Whether or not generative AI (GenAI) has a place in energy workflows is no longer a debate. GenAI has taken hold across the industry, and it’s not just energy professionals firing up a prompt with ChatGPT, Copilot, Gemini, or Grok.
Chatbots are now embedded where we work. Take a good look the next time you start a blank Word document, Copilot is ready to write your next masterpiece. Or not. As anyone who has used GenAI tools can attest, the results can be surprisingly good but often shockingly wrong, misleading, and inconsistent. That’s called ungrounded content.
Grounding Content
Developers of large language models (LLMs) are struggling to understand and measure ungrounded content when it occurs, much less control it. But it’s not surprising that LLMs that are built to be a jack of all trades are master of none. That is, without specialized and deep training in a specific field, and that requires millions in investment.
For many GenAI innovators, the best way to ground content is by using Resource Augmented Generation (RAG). With RAG, you supplement and restrict chatbot output to a dataset, including structured data sources (e.g., databases) and unstructured data. For energy companies, unstructured data comes in the form of AP invoices, JIBs, leases, contracts, well logs, drilling reports, and hundreds of other data types. The problem is that only a fraction of these documents are RAG ready with digital files stranded inside personal and network drives or trapped on paper inside filing rooms.
While many in the energy industry may be ready for GenAI, their data is not.
With the release of Instant Analyst, Enverus has upped the GenAI game for many in our industry by training an LLM on a vast library of research ranging from market intelligence to basin studies, a type of RAG. Their Courthouse edition of Instant Analyst is particularly intriguing for those involved in running title, providing landmen with concise, accurate abstracts from complex legal records spanning decades to build a run sheet in a fraction of the time. But title is just the tip of the iceberg. What about the vast volume of leases and land documents filling filing rooms at every operator?
The Prize
Unlocking all the paper data at an E&P and making it RAG and GenAI ready would be a game changer not just in terms of productivity gains but risk management too. Sticking with land, if an operator could plug a chatbot into their leases, they could surface insights and mitigate risk without a fire drill, such as understanding what leases could be lost if a well is shut-in or tracking important expirations.
While interest in GenAI may be high, energy companies who want to unlock the full potential should build a solid document management foundation first. For those who do it well, the prize is theirs in the form of increased analytical output and a sharper competitive edge.
Document Solutions
According to Gartner and other leading IT researchers, unstructured data is growing much faster than structured data. As energy companies reach for the benefits of GenAI, effective and agile document management is crucial to success. It’s less about fully going paperless as physical documents will continue to play an important role in many energy workflows. It’s about building digitization into your company’s DNA to continuously capture paper data for easier consumption by energy professionals and GenAI.
The most successful digitization requires a nuanced understanding of the myriad document types found in an oil & gas business. That’s because the goal should be to extract key metadata and unlock the entire contents of a document for human and machine readers, not just scanning and loading an image of the document into a PDF.
Equally important is effective management of enterprise content. This entails breaking free from the limitations of network drives and SharePoint and housing your corporate knowledge where land and well files can be easily accessed and fed into an LLM via APIs. The more structure you can add to unstructured data will force multiply the results, such as classifying each document according to a taxonomy or ontology. The latter goes beyond simple, folder-like document hierarchies by cross referencing and adding semantic context, making each document RAG and GenAI ready.
Energy companies looking to accelerate GenAI across departments are turning to Pepper Energy Partners. As a leader in energy-specific digitization for over ten years, we operate alongside your team to transform filing rooms of paper records and the daily flood of docs into organized, classified, and GenAI ready data. Plus, Pepper can build the right document management solution for your team on leading technologies, including Quorum Dynamic Docs, M-Files, and Egnyte.
Talk with our document management experts today to start your digital transformation journey and unlock the full potential of GenAI.
