An Architecture of a Workflow System for Integrated Asset Management in the Smart Oil Field Domain

Integrated asset management (IAM) is the vision of IT-enabled transformation of oilfield operations where information integration from a variety of tools for reservoir modeling, simulation, and performance prediction will lead to rapid decision making for continuous optimization of oil production. In this paper, we discuss the similarities and differences of IAM applications and typical e-Science applications. We then propose an architecture for a workflow system for IAM based on the four key requirements: support for creation, orchestration and management of workflows including those involving legacy tools, support for audit trails and data quality indicators for data objects, usability and extensibility. Our architecture builds upon current research in the scientific workflow area and applies many of its learnings to address the requirements of our system. We propose some implementation strategies and technologies and identify some of the key research challenges in realizing our architectural vision.

[1]  Carl Kesselman,et al.  A Metadata Catalog Service for Data Intensive Applications , 2003, SC.

[2]  Daniel S. Katz,et al.  Pegasus: A framework for mapping complex scientific workflows onto distributed systems , 2005, Sci. Program..

[3]  Rajiv K. Kalia,et al.  Parallel history matching and associated forecast at the center for interactive smart oilfield technologies , 2006, The Journal of Supercomputing.

[4]  R. V. van Nieuwpoort,et al.  The Grid 2: Blueprint for a New Computing Infrastructure , 2003 .

[5]  Thomas Fahringer,et al.  Teuta: A Tool for UML Based Composition of Scientific Grid Workflows , 2005 .

[6]  Abdus Satter,et al.  Integrated Waterflood Asset Management , 1998 .

[7]  Sharanya Eswaran,et al.  Adapting and Evaluating Commercial Workflow Engines for e-Science , 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

[8]  Rajkumar Buyya,et al.  A Taxonomy of Workflow Management Systems for Grid Computing , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[9]  David Garlan,et al.  Architectural Mismatch or Why it's hard to build systems out of existing parts , 1995, 1995 17th International Conference on Software Engineering.

[10]  Jos de Bruijn,et al.  Web Service Modeling Ontology , 2005, Appl. Ontology.

[11]  Mrinal K. Sen,et al.  Autonomic oil reservoir optimization on the Grid , 2005, Concurr. Pract. Exp..

[12]  Yong Zhao,et al.  A notation and system for expressing and executing cleanly typed workflows on messy scientific data , 2005, SGMD.

[13]  Viktor K. Prasanna,et al.  A Semantic Framework for Integrated Asset Management in Smart Oilfields , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[14]  Anne E. Trefethen,et al.  The Data Deluge: An e-Science Perspective , 2003 .

[15]  Matjaz B. Juric,et al.  Business process execution language for web services , 2004 .

[16]  Yong Zhao,et al.  Chimera: a virtual data system for representing, querying, and automating data derivation , 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management.

[17]  Stuart E. Madnick,et al.  Information integration using contextual knowledge and ontology merging , 2003 .

[18]  Jeff Heflin,et al.  LUBM: A benchmark for OWL knowledge base systems , 2005, J. Web Semant..

[19]  Viktor K. Prasanna,et al.  A Model-Based Framework for Developing and Deploying Data Aggregation Services , 2006, ICSOC.

[20]  Simon J. Cox,et al.  Leveraging Windows Workflow Foundation for Scientific Workflows in Wind Tunnel Applications , 2006, 22nd International Conference on Data Engineering Workshops (ICDEW'06).

[21]  Tony Andrews Business Process Execution Language for Web Services Version 1.1 , 2003 .

[22]  Liming Chen,et al.  Engineering Grid Resources' Metadata for Resource and Knowledge Sharing , 2006 .

[23]  Vipul Kashyap,et al.  Semantic and schematic similarities between database objects: a context-based approach , 1996, The VLDB Journal.

[24]  Bertram Ludäscher,et al.  Managing scientific data: From data integration to scientific workflows* , 2006 .

[25]  Gregor von Laszewski Java CoG Kit Workflow Concepts for Scientific Experiments , 2005 .

[26]  Radu Prodan,et al.  ASKALON: a tool set for cluster and Grid computing , 2005, Concurr. Pract. Exp..

[27]  Viktor K. Prasanna,et al.  Model-based Framework for Oil Production Forecasting and Optimization: A Case Study in Integrated Asset Management , 2006 .

[28]  Thomas Heinis,et al.  Developing scientific workflows from heterogeneous services , 2006, SGMD.

[29]  Yong Zhao,et al.  XDTM: The XML Data Type and Mapping for Specifying Datasets , 2005, EGC.

[30]  Edward A. Lee,et al.  CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2000; 00:1–7 Prepared using cpeauth.cls [Version: 2002/09/19 v2.02] Taverna: Lessons in creating , 2022 .

[31]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..