Quantitative Tools Link Portfolio Management With Use Of Technology

E&P Assets From A Portfolio Perspective

John I. Howell III
Portfolio Decisions Inc.
Houston
Roger N. Anderson, Albert Boulanger
Columbia University
Palisades, N.Y.
Bryan Bentz
Bentz Engineering
Stonington, Conn.

Modern portfolio analysis tools give decision-makers in the oil and gas industry analytical support and specific guidance to the intuitive sense that the best balance for the business lies in a combination of tactics and simultaneous actions on multiple projects.

Because of the complexity of considering several projects or tactics simultaneously, decision-makers tend to treat their projects and tactics as independent of anything else in the business.

Yet all of the projects in the business interact with one another. Project interactions arise from factors such as price-resource sharing, performance targets, commercial and market interactions, and technical risk.

Knowledge of how projects interact and how the aggregation of all projects sum to meet balanced business requirements should guide decisions. A portfolio perspective helps the decision-maker understand the total impact on business balance resulting from a single decision.

With portfolio tools, the decision-maker is ultimately able to frame options in terms of the probability of meeting a suite of balanced performance targets.

Basic portfolio management

The following example illustrates many of the concepts of portfolio management and the benefits associated with managing assets from a portfolio perspective (interdependence model) rather than from a project-by-project perspective (independence assumption).

We emphasize the decision-maker's perspective. However, the example also illustrates the impact that engineering, geology, and geophysical technologies can have on the portfolio analysis.

Portfolio management serves as the link between a business strategy and the projects selected to exercise that strategy. Thus, the corporate strategy is the starting point. Decision-makers must select metrics to quantify their strategy as well as multiyear targets for each metric. The targets must be defined for all years of the planning horizon. The time period must be long enough to describe the investment and profit cycle for major projects (e.g., deepwater and large international E&P projects), but not so long that it becomes inaccurate.

Fig. 1 [88,095 bytes] depicts the strategy of a company measuring itself using four metrics-production, reserves, net cash flow (NCF), and earnings-with a 15 year planning horizon. The bars represent the targets, while the area plot represents the company's existing base business.

The base business consists of producing assets, developing assets, and exploration assets. Gaps between the base business (area plot) and the targets (bars) define the performance shortfalls of this company. The gaps clearly define business performance issues the company must resolve.

The company has a small gap in earnings in 1999-2003, which expands dramatically from 2004 until 2013. The company has no gap in production in 1999; however, the production growth goal creates an ever-expanding gap from years 2 until 15.

The reserve metric demonstrates a similar gap, yet here the shortfall begins in the first year and extends over the life of the portfolio. The good news is that the company has excess cash flow between 1999 and 2009 to invest and correct the shortfalls in the other financial and operational metrics.

The decision-maker in this company has a formidable challenge. He must identify the appropriate selection of assets to acquire, divest, and reshape such that excesses in certain metrics can be used to fill shortfalls in other metrics. This challenge goes beyond finding good investments.

Good investments must complement the existing asset base and meet the required performance metrics. The likelihood of solving this performance problem by selecting one project or one tactic at a time is very small.

Using portfolio analysis, the decision-maker can describe a range of projects, including exploration, acquisition, and exploitation opportunities. The portfolio model uses linear programming to determine which projects should be selected, what interest should be taken in the projects, and when the projects should be done. If an appropriate combination of projects can be found that fills the performance gaps, the model is determined to be feasible. If the model is infeasible, additional or different projects may be required, or different performance metrics or targets must be defined. Sources of the infeasibilities represent significant information.

A feasible solution is illustrated in Fig. 2 [88,878 bytes]. The target bars and reference area plots are identical to the targets and base business reference data in Fig. 1. However, Fig. 2 includes a dark line that depicts the portfolio solution. The heavy line is always equal to or exceeds the target bar for all metrics, for all years, except for the reserve target in the year 2013.

This target (or constraint) had to be removed to find a feasible solution. The model will be run against these modified constraints for all subsequent analysis. Any place the heavy line exactly equals the target bar (earnings, 2000-2006; production 2002-2004 and 2006; reserves, 1999-2000), the model is tightly constrained. These metrics and timeframes are the critical performance points for this company.

Efficient frontier

Knowing the model is feasible, the decision-maker can proceed to investigate the range of options he has to meet the performance metrics and balance his business performance.

By running the model with increasing portfolio value targets-cumulative net present value (NPV) of all investments-the decision-maker can define the efficient frontier, as seen in Fig. 3 [40,421 bytes]. Each point on the efficient frontier represents a different collection of projects (a different portfolio). All portfolios on the efficient frontier have two characteristics in common: Each portfolio meets the modified performance metrics, and each portfolio represents the minimum risk collection of projects that generate the appropriate portfolio value.

The efficient frontier characterizes the range of portfolio values ($350-750 million) and the associated risks. Risk for each portfolio is defined as the mean deviation of all outcomes from a given portfolio that produce results less than the target value. Mean deviations are computed with a Monte Carlo analysis, which samples the various possible outcomes associated with each project. The shape of the risk curve is very informative, but the absolute risk measurement provides only limited information to aid the decision-maker.

Probability diagnostics

A much more informative perspective for decision-makers comes from computing the probability of meeting or exceeding each performance target (Fig. 4 [107,203 bytes]).

Figure 4a plots the probability of meeting or exceeding the net cash flow (NCF) targets for two different portfolios. The orange line defines the probabilities associated with the $500 million portfolio shown from the efficient frontier in Fig. 3. The blue line characterizes the probability of meeting the NCF targets for the $750 million portfolio on the efficient frontier.

These results fit the intuition of many decision-makers. The $750 million portfolio is a higher risk portfolio on the efficient frontier, and the likelihood of meeting the NCF targets decreases as the risk on the efficient frontier increases.

Fig. 4b portrays a different result, which is not intuitive to many decision-makers. Fig. 4b clearly illustrates that the probability of meeting the earnings targets are similar in the early years. However, by the fifth year, the probabilities of meeting earnings goals are notably higher (70%) for the high-risk, $750 million portfolio than they are with the $500 million portfolio (40%).

The fact that the probabilities of meeting NCF targets behave inversely to the probability of meeting earnings goals creates a complex situation. The decision-maker dealing with this situation clearly must balance the tradeoffs between meeting these two goals. Decision-makers must realize that the metrics associated with any business are related in very complex ways. High-risk portfolios on the efficient frontier may translate into portfolios with a high probability to meet performance targets.

The same high-risk portfolio may have very low probabilities of meeting other goals. The role of the decision-maker is to understand and manage these tradeoffs. The two sets of curves shown in Fig. 4 compare two portfolios (two points on the efficient frontier curve), for only two metrics. Similar curves can be generated for all the metrics used in the portfolio model.

Project significance

A decision to meet the earnings goal and therefore to select the $750 million portfolio described above leads to investigation of the project contributions to the resulting portfolio.

Fig. 3 in the preceding article illustrates the contribution of each project to the NCF profile for the $750 million portfolio. The X and Y axes define the projects and their contribution to the NCF target for each of the 15 years. The Z axis reflects the magnitude of the NCF contribution by year and project. The back wall of the Project axis sums each project's contribution across all 15 years.

This figure clearly illustrates that production projects No. 4, No. 6, No. 7, and No. 8, exploration project No. 8 and No. 9, play No. 1 and No. 2, and investment No. 1 are the most significant contributors to NCF. The production contribution meets most of the early years' NCF targets, while the exploration projects and Plays No. 1 and No. 2 along with investment No. 1 contribute significantly to the last third of the time period. The NCF of different projects fills NCF needs for different time periods of the model.

Also, production project No. 1 was not selected at all. This reflects a divestment opportunity. Similarly, many of the exploration assets were not selected. However, investment No. 1 (an acquisition) was chosen.

Thus, in this one model, the decision-maker is presented with simultaneous acquisitions and divestment opportunities. This illustrates the power of the interactive analysis of the portfolio model. Projects and tactics are simultaneously evaluated to optimize the business.

Similar diagnostics can be generated for each of the metrics. Learning can be enhanced by comparing plots for different metrics: Projects often have a dominant metric, which causes them to be significant (chosen at a high level) to a given portfolio. The significance of any project is partly a function of the project, partly a function of what the company is trying to achieve, and partly a function of what other projects are available to the portfolio. Therefore, decision-makers need to understand why projects are significant to the portfolio, not just that they are significant.

Some projects will be absolutely critical. This means that they cannot be replaced. Another way to identify critical projects is to observe that if they are removed from the portfolio, no feasible solution can be found.

All too often projects that fail to stand out in the decision-maker's mind end up being critical contributors to the portfolio. Unfortunately, these are the projects that end up low on rank tables or similar scattergrams, and they are thus the frequent targets of divesting programs. Guiding decisions from a portfolio perspective will prevent decision-makers from unknowingly divesting critical assets.

On the other hand, projects that are critical to the portfolio can be identified and enhanced further. Applying the appropriate technologies, as described in the accompanying article, can enhance the significance of an asset.

Another way to view the significance of an asset is to assess the portfolio value of the asset. A given asset may have an NPV of $50 million but a portfolio value of $200 million. How is this differing valuation possible?

If the asset is totally independent of other assets in the portfolio, the portfolio value is $50 million. However, if the asset interacts with other projects, the portfolio value becomes the aggregate improvement in portfolio value associated with this project, as well as all of its interdependent projects. Thus, if the portfolio is optimized without this project available and the aggregate value of the portfolio decreases by $200 million, the net value to the portfolio of this asset is $200 million, not $50 million.

Decison guide

Portfolio tools are designed to guide, not replace, the decision-maker. The portfolio perspective illustrates the possibilities, tradeoffs, and issues associated with the company's strategy and the pool of projects with which the company has to work.

With this perspective, the decision-makers can learn how the metrics and projects interact and use this knowledge to guide decisions rather than fall into the cycle of implementing a solution and dealing with its consequences.

Portfolio management provides a methodology for decision-makers to determine if their strategic targets are achievable. Furthermore, decision-makers can assess the likelihood or probability of meeting their targets.

The tradeoffs associated with any single portfolio option become easily apparent before any option is implemented. Ultimately, these tools allow the decision-maker to focus on issues that help balance business performance.

The portfolio management tool is to the decision-maker as a reservoir simulator is to the reservoir engineer. Neither tool provides "the answer." Yet both tools reduce complex problems to manageable understanding that can be analyzed consistently and logically.

The Authors

John I. Howell III is chief executive officer of Portfolio Decisions Inc. and was formerly at Shell Oil Co. for 21 years.
Roger N. Anderson is executive director of Columbia University's Energy Research Center and was a founder and sits on the board of directors of Bell Geospace Inc.
Albert Boulanger is chief information officer of Columbia's Energy Research Center, and formerly worked at GTE/BBN Internetworking.
Bryan Bentz is president of Bentz Engineering and was formerly at GTE/BBN Internetworking.