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Increasing hydrocarbon production via advanced technologies commonly involves the use of numerical simulation of the associated processes to minimize the risk involved in development decisions. The oil industry today requires much more detailed analysis with a greater demand for reservoir simulations with geological, physical, and chemical models than in the past. Without detailed simulations it is very unlikely that cost effective recovery processes can be developed and applied economically. Although reservoir simulation software is currently available, there are still many obstacles to its widespread and effective use in the upstream oil and gas industry. These include: z Data preparation and output analysis are often extremely time-consuming because of the amount and complexity of the required data. z Large uncertainties associated with the petrophysical properties and methods for incorporating these uncertainties into performance predictions are not currently time- or cost-effective. z Performance optimization using reservoir simulation is tedious and inefficient because of the time and effort required for generating, processing, and analyzing a large number of scenarios. The goal of this dissertation is to design and implement a user-friendly framework to overcome some of the abovementioned obstacles to promote the routine application of reservoir simulation in the processes of design and optimization. The framework includes several modules to identify the variables that have the most impact on hydrocarbon recovery using the concept of experimental design and response surface method. Several oil reservoir simulators such as VIP 1 , ECLIPSE 2 , and UTCHEM are integrated to perform the flow simulations associated with different hydrocarbon recovery processes. The framework implements an economic model that automatically imports the simulation production data to evaluate the profitability of a particular design. A large number of reservoir simulations can be run efficiently using a cluster of computers. This is the first time that a computing platform is developed with all these capabilities. Several field-scale applications are studied using our approach: - Well placement optimization taking into account reservoir and fluid uncertainties, - Surfactant/polymer flooding design and optimization with uncertainties in reservoir characterization, residual oil saturation, surfactant adsorption, price of crude oil and chemical, and discount rate, and - A surfactant remediation process with uncertainties in aquifer properties. According to our experience, the approach proposed in this dissertation can significantly save time for process optimization by a large factor compared to traditional method. This time savings includes for input preparation, postprocessing the simulation results, and the simulation execution time. A case study presented in this work shows that the clock time savings can be of the order of 40 for processing 158 surfactant/polymer simulations using UTCHEM.

This dissertation consists of three essays that are related to inventory management. The first essay models a single-product equity-owned firm which orders products from an outside supplier, borrows short-term capital for solvency, and issues dividends to its shareholders while facing financial risks and demand uncertainty. The firm maximizes the expected present value of the time stream of dividends. If there is a setup cost in this model, we show that an (s, S) replenishment policy is optimal by jointly optimizing the firm's Operational and financial decisions. The analysis is not a straightforward copy of Scarf's argument. The second part of this essay studies the same model with a smoothing cost (instead of a setup cost) and shows that the optimal policy has the same form as the traditional smoothing cost model. Although operational decisions and financial decisions interact with each other in these models, the optimal inventory policies have standard forms. The second essay obtains fill rate formulas for general review inventory models with base-stock-level policies. Ordering decisions in a general review model are made every R (R>=1) periods but demand arises every period. We provide exact fill rate formulas for single-stage model with a general demand distribution. A simple fill rate expression is derived for the model with normally distributed demand. For multi-stage models, we first discuss a general review procedure at each stage and then provide exact fill rate formulas for two-stage and three-stage models. There are parallel streams of literature which analyze identical models except that one stream has backorder costs and the other has fill rate constraints. The third essay clarifies redundancy in the two streams of dynamic inventory models with linear purchase costs. We show that optimal policies for either kind of model can be inferred from the other. That is, inventory fill rate constraints and backorder costs are interchangeable in dynamic newsvendor models.