Data integration support for offshore decommissioning waste management

Offshore oil and gas platforms have a design life of about 25 years whereas the techniques and tools used for managing their data are constantly evolving. Therefore, data captured about platforms during their lifetimes will be in varying forms. Additionally, due to the many stakeholders involved with a facility over its life cycle, information representation of its components varies. These challenges make data integration difficult. Over the years, data integration technology application in the oil and gas industry has focused on meeting the needs of asset life cycle stages other than decommissioning. This is the case because most assets are just reaching the end of their design lives. Currently, limited work has been done on integrating life cycle data for offshore decommissioning purposes, and reports by industry stakeholders underscore this need. This thesis proposes a method for the integration of the common data types relevant in oil and gas decommissioning. The key features of the method are that it (i) ensures semantic homogeneity using knowledge representation languages (Semantic Web) and domain specific reference data (ISO 15926); and (ii) allows stakeholders to continue to use their current applications. Prototypes of the framework have been implemented using open source software applications and performance measures made. The work of this thesis has been motivated by the business case of reusing offshore decommissioning waste items. The framework developed is generic and can be applied whenever there is a need to integrate and query disparate data involving oil and gas assets. The prototypes presented show how the data management challenges associated with assessing the suitability of decommissioned offshore facility items for reuse can be addressed. The performance of the prototypes show that significant time and effort is saved compared to the state-of‐the‐art solution. The ability to do this effectively and efficiently during decommissioning will advance the oil the oil and gas industry’s transition toward a circular economy and help save on cost

Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

Total Constraint Management for Improving Construction Work Flow in Liquefied Natural Gas Industry

Australia has benefited and will continue to benefit significantly from Liquefied Natural Gas (LNG) investments underway. Managing these LNG projects is challenging as they become increasingly complex and technologically demanding. The primary goal of this thesis is to develop a Total Constraint Management (TCM) method to improve construction work flow during LNG construction. Five controlled experiments were conducted and results show that successful implementation of TCM can significantly improve construction productivity and reduce schedule overruns

Improving interoperability on industrial standards through ontologies

Interoperability refers to the effective exchange of information and understanding to collectively pursue common objectives. System developers commonly use ontologies to enhance semantic and syntactic interoperability within this context. This work aims to evaluate the contribution of ontology in making explicit the meaning of the entities described in a Piping and Instrumentation Diagram (P&ID) model and to provide an architecture that allows the representation of a P&ID in ontological knowledge bases. To understand the semantics of the P&ID entities and relations, we map each class of the P&ID to the corresponding entity of the Offshore Petroleum Production Plant Ontology (O3PO). The ontology describes the definition of each vocable associated with the axioms that clarify and regulate the meaning and utilization of this vocabulary. We intend to guarantee that the integration of P&ID with other models respects the original semantics and avoids unintended data exchanges. We follow this ontological analysis with a case study of a model that conforms to the Data Exchange in the Process Industry (DEXPI) specification, intended to provide homogeneous data interchange between CAD systems from diverse vendors. The ontological analysis of the DEXPI P&ID specification, to build a relation with a well-founded ontology, raises a set of desirable properties for a model intended for use in interoperability. While achieving technical interoperability between DEXPI P&IDs and ontologies represented in OWL is evident, we identified several challenges within the realm of semantic interoperability, specifically concerning clarity/intelligibility, conciseness, extendibility, consistency, and essence. These issues present significant hurdles to achieving seamless systems integration. Moreover, if the DEXPI standard were to evolve into a de facto standard for representing P&IDs across a broader range of domains than initially intended, these highlighted issues could potentially bottleneck its adoption and hinder its integration into different systems.Interoperabilidade se refere à troca efetiva de informação e entendimento na busca por objetivos comuns. Neste contexto, desenvolvedores de sistemas comumente utilizam ontologias para aprimorar a interoperabilidade semântica e sintática. O objetivo deste trabalho é avaliar a contribuição da ontologia para tornar explícito o significado das entidades descritas em um modelo de Diagrama de Tubulação e Instrumentação (DTI) e fornecer uma arquitetura que permita a representação de um DTI em bases de conhecimento ontológicas. Para entender a semântica das entidades e relações do DTI, mapeamos cada classe do DTI para a entidade correspondente da Ontologia de Planta de Produção de Petróleo Offshore (O3PO). A ontologia descreve a definição de cada vocábulo associado com os axiomas que esclarecem e regulam o significado e a utilização desse vocabulário. Pretendemos garantir que a integração do DTI com outros modelos respeite a semântica original e, assim, evite trocas de dados não intencionais. Seguimos essa análise ontológica com um estudo de caso de um modelo que se conforma à especificação "Data Exchange in the Process Industry" (DEXPI), destinada a fornecer uma troca de dados homogênea entre sistemas CAD de diversos fabricantes. A análise ontológica da especificação DEXPI DTI, para construir uma relação com uma ontologia bem fundamentada, levanta um conjunto de propriedades desejáveis para um modelo destinado a ser usado na interoperabilidade. Embora a conquista da interoperabilidade técnica entre DTIs DEXPI e ontologias representadas em OWL seja evidente, diversos desafios foram identificados no âmbito da interoperabilidade semântica, especificamente em relação à clareza/inteligibilidade, concisão, extensibilidade, consistência e essência. Essas questões representam obstáculos significativos para alcançar uma integração de sistemas perfeita. Além disso, se o padrão DEXPI evoluir para um padrão de facto para a representação de DTIs em um conjunto mais amplo de domínios do que inicialmente pretendido, essas questões destacadas poderiam potencialmente atrasar sua adoção e dificultar sua integração em sistemas diferentes

J-Park Simulator: An ontology-based platform for cross-domain scenarios in process industry

The J-Park Simulator (JPS) acts as a continuously growing platform for integrating real-time data, knowledge, models, and tools related to process industry. It aims at simulation and optimization in cross-domain and multi-level scenarios and relies heavily on ontologies and semantic technologies. In this paper, we demonstrate the interoperability between different applications in JPS, introduce new domain ontologies into the JPS, and integrate live data. For this, we utilize a knowledge graph to store and link semantically described data and models and create agents wrapping the applications and updating the data in the knowledge graph dynamically. We present a comprehensive industrial air pollution scenario, which has been implemented as part of the JPS, to show how knowledge graphs and modular domain ontologies support the interoperability between agents. We show that the architecture of JPS increases the interoperability and flexibility in cross-domain scenarios and conclude that the potential of ontologies outweighs additional wrapping efforts.National Research Foundation (NRF)Accepted versionThis project is funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. MK gratefully acknowledges the support of the Alexander von Humboldt foundation

Ontology-based solutions for interoperability among product lifecycle management systems: A systematic literature review

During recent years, globalization has had an impact on the competitive capacity of industries, forcing them to integrate their productive processes with other, geographically distributed, facilities. This requires the information systems that support such processes to interoperate. Significant attention has been paid to the development of ontology-based solutions, which are meant to tackle issues from inconsistency to semantic interoperability and knowledge reusability. This paper looks into how the available technology, models and ontology-based solutions might interact within the manufacturing industry environment to achieve semantic interoperability among industrial information systems. Through a systematic literature review, this paper has aimed to identify the most relevant elements to consider in the development of an ontology-based solution and how these solutions are being deployed in industry. The research analyzed 54 studies in alignment with the specific requirements of our research questions. The most relevant results show that ontology-based solutions can be set up using OWL as the ontology language, Protégé as the ontology modeling tool, Jena as the application programming interface to interact with the built ontology, and different standards from the International Organization for Standardization Technical Committee 184, Subcommittee 4 or 5, to get the foundational concepts, axioms, and relationships to develop the knowledge base. We believe that the findings of this study make an important contribution to practitioners and researchers as they provide useful information about different projects and choices involved in undertaking projects in the field of industrial ontology application.Fil: Fraga, Alvaro Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Vegetti, Maria Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Leone, Horacio Pascual. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide