A standardised model to quantify the financial impact of poor engineering information quality in the oil and gas industry

Abstract

Industry needs quality data, but digital formats increase the risk of lost data quality, implying huge risk. The benefits of data quality are difficult to calculate in order to justify the expense. A survey was developed and validated at an operating asset as a precedent. The elements are productivity and production loss, and increased cost and risk. A Monte Carlo method was field tested. The results were presented in graphical and Pareto form to facilitate funding and prioritisation. The results prove that the cost is significant. As a first exploration of the subject, opportunities exist for more sophisticated models, and for investigating causality.