Offshore CO2 storage resource assessment of the northern Gulf of Mexico (Texas-Louisiana)

The Offshore CO2 Storage Resource Assessment of the Northern Gulf of Mexico (Texas-Louisiana) project (a.k.a., "TXLA" study) supports Goals 3 & 4 of the DOE NETL Carbon Storage Program Plan by aiding industry's ability to predict CO2 storage capacity in geologic formations to within +/- 30%. This is achieved by assessing potential regional storage formations in the State and federally regulated portions of the Gulf of Mexico. The objective of the project was to conduct an offshore carbon storage resource assessment of the Gulf of Mexico, Texas — Louisiana study area. This was accomplished by: 1. Assessing the carbon dioxide (CO2) storage capacity of depleted oil and natural gas reservoirs utilizing existing data (well logs, records and sample descriptions from existing or plugged/abandoned wells, available seismic surveys, existing core samples, and other available geologic and laboratory data) from historical hydrocarbon industry activities in the heavily explored portions of the inner continental shelf portions of the Texas and Louisiana Gulf of Mexico coastal areas. 2. Assessing the ability and capacity of saline formations in the region to safely and permanently store nationally-significant amounts of anthropogenic CO2 using existing data. Additionally, the study identified at least one specific site with the potential to store at least 30 million tonnes of CO2, which may be considered in the future for commercial or integrated demonstration projects. The study also engaged the public and other stakeholders in the region through outreach activities to apprise them of the study objectives and results.Bureau of Economic Geolog

Characterizing Small-scale Migration Behavior of Sequestered CO2 in a Realistic Geologic Fabric

AbstractFor typical field conditions, buoyancy and capillary forces grow dominant over viscous forces within a few hundred meters of the injection wells resulting in remarkably different fluid migration patterns. Reservoir heterogeneity and fluid properties are principal factors influencing CO2 migration pathways in the buoyancy/capillarity regime. We study the effect of small-scale heterogeneity on buoyant migration of CO2 in this regime. Capillary channel flow patterns emerge in this regime, as characterized by invasion-percolation simulations in a real meter-scale 2D geologic domain in which sedimentologic heterogeneity has been resolved at sub-millimeter resolution. As the degree of heterogeneity increased in synthetic media, CO2 migration patterns exhibited a spectrum of structures, from ‘dispersed’ capil lary fingers with minimal rock contact to back-filled’compact’ distributions of saturation with much larger storage efficiency

Potential Sinks for Geologic Storage of CO2 Generated in the Carolinas

This document summarizes a scoping study of the current state of knowledge of carbon storage options for our geographic area. The focus is on one aspect of carbon capture and storage—identification of deep saline aquifers in which carbon dioxide (CO2 ) generated in the Carolinas might be stored. The study does not address other aspects of CO2 storage projects, such as capture and compression of the gas, well construction and development, or injection. Transport of CO2 is touched upon in this study but has not been fully addressed. The information contained in this document is primarily from review of published geologic literature and unpublished data. No field data collection has been completed as part of this study. Further work will be necessary to increase confidence in the suitability of the potential CO2 storage sites identified in this report. This study does not address the regulatory, environmental, or public policy issues associated with carbon storage, which are under development at this time.Duke Energy, Progress Energy, Santee Cooper Power, South Carolina Electric and Gas, Electric Power Research Institute (EPRI), Southern States Energy Board (SSEB)Bureau of Economic Geolog

Comparing carbon sequestration in an oil reservoir to sequestration in a brine formation-field study

Geologic sequestration of CO2 in an oil reservoir is generally considered a different class than sequestration in formations which contain only brine. In this paper, the significance and validity of this conceptualization is examined by comparing the performance of CO2 injected into a depleted oil reservoir with the performance of similar injection into non-oil bearing sandstones using a field test at Cranfield Field, Mississippi as a case study. The differences considered are: (1)Residual oil in the reservoir slightly reduces the CO2 breakthrough time and rate of pressure build up as compared to a reservoir containing only brine, because under miscible conditions, more CO2 dissolves into oil than in to brine. (2)Dense wells provide improved assessment of the oil reservoir quality leading to improved prediction as well as verification of CO2 movement in this reservoir as compared to the sparsely characterized brine leg. The value of this information exceeds the risk of leakage. Assessment of the difference made by the presence of residual oil requires a good understanding reservoir properties to predict oil and gas distribution. Stratal slicing, attribute analysis and petrographic analyses are used to define the reservoir architecture. Real-time pressure response at a dedicated observation well and episodic pressure mapping has been conducted in the reservoir under flood since mid-2008; comparison measurements are planned for 2009 in down-dip environments lacking hydrocarbons. Model results using GEM compositional simulator compare well in general to measured reservoir response under CO2 flood; imperfections in model match of flood history document uncertainties Time laps RST logging is underway to validate fluid composition and migration models. Monitoring assessing the performance of the wells during the injection of CO2 suggests that the value of wells to provide field data for characterization exceeds the risk of leakage.Bureau of Economic Geolog

Numerical modeling of CO2 injection into a typical US Gulf Coast anticline structure

AbstractThis paper summarizes recent numerical modeling activities investigating geological CO2 sequestration project at the Cranfield field, Mississippi, USA, performed with the commercial compositional flow simulator CMG-GEM. The oilfield was produced from the 1940’s to the 1960’s but has been the recent recipient of an enhanced oil recovery (EOR) CO2 flood. The subset of actual site operations of interest to the BEG consists of (1) an early phase, object of this paper, in which CO2 is injected into the oil-bearing reservoir (the so-called Phase II) and (2) a second phase (started on December 1, 2009) in which CO2 is injected at a high rate (>100 kt/yr for several years) in the saline aquifer down dip of the reservoir (Phase III). We present the modeling efforts related to the early phase of injection (Phase II, started in July 2008) in which CO2 is injected into the oil-bearing reservoir. The objectives of the modeling effort are to (i) to gain insights on how to approach CO2 injection modeling at the site, (ii) to match recent pressure measurements at several wells including a dedicated observation well, and (iii) to vindicate the necessity of monitoring of reservoir pressure. Its intent is not necessarily to do a full-fledged history match of the historical production period (1940’s–1960’s).We conducted numerous repeat simulation runs to modify boundary conditions, fluid properties, and reservoir properties to match observed fluid responses to production and to injection. A good understanding of subsurface heterogeneities, and composition of the oil and gas components, and boundary conditions of the reservoir is the key to successful history matching. However, allocating the correct distribution of rock properties based on historical geophysical logs remained an area of uncertainty even as additional new data were obtained during characterization because of the complex interplay between depositional environment and strong overprint of diagenetic events. Parameters of utmost importance for a correct description of a flow field, in particular the relationship between porosity and permeability and the nature of permeability spatial variations remain uncertain as well as boundary conditions. The uncertainty was dealt with through sensitivity analyses. Ultimately, the constructed model shows a reasonable match with the data

A developmental approach to diversifying neuroscience through effective mentorship practices: perspectives on cross-identity mentorship and a critical call to action.

Many early-career neuroscientists with diverse identities may not have mentors who are more advanced in the neuroscience pipeline and have a congruent identity due to historic biases, laws, and policies impacting access to education. Cross-identity mentoring relationships pose challenges and power imbalances that impact the retention of diverse early career neuroscientists, but also hold the potential for a mutually enriching and collaborative relationship that fosters the mentee\u27s success. Additionally, the barriers faced by diverse mentees and their mentorship needs may evolve with career progression and require developmental considerations. This article provides perspectives on factors that impact cross-identity mentorship from individuals participating in Diversifying the Community of Neuroscience (CNS)-a longitudinal, National Institute of Neurological Disorders and Stroke (NINDS) R25 neuroscience mentorship program developed to increase diversity in the neurosciences. Participants in Diversifying CNS were comprised of 14 graduate students, postdoctoral fellows, and early career faculty who completed an online qualitative survey on cross-identity mentorship practices that impact their experience in neuroscience fields. Qualitative survey data were analyzed using inductive thematic analysis and resulted in four themes across career levels: (1) approach to mentorship and interpersonal dynamics, (2) allyship and management of power imbalance, (3) academic sponsorship, and (4) institutional barriers impacting navigation of academia. These themes, along with identified mentorship needs by developmental stage, provide insights mentors can use to better support the success of their mentees with diverse intersectional identities. As highlighted in our discussion, a mentor\u27s awareness of systemic barriers along with active allyship are foundational for their role

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Considering faults in CCS

Bureau of Economic Geolog