Lease Analytics
Maximizing Asset Value with Machine Learning

Lease Analytics LLC, a Dallas, TX-based consulting firm, is leveraging next generation machine learning technology to create a major impact on the oil and gas industry. Tom Agnew, President and CEO of Lease Analytics, is an avid innovator with over three decades of expertise in oil and gas. Agnew developed machine learning (ML) technology to analyze complex, unstructured, error-prone data in the land, revenue and legal areas.

According to Agnew, one of the biggest problems in the oil and gas industry back office is “Data Debt.” Data Debt occurs when companies forgo updating and refreshing asset data as their budgets shrink and their companies restructure. For oil and gas companies, the most important asset data is derived from the millions of complex legal and land documents. This data is unstructured and requires considerable time and manpower to interpret. Assets change hands frequently, and terms and provisions silently kick in. When this data is not maintained, errors and omissions build up, and these errors are then passed on to buyers. As assets are bought and sold, the cycle continues.

Many CIOs have been unable to invest in data cleanup to reduce their Data Debt. This is where Lease Analytics comes in. With advanced ML technology and data mining techniques, Lease Analytics analyzes land and accounting records and identifies errors and omissions. This translates to tens of millions of dollars in found revenue while generating significant savings in time and money.

The firm’s core services, Revenue Recovery and Land Data Cleanup, increase cash flow and improve market value. Both of these services leverage analytics to deliver value to clients–generating an uplift in non-operated revenue and delivering cleansed land records.

We recover one percent of total revenue, which in turn unlocks tens of millions of dollars for our clients

“In addition to uncovering missing data, our technology detects anomalies such as poorly documented or incorrectly mapped assets, or agreement terms that are out of compliance,” states Agnew. “Without our technology, most of the errors and omissions would be cost prohibitive for us to find.”

Agnew points out an instance where one of the top five exploration and production (E&P) companies engaged Lease Analytics for Revenue Recovery. The client recognized the impact of the downturn on their non-operated revenue and chose Lease Analytics’ Revenue Recovery service to recover missing revenue. Lease Analytics mapped, reconciled, and loaded this data into its proprietary data warehouse and executed numerous analytical campaigns—mini data mining projects that look for errors and omissions. As a result, Lease Analytics has discovered and collected millions in lost revenue for this client.

For over a decade, Agnew has been using ML and statistical techniques to identify errors and omissions in both land records and incoming revenue payments for large and small E&P companies.

“On average, we recover one percent of total revenue, which in turn unlocks tens of millions of dollars for our clients,” asserts Agnew. “Our technology enables us to have a 100 percent track record of finding and collecting lost revenue.”