In industries where data is key to gaining competitive advantage, artificial intelligence 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 impacting the way the energy business operates.. After taking the role of land manager for the past ten years, Custer has shared how tied to real estate and traditional non-energy 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 data 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 which can vary in length from just two pages to over fifty. 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 task of finding the correct hardcopy lease to begin with.
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 searching processes at Apache but is also 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.
One curious particularity of these leases is that they are often old, with the documents done in handwriting, usually in dated calligraphic and handwritten styles. Some of the later documents were hand type-written. As such there is a lot of variability of legibility, fonts, spacing, and overall document quality. Apache has applied machine learning to complete data organizing and searching processes more effectively and quickly than otherwise would be possible with humans having to read and process each document. Custer notes that there are a variety of document insights that must be considered such as letters, correspondence, or internal memos that are attached to the gas lease itself. The AI-enabled systems allow for significantly improved organization of this additional information due to its ability to classify and categorize the documents. In addition to higher efficiency and effectiveness, using a digitization and ML-based approach here also eliminates the need to store documents on-hand in file cabinets. Instead, these documents, once scanned, can be moved to long-term archival storage for use only when necessary as backup.
The energy is heavily regulated and that this can pose a challenge when attempting technology implementation. Custer however sees AI realistically being applied to the energy industry to many unique use-cases that he envisions for the future. In particular, Custer notes the relative inefficiency of logistics and management of the energy industry. He notes that technological advancements have already been made within the industry and give examples such as seismic imaging that can scan for underground reservoirs and drills that can drill both vertically and horizontally into the ground. Custer recognizes these applications as huge technology advancements within the industry. However, Custer notes that there has been minimal advancement in his domain of land management and business development over the years, referring to how people were apprehensive to begin with but have slowly warmed up to the idea of AI within the energy industry. He adds that this acceptance is particularly prominent when provisions and leases are involved due to its time-saving and file organizing abilities. Apache as well as other energy firms are increasingly welcoming the continual technological advancements enjoying the cost and time-saving benefits.
Custer also elaborates on the time-saving aspect of these AI systems, in particular when applied to a provision known as the ‘consent to assign.’ This provision is involved within a contractual obligation that decides whether ownership can be transferred or not. Custer notes that a consent to assign provision can take hours to review manually while AI-enabled systems can shorten the process to a matter of minutes.
In general, Custer believes that this is just the tip of the iceberg with regards to the ways that AI can dramatically impact the energy industry. He states that there are many possible advancements to be made in the industry that can be learned from other industries such as finance, healthcare, and manufacturing, looking at similar use cases where documents, processes, and data can be effectively leveraged and optimized. Custer notes just how much more efficient data analysis will become and that our ability to extract valuable information from data will only be improved as time goes on.
Credit: Google News