Gauging the Level of Information Needed for Portfolio Management

1. Gauging the Level of Information Needed for Portfolio Management Joanne Pinkney Worldwide Exploration Planning Manager Kerr-McGee Oil & Gas Corp.

2. Agenda • Status of portfolio optimization today • Outline of the granularity issue • The asymmetric approach • Communicating granularity issues • Summary of lessons learned

3. Portfolio Optimization – Where is the industry today? • We have sophisticated software • We have executive management interest in the concept • We have theoretical examples showing us the value-added benefits of a portfolio optimization technique over conventional rank and cut techniques • However, there are still many practical problems associated with effective use of portfolio optimization within a company

4. One of the major challenges: • At what level of granularity do you define your portfolio? VP Strategy VP Planning “ The portfolio model is a strategic level, direction setting tool. Therefore we only need data at the business unit level” “The portfolio model is a planning and capital allocation tool. Therefore we need data on every project in the portfolio”

5. High-Level Approach + it is quick and easy to gather data + can enable clear strategic insights - may give misleading results if business units are composed of different types of projects - Does not allow drill-down into more detail - Cannot model capital allocation decisions Bottom–up Approach + All projects are captured and can be analyzed + Portfolio can be “sliced and diced” in multiple ways - Time-consuming to generate and collect data - Can produce obscure results, which are difficult to interpret Advantages and disadvantages of each approach

6. Is there “a third way”?The “Asymmetric” Approach • The first step is to ask yourself: What questions are we trying to answer? • What combination of projects will meet company goals? • How should we allocate capital on an annual basis? • What is the range of outcomes for a given set of goals? • Does this portfolio database adequately reflect the latest operational updates? • How does Project “X” fit? • What is my long-range plan for each business unit? • How can we structure the database to answer all of these questions? • It is important to consider the questions before commencing data gathering

7. Some input conventions • Projects/opportunities are characterized as: • Base layer • Development projects (infill or major projects) • Exploration prospects • Acquisition or divestment opportunities • Projects can be “fixed” or “floating”: • “Fixed” projects are committed and locked in time – usually short-term Yr 1-2 of the timeframe • “Floating” projects are discretionary, and can be dropped or deferred dependent on their availability

8. Interpretation of the optimum project combination will require knowledge of the types of projects in the asset mix. Optimum combination of projects will include: The layer of fixed projects A sample of the floating projects Fixed layer: Composed of base layer and committed development & exploration projects Reflects decisions already taken Cannot be changed Hence characterizing the floating layer, which represents future investment decisions, is key to the analysis. 1. What combination of projects will meet company goals?

9. First establish which projects are fixed and which are floating. Fixed projects can be aggregated, e.g. group together “base” or PDP layers; approved developments and other committed projects; For floating projects, more detail will be needed : e.g. non-approved developments or infill drilling Exploration prospects and programs Acquisition opportunities Large floating projects may need to be modelled individually But to minimize the number of cases, group similar projects and represent using one “typecase” repeated several times Process steps for Question 1

10. Project groupings within a business unit This leads to an asymmetric database structure

11. Rules of Thumb for aggregation (1)Size and materiality • Establish what amount is significant for your organization for key metrics such as: • Production • Reserves • Capital • This should reflect what management believes is a significant amount. Could be based on: • management approval levels • Commonly used capital allocation buckets • This can be expressed as a % (i.e. of daily production, reserves base or annual capital budget) or a unit amount (e.g. $mm). • Use these significant amounts to set thresholds for aggregation.

12. One example of using typecases: Exploration Prospect Portfolio • Exploration prospects can be grouped by: • Reserves size and range of uncertainty • Chance of success • Development type • Cycle time – discovery to first oil • Using this approach, a prospect case can be described as: • “a 25-50mmboe subsea tieback with an 18 month lead time • and a 1 in 3 chance of success” • This has three benefits: • Reduces the number of cases in the database • Enables easy recognition of the prospect type by all concerned • Allows clear interpretation of the portfolio impact of that type of opportunity • However: where type cases are repeated numerous times, it is critical to make sure that the case used is the most balanced view possible – a slightly optimistic or pessimistic skew will be magnified many times.

13. 2. How should we allocate capital on an annual basis? • Focuses on the development layer opportunities in 1-2yr timeframe • New field developments, e.g. spars and subsea tiebacks • Infill drilling on producing fields • Need to be able to model the investment decisions in a given year • i.e. infill drilling programs must be captured annually, not as one layer • Big risk of going into too much detail – trying to model every project • How do we minimize the number of cases and still allow decision flexibility? Need to consider: • Mandatory vs discretionary expenditures • ‘Significant $ amount’ for capital • Repeatability of infill drilling layers

14. Process Steps for Question 2 • Divide capital expenditures between mandatory or discretionary. Model the decision-making: • Mandatory expenditures represent decisions already taken - can be grouped together • Discretionary projects are the decisions to be taken – greater detail • Split projects into annual expenditure layers – capital is allocated annually • Establish the “significant $ amount” for capital. • Can be based on approval levels or what decision-makers consider significant. • Aggregate projects that are smaller than the $ amount. Aggregate based on organizational or contractual lines: • e.g. International projects together, GOM projects together • Break up infill drilling packages into increments that reflect the $ amount of capital. • Where possible, create incremental layers that are similar year on year - one case can represent future year investment. • Projects can also be aggregated or broken up based on their production contribution

15. 3. What is the range of outcomes for a given set of goals? • If you want to use the range of outcomes to gain insights, it is necessary to have some probabilistic or multi-scenario economics in your database. • Multi-scenario economics are a must for type cases that are repeated numerous times • Time-consuming for data originators - usually meets with resistance • To limit the effort required, consider the goals under scrutiny: • Reserves goals: multi-case economics are important for exploration cases • Near-term production goals: multi-case economics are important for base and development layer cases • Capital or cashflow goals: it is important to have a range of scenarios for any major projects or investments, or any projects with a large range of reserves uncertainty. • Also consider what range of uncertainty is significant for your portfolio

16. Rules of thumb for aggregation (2)Range of uncertainty • Another way to determine which projects need multi-scenario economics is to use this matrix • Uncertainty is defined here as the size of reserves • The “significant amount” for capital and reserves is cross-plotted against reserves uncertainty to establish where multi-case economics are most critical

17. This question should help you decide how often to refresh your portfolio – finding a balance between “evergreen” and updating the data that really matters. Often asked by decision-makers before they ‘buy-in’ to the analysis – they may expect every project to be up to date. This is time-consuming and unnecessary – most project updates will not materially impact the portfolio optimization results. It also makes it difficult to compare optimizations if the database is constantly changing The project updates that will have impact are dependent on: The ‘significant amounts’ for key metrics The selected company goals and constraints Project size, risk, range of uncertainty and repeatability Stage of project lifecycle Also, using typecases rather than specific projects will limit updates 4. Does this portfolio database adequately reflect the latest operational updates?

18. Process steps for Question 4 • “Significant amount” for key metrics: • Only include project updates that have changes greater than the “significant amounts” established for key metrics. • Goals and constraints: • Only include project updates which have changes in metrics or goals that the analysis is focused on • e.g. a cashflow goal is unlikely to be affected by a change in DD&A. • Project size, repeatability, risk, and range of uncertainty • Include updates for large projects or those with many surrogates • For high-risk exploration prospects, changes to the success scenarios may not need to be included (risk will minimize the impact) • Range of uncertainty updates should be included where the mean case increases or decreases. • Stage of project lifecycle: • Some projects will change rapidly in a short timeframe and updates will need to be captured frequently • e.g.major development at the appraisal stage.

19. A common misconception amongst project originators is that their project has more material impact on the portfolio than it actually does. Many individual projects are too small to be significant. In this case a portfolio optimization exercise will not be insightful. Also, if it is not material at the portfolio level, is it worth pursuing? If it is a new venture, it needs to have some other strategic imperative But if part of existing core areas, it may help meet business unit goals Never forget that qualitative strategic fit may be more important than a quantitative portfolio fit E.g. are there operational synergies or partner alignment gains that cannot be modeled quantitatively? 5. How does Project “X” fit?

20. Process flow diagram: Question 5: Portfolio fit

21. 6. What is my long-range plan for each business unit? • Answering this question will: • Require projects to be grouped by business unit and by layer • Understand the contribution of each business unit • e.g. is it a cash cow or a growth engine? • Key metrics can then be defined for each business unit • Will differ depending on business unit role • If database has been properly characterized, should be able to slice business unit contribution by: • Layer • Fixed vs floating • Mandatory vs discretionary • This will help show decision-makers what are the critical elements required to meet business unit performance targets.

22. Communicating portfolio granularity issues • Express projects as a proportion of the total portfolio rather than using absolute amounts. • Use percentages (i.e. % of capital budget, daily production) • Communicate the key metrics and portfolio drivers to originators so they understand where to improve accuracy. • Encourage originators to think in terms of type cases rather than specific projects. • Encourage the use of type cases wherever possible • Work with project teams to ensure that the type cases/floating projects accurately represent: • The range of opportunities available • The number of opportunities available

23. Lessons learned – recap (1) • Know your asset mix/portfolio database before you start data gathering • Classify projects by: • Business unit and layer • Fixed vs floating • Mandatory vs discretionary • Annual expenditure • Assessing materiality and significance is crucial early on: • Establish significant amounts for all key metrics • Use the significant amounts and the project classification to decide which projects to aggregate.

24. Lessons learned – recap (2) • Always model the decision-making behavior • Use type cases to represent a number of opportunities • Use multi-scenario economics for opportunities with a wide range of uncertainty for key metrics • Establish significant threshold for the range of uncertainty • Update projects only when changes are material to the portfolio • Focus on changes to key metrics or major projects that will be scrutinized • And finally… don’t be afraid to play around with the database to establish what projects or metrics do have a significant impact on your results. Be prepared for some iterations and trial and error.