scholarly journals The use of pollutants fate simulation models in the risk analysis

2003 ◽  
Author(s):  
M. Andretta
Keyword(s):  
Risk Analysis ◽  
Simulation Models

Use of disinfection by-products (DBPs) generation simulation models in the risk analysis of secondary water contamination

2020 ◽  
Vol 199 ◽  
pp. 486-492
Author(s):  
Izabela Zimoch ◽  
Ewelina Bartkiewicz
Keyword(s):  
Risk Analysis ◽  
Water Contamination ◽  
Simulation Models ◽  
By Products

A methodology for cost-risk analysis in the statistical validation of simulation models

1981 ◽  
Vol 24 (4) ◽  
pp. 190-197 ◽  
Author(s):  
Osman Balci ◽  
Robert G. Sargent
Keyword(s):  
Risk Analysis ◽  
Simulation Models ◽  
Statistical Validation ◽  
Cost Risk

scholarly journals Tutorial: Parallel Computing of Simulation Models for Risk Analysis

Risk Analysis ◽  
2016 ◽  
Vol 36 (10) ◽  
pp. 1844-1854 ◽  
Author(s):  
Allison C. Reilly ◽  
Andrea Staid ◽  
Michael Gao ◽  
Seth D. Guikema
Keyword(s):  
Parallel Computing ◽  
Risk Analysis ◽  
Simulation Models

scholarly journals Adversarial Risk Analysis for Enhancing Combat Simulation Models

2015 ◽  
Vol 6 (2) ◽  
pp. 82-103 ◽  
Author(s):  
Juho Roponen ◽  
Ahti Salo
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Risk Analysis ◽  
Simulation Models ◽  
The Past ◽  
Combat Simulation ◽  
Decision Making Processes ◽  
Game Theoretic ◽  
Statistical Risk ◽  
Military Combat

Abstract Adversarial Risk Analysis (ARA) builds on statistical risk analysis and game theory to analyze decision situations involving two or more intelligent opponents who make decisions under uncertainty. During the past few years, the ARA approach-which is based on the explicit modelling of the decision making processes of a rational opponent-has been applied extensively in areas such as counterterrorism and corporate competition. In the context of military combat modelling, however, ARA has not been used systematically, even if there have been attempts to predict the opponent’s decisions based on wargaming, application of game theoretic equilibria, and the use of expert judgements. Against this backdrop, we argue that combining ARA with military combat modelling holds promise for enhancing the capabilities of combat modelling tools. We identify ways of combining ARA with combat modelling and give an illustrative example of how ARA can provide insights into a problem where the defender needs to estimate the utility gained from hiding its troop movements from the attacker. Even if the ARA approach can be challenging to apply, it can be instructive in that relevant assumptions about the resources, expectations and goals that guide the adversary’s decisions must be explicated.

scholarly journals ARTIFICIAL NEURAL NETWORK WITH EXPERIMENTAL DESIGN: A FAST TOOL FOR RISK ANALYSIS AND FORECASTING IN AN OIL RESERVOIR, A CASE STUDY

2020 ◽  
Vol 4 (5) ◽  
pp. 40-50
Author(s):  
Nasser Alizadeh Tabrizi
Keyword(s):  
Risk Analysis ◽  
Oil Recovery ◽  
Computing Time ◽  
Simulation Models ◽  
Production Optimization ◽  
Oil Reservoir ◽  
Total Production ◽  
Proxy Model ◽  
Artificial Neural ◽  
Screening Sensitivity

Running simulation models is CPU intensive. In computing expensive tasks such as parameter screening, sensitivity and risk analysis (uncertainty analysis) and production optimization, it can be useful to establish a simple surrogate model (proxy model) that mimics the simulation model with regard to a specific target value (for example, total production) in order to reduce the computing time and to study the available uncertainties in the reservoir and their impacts on production. Artificial Neural Networks (ANN) are one of the main tools used in machine learning. The quality of the ANN as a proxy model is dependent on how the experiments that were used to make and train it are designed. In particular, it is crucial to understand the input parameters such that their respective dependencies, correlations, and ranges are incorporated in the modelling. A combination of simulation runs should be set up that can be used to train the ANN. This task is usually referred to as the design of experiments (DOE) which gives the most informative data sets to train ANN. In this study DOE was used to train the ANN in an oil reservoir under gas injection scenario and the trained ANN, in turn, was applied to create the production profiles which were further used for risk analysis. The accuracy of the results obtained in this study indicates that ANN as a proxy model combined with DOE as a sampling method for training purpose is a fast and reliable tool that can replace the simulator. This dynamic proxy model can be used for risk analysis, production optimization and production forecasting of oil reservoirs under Enhanced Oil Recovery (EOR) methods.

Cost-Risk Analysis in Statistical Validating Simulation Models of Service Processes

2001 ◽  
Vol 34 (29) ◽  
pp. 28-33
Author(s):  
Konstantin N. Nechval ◽  
Nicholas A. Nechval ◽  
Edgars K. Vasermanis
Keyword(s):  
Risk Analysis ◽  
Simulation Models ◽  
Cost Risk

scholarly journals Integrated Spill Modeling for Impact Analysis

2003 ◽  
Vol 2003 (1) ◽  
pp. 943-946
Author(s):  
James F. Bennett ◽  
Walter R. Johnson ◽  
Charles F. Marshall
Keyword(s):  
Risk Analysis ◽  
Environmental Impacts ◽  
Oil Spill ◽  
Impact Analysis ◽  
Oil And Gas ◽  
Oil Spills ◽  
Simulation Models ◽  
Outer Continental Shelf ◽  
Modeling Tools ◽  
Potential Use

ABSTRACT This paper examines the potential use of computer models to estimate environmental impacts from oil spills. Computer simulation models for oil spills have long been used for risk analysis and have continually improved over the past few decades. Beyond risk analysis, however, these modeling tools could contribute to the estimation of the environmental impacts such as species mortality and shoreline contact. Proposed activities such as offshore oil and gas exploration and development can be analyzed using models that integrate spill-simulation capability with environmental resource and toxicological data. To estimate the potential use and reasonableness of such models for oil-spill impact analysis, the authors have applied a commercially available state-of-the-art spill model using previously unavailable historical winds and currents data and spill events extracted from the oil-spill record for the Outer Continental Shelf (OCS) in the Gulf of Mexico (GOM). This is one of the first efforts to bring together such complete data sets for modeling effects on so broad a geographic and temporal scale. Such information is valuable in determining the reasonableness and appropriateness of model use for impact analysis of future exploration, development, and production activities.

Semi Automatic Failure Analysis Based on Simulation Models

2008 ◽  
Author(s):  
Rainer Hamann ◽  
Andreas Uhlig ◽  
Yiannis Papadopoulos ◽  
Erich Ru¨de ◽  
Uwe Gra¨tz ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Simulation Models ◽  
Cost Effective ◽  
Automatic Generation ◽  
System Modelling ◽  
Continuous Assessment ◽  
Fault Trees ◽  
Failure Data ◽  
Local Propagation ◽  
Potential Benefits

Classical risk assessment and risk management which is gaining importance in many industries is usually based on well defined processes and uses techniques like FTA and FMEA. However, classical risk analysis techniques like FTA and FMEA should ideally be automated, at least to some extent and without loss of effectiveness, to enable fast and cost effective iterations of system modelling and risk analysis that can meet the tight cost and time constraints of most offshore projects. This paper is focused on the presentation of a new concept and tool extension for model-based synthesis of fault trees and FMEAs in which these failure analyses are automatically constructed from engineering design models, e.g. simulation models that have been augmented with information about the local propagation of failures. The simulation model is developed in the commercial system modelling tool SimulationX. The proposed process enables the automatic generation of both fault trees and FMEA tables in a single run of the tool, allowing the FMEA and fault trees to share failure data and allowing the FMEA to include failures caused by multiple basic events. As it is a largely automated process, it could be easily iterated to enable the continuous assessment of evolving designs. It provides an automatic generation of fault trees and FMEA tables for multiple top events in a single run of the tool. The potential benefits from application of this technique and tool are substantial and include simplifying the analysis, easing the examination of effects of design modifications on safety and keeping the safety analyses consistent with the design. Furthermore, the presented approach combines the benefits of simulation and risk analysis in one tool. The benefits of this approach are demonstrated by the example of a blow out preventer for a subsea installation valve.

Risk Analysis of Reactor Pressure Vessels Considering Modeling-Induced Uncertainties

2016 ◽  
Vol 1 (4) ◽  
Author(s):  
Matthew E. Riley ◽  
William M. Hoffman
Keyword(s):  
Risk Analysis ◽  
Thermal Shock ◽  
Pressure Vessel ◽  
Simulation Models ◽  
Pressure Vessels ◽  
Reactor Pressure Vessel ◽  
Modeling Process ◽  
Model Form ◽  
Physical Response ◽  
Reactor Pressure

Uncertainties in simulation models arise not only from the parameters that are used within the model, but also due to the modeling process itself—specifically the identification of a model that most accurately predicts the true physical response of interest. In risk-analysis studies, it is critical to consider the effect that all forms of uncertainty have on the overall level of uncertainty. This work develops an approach to quantify the effect of both parametric and model-form uncertainties. The developed approach is demonstrated on the assessment of the fatigue-based risk associated with a reactor pressure vessel subjected to a thermal shock event.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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