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Abstract Conventional oil and gas price forecasts typically include pessimistic, most-likely, and optimistic cases in an attempt to quantify economic uncertainty. An analysis of forecasts presented by industry and governmental organizations illustrates that conventional forecasting methods typically underestimate significantly the full range of uncertainty in oil and gas price forecasts. Economic indicators calculated using such forecasts will not reliably quantify investment risk. In this investigation we compared and contrasted several recently developed methods for quantifying upstream petroleum investment risk due to uncertainty in future prices. We analyzed five forecasting techniques - conventional, bootstrap, Inverted Hockey Stick (IHS), historical, and Sequential Gaussian Simulation (SGS). These techniques were applied to three synthetic projects and 23 industry projects to examine the uncertainty associated with economic indicators such as net present value, investment efficiency, and internal rate of return. Across all 26 projects, the conventional forecasts predicted a narrower range of economic indicator values than the four alternative methods, indicating that conventional methods routinely underestimate uncertainty. All four alternative forecasting techniques can provide operators with more reliable quantification of the uncertainty inherent in their investment decisions than provided by conventional methods currently in widespread use. The four alternative methods have unique strengths and weaknesses that may affect their applicability in particular situations. The SGS methods is the most rigorous and accurate method; however, it is also the most difficult to apply. The IHS method serves as a reasonable approximation, and can be easily incorporated into existing procedures and software. Introduction Investments in the petroleum industry are made under significant uncertainty. According to Capen, uncertainty is underestimated on an almost routine basis. Stermole and Stermole note that uncertainty will be a factor "no matter how comprehensive or sophisticated an investment evaluation may be." Experts have stated that oil and gas producing assets are subject to three classes of uncertainty: technical, political, and economic. Economic uncertainty affects investments within the petroleum industry at least as much as its technical counterpart. Unlike technical uncertainty, which should decrease with production of a reservoir, economic uncertainty does not decrease over the life of a petroleum reservoir. Future oil and gas prices represent a substantial source of economic uncertainty for operators considering exploration and development opportunities. Wiggins claims that price projections are as important as reserves determinations and production forecasts when evaluating hydrocarbon-producing properties. Campbell et al.  affirmed that errors in project valuations are more attributable to price forecasts than any other component. Although we cannot eliminate uncertainty from investment evaluations, we can better quantify the uncertainty by accurately predicting the volatility in future oil and gas prices. Reliably quantifying economic uncertainty will enable operators to make better decisions and allocate their capital with increased efficiency. Price projections within the petroleum industry are often comprised of pessimistic, most-likely, and high cases in an attempt to quantify uncertainty. Typically, these forecasts initially decline or remain flat for a period of time before increasing monotonically. Such price projections are referred to as "hockey stick" forecasts. The California Energy Commission (CEC) published a natural gas price forecast beginning in 1997 that clearly illustrates the characteristic "hockey stick" shape commonly exhibited by conventional price forecasts (Fig. 1). A segment of the CEC natural gas price forecast beginning in 2002 is shown in Fig. 2 along with actual gas price data realized by the market. The CEC forecast clearly underestimates the true range of product price uncertainty. A considerable portion of the actual gas price data falls outside of the high and low extremes presented in the forecast.
This paper was prepared for presentation at the 1999 SPE Annual Technical Conference and Exhibition held in Houston, Texas, 3–6 October 1999.
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.
Abstract Oil price forecasting has been shown to be challenging if not impossible for the long-term. However, the oil price has a major impact on Exploration and Production projects. Historical Project Realized Oil Price (PROP) can be calculated for example projects by summing up the total project revenue using the actual oil prices and dividing through the total amount of oil produced. For different starting dates of example projects, the PROP changes. Determining the PROP for different starting times, a Cumulative Distribution Function (CDF) can be derived. Adjusting this CDF for expected "half cycle breakeven costs" for the low limit and demand considerations for the high case leads to a PROP range that can be used for future project evaluation. Including PROP ranges into project evaluation allows for the selection of the most attractive development option, Value of Information analysis and project Probability of Economic Success (PES) calculation including oil price uncertainty. Furthermore, using PROP ranges rather than oil price scenarios enables a distinction between short-term budget planning and long-term project development. For budget planning, a scenario approach is suggested while for long-term planning PROP ranges should be used. Applying long-term planning on PROP ranges leads to less fluctuation in staff planning and small annual adjustments in PROP range forecasting. Also, using PROP ranges results in increasing PES project hurdles at low oil prices and lower PES hurdles at high oil prices. Hence, at low oil prices the risk averseness of the company is increased. Another effect of using PROP ranges is that at high oil prices robustness of projects to low oil prices is included in the assessment. To investigate the effect of PROP ranges on portfolio PES hurdles and project PES hurdles, a simplified linear-fit-model was developed. The results of the model showed that the project PES hurdles in a Value at Risk assessment can be determined applying the linear-fit-model to quantify the oil price dependency. The required individual project PES hurdles can be adjusted using the linear-fit-model to account for oil price uncertainty.
The Marcellus Shale Gas play has been a popular area of research and investment over the past 10 years. It is commonly cited as the largest shale gas play in the United States with an estimated recoverable gas of 141 TCF; 50 TCF in Pennsylvania. Because of this Marcellus will doublessly have a significant role in meeting the U.S. energy demand in the future. Horizontal drilling with multistage hydraulic fracturing have made production from Marcellus technically achievable though the permeability across the shale formation ranges from 400 to 800 nano-Darcy. Recently, the down-turn of the natural gas market has led to a drastic reduction of rig count in Marcellus, and operators have seen reduced economic performance. This paper presents an after tax economic analysis model for an average shale gas, condensate, and oil well in the liquid rich areas of Marcellus Shale, which can potentially be used to determine the profitability of developing within Marcellus formation. Both deterministic and stochastic economic models are presented. Production decline, cost of drilling, cost of completion, leasing operation expense, effective tax rate, as well as current and future petroleum prices are all incorporated. A comparison between the economic models built in this study and an existing before tax economic model from literature is also presented. Conclusions regarding the current and future development of Marcellus shale have been proposed based on results from the economic model.