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 objective was to be prepared for a total and sudden loss scenario while drilling and coring a challenging well in the Barents sea. A dual-gradient Controlled Mud Level (CML) system with Controlled Mud Cap Drilling (CMCD) mode was installed on the rig to manage minor and/or total losses. Prior to spud of the section, an advanced dynamic simulator with the actual well configuration loaded was used to conduct offline training, and prepare the drilling team and involved service personnel for the operation. Experience from previous wells in the area identified the risk of drilling into karstified carbonate zones with the potential of leading to total and sudden losses. An advanced dynamic simulator was used to reflect the details of the CML system to be used. The rig crew together with the CML operator and other involved service personnel were trained on how to manage a total loss scenario by switching from CML to CMCD mode. All relevant operational procedures were used as a basis for creating training scenarios and operational preparations for the exercises. This paper will briefly present the simulator set-up, the operation/training procedures and results from the training. Feedback from the operation itself will also be described including lesson-learned from utilizing a full-scale dynamic simulator with the actual well loaded during preparation for operation.
A challenging HPHT well was drilled with Managed Pressure Drilling. In the preparation phase, an advanced dynamic drilling simulator was used by the operator in order to test and verify operational procedures as well as the HPHT & MPD well control manual. This drilling simulator is based on advanced dynamic models developed from first principles, and will respond very realistically on the operators actions when "drilling", "circulating", "managing the well control problems" etc; like a "Drilling Flight Simulator". All relevant HPHT effects are modeled, like mud density, compressibility and rheology as function of Pressure and temperature; gas solubility in Oil Based Muds, transient thermal interaction in the well and surroundings etc. An MPD module has been developed in the simulator, allowing testing and training of all relevant procedures and operations related to MPD. Also, the drilling rig characteristics (pumps, surface lines, choke and BOP, MPD toolkit etc.) are modeled according to the real rig specifications. The testing lead to several significant changes to the operator's manual and operational procedures. At the same time it allowed the Engineering team to test and rectify the Drilling program prior to deployment of the operation. The MPD technique was tested in the simulator as well, and the various contingency procedures were assessed. It allowed the rig contractor, operator, and the MPD provider to find the operational routine, communication and best practice prior to start operation offshore. The team's decision trees where reviewed and updated during the simulator training. Since the crews actively had a part in the final revision in the manual, the operator experienced that the crews took an ownership in the making of the manual and the offshore operation. As a resultant effect, the drilling crew and MPD operators rapidly adapted to the MPD procedures agreed upon during training, when operation started. This paper will briefly present the advanced drilling simulator, and focus on the testing and verification of procedures and well control manual. The modifications done will be presented, and the operation itself will also be described, highlighting the benefits of utilizing the drilling simulator prior to operation.
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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.