Neural network analysis of crosshole tomographic images: The seismic signature of gas hydrate bearing sediments in the Mackenzie Delta (NW Canada)

Bauer, Klaus; Pratt, R. G.; Haberland, Christian; Weber, M.

doi:10.1029/2008gl035263

Cited by 27 publications

(23 citation statements)

References 26 publications

(42 reference statements)

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“…Borehole sonic logs have often been used to estimate hydrate saturation [e.g., Tinivella and Carcione , 2001; Guerin and Goldberg , 2002]. Vertical seismic profiling [e.g., Holbrook et al , 1996], ocean bottom cable data [ Hardage et al , 2006], and analysis of exploration seismic data using seismic attributes, full waveform inversion, and other approaches [e.g., Bangs et al , 1993; Bauer et al , 2008; Dai et al , 2008; Korenaga et al , 1997; Kumar et al , 2007; Westbrook et al , 2008; Zillmer , 2006] have also been applied to infer gas hydrate distribution and concentration in natural field settings. In many of these studies, the calibration of seismic velocity as a function of gas hydrate concentration is based on an extension of macroscale and microstructural relationships developed empirically for nonhydrate‐bearing sediments.…”

Section: Introductionmentioning

confidence: 99%

Parametric study of the physical properties of hydrate‐bearing sand, silt, and clay sediments: 2. Small‐strain mechanical properties

et al. 2010

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[1] The small-strain mechanical properties (e.g., seismic velocities) of hydrate-bearing sediments measured under laboratory conditions provide reference values for calibration of logging and seismic exploration results acquired in hydrate-bearing formations. Instrumented cells were designed for measuring the compressional (P) and shear (S) velocities of sand, silts, and clay with and without hydrate and subject to vertical effective stresses of 0.01 to 2 MPa. Tetrahydrofuran (THF), which is fully miscible in water, was used as the hydrate former to permit close control over the hydrate saturation S hyd and to produce hydrate from dissolved phase, as methane hydrate forms in most natural marine settings. The results demonstrate that laboratory hydrate formation technique controls the pattern of P and S velocity changes with increasing S hyd and that the small-strain properties of hydrate-bearing sediments are governed by effective stress, s′ v and sediment specific surface. The S velocity increases with hydrate saturation owing to an increase in skeletal shear stiffness, particularly when hydrate saturation exceeds S hyd ≈ 0.4. At very high hydrate saturations, the small strain shear stiffness is determined by the presence of hydrates and becomes insensitive to changes in effective stress. The P velocity increases with hydrate saturation due to the increases in both the shear modulus of the skeleton and the bulk modulus of pore-filling phases during fluid-to-hydrate conversion. Small-strain Poisson's ratio varies from 0.5 in soft sediments lacking hydrates to 0.25 in stiff sediments (i.e., subject to high vertical effective stress or having high S hyd ). At S hyd ≥ 0.5, hydrate hinders expansion and the loss of sediment stiffness during reduction of vertical effective stress, meaning that hydrate-rich natural sediments obtained through pressure coring should retain their in situ fabric for some time after core retrieval if the cores are maintained within the hydrate stability field.Citation: Lee, J. Y., F. M. Francisca, J. C. Santamarina, and C. Ruppel (2010), Parametric study of the physical properties of hydrate-bearing sand, silt, and clay sediments: 2. Small-strain mechanical properties,

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Section: Introductionmentioning

confidence: 99%

Parametric study of the physical properties of hydrate‐bearing sand, silt, and clay sediments: 2. Small‐strain mechanical properties

et al. 2010

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“…For each iteration step, a data pattern vector is chosen randomly, and the winning neuron with the model vector most similar to the chosen data pattern vector is determined. A learning rule is then applied, where the model vector of the winning neuron and neighboring neurons are updated [ Bauer et al ., , ]. As a result, the model vector of the winning neuron and, to a lesser degree, the model vectors of the neighboring neurons are getting even more similar to the data pattern vector presented in the present iteration step.…”

Section: Self‐organizing Map Methodsmentioning

confidence: 99%

Lithological control on gas hydrate saturation as revealed by signal classification of NMR logging data

et al. 2015

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In this paper, nuclear magnetic resonance (NMR) downhole logging data are analyzed with a new strategy to study gas hydrate‐bearing sediments in the Mackenzie Delta (NW Canada). In NMR logging, transverse relaxation time (T2) distribution curves are usually used to determine single‐valued parameters such as apparent total porosity or hydrocarbon saturation. Our approach analyzes the entire T2 distribution curves as quasi‐continuous signals to characterize the rock formation. We apply self‐organizing maps, a neural network clustering technique, to subdivide the data set of NMR curves into classes with a similar and distinctive signal shape. The method includes (1) preparation of data vectors, (2) unsupervised learning, (3) cluster definition, and (4) classification and depth mapping of all NMR signals. Each signal class thus represents a specific pore size distribution which can be interpreted in terms of distinct lithologies and reservoir types. A key step in the interpretation strategy is to reconcile the NMR classes with other log data not considered in the clustering analysis, such as gamma ray, hydrate saturation, and other logs. Our results defined six main lithologies within the target zone. Gas hydrate layers were recognized by their low signal amplitudes for all relaxation times. Most importantly, two subtypes of hydrate‐bearing shaly sands were identified. They show distinct NMR signals and differ in hydrate saturation and gamma ray values. An inverse linear relationship between hydrate saturation and clay content was concluded. Finally, we infer that the gas hydrate is not grain coating, but rather, pore filling with matrix support is the preferred growth habit model for the studied formation.

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“…The technique used here was developed by Bauer et al (2008), and only its overview will be presented here. The first phase of the analysis is the training.…”

Section: Neural Network Approach For More Than Two Parametersmentioning

confidence: 99%

“…This corresponds to clusters of similar model parameter values being separated by cluster borders. By treating the gradient map as a topographic image, the watershed segmentation algorithm (Vincent and Soille, 1991) can be used to identify the clusters (Bauer et al, 2008).…”

Section: Neural Network Approach For More Than Two Parametersmentioning

confidence: 99%

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Lithology classification from seismic tomography: Additional constraints from surface waves

Stankiewicz

Bauer

Ryberg

2010

Journal of African Earth Sciences

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Neural network analysis of crosshole tomographic images: The seismic signature of gas hydrate bearing sediments in the Mackenzie Delta (NW Canada)

Cited by 27 publications

References 26 publications

Parametric study of the physical properties of hydrate‐bearing sand, silt, and clay sediments: 2. Small‐strain mechanical properties

Parametric study of the physical properties of hydrate‐bearing sand, silt, and clay sediments: 2. Small‐strain mechanical properties

Lithological control on gas hydrate saturation as revealed by signal classification of NMR logging data

Lithology classification from seismic tomography: Additional constraints from surface waves

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