Challenge 2: improving efficiency
On a return on asset (ROA) basis, oil and gas consistently underperforms. On a spectrum that ranges from the high teens (tobacco, railroads, and alcoholic beverages) to the low single digits (health care, consumer discretionary and financial) oil and gas has an ROA of around 7.5%. That’s no surprise. Oil and gas (and other commodity industries with low ROA) companies are traditionally built on tangible assets like drilling rigs, pipes and refineries. Industries with high ROA are built on intangibles like brand and intellectual property.
Oil and gas companies can change that equation. They can bring more data and more sophisticated analysis to bear on their business, leveraging and extracting additional value from their assets — and AI can help.
However, for this to happen, oil and gas companies need to put the data generated by their own assets to work with AI and use it to improve efficiency. This increased efficiency will translate to greater predictability in exploration, accuracy in drilling and completions, efficiency in production, reliability in maintenance, throughout refining, and optimization in transportation and distribution.
For example, on-site chatbots can be used to receive unstructured input from operators in the field when performing maintenance work. The chatbots’ AI can then analyze the issues and offer effective solutions based on an archive of expertise and insight. Other AI systems can monitor drilling and pipelines to identify potential issues, automatically shutting down operations ahead of potentially catastrophic — and costly — failures.
Deployed effectively, AI technologies could have a transformative effect on the industry, ultimately allowing oil and gas organizations to build smaller, more elite teams, that can help deliver value more effectively and efficiently, and position for growth.