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In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region. [ABSTRACT FROM AUTHOR]

Drinking water advisories (DWAs) are a major issue facing many First Nations communities across Canada. This paper analyzes drinking water system data matched to DWA data using a probabilistic neural network (PNN) model to find key factors that influence the occurrence, frequency, duration, and cause of DWAs. First, for all data across Canada and subsequently for a number of data sets for individual provinces, the analyses were completed using an ensembles approach of running the same data set multiple times (in this case, five) to ensure that factors identified were representative of the entire data set and not just the training set. The results were compared with those from previous studies that used data mining techniques. Accuracies above 74% were achieved and key factors influencing each of the models were identified. The PNN models are a practical and powerful diagnostic tool for identifying key system attributes influencing DWAs, which can then be used to develop effective targeted solutions to mitigate the core issues that result in DWAs. These types of models can be used in other applications to identify influential factors and guide decision makers. [ABSTRACT FROM AUTHOR]