The Impacts of Carbon Sequestration on Oil Production
Projects Decision-Making: A Real Option Valuation
Approach
Carlos Alexandre Camargo de Abreu
Federal University of Rio Grande do Norte State, Science and Technology School, Natal RN 59072-970, Brazil
Received: April 04, 2013 / Accepted: June 18, 2013 / Published: January 31, 2014.
Abstract: A traditional real option model is applied to a simulation of an oil production project. This analysis includes a carbon
sequestration structure cost and possible revenues from carbon credit markets. The evaluation focuses on the determination of an optimal timing for the investment in different scenarios, regarding the volatility of the uncertain variable, oil prices. Historical prices data from different moments are used to estimate different prices uncertainty scenarios and its impacts on the decision making on building a carbon sequestration structure. The results are compared between a real option model to the ones obtained using the traditional net present value evaluation. Trigger point of investments are defined for different scenarios with and without carbon sequestration. There is also an analysis of the effects on decision-making in different scenarios for carbon market prices. It is perceived an important difference in the decision making considering the different methods of economic analysis. The real option model is a fundamental valuation tool in periods of high price volatility and higher sunk costs added to a project such as the carbon sequestration structure. Greenhouse gas projects demand high oil prices, positive market trend expectation and volatility.
Key words: Real options, economic evaluation, carbon sequestration, oil prices, uncertainty.
1. Introduction
Companies have traditionally used static indicators and methodologies for economic evaluation of projects based on discounted cash flows resulting in measures as NPV (net present value) and IRR (internal rate of return). These types of approaches are based on the fact that managers will follow a planned budget and schedule from the beginning to the end of the investment project, with incomes, costs and taxes remaining with no change. In this context, managers have a passive role, which does not reflect reality in an uncertain environment.
The traditional economic evaluation model based upon the discounted cash-flow accounts for uncertainty, but just by increasing the discount rate and, as a result,
Corresponding author: Carlos Alexandre Camargo de
Abreu, professor, research fields: real options, petroleum and economic analysis. E-mail: calexandreabreu@ect.ufrn.br.
the higher the uncertainty, the lower the present value of a project. Because of the uncertainties, the performance of the rate of return of the project in the future may be different from what was initially planned. Then, it is important that managers have some flexibility to adapt the project to a new economic reality. Uncertainty has a potential upside and the loss is limited to investment, so the traditional decision-making tends to recommend sub-optimal decision-rules.
Investment projects are normally connected to some kinds of flexibilities which offer decision options to a manager during the operational life of a project. Trigeorgis [1] shows that these flexibilities are concentrated on the decision making process of a manager, that is, the options to delay an investment, abandon an investment, change scale or expand level of production. These options occur accordingly to market variables such as price, costs level, cost reduction rates,
D
macroeconomic environment and other relevant variables in decision-making.
Real option models are important tools in environments where uncertainty is a key variable in the decision-making process. Projects in the oil production industry are directly affected by different types of uncertainties such as prices, technical, technological, among others. Using real options for economic evaluation of a petroleum project is useful since the method internalizes the flexibilities caused by uncertainties resulting in an expanded net present value for a project’s economic analysis. Using and valuing management flexibility to choose the optimal moment to invest in a project with greater costs such as one with a carbon sequestration structure might be the difference between a viable and a not viable project.
The oil and gas industry is an economic sector in which real options models can be used for economic analysis of all kind of projects. Uncertainties influencing decision-making are an important part of business and require valuation models with volatility parameters. Real options have been approached in petroleum projects, considering as uncertain variables mainly the oil prices, but costs, technical and technological uncertainties are also part of real option valuation in the oil industry, as observed in Paddock et al. [2], Tourinho [3], Meyers and Majd [4], Dias and Rocha [5], Lima and Suslick [6], Dias [7] and Abreu et al. [8]. Real option models are being used to evaluate projects involving greenhouse gas emissions in a few different energy industries. Projects such as coal fired power plants with capture and storage of CO2 [9], renewable electricity generation projects [10], electricity generation technologies [11], adoption of photovoltaic technology [12] and petroleum production project [13, 14].
2. Model and Data
The first step in a real option valuation model is the definition of uncertainties that will have influence on the project’s values. This model is a one uncertainty
model and the variable which will not be deterministic is the oil price (P).
This is a real options continuous time model based on the one developed by McDonald and Siegel [15] considered the basic real option model. The model’s main idea is the definition of a trigger investment decision point in which the returns obtained from exploration and possible future development of an oil reservoir has an optimal value (V) which compensates making high investment expenditures (I). Value (V) is subject to the uncertain variable (P) which has a random behavior governed by the geometric Brownian motion stochastic process, observed in Eq. (1):
( )
α( )
σ( )
d∂ V = V p ∂ +t V p z (1)
where, d(V) is the variation of the project’s value subject to the oscillation of oil prices in spot market, α is the expected long-term growth rate of returns of the project, attached to the oil prices, σ is the volatility estimated for the oil prices and dz is the Wiener increment in charge of defining the oscillation tendency. Estimation of (V) is shown in Eq. (2):
= − −
V revenues taxes opex (2)
where, revenues are, price multiplied by the quantity of barrels of oil (in the simulation for this paper, it is used a field of around 163 million barrels), resulted from potential future oil production commercialized in the oil market, tax are all payments made to the government, opex (operational costs) is the summation of all other costs excluding capital investment costs (capex). Information about costs linked to the oil exploration and production are a fixed value of an estimated variable costs of US$0.60 per barrel added to a yearly US$30 million fixed cost (numerical field production simulations indicated a 21 years production). The estimates were based on oil company information for a field of this size. Total operational costs for this simulation are US$343.93 million. The estimation of capital costs, were also based on oil company information with a total value around US$2,075 million. Total per barrel extraction costs for the field, initially without CO2 sequestration structures
are of US$14.83 per barrel. Revenues, taxes and opex are present values estimates of total income, total tax based on this total income and the number of barrels multiplied by the fixed opex.
Real option value in continuous time is estimated through a differential equation which can be obtained by two methods: concepts of dynamic programming and option pricing. In this case, it was used the first since there is an uncertain variable of an asset that is not traded on markets, nor has at least an asset or portfolio on markets that could replicate its volatility and its long term trend, as a proxy. The estimation of volatilities and long term tendency for the oil prices will be detailed on the next section.
The use of dynamic programming as a tool for optimization of a project’s value is based on the idea that this tool breaks the chain of decisions surrounding an uncertain investment, into two components. They are the immediate decision of investing and a valuation function that captures the subsequent decisions of investing in some time in the future [16].
To find the optimal sequence of decisions, the work is done backwards from the last moment that investment could be made to the beginning. At each future decision point the manager will compare payoff for immediate investment (represented by the present value of the project at any future point) to continuation and make the decision of investing or delaying based on the highest value. Eq. (3) is known as the fundamental equation of dynamic optimization or Bellman equation in continuous time and represents the maximization of project’s value in future periods, as shown in Ref. [16]: ( , ) m ax{ ( , ) (1 / d ) [d ( , )]} = π + rF V t V t t E F V t (3)
where, in the left side of Eq. (3) is the return that a decision-maker requires for holding the asset or delaying the investment in the development of the new reservoir using a discount rate. On the right side, there is the immediate flow of profits or dividends from the project represented in the first term, and the second
term is the expected capital gain from oscillation in project’s value in the future.
The summation of both terms is the total expected returns for delaying the investment. Considering that the project will produce profit flows only when decision to invest is taken (π(V, t) = 0) the return for delaying the project will be only the gains from oscillations on the stochastic variable.
Using rules of stochastic calculus detailed in Ref. [17], Dixit and Pindyck [16] where they demonstrate the differential equation which estimates project’s option value presented in Eq. (4):
( )
2( )
2 2( )
( )
( )
( )
2 1 2 0 σ ⎡⎢∂ ∂ ⎤⎥+α⎡⎢∂ ∂ ⎤⎥ ⎣ ⎦ ⎣ ⎦ − = F V F V V V V rF (4) where, [d2 F(V)/d (V)2] is the second derivative, [dF(V)/d(V)] is the first derivative, F is the option value,r is the discount rate,
σ
is the volatility of project’s value based on oil prices and α is the project’s expected future long time based on oil prices trend.Solving Eq. (4) requires three boundary conditions which are determined accordingly to the particular economic dilemma being analyzed. This is a valuation model which aims at the maximization of returns V under a total investment cost I. The equation is going to define an optimal value V and, consequently, a value of oil prices (P) for optimization. The boundary conditions delineate an option curve separating the region where investing is optimal, from where waiting is the best decision. Conditions are in the following equations:
( )
0 = 0 F (5)(
*)
= *− F V V I (6)(
*)
=1 F V (7) The first boundary condition defines the option value as zero when the project reaches that value. The second condition is called the value matching condition. At the optimal moment of investing, option value and termination payoff are equal. The last is the smooth pasting condition which determines that the derivatives
termination payoff and option value are the same at the optimum investment moment.
Solution format which satisfies Eq. (4) is shown in Eq. (8). Dixit and Pindyck [16] consider this equation format as the predominant in real option valuation models following stochastic processes in continuous time. It estimates the delaying option value of the project. Simplifying and doing some algebraic manipulations of Eq. (8) and the three boundary conditions, it is possible to estimate constant A, which is used in the calculation of the option value and the optimum value of project’s return, linked to the uncertain total reservoir production. A and V* are defined in Eqs. (9) and (10). Eq. (11) is the positive root of the second order differential equation of the option valuation. Using Eqs. (8)-(11), it is possible to estimate the petroleum field value with one uncertainty (oil prices) using the basic real options continuous time model developed by McDonald and Siegel [15] to value a company’s flexibility for waiting the optimum moment to take the investment decision, as demonstrated:
( ) ( )
Β1 = F V V (8)(
)
1 * Β − = V I A V (9)(
)
1 *= ⎢⎡Β Β1 1− ⎤⎥ ⎣ ⎦ V I (10)( )
1( ) ( )
2 2( )
1 1 2 α α 2 Β = − σ + σ − (11)The real option model section shows that a real option valuation model applied to the oil industry has two fundamental parameters, long term oil prices trend and level of uncertainty in the oil market. Both of these parameters need quantification before used as an input in the real option model. In this paper, it is used a historical time series of oil prices, to estimate these main parameters.
The long-term oil price trend is a parameter which demonstrates the evolution of prices or the expected growth of prices. Eq. (12) shows the procedure to
estimate the long-term oil price tendency. In Eq. (12), n is the number of price observations of daily closing prices, P(t) is the price of oil on day t and P(t - 1) is oil price in the day before t:
( )
∑
⎜⎝⎛ − ⎟⎠⎞ = 1n ln P(t)P(t 1)α (12)
The uncertainty parameter or volatility level indicates the oscillation of oil prices, in other words, the deviation of oil prices from its mean. As in the other parameter, the data is based on historical oil prices series. For each year of price data, there is an estimate of the yearly mean, based on daily oil prices observations. After that, yearly standard deviation of oil prices is estimated, also based on daily closing prices. Since, there are estimations of mean and standard deviation for every year of the historical series, it is possible to calculate a yearly rate, from which price drifted away from the mean. The last step was to determine the mean of the yearly rates for a specific period as shown in Table 1.
The oil price data were obtained from the Department of Energy of the United States of America. The type of oil prices used in this research to estimate the long term drift was the WTI (West Texas Intermediate) spot price. These parameters are the fundamental drivers that making the difference between real options valuation and traditional NPV economic analysis. The first type of results is about making this comparison, using Case 1.
Estimation of the real option model’s important input parameters (α and σ) are made by using Eqs. (12) and (13), on two selected periods for oil prices time series. The analysis is focused on three different time periods which can be seen in Table 1. Cases 1 and 2 correspond to a less volatile period for oil prices when compared to Case 3. This permits the comparison between different price oscillation scenarios and the understanding of the uncertainty impact on real options results. The trends of oil prices in all three cases have a very close estimation with a very small impact on results, when comparing the simulations. In these three cases are included the costs for CO2 capture,
Table 1 Scenarios and input data.
Scenarios Period (years) Trend (%) Volatility (%) OPEX (MM US$) CAPEX (MMUS$) CO2 revenues (MM
US$) Case 1 2002-2004 24 10 832.47 2,075.16 - Case 2 2006-2008 23 13 832.47 2,075.16 - Case 3 2009-2012 25 20 832.47 2,075.16 - Base Case 1 2002-2004 24 10 343.93 862.50 - Base Case 2 2006-2008 23 13 343.93 862.50 - Base Case 3 2009-2012 25 20 343.93 862.50 - CO2Revenues A1 2002-2004 24 10 832.47 2,075.16 441 CO2Revenues A2 2006-2008 23 13 832.47 2,075.16 441 CO2Revenues A3 2009-2012 25 20 832.47 2,075.16 441 CO2 Revenues B1 2002-2004 24 10 832.47 2,075.16 574 CO2 Revenues B2 2006-2008 23 13 832.47 2,075.16 574 CO2 Revenues B3 2009-2012 25 20 832.47 2,075.16 574
transportation and storage. Still in Table 1, it is observable the base Cases 1, 2 and 3. These are scenarios with trend and volatility levels equal to each one of their correspondent CO2 structure cases, but without inclusion of carbon structures and costs. Using the CO2 case scenarios and comparing to the base cases it is possible to look at the impact of CO2, costs in a real option approach. So, the main differences between both types of cases are the higher total operational and capital costs from CO2 structure cases.
The CO2 costs are, according to McKinsey Company [18], where operational and capital costs for capturing, transporting and storing carbon are estimated for oil fields and other storage possibilities. As defined in Ref. [18], this is a project on an initial demonstration level with its costs for a CO2 structure of 90% (main capture technologies efficiency) carbon abatement. Operational costs per metric tons of CO2, summing up capture, storage and transportation, are near US$18.85. Capital costs are around US$46.79 per metric ton of CO2. Converting these costs to per barrel value (opex—US$3.00/capex—US$7.44) and multiplying to the total field’s production, there is the total opex and capex of Table 1.
The oil field project’s scenarios called “cases CO2 revenues A” (1, 2 and 3) have in their total incomes, revenues from carbon credits markets in addition to the normal oil revenues. Price of carbon credits are
correspondent to a scenario close to one observed on the European Climate Exchange in March and April 2012, in which the prices had oscillated around US$7.00 per metric tons. As done with CO2 costs the revenues values are also converted to CO2 revenues and then multiplied by the total potential of CO2 abatement resulting in the value for carbon credits income in Table 1. Considering a per barrel value there are revenues of US$2.70. Ending the economic analysis there is a final scenario supposing a 30% increase in carbon markets with prices rising to US$9.10. These last scenarios are called “cases CO2 revenues B”.
3. Results
The start of the analysis is based on the cash flow simulation regarding all revenues, traditional costs, CO2 related costs, taxes and other important data. It is possible to estimate both project’s NPV and the real option value associated to the potential investment. Economic evaluation results using real option are fully dependent on the estimation of traditional net present value. Real option results are also called the “expanded” NPV. So, after calculating the results for the two evaluation methods a comparison of the potential financial results for the project is made. All scenarios have a specific level of uncertainty or long-term oil price trend, according to data used. Using Scenario 1, it is demonstrated the difference in
decision-making, between real options and NPV. Applying the traditional evaluation method to the project there is a decision making rule indicating that investing will generate positive potential return, when these are above zero. A “do not invest” indication comes with a negative NPV result, since the project’s revenues will not be high enough to compensate its costs. Also observable in Fig. 1, the dotted straight line represents the NPV valuation.
The portion of the line under the “oil prices” axis, represent the spot market prices (Y axis) that drives the net present value (X axis) to a negative value and an exclusion of the project from a potential investments portfolio, with a “do not invest” decision. The NPV above zero portion of the line shows the oil prices, where the project brings positive returns and should have an indication of a good opportunity to develop the project. In Fig. 1, the investment “trigger point” for net present valuation method is when oil prices reach a minimum of US$/Barrel 55.91, generating a benefit (V) value US$2,075.47 millions, just above the total investment needed to take on the project. Probably, no manager will take the investment and developing decision with prices equal or just above the “trigger price”, but it is a possibility since the returns, above that, already bring some return to the company as visible in Table 2. So, should decision-makers take the investment decision at a return just above the critical oil price? Should the decision-maker wait better market conditions? Until when should a company wait for better prices in the spot market, to make a high investment such as building a structure for sequestrating CO2? The dark curve in Fig. 1 represents
Fig. 1 Real options valuation and net present value (Case 1).
the real option valuation. There are the option values for each level of oil prices. When using real options valuation, there are no negative values for investment options. Since the option value is estimated considering long term future trend expectation and market risk level, it never goes below zero. This occurs because the valuation method considers the possibility of changes in the future oil prices. If the decision-maker has a positive expectation related to oil market behaviour and (or) forecasting of a market with high oscillation, the decision will be made aggregating these variables to the evaluation process. When there is a negative NPV, the real option indicates a positive return since in this scenario there is a long term expectation of a 24% price appreciation and 10% risk in which the prices drive away from its average.
So, besides the negative return at prices below US$/Barrel 55.91, it can rise in the future, based on estimated historical indicators, as shown in the previous session. Real options inserts the flexibility in the economic evaluation of a project valuing the waiting option and giving the decision-maker the option to invest on a better market environment. The revenues from higher prices will turn into greater returns, over very high traditional costs added to CO2 costs, which demands a considerable income for its viability.
In scenario “Case 1”, the investment optimization will occur when oil prices are around US$/Barrel 91.70, generating a benefit of US$4,235.00 millions, much higher than the US$2,075.47 million of capital costs. That moment is when both values of option and NPV are the same, with an expanded NPV value of US$2,160.01 million. Before the real option trigger point, the project has a positive return, but it is not yet optimized. Values above that should always be to take the investment, or else, the investment will never be taken because the waiting value is always higher than the immediate investment. Table 2 shows the differences between an optimized investment, following the rules of investment, based on real options valuation and net
Table 2 Decision-making.
Oil prices (US$) Net present value (MMUS$) Net present value decision Real options (MMUS$) Real options decision
20 -2,164.42 No investing - No investing 25 -1,862.87 No investing 6.10 Wait 30 -1,561.32 No investing 34.54 Wait 35 -1,259.77 No investing 85.41 Wait 40 -958.22 No investing 158.30 Wait 45 -656.57 No investing 252.93 Wait 50 -355.12 No investing 369.10 Wait 55 -53.57 No investing 506.64 Wait 55.91 0.31 Invest 533.46 Wait 60 247.98 Invest 665.42 Wait 65 549.53 Invest 845.33 Wait 70 851.08 Invest 1,046.25 Wait 75 1,152.63 Invest 1,268.10 Wait 80 1,454.18 Invest 1,510.80 Wait 85 1,755.73 Invest 1,774.28 Wait 90 2,057.28 Invest 2,058.47 Wait 91.72 2,160.01 Invest 2,160.01 Invest 95 2,358.83 Invest 2,363.31 Invest 100 2,660.38 Invest 2,688.74 Invest 105 2,961.93 Invest 3,034.71 Invest 110 3,263.48 Invest 3,401.17 Invest
present value. Supposing an investment decision above the trigger point for the NPV with prices around US$70 per barrel, there is an NPV return of US$851.08 million. Real option trigger indicates an investment considering the optimum oil price with a real option value near 2.5 times higher. That is the effect of valuation of manager’s flexibility for investment timing optimization. The greater revenues from elevated oil prices, increases the return over the investment level necessary to build a CO2 sequestration structure with its high fixed costs and, consequently, increases the option value for an estimated risk and trend environment for oil prices.
The main variable defining this difference in field’s estimated value, using real option valuation, comparing both scenarios, are the capital costs required to build a CO2 plant. Sequestrating CO2 increases project’s capital costs in more than two times (near 140% growth). It grows from US$862.50 million to US$2,075.16 million. The increase in oil price, enough to equal option values in the cases with and without CO2 plants is around 56%. This represents an increase
much lower than the ones observed in the capital and operational costs. This happens due to the exponential nature of real options valuation models. Values growth is linked to variables such as risk and long term trend. There is a similar result if both trigger investment points are compared. So, using the decision rules of the two scenarios (Case 1 with CO2 and Base Case 1 without) it is possible to construct Table 3 with the decision-making rules for the development of this simulated potential project, considering investing with a CO2 structure or taking the investment excluding such device.
Making the analysis considering different volatility and trend scenarios the analysis shows diverse values for the real options valuations. All scenarios have a very close long term trend. So, the different trend values have a very small impact when compared, considering the three different historical time periods in which the economic analysis was constructed. The variable with an impact in the evaluation is the volatility or the market risk. When the comparison is
Table 3 Oil prices trigger points.
Cases Oil prices (US$)
Case 1—CO2 structure
Net present value price trigger point 55.91
Real option model trigger point 91.72
Prices to reach real options trigger (Base Case 1) 66.37 Base Case 1—No CO2
Net present value price trigger point 27.69
Real option model trigger point 42.57
made using the estimation of divergent price volatilities, there are considerable differences in the option value for an oil field. The real option model was applied to investment considering CO2 structure costs, in three petroleum prices volatilities scenarios as shown in Table 4. Scenario 1, with a yearly price volatility of 10%, is compared to Scenario 2, which had estimated volatility per year of 13%. Looking at the options decision making prices trigger points, the second scenario has a slight 1.1% increase and field’s value growth of around 2.9%. Comparison to the recent historical data, Scenario 3, with its high oscillation in oil market and, consequently, a higher price volatility estimate of 20% per year, demonstrates a more significant growth of the oil field’s value of 7%, comparing to Scenario 2. A higher price volatility level elevates the decision-maker’s flexibility value.
The reason for superior option values with higher uncertainty is that since a company has the option not to invest if the prices fall, volatility will only have positive effects when prices go upwards. So, there is an opportunity cost of investing now if volatilities are high, due to the monetary values that will be missed with the rise of oil prices. The greater the uncertainty regarding oil price markets along with a positive expected trend by a company or a manager, higher will be the value of an oil field potential investment and its returns over the expenditures made on a CO2 separation plant, transport and storage structure.
An evident result from adding possible revenues from carbon credit markets is that the investment trigger points regarding the oil prices, gets lower. It is possible to compare this evaluation to the cases when
there is no consideration of any revenues from carbon markets, in Table 4. The extra possible income considering prices in carbon markets of US$7.00 per metric ton of CO2, reduces in 8% the trigger investment price for the decision to invest in lower volatility scenarios “CO2 revenues”, A1 and A2. On Case A3, trigger price is around 8.5% lower with the inclusion of extra revenue, observable in Table 5. The total project option value in all three cases is slightly superior even with a trigger price reduction. In Table 5, it is also possible to compare the total field option value, with no carbon revenues, to the option value including revenues, at the optimum oil price shown in Table 4. Supposing oil prices at that level, the potential project would have a value increased in nearly 21% in the first and second Scenarios and 19% on the third revenues CO2 case. This happens due to the higher benefits from added carbon revenue on which the price’s trend and, mainly, its volatility effects, influence the non-linear exponential option value growth. It is also possible to look at the growth of potential oil field’s option value in a better scenario for carbon credits prices. A 25% increase in carbon prices (US$9.10) for the scenarios “Cases B CO2 Revenues”, if compared to the A CO2 revenues scenarios, generates a 4.8% or a near US$146 million increase in potential returns on case B3. Observing Cases B1 and B2, the increase in real option valuation results are of 5.5% with a monetary growth close to US$147 million.
4. Conclusions
The decision-rule using the model is very sensitive to price uncertainty. The greater expected uncertainty;
Table 4 Real options with CO2 structure.
Scenarios Volatility (%) Field’s option value at trigger point (MMUS$) Trigger oil prices (US$)
Case 1—CO2 structure 10 2,160.01 91.72
Case 2—CO2 structure 13 2,222.73 92.76
Case 3—CO2 structure 20 2,378.94 95.35
Table 5 Real options with CO2 structure and revenues.
Scenarios Volatility (%) Field’s option value at trigger point (MMUS$) Trigger oil prices (US$)
CO2 Revenues A1 10 2,167.99 84.54 CO2 Revenues A2 13 2,225.88 85.50 CO2 Revenues A3 20 2,388.72 88.20 CO2 Revenues B1 10 2,154.83 82.10 CO2 Revenues B2 13 2,227.20 83.30 CO2 Revenues B3 20 2,376.98 85.80
there is a greater trigger value to invest in the oil project considering sequestration. Real option models are valuable tools to be used in economic evaluation of projects in the oil industry especially on environments in which uncertainty is a key characteristic. Different oil prices oscillation scenarios will change the investment decision in an oil production project with carbon sequestration structure. As observed, in the oil industry, very high levels of uncertainties are related to prices, making a real options model an important tool to define, if adding a higher cost such as a carbon structure, has its compensations. The tool allows a manager to measure economic impacts on project’s values from probabilistic gains from the existence of possible better economic expectations. The quantification of this flexibility on waiting for better conditions is the key aspect of this model.
Real option valuation does not exclude traditional NPV and other methodology based on discounted cash flow. The last is an important part of the real option valuation models.
Carbon sequestration projects have very high costs. It is of great importance that returns from these projects are optimized. A real option model is a tool that shows in a clear way a decision-making rule which includes an optimization of the moment to invest. This tool might show better possible market conditions in which costs impacts of the CO2 structure on petroleum production investments would be lower.
Oil prices are the fundamental variable which influences a petroleum project’s real option value. Increased oil prices scenarios are clearly favourable for the oil companies to take investments considering the inclusion of CO2 capture, storage and transportation technology. Results of using the real option model shows that optimized investments, in recent oil prices scenarios, are made when the commodity reaches levels close to US$80 and US$90. So, CO2 structure demands a high oil prices scenario. Even if a not optimized investment is considered, there is a necessity of oil prices near the US$70 to have some considerable return, as shown in Table 2. This economic analysis is based on a smaller field. Probably with larger fields, with higher production potential it might be likely to get some reduction on the oil prices trigger investment points.
The price of oil is the most important variable in decision-making but it is not the only one influencing decision-making in petroleum projects with CO2 technology. Expectation of oil price’s positive oscillation plays an important role on the valuation of these high cost projects since the real option model values the opportunity costs of waiting better investment environments.
A very important variable that has a small influence at the present but can be the main incentive for investing in CO2 technology in oil field is the carbon credit markets. Costs for investing in this type of
projects are very high. The CO2 market has the potential to increase revenues and reduce the oil trigger prices for investment optimization. Economic viability would be reached and optimized on low oil market environments. So, an organized and popularized international carbon markets are of great importance to the viability of these investments in a real options point of view, since it has the potential to elevate this type of revenues making a compensation for the CO2 costs.
Along with oil markets, expectations, volatility and carbon revenues, technology development for CO2 structure technology is also a required evolution. As observed CO2 costs represent a great part of the total investment costs in a petroleum project, the possibility of reducing capital expenditures and operational costs due to technological development would have a great impact on the expansion of oil price levels in which a project with CO2 structure could have an optimized return.
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