Inverted Hockey Stick Method Quantifies Price Uncertainty in Petroleum Investment Evaluation

In this paper, we present a new method for quantifying the uncertainty of economic projections due to uncertainty in future oil prices. Traditionally, the petroleum industry has employed what are known as “hockey stick” price forecasts, i.e., monotonically increasing price profiles, in economic calculations to evaluate investment opportunities. Calculations are often run using most-likely, optimistic, and pessimistic price profiles in an attempt to quantify the uncertainty in the resulting economic indicators. These conventional hockey stick methods significantly underestimate uncertainty because they do not reproduce the volatility inherent in oil prices. Stochastic methods that attempt to model price volatility have been used successfully and indicate that there is considerably more uncertainty in oil and gas development projects than has been previously recognized. However, many operators do not use stochastic methods for modeling oil prices, most likely because they require more time and effort to implement than conventional methods.

The Inverted Hockey Stick method presented herein is similar to conventional methods in that only three price realizations are run to quantify uncertainty. However, the high and low cases are designed to better capture the range of possible future price paths. Uncertainty ranges for economic indicators predicted by the new method are comparable to 70-95% probability ranges predicted by the stochastic bootstrap method, significantly greater than the 32-42% ranges predicted by conventional methods. This new method can be easily incorporated into existing economic modeling systems. Recognition of the greater uncertainty in oil and gas investment opportunities, both upside as well as downside, should improve investment decision making.     

Quantification of Oil Price Uncertainty in Economic Evaluation Using Sequential Gaussian Simulation

In this paper, we propose improved methodology for quantifying the uncertainty in petroleum economic evaluations due to uncertainty in future oil prices. Conventional “hockey stick” price forecasts commonly used in industry significantly underestimate uncertainty because they do not reproduce the volatility inherent in oil prices. Some authors have proposed stochastic methods, such as the bootstrap method, to better model oil price volatility. A disadvantage of the bootstrap method, however, is that it can produce price realizations with unrealistically low or high prices. To address this shortcoming, we present two stochastic methods that honor both the historical distribution of oil prices as well as the historically observed variability in oil prices.

The first is an approximate method in which multiple future price realizations are taken directly from different windows of historical uninflated price data. The method is easy to apply and can provide greater insights into the uncertainty and risks associated with project economic evaluation. However, this method can potentially overestimate uncertainty in oil prices and should be used judiciously.

The second method uses sequential Gaussian simulation to generate equiprobable future oil price realizations consistent with both the frequency distribution and temporal variability of historical prices. Although the method is more difficult to apply, it provides a more statistically sound basis for quantifying uncertainty.

The improved methods are compared to conventional methods for three synthetic cases derived from the literature and a field case. Results demonstrate that conventional methods underestimate oil price uncertainty more severely for projects with accelerated cash flow streams, which characterize most petroleum development projects. This indicates that probabilistic methods for quantifying uncertainty in oil prices, such as the sequential Gaussian simulation method proposed here, are needed to provide operators with reliable quantifications of the uncertainties and risks associated with individual projects, which should enable improved investment decision making.     

Estimation of volatility of selected oil production projects

In oil project valuation and investment decision-making, volatility is a key parameter, but it is difficult to estimate. From a traditional investment viewpoint, volatility reduces project value because it increases its discount rate via a higher risk premium. Contrarily, according to the real-option pricing theory, volatility may aggregate value to the project, since the downside potential is limited whereas the upside is theoretically unbounded. However, the estimation of project volatility is very complicated since there is not a historical series of project values. In such cases, many analysts assume that oil price volatility is equal to that of project. In order to overcome such problems, in this paper an alternative numerical method based on present value of future cash flows and Monte Carlo simulation is proposed to estimate the volatility of projects. This method is applied to estimate the volatility of 12 deep-water offshore oil projects considering that oil price will evolve according to one of two stochastic processes: Geometric Brownian Motion and Mean-Reverting Motion. Results indicate that the volatility of commodity usually undervalue that of project. For the set of offshore projects analyzed in this paper, project volatility is at least 79% higher than that of oil prices and increases dramatically in those cases of high capital expenditures and low price.     

The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June 06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.     

石油和化工行业经济回顾与展望

从生产、价格、投资和进出口以及经济效益等方面对2006年前三季度石油和化工行业运行状况进行了分析，认为运行过程中仍然存在投资增速过快、油电价格过高、油品供应偏紧和节能与环保压力大等问题，针对这些问题提出了一些政策建议。认为2007年良好的国内外经济形势将为石油和化工行业的发展提供宽松环境，但一些不确定因素如贸易环境的变化、产能过剩的压力和油价走势的不确定性仍会影响到行业的生存和发展。预计2007年石油和化工行业将继续保持增长势头，增长幅度在15％～18％左右。     

The relationship between international crude oil prices and China’s refined oil prices based on a structural VAR model

With the frequent fluctuations of international crude oil prices and China’s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China’s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a significant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheritance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.     

Production sharing contract: An analysis based on an oil price stochastic process

Assuming that oil price follows the stochastic processes of Geometric Brownian Motion (GBM) or the Mean-Reverting Process (MRP), this paper takes the net present value (NPV) as an economic index and models the PSC in 11 different scenarios by changing the value of each contract element (i.e. royalty, cost oil, profit oil as well as income tax). Then the NPVs are shown in probability density graphs to investigate the effect of different elements on contract economics. The results show that under oil price uncertainty the influence of profit oil and income tax on NPV are more significant than those of royalty and cost oil, while a tax holiday could improve the contractor’s financial status remarkably. Results also show that MRP is more appropriate for cases with low future oil price volatility, and GBM is best for high future oil price volatility.     

随机油价下的油田开发规划优化模型

为了科学合理地制定油田开发规划方案，建立了随机油价下的油田开发规划优化模型。首先利用功能模拟原理（微分模拟方法及神经网络方法）建立油田采油厂产量与其对应影响因素的关联关系，然后在随机油价下，根据油田开发的实际情况，在对决策变量、目标函数、约束条件进行分析的基础上，研究每个采油厂或开发单元的具体情况并建立定产量、定成本、效益最好的产能分配优化模型，并采用改进内点法对优化模型进行求解。采用这一优化过程，成功地解决了油田开发规划中随机油价下的油田（采油厂）产量、工作量及成本的最优分配问题。将该模型应用干中国某油田中后期开发阶段产量规划中，产生了巨大的经济效益。表3参12     

Opportunities and Risks of Natural Gas Investment in China under the Circumstance of Low Oil Prices

After more than 30 years of rapid growth, the Chinese economy has entered the "new normal" of moderately high growth. Due to the effects of multiple factors, the international oil price has remained consistently low. The low oil price has exerted critical effects on international natural gas investment. At the same time, the market-oriented price mechanism of natural gas in China is gradually taking shape; the concept of low carbon development is widely advocated; and the use of natural gas gains popularity in the city. Such factors provide great opportunities for investment in the natural gas market of China, including boiler coal-to-gas transformation, natural gas distributed energy and natural gas vehicles. However, risks also exist, such as the lower competitiveness of natural gas, its excess production capacity and dwindling consumption in some gas consumption industries, an insufficient driving force for facilitating the coal-to-gas transformation of industrial fuel users, reverse substitution of "coal in place of gas" in some enterprises, nontransparent costs of the downstream link of the natural gas price chain, and mismatches and nonsynchronous adjustments in natural gas prices and electricity prices.     

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用过去或现在的管道投资费规划设计未来的管道建设，可能导致建设期投资费不足，需先预测投资费再作规划设计；不同直径、壁厚的油气管道，其投资费亦不相同，需用回归方法来拟合其离散数据。因此，采用灰色预测和二元线性回归方法相结合，建立油气管道投资费的预测数学模型。算例用１４年的价格指数组成时间序列，采用新陈代谢ＧＭ（１，１）动态预测方法预测价格指数；将管道基期投资费按四组小范围的管径规格进行划分，再分别建立二元线性回归模型，并与管径规格不分组的回归模型进行比较，用分组的回归模型计算的基期管道投资费精度高、误差小。计算机运行结果表明预测、回归结果令人满意。经计算、分析、综合后，获得四组不同管径范围的管道投资费预测数学模型，预测出第１５年的管道投资费。由此可预测和计算第１６、１７、１８年的管道投资费。同时，还强调预测与计算中需注意的三个问题。     

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最小经济储量规模的确定,可以减小油气田勘探部署的盲目性,只有找到的探明储量大于最小经济储量规模，该探区才有可能部署勘探开发工作量。文章采用净现值法，根据油气田地理环境、地质特征、流体性质、天然气价格、勘探和开发投资、税收及考虑资金时间价值等情况，建立了最小天然气田经济储量规模模型，并用该模型分别计算了松辽盆地十屋断陷常规气藏不同天然气价格下、不同气藏埋深的最小经济储量规模系列。通过净现值法计算出的最小经济储量规模，提供了十屋断陷天然气勘探规模的经济界限。     

低油价对天然气产业链的影响

2014年下半年以来,国际原油价格迅速下跌,给全球天然气产业链都带来了深刻的影响。如何应对低油价,是所有油气从业者都不能回避的问题。为此,梳理了近年来全球天然气产业链从高油价时期步入低油价时期的发展历程;分析了低油价对全球天然气生产格局、价格,以及发展前景的影响;并着重探讨了持续的低油价为中国的天然气产业链带来的机遇和挑战。结论认为:1油价下跌使油气公司经营业绩下滑,为度过当前困境,油气公司都采取了削减资本支出等措施,对未来油气产量可能会产生不利影响;2低油价使世界天然气价格都有不同程度的下跌,相对原油价格,天然气经济性下降明显;3非常规天然气因其高成本遭受低油价冲击大,国外非常规油气公司破产现象加剧,国内油气企业对待非常规资产的态度差异大,以中石油、中石化为代表的部分企业继续加紧推进其他企业慎重对待;4油气资产并购市场因多个因素影响,市场表现较为"冷静";5中国天然气产业链正处于快速发展阶段,本轮油价下跌对天然气产业链结构改革而言则是机遇与挑战并存。     

吞吐开采稠油油田经济界限研究

以高升油田为例,通过研究近几年单井效益评价结果,采用统计分析方法,确定出区块经济极限日产油;运用盈亏平衡方法,计算在不同原油售价下,措施投入与增油量关系、稠油区块蒸汽吞吐开发的经济极限油汽比,并编制各种经济极限指标模板。各项经济极限指标对实现油井精细管理、措施优选及吞吐井优化注汽具有重要意义。     

胜利油区油气风险勘探成本计算与统计分析

在评价国外油气储量成本计算方法的同时，针对油气勘探投资的长期累积性和探明地质储量在时点分布上的不均衡性，分别引入了物价指数和时间价值因素；对投资和储量的价值进行了相应地调整，对物价水平的变化采用了起始午和当前午2个参照基准，提出了静态储量成本、不变价格储量成本、增量储量成本和动态储量成本等4个概念，并按照单年平均、累积平均和移动平均等不同统计方法建立了相应的数学模型。对胜利油区1986-2002年的勘探和投资数据进行了测算，对储量成本的历史发展趋势和随机分布状态进行了统计分析，分析结果表明，物价和时间价值因素对储量成本计算有显著影响。为了提高成本信息的相关性，使用动态储量成本指标对石油天然气公司的勘探效益进行评价更为恰当。     

辽河油田超稠油蒸汽驱技术界限研究与应用

超稠油的黏度超过了蒸汽驱黏度界限,通常被认为不适合蒸汽驱开发,实际上超稠油蒸汽驱存在启动温度,当油藏温度超过启动温度后,蒸汽驱可以实施.针对辽河油田曙一区超稠油蒸汽驱技术界限认识不清的问题,运用室内实验及数值模拟手段,建立了蒸汽驱启动温度模型,并利用经济效益法建立了不同油价下经济极限产油量预测公式.研究表明:曙一区超稠...     

钻井投资和油气价格对财务盈利能力影响实证分析

财务盈利能力是工程项目经济评价的重要内容之一。反映财务盈利能力的指标主要包括：内部收益率、投资回收期、财务净现值和投资利润率。对于油气田开发工程项目而言，影响财务盈利能力指标的因素很多，但钻井投资与油气价格是两个不可忽视的因素。因此，文章在建立钻井投资和油气价格与反映油气田开发工程项目财务盈利能力各指标数学模型的基础上，实证性分析了某油气田钻井投资和原油价格对财务盈利能力各指标的影响，在其他条件一定的情况下，利用回归拟合方法，拟合出了财务盈利能力各指标与钻井投资和原油价格关系的函数，计算了财务盈利能力各指标对钻井投资和原油价格的弹性系数，为油气田开发工程项目投资决策提供了参考依据。     

S曲线非确定性经济评价方法在油气项目投资决策中的应用,

勘探开发项目经济评价和投资决策分析方法对石油天然气公司的投资决策和投资质量起到非常关键的作用。基于确定性模型的经济评价不能从全貌来评估项目,会产生一定的估值偏差,进而导致较大的投资风险。非确定性模型因此被引入到经济评价和决策分析中,包括决策树、基于贝叶斯定理的VOI方法、蒙特卡洛模拟等,用以解决特定类型的投资决策问题。但是这些传统的非确定性模型和方法不具有通用性,各自存在一定的应用局限性,且不能很好地对项目技术经济风险进行量化和分析。S曲线非确定性经济评价方法(简称S曲线分析方法)提供了一套具有一定通用性的评价方法和决策分析机制,能够较好地完成项目经济风险量化分析、计算盈亏平衡概率,并提供决策所需要的各种比较分析。通过深入分析S曲线分析方法的理论、特点和优势,介绍了实施经验和认识,并通过案例分析展示其在勘探开发项目上的应用。近年很多国际石油公司构建了基于S曲线分析方法的项目经济评价和投资决策分析体系,持续提升经济评价和决策分析水平,这对中国大型石油天然气公司具有借鉴意义,有助于提高投资决策的科学性和有效性。     

利用ARIMA模型预测2007年油价走势

运用Box-Jenkins方法，以2003年初至2006年末的WTI原油月平均价格数据为基础，借助统计分析软件，构建了ARIMA预测模型。利用该预测模型对2007年度的WTI油价走势进行了分析和预测，预测结果以预测值、预测下限和上限的区间数据形式表现出来。预测结果显示，2007年的WTI油价将继续维持高位运行状态，运行区间为60—70美元，桶，价格运行表现出受季节影响较为明显的特征。     

油田技术经济产量预测模型的建立与应用

以技术为中心的传统油田产量预测方法,考虑因素单一,不能直观反映原油价格、生产成本等经济因素和开发投资、产能建设等决策因素对原油产量的影响,难以适应目前以经济效益为中心、科学投资决策的石油开发形势。文中利用多元线性回归定量分析方法,综合考虑经济与技术多种因素,优选出了对油田产量影响显著的生产时间、百万吨产能投资、新建产能、生产成本和油价5个因素,并建立了相应的多元线性回归产量预测模型。在此基础上,研究了如何应用Excel办公软件实现快速回归预测和分析。通过某油田实际参数与产量关系的预测与验证,表明新建立的预测模型简单、实用、可操作性强,预测误差小于2.3%,对油田科学规划决策具有一定指导意义。     

低油价下证实已开发储量评估的影响因素及对策

随着低油价的持续,SEC对于各国际上市石油公司披露的证实油气储量采取更加谨慎态度。如何在低油价下保持储量少核减、少减值,是各石油公司迫在眉睫要解决的问题。文章从证实已开发储量影响因素入手,着重分析了油价、操作成本等因素对证实已开发储量评估结果的影响。研究认为:要确保在低油价下证实已开发储量少核减,销售溶解气参与经济极限计算是最有效的方法;降低和优化操作成本是关键;加强"可靠技术"是增储的主体。