Uncertainty ranking

Uncertainty ranking is helpful for evaluating the criticalness of the different design parameters. Our study involved pioneering tests in order to track the contribution to a threshold event on the basis of classical sensitivity techniques. Initial results showed that uncertainty related to the supporting structure has the most important impact on the variable of interest characterizing sensitivity to unbalance . 

The parametric method is an established statistical technique used for combining variables containing uncertainties, and has been advocated for use within the oil and gas industry as an alternative to Monte Carlo simulation. The main advantages of the method are its simplicity and its ability to identify the sensitivity of the result to the input variables. This allows a ranking of the variables in terms of their impact on the uncertainty of the result, and hence indicates where effort should be directed to better understand or manage the key variables in order to intervene to mitigate downside and/or take advantage of upside in the outcome. 

One significant feature of the Parametric Method is that it indicates, through the (1 + K 2) value, the relative contribution of each variable to the uncertainty in the result. Subscript i refers to any individual variable. (1 + K ) will be greater than 1.0 the higher the value, the more the variable contributes to the uncertainty in the result. In the following example, we can rank the variables in terms of their impact on the uncertainty in UR. We could also calculate the relative contribution to uncertainty. 

Figure 6.12 Ranking of impact of variables on uncertainty in reserves...

Keywords reducing uncertainty, cost-effective information, ranking sources of uncertainty, re-processing seismic, interference tests, aquifer behaviour, % uncertainty, decision tree analysis, value of information, fiscal regime, suspended wells, phased development. 

The ranking process is an important part of deciding an appraisal programme, since the activities should aim to reduce the uncertainty in those parameters which have the most impact on the range of uncertainty in HCIIP or UR. 

When considering exploration economics, the possibility of spending funds with no future returns must be taken into account. A typical world-wide success rate for rank exploration activity is one commercial discovery for every ten wells drilled. Hence a probabilistic estimation of the reserves resulting from exploration activity must take into account the main risks and uncertainties in the volume of hydrocarbons in place, the recoverable hydrocarbons, and importantly the risk of finding no hydrocarbons at all. 

A solution can be diluted by a factor of 200 using readily available pipets (f-mL to fOO-mL) and volumetric flasks (fO-mL to fOOO-mL) in either one, two, or three steps. Limiting yourself to glassware listed in Table 4.2, determine the proper combination of glassware to accomplish each dilution, and rank them in order of their most probable uncertainties. 

Use of absolute risk estimates (i.e., comparison against a target risk value) is more sensitive to uncertainty than is relative use (i.e., risk ranking) (Ref. 4). The reason for this is that, with relative applications, the same methodology and assumptions are usually used to evaluate the various alternatives. As a result, comparative risk estimates are subject to similar uncertainties. 

Frequency domain performance has been analyzed with goodness-of-fit tests such as the Chi-square, Kolmogorov-Smirnov, and Wilcoxon Rank Sum tests. The studies by Young and Alward (14) and Hartigan et. al. (J 3) demonstrate the use of these tests for pesticide runoff and large-scale river basin modeling efforts, respectively, in conjunction with the paired-data tests. James and Burges ( 1 6 ) discuss the use of the above statistics and some additional tests in both the calibration and verification phases of model validation. They also discuss methods of data analysis for detection of errors this last topic needs additional research in order to consider uncertainties in the data which provide both the model input and the output to which model predictions are compared. 

A study of the inventory list of 55,000 commercial substances shows that less than 10% of them account for 99.5% of production. A panel of authorities could provide a qualitative ranking of risk uncertainties which could narrow the list of substances to those of most immediate concern. 

The expert panel can bridge the information gaps of concern for a given chemical and provide a qualitative ranking of those risk uncertainties which are to be clarified by testing. This case-by-case approach will be directed by the basic interests and... 

The first step in the concentrations of cations in plant roots is the transfer of the cations across the plasmalemma of the epidermal cells, and although the subsequent transfer of ions to the xylem is fairly clear (99), it is this transport across the cell membranes of the epidermis where the uncertainty occurs. Studies of the uptake of 46Sc3+ into barley roots have demonstrated that there is no significant uptake beyond the epidermis and first rank of cortical roots of the seminal roots (100). Presumably other polyvalent cations will be immobilised at the same place. 

The MRL was based on a NOAEL of 26 mg/kg/day in the drinking water for 4 days for hepatic effects in mice (Larson et al. 1994b). The NOAEL of 26.4 mg/kg/day was divided by an uncertainty factor of 100 (10 for extrapolation from animals to humans and 10 for human variability) to arrive at the MRL of 0.3 mg/kg/day. A study performed by Moore et al. (1982) found renal effects in CFLP Swiss mice dosed at 65.5 mg/kg/day by gavage in oil. Another study by Larson et al. (1993) found both hepatic (elevated SDH, ALT and AST, hepatocyte necrosis) and renal (proximal tubule necrosis) lesions in Fischer 344 rats and hepatic lesions only in B6C3Fj mice induced by chloroform administered at 34 mg/kg/day once by gavage in oil. Lesions in the Larson et al. (1993) study were ranked as less serious LOAELs. 

Prioritize or rank the uncertainties affecting each risk hypothesis. 

Rank the model components in terms of their uncertainty. 

Uncertainty analysis and sensitivity analysis are tools that provide insight on how model predictions are affected by data precision. One of the issues in uncertainty analysis that must be confronted is how to rank both individual inputs and groups of inputs according to their contribution to overall uncertainty. In particular, there is a need to distinguish between the relative contribution of true uncertainty versus variability (i.e. heterogeneity), as well as to distinguish model uncertainty from parameter uncertainty. This case-study illustrates methods of uncertainty representation and variance characterization. 

And the cost-cutting and uncertainty that inevitably accompany a merger can hurt staff morale as people await the ax. Milton Moskowitz, the pioneer in ranking best places to work, says one reason Merck keeps placing so high on these lists is that it has adamantly refused to merge. People feel, if they do well, they ll move up. They won t be displaced by someone from another company, he notes. 

Dcplclion of stratospheric ozone Ozone depletion projected to result in 10,000 additional annual deaths not ranked liiglier because of the uncertainties in future risk. 

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