Read through our posts covering real world applications of machine learning in oil & gas using AI-driven software and data tools. Learn from seasoned, experienced industry veterans as they go into technical topics related to machine learning, artificial intelligence, risk, and digital oilfield.
We welcome guest writers working in the oil and gas industry. They work with oil and gas machine learning models using AI software to solve complex problems and optimize capital. If you would like to contribute a post or have an idea for us, send us a note.
Analyzing Vista’s Record-Setting Vaca Muerta Wells with Oil and Gas Machine Learning Models
After the Great Coronavirus Shut-In, Vista Oil & Gas turned back on their new Vaca Muerta pad. A pleasant surprise greeted them. Their oil and gas machine learning model showed the highest production the play had ever seen. Both the MDM-2063 and MDM-2061 wells produced over 2,000 bbl/d on average for the month, with peak daily rates breaching 3,000 boe/d.
Why did these wells produce so much? Was it a novel completion design, a geologic hot spot, or a heavy tailwind of flush production? Lets explore this oil and gas machine learning use case. We will put these wells under the microscope with our Vaca Muerta public-data model.
Making sense of it all: extracting actionable core-data from pXRF using PCA and K-means cluster analysis
WHY CARE ABOUT PXRF DATA? Thanks to relatively short scanning times and low operating cost, portable X-ray Fluorescence (pXRF) scanning of geologic core samples has become a burgeoning big data in oil and gas industry, with the technique capable of generating high resolution datasets containing comprehensive major and trace element abundances present in the rock.
We will be using Scikit-learn, an open source machine learning library that supports supervised and unsupervised learning towards a variety of applications. Here we will be utilizing principal component analysis and k-means clustering algorithms to manipulate and segment the big data before visualization.
The process outlined in this blog post develops labeled datasets that can be tied to the core, ahead of the implementation of a neural network. Read the full post here.
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.