Abstract

The net present value of an E&P-project is still the most important investment criteria in oil field acquisitions. Besides the discount rate assumption and the estimation of the strategic value of an E&P-project, the oil price assumption is the most important input parameter in E&P-project calculations. Even small variations in the oil price assumption can have large influence on the resulting project value. Therefore, for a realistic E&P-project valuation it is critical to use sophisticated methods for the estimation of the future oil price.

In the past, it was common practice to simply assume a fixed value for the long-term oil price (flat price); others use forward curves as a forecast (floating price). In probabilistic calculations (e.g. in Monte Carlo simulation) and in using the option pricing theory for valuing real options, stochastic processes are modeled. Here, the oil price is predominantly considered as to follow a Geometric Brownian Motion or a type of a Mean Reverting Process.

This paper presents an improved concept for modeling short- and long-term oil prices. The method is based on the premise that forward and future prices are the markets best guess of future oil prices. The future or forward curve is utilized as the expected value curve for the Mean Reverting Process. Thus, the oil price is modeled in a way that makes the resulting oil price assumption suitable for incorporating it in traditional net present value calculation as well as in sophisticated real option valuations. On the one hand for the discounted cash flow method it is critical to use reasonable short- and long-term values for the oil price, on the other hand for real options valuation it is necessary to model the oil price stochastically. The presented improved method fulfils both basic requirements and is therefore a strong improvement to common E&P-project valuations.

Introduction

Models for an E&P-project valuation incorporate many input parameters. In common net present value calculations these parameters are for example estimated reserves and production rates, operating and capital expenditures, discount rate, government take and oil price. If valuing real options oil price- or project volatilities, net convenience yield, time to option expiration or other model parameters extend the list of input data. All these factors affect the value of the project unequally. In sensitivity analyses this influence on the value of the project can be quantified. Campbell, Campbell and Campbell1 and Olsen et al.2 affirm that the oil price affects the result of an E&P-project valuation more than any other input parameter. Therefore it makes sense to use sophisticated methods for the estimation of the future oil price.

Not only the magnitude of the oil price plays an important role in project valuation. In Monte Carlo simulations and especially in real option valuations the result is critical to the run over time and the volatility of the oil price. Fundamental works on continuous-time option valuation was done by Black and Scholes3 and Merton4 and on discrete-time step models by Cox, Ross and Rubinstein5.

Comprehensive work on real options valuation in E&P-projects was done by Dias6; an extension to Monte Carlo simulation can be found in Zettl7.

In this paper a new concept for modeling the oil price for a more realistic E&P-project valuation is presented. Advantages and disadvantages of using the concept in simple net present value calculation, in Monte Carlo simulation and in real options valuation are highlighted. The paper concludes with an application of the concept to a sample valuation problem.

Concept of Futures Based Oil Price Modeling

The complexity of the model for the oil price used in E&P-project valuations is linked to the type of valuation method applied. For a simple net present value calculation it is not necessary to know the volatility or distribution of the oil price, whereas in Monte Carlo simulations the oil price is modeled by distributions (or stochastic processes) and in real option valuations by stochastic processes.

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