In the consumer industry, breakthrough innovation is transforming the customer experience and creating new revenue opportunities. These breakthroughs are driven by creating a digital replica— or digital twin—of a physical object. This process requires artificial intelligence, data driven machine learning and advanced analytics, as well as technologies already available in the consumer sector, including mobility, virtual and augmented reality, the internet of things, drones and edge computing.
"Using a digital twin, we can virtually try out new ideas and processes and, as a result, make smarter decisions before taking action in the oil field, thereby protecting the health and safety of our workers and the communities where we operate and ensuring compliance"
Take today’s smart home environment, where digitization connects and facilitates communication between our household appliances, entertainment devices and security systems. The same technologies are also revolutionizing the oil and gas industry, where we are deploying a smart ecosystem that is continuously learning from all the connected devices in the field, as well as human expertise. This has enabled companies to deliver operating cost efficiency by “doing more with less,” while transforming the way we interact and manage the oil field.
There are many opportunities for digital twins. The evolution in unconventional drilling has triggered significant growth in the number of facilities, wells, gathering systems, rotating equipment, while spurring the need for continuous, near real-time, monitoring and adjustment of operating conditions. There are other benefits as the industry recruits qualified personnel for the “great crew change.” The growing reliance on a constantly connected/mobile generation is driving the need for technology innovation and use of the digital twin in all facets of oil field operations.
Using a digital twin, we can virtually try out new ideas and processes and, as a result, make smarter decisions before taking action in the oil field, thereby protecting the health and safety of our workers and the communities where we operate and ensuring compliance.
Below are examples of how digital twins are used in the oil field:
• The Production Tech Digital Twin, which uses sensor technologies to monitor equipment uptime and conditions that provide visibility into our operations and reduce “windshield” time for field workers. Field workers can now remotely initiate and schedule work to address abnormal conditions as they receive alerts and direct personnel to a specific location.
• Smart leak detection sensors, along with video-based analytics, monitor conditions for potential leak situations and can help prevent them from occurring. This alleviates the need for field workers to have to visit every site in order to identify and prevent potential leaks. It also improves safety and alerts personnel of potential hazardous situations.
• “Rig-in-a-box” technology at the drilling location streams real-time downhole data during drilling operations and enables edge analytics, resulting in better, faster decisions that reduce non-productive time and improve overall drilling performance.
• Wearable, voice-recognition device technology improves safety and operations maintenance. Taking advantage of this hands-free “remote mentoring,” field personnel can access online equipment manuals or instruction videos using voice commands; follow step-by-step simulations of the needed repairs; and communicate with remote subject matter expects.
Communications infrastructure is an essential component and critical success factor on any digital twin initiative. This is particularly challenging in the oil and gas industry, given that many oil field operations are located in remote areas with limited and, in some places, no access to major communications networks.
The challenges are prioritizing which business process digital twins should be developed that will deliver the most value.To be successful, the oil and gas industry must adopt an agile approach to developing digital twins that allows for a minimum viable product to be delivered to the end users in a timely manner, and establish an iterative approach facilitating the implementation of new learnings to improve the overall experience and results.