Summary Risk analysis has become an integral part of the decision-making process within the Petroleum Industry. Today, petroleum engineers, geoscientists and project managers are using risking tools to evaluate the economic viability of both exploration and development projects. Conoco drilling engineers have combined a drilling cost spread-sheet along with a forecasting and risk analysis program to predict the range of both cost and days necessary to drill a well. The model utilizes risk analysis and incorporates Monte Carlo simulation along with regional cost data to generate drilling cost and time requirements for a well. Using this model, the Conoco drilling engineers effectively evaluate multiple drilling alternatives. subsequently, more informed risk related recommendations from the drilling engineers aids management in the decision-making process of drilling a prospect or developing a project This paper describes the spreadsheet and the risk analysis program used to generate the range of costs and days for a given well. In addition, the paper offers an example of the output data generated from the programs with an interpretation for a sample well. Introduction Historically, drilling engineers have relied on a deterministic approach to developing drilling cost estimates. The strength to this method is its simplicity and clearly set assumptions. To account for uncertainties and risk, the drilling engineer would build a "base" ease cost The "high" and "low" cost estimates were then developed using percentage additions or subtractions from the "base" case. Unfortunately, this approach does not describe the full range of possible outcomes or quantify the likelihood of any particular outcome. In an effort to demonstrate the drilling risks of drilling a prospect or developing a project to management, Conoco drilling engineers have begun performing risk analysis on the drilling expenditures and time through the use of a new drilling cost spreadsheet, a forecasting and risk analysis program which uses Monte Carlo simulation and a standardized methodology for data usage. Spreadsheet Model Our main spreadsheet model for probabilistic drilling cost estimating has a total of 152 line items subdivided into 29 major feature categories (Fig. 1). There are also two "breakout" spreadsheets for providing additional detail (one for detailing the estimate of total days on the well and one for detailing the casing program) (Figs. 2 and 3) for use by the engineer preparing the cost estimate if he or she desires to itemize such detail. Included into our spreadsheet is a summary page and a query sort for the "deterministic" or "most likely" values (cost estimates) of the 29 major feature categories. This query sort, which we call the "Big Rock Sort" lists in descending order, according to cost, the 29 major feature categories, calculates the percentage of the total for each such major feature category, and lists a cumulative percentage for the sorted features (Fig. 4). The "Big Rock Sort" enables us to conveniently identify the "key" cost drivers. We consider those features which account for 80 percent of the total cost estimate as "big rocks", and generally find that relatively few (generally less than 50 percent) of the features fall into our "big rock" category. We have also found that the particular features which fall into the big rock category vary on a case by case basis. To improve the quality of our estimate, we invest additional effort to describe the uncertainty for those features which show up as "big rocks". Our rule-of-thumb in this regard is that we deal probabilistically with those elements of the cost estimate that make up the top 80 percent of the total cost estimate.