Novi Releases Next Generation of AI-Driven Modeling Pipeline, Increasing the Accuracy and Efficiency of PDP Forecasting

Latest product innovation from Novi improves accuracy of wedge forecasting, reserves forecasting, and valuation of acquisitions and divestiture opportunities

AUSTIN, TEXAS (PRWEB) SEPTEMBER 30, 2020

Novi Labs (“Novi”) today announced the release of the next generation of its artificial intelligence-based modeling pipeline. The first application of this patent-pending pipeline integrates a fully automated and highly accurate Proved Developed Producing (PDP) forecasting workflow that is integrated into Novi’s Forecast Engine™. This allows reservoir engineers and financial analysts at oil & gas operators and financial services companies the capability to automate the generation of PDP forecasts with oil and gas AI software, driving improved accuracy of wedge forecasting, reserves forecasting, and valuation of acquisitions and divestiture opportunities.

Novi’s innovative approach to generating PDP forecasts utilizes Novi’s latest AI modeling pipeline to create highly accurate forecasts. Novi has proven the approach while working closely with top tier Operators in multiple oil/gas and gas/condensate shale plays, consistently generating forecasts that are more accurate than existing methods and approaches. The forecasts can be generated in minutes, saving days to weeks of time for engineers, directly driving improved efficiency on one of the most important workflows in Oil & Gas.

Since its inception, Novi’s cloud-based well planning solution has enabled engineering and planning teams to forecast oil, gas, condensate, and water production streams utilizing machine learning-driven pipelines combined with web-based workflow applications. Novi’s core technology is built on machine learning pipelines that leverage a variety of inputs, including Novi proprietary well spacing, stimulation volumes, subsurface characterization, and well production data (among other variables). Novi’s next-generation AI-driven modeling pipeline supports building models trained on data that changes over time such as well spacing, choke, lift, pressure, etc. Novi will continue to evolve these pipelines to help customers better understand the impact of operational intervention on well production, perform counterfactual analysis, automatically classify frac hits, etc.

The forecasts are generated by Novi Forecast Engine™, a self-service software capability that enables the rapid generation of pre-drill and PDP forecasts. Output datasets from Novi Forecast Engine are stored in the Novi Cloud™, which supports direct integration with business intelligence platforms such as Spotfire, Tableau, or PowerBI. Novi Data™ provides insights that support each forecast as part of Novi’s initiative to bring transparency to machine learning and predictive analytics.

“Our new PDP forecasting capabilities utilize patent-pending machine learning algorithms that enable customers to increase operational efficiency and generate higher-quality forecasts in minutes,” said Scott Sherwood, Novi’s CEO. “By increasing accuracy and reducing the manual, time-consuming process of generating PDP forecasts, Novi allows engineering and planning teams to rapidly evaluate A&D opportunities, as well as generate wedge and reserves forecasts in a fraction of the time of current methods,” he said.

About Novi Labs
Novi Labs, Inc. (“Novi”) is the leading developer of artificial intelligence-driven business applications that help the oil & gas industry optimize the economic value of drilling programs. Leveraging cutting-edge data science, Novi delivers intuitive analytics that simplifies complex decisions with actionable data and insights needed to optimize capital allocation. Novi was founded in 2014 and is headquartered in Austin, TX. For more information, please visit https://www.novilabs.com.

Read the press release here: https://www.prweb.com/releases/novi_releases_next_generation_of_ai_driven_modeling_pipeline_increasing_the_accuracy_and_efficiency_of_pdp_forecasting/prweb17434445.htm

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