Portfolio management as a new approach on improvement of ...

African Journal of Business Management Vol. 4(13), pp. 3013-3025, 18 October, 2010 Available online at http://www.academicjournals.org/AJBM ISSN 1993-8233 ©2010 Academic Journals Full Length Research Paper

Portfolio management as a new approach on improvement of financial resources’ allocation: A case

study of the National Iranian Oil Company (NIOC)

Mohammad Reza Moghaddam

Research Institute of Petroleum Industry, Ministry of Petroleum of Tehran, Iran. E-mail: [email protected]. Tel: 0098 021 220 90 347.

Accepted 23 September, 2010

The National Iranian Oil Company (NIOC) is one of the largest exploration and production (E and P) oil companies in the world, which enjoys numerous investment opportunities. The selection and formation of the projects’ basket (portfolio) of this industry needs some considerations on decision making tools, key targets, priorities and constraints of NIOC. Due to NIOC’s vision, which is to be one of the excellent commercial companies of the world in production, refining and export of oil and gas, the necessity of keeping the production level unchanged and enhancing the abilities of production by fields developments and improving the recovery factor, are very important. As regards numerousity of investment opportunities at different stages of exploration, development, exploitation and production, considering all constraints, are not feasible without applying scientific methods. According to these, the model of this paper considers the government expectations, strategies, national targets, priorities and constraints of NIOC and evaluates economic risks of projects, economic evaluation of portfolio, applying MERAK products (for a few pilot projects) in the oil industry and finally, determines the optimum basket of the projects. For implementing the system, an organization is required in each planning departments of NIOC. This system as a decision support system (DSS) is able to use the current data, the government and ministry’s strategies, the policies of CEO and other budgetary and financial considerations, and determines the investment priorities and optimum portfolio of NIOC. Key words: Risk management, portfolio management, upstream industries, software, decision support system.

INTRODUCTION NIOC as one of the largest oil companies in exploration and production (E and P) has various opportunities, while it also faces limited financial resources. Selection of projects (or portfolio) in this industry requires conside-rations on different aspects of decision making such as key objectives, priorities and constraints of NIOC. According to NIOC’s perspectives, as an outstanding commercial oil company, NIOC has to keep the production level unchanged and also increase the capacity of oil production through developing fields and improving the recovery factor. The number of investment opportunities in some aspects such as exploration, development, production, exploitation show that the best portfolio and its dynamics during different periods of time is impossible under consideration of all constraints and restrictions, without applying the scientific methods and related softwares (Schlumberger, 2008). In this direction, a company would be successful which evaluates the

economic risks and establish its portfolio management system according to its existing strategies, aims, priorities and constraints (Alam et al., 2010). The main problem for NIOC is to have a better financial allocation among projects and should be equipped by modern tools for managing its portfolio.

This paper is divided into two sectors; the first, discusses about the methods of ‘risk management’ in the ‘oil and gas industry’, while the second, indicates the desired manner of ‘portfolio management’ for optimum allocation of the financial sources to improve the allocation in the industry. So, a pilot case is used to show the applicability of the model. Risk management in upstream oil and gas industries Risk is defined as an event which leads to negative results.

3014 Afr. J. Bus. Manage.

Table 1. Classification of risks. Kind of risk Effect on economics project Confronting/how to avoid risk Political risks Negative Yes / Multinational portfolio Environmental risks Negative Yes / Many projects Technical risks (Reserves/costs) Negative / Positive Yes / Many fields Economical risks (Price of oil and gas) Positive / Negative Yes / Contract conditions Economical risks (Foreign exchange rate) Positive / Negative Yes / Contract conditions Economical risks (Inflation) Positive / Negative Yes / Contract conditions Commercial risks (Financial) Positive / Negative Yes / Differentiation Commercial risks (Tariffs) Positive / Negative Yes / Hedging

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Figure 1. Oil and gas upstream activities and risky areas.

results. From a scientific view, risk is defined as all events which could lead to positive or negative results (Alam et al., 2010). The oil companies try to recognize the effects of these risks on cash flow investment and long-term value of the company. From the point of investment theory, more efficiency takes place with acceptance of higher risk. Small and independent oil companies react against inefficiency and high risks, trying to decrease such risks. Mid-scale oil companies which are active in the explorations, usually preserve an equal portfolio between exploration and production (Benoit et al., 2000). As such, the large-scale oil companies, which are active in refining, have a weak reaction against the price, and they do not tend to decrease risks. Due to a competitive environment, the technical risks could be recognized and their effects could be restricted. Oil companies try to internationalize their risks on the investment value, cash flow and long-term value of the

company (Wheitside, 1999). Table 1 shows the classification of risks and Figure 1 shows the different stages liable to the risk for upstream levels of oil and gas industry.

Figure 2 shows the effects of the risk on the economic evaluation of oil and gas projects, operational and current costs and finally on ‘net present value’ (NPV). Each parameter which is used in the economics of project is liable to a probability that converts the NPV to a stochastic variable. The methods used in measuring the value and risk are: conventional analysis, decision making analysis and simulation techniques. Conventional analysis By this analysis with a clear procedure, there are many defined parameters. Managers use the cash flow of the

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Figure 2. Economics of oil and gas projects.

company through NPV and rate of return (ROR). Although a deep analysis can provide a better outlook from positive and negative results of a potential investment, but it cannot help the managers to quantify the probability of these results and do some “sensitivity analysis” (Bigdeli et al., 1999). In conventional analysis, the most probable situation is not clear. Decision analysis In most cases, the different sectors of the oil industry use the decision analysis for the stages of the project (after exploration). Simpler, decision analysis imputes the different probabilities of success to the clear events. For each event, this method determines NPV and its related probability. By multiplying NPV to each probability and adding them up for all events, only one result is measurable (Schlumberger, 2008). There are some restrictions by this method that forces the researcher to define some clear events and even limit the number of analyzing variables. Probabilistic simulation technique The probabilistic simulation technique (related to Monte Carlo analysis) is used to determine the distribution

pattern of reserves, especially in the oil industry. By this technique, distribution of associated probability is determined for each key variable and randomized sampling is done for each variable. This process is repeated a thousand times which leads to "probable reserves distribution". However, the average value of distribution indicates the "expected reserves".

The subject of oil reserve is relatively simple, because it composes of less than 10 variables and limited algorithms, whereas for analyzing the cash flow there are more variables and algorithms. To perform financial and economical analysis in the oil industry, Monte Carlo technique is often used. Some large-scale oil companies have developed their domestic and commercial systems so that they could be connected to the ‘international fiscal regime model’. These new systems allow the decision makers to design a model that is more complete for all technical, commercial and economical variables, and as such, show a dependency rate between the variables. Through the probabilistic simulation techniques, it is possible to recognize a full probable distribution for NPV, its standard deviation and EMV feasible for a project or portfolio of projects. Also, this technique could be applied to determine the rate of correlation between projects and integration of risks, especially to calculate the overall risk, value and portfolio cash flow.

Figure 3 shows the sensitivity analysis of upper and


Figure 3. Expected monetary value and net present value.

lower limits of NPV and specifies values according to the related probability; as such, the asset risks are not addable. A risk that is measured by the standard deviation is not independent, in that it depends on the number of assets and risk of portfolios. There are two main mechanisms for diversification: Simple and Markowitzian diversifications.

When the economical efficiency is considered, the assets are not independent (that is, they are not cor-related). As such, efficiency of all assets is independent from the common economic conditions. Under these circumstances, the simple diversification cannot lead to zero-risk, but can lead to minimum rate. In stock market, this risk is known as “market” or “systematic risk”. For an exploration company, the risk of oil price is the main risk. However, the risk is composed of other factors such as gas market, inflation risk and so on.

Markowitzian diversification is applied to decrease the portfolio risk of assets with intense correlation. This diversification uses the analytical optimization portfolio techniques to maximize the efficiency according to a defined level of risk. This method indicates the fact that a combination of assets with low correlation could be faced by less risk in the efficiency of total assets.

These two mechanisms provide a better understanding of risks’ sources. All risk sources influence the risk of efficiency for individual asset. However, this effect depends on the independency and rate of risks toward the total portfolio risk. Anyway, the oil company, regardless of whatever strategy it has, only pursues an objective that maximizes the value of the company according to the defined risk. Portfolio optimization is a method used to determine where to invest. It maximizes the efficiency and the achieved strategy is usable for a long term. To achieve a higher EMV, investment in exploration and new projects is required. Difference between EMV of an asset and its market value, is known

as “risk premium”. Whenever the asset is bought, the added value is considered as the risk premium (Alam, 2009a). If the investor pays the market value of an asset, its value will be increased up to EMV. The added value can compensate the additional risk arising from keeping a risky asset toward a non-risky one. Similarly, for every asset in the portfolio, it is possible to select between the sale of an asset by market price or keeping the asset until it reaches a higher EMV with a defined risk (Alam, 2009b). Some facts of the oil and gas industry are:

(i) Lack of a compatible political basket for strategic long-term planning. (ii) Higher growth of domestic consumption of energy, threatening exports. (iii) Decreasing the production capacity of NIOC. (iv) Limitation of NIOC’s resources for development of the energy sector. (v) Try to fulfill Iran’s share in OPEC. (vi) Necessity of 70 billion dollars investment in the oil and gas sector during the 10 to 15 years period of investment. More so, the oil ‘minister’ has declared his priorities: (i) In the exploration sector (ii) Priority of common oil and gas fields (iii) Priority of gas injection (iv) Recognition of potential capabilities (v) In the production sector (vi) Development and exploration of new fields (vii) Reserves conservation (viii) Gas and water injection (ix) Improving the recovery factor (x) Decreasing the associated gas (xi) In the refinery and petrochemical sector (xii) Self-performance

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Figure 4. Relation among the components of MERAK products.

(xiii) Qualitative and quantitative development (xiv) In the domestic energy consumption sector (xv) Pipeline management (xvi) Storage tanks management (xvii) CNG stations development (Compacted natural gas) (xviii) Attention to the environment (xix) Decreasing the polluting productions (xx) Oil and gas swap (xxi) International relations development (xxii) Preservation of OPEC’s share (xxiii) Preservation and improvement of gas export’s share in the world. The commercial targets of NIOC are: (i) Planning process management with calculation of risks, value and uncertainties. (ii) Quantification of uncertainties in the production process, to forecast needed investment and project plans. (iii) Enjoyment of an optimum commercial and long-term plan. Current planning to determine the optimum basket of projects, confronts the following challenges: (i) Weakness of criteria (unusual) for economical evaluation of the projects. (ii) Using excel-based calculations. (iii) Lack of concentrated economical database (usually on personal computers).

(iv) Lack of optimum portfolio management, based on mental criteria. Hence, ‘portfolio management’ could be used to establish a knowledge-based system for oil and gas industry and applying software to do better economical analyses, determining the investment basket on the light of the minimum risk and maximum value in NIOC’s planning system. Advantages of ‘portfolio management’ are: (i) Board of Directors trust to capital allocation. (ii) Coordination of the 5-year plans and NIOC’s investment plan. (iii) Optimization of risk and companies’ efficiency in the investment plan. (iv) Providing the auditing of investment decisions. (v) Improving the operations through sharing and optimum capital allocation. MERAK software The schema of relations among the component is shown in Figure 4. According to this figure, individual economic evaluation of the projects is done by the PEEP module, while production data are provided through VOLTS module and calculations of risk are done through DTK module. All data are transferred to the ‘capital planning module’, and as such, the ‘optimization portfolio’ will be done along with a basket of projects. Figure 5 shows the


Figure 5. Role of deciding tools in portfolio management process.

relations of every stage assigned to the optimum portfolio.

In this package, it is possible to introduce the strategies of the deciding model in NIOC. The relation of each project in the candidate basket is introduced to the software along with its strategies and assigning priority coefficient. There are 50 pilot projects in this basket and the process is applied to each one. According to Figures 5, 6 and 7, every project is placed on a pre-determined strategy and the strategic index of each project is used to assign the basket of projects. 50 pilots are used to implement “the allocation” of financial resources. Tables 2, 3 and 4 show the estimation of revenue of each project.

Figures 8, 9, 10 and 11 show the income and expected production rate that resulted from performing the projects with any constraints. Tables 5 and 6 show the first and second optimum portfolios arising from optimization of the model.

Pay attention to the following example. First stage: Targeting the oil and gas production and income during the plan. -Target: Producing 15 bln barrels. -Limitation: Max. production of 18 bln barrels of oil during the schedule. -Expected income rate and production of oil and gas is determined by the technical sector and the beneficiaries

of the project. Description of results: The optimum portfolio includes 49 of 50 projects, but 32 projects were not started on time. Description of results: The optimum portfolio includes 49 of 50 projects, but 24 projects were not started on time. Conclusion The National Iranian Oil Company (NIOC), one of the largest exploration and production oil companies in the world, is faced with numerous investment opportunities. The selection and formation of projects’ basket (portfolio) in this industry needs some considerations on decision makings, key targets, priorities and constraints of NIOC. On the one hand, due to NIOC’s vision as one of the excellent commercial companies of the world in produc-tion, refining and export of oil and gas, the necessity of keeping the production level unchanged and enhancing the abilities of production by fields developments and increasing the recovery factor, is very important, whereas on the other hand, the numerousity of investment oppor-tunities at different stages of exploration, development, exploitation, production, complexity of backward and forward linkages of projects and correspondent risks, the

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Figure 6. Introducing the strategies to the model.

Figure 7. Qualification of every project’s relation with model strategies.


Table 2. List of 50 pilots projects.

Candidate set Project name Objective Base business Totals for proposed candidates: 0 Candidates’ set totals: 0 Base business totals: 0 DS2- Buying Office – R2C N/A * DS3- Buying Residential in Kish- R2C N/A * DS4- CNG Bus Plant- R2C N/A * DS6- R2c N/A * DS9- R2C N/A * DS13- Refinery- R2C N/A * M2-Cap-R2C N/A * MS1-R2C N/A * MS4-R2C N/A * MS5-R2C N/A * MS6-South Pars-R2C N/A * MS7-NGL Plant-R2C N/A * MS9-NGL Plant-R2C N/A * MS10-used to be DS10-R2C N/A * UDOD7-R2C N/A * USLGD1-R2C N/A * USOD2-R2C N/A * USOD3-R2C N/A * USOD5-R2C N/A * USOD6-R2C N/A * USOD8-R2C N/A * USOD9-R2C N/A * USOD10-R2C N/A * USOD11-R2C N/A * USOD12-R2C N/A * USOD13-R2C N/A * USOD14-R2C N/A * USOL1-Oil Layer-R2C N/A * USOL2-R2C N/A * USOL3-R2C N/A * USOL4-R2C N/A * DS5 N/A *

Table 3. Total revenue.

Candidate set Project name Objective Base business Totals for proposed candidates: 619222720 Candidates set totals: 619222720 Base business totals: 0 DS2- Buying Office – R2C N/A 38 DS3- Buying Residential in Kish- R2C N/A 5974 DS4- CNG Bus Plant- R2C N/A 645000 DS6- R2c N/A 160309 DS9- R2C N/A 13050057

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Table 3. Contd.

DS13- Refinery- R2C N/A 7670056 M2-Cap-R2C N/A 52363 MS1-R2C N/A 5873220 MS4-R2C N/A 17152210 MS5-R2C N/A 228360 MS6-South Pars-R2C N/A 51426460 MS7-NGL Plant-R2C N/A 2254776 MS9-NGL Plant-R2C N/A 14243453 MS10-used to be DS10-R2C N/A 32159620 UDOD7-R2C N/A 6159660 USLGD1-R2C N/A 2329400 USOD2-R2C N/A 5426964 USOD3-R2C N/A 2720066 USOD5-R2C N/A 1103927 USOD6-R2C N/A 1664915 USOD8-R2C N/A 13531612 USOD9-R2C N/A 12749160 USOD10-R2C N/A 3637776 USOD11-R2C N/A 5967630 USOD12-R2C N/A 4693225 USOD13-R2C N/A 14663427 USOD14-R2C N/A 35365534 USOL1-Oil Layer-R2C N/A 722000 USOL2-R2C N/A 4162697 USOL3-R2C N/A 29619650 USOL4-R2C N/A 345678765 DS5 N/A 23456780

Table 4. Expected income of the oil and gas production rates.

Expected income (bln$) Oil production rate (MMB) Gas production rate (MMB) Date Amount Date Amount Date Amount 2006 2006 2006 2007 2007 2007 2008 2008 2008 2009 522000.000 2009 2009 1100000 2010 575000.000 2010 2010 1550000 2011 619000.000 2011 2200000 2011 1850000 2012 692000.000 2012 2200000 2012 2100000 2013 692000.000 2013 2200000 2013 2100000 2014 696000.000 2014 2200000 2014 2100000 2015 691000.000 2015 2200000 2015 2100000 2016 700000.000 2016 2200000 2016 2100000 2017 701000.000 2017 2200000 2017 2100000 2018 696000.000 2018 2200000 2018 2100000 2019 694000.000 2019 2200000 2019 2000000 2020 696000.000 2020 2100000 2020 2000000 2021 683000.000 2021 2100000 2021 2000000 2022 673000.000 2022 2000000 2022 2000000 2023 666000.000 2023 1460000 2023 1850000 2024 661000.000 2024 1430000 2024 1850000 2025 652000.000 2025 1400000 2025 1600000 2026 621000.000 2026 875000 2026 1500000


Figure 8. Expected income during planning.

Oil Production Profile

Figure 9. Expected oil production rate during the planning (Mean of Oil-Volume).

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Figure 10. Expected gas production rate during planning (Mean of Total-Gas-Production).

Figure 11. Relations of projects with strategies (red=very weak relation, yellow= medium, green=too strong).


Table 5. First optimum portfolio.

Project name Base In Working Interest

Eval. Date

Delay Mean of at-ror (maximized)

Selected By Gen

Candidate Set Totals: 50 2000.00 Base Business Totals: 0.00 Project grid filter totals: 0 49/49 47.46 49 DS2- Buying Office – R2C * � 1.00 2006/1 5 37.17 � DS3- Buying in Kish- R2C * � 1.00 2006/1 0 8.53 � DS4- CNG Bus Plant- R2C * � 1.00 2011/1 5 36.86 � DS6- R2c * � 1.00 2006/1 0 17.17 � DS9- R2C * � 1.00 2011/1 5 39.34 � DS13- Refinery- R2C * � 1.00 2011/1 5 2000.00 � M2-Cap-R2C * � 1.00 2006/1 0 12.97 � MS1-R2C * � 1.00 2006/1 0 17.93 � MS4-R2C * � 1.00 2006/1 0 24.38 � MS5-R2C * � 1.00 2006/1 1 32.59 � MS6-South Pars-R2C * � 1.00 2007/1 0 90.93 � MS7-NGL Plant-R2C * � 1.00 2006/1 5 2000.00 �

Table 6. Second optimum portfolio Project Name Base In Working

Interest Eval Date

Delay Mean of at-ror (maximized)

Selected By Gen

Candidate Set Totals: 50 2000.00 Base Business Totals: 0.00 Project grid filter totals: 0 29/29 53.49 29 DS5 * 1.00 2006/1 5 37.17 � DS1- Buying Office-R2C * 1.00 2006/1 0 8.53 � DS2- Buying Office – R2C * 1.00 2011/1 5 36.86 � DS4- CNG Bus Plant- R2C * 1.00 2006/1 0 17.17 � DS7- R2C 1.00 2007/1 32.59 � DS8-R2C 1.00 2008/1 15.16 � DS13- Refinery- R2C 1.00 2006/1 2000.00 � MS1-R2C 1.00 2009/1 34.56 � MS4-R2C 1.00 2007/1 62.59 � MS5-R2C 1.00 2006/1 38.88 � MS6-South Pars-R2C * 1.00 2011/1 5 39.34 � MS7-NGL Plant-R2C * 1.00 2011/1 5 2000.00 � MS8-R2C * 1.00 2006/1 0 12.97 � USGF1-R2C * 1.00 2006/1 0 17.93 � USGFD3-R2C * 1.00 2006/1 0 24.38 � USGLD1-R2C * 1.00 2006/1 1 32.59 � USOD10-R2C * 1.00 2007/1 0 90.93 � USOD14-R2C * 1.00 2006/1 5 2000.00 �

and correspondent risks, the number of portfolios such as selecting optimum portfolio and its dynamics through time periods, considering all constraints, are not feasible without applying scientific methods. According to these, a model which identifies the expectations, strategies,

targets, priorities and constraints of NIOC, as well as critical success factors (CSFs) and also economic risks of projects, economic evaluation and portfolio management, is presented. However, it applies MERAK products (for a few pilot projects at the upstream level) in this industry

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