Probabilistic decline curve analysis (PDCA) methods have been developed to quantify uncertainty in production forecasts and reserves estimates. However, the application of PDCA in shale gas reservoirs is relatively new. Limited work has been done on the performance of PDCA methods when the available production data are limited. In addition, PDCA methods have often been coupled with Arp’s equations, which might not be the optimum decline curve analysis model (DCA) to use, as new DCA models for shale reservoirs have been developed. Also, decline curve methods are based on production data only and do not by themselves incorporate other types of information, such as volumetric data. My research objective was to integrate volumetric information with PDCA methods and DCA models to reliably quantify the uncertainty in production forecasts from hydraulically fractured horizontal shale gas wells, regardless of the stage of depletion. In this work, hindcasts of multiple DCA models coupled to different probabilistic methods were performed to determine the reliability of the probabilistic DCA methods. In a hindcast, only a portion of the historical data is matched; predictions are made for the remainder of the historical period and compared to the actual historical production. Most of the DCA models were well calibrated visually when used with an appropriate probabilistic method, regardless of the amount of production data available to match. Volumetric assessments, used as prior information, were incorporated to further enhance the calibration of production forecasts and reserves estimates when using the Markov Chain Monte Carlo (MCMC) as the PDCA method and the logistic growth DCA model. The proposed combination of the MCMC PDCA method, the logistic growth DCA model, and use of volumetric data provides an integrated procedure to reliably quantify the uncertainty in production forecasts and reserves estimates in shale gas reservoirs. Reliable quantification of uncertainty should yield more reliable expected values of reserves estimates, as well as more reliable assessment of upside and downside potential. This can be particularly valuable early in the development of a play, because decisions regarding continued development are based to a large degree on production forecasts and reserves estimates for early wells in the play.

The focus of this thesis is on index tracking that aims to replicate the movements of an index of a specific financial market. It is a form of passive portfolio (fund) management that attempts to mirror the performance of a specific index and generate returns that are equal to those of the index, but without purchasing all of the stocks that make up the index. Additionally, we consider the problem of out-performing the index - Enhanced Indexation. It attempts to generate modest excess returns compared to the index. Enhanced indexation is related to index tracking in that it is a relative return strategy. One seeks a portfolio that will achieve more than the return given by the index (excess return). In the first approach, we propose two models for the objective function associated with choice of a tracking portfolio, namely; minimise the maximum absolute difference between the tracking portfolio return and index return and minimise the average of the absolute differences between tracking portfolio return and index return. We illustrate and investigate the performance of our models from two perspectives; namely, under the exclusion and inclusion of fixed and variable costs associated with buying or selling each stock. The second approach studied is that of using Quantile regression for both index tracking and enhanced indexation. We present a mixed-integer linear programming of these problems based on quantile regression. The third approach considered is on quantifying the level of uncertainty associated with the portfolio selected. The quantification of uncertainty is of importance as this provides investors with an indication of the degree of risk that can be expected as a result of holding the selected portfolio over the holding period. Here a bootstrap approach is employed to quantify the uncertainty of the portfolio selected from our quantile regression model.

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Pour commercialiser un véhicule, les constructeurs automobiles doivent se soumettre à la réglementation sur le bruit extérieur. Le règlement de la commission économique pour l'Europe, ECE R51.03, spécifie les niveaux admissibles que peut rayonner un véhicule automobile en roulage. Ce règlement est entré en vigueur depuis le 1er juillet 2016 pour remplacer l'ancien règlement ECE R51.02 (changement de méthode d’essai et sévérisation des niveaux de bruit admissibles). La diminution drastique des niveaux sonores tolérés se fait en trois étapes : passage de 74 dB (A) sous l'ancien règlement, à 68 dB (A) en 2024. Par conséquent, les constructeurs ainsi que les fournisseurs automobiles seront confrontés à un grand défi pour atteindre cet objectif. Ainsi, l'objectif de ces travaux de thèse consiste à développer une aide à la modélisation totale du bruit de passage d’un véhicule, comme le préconisent les essais réglementaires. Le but est de construire des modèles optimaux pour prévoir et évaluer avec précision le bruit que peut rayonner un véhicule en roulage plus tôt dans son cycle de développement, i.e. avant l'étape d'industrialisation. Il faut alors se placer dans la recherche d'un compromis entre précision des estimations, sensibilité aux paramètres, robustesse de la méthode et efficacité numérique. / Currently, to put a vehicle on market, car manufacturers must comply to a certification test of exterior noise. The regulation of the United Nations Economic Commission for Europe, ECE R51-03, specifies permissible levels a rolling motor vehicle can emit. This regulation is applied since July 1st, 2016, to replace the old regulation ECE R51-02 (test method change and tightening of permissible levels). The drastic reduction in noise levels will be done in 3 steps: from 74 dB (A) under the old regulation to 68 dB (A) in 2024. Therefore, manufacturers as well as their suppliers will face a great challenge to achieve this goal. The objective of this thesis is to develop an aid to the modeling of the pass-by noise of a vehicle, as called for in regulatory testing. The goal is to predict and evaluate accurately the noise emissions earlier in the vehicle development cycle, i.e. before the industrialization stage. We must then seek a trade-off between accuracy of estimates, sensitivity to parameters, robustness of the method and numerical efficiency.