Brock Hudson, Managing Director at Carl Marks Advisors, shares his views on the impact AI may have on the future of the tech-forward oil & gas industry.
Current Adoption of AI
One key fact facing the oil and gas industry is that the sector is generating data at a quicker pace than the human mind can meaningfully characterize, catalog, and understand, and that pace is accelerating. The challenge is how can all of this data be assimilated and used? According to the International Energy Agency (IEA), large oil companies generate 1.5 petabytes (1015 bytes) of data every day, yet only 1 percent of the data gathered from oilfields is actually analyzed. A common complaint is that most engineers spend more time searching for, extracting, cleaning and modifying data than actually analyzing it.
Artificial intelligence and machine learning could be helpful to integrate under-utilized data from separate and unstructured data streams into digestible statistics which better correlate events and causalities. Given AI’s ability to automate and optimize data-rich processes, it has the potential to mitigate risk, enhance productivity, remove redundancy and reduce operational costs. As such, artificial intelligence is expected to have a continuing technological impact on the oil and gas industry over the coming years.
Most industry experts agree that the rapid analysis afforded by artificial intelligence could result in faster optimized decisions, elevated operating times, decreased delays, enhanced failure prediction, increased reserve recoveries and enhanced safety analysis and accident prevention. However, there has been a reluctance to implement much of this technology — in part due to the aging demographic of decision-makers in the industry, many of whom have lagging technological skills and who fail to fully understand the business proposition of big data, or due to dated business practices or siloed operations.
Even though technological applications can effectively cut costs, there is an upfront strategic decision, as well as investment, that must be made to fully assimilate a new technology, such as AI, into existing organizational and decision-making processes. This investment requires the integration of new personnel — historically not considered part of the energy talent pool — into the work stream. For example, data scientists and programmers must be brought on to structure the data and create algorithms and outputs that will provide comprehensive insights to the data.
It is often easier for companies to be on the bleeding edge of technology rather than the cutting edge, and there will probably be reluctance to widespread adoption of artificial intelligence until a new generation of leaders who are more comfortable with big data emerge. A few success stories exhibited by trail blazing industry participants and the emergence of industry standards will help hasten adoption.