UNCERTAINTY
SOLV® provides expert analysis of measurement risk and financial exposure to ensure reported oil and gas quantities comply with regulatory standards. Through VERISOLV® Accredited Inspection Services, which include Uncertainty Analysis of gas and liquid hydrocarbons measurement systems, we offer comprehensive support for maintaining compliance and accuracy in reporting.
Our engineers possess over 20 years of experience in uncertainty analysis and have applied their expertise to a wide range of systems, metering configurations, and fluids. We pride ourselves on rapid turnaround for standard systems, providing results with an increased degree of certainty at a reduced cost.
As a UKAS ISO/IEC 17020 accredited and independent company, SOLV® ensures third-party autonomous uncertainty analysis. This accreditation guarantees our services meet the highest standards of impartiality and competence, giving you confidence in compliance with regulatory requirements and the SOLV® ISO 9001:2015 Quality Management System (QMS).
Uncertainty Analysis Methodology
SOLV®’s uncertainty analysis follows a standardized, rigorous procedure. We begin by identifying the sources of uncertainty for each measurement. When there is an algorithm governing the relationship between inputs, we apply it; otherwise, we use primary uncertainty assessment methods such as quadrature (Root Sum Square or RSS), in accordance with the ISO 5168:2005 Flow Measurement Uncertainty standard.
When algorithms are available, we calculate the sensitivity of each uncertainty term through Partial Derivative or Finite Difference methods. These are then combined via RSS to produce the final uncertainty result.
To ensure precision, our results are numerically verified using Monte Carlo Simulation Uncertainty Analysis, which guarantees that results align within 0.01% of the RSS Uncertainty Analysis. This additional step allows SOLV® to confirm the accuracy of the analysis, giving clients complete confidence in the results.
Furthermore, we evaluate the contribution of each input uncertainty source to the overall uncertainty result, allowing us to identify the primary influences on measurement performance. In systems with multiple input streams, we also analyze correlations to ensure comprehensive and reliable results
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