Objective of the paper is to describe and present results of using a "Digital Twin" in Drilling Operations (Planning and Engineering, Training and Operational Support) in the last 10 years for Operators worldwide. The concept of Digital Twin was first introduced by Michael Grieves at the University of Michigan in 2003 through Grieves’ Executive Course on Product Lifecycle Management. Winning a Formula 1 race is no longer just about building the fastest car, hiring the bravest driver and praying for luck. These days, when a McLaren technology group races in Monaco or Singapore, it beams data from hundreds of sensors wired in the car to Woking, England. There, analysts study that data and use complex computer models to relay optimal race strategies back to the driver. The McLaren race crew and the online retailers both harness data and use algorithms to make reasonable projections about the future, Parris explains. The concept is called Digital Twin [1]. A Digital Twin contains information such as a piece of equipment or asset, including its physical description, instrumentation, data and history. A Digital Twin can be created for assets ranging from a well to a piece of equipment to an entire oilfield. For example, a subsea system could have a Digital Twin via a simulation model of a subsea system's components, including the blowout preventer, tiebacks, risers, manifolds, umbilical and moorings. Drilling and extracting simulations can determine whether virtual designs can actually be built using the machines available," GE said. "Last but not least, real-time data feeds from sensors in a physical operating asset are now used to know the exact state and condition of an operating-asset product, no matter where it is in the world"[2].

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.

In this work, we study how Shockley-Read-Hall (SRH) recombination via energy levels in the bandgap, caused by defects or impurities, affects the performance of both photo-filled and pre-filled intermediate band solar cells (IBSCs). For a pre-filled cell, the IB is half-filled in equilibrium, while it is empty for the photo-filled cell in equilibrium. The energy level, density, and capture cross-sections of the defects/impurities are varied systematically. We find that the photo-filled cells are, in general, less efficient than pre-filled cells, except when the defect level is between the conduction band and the IB. In that case, for a range of light intensities, the photo-filled cell performs better than the pre-filled. When the defect level is at the same energy as the IB, the efficiency is above 82% of the defect-free case, when less than 50% of the states at the IB lead to SRH recombination. This shows that even if SRH recombination via the IB takes place, high efficiencies can be achieved. We also show that band gap optimization can be used to reduce the SRH recombination.

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.

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.