Helgeland, Stig (eDrilling) | Oedegaard, Sven Inge (eDrilling) | Kjørstad, Kristian Olaf (Equinor ASA)
Abstract The objective of this paper is to demonstrate how advancedrealtime monitoring (ARM) utilizing advanced hydraulic and mechanical modelling of the drilling process provided early detection of anomalies by giving diagnostic messages during drilling operations. These achievements can minimize non-productive time and invisible lost time and maximize the benefits and value of operations; if they are utilized to its full potential by operations. Some well cases are used to illustrate the methodology and its results. Among problems diagnosed are losses, stuck pipe during drilling and casing running, downhole equipment leakage and improper hole cleaning. In some cases, action was taken based on the diagnostics; and the operational conditions were modified to mitigate the situation. In other cases, the warnings were not taken seriously, the situation worsened until the problem was irreversible and a stuck situation occurred. In one well presented in the current study a stuck pipe situation happened during drilling 8½" section which led to a downtime of more than 20 days. By utilizing the ARM, it couldhave been possible to detect some early signs of the stuck conditions in the wellbore and avoid it. Another stuck situation in awell during 14-inch Casing running, led to downtime of more than 10 days which involved breaking out the casing above the stuck point and performing P&A. The ARM provided early signs of stuck casing that was about to occur, and these signs first started appearing about 15 hours before the pipe got completely stuck. This paper will present the Advanced realtime Monitoring ARM System and the modelling behind this. Also, the plans for further implementation and integration of this in the work processes will be discussed, before results from the first year of utilization will be presented with examples.
- Asia (0.68)
- North America > United States > Texas (0.46)
- Europe > Norway > North Sea > Draupne Formation (0.94)
- Europe > Norway > North Sea > Cromer Knoll Group > Sola Formation (0.94)
Drilling Automated Realtime Monitoring Using Digital Twin
Gholami Mayani, Maryam (eDrilling) | Rommetveit, Rolv (eDrilling) | Oedegaard, Sven Inge (eDrilling) | Svendsen, Morten (eDrilling)
Abstract Having a Digital Twin of the drilling well, pairing digital and physical data combined with predictive analytics and diagnostic messages, improves accuracy in planning and decision making of the drilling operation. It helps the industry to increase safety, improve efficiency and gain the best economic-value-based decision as well as reduce operational cost. Today advanced monitoring is normally done using real-time measurements, compare pre-simulation results with measurements, perform manual diagnostics and run new simulations when abnormalities are seen. All done manually by people. Drilling can move beyond advanced monitoring using Digital Twin's by implementing automatic ‘forward-looking’ and multiple ‘what-if’ simulation to give operations the optimal plan with focus on safety, risk reduction and improved performance. The Digital Twin examples in the current paper can do more advanced and complex automatic forecasting simulations, diagnostics, ‘forward-looking’ and ‘what-if’ simulation as well as predictive analytics in the wellbore in the 2D and 3D simulation view. By using the advanced models (Digital Twin), all relevant challenges and risks were identified during the drilling operations of one well under high pressure high temperature (HPHT) conditions. The stand pipe pressure (SPP), equivalent circulating density (ECD) and temperature behavior were studied during the drilling and circulation of this well. The Digital Twin was also used to evaluate possible losses during 9 7/8″ casing running and cementing with special focus on when casing was passing through the formations. In another well the Digital Twin triggered an early notification regarding high cuttings concentration during drilling 8 ½″ section. The flow rate was adjusted and helped to prevent sidetrack and pack-off due to losses. Morover during drilling 17 ½″ section in another case, large losses were prevented by comparing the modeled active pit calculation and measured tank volume. The Digital Twin enables advanced automatic forecasting simulation, self-diagnostics, automatic ‘forward-looking’, multiple ‘what-if’ simulation and predictive analytics to improve safety, reduce risk, increase drilling performance and reduce costs.
- Well Drilling > Drilling Fluids and Materials > Drilling fluid management & disposal (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Data mining (0.75)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > HP/HT reservoirs (0.70)
- Well Drilling > Drillstring Design > Torque and drag analysis (0.66)
- Information Technology > Data Science > Data Mining (1.00)
- Information Technology > Architecture > Real Time Systems (1.00)
Drilling with Digital Twins
Nadhan, Derek (eDrilling) | Mayani, Maryam Gholami (eDrilling) | Rommetveit, Rolv (eDrilling)
Abstract The industry is undergoing a transition into efficient technologies and it has digitalization written all over it. Digitalization not only should be about data, a fancy software, touchscreens and the internet, it is important that solutions are able to connect within existing work processes and with people for companies to truly lead to more efficient and safer drilling operations. Oil and gas industries are now moving towards using Digital Twin's during the life-cycle of well construction. The concept of Digital Twins was first introduced by Dr. Michael Grieves at the University of Michigan in 2002 through Grieves’ Executive Course on Product Lifecycle Management. Digital Twin is a digital copy of the physical systems and act as a connection between physics and digital world. The digital system gets the real-time data from the mechanical systems which include all functionality and operational status of the physical system. An example from another industry; A Formula 1 team uses data from many sensors used in the car, harnessing data and using algorithms to make projections about what's ahead, and apply complex computer models to relay optimal race strategies back to the driver. Ultimately, to drive faster and safer. By means of the digital twin of the drilling wells during the life cycle of the drilling by combining digital and real-time data together with predictive diagnostic messages there is seen a lot of advantageous in the improvement of accuracy in decision making and results. This again will help the industry to increase safety, improve efficiency and gain the best economic-value-based decision. A Digital Twin driven by real-time data helps to give operations the optimal plan with focus on safety, risk reduction and improved performance. In this paper, the concept will first be explained in creating and utilizing a Digital Twin of your well for drilling and how it will directly influence how Drilling/well engineers, managers and supervisors plan, prepare and monitor their drilling operations and then implement learnings on future wells; for faster and improved decision making with direct relation to predicting and avoiding/mitigating NPT while also optimizing operations along with it. Case examples will be shared, showing value from use of the Digital Twin from first introduced in 2008 up until now where operators around the globe have implemented it for multiple uses in the drilling lifecycle.