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Cognitive Analytics for Oil and Gas

Oil and gas companies are having problems learning from the data to understand the different operational states and failure modes of assets, and uses this learning to provide adequate warning before failures occur so operators can plan for corrective actions thus optimizing their Operations and Maintenance budgets.

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  • SUPPLIER
  • SparkCognition
    SparkCognition is a global leader in cognitive computing analytics. A highly awarded company recognized for cutting-edge technology, SparkCognition develops AI-Powered cyber-physical software for the safety, security, and reliability of IT, OT, and the IoT.The company's technology is capable of harnessing real-time sensor data and learning from it continuously, allowing for more accurate risk mitigation and prevention policies to intervene and avert disasters. SparkCognition???s cognitive software, DeepArmor, is utilized to prevent and detect Malware with a high level of accuracy and efficacy by using advanced Machine Learning and Artificial Intelligence.
  • INDUSTRIES
  • Mining
  • FUNCTIONS
  • Maintenance
  • CONNECTIVITY PROTOCOLS
  • USE CASES
  • Predictive Maintenance
    Predictive maintenance is a technique that uses condition-monitoring sensors and Machine Learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive Maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on Predictive Maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using Predictive Maintenance and Predictive Maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
  • CUSTOMER
  • An large oil and gas company

  • SOLUTION
  • SparkPredict has been deployed on Upstream assets such as Drillstrings and Electrical Submersible Pumps as well as Downstream assets such as pumps in refineries. For assets with no labeled failures, SparkPredict analyzes events and identifies anomalies (unknown operating states, failure conditions, etc.) automatically. SparkPredict uses the identified anomalies to recognize patterns of deviation and raise alarms if significant deviation from normal is observed. SparkPredict leverages cutting edge, cognitive, machine learning techniques to additionally predict asset failures. The cognitive, or reasoning based, nature of our algorithms mean SparkPredict can be deployed to any asset in any location and the insights will adapt to the unique characteristics of that particular asset. In addition, SparkPredict integrates with already installed Asset Monitoring systems or works with data historians, like OSI PI, to leverage pre-existing and/or live streaming data for improved failure predictions.

  • DATA COLLECTED
  • Asset Performance, Asset Status Tracking, Device Diagnostic Status, Fault Detection, Operation Performance
  • SOLUTION TYPE
  • SOLUTION MATURITY
  • Emerging (technology has been on the market for > 2 years)
  • OPERATIONAL IMPACT
  • Impact #1
    [Process Optimization - Remote Diagnostics]
    SparkPredict detects both Stuck Pipe and Wash Out conditions with high accuracy.
    Impact #2
    [Efficiency Improvement - Operation]
    SparkPredict provides an in-context advisory system technicians can use to quickly find documents and other digital resources to address issues, automatically provide meaningful remediation steps, and seamlessly communicate and share data with team member
    Impact #3
    [Efficiency Improvement - Maintenance]
    For Electrical Submersible Pumps (ESPs), SparkPredict predicts failures days in advance.
  • QUANTITATIVE BENEFIT
  • Benefit #1

    The average lead times for detection of these failures is: Stuck Pipe: 1.2 hours, Wash Out: 2.3 hours.

    Benefit #2

    SparkCognition targeted two failure modes, accounting for 85% of all ESP failures, and were able to provide the following median forewarning: Electrical Short: 5.5 days, Mechanical Breakdown: 6 days.

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