Engineers and analysts spend 80% to 90% of their time cleaning up hybrid datasets for machine learning models and analytics. Listen in as Novi Technical Advisors Ted Cross and Kiran Sathaye talk about oil and gas industry challenges with an emphasis on managing data quality, cumbersome workflows and spacing calculation complexity. Novi Data Engine addresses […]
Technical Advisor Ted Cross and Data Scientist Kris Darnell talk about why PDP and PUD forecast uncertainty quantification is so critical to making smart business decisions and how easy it is to get it wrong. Listen in as they take a deep dive into our machine learning technology and the data science behind it. For […]
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 […]
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 […]
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.
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.
Machine learning can help you build unbiased benchmarks for operator performance! Operators, financial services, and investors will all appreciate this one.
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.
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!!