Technical Paper: Building on PRMS To Quantify Risk and Uncertainty in Resource Reconciliations

Society: SPE
Paper Number: 134057
Presentation Date: 2010
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The Petroleum Resources Management System (PRMS) provides a project-based approach to assessing the full range of recoverable quantities in accumulations from early exploration through mature development phases. As new technical information becomes available and/or commercial conditions change, the estimates of recoverable volumes and their associated risk and uncertainty must be updated. The recording of these changes and the contributing factors is termed resource tracking or resource reconciliation.

For publicly traded companies, regulatory agencies define specific change codes. The US Securities and Exchange Commission (SEC) has historically focused on Proved Reserves and registrants reported annual additions (asset purchases, new discoveries and extensions, improved recovery), deletions (production, asset sales) and revisions (technical and commercial-based) to prior year estimates.

Internal systems must apply resource tracking to the company’s full portfolio of projects and are more focused on changes to the expected value (mean) of the estimates. The portfolio may include Reserves, Contingent Resources (discovered not commercial), and Prospective Resources (undiscovered). PRMS further defines sub-classes based on project maturity with the option to quantify associated risk (chance of discovery). Moreover, each resource category has an implied associated confidence factor that discounts volumes for uncertainty. For example, only a portion of Possible volumes may ultimately transfer to Proved. These confidence factors (or “uncertainty discount factors”) may be estimated through parallel probabilistic analyses or based on analog assessments. Changes in confidence-factor must be considered as an additional revision type regarding transfers between categories.

This paper discusses a quantitative approach wherein companies compute an expected value for each project as the sum of the confidence-weighted category volumes and also apply an associated sub-class risk factor. A realistic example illustrates combining risk/confidence-weighting and qualitative descriptions of the key source of changes in estimates to support both internal and external resource tracking requirements.

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