Machine learning models that forecast production for PDP or PUD oil and gas wells may increase accuracy, save engineering time, or replace deterministic models in comparison to in-house methods. These are good reasons to switch to machine learning models and, not coincidentally, these are often the focal points of machine learning sales pitches. However, when […]
Predictive Analytics
Ahead of the Curve: Introducing Novi PDP Oil & Gas Forecasting
It all started as an accident. We passed early time production to a “pre-drill” Novi model. When we saw the results, we were stunned — they were that accurate. Machine learning for PDP oil and gas forecasting was the next frontier. What we had discovered is that existing methods were leaving a whole lot of […]
Machine Learning Regional Profiles for Geologic Insights: Mapping Unconventional Production Drivers in the Williston Basin
What drives unconventional oil production? Of course completions, parent-child, and well spacing all play a huge role — but it all starts with the rocks.
We walk through a powerful new way of visualizing machine learning insights — regional profiles.
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.
the power of analogy : using novi’s contributing oil well data to understand machine learning predictions
Whether exploring for oil offshore Brazil or scaling type curves in Lea County, engineers and geologists rely upon the power of analogy to estimate the productivity of a given area or engineering design choice. Tree-based machine learning models can quantify similarity based on what matters: how it contributes to production.
trust me, I’m an objective engineer: uncovering the inherent bias in oil and gas forecasts
Capital allocation decisions made by engineers that work at E&P companies are completely rational and based on unbiased P50 oil and gas forecasts.
If this is your belief, and it must be a belief, don’t read this post!!