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Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow
A novel workflow is presented for integrating fiber optic Distributed Temperature Sensor (DTS) data in numerical simulation model for the Cyclic Steam Stimulation (CSS) process, using an intelligent optimization routine that automatically learns and improves from experience. As the steam–oil relatio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309087/ https://www.ncbi.nlm.nih.gov/pubmed/32485918 http://dx.doi.org/10.3390/s20113075 |
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author | Feo, Giuseppe Sharma, Jyotsna Cunningham, Stephen |
author_facet | Feo, Giuseppe Sharma, Jyotsna Cunningham, Stephen |
author_sort | Feo, Giuseppe |
collection | PubMed |
description | A novel workflow is presented for integrating fiber optic Distributed Temperature Sensor (DTS) data in numerical simulation model for the Cyclic Steam Stimulation (CSS) process, using an intelligent optimization routine that automatically learns and improves from experience. As the steam–oil relationship is the main driver for forecasting and decision-making in thermal recovery operations, knowledge of downhole steam distribution across the well over time can optimize injection and production. This study uses actual field data from a CSS operation in a heavy oil field in California, and the value of integrating DTS in the history matching process is illustrated as it allows the steam distribution to be accurately estimated along the entire length of the well. The workflow enables the simultaneous history match of water, oil, and temperature profiles, while capturing the reservoir heterogeneity and the actual physics of the injection process, and ultimately reducing the uncertainty in the predictive models. A novel stepwise grid-refinement approach coupled with an evolutionary optimization algorithm was implemented to improve computational efficiency and predictive accuracy. DTS surveillance also made it possible to detect a thermal communication event due to steam channeling in real-time, and even assess the effectiveness of the remedial workover to resolve it, demonstrating the value of continuous fiber optic monitoring. |
format | Online Article Text |
id | pubmed-7309087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73090872020-06-25 Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow Feo, Giuseppe Sharma, Jyotsna Cunningham, Stephen Sensors (Basel) Article A novel workflow is presented for integrating fiber optic Distributed Temperature Sensor (DTS) data in numerical simulation model for the Cyclic Steam Stimulation (CSS) process, using an intelligent optimization routine that automatically learns and improves from experience. As the steam–oil relationship is the main driver for forecasting and decision-making in thermal recovery operations, knowledge of downhole steam distribution across the well over time can optimize injection and production. This study uses actual field data from a CSS operation in a heavy oil field in California, and the value of integrating DTS in the history matching process is illustrated as it allows the steam distribution to be accurately estimated along the entire length of the well. The workflow enables the simultaneous history match of water, oil, and temperature profiles, while capturing the reservoir heterogeneity and the actual physics of the injection process, and ultimately reducing the uncertainty in the predictive models. A novel stepwise grid-refinement approach coupled with an evolutionary optimization algorithm was implemented to improve computational efficiency and predictive accuracy. DTS surveillance also made it possible to detect a thermal communication event due to steam channeling in real-time, and even assess the effectiveness of the remedial workover to resolve it, demonstrating the value of continuous fiber optic monitoring. MDPI 2020-05-29 /pmc/articles/PMC7309087/ /pubmed/32485918 http://dx.doi.org/10.3390/s20113075 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Feo, Giuseppe Sharma, Jyotsna Cunningham, Stephen Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow |
title | Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow |
title_full | Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow |
title_fullStr | Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow |
title_full_unstemmed | Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow |
title_short | Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow |
title_sort | integrating fiber optic data in numerical reservoir simulation using intelligent optimization workflow |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309087/ https://www.ncbi.nlm.nih.gov/pubmed/32485918 http://dx.doi.org/10.3390/s20113075 |
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