Establishing Reasonable Certainty for Reserves Estimates by Utilizing a Combination of Reliable Technologies

Abstract

Abstract The Petroleum Resources Management System (PRMS) (PRMS, 2018) and many regulatory agencies (e.g. US Securities and Exchange Commission – US SEC) require "Reasonable Certainty" for Proved Reserves estimates. The PRMS states that Reasonable Certainty can be demonstrated by use of "definitive geoscience, engineering, or performance data", while the SEC allows the application of reliable technology which they define as a "grouping of one or more technologies (including computational methods) that has been field tested to provide reasonably certain results with consistency and repeatability" (US Code of Federal Regulations § 210.4-10). The guidance provided by the PRMS or SEC for establishing reasonable certainty is general in nature due to the difficulty in explicitly describing all possible scenarios and also allows leeway to use new technologies in the future. In this context, we see the need for more discussion on how a reasonably certain case can be developed utilizing multiple technologies. Reserves estimates are snapshots in time based on the integration of the best data, analysis and forecasts available. Proper application of reliable technologies with all available data can help to refine the uncertainty ranges of reserves. We demonstrate how an overall reasonably certain estimate can be established by utilizing multiple reliable technologies even when each technology individually may not be sufficient to establish reasonable certainty. This approach can also guide how future performance data can be integrated to refine uncertainty ranges. This paper addresses the complex challenge of establishing reasonable certainty in reserves and resource assessments. The paper discusses how multiple reliable technologies may be used in concert to establish reasonable certainty for reserves estimates through the flexibility provided by the PRMS. We share our experiences with establishing reliability based on quality of data and reservoir complexity. The practical discussions in this paper will benefit subsurface teams and reserves estimators across the industry.