One reason oil and gas companies adopt a standard cost coding system is to facilitate benchmarking. NORSOK Z-014 Standard Cost Coding System (SCCS) is an example of this kind of system. This paper describes a set of issues found in a project that attempted to adopt this standard. These were issues whose analysis revealed problems with the standard’s fundamental structure. Further analysis showed that these types of problems are well understood outside the project controls community and resolvable using a classification technique technically known as ‘facets’. The paper provides examples of these issues and indicates how they can be resolved. It also describes the systematic modernization approach adopted by the project to resolve the issues throughout the standard. The aim of this paper is to introduce to the project controls community an understanding of the importance of these issues for raising the quality of their data and the new techniques to provide improved foundations for standard cost coding systems for the oil and gas industry in the 21st Century.

The Upstream Oil and Gas industry is formed of a complex network of contractual structures, highly specialised technical disciplines and technologies that interact across multilateral supply chains for exploration and production of hydrocarbons. The complexity comes with a number of technical, social and environmental risks may affect entire regions and countries. One of the key challenges that organisations face in this industry, is ensuring interoperability across organisational and functional boundaries due to the highly stratified supply chain and deeply specialised technical domains that it creates. This presentation will describe the strategy used to build a robust common foundation for the data; at the centre of which is a stable and resilient data architecture for the oil industry. It will explain how this was achieved including the use of structured foundational design principles and industry data standards including ISO 15926, IDEAS and MODEM.

BORO Solutions applies military-strength semantics in 'Clean' and 'Pure' approach to complex oil country data landscape. Industrial ontology leverages Department of defense framework.

This tutorial provides an illustrative example of how the BORO methodology has been used to re-engineer and improve ‘structural constraints’ in existing frameworks. It provides a nice example of how an ontological analysis can reveal the constraints and identify how they can be improved. The case study is taken from a project that developed a common data foundation for an oil and gas enterprise. One area under analysis was cost management. The starting point for the analysis was the NORSOK Z-014 Standard cost coding system. The tutorial looks at its the structural constraints were identified and remedied.
This is part of a series of tutorials that walk through examples that illustrate how the BORO methodology has been used to re-engineer data in an industrial context.

Data integration of enterprise systems typically involves combining heterogeneous data residing in different sources into a unified, homogeneous whole. This heterogeneity takes many forms and there are all sorts of significant practical and theoretical challenges to managing this, particularly at the semantic level. In this paper, we consider a type of semantic heterogeneity that is common in Model Driven Architecture (MDA) Computation Independent Models (CIM); one that arises due to the data’s dependence upon the system it resides in. There seems to be no relevant work on this topic in Conceptual Modelling, so we draw upon research done in philosophy and linguistics on formalizing pure indexicals – ‘I’, ‘here’ and ‘now’ – also known as de se (Latin ‘of oneself’) or the deitic centre. This reveals firstly that the core dependency is essential when the system is agentive and the rest of the dependency can be designed away. In the context of MDA, this suggests a natural architectural layering; where a new concern ‘system dependence’ is introduced and used to divide the CIM model into two parts; a system independent ontology model and a system dependent agentology model. We also show how this dependence complicates the integration process – but, interestingly, not reuse in the same context. We explain how this complication usually provides good pragmatic reasons for maximizing the ontology content in an ‘Ontology First’, or ‘Ontology then Agentology’ approach.

One of the central concerns of the multi-level modelling (MLM) community is the hierarchy of classifications that appear in conceptual models; what these are, how they are linked and how they should be organised into levels and modelled. Though there has been significant work done in this area, we believe that it could be enhanced by introducing a systematic way to investigate the ontological nature and requirements that underlie the frameworks and tools proposed by the community to support MLM (such as Orthogonal Classification Architecture and Melanee). In this paper, we introduce a key component for the investigation and understanding of the ontological requirements, an ontological sandbox. This is a conceptual framework for investigating and comparing multiple variations of possible ontologies – without having to commit to any of them – isolated from a full commitment to any foundational ontology. We discuss the sandbox framework as well as walking through an example of how it can be used to investigate a simple ontology. The example, despite its simplicity, illustrates how the constructional approach can help to expose and explain the metaphysical structures used in ontologies, and so reveal the underlying nature of MLM levelling.