Technology - Workflows - Geoscientists - Data

The groundwork in digitalization in oil, gas, and energy has already been done by a few notable industry innovators, but more companies are joining the revolution and have started their own digital transition by venturing into the world of cloud platforms, interactive visualizations, analytics, and predictions by applying the first ML algorithms to their datasets. Richer datasets and new workflows - together with ML and AI - have a massive potential to positively influence exploration and production in oil and gas.

Yet, there are many unresolved problems and challenges, and the next phase within digitalization requires the involvement and ownership of domain experts.

We are told that geoscientists will be empowered by data and machines. But what are the benefits, challenges, pros, and cons, and are there workflows more optimal for empowerment than others?

For the human-machine interface, how do we translate our knowledge and experience into algorithms? For data readiness, what does it take for our data to become machine-readable? The data may currently be easily understandable for a domain expert, but how do we ensure the required level of consistency, quality, and context for algorithms and machine learning?

The digital transition still requires a massive mind-shift in the industry. Going forward, geoscientists will need to acquire new skills, learn to use new tools, and adapt to new workflows. How do we ensure that we are bringing everyone along on this journey? Likewise, geoscientists will need to be involved with data scientists, developers, and programmers to ensure that the AI and ML algorithms are capable of obtaining accurate, low risk – high confidence, results that add value to the E&P workflow.

We want to hear about your experiences navigating this territory, and how your organization is ensuring human involvement and geoscience integration in developing algorithms and applications for the exploration and production domains.”