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Search results for: โ€œshaleโ€

  • US shale: outlook and forecasts?

    US shale: outlook and forecasts?

    This model sets out our US shale production forecasts by basin. It covers the Permian, Bakken and Eagle Ford, as a function of the rig count, drilling productivity, completion rates, well productivity and type curves. US shale likely adds +1Mbpd/year of production growth from 2023-2030, albeit flatlining in 2024, then re-accelerating on higher oil prices?

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  • European shale: an overview?

    European shale: an overview?

    Europe has 15 TCM of technically recoverable shale gas resources. This data-file aims to provide a helpful overview, as we expect exploration to re-accelerate. Ukraine has the best shale in Europe, which may even be a motivation for Russian aggression. Other countries with good potential, held back only by sentiment are Romania, Germany, UK, Bulgaria…

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  • US shale gas: the economics?

    US shale gas: the economics?

    This data-file breaks down the economics of US shale gas, in order to calculate the NPVs, IRRs and gas price breakevens. There is a perception that the US has an infinite supply of gas at $2/mcf, but rising hurdle rates and regulatory risk may require higher prices.

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  • CO2 intensity of shale: breakdown by category?

    CO2 intensity of shale: breakdown by category?

    This model disaggregates the CO2 emissions of producing shale oil, across 14 different contributors: such as materials, drilling, fracturing, supply chain, lifting, processing, methane leaks and flaring. CO2 intensity can be flexed by changing the input assumptions. Our ‘idealized shale’ scenario follows in a separate tab, showing how Permian shale production could become ‘carbon neutral’.

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  • Shale: Upgrade to Fiber?

    Shale: Upgrade to Fiber?

    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.

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  • Explaining Shale: Can Machine Learning Capture Complexity?

    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.

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  • China’s Shale Challenge?

    China’s Shale Challenge?

    This data-file quantifies the most-discussed challenges for developing Chinese shale gas, after a review of the technical literature, as well as the solutions suggested to combat them, and our “top ten conclusions” on Chinese shale.

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  • Chevron: SuperMajor Shale in 2020?

    Chevron: SuperMajor Shale in 2020?

    SuperMajorsโ€™ shale developments are assumed to differ from E&Psโ€™ mainly in their scale and access to capital. Access to superior technologies is rarely discussed. But new evidence is emerging. This note assesses 40 of Chevronโ€™s shale patents from 2019, showing a vast array of data-driven technologies, to optimize every aspect of shale.

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  • US shale: the economics?

    US shale: the economics?

    This model breaks down the economics of US shale, including a granular build-up of capex costs across 18 different categories. Our base case requires a $40/bbl oil price for a 10% IRR at a $7.0M shale well with a 1.0 kboed IP30. Economics range from $35-50/bbl. They are most sensitive to productivity.

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  • Marcellus shale: well by well production database?

    Marcellus shale: well by well production database?

    This large data-file tracks activity, well-by-well, across c11,000 wells in the Pennsylvania Marcellus, month-by-month, from 2015-2021. First tier operators stand out, especially as the basin has consolidated. They achieve higher IP rates and have been able to do more with less.

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