Interrelations of the petrophysical, sedimentological and microstructural properties of the Oolithe Blanche Formation (Bathonian, saline aquifer of the Paris Basin)

Casteleyn, Lisa; Robion, Philippe; Collin, Pierre‐Yves; Menéndez, Beatriz; David, Christian; Désaubliaux, Guy; Fernandes, Nathalie; Dreux, Rémi; Badiner, Guillaume; Brosse, É.; Rigollet, Christophe

doi:10.1016/j.sedgeo.2010.07.003

Cited by 42 publications

(36 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The permeability field had a log-normal distribution (Table 2), and 99 % of values lay between 0.31 mD and 3.16 D (consistent with local data for the Dogger aquifer (e.g., Delmas et al 2010;Rohmer and Seyedi 2010;Brosse et al 2010;Casteleyn et al 2010;Lopez et al 2010;Rojas et al 1989)). Stochastic simulations were applied on an initial fine and regular mesh (cell size of 7*7*7 m).…”

Section: Modeling Of Permeability Fieldssupporting

confidence: 75%

Large-Scale $$\hbox {CO}_2$$ CO 2 Storage in a Deep Saline Aquifer: Uncertainties in Predictions Due to Spatial Variability of Flow Parameters and Their Modeling

2015

View full text Add to dashboard Cite

Scarce data and uncertainties in the spatial variation of geological properties lead to different possible models of these heterogeneities. The aim of this study is to compare the pressure results and CO 2 behavior of different permeability field models for a large-scale CO 2 injection in a deep saline aquifer. Five ways of representing heterogeneities are tested and compared. The simplest representation defines homogeneous equivalent properties over the entire domain. A second representation is obtained by considering homogeneous layers. Two other models represent the lateral and vertical variations in permeability in greater detail by geostatistical methods with either a continuous model or a discontinuous model (discrete values). The last model is the semi-homogeneous model combining a heterogeneous area and a homogeneous area depending on the complexity of the flow process. Highly variable predictions arise from the heterogeneities, and significant differences in estimates are obtained using the various modeling methods. The optimum resolution depends on the type of response to be estimated. Averaged properties at large scale are not adequate to estimate the critical pressure propagation far from the well. Averaged properties in the injection area are not sufficient to assess the maximum increase in pressure or the extent of CO 2 migration. Lateral and vertical connectivities, and reservoir compartmentalization modeling are required to obtain reliable results. But the resolution requirements are not to be at the finest scale: The discontinuous model (discrete values) gives satisfactory results compared to the continuous model. The way to represent spatial variability of porosity and pore compressibility is also studied. The influence of these two properties is far lower than that of permeability.

show abstract

Section: Modeling Of Permeability Fieldssupporting

confidence: 75%

Large-Scale $$\hbox {CO}_2$$ CO 2 Storage in a Deep Saline Aquifer: Uncertainties in Predictions Due to Spatial Variability of Flow Parameters and Their Modeling

2015

View full text Add to dashboard Cite

show abstract

“…Based on the analysis of a limited number of rocks, David et al [3] expected a correlation between the permeability k and the capillary coefficient C, as both parameters involve fluid displacement within the pore space. To verify this assumption on a larger dataset, capillaritypermeability data of the present study along with those of recent studies on oolitic limestones [11], dolostones [12], sandstones and carbonates [3] and a variety of sedimentary rocks [13] are reported in Figure 2. An overall positive correlation between C and the rock permeability is observed: a narrow elliptic envelope encompasses 97% of the data allowing the prediction of the rock permeability with a maximum error of about one order of magnitude.…”

Section: Resultsmentioning

confidence: 54%

Detection of moving capillary front in porous rocks using X-ray and ultrasonic methods

et al. 2015

Self Cite

View full text Add to dashboard Cite

(2015) Detection of moving capillary front in porous rocks using X-ray and ultrasonic methods. Front. Phys. 3:53. doi: 10.3389/fphy.2015.00053 Detection of moving capillary front i porous rocks using X-ray and ultrasonic methods Several methods are compared for the detection of moving capillary fronts in spontaneous imbibition experiments where water invades dry porous rocks. These methods are: (i) the continuous monitoring of the mass increase during imbibition, (ii) the imaging of the water front motion using X-ray CT scanning, (iii) the use of ultrasonic measurements allowing the detection of velocity, amplitude and spectral content of the propagating elastic waves, and (iv) the combined use of X-ray CT scanning and ultrasonic monitoring. It is shown that the properties of capillary fronts depend on the heterogeneity of the rocks, and that the information derived from each method on the dynamics of capillary motion can be significantly different. One important result from the direct comparison of the moving capillary front position and the P wave attributes is that the wave amplitude is strongly impacted before the capillary front reaches the sensors, in contrast with the velocity change which is concomitant with the fluid front arrival in the sensors plane.

show abstract

“…3(B)), with clearly lower values for the RP samples compared to the UP samples. 3 Therefore both microstructural arrangements selected in this study (Fig. 2) are reported to have a specific response for the acoustic and fluid flow properties.…”

Section: Resultsmentioning

confidence: 94%

“…1 However, several studies concerning both type of exploitations showed that the Oolithe Blanche forms a complex carbonate reservoir presenting heterogeneous petrophysical and microstructural properties. [2][3][4][5][6] Many studies using P-wave velocities measurements have shown that dynamic moduli of carbonate rocks are controlled by several microstructural parameters such as rock fabric, pore type and shape, porosity and pore fluid, making it difficult to attribute changes in seismic expression to any one parameter. 3,4,[6][7][8][9][10][11][12][13][14][15] Elastic waves are, in essence, small mechanical perturbations and are therefore affected by the rock microstructure and rock deformation processes.…”

Section: Introductionmentioning

confidence: 99%

Influence of microporosity distribution on the mechanical behavior of oolithic carbonate rocks

Regnet

David

Fortin

et al. 2015

Geomechanics for Energy and the Environment

View full text Add to dashboard Cite

Interrelations of the petrophysical, sedimentological and microstructural properties of the Oolithe Blanche Formation (Bathonian, saline aquifer of the Paris Basin)

Cited by 42 publications

References 24 publications

Large-Scale $$\hbox {CO}_2$$ CO 2 Storage in a Deep Saline Aquifer: Uncertainties in Predictions Due to Spatial Variability of Flow Parameters and Their Modeling

Large-Scale $$\hbox {CO}_2$$ CO 2 Storage in a Deep Saline Aquifer: Uncertainties in Predictions Due to Spatial Variability of Flow Parameters and Their Modeling

Detection of moving capillary front in porous rocks using X-ray and ultrasonic methods

Influence of microporosity distribution on the mechanical behavior of oolithic carbonate rocks

Contact Info

Product

Resources

About