Defining Coalbed Methane Exploration Fairways in East-Central Texas

The Bureau of Economic Geology at The University of Texas at Austin has developed a basin-scale coal bed methane producibility and exploration model based on a decade of Gas Research Institute-supported research performed in the San Juan, Sand Wash (Greater Green River), and Piceance Basins, as well as reconnaissance studies of several other producing and prospective coal basins in the United States. As part of a cooperative agreement between the Bureau of Economic Geology (BEG) and the U.S. Geological Survey (USGS), BEG is to provide a preliminary assessment of the coal bed methane potential of the east-central Texas Gulf Coast coal basins based on previously published literature and data. The objective of this report is to discuss the application of the producibility model in defining coalbed methane exploration fairways in an east-central Texas coal basin. The producibility model indicates that tectonic/structural setting, depositional systems and coal distribution, coal rank, gas content, permeability, and hydrodynamics are critical controls to coalbed methane producibility. However, simply knowing a basin's geologic and hydrologic characteristics will not lead to a conclusion about coalbed methane producibility because it is the interplay among geologic and hydrologic controls on production and their spatial relation that governs producibility. High producibility requires that the geologic and hydrologic controls be synergistically combined. That synergism is evident in a comparison of the prolific producing San Juan Basin and marginally producing Sand Wash and Piceance Basins, where high productivity is governed by (1) thick, laterally continuous coals of high thermal maturity; (2) adequate permeability; (3) basinward flow of groundwater through coals of high rank and gas content orthogonally toward no-flow boundaries (regional structural hinge lines, fault systems, facies changes, permeability contrasts, and/or discharge areas); (4) generation of secondary biogenic gases; and (5) conventional trapping along those boundaries to provide additional gas beyond that generated during coalification. Understanding the dynamic interaction among these key geologic and hydrologic controls will be critical for delineation of exploration fairways in east-central Texas frontier basins and for targeting "sweet spots" along the Gulf Coast.Bureau of Economic Geolog

Deep Visual Representation Learning for Classification and Retrieval: Uncertainty, Geometry, and Applications

Deep visual representation learning is the process by which deep neural networks discover a low-dimensional latent feature space, or embedding space, of visual data such that distance serves as a proxy for semantic dissimilarity. We consider deep visual representation learning tailored for classification and retrieval applications, that is, the representation is trained to discriminate between inputs belonging to different classes. In particular, we explore two facets of these visual representations: their stochasticity and their geometry. The vast majority of losses, or methods, used to discover visual representations operate on deterministic embeddings where an input projects to a single point in the embedding space. Methods that produce stochastic embeddings, in contrast, project an input to a random variable whose distribution reflects its uncertainty in the semantic space. Capturing uncertainty in the embedding space is useful for robust classification and retrieval, informing downstream applications, and interpreting representations. Our primary focus is designing novel loss functions for discovering stochastic visual representations that perform equivalently or better than deterministic alternatives, are efficient and tractable, and are more robust. The secondary focus is on the geometry of the representation. The three geometries are Euclidean, spherical, and hyperbolic, and each induce constraints on the latent space. In conjunction with designing stochastic embedding methods, we empirically explore the three geometries. We propose two novel stochastic methods: (1) the Stochastic Prototype Embedding using Gaussians in Euclidean space and (2) the von Mises-Fisher loss using von Mises-Fisher distributions in spherical space (i.e., on the unit hypersphere). While each of the three geometries has benefits, we find that spherical methods produce the strongest discrimination between classes and thus are well-suited for the downstream retrieval and classification applications that act on the learned representations. Our tertiary focus involves the application of discriminative visual representations, appealing to practitioners via two large-scale empirical studies. The first unifies few- and zero-shot egocentric action recognition---and more generally, few- and zero-shot classification---verifying that the same representation can be used jointly for both tasks without degrading generalization. The second explores clustering pretrained embeddings with results that emphasize (1) the benefit of spherical representations, (2) the value of shallow, unsupervised clustering methods, for example hierarchical agglomerative clustering, when carefully tuned and benchmarked, and (3) the fragility of recent supervised, deep clustering methods operating on embeddings with more uncertainty (i.e., less discrimination).</p

Fast algorithm for detecting community structure in networks

It has been found that many networks display community structure -- groups of vertices within which connections are dense but between which they are sparser -- and highly sensitive computer algorithms have in recent years been developed for detecting such structure. These algorithms however are computationally demanding, which limits their application to small networks. Here we describe a new algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster than previous algorithms. We give several example applications, including one to a collaboration network of more than 50000 physicists.Comment: 5 pages, 4 figure

Emergency Department Pain Management Following Implementation of a Geriatric Hip Fracture Program

Introduction: Over 300,000 patients in the United States sustain low-trauma fragility hip fractures annually. Multidisciplinary geriatric fracture programs (GFP) including early, multimodal pain management reduce morbidity and mortality. Our overall goal was to determine the effects of a GFP on the emergency department (ED) pain management of geriatric fragility hip fractures. Methods: We performed a retrospective study including patients age ≥65 years with fragility hip fractures two years before and two years after the implementation of the GFP. Outcomes were time to (any) first analgesic, use of acetaminophen and fascia iliaca compartment block (FICB) in the ED, and amount of opioid medication administered in the first 24 hours. We used permutation tests to evaluate differences in ED pain management following GFP implementation. Results: We studied 131 patients in the pre-GFP period and 177 patients in the post-GFP period. In the post-GFP period, more patients received FICB (6% vs. 60%; difference 54%, 95% confidence interval [CI] 45–63%; p<0.001) and acetaminophen (10% vs. 51%; difference 41%, 95% CI 32–51%; p<0.001) in the ED. Patients in the post-GFP period also had a shorter time to first analgesic (103 vs. 93 minutes; p=0.04) and received fewer morphine equivalents in the first 24 hours (15mg vs. 10mg, p<0.001) than patients in the pre-GFP period. Conclusion: Implementation of a GFP was associated with improved ED pain management for geriatric patients with fragility hip fractures. Future studies should evaluate the effects of these changes in pain management on longer-term outcomes

Coalbed Methane Potential of the Greater Green River, Piceance, Powder River and Raton Basins

Coalbed methane potential of the Greater Green River, Piceance, Powder River, and Raton Basins was evaluated in the context of geologic and hydrologic characteristics identified in the San Juan Basin, the nation's leading coalbed methane producing basin. The major comparative criteria were (1) coalbed methane resources, (2) geologic and hydrologic factors that predict areas of high gas producibility and high coalbed reservoir permeability, and (3) coalbed thermal maturity. These technical criteria were expanded to include structure, depositional systems, and database and then combined with economic criteria (production, industry activity, and pipeline availability) to evaluate the coalbed methane potential of the basins. The Greater Green River and Piceance Basins have primary potential to make a significant near-term contribution to the nation's gas supply. These basins have large gas resources, high-rank coals, high gas contents, and established coalbed methane production. The Greater Green River Basin has numerous coalbed methane targets, good coal-seam permeability, and extensive hydrologic areas favorable for production. The Powder River and Raton Basins were judged to have secondary potential. Coal beds in the Powder River Basin are thermally immature and produce large volumes of water; the Raton Basin has a poor database and has no gas pipeline infrastructure. Low production and minimal industry activity further limit the near-term potential of the Raton Basin. However, if economic criteria are discounted and only major technical criteria are considered, the Greater Green River and Raton Basins are assigned primary potential. The Raton Basin's shallow, thermally mature coal beds of good permeability are attractive coalbed methane targets, but low coal-seam permeability limits the coalbed methane potential of the Piceance Basin.Bureau of Economic Geolog

Preventing Isolated Perioperative Reintubation: Who is at highest risk?

Objectives: 1. We aim to characterize IPR nationally through a retrospective review of the National Surgical Quality Improvement Program participant user file (NSQIP PUF). 2.Identify risk factors for IPR including analysis of procedure type and preoperative characteristics.https://jdc.jefferson.edu/patientsafetyposters/1041/thumbnail.jp