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Data provenance is the chronology of an object, i.e., all the transformations and actions that modified the state of the object from the time of creation to the present. End-to-end data provenance is the ability to account not only for the data provenance of a single object, but also to account recursively for the data provenance of all other objects that affected the state of the object. The purpose of data provenance is to help ensure data integrity.Modeling has become an important aspect of shared earth problems. As the role and extent of modeling of the subsurface expands and its use is taken up by larger teams, determining the provenance of data becomes ever more important. The explosion of data and the appearance of increasingly diverse teams require the establishment of better data provenance to guard the integrity of the modeling results. Such a data provenance model is presented in this paper.The economic consequences of such an approach are significant: better integrity of modeling results leads to more confidence in those results, which reduces perceived risk and, in turn, can improve the economic performance of field development.The solution to the problem of end-to-end data provenance has six elements:• The ability to uniquely identify objects of the shared earth problem • An object repository that supports federation and extensibility • The ability to associate an immutable change history with each object • The loose coupling of metadata and actual data of an object • Client-side caching of actual data • A comprehensive security model for the object.Each of these elements solves an important aspect of ensuring data integrity. Such an approach is increasingly important as model-based approaches become ubiquitous, and the available data become more voluminous, and the teams that handle the data and modeling results become larger and more diverse.
Data provenance is the chronology of an object, i.e., all the transformations and actions that modified the state of the object from the time of creation to the present. End-to-end data provenance is the ability to account not only for the data provenance of a single object, but also to account recursively for the data provenance of all other objects that affected the state of the object. The purpose of data provenance is to help ensure data integrity.Modeling has become an important aspect of shared earth problems. As the role and extent of modeling of the subsurface expands and its use is taken up by larger teams, determining the provenance of data becomes ever more important. The explosion of data and the appearance of increasingly diverse teams require the establishment of better data provenance to guard the integrity of the modeling results. Such a data provenance model is presented in this paper.The economic consequences of such an approach are significant: better integrity of modeling results leads to more confidence in those results, which reduces perceived risk and, in turn, can improve the economic performance of field development.The solution to the problem of end-to-end data provenance has six elements:• The ability to uniquely identify objects of the shared earth problem • An object repository that supports federation and extensibility • The ability to associate an immutable change history with each object • The loose coupling of metadata and actual data of an object • Client-side caching of actual data • A comprehensive security model for the object.Each of these elements solves an important aspect of ensuring data integrity. Such an approach is increasingly important as model-based approaches become ubiquitous, and the available data become more voluminous, and the teams that handle the data and modeling results become larger and more diverse.
This paper illustrates strategies, methods, and techniques that have been successfully utilized to model and deploy collaborative solutions to help teams and individuals within and across companies execute business processes efficiently and consistently, whilst ensuring adherence to norms, standards and agreed guidelines for the benefits of shareholders and contractual parties. Cases are presented related to Integrated Reservoir Management, E&P Technical Information Management, and Surface Rights Management for O&G operations. Whilst some contextual aspects of information technology are mentioned in this paper, the focus rather lies in the aspects of value delivery through process governance, workflow automation efficiencies, process improvement, and reduction of time to learn how things must be done on a day to day basis by newcomers. Business process management (BPM) is a technique that brings efficiency and effectiveness to the execution of processes. Rather than replacing big systems, companies are looking at technologies that facilitate implementing operating processes on a workflow orchestration platform that establishes a controlled environment to execute defined activities efficiently. The convergence of business process modeling, business rules engines, process performance monitoring and human workflows has led to adoption of integrated systems leveraging BPM platform solutions. Wide market availability of BPM platform solutions makes possible chosing from diverse types of architecture, functionality, usability and integration capabilities to meet the information management requirements of an operating company. However, adherence to Business Process Modelling Notation (BPMN) and to data and integration standards, is critical to balance governance of processes with agility and flexibility to adapt workflows, especially in companies that require to scale-up workflows initially created for the needs of one functional or organizational area across and into others. Using standards is critical in cases where collaborative workflows require integration and interaction with data, calculations, and transactions performed in existing back office and/or Petrotechnical systems. Successful BPM implementations are those that become adopted by business users and create incremental value. This requires a systematic approach to process improvement opportunity identification, creating realistic business cases, defining and tracking process performance metrics, communicating value and strategic alignment lead by management, and effective management of change.
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