Digital technologies in the energy transition

Shale Productivity: Our “Top 50” Improvements

Critics still downplay shale productivity. This simple data-file compiles fifty examples of genuine improvements across the industry since 2015. A “one line” summary is provided for each one.

Explaining Shale: Can Machine Learning Capture Complexity?

Machine learning predicts 78% of the variance in shale well productivity, suggesting $1M/well savings and 19-97% resource uplifts. This data-file presents the correlation matrix between 22 inter-related variables which co-vary with well productivity. The complexity requires “big data” approaches. We see upside from Machine Learning in shale.

Machine Learning to Optimise Rod Pumps

This data-file summarises progress using machine learning to maximise production from mature wells, by detecting errors and optimising production. There is potential to lower global decline rates by c100kbpd per annum for over a decade, and increase each well’s NPV by $0.1M.

April 30, 2019

Can technology revive offshore oil?

Can technology revive offshore and deep-water? This note outlines our ‘top twenty’ opportunities. They can double deep-water NPVs, add c4-5% to IRRs and improve oil price break-evens by $15-20/bbl.

Do “digital” completions lift Permian IRRs?

We have modelled the economic uplift of extra digital instrumentation on a typical Permian well. At $50/bbl oil, c$0.4M of extra instrumentation costs, which add 10% to well-productivity, will raise overall NPV by $1M and IRR by 5pp per well.

Production Losses at a Giant Offshore Oilfield

This data-file breaks down the production losses at a giant offshore oilfield, across five categories and ten sub-categories. They are addressable with digital oilfield technologies, as shown by our notes. Advanced algorithms such as BP’s Apex solution, are capable of reducing the losses — particularly in the largest categories. Halving them could increase output by…