A Framework to Capture the Relationships in Drilling Data and the Propagation of Uncertainty

Abstract Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The SPE has formed a cross-disciplinary technical sub-committee to investigate how to describe and propagate drilling data quality and uncertainty. The sub-committee is a cooperation between the Drilling System Automation, Wellbore Positioning, and Drilling Uncertainty Prediction Technical Sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated, and ranked with the most compelling user story analyzed in detail. From this approach, a series of multi-disciplinary workflow - drilling data generators - can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one "user story" focused on capturing the description of the quality and uncertainty of depths. Multiple "use cases" have been extracted from this single "user story" exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic graphs to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted "use cases", showing that the semantic graph method has the potential to describe a broad range of complex drilling related processes. The study also demonstrates that use of a parallel graph, employing other notions that do not expressly refer to the processes that generated the data can capture the description of how uncertainty propagates between each of those concepts. This paper describes the development of an initial reference implementation of semantic graph manipulation, the associated vocabulary and the description of uncertainty and quality notions and their linkage in terms of uncertainty propagation. This reference implementation will be available as open source to the industry drilling community allowing software solutions that capture and describe the generation of drilling data through multi-disciplinary workflows, and how they relate in terms of uncertainty propagation.