Analysis of time-lapse seismic and production data for reservoir model classification and assessment

Rafael Souza, David Lumley, Jeffrey Shragge, Alessandra Davolio, Denis Jose Schiozer

Research output: Contribution to journalArticle

Abstract

The heterogeneous distribution of reservoir properties is one of the most important uncertainties in static and dynamic reservoir modelling. Petrophysical properties are usually interpolated within reservoir models from sparse well-log data, which can lead to highly uncertain estimates at inter-well locations that directly affect the reliability of fluid-flow model predictions of reservoir behaviour. To address this issue, one approach is to build an ensemble of equiprobable models that combine different geostatistical realisations of reservoir properties that ideally span the range of potential outcomes. While this process captures the impact of reservoir property distributions on the model response, a major challenge is classifying the subset of models in the ensemble best representing reservoir fluid-flow behaviour. Time-lapse seismic attributes are useful for reducing such uncertainties, since they image fluid-movement trends that provide insights regarding fault locations and distribution of reservoir properties, such as permeability and porosity. Accordingly, we introduce a methodology combining 4D seismic amplitude attributes and reservoir production data to classify fluid-flow models. This classification is based on applying thresholds for independent seismic and production objective functions. We develop and apply a new formulation of local dissimilarity maps to quantify differences between observed and modelled 4D seismic amplitudes. We test our methodology on the benchmark case UNISIM-I developed from observations from the Namorado Field, Campos Basin, Brazil. By comparing injection and production rates in relation to 4D seismic amplitude trends within each region, we identify nine models out of an ensemble of 100 that are judged optimal via the required seismic and production objective function thresholds. Thus, we obtain an improved quantitative evaluation of the impact of reservoir production on the 4D seismic signal. Combining seismic and production data offers interpretation scenarios that automatically identify realistic fluid-flow models that would be helpful for updating reservoir properties.

Original languageEnglish
Pages (from-to)1561-1587
Number of pages27
JournalJournal of Geophysics and Engineering
Volume15
Issue number4
DOIs
Publication statusPublished - Aug 2018

Cite this

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title = "Analysis of time-lapse seismic and production data for reservoir model classification and assessment",
abstract = "The heterogeneous distribution of reservoir properties is one of the most important uncertainties in static and dynamic reservoir modelling. Petrophysical properties are usually interpolated within reservoir models from sparse well-log data, which can lead to highly uncertain estimates at inter-well locations that directly affect the reliability of fluid-flow model predictions of reservoir behaviour. To address this issue, one approach is to build an ensemble of equiprobable models that combine different geostatistical realisations of reservoir properties that ideally span the range of potential outcomes. While this process captures the impact of reservoir property distributions on the model response, a major challenge is classifying the subset of models in the ensemble best representing reservoir fluid-flow behaviour. Time-lapse seismic attributes are useful for reducing such uncertainties, since they image fluid-movement trends that provide insights regarding fault locations and distribution of reservoir properties, such as permeability and porosity. Accordingly, we introduce a methodology combining 4D seismic amplitude attributes and reservoir production data to classify fluid-flow models. This classification is based on applying thresholds for independent seismic and production objective functions. We develop and apply a new formulation of local dissimilarity maps to quantify differences between observed and modelled 4D seismic amplitudes. We test our methodology on the benchmark case UNISIM-I developed from observations from the Namorado Field, Campos Basin, Brazil. By comparing injection and production rates in relation to 4D seismic amplitude trends within each region, we identify nine models out of an ensemble of 100 that are judged optimal via the required seismic and production objective function thresholds. Thus, we obtain an improved quantitative evaluation of the impact of reservoir production on the 4D seismic signal. Combining seismic and production data offers interpretation scenarios that automatically identify realistic fluid-flow models that would be helpful for updating reservoir properties.",
keywords = "fluid-flow model, time-lapse (4D) seismic, ensemble of models, history matching, model selection, reservoir property update, REPRESENTATIVE MODELS, LATIN HYPERCUBE, QUANTIFICATION",
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year = "2018",
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Analysis of time-lapse seismic and production data for reservoir model classification and assessment. / Souza, Rafael; Lumley, David; Shragge, Jeffrey; Davolio, Alessandra; Schiozer, Denis Jose.

In: Journal of Geophysics and Engineering, Vol. 15, No. 4, 08.2018, p. 1561-1587.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Analysis of time-lapse seismic and production data for reservoir model classification and assessment

AU - Souza, Rafael

AU - Lumley, David

AU - Shragge, Jeffrey

AU - Davolio, Alessandra

AU - Schiozer, Denis Jose

PY - 2018/8

Y1 - 2018/8

N2 - The heterogeneous distribution of reservoir properties is one of the most important uncertainties in static and dynamic reservoir modelling. Petrophysical properties are usually interpolated within reservoir models from sparse well-log data, which can lead to highly uncertain estimates at inter-well locations that directly affect the reliability of fluid-flow model predictions of reservoir behaviour. To address this issue, one approach is to build an ensemble of equiprobable models that combine different geostatistical realisations of reservoir properties that ideally span the range of potential outcomes. While this process captures the impact of reservoir property distributions on the model response, a major challenge is classifying the subset of models in the ensemble best representing reservoir fluid-flow behaviour. Time-lapse seismic attributes are useful for reducing such uncertainties, since they image fluid-movement trends that provide insights regarding fault locations and distribution of reservoir properties, such as permeability and porosity. Accordingly, we introduce a methodology combining 4D seismic amplitude attributes and reservoir production data to classify fluid-flow models. This classification is based on applying thresholds for independent seismic and production objective functions. We develop and apply a new formulation of local dissimilarity maps to quantify differences between observed and modelled 4D seismic amplitudes. We test our methodology on the benchmark case UNISIM-I developed from observations from the Namorado Field, Campos Basin, Brazil. By comparing injection and production rates in relation to 4D seismic amplitude trends within each region, we identify nine models out of an ensemble of 100 that are judged optimal via the required seismic and production objective function thresholds. Thus, we obtain an improved quantitative evaluation of the impact of reservoir production on the 4D seismic signal. Combining seismic and production data offers interpretation scenarios that automatically identify realistic fluid-flow models that would be helpful for updating reservoir properties.

AB - The heterogeneous distribution of reservoir properties is one of the most important uncertainties in static and dynamic reservoir modelling. Petrophysical properties are usually interpolated within reservoir models from sparse well-log data, which can lead to highly uncertain estimates at inter-well locations that directly affect the reliability of fluid-flow model predictions of reservoir behaviour. To address this issue, one approach is to build an ensemble of equiprobable models that combine different geostatistical realisations of reservoir properties that ideally span the range of potential outcomes. While this process captures the impact of reservoir property distributions on the model response, a major challenge is classifying the subset of models in the ensemble best representing reservoir fluid-flow behaviour. Time-lapse seismic attributes are useful for reducing such uncertainties, since they image fluid-movement trends that provide insights regarding fault locations and distribution of reservoir properties, such as permeability and porosity. Accordingly, we introduce a methodology combining 4D seismic amplitude attributes and reservoir production data to classify fluid-flow models. This classification is based on applying thresholds for independent seismic and production objective functions. We develop and apply a new formulation of local dissimilarity maps to quantify differences between observed and modelled 4D seismic amplitudes. We test our methodology on the benchmark case UNISIM-I developed from observations from the Namorado Field, Campos Basin, Brazil. By comparing injection and production rates in relation to 4D seismic amplitude trends within each region, we identify nine models out of an ensemble of 100 that are judged optimal via the required seismic and production objective function thresholds. Thus, we obtain an improved quantitative evaluation of the impact of reservoir production on the 4D seismic signal. Combining seismic and production data offers interpretation scenarios that automatically identify realistic fluid-flow models that would be helpful for updating reservoir properties.

KW - fluid-flow model

KW - time-lapse (4D) seismic

KW - ensemble of models

KW - history matching

KW - model selection

KW - reservoir property update

KW - REPRESENTATIVE MODELS

KW - LATIN HYPERCUBE

KW - QUANTIFICATION

U2 - 10.1088/1742-2140/aab287

DO - 10.1088/1742-2140/aab287

M3 - Article

VL - 15

SP - 1561

EP - 1587

JO - Journal of Geophysics and Engineering

JF - Journal of Geophysics and Engineering

SN - 1742-2132

IS - 4

ER -