In industries where data is key to gaining competitive advantage, artificial intelligence (AI) and machine learning have become necessities. This is most definitely the case in the oil and gas industries that ebb and flow over time as market demand waxes and wanes for critical resources we’ve come to depend on.
In a recent AI Today podcast episode, Tim Custer, senior vice president of North America land, business development, and real estate with Apache, a major energy firm, shares how AI is affecting the way the energy business operates. After taking the role of land manager for the past 10 years, Custer has shared how tied to real estate and traditional nonenergy businesses the oil and gas sector is and the role that machine learning and AI is playing to greatly change the way that the energy industry deals with documents.
According to Custer, AI and machine learning are extracting valuable insights from unstructured data. The oil and gas industry is particularly dependent on an intricate set of processes and document-centric needs for land leases. Gas leases are vital to the energy industry as they determine legal rights and claims to an oil or gas deposit while regulating the trade and extraction of those resources. At Apache, Custer notes they have around 60,000 paper and document-centric leases that can vary in length from just two pages to more than 50 pages. Moreover, there are provisions contained on each page that must be located and interpreted every time an inquiry is made on a lease. This task can prove quite laborious with the added step of finding the correct hardcopy lease, which isn’t always easy to locate.
The first step to wrangling control of these leases is to digitize the documents so that machines can understand them. Apache has succeeded in digitizing the majority of their gas leases using optical character recognition (OCR) and natural language processing (NLP). They are capable of searching through these documents for not only the required lease but the provision within it in a matter of seconds. This not only speeds up the searching processes at Apache but also provides huge time-saving for teams in need of specific provisions for their projects.
Custer continues by describing the optimization of the process as grouping provisions with like wording across vast amounts of data. These digitization and NLP systems ensure higher data integrity by increasing accuracy and removing human interpretation.