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At least 649 records · Page 36

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DOE OSTI2021

Project Risk Assessment and Monitoring for CarbonSAFE Illinois - Macon County (Task 2)

A risk evaluation performed for CarbonSAFE Macon County included performing a risk assessment for the overall project and the identification of technical project risks. These tasks were accomplished through a combination of risk assessment meetings, workshops, expert solicitation, and identifying and implementing risk mitigation or prevention activities. Information from previous carbon storage projects and from the execution of the project (including drilling a test well and conducting 2D seismic survey) were assessed to identify future risks towards the development of a storage complex.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

SWP Summary Report for Individual Legacy Well Leakage Risk Analysis at Farnsworth Unit Site: Open Well Scenario

This report summarizes results of the individual legacy well leakage risk analysis of the Farnsworth Unit site (FWU) for Open Well scenario. NRAP tools Integrated Assessment Model for Carbon Storage (NRAP-IAM-CS) and Reservoir Reduced-Order Model Generator (RROMGen) were used (Dilmore et al., 2016; Pawar et al., 2016; King, 2016). This is part one of the additional analysis addressing SWP BP4 Subtask 7.4.1.2: Extend quantitative brine and CO2 leakage calculations (Chu et al., 2020).

58 GEOSCIENCES↗

DOE OSTI2022

Computed Tomography Scanning and Geophysical Measurements of the Wellington 1-32 Core

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the U.S. Department of Energy’s (DOE), National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core from the Wellington 1-32 well (API 15-191-22591), a small-scale field test site in the Wellington Field, in Sumner County, Kansas. Wellington 1-32 was drilled in association with the Kansas Geologic Survey’s (KGS) Phase I pre-feasibility study under the DOE-NETL Carbon Storage Assurance Facility Enterprise (CarbonSAFE) program, with the goal of utilizing the Arbuckle Group as a reservoir for CO 2 storage and the potential for stacked reservoir enhanced oil recovery from Mississippian reservoirs.

58 GEOSCIENCES↗

DOE OSTI2022

Self-Healing Cements with Improved Toughness at Casing and Formation Interfaces for Subsurface Applications

The goal of this project (developed for Topic 2) is to demonstrate that PNNL’s self-healing cement (US Patent 20180044570A1) is a superior well construction material compared to conventional cement and to accelerate its commercial adoption. The project also enables a pathway toward a rigorous and reproducible EGS development approach that can be extended to high-temperature unconventional oil and gas reservoirs as well as low temperature carbon storage reservoirs. To achieve this goal, the project’s objectives are to answer remaining technical questions and to address identified technical and market barriers. This project involves collaboration between PNNL, ARE, Cyrq Energy (under the auspices of ARE), and RC. This team demonstrates a strong technical partnership, linking theoretical and laboratory expertise with field-based geothermal resource management and well completions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Corrinoids as model nutrients to probe microbial interactions in a soil ecosystem

Earth’s soils are habitats for microbial communities that drive biogeochemical cycling, plant growth, and carbon storage and persistence. The thousands of microbial species living in soil form an intricate web of interactions involving the exchange of molecules produced by different microbes. Understanding in detail how these molecular exchanges occur and how they shape microbial communities may lead to new methods to improve soil health, bioremediation efforts, and better understanding of biogeochemical processes. The overall goal of this research is to gain a deeper knowledge of the microbial interactions that drive soil community structure. However, the high functional and genomic diversity in soil microbiomes has posed a challenge for current microbiology methods to achieve this goal. This research leverages a model group of key metabolites related to cobalamin (vitamin B 12 ), known as corrinoids, to investigate microbial interactions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Computed Tomography Scanning and Petrophysical Measurements of Oriskany Core across Eastern Ohio

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core material from three Ohio wells. These wells are listed below along with their American Petroleum Institute (API) and Ohio Geological Survey (OGS) identification numbers: - New York Central System 1 well (API 34085200170000, OGS Core 855) - Herren Well (API 34099201650000, OGS Core 2914) - Garvin-King Well (API 34121215610000, OGS Core 2939) The primary impetus of this work was to capture a detailed digital representation of the available core from all three wells. The collaboration between the U.S. Department of Energy’s (DOE) NETL and the Ohio Department of Natural Resources, Division of Geological Survey enables other research entities to access information about this potential carbon storage location and its surrounding formations.

58 GEOSCIENCES↗

DOE OSTI2024

Deploying a Publicly Available and Living National Oil and Gas Well Geodatabase: CO2-Locate: A Dynamic Database & Tool

Emission mitigation and safe geologic carbon storage require an understanding of local wellbore infrastructure, yet well data are siloed across many entities. Addressing this challenge, the National Energy Technology Laboratory published CO2-Locate, an integrated and dynamic national wellbore geodatabase. CO2-Locate offers up to date well data spanning more than 40 federal, state, and tribal entities, as well as spatially summarized insights designed to support commercial, regulatory, and research communities as they strive to curb climate change through a national energy transition.

Pfander, Isabelle↗

DOE OSTI2024

Comparison of MeshGraphNet Techniques for Subsurface Behavior Prediction during CO2 Sequestration

Carbon sequestration is a vital part of the effort to mitigate anthropogenic climate change. Previously, we have shown that Graph Neural Networks (GNNs) provide the ability to extract meaningful insights during prediction of subsurface behavior in carbon storage projects. However, these models have struggled with long-term prediction accuracy due to error accumulation caused by autoregressive prediction. This research leverages the Illinois Basin – Decatur Project (IBDP) dataset to examine strategies for minimizing loss over time in a MeshGraphNet GNN model to improve reliability of predictions while minimizing inferencing time.

Holcomb, Paul↗

DOE OSTI2024

Evaluating the Nation's Pipeline Infrastructure with NETL's Advanced Infrastructure Integrity Model (AIIM)

This poster is a part of BIL-EDX4CCS Task 36: Advanced Infrastructure Integrity Modeling to Evaluate Existing Energy Infrastructure Reusability and Risk, the goal of which is to produce a smart tool that will assess existing energy infrastructure reusability and risk using the Advanced Infrastructure Integrity Model (AIIM). This model forecasts lifespan and potential risk using a multitude of factors such as incidents reports, structural characteristics, and the surrounding environment. The project aims to provide scientific insights for a better understanding of carbon storage (CS), potential to support CS stakeholder needs, national decarbonization, and mitigating climate change. AIIM will utilize an energy infrastructure database as its input, developed by acquiring publicly available data as well as NETL derived products. These resources include incidents, geohazards, and infrastructure variables. Soil data in the form of rasters and pipeline incident reports were processed and a script was developed to count the number of times features such as roads, railroads, and rivers intersected with pipeline segments which were then converted to points. Distance to oil and natural gas wells, petroleum ports, intermodal freight facilities, and geologic structures were also calculated. After data preparation and quality control was completed, the data was integrated into the pipeline points. Once models are complete, a smart tool will be created in the form of an online dashboard.

Malay, Caleb↗

DOE OSTI2024

Efficient Dimension Reduction of Complex Three-dimensional CO2 Saturation using Deep Learning Models

In the domain of deep learning (DL), dimension reduction is crucial for enhancing training efficiency and mitigating overfitting, particularly when managing complex data such as three-dimensional (3D) saturation data. The 3D saturation data in the context of geological carbon storage (GCS) presents unique challenges due to its inherent sparsity and the abrupt transitions at plume boundaries, known as shock fronts. To address the challenges, we proposed a novel DL framework that integrates dimension reduction with advanced 3D reconstruction techniques. Our model leveraged latent variables derived from 2D average saturation data, offering a robust and efficient solution tailored to the intricate dynamics of 3D saturation fields. The proposed framework can extract the critical features of the high-dimensional data while reducing the variable numbers, which is more tractable for DL models and enhances the model robustness and accuracy. Therefore, it provides a novel approach for modeling and analyses in complex geological scenarios, which finds great potential applications in environmental monitoring and energy storage.

Wang, Hongsheng↗

DOE OSTI2024

Final Technical Report: Center for Mechanistic Control of Unconventional Formations

The overarching mission of the Center for Mechanistic Control of Unconventional Formations (CMC-UF) was to garner cross-cutting, fundamental, geoscience knowledge to achieve mechanistic control over the strongly coupled nonequilibrium physical and geochemical processes in extreme geological environments including shale, mudstone, marls, and other tight rocks with nanoscale pores. Collectively, these are referred to as unconventional formations and they often play the role of seals for other subsurface storage formations. The fundamental knowledge garnered by CMC-UF enabled science-based management of US unconventional formations for subsurface storage of carbon dioxide and TWh quantities of renewable energy as hydrogen and/or compressed air over longer timeframes as well as for natural gas production, with reduced environmental impacts, in the short term.

58 GEOSCIENCES↗

DOE OSTI2024

Overview of SMART Initiative

The objective of the SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to show how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations in three main areas: real-time visualization, virtual learning, and real-time forecasting. This presentation reports the status of SMART initiative for demonstrating: (a) virtual learning during the pre-injection permitting phase, and (b) ML-assisted operational decision making and visualization.

Siriwardane, Hema↗

DOE OSTI2024

Rock Characterization for Subsurface Energy Applications

An overview of the Fossil Energy and Carbon Management mission for carbon management was given, along with information on the role the Research and Innovation Center plays in understanding aspect of research needed to accelerate geologic carbon storage nationwide.

Crandall, Dustin [NETL]↗

DOE OSTI2024

Reservoir rocks interacting with CO2/brine under subsurface CO2 sequestration conditions

The Cedar Keys/Lawson formation in the U.S. is considered as a potential candidate host reservoir for carbon storage. Reporting the knowledge of geochemically induced changes to the permeability and porosity of host CO2 storage sandstone will enable us to gain a deeper insight of the long-term reservoir behavior under the CO2 storage conditions. This study suggests that mineral dissolution and mineral precipitation could occur in the host deposit altering its characteristics for CO2 storage over time.

Soong, Yee↗

DOE OSTI2024

Reservoir rocks interacting with CO2 /brine under CO2 sequestration conditions

Soong, Yee↗

DOE OSTI2024

Electromagnetic inversion for a baseline conductivity model at the Kemper CarbonSAFE site

Geophysics is one component of a successful monitoring strategy for carbon storage sites. Seismic surveys are a common element of these strategies, with repeat surveys occurring about every 5 years. However, seismic is expensive, the sensitivity to CO2 saturation is limited, and it requires a large footprint on the surface (Yurikov et al., 2022). Electromagnetic (EM) methods offer repeat surveys at a significantly lower cost, the ability to utilize airborne methods to increase coverage, and potentially provide enough information to increase the time between seismic surveys. Specifically, we focus on semi-airborne EM, with a grounded dipole transmitter, and airborne magnetic field receivers as described and modeled in Kohnke et al. (2023).

Kohnke, Colton↗

DOE OSTI2025

MRCI Task 2: Addressing Key Technical Challenges - Executive Summary Report

This report provides a high-level overview of Task 2 products that were developed through defining carbon storage systems, Precambrian basement structure and stress, developing regional technical collaboration, modeling and risk assessment. Subtask 2.1: Defining Sub-Regional CS/CCUS Systems Subtask 2.2: Defining Precambrian basement faulting/stress Subtask 2.3: Developing industrial partnerships and regional technical collaborations Subtask 2.4: Conducting regional/subregional analysis Subtask 2.5: Assessing and managing risk for potential commercial-scale storage complexes

MRCI,Technical Challenges↗

DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

CO2 geologic storage↗