WoodMac Enters into Partnership with Novi Labs

Research and consultancy firm Wood Mackenzie said it has entered into a strategic data partnership with Novi Labs, aiming to enhance its Lens Lower 48 solution with Novi’s well-level production data and advanced lease-to-well algorithms.

The collaboration provides Wood Mackenzie customers with access to Novi’s proprietary data across a region where over 4.4 million wells generate more than 20 percent of global liquids and gas supply, Wood Mackenzie said in a news release.

“The partnership provides access to Novi’s licensed proprietary oil, gas, and water production data for more than 25,000 wells and advanced lease-to-well algorithms trained on the same well actuals in major supply driving states such as Texas, Oklahoma, and Louisiana,” the company said.

According to the release, Novi’s upstream data, which “provides the most accurate well-level production information available,” will support advanced AI workflows and machine learning applications.

“Novi’s proprietary data and lease-to-well algorithms establish a new standard for production data accuracy not previously achievable,” Novi CEO Scott Sherwood said. Built on thousands of actual well records from operators across every major unconventional basin, our AI-powered technology delivers unmatched production data accuracy for more confident, data-driven investment decisions. Through our partnership with Wood Mackenzie, this data is now accessible to stakeholders across the energy value chain, including energy producers and investors”.

“The Lower 48 represents the world’s most dynamic upstream market, and accurate well detail data is absolutely critical for US analysis that feeds into integrated global modeling,” Joe Midgley, global head of upstream and carbon management at Wood Mackenzie, said. “Our partnership with Novi transforms our ability to serve customers with the most trusted well-level production data available, combined with our industry-leading cost intelligence and global market perspective”.

“Timely and high-quality trusted data underpins how existing operators, new entrants and investors can deliver winning strategies,” Midgley added. “In a market dominated by high-priced incumbent providers, this partnership creates a compelling alternative that combines best-in-class data with flexible, user-friendly analytics for the comprehensive intelligence our customers need to navigate consolidation, identify opportunities, and optimise their portfolios in the world’s largest oil and gas producing region”.

AI-Powered Analysis Shows Trillion-Barrel Opportunity in Existing Fields

Earlier in the month, Wood Mackenzie said its study using new artificial intelligence-powered tools shows that existing fields could yield 470 billion to over 1,000 billion additional barrels through proven recovery methods, which could meet demand through 2050.

The analysis examined over 30,000 fields worldwide using machine learning to identify similar analogs across more than 60 parameters using proprietary data on reservoir geology, hydrocarbon quality, in-place resources, operator access to finance and technology, costs and fiscal terms, Wood Mackenzie said in an earlier news release.

The research reveals that national oil companies (NOCs) and state-controlled enterprises operate fields containing more than 320 billion barrels of upside potential if the top-quartile recovery factor is achieved and 700 billion barrels on a best-in-class recovery basis.

Iran, Venezuela, Iraq, and Russia were found to have the largest recovery upside potential globally. Major international oil companies, despite operating above-average quality fields, control only 6 percent of global upside potential due to their already strong performance levels, according to the release.

Upside potential lies almost exclusively within onshore and shallow offshore fields, accounting for 63 percent and 31 percent of best-in-class opportunities, respectively, Wood Mackenzie said.

“Improved recovery factors will be essential to meeting future demand for oil as there are unlikely to be anywhere near enough new fields to offset the natural decline of existing supply,” Andrew Latham, SVP for energy research at Wood Mackenzie, said. “Using AI to help understand the potential of existing fields and gauge how much the world will need new fields in [the] future represents a significant advance from traditional field analogue methods based on filtering that produce biased results”.

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