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!
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.
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.
