Stochastic Model and Sensitivity Analysis of the Economics of a "Shale" Development Program

Summary This paper presents the results of an economic modeling project to determine the economic value of a multi-well shale drilling program. A model was constructed to simulate a multi-year 200 well horizontal drilling program, and to enable a deterministic evaluation of the drilling program in terms of the customary yardsticks of Net Present Value, Return on Investment, etc. This model served as the basis for a evaluation of the drilling program. Both the deterministic and stochastic models of the drilling program were used as an input to a financial model of a small hypothetical public company producing approximately 9,000 BOEPD before the commencement of the shale drilling program. This small hypothetical company should be seen as a benchmark company as it represents the average financials of a typical E&P company after aggregating the assets, liabilities and income statements of 14 junior U.S. E&P companies with a similar size in terms of production volumes. This linkage of the drilling evaluation model to the financial metrics of this benchmark company measures the impacts of the drilling program on the company‟s financial performance and NAV valuation. The resultant model and the performance of a sensitivity analysis of the various model uncertainties on the company‟s financial performance and equity valuation is a major technical contribution of this paper. The paper also compares the financial results of drilling in the oil, wet gas and dry gas legs of a shale play, examining the effects of the initial production rates, decline rates, development efficiency and price variability on the financial metrics of the company and the specific capital requirements. A second technical contribution of the paper is to illustrate the usage of the model including stochastics to determine the optimal development path for the reservoir in order to maximize the benchmark company‟s financial metrics and NAV valuation, by comparing a best case development vs. a less optimal development path.