The resource sector is particularly appropriate for advanced financial and economic modelling, and is one of the most interesting and challenging in this respect perspective.
General themes in model design typical revolve around:
- The role of uncertainty. The industry is naturally faced with making large scale decisions in the face of great uncertainty or risk, and typically there is a requirement for these to be represented in business cases, as well as for overall corporate planning purposes. Beyond the core topics of sensitivities and scenarios, additional approaches (such as Monte Carlo simulation) are required in order to reflect and calculate uncertainty distributions, and to inform decision-makers about the likelihood of success, risk mitigation actions or evaluation of alternate testing programs. Uncertainty analysis also applies in the areas of volumetric uncertainty, value of information and testing decisions, and so on.
- The presence of optimisation situations. Examples of optimisation problems include portfolio composition and participation (% stake in each asset), portfolio sizing, project timing, and decision-selection amongst competing alternatives. Addressing these problems requires highly flexible models (that are formulated in the right way) as well as (potentially) the use of additional tools, VBA, or add-ins. The solution to optimisation situations would ideally also need to take uncertainty into account (E.g. “What is the right level of investment so that a plan will be met with 90% confidence?”
- The requirement to build highly flexible models. Models may require that the time axis be flexible (e.g. so that some (but not all) items can be shifted in time, such as being able to delay the first oil date without altering the timing of some activities in the development phase), or that new data sets can be added or deleted (for example due to acquisitions or disposals) with the consolidated model requiring no (or only very minimal) adjustment.
- The size of the inputs data sets. Due to the potential large size of the data sets and models, there are a number of competing possible ways to structure models – crudely those which are more like traditional corporate finance models versus a more database-driven approach. In practice the right approach is often a combination of both, together with the disciplined use of best practice techniques in model formatting and layout. In addition, the advanced use of lookup functions can allow model to be developed in which new data can be added (or old data deleted) without the formulae having to be re-built or re-linked, and VBA macros can be used to create functionality that consolidates together the results of queries applied to data sets that are distributed across multiple files, thus providing a far greater capacity to deal with large data sets than one may initially expect to be possible.
- Ensuring that economic concepts and tools are correctly used and interpreted. Issues relating to sunk cost, discount rates, project evaluation metrics (NPV, IRR) and project valuation (impact of negative and positive risks and uncertainties, and real options) all need adequate consideration.
More specifically, questions that need to be addressed typically relate to:
- Cost estimation and budgeting (e.g. capex and opex uncertainty, incl. event risks)
- Optimal decision-making, real options and valuing flexibility (e.g. appropriate forms of subsurface testing, value of information analysis and Bayesian techniques)
- Exploration and discovery risk (incl. multiple prospects within a licence and dependencies)
- Portfolio sizing and optimisation (e.g. optimal sizing of for E&A portfolios, aggregate reserves calculations)
- Drilling uncertainty, drill program planning and optimisation of portfolios of drilling projects e.g. reflecting uncertainties in schedules, discoveries, and finance and resource constraints
- Volumetric uncertainty estimation
- Capturing dependencies e.g. between porosity and oil saturation in calculations
- Mapping volume to non-linear cost curves (e.g. switching engineering designs according to volume)
- Production forecasting and modelling (e.g. decline modelling, operational breakdown possibilities)
- Schedule risk analysis and integrated cost-schedule risk modelling
- Price forecasting
- Incentive scheme optimisation e.g. design of fiscal regimes
- Integrated business cases and valuation modelling: volumetrics, capex, opex, production, profits, taxes
- Business cases, valuation, and uncertainty modelling of new technologies (oil recovery, unconventional gas, wind, bio-fuels etc.)
- Financing issues (e.g. debt modelling, project finance, supporting of equity raises, use of non-conventional financing techniques)
- Tax calculations and optimisation
- Advanced valuation techniques (combining cash flow, uncertainty analysis, real options etc.
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