Crude oil is by far the most important commodity to humans after water and food. Having a continuous and affordable supply of oil is considered a basic human right in this day and age. That is the main reason oil companies are in a constant search of cost effective ways and technologies that allow for an improved oil recovery rate. This would improve profitability as well. What almost everyone knows and dreads at the same time is that oil is an exhaustible resource. This means that as more oil is being produced every day, the amount of oil that remains to be produced shrinks even more. With almost all big oil fields worldwide having already been discovered, the challenge of finding new reserves grows harder and harder. A question that has always been asked is “when are we going to run out of oil?” Given the available technologies and techniques, no one could give an exact answer and if someone does, he/she would not be 100% sure of that answer. This study tries to approximate future oil production rates to the year 2050 using the Hubbert model. There are different models or tools to estimate future oil production rates, but the reason that the Hubbert model was chosen for this study is its simplicity and data availability. As any forecast, this study depends heavily on past trends but also factors in the current conditions. It is safe to say that this forecast (study) is as any other forecast, in which it will probably not mirror exactly what will happen in the future. Still, forecasts have to be done, especially for such an important commodity. This study predicts that the total oil to be recovered is 4.1 trillion barrels. It also shows that most major oil-producing countries are either passed or about to pass their peaks.

Since Hubbert proposed the "peak oil" concept to forecast ultimate recovery of crude oil for the U.S. and the world, there have been countless debates over the timing of peak world conventional oil production rate and ultimate recovery. From review of the literature, forecasts were grouped into those that are like Hubbert's with an imminent peak, and those that do not predict an imminent peak. Both groups have bases for their positions. Viewpoints from the two groups are polarized and the rhetoric is pointed and sometimes personal. A big reason for the large divide between the two groups is the failure of both to acknowledge the significant uncertainty in their estimates. Although some authors attempt to quantify uncertainty, most use deterministic methods and present single values, with no ranges. This research proposes that those that do attempt to quantify uncertainty underestimate it significantly. The objective of this thesis is to rigorously quantify the uncertainty in estimates of ultimate world conventional oil production and time to peak rate. Two different methodologies are used. The first is a regression technique based on historical production data using Hubbert's model and the other methodology uses mathematical models. However, I conduct the analysis probabilistically, considering errors in both the data and the model, which results in likelihood probability distributions for world conventional oil production and time to peak rate. In the second method, I use a multiple-experts analysis to combine estimates from the multitude of papers presented in the literature, yielding an overall distribution of estimated world conventional oil production. Giving due consideration to uncertainty, Hubbert-type mathematical modeling results in large uncertainty ranges that encompass both groups of forecasts (imminent peak and no imminent peak). These ranges are consistent with those from the multiple-experts analysis. In short, the industry does not have enough information at this time to say with any reliability what the ultimate world conventional oil production will be. It could peak soon, somewhere in the distant future, or somewhere in between. It would be wise to consider all of these possible outcomes in planning and making decisions regarding capital investment and formulation of energy policy.

Senior Honors thesis--Regis University, Denver, Colo., 2009. / Title from PDF title page (viewed on May 22, 2009). Includes bibliographical references.