Reservoir
The US Department of Energy and Pennsylvania Department of Environmental Protection will convert a horizontal Utica Shale gas well into an enhanced geothermal system. Building directly on horizontal drilling and completion practices developed in the Utica, the project will include evaluation of optimal well orientation, lateral placement, and spacing.
An asset swap with PDVSA helps consolidate the supermajor’s heavy-oil operations in the country.
Co-owner Chevron confirmed the find at the Bandit prospect offshore Louisiana and suggested it may become a subsea tieback to existing faciltiies operated by Occidental Petroleum.
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The paper describes a method to match reaction kinetics from coreflooding experiments. -
For those with an oil company to sell, an oil price of $100/bbl is not what it used to be.
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Researchers from Skoltech have trained a neural network to recognize rock samples in core box images efficiently. The process has sped up analysis by up to 20 times and made it possible to automate the description of rock samples.
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Chevron assets in Texas and Colorado have earned high marks for control of methane emissions under a pilot program with Project Canary, paving the way for its sale of responsibly sourced gas.
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The paper investigates estimation of optimal design variables that maximize net present value for life-cycle production optimization during a single-well CO2 huff ‘n’ puff process in unconventional oil reservoirs.
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Unconventional resource exploitation has three key technology and application focus areas—increasing implementation of fracture diagnostics, enhancing production and recovery from new well completions as well as existing wells through improved stimulation and restimulation design and execution quality, and developing enhanced oil recovery methods.
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The paper presents key lessons learned in efficiently designing pressure-buildup tests in tight sandstone reservoirs.
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This paper discusses the challenges presented by factors such as infrastructure, types of primary energy, and investment.
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The paper describes an approach to history matching and forecasting that does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance.
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This paper presents a physics-informed neural network technique able to use information from fluid-flow physics as well as observed data to model the Buckley-Leverett problem.
