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.
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!!
First large bankruptcy filing begs the question :: is there hidden value in Whiting’s un-drilled acreage worth bidding on?
When Whiting declared bankruptcy, we put their acreage under the machine learning microscope.
What do production expectations and breakevens look like across their Williston portfolio? Read the answers in this post.
Playing enough short game to get to the long game :: revisiting the Parsley-Jagged deal at $30 strip
When Parsley Energy announced their acquisition of Jagged Peak in October 2019, WTI was trading at $55/bbl. Though the all-stock nature of the transaction protected their downside, Parsley still faced the question: “how can we economically develop the Jagged Peak acreage?”
In this post, we study completions and spacing options to maximize acreage value. Click through to read our analysis.
The confidence game: optimizing completions for XEC DUC wells in the Delaware using model confidence as a proxy for risk
Optimizing DUC inventory is something that many operators unfortunately have to wrestle with after pauses in completions activity.
Machine learning models can help you design the right completions for each well — and Lead-in sentence then understand the risk and uncertainty to prioritize your capital allocation.
DUC, DUC, GOOSE: In a sub-$30 oil environment how can NBL maximize returns with less CAPEX spend in their Delaware asset?
In this post, we analyze Noble’s Delaware DUC inventory during the great coronavirus shut-in. How can smart operators optimize their activity to survive low prices?