Optimising PSC - Economics

Optimizing PSC Contracts for Development of Marginal Fields: An Equatorial Guinea Study

Antonio C. Fernandez

Texas A&M University Master of Engineering Petroleum Engineering

December 2008

Optimizing PSC Contracts for Development of Marginal Fields: An EG Study Page 2

Table of Contents

1. Abstract ..................................................................................................................... 4

2. Conclusion ................................................................................................................ 4

3. Introduction ............................................................................................................... 5

4. Method ...................................................................................................................... 7 4.1. Commercial Uncertainties ............................................................................................................... 7

4.2. Operational Uncertainties ............................................................................................................... 8

4.3. Geotechnical Uncertainties ............................................................................................................. 8

4.4. Contract Uncertainties ..................................................................................................................... 9

4.5. Technical/Commercial Parity ........................................................................................................ 10

4.6. IRR-Based Production Sharing ..................................................................................................... 10

5. Discussion................................................................................................................ 10

List of Figures

Figure 1–Target Marginal Fields ............................................................................................................... 14 Figure 2–R-Factor System Sensitivity Analysis (From Johnston 2003) ................................................... 14 Figure 3–Risk Analysis Flow Diagram (From Johnston 1994) .................................................................. 15 Figure 4–Historical Oil Prices and Projections (From Johnston 2003) ..................................................... 15 Figure 5–The Effect of Shrinking Excess Capacity on Price Volatility (Data from EIA) ............................ 16 Figure 6–Forecasted Oil Prices (Historical Data from EIA) ...................................................................... 16 Figure 7–Random Samples from Monte Carlo Simulation of Oil Prices ................................................... 17 Figure 8–License Status in Equatorial Guinea (From GEPetrol) .............................................................. 18 Figure 9–Exploration & Production Activity Map in Equatorial Guinea (From GEPetrol) ......................... 19 Figure 10–Seismic Activity on Equatorial Guinea Shelf (From GEPetrol) ................................................ 20 Figure 11–Technical vs. Commercial Success Probability Plot Showing Threshold Field Size ............... 20 Figure 12–Random Sample From Monte Carlo Simulation of Well Shedule ............................................ 21 Figure 13–Monte Carlo Results (Current Fiscal System) ......................................................................... 22 Figure 14–Scatter Plot Under Current Fiscal System ............................................................................... 22 Figure 15–Monte Carlo Results (No Fiscal System, i.e. Technical Success Scenario) ........................... 23 Figure 16–Scatter Plot Under No Fiscal System, i.e. Technical Success ................................................ 23 Figure 17–Monte Carlo Results (Under Proposed IRR-Based Profit Oil Splits) ....................................... 24 Figure 18–Scatter Plot Under Proposed IRR-Based Profit Oil Splits ....................................................... 24 Figure 19–Contractor Profit Under Proposed IRR-Based Profit Oil Splits ................................................ 25 Figure 20–Government Profit Under Proposed IRR-Based Profit Oil Splits ............................................. 25

List of Tables


Table 1–Equatorial Guinea Sample Profit Oil Splits .................................................................................. 9 Table 2–Sample IRR-Based Sliding Scale Profit Oil Splits ....................................................................... 10


Optimizing PSC Contracts for Development of Marginal Fields: An Equatorial Guinea Study

Antonio C. Fernandez

1. Abstract Production Sharing Contracts (PSCs) with profit oil splits dictated by production rates make the exploration of “marginal fields” – those fields uneconomic under current terms but economic given parity between technical success and commercial success (Fig. 1) – prohibitive because royalties become regressive. Although widely used throughout the industry because of their simplicity, production-rate splits are so regressive that they penalize smaller fields while at the same time cheat the government of upside from large fields. So strongly regressive are these royalties that with most marginal discoveries, government take is invariably higher and the economics for producing such fields becomes exorbitant. The purpose of this study is to produce profit oil splits that are progressive, increasing the government take as the project creates more profit. Rate of return (IRR)-based profit oil splits optimize the state’s objective to bring bypassed oil to market by reducing minimum field size and production requirements without prejudicing the state’s entitlement to share in upside. Marginal fields in Equatorial Guinea (EG) were studied and used as exemplary cases of the type of marginal fields that oil companies are beginning, and will continue, to encounter as world demand rises and global production/reserves decline.

2. Conclusion The profit oil splits within current EG PSCs, which are based on cumulative production, are regressive. In fact, around 65% of potential projects would actually lose money under current tax schemes (IRR of 0%). The average project stands to make an IRR of only 8.5%, far below the typical hurdle rate of potential projects for international oil companies (IOCs) to consider (typically 15%-25%). Given parity between technical success and commercial success, however, the same fields present a mean IRR of 59%, with only a 13% chance that the project suffers a negative cash flow; therefore, there is room for negotiations between the IOC and the government such that downside risk in the part of IOCs is mitigated whilst at the same time the government can be entitled to share in the upside of a discovery. The IRR-based profit-oil splits model devised in this study mitigated the downside risk by ensuring that the average IRR is approximately 30% with only a 16% chance of an IRR of 0%, while at the same time it ensured that the state collected its share of the upside, as the IOC’s IRR maxed out at around 56% in a process that allowed the state to capture most of the incremental profit oil.


3. Introduction EG is a well-established, major West Africa hydrocarbon province that, unlike Nigeria, Gabon, and to a lesser extent Angola, still retains a large portion of its hydrocarbon potential unexplored and underdeveloped. Because existing PSC terms impose a large minimum field size to achieve commerciality, Equatorial Guinea possesses stranded oil & gas and exploration opportunities. Companies have cursorily explored these relinquished blocks in the past and abandoned them because accumulations of 10 million barrels or less are generally unattractive at the current one-size-fits-all production sharing terms mandated by Equatorial Guinea. This “cookie cutter” approach to PSCs is currently mandated by the EG government on every concession block agreement, but the government has indicated willingness to negotiate more moderate PSC terms to develop these fields. The inherent instability of the current PSC contracts has resulted in some projects not being developed although they are economically attractive in general. On the National Oil Company (NOC, usually acting on behalf of the government’s interest) side, when it enters into negotiations with an IOC which it expects to provide capital, technology, and expertise it wants to ensure that it obtains the best possible deal given the country’s specific circumstances (Bindemann 1999). The NOC will take a number of elements into account and evaluate them under different scenarios such as reserve discoveries, variations in oil prices, operating costs, and field development. The objective, presumably, is to maximize the country’s revenue under each scenario. However, the derivation of PSC terms often produces inefficient contracts that make the development of marginal fields financially prohibitive. A marginal field, although still commercially attractive, is small and therefore its project economics are greatly affected with even the slightest change in PSC terms. The government, therefore, has to find the optimal, or efficient, contract for its country. Applying the definition of economic efficiency with Pareto optimality from welfare economics to contract theory, it can be concluded that a contract is efficient when there is no way to rearrange the allocation of goods in a way that makes one person better off without harming another (Bindemann 1999; Hall and Lieberman 2001). But how can an NOC make the contract such that every possible Pareto improvement is exploited given the varying degrees of uncertainty that should be taken into account during negotiations? Indeed, it must create a contract which addresses the main unknown factors in oil exploration and development, which according to Bindemann (1999) are:

- Existence of commercial hydrocarbon accumulations - Type of resource (i.e. oil, gas, and/or condensate) - Size of deposit - Economic viability of development - Technological requirements - Commodity uncertainties - General economic and political risks

On the other hand, the IOC faces uncertainties in both the exploration and production periods. The following uncertainties affect a project’s economics in the exploration period (Bindemann 1999):


- No discovery - Discovery is not commercial - Costs

The two main uncertainties encountered by the IOC during production are:

- Operating costs - Commodity prices

Whether a contract has production-based sliding scale profit oil or a fixed production share, the economics of such contracts are too rigid to allow for the commercialization of small fields. In other words, the rigidity of the production share splits makes the risks associated with the uncertainties above too great. A rate-of-return based scale, on the other hand, would ensure development projects for marginal fields to be lucrative because the government’s take is not dictated prior to exploration, thereby economically limiting exploration if the economics do not match. In other words, the project can come online without getting terminated prematurely. This type of flexibility will allow the market to effectively exploit the country’s natural resources. It will also create cash-generating projects for the government, stimulate the economy by increasing local investment, and create jobs. On the other spectrum of field-size, a rate-of-return-based contract will allow the government to obtain a larger piece of the pie. For example, if a large field is discovered and a fixed production share or production-based sliding scale profit oil dictates the profit oil splits, the country may be missing out on the incremental production. In essence, it has a dampening effect: contractor potential upside from price increases is diminished, but the downside is also protected. Likewise, if costs are relatively higher, the IRR sliding scale mitigates the negative impact, and if costs are lower both the contractor and government benefit. Fig. 2 illustrates this dampening effect using a fiscal system dictated by R-Factors, profit factors that some countries have begun to use and are determined from a formula or series of formulas. For this example, the R-Factor system accomplishes the dampening effect, although R-Factor formulas can be biased by the NOC and are therefore not the method of choice in this study. The objective with sliding scale systems is to create a relationship whereby the government percentage flexes upward as profitability increases. In general, it is better for both parties when government take is a function of profitability, but sliding-scale taxes and other attempts at flexibility should be based on profitability, not production rates, in order to be truly efficient. The aim of this new profit-sharing scheme, therefore, will be to optimize the state’s objective to bring bypassed oil to market by reducing minimum field size and production requirements without prejudicing the state’s entitlement to share in upside. Regardless of the volatility of uncertainties in the exploration and production stages, a fair division of benefits between the government and the oil company will be achieved. The PSC scheme will also allow the state to “skim-off” possible windfall profits without prejudicing the commercial viability of marginal or average fields. As a result of such PSC terms, marginal fields will be developed, thereby stimulating the economy and creating jobs stemming from renewed industry investment in the country.


4. Method The main uncertainties in exploration and production, as outlined in Fig. 3, were divided into commercial, operational, geotechnical, and contract inputs into the discounted cash flow (DCF) analysis of the sample project, which culminated in an IRR for the project. In order to determine the difference between technical and commercial success, all government-related taxes, royalties, participation, and fees were zeroed. Finally, an IRR-based PSC contract was created whereby the government participation in the marginal field was determined only after calculating the IRR of the project.

4.1. Commercial Uncertainties Overoptimism has plagued the oil industry for the past 30 years, as companies have dramatically overestimated oil prices and, to a lesser extent, underestimated costs. Fig. 4 illustrates this phenomenon, plotting oil price estimates from reputable firms and think tanks versus actual prices. Further, going over-budget seems to happen a lot more often than bringing projects in on-time and under budget, particularly with frontier regions. Since the expectations of these variables are amongst the most significant drivers behind competitive bidding, encapsulating realistic ranges over time will help in ensuring that these economic drivers do not provide a substantial bias in the project, as the main goal is to study the fields that are uneconomic under current terms but economic given parity between technical success and commercial success.

4.1.1 Oil Prices The single most important variable that affects a project’s bottom line is oil prices. Several agencies and independent analysts have published their own projections on what crude prices will look like in the long-term, and there are conflicting opinions about oil price trends for the next couple of years, let alone decades. A stochastic model proposed by Costa Lima, Suslick, and Avansi (2008), based on a simple multiple regression considering a two-year lagged price, has an overall coefficient of determination (R2) of 0.917. In other words, 91.2% of the variation in annual crude oil prices between 1982 and 2007 can be explained by the combined variation in the two lagged years. The equation is:

pt = -0.128 – 0.244p(t-1) + 1.299p(t-2) ............................................................................... (1) where pt is the price in time; pt-1 is the lagged price in the time t-1; and pt-2 is the lagged price in the time t-2. Given that the DCF in this study goes to 2050, the purpose of estimating crude prices is not to guess the price, but instead to probabilistically capture the volatility that crude prices could follow. Macroeconomically, as excess capacity (i.e. world oil capacity minus world oil production) decreases, the price of crude oil has become more and more volatile. Fig. 5 shows how this phenomenon has affected crude prices, particularly after 2002. Keeping this in mind, the oil price forecast used in this study uses Eq. 1 above as the backbone for the upward trend in the forecast, but ensures volatility month-to-month through a series of triangular distributions for downside and upside volatility factors. These volatility factors were used in the price estimate for each month. The ultimate estimate was also a triangular distribution, with the “minimum” being the result from the downside volatility, the “maximum” being the result from the upside


volatility, and the “most likely” parameter being the result of the two-year lagged simple multiple regression. Fig. 6 shows the upward trend of the forecasted prices, as well as a range of 1 standard deviation and 2 standard deviations over time. Examples of forecasts resulting from this probabilistic manipulation are provided in Fig. 7. The results were used in the Monte Carlo simulation of the DCF for the project. Note that although there is a general upward trend, volatility month-to-month, as well as price in general, can vary greatly using this model. This is precisely the type of uncertainty that should be captured when forecasting oil prices.

4.1.2 Cost and Timing Estimates In order to portray an accurate cash flow over time, cost and time estimates were escalated and determined probabilistically from a distribution of values. Current-day estimates of exploration well costs, development well costs, tie-in costs, facilities costs, operating expenses, abandonment costs, and time lags for facilities/pipeline construction were probabilistically inserted into the DCF calculations and escalated at an annual rate of 3% for inflationary considerations.

4.2. Operational Uncertainties The proper way to determine when a well should be shut-in is to determine its “economic limit”, the production rate where it costs more to produce the hydrocarbons than those hydrocarbons are worth (Allen 2003). Given the computational complications that determining the economic limit presents, a minimum rate per well was probabilistically assessed to each well. Since the time to drill a well varies greatly depending on the geology, infrastructure, and rig capacity, the time to drill a well was probabilistically inserted into the well schedule using a triangular distribution with a most likely case being 5 months, the current industry average for onshore/nearshore drilling in West Africa.

4.3. Geotechnical Uncertainties Specific to the region, Fig. 8 shows the Equatorial Guinea open and licensed acreage, while Fig. 9 shows the recent exploration and development activity. Note the area around Ceiba (discovered in 1999) and Okume (discovered in 2001) is open and on the shelf. Fig. 10 shows the regional seismic data on the shelf. From the figures, it seems that the industry has explored the area in several campaigns, found two fields, and subsequently left the area. The two fields produce from the Albian (younger) system, a post-salt system that generates from the drift section source sequences deposited during the period of restricted circulation prior to the final detachment of Africa from South America. The rest of the system is likely to be oil because the downdip fields are oil. Since the charge is coming from downdip, if there were a large gas component it would likely have displaced the oil in the basinward fields.* After reviewing the geologic setting of Equatorial Guinea, it is obvious that the hydrocarbon potential of the eastern part of the Rio Muni Basin has not been exhausted. The location of the Ceiba and Okume oil fields in this area, plus a number of other undeveloped discoveries in the area, show the presence of at least one active petroleum system.

* Personal communication with RSK Executive Director of Geology.


More, but smaller fields are therefore expected, as the area was historically explored with a strategy that required prospects to have amplitude anomalies. A different strategy of finding smaller fields and not requiring amplitude anomalies will probably locate the prospects previous operators found uneconomic. These fields are on average 10 million barrel accumulations or less, with geotechnical chances of success in the post-salt play of 25-40%.

4.4. Contract Uncertainties The Ministry of Mines and Energy is the overall regulatory and administrative body for the petroleum industry in Equatorial Guinea. GEPetrol was established as the NOC of the Republic of Equatorial Guinea, and new upstream contracts provide for direct state participation through GEPetrol, as it acts as an agent for the state’s share of hydrocarbons. Under new PSCs, work obligations are negotiable, as is the overall structure of the PSC:

- Bonuses: signature bonus, discovery bonus, and production bonus; - Cost oil/gas: after royalty a fixed percentage of production is available for the recovery of operating and capital costs; - Profit oil/gas: remaining production after cost recovery is divided between the investor and the government on a sliding scale basis linked to cumulative production; - Royalty and income taxes: paid by the contractor, usually 10% and 25%, respectively; - Training fees: paid by the contractor, usually $100,000 per annum during the exploration stage and $200,000 per annum during the production stage.

New PSCs provide for a minimum of 20% state participation, and GEPetrol is carried for all costs prior to first production, at which time the contractor is reimbursed for its pro-rata share of costs incurred prior to the date of first production. The cost recovery ceiling has been mixed between contracts, but is a minimum of 60% and typically the contractor is allowed 100% cost recovery. Typical cumulative production based profit splits are given in Table 1.

Table 1–Equatorial Guinea Sample Profit Oil Splits**

Cumulative Production (mmbbl) Government Share (%) Contractor Share (%)

0‐200 20 80

200‐350 30 70

350‐450 40 60

450‐550 50 50

>550 60 40

For the sake of simplicity, all contract uncertainties in the study have been grouped into the “government take” umbrella, as it can be argued that each government provision is, in fact, a tax.

**Personal communication with Wood Mackenzie.


4.5. Technical/Commercial Parity The range of field sizes not economic under EG’s current PSCs but economic given parity between technical and commercial success were determined by modeling the DCF twice, once with all the contract uncertainties as detailed above and once with the contract uncertainties (i.e. government take) zeroed out. In effect, these fields are the marginal fields this study attempted to make economic. According to Johnston, the subject of success probability centers on the difference between technical and commercial success. The difference, he states, is development threshold field size. As the threshold field size approaches zero, as it virtually has in the United States, the difference begins to disappear. However, this difference has not disappeared in countries like Equatorial Guinea, so threshold analysis is used as a decision analysis metric whether or not to even attempt exploration efforts. Fig. 11 illustrates the effect of development threshold field size on success probability.

4.6. IRR-Based Production Sharing A well schedule was used to determine the production on a monthly basis (Fig. 12) based on the geologic chance of success as outlined above, which was then fed to the DCF to populate production and drilling data. Using the DCF for technical parity (that is to say, government take is zero), the IRR for the project after the end of each month was calculated. Based on the IRR at the end of each month, government take was subsequently calculated based on the tranches from Table 2.

Table 2–Sample IRR‐Based Sliding Scale Profit Oil Splits

Project IRR Government Take Contractor Take < 20% 0% 100%

21% - 25% 50% 50% > 26% 95% 5%

For example, if the IRR at the end of a month is 30%, then the first 20% would get taxed at 0%, while the next 5% would get taxed at 50%, and the remaining IRR would get taxed at 95% for an overall government take of 24.2% and a contractor take of 75.8%. In other words, the net revenue interest (NRI) for the contractor during that month would be 75.8%.

5. Discussion The ultimate objective of a flexible system is to create a framework that can honor the mutuality of interest between the host government and the contractor and provide an equitable arrangement for both the highly profitable and the less profitable discoveries. Systems with flexible terms are becoming standard in the industry, as countries attempt to encompass a range of economic conditions to their PSCs. The most common method used today for creating a flexible fiscal system is with sliding scale terms, which typically impose a progressively smaller share of profit oil for the contractor as production rates increase (Johnston 2003). However, in order to truly be a progressive system, government take should be a function of profitability, not the other way around. Production rate sliding scale terms, which attempt at


creating a progressive taxation system, are actually regressive. In fact, marginal fields in EG are uneconomic under the current production-based sliding scale terms, with expected IRR of 8.5% and 65% of cases netting an IRR of 0% (Figs. 13 and 14), but are lucrative given parity between technical and commercial success (Figs. 15 and 16), and can be economic under IRR-based sliding scale terms, with expected IRR of 30% and only 16% of cases netting an IRR of 0% (Figs. 17 and 18). This is because the current terms are essentially based on gross revenues and do not take into account any of the other uncertainties that affect a project. IRR contracts directly take into account such things as production profiles, commodity prices, capital and operating costs, and time value of money because government take is based on project profitability. The argument for royalties, bonuses, cost recovery limits, and other forms of taxation is that they provide a guarantee that the government will benefit in the early stages of production (Johnston 2003), but this is a point of view that is too short-term. Adopting such fiscal conditions actually hinders activity in the country, as evidenced by the low expected IRR of the marginal fields when compared to their otherwise healthy IRR under no taxation. Even if a project is already on-line, royalties can prematurely cause production to become uneconomic – to the disadvantage of both industry and government (Johnston 2003). Using IRR-based sliding scale terms, not only will the project be lucrative to industry as evidenced by the expected IRR using those terms and Fig. 19, which shows the expected profit by the contractor, but as Fig. 20 shows, the government still receives the lion’s share of profits, as new projects come on-line and the government captures most of the upside of a project. One disadvantage to the IRR-based sliding scale system is that the progressive nature of the fiscal system delays the government’s share of revenues, whereas royalties and bonuses guarantee some revenue. Although beyond the scope of this study, it would be beneficial to study how much royalties/bonus a potential PSC scheme with both IRR-based sliding scale profit oil splits and royalties and bonuses could accommodate before the time value of money effects of royalties and bonuses make the fiscal system regressive. Another perceived disadvantage is that the project’s IRR is directly affected by the contractor’s decisions, and contractors can implement gold plating whereby operation and maintenance costs are unnecessarily accrued to hamper the true IRR of the project. According to Johnston (1994), generally speaking companies would likely develop an oilfield in the same way under an IRR system as any other system. However, the integration that large IOCs present could offer a scenario whereby a division of the company performs work and is charge by another division of the same company, but a different subsidiary. Although beyond the scope of this study, this potential problem should easily be solved with moderate involvement on the part of the NOC in the project and/or the hiring of a third-party, independent auditing firm. Other future studies could combine Kokolis, et al.’s scenario selection for valuation of multiple prospect opportunities (1999) with the IRR-based sliding scale economic models to obtain a more realistic scenario, for concession blocks often have more than one prospect and/or producing horizons. Lastly, more imaginative variables could be studied for PSCs, particularly incentivized PSCs linked to specific key performance indicators. Wadood (2006), for example, suggests incentives


to reduce costs and reserve replenishment rate, i.e. encourage contractors to continue investing beyond the initial exploration stage such that reserves are replenished towards the end of the agreement.


References List

Allen, Fraser H. and Seba, Richard D. Economics of Worldwide Petroleum Production. Oil & Gas Consultants International (OGCI), Inc. (January 1993) 12.

Bindemann, K. Production-Sharing Agreements: An Economic Analysis. Oxford Institute for Energy Studies. (October 1999) 29-32.

Costa Lima, E.G. et al. The Impact of Some Real Options on the Efficient Frontier of Portfolios of Oil Production Projects. SPE-116440-MS presented at 2008 SPE Annual Technical Conference and Exhibition, 21-24 September 2008, Denver, CO, USA.

Hall, R. and Lieberman, M. 2001. Microeconomics Principles and Applications, Second Edition, 409-11. Cincinnati, Ohio: South-Western College Publishing.

Johnston, D. 1994. International Petroleum Fiscal Systems and Production Sharing Contracts, 94-115. Tulsa, Oklahoma: PennWell Publishing Company.

Johnston, D. 2003. International Exploration Economics, Risk, and Contract Analysis, 25-88. Tulsa, Oklahoma: PennWell Publishing Company.

Kokolis, G. et al. Scenario Selection for Valuation of Multiple Prospect Opportunities: A Monte Carlo Play Simulation Approach. SPE-52977 presented at 1999 SPE Hydrocarbon Economics and Evaluation Symposium, 20-23 March 1999, Dallas, TX, USA.

Wadood, S. Production Sharing Agreements–An Initiative to Reform. SPE-103603 presented at 2006 SPE Annual Technical Conference and Exhibition, 24-27 September 2006, San Antonio, TX, USA.


List of Figures

Figure 1–Target Marginal Fields

Key

‐ Field

‐ Economic

‐ Subeconomic

Large Fields

Current Economic Limit

Threshold Field Sizes Under Current PSC Terms

Small Fields

Large Fields

New Economic Limit

Threshold Field Sizes Under Proposed PSC Terms

Small Fields

TARGETFIELDS

Figure 2–R‐Factor System Sensitivity Analysis (From Johnston 2003)


Figure 3–Risk Analysis Flow Diagram (From Johnston 1994)

Figure 4–Historical Oil Prices and Projections (From Johnston 2003)


Figure 5–The Effect of Shrinking Excess Capacity on Price Volatility (Data from EIA)

Figure 6–Forecasted Oil Prices (Historical Data from EIA)


Figure 7–Random Samples from Monte Carlo Simulation of Oil Prices


Figure 8–License Status in Equatorial Guinea (From GEPetrol)


Figure 9–Exploration & Production Activity Map in Equatorial Guinea (From GEPetrol)


Figure 10–Seismic Activity on Equatorial Guinea Shelf (From GEPetrol)

Figure 11–Technical vs. Commercial Success Probability Plot Showing Threshold Field Size (Johnston 1994)


Figure 12–Random Sample From Monte Carlo Simulation of Well Shedule

Project Month # Wells # Wells Total ProductionOnline Abandoning (bbls/month) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 012 1 0 49798 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 013 1 0 48027 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 014 1 0 46348 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 015 1 0 44754 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 016 1 0 43240 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 017 1 0 41801 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 018 2 0 63789 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 019 2 0 61630 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 020 2 0 59571 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 021 2 0 57606 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 022 2 0 55729 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 023 2 0 53936 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 024 3 0 52221 ON ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 025 2 1 50581 ON ON OFF 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 026 2 0 49012 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 027 2 0 47509 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 028 2 0 46068 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 029 2 0 44688 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 030 3 0 43364 ON ON 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 031 2 1 42094 ON ON 0 OFF 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 032 2 0 40875 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 033 2 0 39704 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 034 2 0 38579 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 035 2 0 37498 ON ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 036 3 0 53775 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 037 3 0 52254 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 038 3 0 50791 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 039 3 0 49383 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 040 3 0 48028 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 041 3 0 46723 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 042 3 0 45466 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 043 3 0 44254 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 044 3 0 43087 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 045 3 0 41961 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 046 3 0 40874 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 047 3 0 39826 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 048 3 0 38815 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 049 3 0 37838 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 050 3 0 36895 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 051 3 0 35983 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 052 3 0 35102 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 053 3 0 34251 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 054 3 0 33427 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 055 3 0 32630 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 056 3 0 31860 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 057 3 0 31114 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 058 3 0 30391 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 059 3 0 29692 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 060 3 0 29014 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 061 3 0 28358 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 062 3 0 27722 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 063 3 0 27105 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 064 3 0 26507 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 065 3 0 25927 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 066 3 0 25365 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 067 3 0 24819 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 068 3 0 24289 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 069 3 0 23774 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 070 3 0 23275 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 071 3 0 22790 ON ON 0 0 ON 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Well Schedule


Figure 13–Monte Carlo Results (Current Fiscal System)

-0.1 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Figure 14–Scatter Plot Under Current Fiscal System

-20

-10 0 10 20 30 40 50 60

IRR


Figure 15–Monte Carlo Results (No Fiscal System, i.e. Technical Success Scenario)

5.0% 90.0% 5.0%

0.000 1.227

-0.2 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

IRR

IRR

Minimum 0.0000Maximum 1.7218Mean 0.5919Std Dev 0.3834Values 4973 / 5000Filtered 27

Figure 16–Scatter Plot Under No Fiscal System, i.e. Technical Success

-20

-10 0 10 20 30 40 50 60

Field Size (mmbbls)

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

IRR

IRR vs Field Size (mmbbls)

IRR vs Field Size (mmbbls)

X Mean 19.9905X Std Dev 10.8216Y Mean 0.5919Y Std Dev 0.3834Pearson Corr Coeff 0.2375

@RISK Student VersionFor Academic Use Only


Figure 17–Monte Carlo Results (Under Proposed IRR‐Based Profit Oil Splits)

5.0% 90.0% 5.0%

0.000 0.490

-0.1 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

New IRR

New IRR

Minimum 0.0000Maximum 0.5636Mean 0.3037Std Dev 0.1714Values 5000

Figure 18–Scatter Plot Under Proposed IRR‐Based Profit Oil Splits

-20

-10 0 10 20 30 40 50 60

technical / Field Size (mmbbls)

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

New

IRR

New IRR vs technical / Field Size (mmbbls)

New IRR vs technical / Field Size (mmbbls)

X Mean 19.9999X Std Dev 10.8023Y Mean 0.3037Y Std Dev 0.1714Pearson Corr Coeff 0.1895


Figure 19–Contractor Profit Under Proposed IRR‐Based Profit Oil Splits

5.0% 90.0% 5.0%

0.0 147.4

-50 0 50 100

150

200

250

300

350

400

Values in Millions

0.00

0.05

0.10

0.15

0.20

0.25

Contractor Profit

Contractor Profit

Minimum 0.0000Maximum 367958691.6771Mean 50364471.7034Std Dev 48934239.2675Values 5000


Figure 20–Government Profit Under Proposed IRR‐Based Profit Oil Splits

5.0% 90.0% 5.0%

0.000 0.521

-0.2 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Values in Billions

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Government Profit

Government Profit

Minimum 0.0000Maximum 1.567E+009Mean 156903652.6136Std Dev 183211006.4588Values 5000


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Optimising PSC - Economics