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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow.

Giuseppe Feo1, Jyotsna Sharma1, Stephen Cunningham2

  • 1Department of Petroleum Engineering Louisiana State University, Baton Rouge, LA 70803, USA.

Sensors (Basel, Switzerland)
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new workflow integrating fiber optic Distributed Temperature Sensing (DTS) data into Cyclic Steam Stimulation (CSS) models. This enhances heavy oil recovery by accurately estimating steam distribution and improving predictive accuracy.

Keywords:
automated history matchcyclic steam stimulationdistributed fiber optic sensingenhanced oil recoveryintelligent optimization algorithmnumerical reservoir simulation

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Area of Science:

  • Petroleum Engineering
  • Reservoir Engineering
  • Data Science

Background:

  • Cyclic Steam Stimulation (CSS) is crucial for heavy oil recovery.
  • Accurate downhole steam distribution is key for optimizing CSS operations.
  • Integrating real-time data into numerical models remains a challenge.

Purpose of the Study:

  • To develop and validate a novel workflow for integrating Distributed Temperature Sensing (DTS) data into CSS numerical simulation models.
  • To improve the accuracy of steam distribution estimation and reduce uncertainty in predictive models.
  • To demonstrate the value of continuous fiber optic monitoring for operational decision-making.

Main Methods:

  • Implementation of an intelligent optimization routine for data integration.
  • Utilizing field data from a California heavy oil CSS operation.
  • Employing a stepwise grid-refinement approach with an evolutionary optimization algorithm.

Main Results:

  • Accurate estimation of steam distribution along the entire well length.
  • Simultaneous history matching of water, oil, and temperature profiles.
  • Detection of steam channeling and assessment of remedial workover effectiveness in real-time.

Conclusions:

  • The integrated workflow significantly reduces uncertainty in predictive models for CSS.
  • DTS data integration enhances the understanding and optimization of thermal recovery processes.
  • Continuous fiber optic monitoring provides valuable real-time insights for operational adjustments and issue resolution.