Seismic attributes optimization and application in reservoir prediction

Gao, Jun; Wang, Jianmin; Meihou, Yun; Huang, Baoshun; Zhang, Guocai

doi:10.1007/s11770-006-4007-z

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…It is diffi cult to predict the shape and range of a sand body by the analysis of seismic attributes. Of course, a mapping from the properties of sand bodies to instantaneous attributes can be established by using multivariable statistical regression or neural network (Gao et al, 2006). However, this relation mostly represents statistical characteristics and is suitable for the prediction of macro sedimentary environments, but it is diffi cult to use to predict sedimentary details and microfacies.…”

Section: Figures 6 and 7 Show The Instantaneous Attributes For The Sementioning

confidence: 99%

Seismic reflection characteristics of fluvial sand and shale interbedded layers

Xiong²,

Zhou

et al. 2008

Appl. Geophys.

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A sedimentary geological model is established in order to study the seismic refl ection characteristics of channel sand bodies. Synthetic seismic shot gathers are simulated using the acoustic wave equation and then are prestack time migrated. On the imaged data, the refl ection characteristics and instantaneous attributes are analyzed and log-constrained impedance inversion is tested. Because of wave fi eld interference, the experimental results show that seismic events do not defi nitely correspond to the channel sand bodies and that seismic modes of occurrence do not represent the actual ones. The seismic events formed by wave interference may lead to errors and pitfalls in sand body interpretation. The corresponding relations between instantaneous seismic attributes and sedimentary sands are not well established. Log-constrained impedance inversion improves the resolution of channel sands. However, if the inverted resolution is forced to be too high, artifacts related to the initial model may occur.

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Section: Figures 6 and 7 Show The Instantaneous Attributes For The Sementioning

confidence: 99%

Seismic reflection characteristics of fluvial sand and shale interbedded layers

Xiong²,

Zhou

et al. 2008

Appl. Geophys.

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“…Dorrington et al presented the selection method of seismic attribute by using a genetic algorithm, which can select the optimal number and type of seismic attributes for the prediction of porosity [ 10 ]. Gao et al presented a novel selection method called constrained main component analysis [ 14 ]. Ahmed et al introduced the abductive networks to predict reservoir properties from seismic attributes.…”

Section: Introductionmentioning

confidence: 99%

A novel selection method of seismic attributes based on gray relational degree and support vector machine

Huang

Yang²,

et al. 2018

PLoS ONE

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The selection of seismic attributes is a key process in reservoir prediction because the prediction accuracy relies on the reliability and credibility of the seismic attributes. However, effective selection method for useful seismic attributes is still a challenge. This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). The proposed method has a two-hierarchical structure. In the first hierarchy, the primary selection of seismic attributes is achieved by calculating the GRD between seismic attributes and reservoir parameters, and the GRD between the seismic attributes. The principle of the primary selection is that these seismic attributes with higher GRD to the reservoir parameters will have smaller GRD between themselves as compared to those with lower GRD to the reservoir parameters. Then the SVM is employed in the second hierarchy to perform an interactive error verification using training samples for the purpose of determining the final seismic attributes. A real-world case study was conducted to evaluate the proposed GRD-SVM method. Reliable seismic attributes were selected to predict the coalbed methane (CBM) content in southern Qinshui basin, China. In the analysis, the instantaneous amplitude, instantaneous bandwidth, instantaneous frequency, and minimum negative curvature were selected, and the predicted CBM content was fundamentally consistent with the measured CBM content. This real-world case study demonstrates that the proposed method is able to effectively select seismic attributes, and improve the prediction accuracy. Thus, the proposed GRD-SVM method can be used for the selection of seismic attributes in practice.

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“…At present, certain progress has been made in the identification of microfacies and sand body depiction [2][3][4][5][6][7]. The studies are mainly about combining seismic attribute analysis technology with depositional environment, high resolution 3D seismic data, drilling data respectively to study the distribution of reservoir sand body and reservoir microfacies.…”

Section: Introductionmentioning

confidence: 99%

The Optimization and Application of Seismic Attribute – In Depicting Reservoir Sand Bodies

Ren

Song

Xia

2013

AMR

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Right now the application of seismic attributes in China is almost only qualified. Given this situation 2D crossplot analysis, the unary linear regression method and the method of fuzzy clustering analysis are applied to get the seismic attributes from the 3D data volume of oilfield C. The seismic attributes are then quantified and the ones which are good for depicting the reservoir are found and fine depict, thus the application of the seismic attributes is more specifically. It provides good idea and method for the optimization and application of seismic attributes.

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Seismic attributes optimization and application in reservoir prediction

Cited by 8 publications

References 3 publications

Seismic reflection characteristics of fluvial sand and shale interbedded layers

Seismic reflection characteristics of fluvial sand and shale interbedded layers

A novel selection method of seismic attributes based on gray relational degree and support vector machine

The Optimization and Application of Seismic Attribute – In Depicting Reservoir Sand Bodies

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