Guest Article
Data Management
The development of a corporate portfolio view is probably the most daunting task faced by economists working in the E&P industry. Large amounts of data coming from many different sources enter the process in as many different formats. In the portfolio process, the data and its assumptions have to be approved by a number of corporate line managers and multiple adjustments are typically required before the final approval is given and the ultimate assessment can be made.
An efficient portfolio process requires a system which ensures that the data gathering and data capture processes are consistent, data integrity is secured and there is a fully integrated workflow. Although computing technology has been the cornerstone in portfolio management for several decades, the systems used in most organizations consist of a collection of stand-alone applications which are poorly integrated. As a result, vast amounts of human resources are dedicated to the low value activity of transferring data between different systems.
Palantir has long understood the short comings of current practices and has developed a suite of integrated software tools known as the PalantirSUITE® which greatly enhance the process of portfolio modelling. In addition to developing static portfolio consolidations using PalantirCASH®, the tools allow for dynamic portfolio analysis. PalantirPLAN® enables managers to create on-the-fly scenario analysis and what-if scenarios can be developed to provide insight into whether future production targets are met when alternative development scenarios are chosen for one or several key projects (Fig 1). The PalantirTREE® application allows for probabilistic portfolio analysis. Financial statements can be generated using the PalantirFINANCIALS® application.
Data Migration
In the assessment of an investment proposal that relates to a joint venture, individual investors will evaluate the investment option in the context of their portfolio. It is therefore of crucial importance to develop an understanding of the business opportunity from the perspective of the other stakeholders. Hence economists will be forced to model one or even multiple portfolios of their competitors.
Although companies will not have access to the private view that their peers hold on their portfolio, relevant information can be purchased from several data-gathering organizations.
Palantir has developed a number of data loader tools that enable companies to rapidly load large amounts of data from products like QUE$TOR™ from IHS inc. or Global Economic Model (GEM) from Wood Mackenzie. The data that relates to equity positions and production and cost forecasts are loaded into PalantirCASH® and a cash flow can be developed for each of the individual assets of a given company (Figure 2). These cash flows can subsequently be analyzed using a library of tax regimes, reporting templates and the project aggregation features of PalantirCASH® and PalantirPLAN®.
Portfolio Risking
One of the great successes of modern financial risk modeling is the application of computing technology to simulate complex continuous probability distributions associated with the value metrics of real-life assets. The Monte Carlo simulation technique is the best known example and is widely applied by economists in the E&P industry. However, a Monte Carlo simulation in which project economics are aggregated into a large asset portfolio is rarely undertaken. The main reason is that present computing systems cannot handle the vast amounts of data generated in a Monte Carlo simulation of a large asset portfolio.
Palantir has developed a pragmatic approach to create a risked portfolio view. Following this method, the continuous distribution of feasible project outcomes is approximated by a small number of probability-weighted discrete scenarios (Willigers, 2009). In a corporate portfolio simulation, a project sample would be drawn from these discrete distributions as opposed to the original continuous distributions. If either a global assumption needs revision or a single asset requires recalculating, the economics of a relatively small number of scenarios would be computed. Thus, there is no need to store or recalculate a large number of outcomes (typically more then 2,000) for each project, as would be required in a conventional Monte Carlo simulation.
Risked Portfolio Valuation
The following procedure describes how a risked portfolio view can be developed using software from Palantir in combination with information from third party data providers. The valuation process consists of four phases:
1. The development of a cash flow model for individual assets that constitute an asset portfolio using third party data.
2. Given that third party data typically only provides a single base-case scenario, two additional scenarios: a conservative and optimistic scenario, are created. This is achieved by applying a generic multiplier to
Drillers and Dealers :::
::: December 2011 Edition
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