History Matching of 4D Seismic Data Attributes using the Ensemble Kalman Filter

Handle URI:
http://hdl.handle.net/10754/294183
Title:
History Matching of 4D Seismic Data Attributes using the Ensemble Kalman Filter
Authors:
Ravanelli, Fabio M.
Abstract:
One of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Because of different sources of uncertainties the numerical models employed are often only crude approximations of the reality. This problem is tackled by the conditioning of the model with production data through data assimilation. This process is known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse seismic data and automated history matching software tools. One of the most promising data assimilation techniques employed in the oil industry is the ensemble Kalman filter (EnKF) because its ability to deal with highly non-linear models, low computational cost and easy computational implementation when compared with other methods. A synthetic reservoir model was used in a history matching study designed to predict the peak production allowing decision makers to properly plan field development actions. If only production data is assimilated, a total of 12 years of historical data is required to properly characterize the production uncertainty and consequently the correct moment to take actions and decommission the field. However if time-lapse seismic data is available this conclusion can be reached 4 years in advance due to the additional fluid displacement information obtained with the seismic data. Production data provides geographically sparse data in contrast with seismic data which are sparse in time. Several types of seismic attributes were tested in this study. Poisson’s ratio proved to be the most sensitive attribute to fluid displacement. In practical applications, however the use of this attribute is usually avoided due to poor quality of the data. Seismic impedance tends to be more reliable. Finally, a new conceptual idea was proposed to obtain time-lapse information for a history matching study. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region was demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with full scale surface surveys. This approach relies on the higher velocity sensitivity to fluid displacement at higher frequencies. The velocity effects were modeled using the Biot velocity model. This method provided promising results leading to similar RRMS error reductions when compared with conventional history matched surface seismic data.
Advisors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Committee Member:
Alkhalifah, Tariq; Sun, Shuyu ( 0000-0002-3078-864X )
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Program:
Earth Sciences and Engineering
Issue Date:
May-2013
Type:
Thesis
Appears in Collections:
Theses; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.advisorHoteit, Ibrahimen
dc.contributor.authorRavanelli, Fabio M.en
dc.date.accessioned2013-06-19T08:44:52Z-
dc.date.available2013-06-19T08:44:52Z-
dc.date.issued2013-05en
dc.identifier.urihttp://hdl.handle.net/10754/294183en
dc.description.abstractOne of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Because of different sources of uncertainties the numerical models employed are often only crude approximations of the reality. This problem is tackled by the conditioning of the model with production data through data assimilation. This process is known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse seismic data and automated history matching software tools. One of the most promising data assimilation techniques employed in the oil industry is the ensemble Kalman filter (EnKF) because its ability to deal with highly non-linear models, low computational cost and easy computational implementation when compared with other methods. A synthetic reservoir model was used in a history matching study designed to predict the peak production allowing decision makers to properly plan field development actions. If only production data is assimilated, a total of 12 years of historical data is required to properly characterize the production uncertainty and consequently the correct moment to take actions and decommission the field. However if time-lapse seismic data is available this conclusion can be reached 4 years in advance due to the additional fluid displacement information obtained with the seismic data. Production data provides geographically sparse data in contrast with seismic data which are sparse in time. Several types of seismic attributes were tested in this study. Poisson’s ratio proved to be the most sensitive attribute to fluid displacement. In practical applications, however the use of this attribute is usually avoided due to poor quality of the data. Seismic impedance tends to be more reliable. Finally, a new conceptual idea was proposed to obtain time-lapse information for a history matching study. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region was demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with full scale surface surveys. This approach relies on the higher velocity sensitivity to fluid displacement at higher frequencies. The velocity effects were modeled using the Biot velocity model. This method provided promising results leading to similar RRMS error reductions when compared with conventional history matched surface seismic data.en
dc.language.isoenen
dc.subjectHistory Matchingen
dc.subjectData Assimilationen
dc.subjectTime-Lapse Dataen
dc.subject4D Seismicen
dc.subjectEnsemble Kalman Filteren
dc.subjectCrosswellen
dc.titleHistory Matching of 4D Seismic Data Attributes using the Ensemble Kalman Filteren
dc.typeThesisen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberAlkhalifah, Tariqen
dc.contributor.committeememberSun, Shuyuen
thesis.degree.disciplineEarth Sciences and Engineeringen
thesis.degree.nameMaster of Scienceen
dc.person.id118499en
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