3‐D reservoir characterization by stratigraphic inversion and pattern recognition

Johann, Paulo; Fournier, F.; Dequirez, P. Y.; Blanchet, Claude

doi:10.1190/1.1826484

“…3-D seismic data play an important role in describing external and internal complexities of reservoirs away from the wellbore and to define geometric description of structural and stratigraphic aspects of the reservoirs (ref. 1). Seismic amplitude variations are linked to changes in acoustic impedances that we can be trying to relate to reservoir properties.…”

Section: Introductionmentioning

confidence: 99%

Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil

Paulo¹,

D.²,

Barroso³

2001

Proceedings of SPE Latin American and Caribbean Petroleum Engineering Conference

0

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractUsually, seismic data is used in a qualitative approach to detect changes in the waveform and to pick acoustic continuity of a peak and/or a through as a structural mapping tool. The seismic interpretation is a qualitative process for building a geological model. Today, many works try to use the seismic information in a quantitative approach. Seismic interpretation in a quantitative approach is a key process in the integration of geoscience data at scales from basin-wide studies, reservoir focused and field-development process. Quantitative modeling could be deterministic and/or probabilistic. We use, in many steps of a seismic processing sequence, examples of quantitative deterministic modeling like seismic migration, some seismic inversion methodology, etc. Probabilistic modeling can be gathered in two groups: multivariate statistics and geostatistics approaches. Close to probabilistic modeling, we have also the neural network method. In this paper, we focus on the application of neural network modeling for seismic pattern recognition (seismic facies analysis) applied an ultra-deepwater turbidite oilfield reservoir in Campos Basin, offshore Brazil.

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Unsupervised seismic facies analysis using wavelet transform and self-organizing maps

Matos

¹

,

Osório²,

Johann

³

2007

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Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps ͑SOMs͒ as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.

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Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil

Johann

¹

,

Castro

²

,

Barroso

³

2001

SPE Latin American and Caribbean Petroleum Engineering Conference

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractUsually, seismic data is used in a qualitative approach to detect changes in the waveform and to pick acoustic continuity of a peak and/or a through as a structural mapping tool. The seismic interpretation is a qualitative process for building a geological model. Today, many works try to use the seismic information in a quantitative approach. Seismic interpretation in a quantitative approach is a key process in the integration of geoscience data at scales from basin-wide studies, reservoir focused and field-development process. Quantitative modeling could be deterministic and/or probabilistic. We use, in many steps of a seismic processing sequence, examples of quantitative deterministic modeling like seismic migration, some seismic inversion methodology, etc. Probabilistic modeling can be gathered in two groups: multivariate statistics and geostatistics approaches. Close to probabilistic modeling, we have also the neural network method. In this paper, we focus on the application of neural network modeling for seismic pattern recognition (seismic facies analysis) applied an ultra-deepwater turbidite oilfield reservoir in Campos Basin, offshore Brazil.

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3‐D reservoir characterization by stratigraphic inversion and pattern recognition

Cited by 3 publications

References 0 publications

Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil

Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil

Unsupervised seismic facies analysis using wavelet transform and self-organizing maps

Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil

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