Oil and gas companies are increasingly honing their use of artificial intelligence (AI) in their day-to-day operations to keep costs low and to retain vital information.
Last month, for example, Fluor Corp. reported its use of AI-based systems to “predict, monitor, and measure the status of engineering, procurement, fabrication, and construction [of] megaprojects from inception to completion.” By using AI and analytic technologies from IBM Watson, Fluor says it is able to deliver “predictive analytics capability” for its megaprojects.
The partnership with IBM Watson, Fluor says, “forms the foundation for big data analytics and diagnostic systems that help predict critical project outcomes and provide early insights into the health of projects.”
Fluor noted, “Large capital projects, especially in the energy and chemicals and mining and metals markets, are incredibly complex with enormous amounts of data, people, and moving parts that are constantly changing and need to be understood to keep a project on schedule and budget.”
Gaining insights
The company says that it has gained insights from project data in nearly real-time and now understands the implications of changing factors through the introduction of the EPC Project Health Diagnostics (EPHD) and Market Dynamics/Spend Analytics (MD/SA) systems.
The company explains, “Developed with IBM Research and IBM Services, working collaboratively with Fluor, these innovative tools help to identify dependencies and provide actionable insights by fusing thousands of data points across the entire life cycle of capital projects.”
The company, it says, “can now leverage a wealth of experience from across its entire historical data store and global workforce to quickly understand markets and monitor project factors impacting cost and schedule to drive improved certainty and cost efficiency across the entire project scope.”
Arvind Krishna, senior vice-president and director of IBM Research, said, “Harnessing the power of data to make meaningful insights will alter how megaprojects around the world are designed, built, and maintained. Together with IBM, Fluor is embracing artificial intelligence as an engine for transformation in data-driven industries that are ripe for innovation.”
Ray Barnard, Fluor’s senior executive vice-president of systems and supply chain, said, “The ability to rapidly analyze and comprehend big data that drives decisions at any point throughout the engineering, procurement, fabrication, and construction of today’s megaprojects is an imperative for the success of our company and the protection of our clients’ capital investments.”
The EPHD and MD/SA system tools assess the status of a project by “predicting issues such as rising costs or schedule delays based on historical trends and patterns, gaining earlier insights from many sets of complex factors across project execution, and identifying the root causes of issues and the potential impacts of changes as input to the decision-making process including estimate analysis, forecast evaluation, project risk assessment and critical path analysis,” says Fluor.
Retaining knowledge
Another example of the use of AI is seen in Australian independent Woodside Energy, which also turned to IBM Watson to aid in the retention of senior experts’ knowledge and make it possible for junior employees to locate, analyze, and learn from them.
“In an industry requiring absolute accuracy, it is critical Woodside’s engineers avoid guesswork. To ensure precision, they rely heavily on historical context and procedural information. Unfortunately, every time an expert with years of knowledge and knowhow retires, that experience walks out the door with them,” the company said.
Caitlin Bushell, a graduate process engineer for Woodside, explains, “Once Watson learned the natural language the staff use and what they wanted to know, it could accurately match data with what they needed.
“It’s helped our engineers get up to speed very quickly on what has already been done and how they were managed in the past. We can learn from the past, and there’s no need to reinvent the wheel,” she said.
