Uncertainties in a risk management context in early phases of offshore petroleum field development

The current risk management approach for the Norwegian offshore petroleum industry came into effect in 2001 and has been stable with minor changes for 15 years. Relatively few new installations were slated for development until quite recently, and several new projects have been started in the last few years. The paper considers the risk management approach in the pre-FEED phase and builds on two case studies selected from the most recent cases. These case studies have been evaluated with respect to how uncertainties are considered in the early phase, based on the submission of the Plan for Development and Operation, their evaluations by authorities and the supporting documents. Both case studies involve new concepts for which there is no experience from similar environments and/or water depths. In spite of what could have been expected, the case studies conclude that uncertainties have not been in focus at all during concept development. This appears to be definitely the case for the licensees, but also to be the case for the authorities. Some suggestions are presented for what could have been considered by the licensees and authorities.     

原子吸收法测定铜检出限的测量不确定度评估

对原子吸收法测定铜检出限的测量不确定度进行分析探讨,建立了不确定度的评估方法。影响铜检出限测量不确定度的主要因素包括标准溶液不确定度;拟合曲线不确定度;检测仪器不确定度;吸光值量化误差不确定度等,提供了上述各因素的计算方法及过程。     

Multi-criteria decision-making considering risk and uncertainty in physical asset management

In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and cost-benefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.     

The effect of recycling price uncertainty on municipal waste management choices

This paper analyzes the effect of price uncertainty and irreversible investment on the decision of municipalities to switch from landfill waste disposal to recycling by developing a model to predict recycling adoption behavior and applying it to empirical data. It is shown that uncertainty regarding the price of recycled materials may induce a risk neutral municipality to prefer landfill disposal, even when recycling is less expensive. A model is developed to describe the switching process and estimate its parameters using empirical data from 79 municipalities in Israel. The model is then used to predict municipalities' recycling adoption decisions under various assumptions regarding price uncertainty. The results support the hypothesis that price uncertainty is a major obstacle for recycling. Finally, several options for price stabilization are sketched and it is argued that these policies may be effective in establishing viable recycling markets.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

An Interval-Parameter Waste-Load-Allocation Model for River Water Quality Management Under Uncertainty

A simulation-based interval quadratic waste load allocation (IQWLA) model was developed for supporting river water quality management. A multi-segment simulation model was developed to generate water-quality transformation matrices and vectors under steady-state river flow conditions. The established matrices and vectors were then used to establish the water-quality constraints that were included in a water quality management model. Uncertainties associated with water quality parameters, cost functions, and environmental guidelines were described as intervals. The cost functions of wastewater treatment units were expressed in quadratic forms. A water-quality planning problem in the Changsha section of Xiangjiang River in China was used as a study case to demonstrate applicability of the proposed method. The study results demonstrated that IQWLA model could effectively communicate the interval-format uncertainties into optimization process, and generate inexact solutions that contain a spectrum of potential wastewater treatment options. Decision alternatives can be generated by adjusting different combinations of the decision variables within their solution intervals. The results are valuable for supporting local decision makers in generating cost-effective water quality management strategies.     

The reliability of experts' opinions in constructing a composite environmental index: The case of ESI 2005

The complexity of the environment demands a well-constructed composite environmental index (CEI) to provide a useful tool to draw attention to environmental conditions and trends for policy purposes. Among the common difficulties in constructing a proper CEI are uncertainties due to the selection of the most representative underlying variables or indicators. A degree of uncertainty accompanies experts' judgments, and to deal with vague, subjective or inconsistent information, logic other than classic is required. This study analyzes a procedure that uses different experts' opinions in constructing a CEI, with the use of paraconsistent annotated logic. For this, a sensitivity analysis of the Environmental Sustainability Index (ESI 2005) was used as an example to assess the reliability of experts' opinions. The uncertainty due to the disagreement in experts' opinions clearly indicates that the forms we presently use to measure and monitor the actual environment are insufficient, that is, there is a lack of a “science of sustainability”.     

Methane and nitrous oxide emissions from a subtropical coastal embayment (Moreton Bay, Australia)

Surface water methane (CH₄) and nitrous oxide (N₂O) concentrations and fluxes were investigated in two subtropical coastal embayments (Bramble Bay and Deception Bay, which are part of the greater Moreton Bay, Australia). Measurements were done at 23 stations in seven campaigns covering different seasons during 2010-2012. Water-air fluxes were estimated using the Thin Boundary Layer approach with a combination of wind and currents-based models for the estimation of the gas transfer velocities. The two bays were strong sources of both CH₄ and N₂O with no significant differences in the degree of saturation of both gases between them during all measurement campaigns. Both CH₄ and N₂O concentrations had strong temporal but minimal spatial variability in both bays. During the seven seasons, CH₄ varied between 500% and 4000% saturation while N₂O varied between 128 and 255% in the two bays. Average seasonal CH₄ fluxes for the two bays varied between 0.5 ± 0.2 and 6.0 ± 1.5 mg CH₄/(m²·day) while N₂O varied between 0.4 ± 0.1 and 1.6 ± 0.6 mg N₂O/(m²·day). Weighted emissions (t CO₂-e) were 63%-90% N₂O dominated implying that a reduction in N₂O inputs and/or nitrogen availability in the bays may significantly reduce the bays' greenhouse gas (GHG) budget. Emissions data for tropical and subtropical systems is still scarce. This work found subtropical bays to be significant aquatic sources of both CH₄ and N₂O and puts the estimated fluxes into the global context with measurements done from other climatic regions.     

Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results

The chemical mass balance (CMB) model was applied for source apportionment of PM_2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.     

Using stochastic risk assessment in setting information priorities for managing dioxin impact from a municipal waste incinerator

The objectives of this study were to assess site-specific carcinogenic risk of incinerator-emitted dioxins in a manner reflecting pollutant transfer across multimedia and multi-pathways. The study used site-specific environmental and exposure information and combined the Monte Carlo method with multimedia modeling to produce probability distributions of risk estimates. The risk estimates were further categorized by contaminated environmental media and exposure pathways that are experienced by human receptors in order to pinpoint significant sources of risk. Rank correlation coefficients were also calculated along with the Monte Carlo sampling to identify key factors that influenced estimation of risk. The results showed that ingestion accounted for more than 90% of the total risk and that risk control on ingestion of eggs, aboveground vegetables, and poultry should receive priority. It was also found that variation of parameters with variability accounted for around 35% of the total risk variance, while uncertainty contributed to the remaining 65%. Intake rates of aboveground vegetables, eggs, and poultry were the key parameters with the largest contribution to variance. In addition, sufficient sampling and analysis of dioxin contents in eggs, aboveground vegetables, poultry, soil, and fruit should be performed to improve risk estimation because the variation in concentrations in these media accounted for the largest overall risk variance. Finally, focus should be placed on reduction of uncertainty associated with the risk estimation through ingestion of aboveground vegetables, eggs, poultry, fruit, and soil because the risk estimates associated with these exposure pathways had the largest variance.