Multiattribute seismic analysis

Russell, Brian; Hampson, Daniel P.; Schuelke, J.S.; Quirein, John

doi:10.1190/1.1437486

Cited by 114 publications

(37 citation statements)

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“…As was observed during the multiattribute analysis, the sample size was substantially increased from six (one sample per well) to forty-three (an average of six samples per well). Hence, this method is most appropriate in areas of limited well control (Russell et al 1997;Hampson et al 2001). …”

Section: Discussionmentioning

confidence: 99%

“…We used a volume-based seismic attribute study as described by Russell et al (1997) and Hampson et al (2001) due to the thickness (80-230ft/24-70m) and expected stratigraphic complexity of this interval. We sought to predict porosity, as measured by the density porosity log, because of its direct relation to depositional facies at the Appleton Field (Benson 1988;Benson et al 1996) and because it is an important variable controlling hydrocarbon production.…”

Section: Methodsmentioning

confidence: 99%

“…This approach seeks to find empirical correlations between seismic attributes and log-derived physical properties (e.g., porosity, lithology, bed thickness) through methods such as multivariate linear regression (MLR) and artificial or probabilistic neural networks (ANN/PNN; Schultz et al 1994 a & b;Russell et al 1997;Hampson et al 2001).…”

Section: Introductionmentioning

confidence: 99%

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Integrated Geologic-Engineering Model for Reef and Carbonate Shoal Reservoirs Associated With Paleohighs: Upper Jurassic Smackover Formation, Northeastern Gulf of Mexico

Mancini¹

2003

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Progress on the project is as follows: geoscientific reservoir characterization is completed.The architecture, porosity types and heterogeneity of the reef and shoal reservoirs at Appleton and Vocation Fields have been characterized using geological and geophysical data. The study of iv rock-fluid interactions has been completed. Observations regarding the diagenetic processes influencing pore system development and heterogeneity in these reef and shoal reservoirs have been made. Petrophysical and engineering property characterization has been completed. Porosity and permeability data at Appleton and Vocation Fields have been analyzed, and well performance analysis has been conducted. Data integration is up to date, in that, the geological, geophysical, petrophysical and engineering data collected to date for Appleton and Vocation and Vocation Field reservoirs have been developed. These models are being tested. The geophysical interpretation for the paleotopographic feature being tested has been made, and the study of the data resulting from drilling of a well on this paleohigh is in progress. Numerous presentations on reservoir characterization and modeling at Appleton and Vocation Fields have been made at professional meetings and conferences and a short course on microbial reservoir characterization and modeling based on these fields has been prepared.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Integrated Geologic-Engineering Model for Reef and Carbonate Shoal Reservoirs Associated With Paleohighs: Upper Jurassic Smackover Formation, Northeastern Gulf of Mexico

Mancini¹

2003

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“…The final training result is then applied to the entire 3D volume. For more details on multiattribute analysis from seismic attributes, the reader is referred to Russell et al (1997).…”

Section: Multiattribute Regression Analysismentioning

confidence: 99%

Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

et al. 2017

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An integrated innovative multidisciplinary approach has been used to estimate effective porosity (PHIE), shale volume (V sh ), and sand probability from prestack angle gathers and petrophysical well logs within the Lower Triassic Havert Formation in the Goliat field, Southwest Barents Sea. A rock-physics feasibility study revealed the optimum petrofacies discriminating ability of extended elastic impedance (EEI) tuned for PHIE and V sh . We then combined model-based prestack inversion outputs from a simultaneous inversion and an EEI inversion into a multilinear attribute regression analysis to estimate absolute V sh and PHIE seismic attributes. The quality of the V sh and PHIE prediction is shown to increase by integrating the EEI inversion in the workflow. Probability distribution functions and a priori petrofacies proportions extracted from the well data are then applied to the V sh and PHIE volumes to obtain clean and shaly sand probabilities. A tectonic-controlled point-source depositional model for the Havert Formation sands is then inferred from the extracted sand bodies and the seismic geomorphological character of the different attributes. IntroductionThe past two decades have seen a significant increase in the use of quantitative seismic interpretation methods to characterize the subsurface. Quantitative seismic interpretation techniques are increasingly being implemented into reservoir characterization schemes to minimize hydrocarbon exploitation risks. The measured seismic data respond to the contrasts in the effective elastic properties in the subsurface area of investigation. However, the main goal is usually aimed at characterizing the different lithofacies, porosity distribution, and fluid content responsible for the effective elastic response. These underlying properties are only indirectly measured using the available proxies in the seismic data, which are then transformed to the geologic variables of interest using rock physics.Direct hydrocarbon indicators (DHIs) from seismic data are not always successful because the seismic amplitude is a composite response of the saturated rock. Therefore, it is important to spend as much time characterizing the lithology response before fluid anomaly hunting. Reservoir quality assessment from seismic, well log, and laboratory data requires an integrated approach involving geology, petrophysics, rock physics,

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“…The primary data type used for exploration of reservoir properties in the oil and gas industry is seismic reflection (e.g., Russell et al 1997;Hart & Balch 2000). Seismic and well log data are used to infer properties such as porosity and water saturation.…”

Section: Introductionmentioning

confidence: 99%

Imaging fracture permeability using magnetotellurics

Kirkby

Heinson

Holford

2016

ASEG Extended Abstracts

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SUMMARYWe present 1D anisotropic inversions of magnetotelluric data in two regions of the Otway Basin; Koroit, Victoria, and Penola, South Australia. In the Koroit region we have delineated an electrically anisotropic layer at approximately 2.5 to 3.5 km depth; this corresponds to the upper part of the Lower Cretaceous Crayfish Group, a known reservoir unit. The anisotropy strike is consistent between stations at approximately 160° east of north. We interpret the anisotropy at Koroit as resulting from pervasive NNW oriented, fluid-filled fractures, resulting in enhanced bulk electrical and hydraulic conductivity. This interpretation is consistent with permeability data from well formation tests. It is also consistent with the orientation of mapped faults in the area, which are favourably oriented for reactivation in the current stress field. In Penola, no persistent anisotropic layer has been defined even though the areas are geologically similar. The difference in the resistivity structure may reflect differences in the density of fractures or their fill material. Alternatively, it may reflect small differences in the amount by which the fractures are open, resulting from differences in the stress field and fracture orientation in each area.

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Multiattribute seismic analysis

Cited by 114 publications

References 0 publications

Integrated Geologic-Engineering Model for Reef and Carbonate Shoal Reservoirs Associated With Paleohighs: Upper Jurassic Smackover Formation, Northeastern Gulf of Mexico

Integrated Geologic-Engineering Model for Reef and Carbonate Shoal Reservoirs Associated With Paleohighs: Upper Jurassic Smackover Formation, Northeastern Gulf of Mexico

Prestack inversion and multiattribute analysis for porosity, shale volume, and sand probability in the Havert Formation of the Goliat field, southwest Barents Sea

Imaging fracture permeability using magnetotellurics

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