### Abstract

Polymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and by implementing a closed-loop production optimization scheme. However, the formulation of an optimal production strategy should be based on uncertain production forecasts resulting from uncertainty in for example spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, and other factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This paper presents a feedback control framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses on assessing uncertainty in reservoir modeling and production forecasts originated mainly by uncertain geologic scenarios and heterogeneity. This uncertainty is mapped in a metric space created by comparing multiple reservoir models and measuring differences in effective heterogeneity related to well connectivity and well responses characteristic of polymer flooding. Continuously monitored production data are used to refine the prior uncertainty scores using a Bayesian inversion algorithm. In contrast to classical approach of history matching by model perturbation, a model selection problem is implemented where highly probable reservoir models are selected to represent the posterior uncertainty in production forecasts. The model selection procedure yields the posterior uncertainty associated with the reservoir model. The production optimization problem is solved using the posterior models and using a proxy model of polymer flooding to rapidly evaluate the objective function and response surfaces to represent the relationship between well controls and an economic objective function. The value of the feedback control framework is demonstrated with a synthetic example of polymer flooding where the economic performance was maximized.

Original language | English (US) |
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Title of host publication | Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011 |

Pages | 1152-1169 |

Number of pages | 18 |

State | Published - Jun 7 2011 |

Event | SPE Reservoir Simulation Symposium 2011 - The Woodlands, TX, United States Duration: Feb 21 2011 → Feb 23 2011 |

### Publication series

Name | Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011 |
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Volume | 2 |

### Conference

Conference | SPE Reservoir Simulation Symposium 2011 |
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Country | United States |

City | The Woodlands, TX |

Period | 2/21/11 → 2/23/11 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Geochemistry and Petrology
- Modeling and Simulation
- Energy Engineering and Power Technology
- Geotechnical Engineering and Engineering Geology

### Cite this

*Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011*(pp. 1152-1169). (Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011; Vol. 2).

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*Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011.*Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011, vol. 2, pp. 1152-1169, SPE Reservoir Simulation Symposium 2011, The Woodlands, TX, United States, 2/21/11.

**Feedback control of polymer flooding process considering geologic uncertainty.** / Mantilla, Cesar A.; Srinivasan, Sanjay.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Feedback control of polymer flooding process considering geologic uncertainty

AU - Mantilla, Cesar A.

AU - Srinivasan, Sanjay

PY - 2011/6/7

Y1 - 2011/6/7

N2 - Polymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and by implementing a closed-loop production optimization scheme. However, the formulation of an optimal production strategy should be based on uncertain production forecasts resulting from uncertainty in for example spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, and other factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This paper presents a feedback control framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses on assessing uncertainty in reservoir modeling and production forecasts originated mainly by uncertain geologic scenarios and heterogeneity. This uncertainty is mapped in a metric space created by comparing multiple reservoir models and measuring differences in effective heterogeneity related to well connectivity and well responses characteristic of polymer flooding. Continuously monitored production data are used to refine the prior uncertainty scores using a Bayesian inversion algorithm. In contrast to classical approach of history matching by model perturbation, a model selection problem is implemented where highly probable reservoir models are selected to represent the posterior uncertainty in production forecasts. The model selection procedure yields the posterior uncertainty associated with the reservoir model. The production optimization problem is solved using the posterior models and using a proxy model of polymer flooding to rapidly evaluate the objective function and response surfaces to represent the relationship between well controls and an economic objective function. The value of the feedback control framework is demonstrated with a synthetic example of polymer flooding where the economic performance was maximized.

AB - Polymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and by implementing a closed-loop production optimization scheme. However, the formulation of an optimal production strategy should be based on uncertain production forecasts resulting from uncertainty in for example spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, and other factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This paper presents a feedback control framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses on assessing uncertainty in reservoir modeling and production forecasts originated mainly by uncertain geologic scenarios and heterogeneity. This uncertainty is mapped in a metric space created by comparing multiple reservoir models and measuring differences in effective heterogeneity related to well connectivity and well responses characteristic of polymer flooding. Continuously monitored production data are used to refine the prior uncertainty scores using a Bayesian inversion algorithm. In contrast to classical approach of history matching by model perturbation, a model selection problem is implemented where highly probable reservoir models are selected to represent the posterior uncertainty in production forecasts. The model selection procedure yields the posterior uncertainty associated with the reservoir model. The production optimization problem is solved using the posterior models and using a proxy model of polymer flooding to rapidly evaluate the objective function and response surfaces to represent the relationship between well controls and an economic objective function. The value of the feedback control framework is demonstrated with a synthetic example of polymer flooding where the economic performance was maximized.

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M3 - Conference contribution

AN - SCOPUS:79957896747

SN - 9781617823862

T3 - Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011

SP - 1152

EP - 1169

BT - Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2011

ER -