OAG Analytics: Optimizing Wells with Machine Learning

Numerous oil and gas companies that are keen on maximizing their well production are turning to technologies like machine learning and big data analytics to enhance performance, reduce non-productive time, and minimize the total cost of operations. Well planning is one of the most demanding aspects of drill engineering; and for it to be carried out in a productive manner, it is crucial for data to be leveraged and analyzed effectively. The oil and gas industry has an abundance of data, but the methods of analyzing and eliciting meaningful insights from that data to optimize the overall drilling process are fraught with challenges. OAG Analytics, a Texas-based oil and gas-focused software company, has developed a self-service AI and machine learning software—Insights Workflow™—to help create profitable wells and enhance the overall digital well planning process. The SaaS product delivers meaningful insights needed to optimize unconventional wells and enable subsurface professionals like petroleum engineers and geophysicists to configure cross-functional datasets to accurately predict well production and design better wells in a cost-effective manner with higher internal rate of return. “We provide a tool that enables firms to much more accurately optimize the design and profitability of wells,” begins Luther Birdzell, founder and CEO, OAG Analytics.

OAG’s Insights Workflow™ platform increases the efficiency of well planning by streamlining data management and providing transparency and control over the analysis process, enabling users to align the data based on their specific objectives and choose the parameters for analysis accordingly. Transparency and control over the data that is being ingested for analysis and the machine learning algorithms used to evaluate it are some of the capabilities offered by OAG’s platform. “These are two concepts virtually the entire industry is aligned on,” adds Birdzell.