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At least 289 records · Page 16

LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts fully and partially supported by the fundamental shale portfolio to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins.

04 OIL SHALES AND TAR SANDS↗

A decadal view of the LANL HED/ICF Program [Slides]

The vast majority of nuclear weapon yield is generated in a regime of extreme temperature, pressure, and density. Without explosive nuclear testing, HED experiments on ICF facilities are the only way to approach and probe these extreme conditions that affect nuclear weapon performance.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Transport @ LANL: An Overview [Slides]

Deterministic neutron transport requires discretization of the Boltzmann transport equation in space, angle, energy, and time. Discretization in energy is typically performed with the multigroup approximation, which tries to preserve continuous energy cross section information by using a weighted average over discrete energy groups. There are two important requirements for accurate multigroup cross sections: appropriate energy group boundaries and an accurate weight function. The former can be selected with appropriate optimizers; the latter either requires flux solutions from comparable simulations or expert knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Equation of State Optimization and Uncertainty Quantization: Implementation in the LANL EOS Production Code OpenSesame

We detail the approaches of particle swarm optimization and Bayesian inference through Markov chain Monte Carlo for equation of state development. This work includes formulation of the modeling for the equation of state, numeric optimization of the parametric models via particle swarm optimization, and generation of probability distributions of equations of state from Markov chain Monte Carlo.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Requirements for an in-house synchrotron radiation source at LANL

Synchrotron measurements for diffraction characterization and high resolution tomography require: (1) unable monochromatic X-ray incident energy from 65keV to 100keV. 65keV need is defined by Ta and W absorption edges at 67keV, 100keV need is defined by need to penetrate U/Pu sample thickness of 0.5 mm (2) focused flux density of the monochromatic beam on a beam spot of ~100 µm 2 greater than 10 10 photons per second to allow some slower kinetics experiments such as heating, cooling for CTE measurements (but not quenching, solidification etc. which require <0.1 s resolution) (3) Δλ/λ resolution of 0.3% or better across the energy spectrum. This can be achieved by monochromator as long as flux needs are met in the monochromatic beam.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Overview of the LANL Double Shell Campaign [Slides]

A volume burning ignition target allows for high temperature with comparitvely lower areal density. The large number of interfaces allows for a deep investigation of the impact of hydrodynamic instabilities on implosion quality. Machine learning is currently being leveraged to improve the quality of the design. Due to the presence of high-z material we must take a deeper level of hohlraum physics into account compared to single shell designs.

36 MATERIALS SCIENCE↗