Novi Labs

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.

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. Understanding the impact and relative importance of the different geologic variables requires REGIONAL PERSPECTIVE. Only then do you have sufficient variation in the underlying properties to learn their effects. The […]

Investing in Shale Oil and Gas Wells: Are you the House or the Player?

We want to invest in shale oil and gas wells that operate like a casino. Why? Casinos make lots of money based on a simple concept. Players under all possible outcomes vs the House are at a disadvantage. The probability that they will lose is always greater than the probability that they will win. Casino […]

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 […]

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

“What do you mean the model doesn’t use **INSERT PET GEOLOGIC VARIABLE HERE**?!” Anyone who’s built and reviewed enough machine learning models and SHAP values will instantly recognize the question above. The algorithm dropped some geologic variable in the feature selection process due to high correlation with another input geologic variable, and someone in the […]

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 oil well 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. […]

The Changing Impact of Oil Well Design Completions Through Time: URTeC 2020 Novi Paper Summary

“Upsizing well completions increase production 37.5%*!!!” Sure, but when? Where that asterisk lands will have a huge impact on the returns of oil well design completions. If it’s Peak Rate, that might shorten payback. If it’s EUR — now you might have a real valuable well. Oil and gas machine learning models can be trained […]

Building Unbiased Benchmarks with Machine Learning Oil and Gas Modeling: URTeC 2020 Novi Paper Summary

90% of American drivers say they’re better than average, and 90% of Shale focused Operators have “peer-leading” breakevens, returns and well production forecasts. Suuuure they do! How do you cut through Investor Relations fluff to identify top-performing operators to learn from, or underperformers to acquire? We will use machine learning to build unbiased benchmarks for […]

how does a 3 billion barrel discovery turn into a $3 billion impairment in oil and gas plays?

Involvement in exploring a substantial discovery and bringing it to development is an exciting opportunity. There is nothing more valuable to a company than taking an unknown prospect, acquiring the land for low cost, and turning it into a cash generating machine. Finding such an oil and gas play is the motivation for many teams. […]

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. We use geologic and engineering similarities to group oil and gas wells, then we use an average of that group to […]

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 unimpacted by their own experiences or personal exposure to the play. Oil well forecasting & completions design recommendations are objective, uncolored by cultural influence, and based on unbiased P50 estimates. If this is your belief, and it must be a […]

first large bankruptcy filing begs the question :: is there hidden value in Whiting’s un-drilled acreage worth bidding on?

Earlier this month, Whiting Petroleum (NYSE: WLL) declared bankruptcy, becoming the first high-profile E&P casualty in the Coronavirus downturn. While it’s unclear how exactly the Chapter 11 process will turn out, we’ve taken the opportunity to dig through their inventory looking for potential bargains using our machine learning software. We calculated breakeven prices for their […]

the confidence game: optimizing completions for XEC DUC wells in the Delaware using model confidence as a proxy for risk

Introduction: Problem we are trying to solve; methodology we are applying to solve it We have written a couple of posts about optimizing completions on DUC (Drilled but UnCompleted) well inventory given the extreme constraints on capital that Operators are facing. In our first post, we focused on QEP’s inventory in the Midland, and then […]

DUC, DUC, GOOSE: In a sub-$30 oil environment how can NBL maximize returns with less CAPEX spend in their Delaware asset?

Given the extreme capital constraints imposed by a lower commodity price environment, many operators have announced CAPEX reductions and significant rig layoffs. Given these reductions, it would make sense to apply what capital remains to complete through DUC (Drilled, but UnCompleted) wells. If this is true, it would also make sense to revisit the completion […]

change of plans: how QEP could optimize returns in their Midland asset in a low-price environment

Should QEP change its development strategy in this lower price environment? Using our machine learning models and Novi software we analyzed QEP’s Midland Asset development plans to determine the best plan going forward. Based on evaluation of the data, QEP should consider: Downsizing completion designs in primary zones. Discontinuing development of non-core zones. How much […]

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 […]

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