Oil markets: finding the balance?

oil market supply demand balance

Our oil market supply demand balance is informed by a 45-line model, running month-by-month out to 2025. This download contains both the model, and a 4-page summary of our outlook, from mid-2021.


After ten years forecasting oil markets, our humble conclusion is that all oil models are wrong. Some are nevertheless useful. To be most useful, our model takes a Monte Carlo approach to the key uncertainties, to quantify the “risk” of positive and negative surprises (illustrative example below).

Please download the model to see, and to flex our input assumptions. Included with the download is a PDF summary of our oil price thesis in mid-2021,  which is also available separately, linked here.

Important note. The past 2-3 years have been a nightmare for oil market supply demand balance forecasting. We think there is over 3Mbpd of oil demand pent up still to recover in 2022+ from post-COVID. Also, to state the obvious, if there is a major disruption to Russian oil supplies, then oil markets will be under-supplied. And so we do not think we can currently add value by ‘forecasting’ oil markets at all in 2022. We will re-visit this topic in depth in 2023.

Can super-computers lower decline rates?

can computers lower oil production decline rate

Advanced reservoir modelling can stave off production declines at complex offshore assets. This data-file illustrates how, tabulating production estimates based on a technical paper published by Eni, an industry leader in applying high-speed computing power in its upstream operations.


Specifically, the paper simulates an offshore field-cluster in a single, Integrated Asset Model that covers 31 wells, drilled into 3 reservoirs (each is modelled in detail, with a total of 1.9M reservoir cells), 34 pipes, 4 oil platforms and 3 delivery points. Each iteration of this model takes an average of 3.5-hours to run.

Production can be uplifted by 60% according to the simulation, both in terms of EUR and in terms of year 5-7 production rate. 9pp of the uplift is achieved by simple reservoir optimisation. Another 21pp of uplift is achieved by identifying the key bottleneck, and building a new separation & boosting platform to alleviate it. A further 29pp of uplift comes from optimising the development plan for the new platform.

Emerging digital technologies appear to be keeping LT oil-markets better supplied than many expect, with production upside for the industry’s technology-leaders.

Shale: Upgrade to Fiber?

DAS Quest for Idealized Completion

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.

Shale EOR: Container Class

Shale-EOR summary

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.

Machine Learning to Optimise Rod Pumps

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.

U.S. Shale: Winner Takes All?

machine learning on permian seismic

Shale is a ‘tech’ industry. And the technology is improving at a remarkable pace. But Permian technology is improving faster than anywhere else. These are our conclusions after reviewing 300 technical papers from 2018. We address whether the Permian will therefore dominate future supply growth.

Production Losses at a Giant Offshore Oilfield

production losses at an 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.

Where will Permian production peak?

Permian productivity

This model shows how the Permian’s ultimate production plateau will be determined by the rig count, drilling efficiency, well productivity and decline rates. It includes a 10Mbpd scenario where productivity flatlines from here; and our 20Mbpd scenario where productivity continues rising at an 11% CAGR. Economic assumptions are also included to visualise capex and FCF, under different commodity scenarios.

Copyright: Thunder Said Energy, 2019-2023.