Smart Oilfield

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Big Data at CiSoft IO

Integrated Optimization (IO) is a fast-paced research project that seeks to apply core computer science concepts to challenging problems in the oilfield domain. Specifically, our objective is a creative synthesis of ideas from a wide variety of domains -- software architecture, distributed and grid computing, artificial intelligence, semantic web, etc. -- in an attempt to create a knowledge management and workflow management layer for oilfield applications.

  • The project offers a host of challenging opportunities for motivated graduate students.
  • The IAM project is part of the joint USC-Chevron CiSoft effort to realize the vision of an integrated, instrumented, intelligent oilfield.
  • The IAM project is part of the Smart Oilfield project being done at the Center for Energy Informatics.
  • The project team includes computer scientists from USC, domain experts from Chevron, and technology advisors from Microsoft and Avanade.

In the CiSoft Big Data effort as part of the Integrated Optimization (IO) project, the current focus is on Big Data technologies. Big data is popularly characterized using the three V's: variety, volume, and velocity - indicating the heterogeneity of involved datasets, scales at which data is generated, and urgency in analysis. Large number of applications related to Big Data have emerged recently. We focus on the following research aspects of Big Data.

Research

The delivery of enriched information from technical analysis into real time operational domains is one of the main challenges addressed by Integrated Asset Management (IAM). An IAM system should ensure proper coordination between data collection sources and data processing destinations. Ultimately, meeting these conditions increases the demand for rapid delivery of relevant data to applications at the desired frequency and/or density, and synchronized in time over multiple sources. Large volumes of data from multiple sources result from progressively improving new capabilities for well measurement, seismic data acquisition, and continuous data collection.

The current areas of research in this project are as follows:

Publications

  • Recent publications
    • Event-driven Information Integration for the Digital Oilfield, Om Prasad Patri, Vikram Sorathia and Viktor K. Prasanna, SPE Annual Technical Conference and Exhibition (ATCE), Society of Petroleum Engineers (SPE), October 2012. [pdf]
    • Semantic Image Clustering Using Object Relation Network, Na Chen and Viktor K. Prasanna, to appear in Proceedings of the Computational Visual Media Conference, 2012. [pdf]
    • Learning to Rank Complex Semantic Relationships, Na Chen and Viktor K. Prasanna, to appear in International Journal on Semantic Web and Information Systems, 2012. [pdf]
    • Predicting Communication Intention in Social Networks, Charalampos Chelmis and Viktor K. Prasanna, ASE/IEEE International Conference on Social Computing (SocialCom), September, 2012. [pdf]
    • Microblogging in the Enterprise: A few comments are in order, Charalampos Chelmis and Viktor K. Prasanna, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August, 2012. [pdf]
    • Enterprise Wisdom Captured Socially, Charalampos Chelmis, Vikram Sorathia and Viktor K. Prasanna, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August, 2012. [pdf]
    • Rankbox: An Adaptive Ranking System for Mining Complex Semantic Relationships Using User Feedback, Na Chen and Viktor K. Prasanna, in Proceedings of 13th IEEE International Conference on Information Reuse and Integration, August, 2012. [pdf]
    • Understanding Web Images by Object Relation Network, Na Chen, Qian-Yi Zhou and Viktor K. Prasanna, in Proceedings of the 21th international conference on World Wide Web, April, 2012. [pdf]
    • Social Networking Analysis: A State of the Art and the Effect of Semantics, Charalampos Chelmis and Viktor K. Prasanna, IEEE Third International Conference on Social Computing (SocialCom), October, 2011. [pdf]
    • Predicting Missing Provenance using Semantic Associations in Reservoir Engineering, Jing Zhao, Karthik Gomadam, and Viktor K. Prasanna, IEEE International Conference on Semantic Computing (ICSC), 2011. [pdf]
    • Querying Provenance in Distributed Environment, Jing Zhao, Yogesh Simmhan, Karthik Gomadam, and Viktor K. Prasanna, International Journal of Computers and Their Applications (IJCA) Special Issue on Scientific Workflows, Provenance and Their Applications, Vol. 18, No. 3, September, 2011. [pdf]
    • Integrating Provenance Information in Reservoir Engineering, Jing Zhao, Na Chen, Karthik Gomadam, Viktor K. Prasanna, IEEE/WIC/ACM International Conference on Web Intelligence, 2010. [pdf]
    • Inteligent Model Management And Visualization for Smart Oilfields, Charalampos Chelmis, Amol Bakshi, F. Seren Burcu, Karthik Gomadam, Viktor K. Prasanna, SPE Western Regional Meeting "From Mountains to Main Street", May, 2010. [pdf]
    • Semantic Web Technologies for Event Modeling and Analysis: A Well Surveillance Use Case, Tao Zhu, Amol Bakshi, Viktor K. Prasanna, Roger Cutler and Scott Fanty, Intelligent Energy Conference, Society of Petroleum Engineers (SPE), 2010 [pdf]
  • Complete List

Group Members

Relevant Courses at USC

  • CSCI 548: Information Integration on the Web
  • CSCI 586: Database Systems Interoperability
  • CSCI 561: Foundations of Artificial Intelligence
  • CSCI 573: Advanced Artificial Intelligence
  • CSCI 578: Software Architectures
  • CSCI 670: Advanced Analysis of Algorithms


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