addressing uncertainty in oil and natural gas industry greenhouse

Since an understanding of the magnitude and sources of GHG emissions is critical to properly managing
these emissions, employing a consistent approach can significantly improve industry-wide, comparable
estimates of emissions, and emission reductions.
Higher quality GHG data leads to higher certainty of emission assessments, and improved confidence in
the data reported. This is true for national and government assessments, and is also important at the
entity, or facility level. To ensure that a company’s strategies and forward-looking actions are based on
the most robust data set and most appropriate computational methods, it is important that this data set and
method be based on four key factors (“The Four C’s”).
Comparability, Consistency, Certainty, Confidence
1.2 Overview of Uncertainty Terminology
The API in Chapter 13 of its Manual of Petroleum Measurement Standards (MPMS), provides detailed
guidance on statistical concepts and procedures for addressing the statistical procedures that should be
followed when estimating a true quantity from measurements–or models–and when deriving the
confidence interval of the results (API, 1985). That chapter also examines sources of error and
recommends how to develop a statement of the overall range of uncertainty of the results obtained. Some
of the key terms used in the API MPMS are presented in Exhibit 1-1.
EXHIBIT 1-1: SELECTED TERMINOLOGY
• Accuracy – Ability to indicate values that closely approximate the true value of the measured variable.
• Bias – Any influence on a result that produces an incorrect approximation of the true value of the variable being
measured. Bias is the result of a predictable systematic error.
• Confidence interval (or range of uncertainty) – The range or interval within which the true value is expected
to lie with a stated degree of confidence.
• Confidence level – The degree of confidence that may be placed on an estimated range of uncertainty.
• Error – The difference between true and observed values.
• Precision – The degree to which data within a set cluster together.
• Random error – An error that varies in an unpredictable manner when a large number of measurements of the
same variable are made under effectively identical conditions.
• Spurious error – A gross error in procedure (for example, human errors or machine malfunctions).
• Systematic error – An error that, in the course of a number of measurements made under the same conditions
on material having the same true value of a variable, either remains constant in absolute value and sign, or
varies in a predictable manner. Systematic errors result in a bias.
• Variance – The measure of the dispersion or scatter of the values of the random variable about the mean.
Source: API MPMS Chapter 13.1
Pilot Version, September 2009 1-2