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Determination of the total organic carbon (TOC) from well logs is an important step in formation evaluation of shale reservoirs. The ΔlogR model is one of the most commonly used methods in determining the TOC of the source rocks. Wang et al. (2016) made three revisions on ΔlogR model to enhance the prediction accuracy, one of which is to use a changeable slope of log 10 R t versus porosity logs to replace a fixed value. Based on the original ΔlogR model and Wang's revision, we propose an improved model for a better TOC estimation in this paper. Specifically, the approximate linear baseline is replaced by a theoretical one. Under this condition, both the slope and resistivity and porosity log values of the baselined rocks vary from depth to depth. The determination of theoretical baseline is presented, and the varied baseline log values are obtained by a simple mathematical technique. In addition to the shale play of Western Canada Sedimentary Basin, two study areas of Sichuan Basin and Ordos Basin in China, were used to assess the applications of the proposed model and its improvement over the original model. The analysis and discussions presented in this paper indicate that the improved model offers more reliable results in shale plays with large lithological variations. Also, the TOC content of the three study areas was estimated using the proposed model. The results showed that the TOC values estimated from well logs and core analysis are in good agreement. [ABSTRACT FROM AUTHOR]

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This paper presents how the Erlangization method can be employed to estimate probabilities for the time-dependent behaviour of the level in a Markov modulated fluid flow. Formulas are given for estimating the probability of transitions to any fluid level, starting from an initial level in either an increasing or a decreasing stage in the phase process, prior to a finite time-point of interest. This methodology is applied to a model developed to study the evolution of uncontrolled fire perimeter for forest fires in Ontario, Canada. The model estimates the relative effectiveness of fire suppression under various fire-weather and fuel type scenarios, focusing on containment and escape probabilities. Numerical results are provided for illustrative purposes. [ABSTRACT FROM AUTHOR]

Pediatric obesity is an urgent and complex public health issue. Approximately one-third of Canadian children are overweight or obese, a proportion that highlights the need for effective and accessible services to improve short- and long-term health risks. In our experience, we have encountered a number of challenges common in pediatric obesity management across our clinical and research centers. For the purpose of this review, these challenges and our real-world experiences are grouped as issues that span (i) caring for children, adolescents, and families, (ii) collaborating with colleagues and (iii) working within the health care system. Collectively, we highlight a number of lessons learned from our years of experience and detail ongoing initiatives designed to optimize health services for managing obesity for children and adolescents in Canada. [ABSTRACT FROM AUTHOR]

This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature. [ABSTRACT FROM AUTHOR]