Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information. / text

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This dissertation combines studies of aeolian bedforms and aeolian dune-field patterns to create a comprehensive set of tools that can be used in tandem (or separately) to extract information about climate change and landscape evolution, and to identify the controls on formation for specific modern dune fields or ancient aeolian sequences. The spatial distribution of surface processes, erosion/deposition rates, and lee face sorting on aeolian dunes are each a function of the incident angle. This correlation between stratification style and incidence angle can be used to develop a “toolbox” of methods based on measurements of key suites of parameters found in ancient aeolian deposits. Information obtained from the rock record can be used as input data for different kinds of numerical models. Regional-scale paleowind conditions can be used to validate paleoclimate and global circulation models. Understanding the natural variability in the Earth’s climate throughout its history can help predict future climate change. Reconstructed wind regimes and bedform morphologies can be used in numerical models of aeolian dune-field pattern evolution to simulate patterns analogous to those reconstructed from ancient aeolian systems. Much of the diversity of aeolian dune-field patterns seen in the real world is a function of the sediment supply and transport capacity, which in turn determine the sediment availability of the system. Knowledge of the sediment supply, availability, and transport capacity of aeolian systems can be used to predict the amount of sand in the system and where it might have migrated. This information can be extremely useful for development and production of oil and gas accumulations, where a discovery has been made but the spatial extent of the aeolian reservoir is unknown. / text

Ce travail théorique vise à étudier, via les méthodes Premiers Principes, les propriétés des complexes de métaux de transitions, left[Mleft(dmbpyright)_{3}right]^{n+}nCi^{-} pour un usage en batterie. Pour cette étude ab-initio, les composés mono et bi-nucléaires ont été retenus. La pertinance de notre modélisation a été validée sur les composés mononucléaires. Nous nous sommes interessé au complexes de Fe, Ru et Cu pour lesquels une validation expérimentale était possible. Notre étude a principalement consisté à faire varier les degrés de liberté que nous possédons pour optimiser le voltage et la cinétique de chargement des batteries. Pour cela, nous avons fait varier le TM = Fe, Ru, et Cu, la nature des contre-ions Ci^{-}=PF_{6}^{-}, TFSI^{-} et ClO_{4}^{-} en interaction avec le polymère lors du processus de charge, ainsi que la longeur de la chaîne alkyl qui sépare les deux monomers dans le cas des composés binucléaires. Le composé à base de Fe avec une chaîne -left(CH_{2}right)_{n=6}- a été retenu comme le meilleur candidat pour une application batterie. Le composé à base Ru montre un comportement proche de celui du Fe, quant-au complexe de Cu, il présente des changements de géométrie locale sous chargement trop importants, le rendant peu apte à conduire à une cinétique efficace. Cette étude nous a permis de déterminer que l'approximation PBE était le meilleur choix possible pour modéliser nos complexes dans les conditions de fonctionnement en batterie (dans le champ créé par les contre-ions) et que l'approximation PBE0, généralement utilisée dans la littérature, ne pouvait rendre compte de la physico-chimie de nos composés dans de telles conditions.De surcroît, nous avons dévelopé pour le complexe de Fe, un potentiel atomistique de type “Champ de forces” de manière à pouvoir aborder les aspects dynamiques impliquant de plus grandes tailles de boîte de simulation. Ici, nous modélisons une structure 3D, totalement réticulée à partir de nos monomères à base de Fe. Nous nous sommes servi de la base de donnés DFT que nous avions généré (énergies, géométries, état de spin et fréquences vibrationnelles calculées) pour ajuster les paramètres entrant dans l'écriture du modèle. La construction de la géométrie initiale du polymère 3D a nécessité l'écriture d'un code de calcul visant à produire un arrangement complétement réticulé et à assigner les charges effectives issues des calculs DFT. Ce modèle nous a permis de déterminer les coefficients de diffusion des contre-ions pour les états totalement chargé et non-chargé. Un calcul plus ambitieux vise à déterminer les chemins de diffusion des contre-ions lors d'un processus de chargement en considérant un seul centre de degré d'oxydation 3+ au centre du polymère 3D, pour lequel les centres actifs possèdent un degré d'oxidation 2+. Les contre-ions assurent la neutralité globale.Keyword: Polymer, Electrochemistry, Li-ion Battery, DFT, Force Field development, 3D structure, Atomistic modeling / Abstract Standard redox potentials for mono and bi-nuclear transition metal (TM) complexes left[Mleft(dmbpyright)_{3}right]^{n+}nCi^{-}, have been investigated using First Principles Calculation. Three metal centers are investigated: Fe, Ru, and Cu. Our modeling is validated on mono-nuclear compounds. This approach consists in determining the best small polymer (bi-nuclear) made out of these monomers for a battery application. For that, we varied the three available degrees of freedom i.e., the nature of the central TM atom (Fe, Ru, and Cu), counter-ions Ci=PF_{6}^{-}, TFSI^{-} and ClO_{4}^{-} in interaction with the polymer, and the alkyl chain -left(CH_{2}right)_{n}- of length n that connects both mono-nuclear in the bi-nuclear compound. The Iron compound with -left(CH_{2}right)_{n=6}- is found to be the best candidate. The left[Culeft(dmbpyright)_{2}right]^{n+}nCi^{-} complex shows too much structure deformation upon loading, making it less reliable for cathode material. Moreover, we studied two XC functional, PBE and PBE0 and found, for three complexes PBE approximation retains the ligand field picture whereas PBE0 functional induces an exaggerated and unexpected band dispersion by dissolving the ligand field picture expected for the octahedral environment of the TM in the studied complexes. These findings validate that hybrid functional for which it was designed to localize and cancel self-interaction error does not work for all system. More particularly, the PBE0 approximation fails to model the three complexes (Fe, Ru, and Cu) in functional conditions (in the field made by the counter-ions).Abstract Further, we have developed an atomistic potential relying on the Force Field scheme for the Iron complex in order to study the dynamical properties of this compound at larger simulation scale (3D reticulated polymerization made of our Fe complex monomers). We made an intensive use of our DFT data (energies, geometries, spin-state configurations and calculated vibrational properties) to develop the required parameters entering the model. Moreover, computational techniques (written python language) were developed specifically to create a 3D structure of transition metal complexes satisfying the condition to be fully reticulated. Bounding conditions had to be designed and a procedure aiming at fixing reliable and physical effective charges on each atom of the simulation cell (compatible with DFT results) were developed. Our first simulations have been attached to calculate the diffusion coefficients of the counter-ions in both the fully loaded and unloaded states. A more ambitious and realistic calculation aims at investigating the paths of the counter-ions when one single center starts to be loaded in an unloaded environment.Abstract Keyword: Polymer, Electrochemistry, Li-ion Battery, DFT, Force Field development, 3D structure, Atomistic modeling

[pt] Os custos de instalação e as taxas de produção ao longo da vida de um reservatório de óleo e gás são influenciados diretamente pela localização, quantidade e capacidade das Unidades Estacionárias de Produção (UEPs). A distância entre um poço e a UEP a qual foi alocado é um fator impactante na perda de carga a que os fluídos são submetidos. A dissipação de energia aumenta quando essa distância é maior e todo o sistema de produção recebe a interferência negativa desta perda, o que compromete as taxas de recuperação. A necessidade de respeitar as restrições de capacidade das UEPs faz com que outras decisões precisem ser tomadas no mesmo momento em que se decide a localização de cada uma. Este trabalho descreve um modelo baseado em Algoritmos Genéticos para a otimização simultânea da quantidade, localização e dimensionamento de Unidades Estacionárias de Produção (UEPs). Para lidar com as restrições lineares e não lineares do problema utiliza-se a técnica chamada de GENOCOP III - Genetic Algorithm for Numerical Optimization of Constrained Problems e funções de penalidade. O objetivo da otimização é maximizar o Valor Presente Líquido (VPL) que depende da curva de produção de cada configuração obtida como possível solução. Para obter a curva de produção são realizadas simulações de reservatório que utilizam tabelas de escoamento multifásico para representar o sistema de produção externo ao reservatório. O modelo de solução foi testado em um modelo de reservatório baseado em um caso real. Os resultados encontrados indicam que a utilização deste modelo de solução como ferramenta pode auxiliar a tomada de decisão dos especialistas responsáveis pelo desenvolvimento de campos de petróleo. / [en] Installation costs and production rates over the life of an oil and gas reservoir are directly influenced by the location, number and capacity of the Production Units. The distance between a well and the Production Unit to which it has been allocated is an important factor in the loss of fluids pressure. The power dissipation increases when the distance is bigger and the entire production system receives the negative interference of this loss, compromising recovery rates. There is a need to take into account restrictions that apply to the capacity of Production Unit at the same time as there localization are decided. This paper describes a model with genetic algorithms for the simultaneous optimization of the quantity, location and sizing of Production Units. To deal with the constraints of the problem we use a technique called GENOCOP III - Genetic Algorithm for Numerical Optimization of Constrained Problems. The goal of the optimization is to maximize the Net Present Value (NPV) which depends on the production curve of each configuration obtained as a possible solution. The production curves are obtained by reservoir simulations with multiphase flow tables that represent the system external to the reservoir. The solution model was tested in a reservoir model based on a real case. The results indicate that using this solution model as a tool can assist the decision making of experts responsible for oil field development.