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 […]
Machine learning in oil & gas blog
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 typically publish a post every two to three weeks sharing our machine learning models and use cases for clients. Topics range from ML basics to deal evaluations and groundbreaking completions studies. We also feature thought pieces that cover bias, uncertainty and decision making, like this post on the risks of developing big discoveries.
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.
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 […]
What can chess teach us about artificial intelligence in oil and gas?
If, like me, you haven’t seriously played chess since you were young, you may find the advancements in chess artificial intelligence (or chess “engines”) incredible. But can chess teach us about the future of artificial intelligence in oil and gas?
Chess has long been a focus of artificial intelligence research. The game offers both clear rules (to easily measure success) and sufficient complexity (with at least 10^120 possible moves, no computer will ever be able to “solve” the game, unlike checkers). We certainly have complexity in the oilfield – but we also have the ability to measure success in the form of barrels produced, feet of pay encountered, or time to process a seismic volume.
In this post, we’ll walk through the latest AI in oil and gas market developments in chess and how they might impact the oilfield. Read the full post here.
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.
Investing in Shale Oil and Gas Wells: Are you the House or the Player?
Are you the House or the Player?
Industry execGarth Stotts continues his series on bias, probability, and risk in the Shale patch with his latest message: successful investments in shale require an understanding of the REAL probabilities of your opportunities, from inputs all the way to cashflow.
Only then will you be in control — and be able to win repeatedly.
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.
Announcing the Novi Labs Machine Learning in Oil & Gas Blog Student Contest
We know that many current students have had their summer plans upended with internships canceled or delayed. To give those students a chance to still differentiate themselves, we are announcing the Novi Labs Machine Learning in Oil & Gas Blog Student Contest. We will pay $500 each to any posts selected for publication to our […]
how does a 3 billion barrel discovery turn into a $3 billion impairment in oil and gas plays?
Why do operators stay in oil and gas plays long after it’s clear that they are uneconomic?
There’s a big difference between finding Big Tuna and getting it in the boat. You must understand uncertainty and feedback loops to make good decisions. Data-driven models offer one potential solution.
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?
change of plans: how QEP could optimize returns in their Midland asset in a low-price environment
In this post, we analyze QEP’s Midland Basin inventory, studying the impact of a range of completions designs.
five ways smart digital oilfield technology & applications might save the Shale industry
DIGITAL OILFIELD TECHNOLOGY OVERVIEW : Let’s face it, the last two to three weeks have been a complete train smash for shale operators and investors. We are price takers at the end of the day, so we as an industry are all trying to do what we always do – pick ourselves up by the bootstraps and find a way forward. I have some ideas utilizing digital oilfield applications that we can use as a foundation to stand on.
In this post, I’m going to discuss where things are at, Level set on what response vectors the shale industry has taken thus far and much more. Read the full report here.
less than 10% of remaining Bakken inventory is in the play core
With the current oil price downturn, many have started to wonder whether activity in the Bakken will ever return to its previous heights. Using our machine learning models, we estimate that <10% of the remaining inventory is Tier 1, with the real number perhaps <5% due to surface constraints. With lower-tier locations predicted to produce […]
oil and gas well spacing and completions: untangling complex interactions with machine learning
How much of the oil & gas industry’s recent underperformance comes from misapplication of large completions designs with tight spacing configurations?