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Unifying Streaming and Historical Telemetry Data For Real-time Performance Reporting

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"In the oil and gas industry, utilizing vast amounts of data has long been identified as an important indicator of operational performance. The measurement of key performance indicators is a routine practice in well construction, but a systematic way of statistically analyzing performance against a large data bank of offset wells is not a common practice. The performance of statistical analysis in real-time is even less common. With the adoption of distributed computing platforms, like Apache Spark, new analysis opportunities become available to leverage large-scale time-series data sets to optimize performance. Two case studies are presented in this talk: the rate of penetration (ROP) and the amount of vibration per run.

By collecting real-time, telemetry data and comparing it with historic sample datasets within the Databricks Unified Analytics Platform, the optimization team was able to quickly determine whether the performance being delivered matched or exceeded past performance with statistical certainty. This is extremely important while trying new techniques with data that is highly variable. By substituting anecdotal evidence with statistical analysis, decision making is more precise and better informed. In this talk we'll share how we accomplished this and the lessons learned along the way."

Publicado en: Datos y análisis
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Unifying Streaming and Historical Telemetry Data For Real-time Performance Reporting

  1. 1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics
  2. 2. Unifying Streaming and Historical Data for Real- Time Performance Reporting Daniel Antonio, Halliburton #UnifiedAnalytics #SparkAISummit © 2019 Halliburton. All Rights Reserved.
  3. 3. Data Performance Expectations
  4. 4. Data Performance Expectations Evidence Utilized to Make Decisions
  5. 5. Oil and Gas Data  Highly variable time-series data  Large data sets per well  Millions of rows  Thousands of columns  Difficult to normalize and compare  Geological factors  Equipment factors  Operational factors
  6. 6. Business Need: Quickly Compare Current Operations with Historic Data Historic Operational Data Real-Time Sample Data ?
  7. 7. Solution Workflow SPE-195023-MS • Using Hypothesis Testing to Evaluate Key Performance Indicators in Real Time: an Edge Computing Use Case • P. Kowalchuk Data Visualization Modeling Data Transformation Communicating Results Data Collection and Import
  8. 8. Analytics Process for Modeling Real-Time Data Evaluate Raw Data Bootstrap with Apache Spark™ Hypothesis Testing 10x Reduction in Bootstrap process time
  9. 9. Solution Workflow SPE-195023-MS • Using Hypothesis Testing to Evaluate Key Performance Indicators in Real Time: an Edge Computing Use Case • P. Kowalchuk Data Visualization Modeling Data Transformation Communicating Results Data Collection and Import
  10. 10. Analytics Use Case Development Process • Role-based access controls • Use case playbook • Scripts available for future use cases Data Governance and Organization TransformationStorageIngest Usage Data Available for Consumption Custom Analysis and Compute Self-Serve AnalyticsAutomated File Transfer
  11. 11. Results  Proven viability for bringing distributed computing resources into a real-time environment  Once models are generated, usage at the edge empowers decision makers  Provides unbiased view of data  Project details available in Society of Petroleum Engineers paper SPE-195023-MS
  12. 12. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

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