Seismic driven reservoir characterization for porosity estimation
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1
Seismic driven reservoir characterization for porosity estimation (2015)
DE PB NWISBN: 9783659491801 bzw. 3659491802, in Deutsch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Schweiz, Versandfertig innert 4 - 7 Werktagen.
An integrated approach for thin bed reservoir delineation using well and 3D seismic based reservoir characterization, In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas; a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control. Taschenbuch, 11.04.2015.
An integrated approach for thin bed reservoir delineation using well and 3D seismic based reservoir characterization, In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas; a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control. Taschenbuch, 11.04.2015.
2
Seismic driven reservoir characterization for porosity estimation (2015)
~EN PB NWISBN: 9783659491801 bzw. 3659491802, vermutlich in Englisch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Österreich, zzgl. Versandkosten.
An integrated approach for thin bed reservoir delineation using well and 3D seismic based reservoir characterization In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas; a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control. 11.04.2015, Taschenbuch.
An integrated approach for thin bed reservoir delineation using well and 3D seismic based reservoir characterization In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas; a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control. 11.04.2015, Taschenbuch.
3
Seismic driven reservoir characterization for porosity estimation
DE NWISBN: 9783659491801 bzw. 3659491802, in Deutsch, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten.
In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas; a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control.
In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas; a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control.
4
Seismic driven reservoir characterization for porosity estimation
DE NWISBN: 9783659491801 bzw. 3659491802, in Deutsch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Lieferzeit: 11 Tage, zzgl. Versandkosten.
In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas, a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control.
In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas, a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control.
5
Seismic driven reservoir characterization for porosity estimation - An integrated approach for thin bed reservoir delineation using well and 3D seismic based reservoir characterization
DE PB NWISBN: 9783659491801 bzw. 3659491802, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Seismic driven reservoir characterization for porosity estimation: In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control. Englisch, Taschenbuch.
Seismic driven reservoir characterization for porosity estimation: In the present research, 3D post-stack seismic dataset was evaluated along with 38 wells from Boonsville Field, Fort Worth Basin, Texas, USA. Fluvio-deltaic deposit of Bend Conglomerate from Pennsylvanian age was the main target reservoir of this study. Single and multi-attribute analysis has been done on selected data using a multi linear regression transforms to derive the porosity maps at the Runaway and Vineyard Formations. Total of six seismic attributes namely, seismic amplitude, integrated trace, amplitude envelope, instantaneous phase, instantaneous frequency, and acoustic impedance (AI) are used in current study. A slice of 10ms was obtained for each attribute and are used to derive the porosity distribution maps. Porosity of the selected horizons was measured using the single & multi-attributes. The cross-validation analysis of predicted and actual porosity at well locations indicated that multi-attribute transforms produced the porosity map with 90% accuracy whereas a single AI attribute produced only 70% prediction. This study indicates that multi-attribute transformation is more accurate and can be used for accurate porosity estimation away from well control. Englisch, Taschenbuch.
6
Seismic driven reservoir characterization for porosity estimation
DE NWISBN: 3659491802 bzw. 9783659491801, in Deutsch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
7
Seismic driven reservoir characterization for poro (2015)
~EN PB NWISBN: 9783659491801 bzw. 3659491802, vermutlich in Englisch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
8
Seismic driven reservoir characte (2015)
DE PB NWISBN: 9783659491801 bzw. 3659491802, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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