Rapid Data Integration and Analysis for Upstream Oil and Gas Applications

The increasingly large number of sensors and instruments in the oil and gas industry, along with novel means of communication in the enterprise has led to a corresponding increase in the volume of data that is recorded in various information repositories. The variety of information sources is also expanding: from traditional relational databases to time series data, social network communications, collections of unsorted text reports, and linked data available on the Web. Enabling end-to-end optimization considering these diverse types of information requires creating semantic links between them. Though integration of data across silo-ed databases has been recognized as a problem for a long time, it has proven to be difficult to accomplish due to the complexity of the data arrangement within databases, scarcity of metadata that describe the content, lack of a direct mapping between related entities across databases, and the several types of data represented within a database. In addition, there are large amounts of unstructured text data such as text entries in databases and document repositories. These contain valuable information on processes from the field but there is currently no method to convert this raw data to useable information. The Center for Interactive Smart Oilfield Technologies (CiSoft) is a USC-Chevron Center of Excellence for Research and Academic Training on Smart Oilfield Technologies. We describe the Integrated Optimization project at CiSoft which has the goal of developing a framework for automated linking of heterogeneous data sources and analysis of the integrated data in the context of upstream applications.

[1]  Vikrambhai S. Sorathia,et al.  The process-oriented event model (PoEM): a conceptual model for industrial events , 2014, DEBS '14.

[2]  Jim Crompton,et al.  Information Architecture Strategy for the Digital Oil Field , 2007 .

[3]  Viktor K. Prasanna,et al.  Computational models of technology adoption at the workplace , 2014, Social Network Analysis and Mining.

[4]  Viktor K. Prasanna,et al.  Predicting Compressor Valve Failures from Multi-Sensor Data , 2015 .

[5]  Anna Wu,et al.  Detecting professional versus personal closeness using an enterprise social network site , 2010, CHI.

[6]  Viktor K. Prasanna,et al.  Predicting Failures from Oilfield Sensor Data using Time Series Shapelets , 2014 .

[7]  Eamonn J. Keogh,et al.  Time series shapelets: a new primitive for data mining , 2009, KDD.

[8]  Zellig S. Harris,et al.  Distributional Structure , 1954 .

[9]  Heiner Stuckenschmidt,et al.  Ontology Alignment Evaluation Initiative: Six Years of Experience , 2011, J. Data Semant..

[10]  Viktor K. Prasanna,et al.  UFOMQ: An Algorithm for Querying for Similar Individuals in Heterogeneous Ontologies , 2015, DaWaK.

[11]  Viktor K. Prasanna,et al.  Influence in social networks: A unified model? , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[12]  Sergey Brin,et al.  Extracting Patterns and Relations from the World Wide Web , 1998, WebDB.

[13]  Vikrambhai S. Sorathia,et al.  Semantic Social Network Analysis for the Enterprise , 2015, Comput. Informatics.

[14]  Viktor K. Prasanna,et al.  UFOM: Unified fuzzy ontology matching , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[15]  Vikrambhai S. Sorathia,et al.  Event-driven Information Integration for the Digital Oilfield , 2012 .

[16]  Eamonn J. Keogh,et al.  Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets , 2013, SDM.

[17]  Viktor K. Prasanna,et al.  Event Extraction from Unstructured Text Data , 2015, DEXA.

[18]  Michael R Brule,et al.  Big Data in Exploration and Production: Real-Time Adaptive Analytics and Data-Flow Architecture , 2013 .

[19]  Ahmed Abou-Sayed,et al.  Data Mining Applications in the Oil and Gas Industry , 2012 .

[20]  H. Edmund Stiles,et al.  The Association Factor in Information Retrieval , 1961, JACM.