A Scalable Parallel In-Situ Combustion Reservoir Simulator for Large Scale Models

2019 ◽  
Author(s):  
Ruijian He ◽  
Shuhong Wu ◽  
Zhangxin Chen ◽  
Bo Yang ◽  
Hui Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
In Situ Combustion ◽  
Scale Models ◽  
Reservoir Simulator

Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications

2020 ◽  
Vol 36 (1_suppl) ◽  
pp. 321-344 ◽  
Author(s):  
Massimiliano Pittore ◽  
Michael Haas ◽  
Vitor Silva
Keyword(s):  
Risk Assessment ◽  
Survey Data ◽  
Natural Hazards ◽  
Large Scale ◽  
Probabilistic Models ◽  
Probabilistic Framework ◽  
Variable Resolution ◽  
Exposure Modeling ◽  
Scale Models

In risk assessment, the exposure component describes the elements exposed to the natural hazards and susceptible to damage or loss, while the vulnerability component defines the likelihood to incur damage or loss conditional on a given level of hazard intensity. In this article, we propose a novel adaptive approach to exposure modeling which exploits Dirichlet-Multinomial Bayesian updating to implement the incremental assimilation of sparse in situ survey data into probabilistic models described by compositions (proportions). This methodology is complemented by the introduction of a custom spatial aggregation support based on variable-resolution Central Voronoidal Tessellations. The proposed methodology allows for a more consistent integration of empirical observations, typically from engineering surveys, into large-scale models that can also efficiently exploit expert-elicited knowledge. The resulting models are described in a probabilistic framework, and as such allow for a more thorough analysis of the underlying uncertainty. The proposed approach is applied and discussed in five countries in Central Asia.

Development and Application of an In-Situ Combustion Reservoir Simulator

1980 ◽  
Vol 20 (01) ◽  
pp. 39-51 ◽  
Author(s):  
Gary K. Youngren
Keyword(s):  
Heat Transfer ◽  
Fluid Flow ◽  
Solution Method ◽  
Oil And Gas ◽  
Three Dimensions ◽  
Volatile Components ◽  
In Situ Combustion ◽  
Gas Phases ◽  
Reservoir Simulator

Youngren, Gary K., SPE-AIME, ARCO Oil and Gas Co. Abstract This paper describes a three-dimensional, three-phase in-situ combustion reservoir simulator that rigorously models fluid flow, heat transfer, and vaporization/ condensation. It has five components: water, oxygen, nonvolatile oil, and two arbitrary volatile components. The volatile components partition between the oil and gas phases. The physical mechanisms modeled, the comprehensive mathematical solution method employed, and four applications of the simulator are presented. The applications demonstrate that the simulator can be used to interpret laboratory results and predict the effects of reservoir characteristics and operating strategy on field performance. Introduction Crookston et al. and Farouq Ali thoroughly reviewed previous developments in the mathematical simulation of in-situ combustion processes. Briefly, the earliest studies modeled certain aspects of the process using simple assumptions for the remaining process using simple assumptions for the remaining features in order to make the problem tractable. For example, Chu modeled one-dimensional thermal conduction, convection, and the thermal effects of vaporization and condensation, but multi phase fluid flow effects were simplified by assuming constant fluid saturations. Smith and Farouq Ali simulated conduction, convection, heat losses, and heat generation in two-dimensions, but assumed single-phase flow and constant fuel consumption. Recently, Farouq Ali and Crookston et al. described comprehensive three-phase, two-dimensional simulators that model the most essential features of in-situ combustion; however, results were presented only for hypothetical one- and two-dimensional examples with relatively few grid blocks.The objective of this work was to develop an in-situ combustion simulator that would rigorously model fluid flow, heat transfer, and vaporization/ condensation and still be efficient enough to allow simulation of realistic reservoir problems. Accordingly, the simulator employs a stable, efficient, highly implicit solution method. It is formulated to handle three dimensions, three phases, five components, gravity and capillary forces, heat transfer by convection and conduction within the reservoir and conductive heat loss to adjacent strata. Quantitative data on high-temperature combustion kinetics of crude oils in porous media is inadequate to allow rigorous treatment of reaction kinetics; thus, the combustion reaction is treated simply, yet realistically, by assuming that the combustion rate is limited only by the oxygen flux. This paper first describes the simulator, outlining the physical mechanisms modeled and the numerical solution method employed. It concludes by presenting analysis of real laboratory and field data in one, two, and three dimensions. Simulator Description Physical Properties Physical Properties The most significant features of the simulator are listed in Table l and detailed in Appendix A.The simulator has five components: water, nonvolatile (dead) oil, oxygen, and two arbitrary volatile components that partition between the oil and gas phases. The last four components are considered insoluble in water. The last two components are arbitrary and may be any one of the combinations: nitrogen (N2) and solution gas, N2 and carbon dioxide (CO2), N2 and a distillable hydrocarbon, CO2 and solution gas, or CO2 and a distillable hydrocarbon. SPEJ p. 39

Inferring controls on basal drag in the Amundsen Sea sector of Antarctica

2020 ◽  
Author(s):  
Robert Arthern ◽  
Rosie Williams ◽  
Kelly Hogan ◽  
Alex Brisbourne ◽  
Andrew Smith ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Large Scale ◽  
Inverse Methods ◽  
Satellite Observations ◽  
Small Scale ◽  
Amundsen Sea ◽  
Ice Flow ◽  
Scale Models ◽  
In Situ Observations

<p>We consider a variety of ways that the basal drag that acts to resist the sliding of an ice sheet can be inferred from satellite observations, or from in situ observations. Three approaches are considered here. (1) use of inverse methods combined with large scale models of ice flow. (2) spectral analysis of basal topography combined with a theory of ice flow near small scale undulations, and (3) seismic methods that probe the physical characteristics of the subglacial sediment. Consideration is given to which sliding relationships are consistent with the available observations, and to identifying measurements that could help reduce ambiguity in sliding laws.</p>

ROCK OIL RESERVOIR STRUCTURE INFLUENCE ON THE FLAME STABILITY OF THE IN-SITU COMBUSTION RECOVERY METHOD

2019 ◽  
Author(s):  
Lucas Henrique Pagoto Deoclecio ◽  
Filipe Arthur Firmino Monhol ◽  
Antônio Carlos Barbosa Zancanella
Keyword(s):  
Flame Stability ◽  
Oil Reservoir ◽  
In Situ Combustion ◽  
Recovery Method

Ceramic monolith- and foam-structured catalysts via in-situ combustion deposition for energetic applications

2018 ◽  
Vol 42 (3) ◽  
pp. 405-418
Author(s):  
Cristina ITALIANO ◽  
Lidia PINO ◽  
Massimo LAGANÀ ◽  
Antonio VITA
Keyword(s):  
In Situ Combustion ◽  
Structured Catalysts

scholarly journals IMPROVING SYSTEM READINESS, STAFF PREPAREDNESS AND PATIENT SAFETY IN A NEW SINGLE FAMILY ROOM NICU THROUGH IN SITU SIMULATION

2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e16-e16
Author(s):  
Ahmed Moussa ◽  
Audrey Larone-Juneau ◽  
Laura Fazilleau ◽  
Marie-Eve Rochon ◽  
Justine Giroux ◽  
...  
Keyword(s):  
Patient Safety ◽  
Repeated Measures ◽  
Large Scale ◽  
Multidisciplinary Teams ◽  
Single Family ◽  
In Situ Simulation ◽  
Ensure Patient Safety ◽  
System Readiness ◽  
Dependent Samples

Abstract BACKGROUND Transitions to new healthcare environments can negatively impact patient care and threaten patient safety. Immersive in situ simulation conducted in newly constructed single family room (SFR) Neonatal Intensive Care Units (NICUs) prior to occupancy, has been shown to be effective in testing new environments and identifying latent safety threats (LSTs). These simulations overlay human factors to identify LSTs as new and existing process and systems are implemented in the new environment OBJECTIVES We aimed to demonstrate that large-scale, immersive, in situ simulation prior to the transition to a new SFR NICU improves: 1) systems readiness, 2) staff preparedness, 3) patient safety, 4) staff comfort with simulation, and 5) staff attitude towards culture change. DESIGN/METHODS Multidisciplinary teams of neonatal healthcare providers (HCP) and parents of former NICU patients participated in large-scale, immersive in-situ simulations conducted in the new NICU prior to occupancy. One eighth of the NICU was outfitted with equipment and mannequins and staff performed in their native roles. Multidisciplinary debriefings, which included parents, were conducted immediately after simulations to identify LSTs. Through an iterative process issues were resolved and additional simulations conducted. Debriefings were documented and debriefing transcripts transcribed and LSTs classified using qualitative methods. To assess systems readiness and staff preparedness for transition into the new NICU, HCPs completed surveys prior to transition, post-simulation and post-transition. Systems readiness and staff preparedness were rated on a 5-point Likert scale. Average survey responses were analyzed using dependent samples t-tests and repeated measures ANOVAs. RESULTS One hundred eight HCPs and 24 parents participated in six half-day simulation sessions. A total of 75 LSTs were identified and were categorized into eight themes: 1) work organization, 2) orientation and parent wayfinding, 3) communication devices/systems, 4) nursing and resuscitation equipment, 5) ergonomics, 6) parent comfort; 7) work processes, and 8) interdepartmental interactions. Prior to the transition to the new NICU, 76% of the LSTs were resolved. Survey response rate was 31%, 16%, 7% for baseline, post-simulation and post-move surveys, respectively. System readiness at baseline was 1.3/5,. Post-simulation systems readiness was 3.5/5 (p = 0.0001) and post-transition was 3.9/5 (p = 0.02). Staff preparedness at baseline was 1.4/5. Staff preparedness post-simulation was 3.3/5 (p = 0.006) and post-transition was 3.9/5 (p = 0.03). CONCLUSION Large-scale, immersive in situ simulation is a feasible and effective methodology for identifying LSTs, improving systems readiness and staff preparedness in a new SFR NICU prior to occupancy. However, to optimize patient safety, identified LSTs must be mitigated prior to occupancy. Coordinating large-scale simulations is worth the time and cost investment necessary to optimize systems and ensure patient safety prior to transition to a new SFR NICU.

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