The term "reliable technology" as related to oil and gas reserves estimation is a recent development in reserves classification. This term was introduced in the updated regulations published by the US Securities and Exchange Commission (SEC) in late 2008. This paper will describe how reserves evaluators can use scientific practices to identify such reliable technologies and document that these technologies meet defined requirements. The paper will also explore the how Petroleum Resource Management System (PRMS) addresses the concept of reliable technologies and defines different levels of reliability to classify reserves estimates.
Reserves evaluators currently have limited guidance from the SEC or PRMS on how reliable technologies should be demonstrated. Since the use of new technologies is fundamental to advancing technical methods and practices in the Geosciences and Petroleum Engineering, the proposed approaches noted in this paper will provide useful support to enable the application of new technologies in reserves estimation and classification.
The paper draws on scientific practices and legal evidence rules (previously described by Miller 1997) to support the use of the scientific method for satisfying the requirements of reliability. The key steps of the scientific method are reviewed and adapted to the needs of demonstrating a reliable technology. The paper explains that the technology need be supported by both scientific theory and empirical data from experimental results. Also limitations on the technology including key assumptions and required conditions for successful application are discussed as part of the work needed to properly use the technology. Finally the paper will describe the level of empirical data needed for proper qualification of the reliable technology.
The recent publication of the SEC reserves reporting rules, which draw partially from the PRMS, has increased interest in deeper understanding of both these SEC rules and the PRMS. This paper will assist those who develop and use petroleum reserves data in understanding how technologies can be qualified to contribute to reliable reserves estimates and proper classification.