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

[摘要] ENGLISH ABSTRACT: Industrial assets rely on thousands of data points to run safely, responsibly and profitably. The digital era has introduced the risk that control of data quality is lost. Achieving and maintaining asset data quality control is expensive. Although this issue is instinctively understood by engineers and technicians, a review of the literature indicates that the true impact of poor asset data quality is difficult to quantify. This makes it difficult to justify the expense required to rectify the deficiencies in engineering data. Consequently, the problem is often not rectified. This leads to a perpetuation of the problem and increasing risk, inefficiency and frustration. Problems surrounding engineering information quality have been implicated in several well-publicized industry disasters.Justifying the expense is difficult because the benefits are neither immediately obvious nor able to be calculated using a defensible method. No defensible method to calculate the financial impact of engineering data quality has been found for the oil and gas industry. This research study addresses this challenge. The research objective of the present study its therefore to develop a standardized model to quantify the financial impact of poor engineering information quality in the oil and gas industry.This study defines engineering information in the oil and gas industry as information about asset design and machinery. It is generated during design and is required throughout the asset life. The target audience is senior management in the oil and gas industry, where authority for approval for data quality initiatives is held.A review of the literature has shown precedent in related industries, but none in the oil and gas industry. The precedent in other industries, coupled with an analysis of several potential approaches, revealed that a survey-based research design was appropriate for this problem. A survey questionnaire was therefore developed from a literature review and validated during a series of structured interviews at an operating asset. The contents of the validated survey questionnaire indicated that the financial impact of poor engineering information quality consist of the four categories of productivity loss, increased cost, reduced production and increased risk.Using the survey questionnaire as a basis, a model was developed to calculate the cost of poor engineering information quality, both deterministically and stochastically. Following a review of commonly used numerical methods, it was concluded that Monte Carlo simulation was the most applicable approach for the stochastic model. Data collected during the survey validation structured interviews was used to populate a laboratory data set, which was used to test the model.The construction and testing of the model enabled a case study of actual field data from another operating asset. The results of the case study were discussed and interpreted in the thesis.The results of the model are intended to serve as inputs for senior managers to assign funding to engineering information quality improvement. In order to present the data in the most acceptable form, a review of the literature around organisation decision-making and information presentation requirements was undertaken. The review indicated that the target audience was comfortable with uncertainty but was at risk of cognitive strain. The cognitive strain could be reduced by presenting information graphically and reporting the confidence of the result. An appropriate data presentation and management report was therefore developed. This included reporting results in Pareto form. For this reason, a taxonomy was developed and validated by a series of unstructured interviews with senior managers. These Pareto results enable the prioritisation of data quality improvement drives.Both the initial structured interviews and case study results proved the original contention that the cost of poor engineering information quality is not insignificant and presents an opportunity for improvements in the oil and gas industry that is competitive with other opportunities.This study is a first exploration of the subject. Many opportunities for future research have been identified, including more sophisticated statistical models, exploration of causality and the mechanistic properties of poor engineering information quality.

[发布机构] Stellenbosch University