The oil and gas industry’s wealth of historical data, in up, mid and downstream processes and available resources from large enterprises, makes it ripe for AI applications. Here are five ways in which AI is helping O&G companies get the most from their data and unstructured information, so that they can make major improvements that were difficult to unearth before, whether measured by end user satisfaction, revenue, profits per well or efficiency.

AI’s ability to deal with both structured data and unstructured information are in high relief when it comes to capital planning: By being able to read and process both structured data like standardised forms or excel sheets, and unstructured information like differing P&Ls, maps, billings, land-use records, and contracts, AI can process these inputs into analytics-ready data that can help companies find profits that may have stayed hidden otherwise. According to a Financial Times article, the French gas company Total has partnered with Google Cloud to develop AI technologies to help estimate the energy output of sites, and improve the imaging analysis of both site and rock samples.

“Natural language processing (NLP) based AI can do a great deal when it comes to running more accurate simulations of all sorts.”

NLP can help energy decision-makers tame the multitude of variables involved in considering the purchase of new land, new equipment, or even other companies. These could be oil well information from various locations which uses different terminologies. By using NLP, applications can be trained to read these unstructured inputs and produce structured data for predictive data models to ingest. With better data sources, companies can find out the likely results of their plans, and therefore better judge if the potential gains of new investments outweigh the risks.

Just as AI can help companies figure out where best to spend its money, it can also help them keep more of what they have through smart asset management. For instance, AI platforms might integrate with a company’s fleet to ensure maintenance happens when it needs to, analyse a well’s output to see how it compares to others, or provide data that help improve the ways in which investments are allocated across locations. The company Schneider Electric, for instance, supplies sensors to help oil producers’ pumps run more efficiently and are connected to cloud based analytics dashboards which allows 24/7 monitoring. This reduces costly equipment downtime in the event where servicing is needed.

AI can help in many other ways with managing another kind of resources, its people, whether by teaching safety procedures, general onboarding, or preparing them to take on new jobs or skills. Because such processes are complex, the automation needs to be sophisticated enough to read and understand unstructured documents, in order to automate the process. AI can also help deploy educational resources in a hyperpersonalised way that matches each worker’s style, and the lesson plans can be updated on the fly. In fact, similar methods can be used to educate potential and existing customers and investors about a company’s goals and its future prospects.

When it comes to answering intricate questions, chatbots are increasingly relied on to help clients as well as workers get the information they need. Through NLP and connected microservices, bots are able to understand complicated queries, search connected data repositories and supply precise answers quickly to elaborate questions, saving time and money. For instance, Shell Lubricants has been using an AI-powered chatbot since 2018 to help industrial clients zero in on the exact product that meets the specifications they need. And Shell remains convinced that conversational and connected AI will remain essential to its future plans: in early 2020 the Wall Street Journal reported that the company had budgeted to teach thousands of its petroleum engineers, chemists, geophysicists, and other employees more about AI applications via online education classes.

The oil and gas market’s volatility is putting pressure globally on energy enterprises to improve their efficiencies and business processes. With these rising expectations, organisations must continue to adopt a connected AI approach similar to the examples shared above, where various AI components work together to facilitate complex cognitive work. The result of this approach would improve knowledge workers allocation towards upstream processes for better productivity and returns on investment.

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