The database evaluates 950 technical papers that have been presented at shale industry conferences from 2018-2020. We have summarised each paper, categorized it by topic, by author, by basin, ‘how digital’ and ‘how economically impactful’ it is.
The aim is to provide an overview of shale R&D, including the cutting edge to improve future resource productivity. We estimate 2020 was the most productivity-enhancing set of technical papers of any year in the database.
Recent areas of innovation include completion design, fracturing fluids, EORand machine learning. We also break down the technical papers, company-by-company, to see which operators and service firms have an edge (chart below).
Completing a shale well depends on over 40 variables. Each one can be optimised using data. It follows that next-generation data could deliver next-generation shale productivity.
This note focuses on the most exciting new data methodology we have seen across the entire shale space: distributed acoustic sensing (DAS) using fiber-optic cables. It has now reached critical mass.
DAS will have six transformational effects on the shale industry. Leading operators and service companies are also assessed.
This data-file quantifies the leading companies in Distributed Acoustic Sensing (DAS), the game-changing technology for enhancing shale and conventional oil industry productivity.
For operators (chart above), our rankings are based on assessing patents, technical papers and discussions with industry-participants.
For Services (chart below), our work summarises the companies, the ownership (e.g., public vs private), their offerings, their size and the technical papers they have filed.
This data-file summarises 25 of the most recent technical papers around the industry, using fiber-optic cables for Distributed Acoustic Sensing (DAS). The technology is hitting critical mass to spur shale productivity upwards.
For each study, our data-file tabulates the company involved, the country of application, the specific purpose and a short summary of findings.
Technical data are also tabulated from some of these papers, including for warm-back analysis, perforation design and cluster flow-allocations.
There is only one way to decarbonise the energy system: leading companies must find economic opportunities in better technologies. No other route can source sufficient capital to re-shape such a vast industry that spends c$2trn per annum. We outline seven game-changing opportunities. Leading energy Majors are already pursuing them in their portfolios, patents and venturing. Others must follow suit.
This data-file decomposes the drivers of shale productivity in Alberta’s Duvernay play, across a correlation-matrix of 23 different variables.
Machine learning can be used to predict 78% of the variance in wells’ performance from this data-set, surpassing the 19-67% predictive power of regression models (chart above). Accordingly, $1M/well savings are suggested, while well productivity can improve by 19-97%.
Shale is a data industry. “Big data” approaches are the only way to capture the complex inter-correlations within shale’s productivity drivers. As shown below, well EURs are meaningfully correlated with 12 variables. The “largest” driver is “proppant placed”, which is itself meaningfully correlated with 16 other variables.
Machine learning is still in its infancy in the shale patch, representing c2% of total industry-research. It presents material upside.
This model assesses the economics of a shale-EOR huff’n’puff project. NPVs and IRRs can be stress-tested as a function of oil prices, gas prices, production-profiles, EUR uplifts and capex costs. Our input assumptions are derived from technical papers. We think that economics are increasingly exciting, as the technology is de-risked. As more gas is stranded in key shale basins, base case IRRs rise from c15% well-level IRRs to c20%.
Is Shale-EOR the next wave of unconventional upside? The topic jumped into the ‘Top 10’ most researched shale themes last year. Stranded in-basin gas will improve the economics. Production per well can rise by 1.5-2x. The theme could add 2.5Mbpd to YE25 output.
This data-file summarises progress using machine learning to maximise production from mature wells by detecting errors and optimising production. The algorithms are getting more accurate.
Methodology. First, we tabulate the accuracy of prior ML studies, touching on initiatives from Equinor, Conoco and Concho. Next, we focus in on an excellent, recent technical paper, achieving 5-10% production uplifts using machine learning to optimise 300 wells at the Bahrain oilfield.
Hence we constructed a simple model for digitising rod pumps: we estimate $100k of NPV can be created through instrumenting a typical rod pump well early in its life.
Global decline rates can be lowered by c100kbpd per annum for over a decade, using these improved algorithms.
This model assesses the production-uplifts and well-level economics of re-fracturing shale wells in the Permian and the Eagle Ford, to improve recovery of previously missed pay. The opportunity is interesting but not quite game-changing.
Economic breakevens are seen at c$45/bbl under our base-case assumptions. The most likely NPV uplift is c$0.5M/well. However higher prices and process-enhancements can unlock $2-3M of NPV10 per well.
Input assumptions are informed by disclosures from Occidental and Devon Energy, the two E&Ps that dominate the technical literature. They are summarised in the ‘notes’ tab. Please download the file to stress-test the assumptions…
