The oil and gas industry has changed very rapidly in recent years, with new technologies being adopted by the energy sector to meet the challenges of a digital economic landscape. Artificial intelligence is an exciting new technological field when it comes to the oil and gas, but there are significant challenges that the industry needs to overcome before AI can really be incorporated and realise its full potential.
If you look at the oil and gas industry now, what you have is tons of data across the board, but those tend to be very siloed. Individual plants and individual machines generate a lot of data, but it leads almost to what I call a bit of a dichotomy.
- Global Head of Digital Products, Baker Hughes
Below are what we believe, the biggest huddles for AI implementation in the Oil & Gas industry.
#1 Approach
Software industry approach of making fast & iterative (agile) adjustments and bug fixes need to be reconciled with the E&P industry’s need for safety, accuracy, predictability and control
#2 Focus
AI technology providers are often industry agnostic with little E&P knowledge and experience, making it frustrating for both sides to communicate, engage & deliver AI solutions.
#3 Risk-appetite
There is an inherent lack of attention to proactive risk management in agile (big data) projects. E&P companies want to make sure appropriate risk management process is used.
#4 Data handling
Data ownership and a proprietary culture hamper collaboration and the assembly of large data sets. Data must be assembled in the cloud, data centre or air-gapped systems for AI projects to work
#5 Process understanding
Storing data is easier than gaining value from it. Preparation is time-consuming but often overlooked. Machine learning still needs a lot of human input & expertise before delivering useful results
#6 Lack of AI strategy
Many organisations don’t have a mature AI strategy yet. Evaluating AI projects using a structured portfolio consultancy approach maximises the chance of success