Clay content, porosity and velocity of Amazon fan sediments determined from ODP Leg 155 cores and wireline logs

Pirmez, Carlos; Flood, Roger D.; Baptiste, John; Yin, Hezhu; Manley, P. L.

doi:10.1029/96gl03469

Cited by 14 publications

(11 citation statements)

References 6 publications

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“…Robusl•ness of this relationship was examined by comparison to alternative curve-fit methods and data subsets; we concluded that (5) is probably accurate to _+0.02 km/s. Flood et al [1997] estimate in situ velocity for Amazon Fan sites without velocity logs using the Biot [1962] theory as described by Stoll [1989]. However, this requires the estimation of several unknown parameters.…”

Section: Primary Control Of Velocity: Porositymentioning

confidence: 99%

“…Reliability of these shale fraction logs (Figure 2) was confirmed by comparison to core descriptions [Flood, Piper et al, 1995], smear slides [Flood, Piper et al, 1995], and logs of apparent volumetric photoelectric factor, a lithology-sensitive combination of the density and photoelectric factor logs [Schlumberger, 1989]. Pirmez et al [1997] determined clay content by applying a locally based, empirical equation to all Amazon Fan CGR logs. Consequently, their lithology logs differ slightly from ours.…”

Section: Introductionmentioning

confidence: 95%

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Velocity‐porosity relationships for water‐saturated siliciclastic sediments

Erickson¹,

Jarrard²

1998

J. Geophys. Res.

105

189

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Abstract. Seeking a global empirical relationship between compressional wave velocity and porosity for siliciclastic sediments, we have brought together an extensive suite of both new and published log-and core-based data. We undertook a detailed statistical analysis of Ocean Drilling Program data from Amazon Fan to examine variables affecting compressional velocity in shallow, unconsolidated sediments. We identify three dominant variables (porosity, shale fraction, and consolidation history) and present two empirically determined boundary curves (one for normally consolidated sediments and a second for highly consolidated environments (e.g., accretionary prisms)). These two empirical relationships predict the compressional velocity of siliciclastic sedimentary rocks with water-filled pores as a function of porosity and clay content for the full range of observed porosities. Velocities of siliciclastic sedimentary rocks decrease rapidly with both increasing porosity and increasing clay content. At fractional porosities higher than about 0.4, fluid dominates the elastic properties, and velocity exhibits a subtle dependence on porosity. Remarkably, the Amazon Fan data show that clay content has no direct influence on velocity at high porosities. Both clay content and sorting do indirectly affect velocity, through their control of porosity. Burial affects velocity not only by compaction-related porosity decrease but also by pressure-induced increase of intergrain coupling. Because of the sensitivity of velocity to consolidation history, particularly at intermediate fractional porosities of about 0.30-0.40, no single velocity-porosity relationship can apply to all high-porosity sediments. The two proposed relationships fit the majority of published and new data. They are applicable, however, only for normally pressured, in situ conditions and water-filled pores.

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Section: Primary Control Of Velocity: Porositymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Velocity‐porosity relationships for water‐saturated siliciclastic sediments

Erickson¹,

Jarrard²

1998

J. Geophys. Res.

105

189

View full text Add to dashboard Cite

show abstract

“…Reliability of these shale fraction logs was confirmed by comparison to core descriptions [Flood, Piper et al, 1995], smear slides [Flood, Piper et al, 1995] and logs of apparent volumetric photoelectric factor, a lithology-sensitive combination of the density and photoelectric factor logs [Schlumberger, 1989a]. Pirmez et al [1997] determined clay content by applying a locally-based, empirical equation to all Amazon Fan CGR logs. Differences between their lithology logs and ours are too small to affect overall data patterns and conclusions.…”

Section: '•mentioning

confidence: 98%

“…Resistivities of high-porosity sediments are relatively unknown, and the effect of clay conduction in high-porosity sediments has not been examined previously in detail. Most previous studies of resistivity of high-porosity sediments [Patinode and Wyllie, 1950;Zierfuss and van der Vliet, 1956;Hutt and Berg, 1968;Jackson et al, 1978Jackson et al, , 1993Boyce, 1968;Pirmez et al, 1997;Henry, 1997] are based mainly on core measurements, but core expansion or disturbance can bias such results. This study, in contrast, uses only in situ data.…”

Section: Law As Modified By Winsauer and Mccardell [1953] Is Ff -' mentioning

confidence: 99%

Porosity/formation‐factor relationships for high‐porosity siliciclastic sediments from Amazon Fan

Erickson

Jarrard

1998

Geophysical Research Letters

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Abstract. The electrical resistivity of siliciclastic rocks is a function both of pore-fluid resistivity and of formation factor (FF), an intrinsic rock property. For low-porosity rocks, FF depends on clay conduction and porosity. In contrast, we find that FF of high-porosity sediments (fractional porosities of 0.3-0.6) from Amazon Fan is controlled primarily by porosity and pore geometry; clay conduction is a minor effect. Porosity vs. formation factor (FF) plots for Amazon Fan well logs demonstrate two distinct trends which are dependent on the amount of shale present. Muds, with more than about 0.4 shale fxaction, follow a trend that increases to much higher FF as porosity decreases, compared to sands and slightly muddy sands. This separation is of opposite sign to the separation between low-porosity sandstones and shales attributable to clay conduction. We conclude that in high-porosity muds, presence of clays reduces formation conductivity by increasing the tortuosity of pores.

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“…Most wellbore stability and seal integrity problems are associated with soft mudrock formations. For soft mudrock samples retrieved from the same geological formation, the clay fractions (particle size less than 2 µm) could have significant variations with burial depths [ Day‐Stirrat et al ., ; Pirmez et al ., ; Smith et al ., ]. Variations in amount and type of clay minerals present in soft mudrocks have contributed to their changes in compression behavior, swelling behavior, strength, permeability, and fabric orientation distribution [ Day‐Stirrat et al ., , ; Dewhurst et al ., ; Mondol et al ., ; Olson and Mesri , ; Schneider , ; Skempton , ; Yang and Aplin , ].…”

Section: Introductionmentioning

confidence: 97%

Quantifying structural states of soft mudrocks

Wong

2016

JGR Solid Earth

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In this paper, a cm model is proposed to quantify structural states of soft mudrocks, which are dependent on clay fractions and porosities. Physical properties of natural and reconstituted soft mudrock samples are used to derive two parameters in the cm model. With the cm model, a simplified homogenization approach is proposed to estimate geomechanical properties and fabric orientation distributions of soft mudrocks based on the mixture theory. Soft mudrocks are treated as a mixture of nonclay minerals and clay‐water composites. Nonclay minerals have a high stiffness and serve as a structural framework of mudrocks when they have a high volume fraction. Clay‐water composites occupy the void space among nonclay minerals and serve as an in‐fill matrix. With the increase of volume fraction of clay‐water composites, there is a transition in the structural state from the state of framework supported to the state of matrix supported. The decreases in shear strength and pore size as well as increases in compressibility and anisotropy in fabric are quantitatively related to such transition. The new homogenization approach based on the proposed cm model yields better performance evaluation than common effective medium modeling approaches because the interactions among nonclay minerals and clay‐water composites are considered. With wireline logging data, the cm model is applied to quantify the structural states of Colorado shale formations at different depths in the Cold Lake area, Alberta, Canada. Key geomechancial parameters are estimated based on the proposed homogenization approach and the critical intervals with low strength shale formations are identified.

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Clay content, porosity and velocity of Amazon fan sediments determined from ODP Leg 155 cores and wireline logs

Cited by 14 publications

References 6 publications

Velocity‐porosity relationships for water‐saturated siliciclastic sediments

Velocity‐porosity relationships for water‐saturated siliciclastic sediments

Porosity/formation‐factor relationships for high‐porosity siliciclastic sediments from Amazon Fan

Quantifying structural states of soft mudrocks

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