Oil Companies Drive the Energy Transition?

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.

Explaining Shale: Can Machine Learning Capture Complexity?

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.

 

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. 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.

Can technology revive offshore oil?

The appetite to invest in new offshore oil projects has been languishing, due to fears over the energy transition, a preference for share-buybacks, and intensifying competition from short-cycle shale. So 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. If the data can uplift production by 2.5%, then c$0.4M of instrumentation costs would “pay back” (i.e., break even). If the data can uplift production by 10%, it would add +$1M of NPV and +5% IRR per well. These numbers are all shown at $50/bbl, but you can flex the inputs in our model.

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 c55kbpd.