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

Coetzer, Emile Otto (2018-12)

Thesis (MEng)--Stellenbosch University, 2018.

Thesis

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.

AFRIKAANSE OPSOMMING: Industriële aanlegte maak staat op duisende data-punte om veilig, verantwoordelik en winsgewend te kan bedryf. Die digitale era het ‘n nuwe risiko meegebring: die moontlikheid dat beheer oor die kwaliteit van die data verloor kan word. Die daarstelling en instandhouding van aanleg-data van toereikende kwaliteit is duur. Alhoewel hierdie probleem instinktief verstaan word deur ingenieurs en tegnici in die industrie, dui ‘n oorsig van die literatuur aan dat dit kompleks is om die ware impak van ontoereikende kwaliteit van aanleg-data te kwantifiseer. Dit maak dit moeilik om die onkoste te regverdig om die vereiste kwaliteit te bereik. Die probleem word gevolglik dikwels nie aangespreek nie, wat voortgesette verhoogde risiko, oneffektiwiteit en frustrasie tot gevolg het. Probleme rondom die kwaliteit van ingenieurs-inligting word aangehaal in verskeie hoogs-gepubliseerde industriële rampe. Die regverdiging van die onkoste is moeilik omdat die voordele van data van die aangewese kwaliteit beide nie voor die hand liggend is nie, en ‘n geloofwaardige metode om dit te bereken nie beskikbaar is nie. Geen verdedigbare metode is gevind om die finansiële impak van lae-kwaliteit aanleg-data in die energie-industrie te bereken nie. Hierdie navorsing spreek hierdie leemte aan. Ingenieurs-inligting in die energie-industrie word in hierdie studie gedefinieer as inligting wat verband hou met die ontwerp van industriële aanlegte en gepaardgaande masjinerie. Hierdie inligting word grootliks gegenereer tydens ontwerp en word benodig tydens die totale leeftyd van die aanleg. Die navorsingsdoelwit is om ‘n gestandardiseerde model te ontwikkel vir die berekening van die finansiële impak van ontoereikende ingenieurs-inligting in die energie-industrie. Die doelwitgehoor van die navorsing is senior bestuurders in die energie-industrie, waar die goedkeuring gesetel is om fondse te bewillig. ‘n Oorsig van die literatuur toon aan dat daar geen so ‘n metode in die energie-industrie bestaan nie, maar dat verwante industrieë alreeds soortgelyke studies aangepak het. Hierdie voorbeelde, tesame met ‘n analise van verskeie potensiële navorsingsbenaderings, wys daarop dat ‘n opname-metode aangewese is vir hierdie probleem. ‘n Opname-vraelys is gevolglik ontwikkel vanuit ‘n literatuurstudie, en is bekragtig deur middel van ‘n reeks gestruktureerde onderhoude by ‘n aanleg wat tans in bedryf is. Die inhoud van die finale vraelys dui daarop dat die koste van ontoereikende kwaliteit van ingenieurs-inligting toegeskryf kan word aan vier hoof-kategorieë, naamlik verlaagde produktiwiteit, addisionele koste, verminderde produksie en verhoogde risiko. Op grond van die vraelys is ‘n model ontwikkel om the koste van ontoereikende kwaliteit van ingenieurs-inligting te bereken, beide deterministies en stochasties. Vir die stochastiese model is ‘n Monte-Carlo-simulasie gekies, gebaseer ‘n oorsig van algemene numeriese metodes. Tipiese grootte-ordes vir elke vraag in die vraelys is ook verkry tydens die gestruktureerde onderhoude. Die model is getoets deur gebruik te maak van hierdie laboratorium-data. Die beskikbaarheid van ‘n bewese model het ‘n gevallestudie moontlik gemaak. Werklike opname-data vanuit ‘n ander aanleg is aangewend en werklike resultate in die industrie is bereken. Die resultate van die gevallestudie word in die tesis bespreek en geinterpreteer. Die doel van die resultate van die model is om as insette te dien sodat senior bestuurders kan evalueer of fondse bewillig moet word om die kwaliteit van ingenieurs-inligting te verbeter. Ten einde die resultate in die mees aanvaarbare manier aan te bied, is ‘n oorsig gedoen van die literatuur rondom besluitnemingsdinamika in organisasies en die formaat waarin resultate voorgelê moet moet. Die literatuuroorsig se slotsom is dat die teikengehoor gemaklik is met onsekerheid, maar grafiese aanbieding verkies en geneig is tot kognitiewe spanning. Gevolglik is ‘n bestuursverslag ontwikkel wat die aanbieding van resultate in die aangewese formaat bevat. Die bestuursverslag sluit ook die aanbieding van resultate in Pareto-formaat in. Vir hierdie doel is ‘n hierargie ontwikkel en voorgelê vir kommentaar aan ‘n aantal senior bestuurders. Hierdie Pareto-verslae maak dit moontlik om aktiwititeite rondom die verbetering van die kwaliteit van ingenieurs-inligting te kan prioritiseer. Beide die aanvanklike onderhoude en die gevallestudie-data bevestig die aanvanklike standpunt dat die koste van ontoereikende kwaliteit van ingenieurs-inligting beduidend genoeg is om te kan kompeteer vir befondsing met ander verbeterings-inisiatiewe. Hierdie studie is ‘n eerste verkenning van die onderwerp. Verskeie geleenthede vir verdere navorsing is ontbloot, insluitende die ontwikkeling van meer gesofistikeerde statistiese modelle en verkenning van die eienskappe en oorsake van ontoereikende kwaliteit van ingenieurs-inligting.

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