Prospects for finding the most valuable potential resource of the moon: Water
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Muller, P. M.↗
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Resource Prospector: An Update on the Lunar Volatiles Prospecting and ISRU Demonstration Mission
Over the last two decades a wealth of new observations of the moon have demonstrated a lunar water system dramatically more complex and rich than was deduced following the Apollo era. Lunar water, and other volatiles, have the potential to be a valuable or enabling resource for future exploration. The NASA Human Exploration and Operations Mission Directorate (HEOMD) have selected a lunar volatiles prospecting mission for a concept study and potential flight in CY2021. The mission includes a rover-borne payload that (1) can locate surface and near-subsurface volatiles, (2) excavate and analyze samples of the volatile-bearing regolith, and (3) demonstrate the form, extractability and usefulness of the materials.
Human Exploration and Operations Mission Directora↗
Resource Prospector: An Update on the Lunar Volatiles Prospecting and ISRU Demonstration Mission
Over the last two decades a wealth of new observations of the moon have demonstrated a lunar water system dramatically more complex and rich than was deduced following the Apollo era. Lunar water, and other volatiles, have the potential to be a valuable or enabling resource for future exploration. The NASA Human Exploration and Operations Mission Directorate (HEOMD) have selected a lunar volatiles prospecting mission for a concept study and potential flight in CY2021. The mission includes a rover-borne payload that (1) can locate surface and near-subsurface volatiles, (2) excavate and analyze samples of the volatile- bearing regolith, and (3) demonstrate the form, extractability and usefulness of the materials.
Moon↗
Shuttle Experimental Radar for Geological Exploration (SERGE) project: Field work relating to the Shuttle Experimental Radar A (SIR-A) in Brazil (phase 2)
The ground observations undertaken over the northern position of Minas Gerais State, and part of Distrito Federal from 7 to 12 December 1982, along the Space Shuttle 2 flying orbit 22 of November 1981 are described. Field data related mostly with lithology, geological structures and forest cover, and specific geomorphological and pedological aspects were collected. Ground data are applied to evaluate the SIR-A Experiment, developed in the Space Shuttle-2 mission for natural resources mapping and prospecting.
Balieiro, M. G.↗
Spillage of Lunar Polar Crater Volatiles onto Adjacent Terrains: The Case for Dynamic Processes
We present an investigation of the release and transport of lunar polar crater volatiles onto topside regions surrounding the cold traps. The volatiles are liberated via surface energization processes associated with the harsh space environment, including solar wind plasma sputtering and impact vaporization. We find that some fraction of these volatiles can migrate from crater floors onto topside regions (those regions directly adjacent to and above the polar crater floors), and that these surrounding terrains should contain a sampling of the material originating within the crater itself. It is concluded that the nature of the volatile content on crater floors can be obtained by sampling the surface volatiles that have migrated or "spilled out" onto the adjacent terrain. This "spillage" effect could make human or robotic prospecting for crater resources significantly easier, since an assessment may not require direct entry into the very harsh polar crater environment. We also suggest that there are dynamic processes actively operating on the crater floors, and we estimate their source rates assuming dynamic equilibrium of the observed water frost and our modeled loss rates.
Vaporization↗
HIPSTER (Harmonized Impacts across Products, Scenarios, and Technologies for Environmental and Resource metrics) [SWR-24-17]
Development of a prototype, code-based life cycle assessment framework, the Harmonized Impacts across Products, Scenarios, and Technologies for Environmental and Resource metrics (HIPSTER), to coherently assess outputs of prospective NREL models across environmental and resource use metrics. Eventually, HIPSTER is aimed to also cover socioeconomic and justice metrics. At present, NREL model scenario impact assessments are performed incoherently, as they as based on varying assumptions and inputs, specifically life cycle inventories, and cover different system boundaries. HIPSTER was created to align the assumptions and boundaries with those of cradle-to-grave life cycle assessment. In FY22, HIPSTER was successfully applied using scenarios from the ReEDS™ model, NREL's flagship capacity expansion model resulting in cradle-to-grace time-series life cycle impacts across LCA midpoints and resource use metrics.
Ghosh, Tapajyoti↗
GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources
The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.
15 GEOTHERMAL ENERGY↗
Hawthorne Nevada Deep Direct-Use Feasibility Study - Data Used for Geothermal Resource Conceptual Modeling and Power Capacity Estimates
This data submission includes several data components that were used to develop a conceptual model and power capacity-estimates of two low-temperature geothermal resources that define geothermal prospect A at Hawthorne, Nevada. Data are sourced from a combination of legacy publicly-available data and more recent data acquisition conducted by the US Navy Geothermal Program Office (2008-2013) and the Great Basin Center for Geothermal Energy at the University of Nevada, Reno (2008-2010). Data sets include compiled fluid geochemistry data, down-hole temperature logs for wells in the vicinity of prospect A, 2 meter temperature survey data, temperature-spinner logs acquired in well HWAAD-2A, fracture picks from image log data acquired in wells HWAAD-2 and HWAAD-3, and X-Ray Diffraction (XRD) analyses on cuttings from wells HWAAD-2A and HWAAD-3. These data have been reviewed for errors and inconsistencies, but it is possible that few errors could still remain. The resource conceptual model and power capacity estimates are included in the final report to the US Department of Energy, and are presented in a manuscript by Ayling and Hinz. A link to the manuscript published in Geothermics is linked below in this submission.
15 GEOTHERMAL ENERGY↗
Basin-scale study of CO 2 storage in stacked sequence of geological formations
Commercial scale decarbonization through carbon capture and storage may likely involve many CO 2 storage projects located in close proximity. The close proximity could raise concerns over caprock integrity associated with reservoir pressure buildup and interference among adjacent projects. Commercial-scale injection will also require large prospective CO 2 storage resource and high injectivity in the targeted storage formations. To accommodate the need for both large resource and high injectivity, project operators could consider injecting CO 2 into a stacked sequence of formations. This analysis investigates the benefits of injecting CO 2 into a vertically stacked sequence of saline formations, over injecting the same amount of CO 2 into a single saline formation, in addressing these challenges. Our analysis shows that injecting into the stacked sequence mitigates the extent of pressure buildup among the stacked formations, while still achieving the same or greater target CO 2 storage volumes. Among cases modeled, the resulting pressure buildup front is most reduced when each storage site distributes injection volumes over several wells, each of which injects a portion of the total CO 2 mass across the stacked sequence. This favorable case not only results in the smallest CO 2 aerial footprint, but also shows the largest reduction in the pressure buildup at the top of perforation at the injection wells (upwards of approximately 46% compared to the single-formation storage), the result of which is crucial to maintain caprock integrity. This analysis provides insights into required decision-making when considering multi-project deployment in a shared basin.
42 ENGINEERING↗
Genome sequences of key bacterial symbionts of entomopathogenic nematodes: Xenorhabdus cabanillasii DSM17905, Xenorhabdus ehlersii DSM16337, Xenorhabdus japonica DSM16522, Xenorhabdus koppenhoeferii DSM18168, and Xenorhabdus mauleonii DSM17908
ABSTRACT Xenorhabdus species are bacterial symbionts of entomopathogenic Steinernema nematodes, in which they produce diverse secondary metabolites implicated in pathogenesis. To expand resources for natural product prospecting and exploration of host-symbiont-pathogen relationships, the genomes of Xenorhabdus cabanillasi, Xenorhabdus ehlersii , Xenorhabdus japonica , Xenorhabdus koppenhoeferii , and Xenorhabdus mauleonii were analyzed.
Robertson, Raegan↗
Commercial prospects for extraterrestrial materials
Prospects for using lunar resources as materials for spaceborne construction are examined. The use of lunar construction materials is considered economically justifiable in the case of such large scale projects as space power stations (SPS). A proposed scenario for the acquisition and space processing of lunar materials involves the use of space shuttles to deliver an assembly base to earth orbit, where a solar powered mass driver reaction engine rocket is assembled and used to bring sections of a lunar base from low earth orbit to the moon. The rocket would then be positioned at the L2 equilibrium point in order to catch lunar soil propelled into space by a mass driver and bring it to the assembly base for chemical processing. Cost factors would be comparable to those of the terrestrial deployment of the first SPS, and such a project could be in operation before the end of the century.
Criswell, D. R.↗
NASAs Evolvable Mars Campaign: Mars Moons Robotic Precursor
Human exploration missions to the moons of Mars are being considered within NASA's Evolvable Mars Campaign (EMC) as an intermediate step for eventual human exploration and pioneering of the surface of Mars. A range of mission architectures is being evaluated in which human crews would explore one or both moons for as little as 14 days or for as long as 500 days with a variety of orbital and surface habitation and mobility options being considered. Relatively little is known about the orbital, surface, or subsurface characteristics of either moon. This makes them interesting but challenging destinations for human exploration missions during which crewmembers must be able to effectively conduct scientific exploration without being exposed to undue risks due to radiation, dust, micrometeoroids, or other hazards. A robotic precursor mission to one or both moons will be required to provide data necessary for the design and operation of subsequent human systems and for the identification and prioritization of scientific exploration objectives. This paper identifies and discusses considerations for the design of such a precursor mission based on current human mission architectures. Objectives of a Mars' moon precursor in support of human missions are expected to include: 1) identifying hazards on the surface and the orbital environment at up to 50-km distant retrograde orbits; 2) collecting data on physical characteristics for planning of detailed human proximity and surface operations; 3) performing remote sensing and in situ science investigations to refine and focus future human scientific activities; and 4) prospecting for in situ resource utilization. These precursor objectives can be met through a combination or remote sensing (orbital) and in-situ (surface) measurements. Analysis of spacecraft downlink signals using radio science techniques would measure the moon's mass, mass distribution, and gravity field, which will be necessary to enable trajectory planning. Laser altimetry would precisely measure the moon's shape and improve the accuracy of radio science measurements. A telescopic imaging camera would map the moon at submeter resolution and photograph selected areas of interest at subcentimeter resolution and a visible and near-infrared (0.4-3.0 mm) imaging spectrograph would produce a global map of mineral composition variations at a resolution of tens of meters and maps of selected areas of interest at meter resolution. Additional remote sensing capabilities could include a thermal infrared imager (heat flow, thermal inertia, and grain size distributions), a gamma-ray and neutron detector (atomic composition), a ground-penetrating radar (internal structure), and a magnetometer and Langmuir probe (magnetic properties and plasma field). Once on the surface of Phobos or Deimos, necessary instrumentation would include a penetrometer (regolith compressive strength), a motion-imagery camera (to observe the penetrometer tests before, during, and after contact), a dust-adhesion witness plate and camera (dust levitation), a microimager (dust particle sizes and shapes), and an alpha-proton-X-ray, X-ray fluorescence, Mossbauer, or Raman spectrometer (atomic and mineral composition of surface materials) and an optional temperature probe (regolith thermal properties). A variety of robotic mission design options to enable both orbital and surface measurements are being considered that include fully integrated and modular approaches. In-situ measurements from at least one surface location would be required, with additional measurement locations possible through use of multiple landers, through propulsive relocation of a single lander, or through electromechanical surface translation by a walking or hopping lander vehicle, which could also serve to evaluate such mobility capabilities for subsequent human missions. Preliminary orbital analysis suggests that remote sensing would likely be performed while in a distant retrograde orbit around the target moon. Mission design options to enable characterization of both Mars’ moons in a single mission are also being studied.
Gernhardt, Michael L.↗
Analysis of Selected Publicly Available Geothermal Exploration Data Gaps
As part of a United States Department of Energy (DOE) supported retrospective analysis of DOE's Play Fairway Analysis (PFA) projects, the National Renewable Energy Laboratory (NREL) compiled and analyzed publicly available geothermal exploration datasets to identify and highlight data gaps in areas prospective for hosting geothermal resources. The analysis was intended to understand the existing geographic coverage of selected datasets commonly utilized both by the PFA projects and geothermal developers during resource assessments including geologic mapping, temperature gradient drilling, and aeromagnetic, gravimetric, and lidar surveys. Results indicate that broad areas of the western United States estimated to have geothermal potential lack sufficient geologic and geophysical coverage necessary for even regional resource exploration. The study directly informed the recent Geoscience Data Acquisition for Western Nevada, or GeoDAWN - which united DOE's Geothermal Technologies Office (GTO) with the U.S. Geological Survey (USGS) of the U.S. Department of the Interior to assist U.S. needs for energy and critical minerals. The study also has the potential to inform public investment in further data acquisition for characterization of the Earth both for geothermal and other natural resource assessments.
data↗
Basin and Range Investigation for Developing Geothermal Energy: Exploration Data
This data package includes exploration material from the Basin & Range Investigation for Developing Geothermal Energy [in Hidden Systems] project (BRIDGE), which is part of a broader initiative to advance the exploration of hidden geothermal resources in the Basin & Range Province of the western U.S. Data modalities include a helicopter-borne time-domain electromagnetic survey, magnetotellurics, 2-meter temperature measurements, ground-based gravity and legacy aeromagnetic surveys, geochemistry, geologic mapping, LiDAR analysis, 3D models, associated geospatial data, and a bibliography of existing data and references utilized in prospect characterization and conceptual modeling. Key files are in CSV, Geosoft, and Geotools formats. Please refer to READMEs for dataset-specific information. Where applicable, acquisition data and inversion models for a particular prospect or area of interest are organized separately. This BRIDGE data package is the product of a collaboration led by Sandia National Laboratories with partners from Geologica Geothermal Group, Inc., the U.S. Navy Geothermal Program Office, and consultants Steven Sewell (Australis Geoscience Ltd) and William Cumming (Cumming Geoscience). The project's areas of interest (AOIs) are based off priority areas of interest in the southwestern portion of the Nevada Play Fairway map, distribution across tectonic provinces, accessibility, and the project team's extensive experience in the region. AOIs cover about a dozen basins that include unexplored prospects, partially explored prospects, and some developed analogue resources that provide validation cases. Many unexplored and partially explored prospects are on U.S. Department of Defense (DoD) land, though adjacent lands are included as well.
15 GEOTHERMAL ENERGY↗
Integration and Ruggedization of a Commercially Available Gas Chromatograph and Mass Spectrometer (GCMS) for the Resource Prospector Mission (RPM)
The Resource Prospector is a mission to prospect for lunar volatiles (primarily water) at one of the two lunar poles, as well as demonstrate In-Situ Resource Utilization (ISRU) on the Moon. The Resource Prospector consists of a lander, a rover, and a rover-borne scientific payload. The Regolith and Environment Science and Oxygen & Lunar Volatile Extraction (RESOLVE) payload, will be able to (1) locate near subsurface volatiles, (2) excavate and analyze samples of the volatile-bearing regolith, and (3) demonstrate the form, extractability and usefulness of the materials. The gas chromatograph mass spectrometer (GCMS) is the primary instrument in the RESOLVE instrumentation suite responsible for identification and quantification of the volatiles evolved from the lunar regolith. Specifically, this instrument must have: a low mass, a low power consumption, be able to perform fast analyses of samples ranging from less than one to greater than ninety nine percent water by mass, be autonomously controlled by the payload's software and avionics platform, and be able to operate in the harsh lunar environment. The RPM's short mission duration is the primary driver of the requirement for a very fast analysis time currently base lined at less than 2 minutes per sample. This presentation will discuss the requirements levied upon the GCMS design, lessons learned from a preliminary field demonstration deployment, the current design, and the path forward.
Loftin, Kathleen↗
Integration and Ruggedization of a Commercially Available Gas Chromatograph and Mass Spectrometer (GCMS) for the Resource Prospector Mission (RPM)
The Resource Prospector is a mission to prospect for lunar volatiles (primarily water) at one of the two lunar poles, as well as demonstrate In-Situ Resource Utilization (ISRU) on the Moon. The Resource Prospector consists of a lander, a rover, and a rover-borne scientific payload. The Regolith and Environment Science and Oxygen & Lunar Volatile Extraction (RESOLVE) payload, will be able to (1) locate near subsurface volatiles, (2) excavate and analyze samples of the volatile-bearing regolith, and (3) demonstrate the form, extractability and usefulness of the materials. The gas chromatograph mass spectrometer (GCMS) is the primary instrument in the RESOLVE instrumentation suite responsible for identification and quantification of the volatiles evolved from the lunar regolith. Specifically, this instrument must have: a low mass, a low power consumption, be able to perform fast analyses of samples ranging from less than one to greater than ninety nine percent water by mass, be autonomously controlled by the payload's software and avionics platform, and be able to operate in the harsh lunar environment. The RPM's short mission duration is the primary driver of the requirement for a very fast analysis time currently base lined at less than 2 minutes per sample. This presentation will discuss the requirements levied upon the GCMS design, lessons learned from a preliminary field demonstration deployment, the current design, and the path forward.
Loftin, Kathleen↗
Expert systems built by the Expert: An evaluation of OPS5
Two expert systems were written in OPS5 by the expert, a Ph.D. astronomer with no prior experience in artificial intelligence or expert systems, without the use of a knowledge engineer. The first system was built from scratch and uses 146 rules to check for duplication of scientific information within a pool of prospective observations. The second system was grafted onto another expert system and uses 149 additional rules to estimate the spacecraft and ground resources consumed by a set of prospective observations. The small vocabulary, the IF this occurs THEN do that logical structure of OPS5, and the ability to follow program execution allowed the expert to design and implement these systems with only the data structures and rules of another OPS5 system as an example. The modularity of the rules in OPS5 allowed the second system to modify the rulebase of the system onto which it was grafted without changing the code or the operation of that system. These experiences show that experts are able to develop their own expert systems due to the ease of programming and code reusability in OPS5.
Jackson, Robert↗
Assessment of oil and gas fields in California as potential CO 2 storage sites
California's total annual greenhouse gas (GHG) emissions (425.3 MtCO 2 e) in 2018 were about 6.4% of the US total (6,677 MtCO 2 e) and around 1% of global emissions. About 39% of 2018 GHG emissions in California were from the industrial and electrical sectors. Many of these emissions were from large stationary point sources and were suitable for carbon capture retrofit with subsequent storage of the captured carbon dioxide (CO 2 ) in geological formations. Previous studies of California found suitable geology and CO 2 storage resource. This study refines and furthers prior work using a three-stage screening process of oil fields, gas fields, and underground natural gas storage (UGS) sites by combining criteria from previous studies while excluding sites that pose technical risk or are located in regions with surface restrictions including sensitive habitats and dense populations. In the first stage, 129 CO 2 storage sites in California were identified using qualification criteria based upon formation properties including geological conditions and pore pressure. The second stage identified sensitive sites by applying conservative screens including seismic activity, faulting, population density, restricted lands, and sensitive habitats. During the third stage, 61 CO 2 potential storage sites were identified by subtraction of stage 2 areas from stage 1. The potential storage volume in the third stage ranged from 1.0 to 2.0 GtCO 2 . Finally, we applied a scoring system with seven parameters to rank the 61 potential sites based on subsurface technical criteria. The scored sites are classified as high priority, medium priority, and sites for future study. Prospective CO 2 storage sites with high and moderate priority were selected and linked to CO 2 sources. There are 14 prospective sites (above 20 MtCO 2 storage resource per site) with a total storage resource of 1024 MtCO 2 distributed in Northern and Southern California. Of these sites, there are 9 potential CO 2 -EOR sites and 1 depleted oil field with a total estimated CO 2 storage volume of ~800 MtCO 2 in the Southern San Joaquin and Ventura Basin. These 10 prospective sites with a storage resource greater than 20 MtCO 2 could potentially deliver more than 20 years of storage with an average injection rate of 40 MtCO 2 /year. The remaining 4 highly prospective sites are in Northern California. Additionally, study results suggest that saline formations should be re-evaluated in concert with storage in oil, gas, and natural gas storage reservoirs.
58 GEOSCIENCES↗