Recent oil market developments have made the understanding of both short term and long term oil price term structure and volatility an essential component in the decision making process. Short term volatility and the oil forward curve term structure have always been important for producers, storage operators and speculators in deciding how much to add or withdraw from inventories and what production volumes are needed to smooth the production and storage cycle, and how to arbitrage term spreads through the use of futures and options trading instruments In the first chapter, we start by looking at the oil futures price curve over time, and examine some of its properties such as its correlation and variance structure. Then we perform Principle Component Analysis (PCA) on the futures curve over our study period to glean some insights about the dominant statistical components driving the overall futures curve movement and volatility, and how these dynamics are changing over time. Next we move to the second object of investigation in this chapter, namely modeling of the oil volatility process, and examine how volatility has tended to react to inventory forward cover and futures curve term structure spread, and highlight some key findings. Then we employ a Vector Autoregressive model (VAR) to understand how oil price volatility interacts with other oil fundamental variables and relevant economic time series, such as the overall business cycle, industrial production, the level of commercial inventory and the share of OPEC spare capacity to world demand. Here, we look at the spot (or near month) oil price volatility only, as opposed to the entire future curve. Finally, we look at some causal and cointegration relationships between relevant variables. In the second chapter, we develop and solve a model of a dominant firm aiming to maximize profit by choosing optimal production and storage. We assume that the firm is a large and strategic player in this market, and can influence market prices through its supply decisions. We will also assume that the market has many small competitive producers, and we call them the competitive fringe. Supply by the fringe is uncertain and is often prone to shocks, which could be either positive (reflecting new temporary production boosts for example) or negative (due to disruptions caused by operational or geopolitical events). The dominant firm takes a stochastic supply of a competitive fringe as given, and then determines the optimal oil production policy and storage policy. Thus, the dominant producer factors-in this uncertainty in supply by other producers before making its supply decision. We assume aggregate demand has to equal the sum of both stochastic fringe supply, and the supply by the dominant firm. In particular, the overall market supply inherits the uncertainty introduced by the stochastic shocks to fringe supply. As a consequence, the equilibrium market price will also be a function of those shocks. To see how the model behaves given these assumptions, we solve a dynamic maximization model numerically then compare our findings to that of the perfectly competitive case to glean some insights on how dominant producers might balance their production and storage decisions. In the third chapter, we consider the decision of an oil producing company that faces a downward sloping demand curve, and an increasing price trend, with some randomness. The firm wishes to maximise its life-time profit buy choosing the optimal policy for adding investing in additional production capacity. Adding production capacity is costly, as is typical in large oil projects. The firm chooses between three additional levels of production, a small production increment, a medium increment, and a large increment. The cost of additional increments is linearly proportional to their size. In addition, the firm also faces a constant marginal production cost per barrel. As a simplification, we assume the the inverse demand curve is continuously shifting to the right, with some noise. This assumption reflects the ever increasing pressure on prices in the long run due to increased population growth and global energy needs, in addition to the continuous depletions of conventional oil resources, and the need for oil companies to go to harder and more costly plays in order to meet future supply needs.