Sustainable energy solutions: Well retrofit analysis and emission reduction for a net-zero future in the Intermountain West, United States of America.

To achieve net-zero emissions by 2050, we need economic means of sequestering carbon dioxide (CO2) and reducing greenhouse gas emissions (GHG). We analyze the sequestration potential of the Intermountain West (I-West) region, US, as a primary energy transition hub through analysis of wellbore retrofit potential and emission reduction in both fugitive gas abatement and flare gas. We selected the I-West region due to its abundant energy sources and oil and gas production legacy. Preliminary analysis hints that well retrofits can breathe new life into a well at a fraction of the cost of a new drill. With millions of potential candidates in the US, even a modest fraction (1% or less) suitable for retrofit could accelerate the shift to large-scale CO2 sequestration. Fugitive gas, the unintentional release of wellbore gases such as methane, is a significant emissions source. Through conservative analysis, it is estimated that wellhead leakage alone may account for 5 million tonnes of carbon dioxide equivalent (CO2e) emissions. We conclude by assessing the CO2 emissions from flaring, which is the burning of associated gas during well operations, conservative analysis indicates flaring contributes another 2 million tonnes of CO2 emissions to the region. We find that with targeted retrofit and better controls on emissions sources, the I-West region can make a significant impact in the nation's push to become net-zero. This study outlines economic feasibility and actionable items to achieve the critical reductions in emissions and increases in sequestration necessary to attain net zero.

Predictive scale-bridging simulations through active learning.

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Flaring volumes in the intermountain west region: A geospatial analysis of satellite and operator-reported data with viable mitigation strategies.

Burning associated gas has been a prevailing problem across the world for decades. This practice consumes billions of (US) dollars' worth of valuable natural gas, contributes billions of metric tons of carbon dioxide (CO2) to the atmosphere, and releases volatile chemicals to nearby communities. To assess the prevalence of wellbore flaring within the Intermountain West (I-West) region, we analyzed data from the Nightfire project and contrasted it with wellbore surface hole locations. Consequently, we will permit the analysis of the flare data on a geospatial scale and compare it with operator self-reported flaring volumes. Through this analysis, we found that New Mexico is by far the largest flaring state in the I-West region, with most of its flare gas coming from the Permian Basin. Additionally, we found that satellite data estimated volumes that were 165% larger than those self-reported by the operators. Although some of this could be an overestimation from the Nightfire project, the size of the discrepancy indicates that there may be an underestimation of flared volumes that operators report to the state. A better understanding of the discrepancy source can be identified by linking the satellite flare volume to individual wells and operators, and potential solutions may be implemented to assist New Mexico's recent waste laws in reducing Permian flared volumes. We also proposed economic solutions that could substantially reduce the flared volume through flare gas utilization through on-site processing, the construction of small spur lines, and the development of a local sink for methane.

Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media.

Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications.