Leveraging artificial intelligence (AI) technology is proving not just worthwhile but also transformative for those working in a multitude of industries. Oil and gas executives are no exception: Using disruptive technology applications like machine learning (ML) and predictive analytics can help industry insiders improve operations and could cut costs by $50 billion in upstream oil and gas activities alone. However, AI’s impact on the industry extends beyond cost savings, offering several potential benefits. It’s vital oil and gas executives understand what’s at stake — and what they stand to gain.
How AI shines
Big data is a core element of AI functionality, going beyond dated information buried in spreadsheets and moving toward up-to-date actionable intelligence. Oil and gas executives can collect and leverage big data in many ways to gain an edge. For example, according to the Engineering360 article, data-driven technology solutions such as wireless networks, remote sensors, and analytics software help industry insiders gather, track, and interpret data to maintain optimal production and efficiency. However, while incredibly valuable, AI goes far beyond smart data.
AI technology really shines in its potential to enhance human worker capabilities, freeing them up for greater strategic thinking and enabling smarter decision-making. Thanks to machine learning capabilities, AI-driven software can take vast amounts of unstructured data, such as seismic activity, and not only predict potential problems but also recommend solutions. In cases where data is incomplete or unreliable, AI-driven systems can use “fuzzy logic” to overcome data information gaps — essentially making sense of inconsistent data, a task that’s likely harder for humans to accomplish with the same accuracy levels. AI technology has the potential to propel the oil and gas industry into the future, reaching many aspects of the oil and gas production pipeline.
Making a business case
So, how are industry insiders employing this technology — and what are some specific use cases? More complex and seemingly popular AI applications in the industry include virtual assistants and intelligent robots. These robots, for example, are “designed with AI capabilities for hydrocarbon exploration and production, to improve productivity and cost-effectiveness while reducing worker risk.” Exxon Mobil Corp. executives are using robots to detect natural seeps on ocean floors, helping to preserve the ecosystem while they discover new resources. Others have introduced AI-driven platforms, called “cognitive workers,” that support operations in the field. These platforms, like IPSoft’s Amelia, help human workers complete tedious, hard-to-replicate, or even dangerous tasks.
Leaders of the AI software company Nervana Systems are harnessing AI to revolutionize oil exploration. They use deep learning to train an “artificial neural network how to find oil and gas using data from geoseismic studies.” Humans give examples using data sets, and the network then learns by example. Those at intelligent software technology company Kpler are geotracking energy vessels and using deep machine learning to model trends, according to the OilPrice.com article.
Oil and gas executives in other companies incorporate digital oil field technologies into their production processes. By setting up IoT field devices that measure different data points, analysts can use AI technology to gather predictive intelligence, which can help to maximize efficiency and safety by pinpointing unsuspected safety problems and predicting potential failures.
While business use cases can highlight specific benefits, what are the overall advantages oil and gas leaders stand to gain? Here are four key ways in which AI technology can positively impact oil and gas operations:
- Improve operational efficiency.
- Employ greater safety measures and tactics.
- Elicit more cost savings.
- Leverage vital predictive intelligence.
The time is now: Industry-specific AI technologies are in place for savvy oil and gas executives looking to gain an edge. The first step to leveraging AI is to harness your company’s data. When starting a data analytics practice, consider master data management (MDM), system integration, and creating a data lake. AI requires smart data; once you’ve cleaned and organized your data, you can begin your journey toward AI-driven analytics, helping you achieve more strategic operations and greater efficiencies.