The oil and gas industry has been on a roller coaster ride over the last few years. Falling prices have squeezed cash flows and forced companies to reconsider their exploration and production strategy. Today, oil and gas companies are grappling with a major challenge: How to use technology to better address fluctuations in demand and pricing. While oil and gas companies have been slower to adopt new technologies, these companies also recognize that to compete and win in the digital age they must transform their business models, processes, and IT operations. Benjamin Beberness, vice president and global head of the Oil and Gas Industry Business Unit at SAP, recently spoke on the S.M.A.C. Talk Technology Podcast about the importance of machine learning to the oil and gas industry.
IoT data and machine learning: real-time insights create new business models
Real-time information access across multiple touch points is one of the key needs for oil and gas companies. That’s now being achieved thanks to Internet of Things (IoT) sensor data, which brings unprecedented insight into process performance. Proprietary exploration and extraction data – like real-time drillbit visualization or detailed geological models – represent an oil and gas company’s competitive advantage. This information becomes even more powerful when companies are able to combine this with data from the drilling process and real-world demand fluctuations.
“We want to make sure [oil and gas] employees have the information they need, when and where they need it, on the device they need it,” says Beberness at the beginning of the S.M.A.C. Talk Technology Podcast. “Companies don’t want their employees to have to be going back and forth between the office and the field.”
But once employees have access to this data, what happens next? The data must be translated into actionable insights. This is where artificial intelligence (AI) and machine learning are making a difference.
When oil prices were high, many oil and gas companies followed a “production at all costs” approach. AI tools help upstream oil and gas companies to shift away from this business model and make maximum use of all assets. When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. Companies can integrate data collected across all production touch points into a single platform for analysis and forecasting.
By leveraging the power of AI and machine learning, companies can perform precise profit and loss calculations, optimizing production cost per barrel based on ever-changing factors. For example, a company could tie predictive information gained from data visualization with current market demand and financial reports to strategically plan its next drilling operation. Companies can shift their business models to “produce in context” rather than “produce at all costs.” According to Beberness, this business model shift makes it easier for oil and gas to deal with the “new norm” of low-cost oil.
“[Oil and gas] companies are looking for that agility to leverage technology, to help them balance that out and be able to deal with the ebbs and flows,” says Beberness. “Historically, there’s been ebbs and flows, and this time we’re kind of stuck in an ebb.”
AI algorithms can help oil and gas companies better navigate current and future market ebbs, like a consumer shift towards electric cars or autonomous vehicles. McKinsey predicts that the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. For example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease the number of dry wellheads by 10%. Predictive maintenance may increase asset uptime by up to five percent.
New opportunities, but cybersecurity concerns persist
In the digital economy, a company’s proprietary information must be secured from cybersecurity threats. This is especially true for companies in the oil and gas industry that increasingly rely on a vast network of sensor data to make strategic business decisions. As oil and gas companies consider solution partners for their IoT and machine learning needs, they must ensure that the companies they choose offer the most robust cybersecurity possible, says Beberness.
The digital energy revolution is creating new products, new business models, and new ways of working. For more information on how the Internet of Things and machine learning are disrupting the oil and gas industry, listen to Benjamin Beberness on the S.M.A.C. Talk Technology Podcast.