Casinos make money. Lots of money and it is simple to understand why. Players under all possible outcomes vs the House are at a disadvantage. The probability that they will lose is greater than the probability that they will win. Always. There is no way (legally) to change the probability of the outcome in the […]
Machine learning in oil and gas
Read through our Masters Series covering real world applications of machine learning in oil & gas companies. Seasoned, experienced veterans present their findings and outcomes with visual delivery.
Analyzing Vista’s Record-Setting Vaca Muerta Wells with Machine Learning
When Vista Oil & Gas turned back on their new Vaca Muerta pad after the Great Coronavirus Shut-In, they were greeted with a pleasant surprise: 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. […]
Making sense of it all: extracting actionable core-data from pXRF using PCA and K-means cluster analysis
Editor’s note: we are excited to present this work, the winner of Novi’s inaugural Student Blog Contest. Many thanks to Esben and the other submissions we received! This blog post highlights features and functionality of an early developmental version of CorePy, a collaborative effort with Toti Larson and other research team members of the Mudrock […]
geoSHAP: estimating rock quality using machine learning
“What do you mean the model doesn’t use **INSERT PET GEOLOGIC VARIABLE HERE**?!” Anyone who’s built and reviewed enough machine learning models will instantly recognize the question above — some geologic variable (could be carbonate %, hydrocarbon pore volume, permeability, anything) is dropped in the feature selection process due to high correlation with another input […]
Water, water everywhere: understanding water production drivers and improving forecasts in the Williston Basin
Editor’s note: after URTeC, this paper was featured in JPT. Click through to learn more. How often does an engineer dash off a simple water forecast, doing something like applying a flat WOR to their oil prediction? It’s easy to ignore water, but unexpectedly high production can damage well economics, or–in the worst cases–force shut-in […]
The Changing Impact of Completions Through Time: URTeC Preview
“Upsizing completions increases production 37.5%*!!!” Sure, but when? Where that asterisk lands will have a huge impact on the returns of any completions decision. If it’s Peak Rate, that might shorten payback. If it’s EUR — now you might have a real valuable well. Machine learning models can be trained to predict a time series […]
Building Unbiased Benchmarks with Machine Learning :: URTeC Preview
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? Simply looking at raw production numbers without the context […]
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?
Involvement in exploring a substantial discovery and bringing it too 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 a play is the motivation for many teams. It can be […]
the power of analogy :: using novi’s contributing 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. Wells are grouped according to geologic and engineering similarity, and an average of that group is used for a prediction. Decision-tree […]
trust me, i’m an objective engineer: uncovering the inherent bias in oil & 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. Recommendations are objective, uncolored by cultural influence, and based on unbiased P50 estimates. If this is your belief, and it must be a belief, please stop reading this post […]
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 […]
playing enough short game to get to the long game :: revisiting the Parsley-Jagged deal at $30 strip
Golf, like drilling oil wells, is a strategy game. When do I go for the green versus laying up and saving par? Sub $30 oil demands a great short game while still preserving upside when it’s time to deploy the long game.
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 algorithms, compute and smart digital oilfield software applications might save the Shale industry
We must lean out to survive this price shock. Novi founder Jon Ludwig provides some historical context and outlines the case for operators and finance professionals serving the industry to adopt digital oilfield technologies now.
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 […]
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?