Novi is excited to be exhibiting at BOOTH 3011! Click for details.
Conference Presentations
THREE Novi papers recognized as Best of URTeC 2020!
We are excited to announce that three of our papers have been recognized as Best of URTeC 2020! At Novi, our team pushes the industry forward every day with advances in machine learning for oil and gas developments, and we are thrilled to see the hard work of the team recognized. Click through for the […]
Novi Attends URTeC 2021! Click for presentation schedule & booth details.
Novi is excited to be exhibiting at BOOTH 4601 and presenting five papers! Click through for the full schedule.
Novi geoSHAP – estimating rock quality using SHAP values in machine learning models: URTeC 2020 Novi Paper Summary
Can a machine learning model learn where the play sweet spots are, just from raw well logs? The answer is YES. Using logs (or any input geo variables), plus a principal components analysis, gives the model everything it needs to learn what drives production.
This is the subject of our most requested URTeC 2020 paper — geoSHAP: A Novel Method of Deriving Rock Quality Index from Machine Learning Models and Principal Components Analysis. Click through to learn more!
Water, water everywhere: oil well water analysis with machine learning models to improve produced water forecasts in the Williston Basin: URTeC 2020 Novi Paper Summary
How often does an engineer dash off a simple produced water analysis, doing something like applying a flat WOR to their oil prediction? It’s easy to ignore water, but unexpectedly high production, leading to more produced water, can damage well economics. In the worst cases, it can force shut-in if disposal capacity is full.
Fortunately, advanced machine learning methods developed for oil can be applied to water. These technique help disentangle the complex interactions of completions, geology, and spacing.
This is the subject of our URTeC 2020 (and JPT-featured) paper. Read the summary here.
The Changing Impact of Oil Well Design Completions Through Time: URTeC 2020 Novi Paper Summary
Using a single scaling factor instead of a time series can ruin your well economics. An upsized completion might increase your production 20%, but knowing whether that applies to peak rate or EUR can have a huge impact on your well economics.
Machine learning models can predict a time series of production. This means you can evaluate the impact of completions design over the life of a well. Read the URTeC paper summary here.
Building Unbiased Benchmarks with Machine Learning Oil and Gas Modeling: URTeC 2020 Novi Paper Summary
Machine learning can help you build unbiased benchmarks for operator performance! Operators, financial services, and investors will all appreciate this one.