Zhou Received25 Dec Published12 May Abstract Wandering of oil prices at lower values and the bitter reality have forced people to look for a more accurate valuation method for overseas oil and gas extraction of China. However, the currently available resource classification source binary option, discount cash flow DCF method, and real option method all suffer from their own disadvantages.
This paper identifies multiple uncertainty factors such as oil prices and reserves. It then investigates the transmission mechanism of how each uncertainty factor impacts the oil and gas extraction value and quantifies the transmission efficiency. The probability distribution patterns of each uncertainty factor have been determined; the trinomial tree option pricing model is modified, with consideration upon the nonstandardness of the probability distribution.
Decision points and strategies space are designed in accordance with the practical oil and gas production; and the Bermuda option is adopted to replace the conventional бинарные опционы и казино tree model with the probability-based tree. Finally, a backward algorithm is developed to calculate the probability at each decision point, which avoids difficulties in determining the asset volatility ratio; and a case study is presented to demonstrate application of the proposed method.
Results show that decision rights for overseas investment are moment of execution of a Bermuda option transaction.
The value of extraction does not yet necessarily grow with higher uncertainty, and instead, it is under joint effects of the cash flow and strategy space. So, valuation should incorporate the composite value of future cash flow and decision rights.
Volatility of the value of extraction is not solely dependent on the oil price, but affected by multiple factors. Similar to the Bermuda option, the decision-making behavior for oil and gas extraction occurs only at finite decision points, to which the trinomial tree option pricing model is applicable.
The adoption of probability distribution can to a great extent exploit the uncertain information. Replacement of the decision-based tree with the probability-based tree provides more accurate probability distribution of the calculated value of extraction, and moreover the disperse degree of the probability can reflect how high risks are, which is conducive to decision-making for investment.
After nearly 20 years of international development, oil and gas companies of China have possessed some oil and gas blocks in the central Asia, Africa, etc. However, these blocks are often seen with inferior opulence in resources and in many cases, they are located in high-risk countries [ 4 ].
In recent years, China has further increased its investment in overseas oil and gas assets, resulting in a significant increase in overseas oil and gas production. Hence, the investment evaluation for oil and gas extraction is of extreme importance. With the prolonged downturn of oil prices, there will be more opportunities to acquire overseas oil and gas assets; however, higher requirements are also raised up upon decision-making in acquisition and extraction.
For oil and gas blocks that have been through deepened exploration and already put into development, valuation is relatively simple. The available elaborate geological and production data are sufficient to build the secret binary options video model and gas production forecast model, which are able to offer relatively precise valuation of assets [ 5 ].
Nonetheless, as for oil and gas blocks with unclear geological setting and incomplete data, the existing conventional method fails to accomplish asset valuation, due to its high uncertainty [ 6 ].
Accordingly, a valuation method specific to oil and gas extraction with higher uncertainty is required. Based on the Modified Trinomial Tree Option Pricing Model, and considering the fluctuation of asset uncertainty, this research explores the value of decision rights at each decision point in overseas oil and gas asset evaluation and proposes an asset evaluation method.
Firstly, this paper presents an overview of existing evaluation methods for overseas oil and gas assets and reveals that the existing methods have some defects in calculating the value of decision rights under uncertain conditions.
Secondly, it identifies some uncertainty factors of overseas oil and gas assets, analyzes how these factors exert impacts on the asset value, and constructs a formula to estimate the cash flow of oil and gas asset development.
Thirdly, it holds that there are five main decision points and three strategy types in the oil and gas investment process. By improving the trinomial tree option pricing model and combining with the distribution and types of uncertainty factors, this paper finally presents an evaluation method that is based on the probability tree of oil and gas exploration and development and that can be used to calculate the value of decision rights at each decision point in inverse order and the overall oil and gas asset value, and also it gives some examples to illustrate the application of this method.
Literature Review The evaluation of oil and gas moment of execution of a Bermuda option transaction refers to the economic benefit evaluation of the future exploration, development, and sales process of oil and gas assets. Besides the direct extraction value, the value of overseas oil and gas assets also includes strategic value, social value, and political value, which are not within the scope of this paper.
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In terms of valuation of oil and gas extraction with higher uncertainty, the current research approach can be divided into two groups: One is the quantitative analysis based on modelling and computing, including the discounted cash flow and real options valuation.
For the qualitative analysis, the Delphi method is susceptible to personal preferences and knowledge limitations of surveyed experts and has been discarded as the core valuation method for investment decision-makers.
The resource classification method [ 78 ] is a semiquantitative method for comprehensive valuation of oil and gas assets, involving grading hydrocarbon resources in accordance with reserve quantities, recovery difficulties, oil and gas quality, external risks, etc. This method is relatively accurate and moreover simple and can easily determine at which grade a certain oil and gas asset stays. However, comprehensive grading based on this method is somewhat subjective and has difficulties in deciding which has higher values, a block with a smaller scale and yet better quality, or the one with secondary quality but expanded reserve quantities?
Under such circumstances, weights shall be assigned to each grading indicator by the decision makers, and therefore, this semiquantitative method is applicable to fuzzy comparison and preliminary screening out of multiple blocks.
Valuation of overseas oil and gas extraction demands more precise quantitative research to support investment decision-making, especially during the current downturn of oil prices. With respect to quantitative research, the mainstream method should be the discounted cash flow DCF method based on input and output.
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Moment of execution of a Bermuda option transaction DCF method [ 9 ] is widely used in the oil and gas industry and is able to present relatively high precision in the case of oil and gas blocks with lower certainty. Taking the time value of money into consideration, it precisely calculates the value of extraction by estimating the exploration and development investment, operation expenditure, binary options passive income mortgages and taxes and predicting the sales revenue of crude.
Nevertheless, the estimation of investment, cost, and sales revenue in this method requires low uncertainty of the moment of execution of a Bermuda option transaction object; otherwise the calculated NPV net present value will not have credibility [ 10 ].
Moreover, the DCF method only considers the value of future cash flow and neglects the value of decision-making. Clearly, for overseas oil and gas extraction, the value of decision-making is embodied as the ability to give up exercising rights, which means stopping exploitation in the case of money-losing oil and gas investment [ 11 ].
This is a limitation of the DCF method. Some scholars introduce the option method to deal with uncertainty of overseas oil and gas extraction, calculate the comprehensive value, and determine the investment timing. The real options method [ 1213 ], as an evaluation approach, estimates the financial value of oil and gas extraction through the DCF process, then calculates the option value using the volatility ratio of the asset value, and at last concludes the comprehensive value.
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The real option method is able to better mimic the decision-making behavior and measure the value of the decision right, by considering and calculating values of options to defer investment and to abandon. Yet, some issues still exist in terms of the basic assumption and actual implementation and have not been well handled.
Firstly, it is hard to measure the volatility ratio of the value of extraction. Transactions of oil and gas assets are characterized by their small quantity and discontinuation, and thus the value volatility ratio cannot be directly calculated.
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Generally, the real option method [ 121415 ] uses the oil price fluctuation to represent the undulation of the oil and gas asset value, between which the consistency has not been confirmed yet. In fact, the value of oil and gas assets is not only related to oil prices but also related to many other factors such as the level of risks associated with the resources, the local political and economic status, and laws.
This is considered a major flaw of this method. Secondly, there is no complete market for oil and gas assets, like that for option transactions. The excessively limited quantity of buyers and sellers decides that the transaction is not real-time, and various options cannot be exercised in a timely fashion. Furthermore, the transaction value of assets is largely determined through the game between the two sides and the transaction value of options is hard to be estimated.
In addition, some studies [ 16 ] reject two stylized facts of real options on oil: one is that the correlation of the returns on oil and the stock market is positive; the other is that it is invariant to changes in oil price volatility.
They state that the widespread idea that higher volatility leads to increased value and postponed investment is not necessarily valid.
To sum up, regarding valuation of overseas oil and gas extraction, the qualitative method suffers from insufficient precision, the DCF method fails to capture the value of decision rights, and the basic assumption of the real option moment of execution of a Bermuda option transaction is questionable. Therefore, it is required to think about it further to random binary options these defects. Analyzing Uncertainties of Overseas Oil and Gas Extraction The value of the decision rights for overseas oil and assets roots in uncertainty, and thus we should first clarify in which aspects uncertainty is embodied, then analyze how these uncertain factors impact the assessed value of assets, and at last characterize variations of these uncertainty factors.
Identification of Uncertainty Factors Overseas oil and gas extraction are subject to various uncertainty factors, of which extensive identification investigation and subsequent risk quantification and asset valuation have been carried out. Most scholars [ 17 — 20 ] focus on uncertainty in the geology, which mainly include the reserves, quality, depth, utilization rate, production rate, and decline rate of production. Some scholars [ 2122 ] also consider the external environment, including the political and economic environment, sovereignty credit, and global oil price.
Besides, the geographic location, topographical setting, hydrogeological background, and technical proficiency of operators, which are concerned with the discovery, development, construction, and operating costs during hydrocarbon recovery, also have internet trading training upon the value of assets.
In summary, the uncertainty factors can be classified into the following categories, as shown in Table 1.