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Integration of rock physical signatures with depositional environments: A case study from East Coast of India
Journal of Applied Geophysics 148 (2018) 256–264
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Journal of Applied Geophysics journal homepage: www.elsevier.com/locate/jappgeo
Integration of rock physical signatures with depositional environments: A case study from East Coast of India Samit Mondal a, Ashok Yadav a, Rima Chatterjee b,⁎ a b
Reliance Industries Limited, Mumbai 400701, India Department of Applied Geophysics, IIT (ISM), Dhanbad 826004, India
a r t i c l e
i n f o
Article history: Received 3 April 2017 Received in revised form 15 November 2017 Accepted 4 December 2017 Available online 6 December 2017
a b s t r a c t Rock physical crossplots from different geological setup along eastern continental margin of India (ECMI) represent diversified signatures. To characterize the reservoirs in rock physics domain (velocity/modulus versus porosity) and then connecting the interpretation with geological model has been the objectives of the present study. Petrophysical logs (total porosity and volume of shale) from five wells located at sedimentary basins of ECMI have been analyzed to quantify the types of shale such as: laminated, dispersed and structural in reservoir. Presence of various shale types belonging to different depositional environments is coupled to define distinct rock physical crossplot trends for different geological setup. Wells from three different basins in East Coast of India have been used to capture diversity in depositional environments. Contact model theory has been applied to the crossplot to examine the change in rock velocity with change in reservoir properties like porosity and volume of shale. The depositional and diagenetic trends have been shown in the crossplot to showcase the prime controlling factor which reduces the reservoir porosity. Apart from that, the effect of geological factors like effective stress, sorting, packing, grain size uniformity on reservoir properties have also been focused. The rock physical signatures for distinct depositional environments, effect of crucial geological factors on crossplot trends coupled with established sedimentological models in drilled area are investigated to reduce the uncertainties in reservoir characterization for undrilled potentials. © 2017 Elsevier B.V. All rights reserved.
1. Introduction Lithofacies successions from diverse depositional environments show distinctive patterns in rock physics plane (Modulus/velocity versus porosity) (Avseth et al., 2005; Mauricio, 2005). Rock physics establishes the relation between sedimentological properties and elastic moduli (Dutta et al., 2006). The change in elastic properties (velocity or elastic moduli) of rock with petrophysical properties (porosity, saturation, volume of shale) depicts the nature of deposition, occurrences of diagenetic effects or compaction experienced by the reservoir (Mukerji et al., 2001; Sarasty and Stewart, 2003; Avseth et al., 2005; Omudu et al., 2008). Several researchers deal with the ratio of P-wave and S-wave velocities as lithology and pore fluid indicator (Ødegaard and Avseth, 2004; Gupta et al., 2012; Ba et al., 2013; Gegenhuber and Pupos, 2015; Tucovic et al., 2016). Han and Batzle (2004) have discussed fluid saturation effect on seismic velocities due to the coupling between P and S-waves through the shear modulus and bulk density. Rock physics trends appear more discretely in the modulus-porosity plane than in the velocity-porosity plane. Bulk modulus shows sensitivity to pore fluid (water) and deformation produced by a seismic wave resulting in change of pore volume. Shear modulus is not affected by different fluids. ⁎ Corresponding author. E-mail address: [email protected] (R. Chatterjee).
https://doi.org/10.1016/j.jappgeo.2017.12.005 0926-9851/© 2017 Elsevier B.V. All rights reserved.
The work space for rock physics analysis is the rock physics plane that may be (a) velocity-porosity; (b) impedance-porosity; and/or (c) modulus-porosity plane. Primarily we prefer elastic moduli-porosity planes to correlate change of fluid effect in bulk modulus in comparison with velocity-porosity planes for diagnosing rocks (Durrani et al., 2014). Recently Singha and Chatterjee (2017) have demonstrated rock physics modeling of reservoirs in Krishna-Godavari (K-G) basin. The rock physics diagnostic models such as contact cement, constant cement and friable sand are chosen to characterize reservoir sands of two wells in the K-G basin. Using drilled well information, the depositional environments can be characterized by plotting the change in porosity with volume of shale content (Thomas-Stieber plot) at well locations (Thomas and Stieber, 1975). The plot represents quantitative estimation of laminated, structural or dispersed shale present in the system. The crossplots for different depositional environments (which represent laminated, blocky, and diagenetic or dispersed shaly sand reservoirs) are then analyzed in rock physics plane (elastic moduli or velocity versus porosity). From these plots, rock physics models are established for different depositional setup at drilled well locations. The models correlate elastic properties with petrophysical or reservoir properties. The reservoir architecture of each of these depositional elements is a function of the interaction between sedimentary process, sea-floor morphology, and sediment grain-size distribution (Posamentier et al., 2003). Further the study results/ understanding for different depositional setups can be coupled
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with elastic properties derived from seismic data at undrilled locations which is a future scope of work, would add significant values in reservoir characterization. In this study we have captured different depositional environments across the basins in eastern continental margin of India. The interpreted petrophysical logs (total porosity and volume of shale) have been analyzed using Thomas-Stieber(T-S) model. This particular model is useful to estimate net-to-gross (sand/shale ratios) and sand porosity in shaly sand sequences for different shale configurations (Dutta, 2009). Further data has been plotted in elastic space to inspect the change in rock properties (velocity/ modulus)with change in porosities which are related to different depositional setups. Diagenetic trend, depositional trend, laminated/blocky trend for different reservoirs from Mahanadi, Krishna-Godavari (K-G) and Cauvery basins are shown in this case study in elastic (velocity-porosity) space. 2. Theory of rock physics modeling During burial, the porosity of sediments changes dramatically through diagenesis. Diagenesis represents all mechanical and chemical alteration of rock after deposition (Avseth et al., 2005). Fig. 1 represents crossplot of petrophysical property (porosity) with rock properties (modulus or velocity of the rock). Rock as a whole consists of minerals (which form the matrix of the rock) and fluid present within the pore spaces. Rockphysics model in elastic space starts with bounds which provide a frame work for understanding the elastic properties of the sediments. The simplest bounds were introduced by Voigt (1910) and Reuss (1929) where they used arithmetic and harmonic average of the moduli of constituent minerals respectively. The best bounds for isotropic elastic mixture defined as giving the narrowest range of possible elastic moduli without specifying the geometrics of the constituent minerals are the Hashin-Shtrikman (H/S) bounds (Hashin and Shtrikman, 1963; Mavko et al., 1998).The mathematical formulation was established using mixture of two constituents and their volume fractions (shown in black and red line for upper and lower bounds). Before deposition, the sediments exist
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as particles suspended in water. The Reuss or H/S lower bound describes the moduli of suspension of grains in pore fluid. When they are first deposited in water bottom, the corresponding porosity is termed as critical porosity, Φ0 (Nur, 1992). Upon burial, rock becomes stiffer due to geological processes like compaction and cementation which move the points off the lower bound (Reuss bound) in velocity-porosity space. When diagenesis increases, the rock properties (velocity/modulus) fall along steep trajectories (diagenetic trend) that extend upward from the Reuss bound at critical porosity toward the mineral end point at zero porosity. In this context, Modified H/S upper bound was introduced by Avseth et al. (2005) (green dotted line in Fig. 1) describing a mixture of newly deposited sediments at critical porosity with additional mineral (diagenetic), instead of describing a mixture of minerals and pore fluid. On the other hand, porosity controlled by sedimentation, is generally expected to yield flatter trend (follows lower bound) which often termed as depositional trend. Three different rock physics models namely; friable Sand/ Shale, contact cement and constant cement (Avseth et al., 2005) have been discussed. 2.1. Friable Sand/Shale Model The model was introduced by Dvorkin and Nur in 1996.This model for unconsolidated sediments assumes porosity reduction from the initial sand pack value (Critical porosity) due to the deposition of solid matter away from the grain contacts that result in gradual stiffening of the rock. This porosity reduction for clean sandstone is caused by depositional sorting (follows sorting trend shown in Fig. 1) and packing. Clarke (1979)analyzed the effect of grain sorting on reservoir properties and concluded that, in some clastic rocks, reservoir quality is affected more by bimodality than by diagenesis (Dvorkin and Gutierrez, 2001). 2.2. Contact cement model During burial, sands are likely to become cemented sandstones. The cement may be diagenetic quartz, calcite or other minerals. The contact
Fig. 1. Schematic depiction of bounds. Sediments are initially deposited along the lower bound and then sorting-compaction-digenesis starts after critical porosity. Lines for contact models (diagenetic) and friable trend (sorting) are shown. (After Mavko et al., 1998.)
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2.3. Constant cement model The model (Avseth, 2000) is a combination of the friable-sand/shale model and the contact cement model. It assumes that the sands of varying porosity all have the same amount of contact cement, and porosity reduction is solely due to non-contact pore filling materials. Porosity initially decreases from critical limit, Φ0 to Φb due to cementation. From Φb, porosity decreases as in the case of friable sand model. There is deposition of solid phase away from the grain contacts.
3. Case study
Fig. 2. Geographical map of India showing approximate locations of study wells in East Coast of India. Table of well names (pseudo names) and overburden thickness have been shown. (Image source: www.google.co.in/maps.)
cement model (Dvorkin and Nur, 1996) describes the porosity reduction from initial sand pack due to uniform deposition of cement layers on the surface of grains. This drastically increase stiffness of the sandstone by reinforcing the grain contact.
The wells used in this case study are located in three different sedimentary basins on eastern continental margin of India (ECMI) (Fig. 2). In this study we have considered five wells drilled in different depositional setups. Wells from three basins namely; Mahanadi, K-G and Cauvery have been showcased. All the reservoirs are clastic with dominance of quartz, K-Feldspar and Plagioclase Feldspar. Well A and B are located in shallow water of Mahanadi basin, Well C and D are in deep-water K-G basin. Although all the four reservoirs encountered in Wells A, B, C and D are in Tertiary, there is variability in sediment thickness over the reservoir ranging 1300 m to 2400 m (Fig. 2). This leads to differences in quantum of compaction or diagenesis in the reservoirs. Well E is located in deep-water Cauvery basin where the overburden sedimentary thickness is 2641 m. The reservoirs are in Cretaceous age and there is significant presence of diagenetic cementations. In this case study, few shallow channels or delta bar reservoir has been interpreted as friable
Fig. 3. Vp-Porosity cross-plot for four different depositional environments. Depositional (dashed pink) and diagenetic trends (dashed blue) are put in the cross plots. Q indicates sand points and C indicates clay rich points. (a) Fluvial deposits of the Miocene Guayabo Formation from the Llanos Basin, Colombia (both dispersed and laminated lithofacies). (b) Corresponds to Miocene, mud-rich, deep-water deposits from offshore West Africa (laminated lithofacies). (c) Shows sand-rich deep-water deposits from offshore Gulf of Mexico (blocky lithofacies) and (d) illustrates a shallow marine deposits from the Miocene Leon Formation, in the Llanos Basin (Colombia) which shows the predominance of dispersed sand-clay mixtures and occurrence of some subordinate laminated lithofacies. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (Modified after Mauricio, 2005.)
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Fig. 4. Thomas-Stieber plots of Well A. Laminated shale percentage lines are shown in deep blue lines. Data are colored in volume of shale percentage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (Shale, clean sand, laminated and dispersed cartoon courtesy: after Yared et al., 2010.)
sand deposition which follows depositional trend. Depositional environment has been studied through log responses and other geological data (Unpublished Reliance Internal project report). For deeper channel, lobe or turbidite system, there are presence of diagenetic cements and it follows diagenetic trend. Fig. 3 depicts velocity-porosity cross plots (Mauricio, 2005) of different depositional setups (shallow marine, fluvial, deep water etc.) which showcases examples from various basins. In our case study, similar trend of the data has been observed and interpreted for different depositional setups. To avoid the effect of fluid on elastic
properties, fluid substitution (Gassmann, 1951) of the logs to 100% brine was performed by methodology as suggested by Dejtrakulwong and Mavko (2011). It is worth mentioning that most of the reservoirs studied in this paper are hydrocarbon saturated with minimum water saturation in the range of 15–20%. Gamma ray, compressional and shear velocity, density, resistivity, neutron logs were available for all the reservoirs. Total porosity was calculated from neutron-density logs. Volume of shale (Vsh) logs were generated from gamma ray and sigma logs. Brine substituted points are plotted in velocity- porosity crossplot and
Fig. 5. Velocity-Porosity plot of Well A and Well B. Rock physics model lines (Friable sand in dashed pink with varying in fraction of shale from 0.0–0.7 and friable shale in dashed pink) are shown. Zones plotted are marked in red in volume of shale (Vsh) logs for individual wells. Red arrow shows the direction of increase in effective stress. Modified H/S Upper bound for this combination of minerals has been shown in green dotted line. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 6. Thomas-Stieber plots of Well C. Laminated shale percentage lines are shown in deep blue lines. Data are colored in volume of shale percentage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (After Yared et al., 2010.)
the cement model lines have been established using the parameters (percentage of cement, minerals, fluid type, effective stress) from petrographic report, X-ray diffraction report, modular dynamic tester, formation evaluation report. We have referred Fig. 3 as an analogy for various trends in velocity-porosity crossplot where Fig. 3(a) represents fluvial deposits of the Miocene Guayabo Formation from the Llanos Basin, Colombia (both dispersed and laminated lithofacies). Fig. 3(b) represents Miocene mud-rich, deep-water deposits from offshore West Africa (laminated lithofacies). Fig. 3(c) represents sand-rich deep-water deposits from offshore Gulf of Mexico (blocky lithofacies) and Fig. 3(d) illustrates a shallow marine deposit from the Miocene
Leon Formation, in the Llanos Basin (Colombia) which shows the predominance of dispersed sand-clay mixtures and occurrence of some subordinate laminated lithofacies. Our data trend is then compared with one of the crossplots in the figure with corresponding geologic setup. 4. Shallow marine delta bar deposits Reservoirs of Well A and Well B (marked in red rectangle in volume of shale log in Fig. 5) in Mahanadi basin were deposited in shelf as deltaic bar (Unpublished Reliance Internal report). T-S plot of Well A shows dominant laminated trend (in transition from clean sand to shale) in
Fig. 7. Velocity-Porosity plot of Well C. Rock physics model lines (Constant cement model with 3% quartz cement shown in dashed blue with varying in of shale from 0.0–0.7 and friable shale line in dashed pink) are shown. The reservoir zone plotted are shown in red marker in volume of shale log of Well C. Red arrow shows the direction of grain size uniformity and arrow in magenta represents the direction of poor sorting. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. This trend is also expected in Well B which are evident in other plot (Fig. 5). In velocity-porosity plot as shown in Fig. 5, there is slightly decrease in velocity with decrease in porosity. This is probably the result of clean sands have higher velocity compared to shaly sand. This represents largely laminated shaly-sand reservoir for both of Wells A and B in Fig. 5. The trend of laminated shaly-sand reservoir is analogous to the trend of deep-water laminated reservoir of offshore West Africa as shown in Fig. 3(b). Although the lithology of deep-water reservoir of West Africa is mud rich, but the trend of this laminated reservoir in velocity-porosity plane is similar to the trend as observed in Wells A and B in Fig. 5. In elastic space, the data points follow depositional trend. Well A is having higher effective stress acting on the reservoir compared to the effective stress on reservoir in Well B. The reason is sediment thickness
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above the reservoir in Well A is higher than that of Well B (Fig. 2). The direction of change in effective stress in rock physics cross-plot has been shown in red arrow in this figure (Zimmer, 2003).Green arrow shows the laminated trend and arrow in black depicts the depositional trend. Rock velocities are directly proportional to sediment thickness above the reservoir. More overburden translates into higher rock velocity. Accordingly, the point in the crossplot will move along the red arrow. 5. Deep water slope fan lobe deposits Reservoirs belonging to Well C and D in K-G basin were deposited as fan lobe in slope set up. These wells are positioned in different lobes and have different types of reservoirs in terms of lamination. The stacking
Fig. 8. (a) velocity-porosity plot of Well D. Rock physics model lines (constant cement model with 3% quartz cement shown in dashed blue with varying in fraction of shale from 0.0–0.7) are shown. Reservoir zone plotted are marked in red in volume of shale log of Well D. (b) Thin section petrography image of core within the reservoir does show presence of quartz overgrowth (represented as Qo in cross section and SEM image) which is exclusively shown in SEM image in BW (Inset 1). Blue colored areas indicate primary porosity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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patterns depend on the geological parameters like proximity or distance from axis of the lobe. Well C contains mostly amalgamated blocky sandstone reservoir whereas reservoir in Well D is laminated. T-S plot for reservoir (marked in red rectangle in Vsh log in Fig. 7) in Well C has been shown in Fig. 6. We notice less number of data points in transition zone (shaly-sand) in T-S plot for Well C as compared to T-S plot for Well A (Fig. 4) as Well C reservoir is blocky. In rock physics crossplot (Fig. 7 and 8a), the data clusters for Well C are analogous to sand-rich deep-water deposits from offshore Gulf of Mexico shown in Fig. 3(c). We have performed rock physics modeling using constant cement model. Shales for the reservoirs are same in character and can be modeled with friable shale model. Sands are interpreted with constant cement model where especially Well D shows locally dispersed trend with increase in velocity with minimum increase in porosity (Fig. 8a). The dispersed trend (inverted ‘V’ shaped in velocity-porosity plane) for Well D is similar to the trend of dispersed Miocene Leon Formation shown in Fig. 3(d). Probably, Well C is located along the axis of lobe and hence the reservoir has higher net-to-gross compared to Well D (Unpublished Reliance Internal project report). The cement is basically quartz cement as overgrowth and there are evidences of occasionally occurred calcareous cements. The timings of calcite and quartz cementation are different in geological time scale. Quartz cementation of quartz-rich sandstones initiates in association with smectite-to-illite transition in embedding shales (Avseth et al., 2009). Quartz cement is responsible for much of the porosity and permeability reduction in well sorted sandstones (Bjørlykke and Egeberg, 1993). The thin section petrography and scanning electron microscopy (SEM) images show imprints of quartz overgrowth (Fig. 8b). The magnified view of scanning electron microscopy image in inset shows poorly crystallized mixed-layerillite/smectite that occurs as coatings on grains and has impeded the development of quartz overgrowths (Qo). This was postdated by precipitation of well crystallized authigenic kaolinite (K) that is attached to the surfaces of grain sand quartz overgrowths.
for turbidite depositions, the largest grains in suspension will be concentrated near the bottom of the current and thus both the concentration of sediment in suspension and maximum grain size in suspension, decrease upwards from the bottom (Bjørlykke, 2010). But this type of ideal case can also be changed depending upon the type of turbidite or sediment gravity flow and the sediment support mechanism, viscosity, matrix strength, turbulence etc. what we have experienced in this basin. Well E reservoir consists of both blocky and laminated sequence. The sandstones contain secondary minerals assemblages that include pyrite, siderite, quartz overgrowths, calcite, kaolinite and glauconite. Calcite streaks mainly occur in blocky sand and their responses in elastic domain have been discussed later. T-S plot shown in Fig. 9 depicts laminated reservoir. The selected zone is near top of the reservoir marked red rectangle in Vsh log in Fig. 10(a) and it excludes the blocky sands which contains streaks. Fig. 10(a) shows velocity versus porosity plot of Well E which depicts the reservoir is mainly laminated. Fig. 10(b), displays thin section petrography image showing presence of calcareous sandstones within reservoir interval. The laminated trend is analogous to deep-water laminated reservoir of offshore West Africa which is shown in Fig. 3(b). Rock physics modeling has been carried out using constant cement model. In transition from sand to shale, velocity slightly decreases with decrease in porosity as seen in deepwater laminated reservoir in Fig. 3(b). Fig. 11 represents rock physics crossplot of blocky sand reservoir and the zone in marked with red rectangle in Vsh log in inset. Presence of calcite streaks are very common especially inblocky sand compared to zones characterized by laminated shale-sand sequence toward top of the reservoir. This type of cementation occurs due to the leaching of Plagioclase Feldspar within the reservoir. The data points follow diagenetic trend and modeled using constant cement. Calcareous streaks are having low porosity and higher moduli than the laminated reservoir points. These points can be identified in rock physics crossplots as outliers as far as contact model is concerned.
6. Deep-water canyon filled turbidities deposits 7. Conclusions Reservoirs in Well E located at Cauvery basin, were deposited as canyon filled turbidities in slope setup. Early diagenetic material present in the reservoir are mainly calcareous in nature. Generally,
Different depositional environments show significant changes in rock physical signature which has been captured in this study. T-S plot has
Fig. 9. Thomas-Stieber plots of Well E. Laminated shale percentage lines are shown in deep blue lines. Data are colored in volume of shale percentage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (After Yared et al., 2010.)
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Fig. 10. (a) velocity-porosity plot of Well E. Red marker in volume of shale (Vsh) log does represent zone plotted in crossplot. Rock physics model lines (constant cement model with 3.5% calcite cement shown in dotted blue with varying in fraction of shale from 0.0–0.7) are shown. (b) Thin section petrography image within the reservoir zone shows presence of calcareous sandstones. Blue colored areas indicate primary porosity. The pink stained mineral is Calcite labelled as Ca. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
been used to quantify laminated, structural or dispersed shale present in the reservoir. In rock physics crossplots, the data exhibits change in velocity with change in reservoir porosity. But, the reason of porosity reduction is sorting/depositional, diagenesis or combination of all; has been analyzed further. The depositional and diagenetic trends have been identified in velocity-porosity crossplot, where data have been interpreted using different rock physics models. Further these trends have been interpreted as laminated, blocky, calcareous and locally dispersed. In addition, the impact of effective stress on the data has been shown exclusively for non-diagenetic reservoir. On the basis of lamination or blocky nature of the reservoir, the rock physics crossplots have been integrated with depositional setups like position of the well in complete lobe setup or stacked patterns change along and away from the lobe axis. This represents the variability in geological factors controlling
elastic properties within the same or different setups. Behavior of elastic properties for different depositional setups at drilled location, coupled with elastic properties derived from seismic data will add significant values in risk assessment in undrilled location. Acknowledgement Authors are thankful to Reliance Industries Limited for giving necessary permission to publish this study. We would like to especially thank Mr. Neeraj Sinha, Mr. Ajoy Biswal, Dr. Lalaji Yadav and Dr. Nabarun Pal for their valuable inputs and comments in course of this study. Authors also appreciate entire Quantitative Interpretation Group, Sedimentology Group and Petrophysics Group for their proactive support in carrying out this work.
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Fig. 11. Velocity-porosity plot of Well E. Red marker in volume of shale (Vsh) log does represent zone plotted in crossplot. Rock physics model lines (constant cement model with 3.5% calcite cement shown in dotted blue with varying in volume of shale from 0.0–0.7) are shown. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
References Avseth, P., 2000. Combining Rock Physics and Sedimentology for Seismic Reservoir Characterization in North Sea Turbidite Systems. (Ph. D. thesis). Stanford University. Avseth, P., Mukerji, T., Mavko, G., 2005. Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge University Press https://doi.org/10.1017/CBO9780511600074. Avseth, P., Jørstad, A., Wijngaarden, A., Mavko, G., 2009. Rock physics estimation of cement volume, sorting, and net-to-gross in North Sea sandstones. TLE, January: 98–108 https://doi.org/10.1190/1.3064154. Ba, J., Cao, H., Carcione, J., Tang, G., Yan, X.F., Sun, W.T., Nie, J.X., 2013. Multiscale rock physics templates for gas detection in carbonate reservoirs. J. Appl. Geophys. 93:77–82. https://doi.org/10.1016/j.jappgeo.2013.03.011. Bjørlykke, K., 2010. Petroleum Geoscience: From Sedimentary Environments to Rock Physics. Copy right. 2010. Springer-Verlag, Berlin Heidelberg. https://doi.org/10.1007/978-3-642-02332-3. Bjørlykke, K., Egeberg, 1993. Quartz cementation in sedimentary basins. Am. Assoc. Pet. Geol. Bull. 77, 1538–1548. Clarke, R.H., 1979. Reservoir properties of conglomerates and conglomeratic sandstones. Am. Assoc. Pet. Geol. Bull. 63, 799–809 (no. 5). Dejtrakulwong, P., Mavko, G., 2011. Fluid Substitution for Laminated Sand-Shale Sequences, Expanded Abstract, SEG Annual Meeting, San Antonio. :pp. 2183–2187 https://doi.org/10.1190/1.3627642. Durrani, M.Z.A., Willson, K., Chen, J., Tapp, B., Akram, J., 2014. Rational rock physics for improved velocity prediction and reservoir properties estimation for granite wash (tight sands) in Anadarko Basin, Texas. Int. J. Geophys.:1–15 https://doi.org/10.1155/2014/ 209351 (Article ID 209351). Dutta, T., 2009. Integrating Sequence Stratigraphy and Rock-Physics to Interpret Seismic Amplitudes and Predict Reservoir Quality. (Ph. D. thesis). Stanford University. Dutta, T., Mukerji, T., Mavko, G., Avseth, P., 2006. Reservoir Quality Prediction by Integrating Sequence Stratigraphy and Rock Physics, Expanded Abstract, SEG Annual Meeting, New Orleans. :pp. 1811–1815 https://doi.org/10.1190/1.2369876. Dvorkin, J., Gutierrez, M.A., 2001. Textural Sorting Effect on Elastic Velocities, Part II: Elasticity of a Bimodal Grain Mixture, Society of Exploration Geophysicist Annual Meeting, Expanded Abstracts. Dvorkin, J., Nur, A., 1996. Elasticity of high-porosity sandstones: theory for two North Sea datasets. Geophysics 61:1363–1370. https://doi.org/10.1190/1.1444059. Gassmann, F., 1951. Über die elastizitätporösermedien: Vierteljahrss-chrift der NaturforschendenGesellschaft in Zurich. 96 pp. 1–23 (The English translation of this paper is available at http://sepwww.stanford.edu/sep/berryman/PS/gassmann.pdf). Gegenhuber, N., Pupos, J., 2015. Rock physics template from laboratory data for carbonates. J. Appl. Geophys. 114:12–18. https://doi.org/10.1016/j.jappgeo.2015.01.005. Gupta, S., Chatterjee, R., Farooqui, M., 2012. Rock physics template (RPT) analysis of well logs and seismic data for lithology and fluid classification in Cambay Basin. Int. J. Earth Sci. 101:1407–1426. https://doi.org/10.1007/s00531-011-0736-1. Han, D., Batzle, M.L., 2004. Gassmann's equation and fluid-saturation effects on seismic velocities. Geophysics 69 (2), 398–405. Hashin, Z., Shtrikman, S., 1963. A variational approach to the elastic behavior of multiphase materials. J. Mech. Phys. Solids 11, 127–140.
Mauricio, J., 2005. Integrating Geology, Rock Physics and Seismology for Reservoir Quality Prediction. (Ph.D Thesis). Stanford University. Mavko, G., Mukerji, T., Dvorkin, J., 1998. The Rock Physics Handbook. Cambridge University Press https://doi.org/10.2277/0521543444. Mukerji, T., Avseth, P., Mavko, G., Takahashi, I., Gonzalez, F., 2001. Statistical rock physics: combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization. Lead. Edge 20 (3), 313–319. Nur, A., 1992. Critical porosity and the seismic velocities in rocks. EOS, Trans. Am. Geophys. Un. 73, 43–66. Ødegaard, E., Avseth, P., 2004. Well log and seismic data analysis using rock physics templates. First Break 22:37–43. https://doi.org/10.3997/1365-2397.2004017. Omudu, L.M., Ebeniro, J.O., Xynogalas, M., Osayande, N., Olotu, S., 2008. Fluid discrimination and reservoir characterization from onshore Niger Delta. Proceedings of 78th Society of Exploration Geophysicists International Exposition and Annual Meeting, Las Vegas, NV, USA, November. 4, pp. 1996–2000. Posamentier, Henry, W., Kolla, V., 2003. Seismic geomorphology and stratigraphy of depositional elements in deep-water settings. J. Sediment. Res. 73, 367–388 (May 2003, published online January 1, 2003). Reuss, A., 1929. Berechnung der Fliessgrenzen von Mischkristallen. Z. Angew. Math. Mech. 9, 49–58. Sarasty, J.J., Stewart, R.R., 2003. Analysis of well-log data from the white rose oilfield, offshore Newfoundland. CREWES Res. Rep. 15, 1–16. Singha, D., Chatterjee, R., 2017. Rock physics modeling in sand reservoir through well log analysis, Krishna-Godavari basin, India. Geomech. Eng. 13 (1), 99–117. Thomas, E., Stieber, S., 1975. The Distribution of Shale in Sandstones and Its Effect Upon Porosity: 16th SPWLA Annual Logging Symposium. Tucovic, N., Bartetzko, A., Wessling, S., Schön, J., Gegenhuber, N., 2016. Resistivity and Acoustic Impedance Based Rock Physics Templates for Enhanced Well Placement and Reservoir Understanding, 78th EAGE Conference and Exhibition, Extended Abstracts, We STZ013. Unpublished Reliance Internal report on project: Cataloguing and Integrating Depositional Environment, Attributes, Rock Physics in Reliance Drilled Areas. Unpublished Reliance Internal Reports on Sedimentological Evaluation of Core Plug Samples. Voigt, W., 1910. Lehrbuch der Kristallphysik. Teubner, Leipzig. Yared, K., Pelorosso, M., Altintutar, I., Buster, D., Manuel, E., Doyle, J., Russell, C., 2010. Novel Approach to Quantifying Deepwater Laminated Sequences Using Integrated Evaluation of LWD Real-Time Shear, Porosity, Azimuthal Density and High Resolution Propagation Resistivity: Deepwater Drilling and Completion Conference, Galveston, Texas, 5–8 October (SPE paper 134515). Zimmer, M., 2003. Controls on the Seismic Velocities of Unconsolidated Sands: Measurements of Pressure, Porosity and Compaction Effects. (Ph. D. dissertation). Stanford University.
Further reading Avseth, P., Mavko, G., Dvorkin, J., Rykkje, J., 2000. Rock Physics diagnostics of North Sea sands: link between microstructure and seismic properties. Geophys. Res. Lett. 27 (17):2761–2764. https://doi.org/10.1029/1999GL008468.
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Journal of Applied Geophysics journal homepage: www.elsevier.com/locate/jappgeo
Integration of rock physical signatures with depositional environments: A case study from East Coast of India Samit Mondal a, Ashok Yadav a, Rima Chatterjee b,⁎ a b
Reliance Industries Limited, Mumbai 400701, India Department of Applied Geophysics, IIT (ISM), Dhanbad 826004, India
a r t i c l e
i n f o
Article history: Received 3 April 2017 Received in revised form 15 November 2017 Accepted 4 December 2017 Available online 6 December 2017
a b s t r a c t Rock physical crossplots from different geological setup along eastern continental margin of India (ECMI) represent diversified signatures. To characterize the reservoirs in rock physics domain (velocity/modulus versus porosity) and then connecting the interpretation with geological model has been the objectives of the present study. Petrophysical logs (total porosity and volume of shale) from five wells located at sedimentary basins of ECMI have been analyzed to quantify the types of shale such as: laminated, dispersed and structural in reservoir. Presence of various shale types belonging to different depositional environments is coupled to define distinct rock physical crossplot trends for different geological setup. Wells from three different basins in East Coast of India have been used to capture diversity in depositional environments. Contact model theory has been applied to the crossplot to examine the change in rock velocity with change in reservoir properties like porosity and volume of shale. The depositional and diagenetic trends have been shown in the crossplot to showcase the prime controlling factor which reduces the reservoir porosity. Apart from that, the effect of geological factors like effective stress, sorting, packing, grain size uniformity on reservoir properties have also been focused. The rock physical signatures for distinct depositional environments, effect of crucial geological factors on crossplot trends coupled with established sedimentological models in drilled area are investigated to reduce the uncertainties in reservoir characterization for undrilled potentials. © 2017 Elsevier B.V. All rights reserved.
1. Introduction Lithofacies successions from diverse depositional environments show distinctive patterns in rock physics plane (Modulus/velocity versus porosity) (Avseth et al., 2005; Mauricio, 2005). Rock physics establishes the relation between sedimentological properties and elastic moduli (Dutta et al., 2006). The change in elastic properties (velocity or elastic moduli) of rock with petrophysical properties (porosity, saturation, volume of shale) depicts the nature of deposition, occurrences of diagenetic effects or compaction experienced by the reservoir (Mukerji et al., 2001; Sarasty and Stewart, 2003; Avseth et al., 2005; Omudu et al., 2008). Several researchers deal with the ratio of P-wave and S-wave velocities as lithology and pore fluid indicator (Ødegaard and Avseth, 2004; Gupta et al., 2012; Ba et al., 2013; Gegenhuber and Pupos, 2015; Tucovic et al., 2016). Han and Batzle (2004) have discussed fluid saturation effect on seismic velocities due to the coupling between P and S-waves through the shear modulus and bulk density. Rock physics trends appear more discretely in the modulus-porosity plane than in the velocity-porosity plane. Bulk modulus shows sensitivity to pore fluid (water) and deformation produced by a seismic wave resulting in change of pore volume. Shear modulus is not affected by different fluids. ⁎ Corresponding author. E-mail address: [email protected] (R. Chatterjee).
https://doi.org/10.1016/j.jappgeo.2017.12.005 0926-9851/© 2017 Elsevier B.V. All rights reserved.
The work space for rock physics analysis is the rock physics plane that may be (a) velocity-porosity; (b) impedance-porosity; and/or (c) modulus-porosity plane. Primarily we prefer elastic moduli-porosity planes to correlate change of fluid effect in bulk modulus in comparison with velocity-porosity planes for diagnosing rocks (Durrani et al., 2014). Recently Singha and Chatterjee (2017) have demonstrated rock physics modeling of reservoirs in Krishna-Godavari (K-G) basin. The rock physics diagnostic models such as contact cement, constant cement and friable sand are chosen to characterize reservoir sands of two wells in the K-G basin. Using drilled well information, the depositional environments can be characterized by plotting the change in porosity with volume of shale content (Thomas-Stieber plot) at well locations (Thomas and Stieber, 1975). The plot represents quantitative estimation of laminated, structural or dispersed shale present in the system. The crossplots for different depositional environments (which represent laminated, blocky, and diagenetic or dispersed shaly sand reservoirs) are then analyzed in rock physics plane (elastic moduli or velocity versus porosity). From these plots, rock physics models are established for different depositional setup at drilled well locations. The models correlate elastic properties with petrophysical or reservoir properties. The reservoir architecture of each of these depositional elements is a function of the interaction between sedimentary process, sea-floor morphology, and sediment grain-size distribution (Posamentier et al., 2003). Further the study results/ understanding for different depositional setups can be coupled
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with elastic properties derived from seismic data at undrilled locations which is a future scope of work, would add significant values in reservoir characterization. In this study we have captured different depositional environments across the basins in eastern continental margin of India. The interpreted petrophysical logs (total porosity and volume of shale) have been analyzed using Thomas-Stieber(T-S) model. This particular model is useful to estimate net-to-gross (sand/shale ratios) and sand porosity in shaly sand sequences for different shale configurations (Dutta, 2009). Further data has been plotted in elastic space to inspect the change in rock properties (velocity/ modulus)with change in porosities which are related to different depositional setups. Diagenetic trend, depositional trend, laminated/blocky trend for different reservoirs from Mahanadi, Krishna-Godavari (K-G) and Cauvery basins are shown in this case study in elastic (velocity-porosity) space. 2. Theory of rock physics modeling During burial, the porosity of sediments changes dramatically through diagenesis. Diagenesis represents all mechanical and chemical alteration of rock after deposition (Avseth et al., 2005). Fig. 1 represents crossplot of petrophysical property (porosity) with rock properties (modulus or velocity of the rock). Rock as a whole consists of minerals (which form the matrix of the rock) and fluid present within the pore spaces. Rockphysics model in elastic space starts with bounds which provide a frame work for understanding the elastic properties of the sediments. The simplest bounds were introduced by Voigt (1910) and Reuss (1929) where they used arithmetic and harmonic average of the moduli of constituent minerals respectively. The best bounds for isotropic elastic mixture defined as giving the narrowest range of possible elastic moduli without specifying the geometrics of the constituent minerals are the Hashin-Shtrikman (H/S) bounds (Hashin and Shtrikman, 1963; Mavko et al., 1998).The mathematical formulation was established using mixture of two constituents and their volume fractions (shown in black and red line for upper and lower bounds). Before deposition, the sediments exist
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as particles suspended in water. The Reuss or H/S lower bound describes the moduli of suspension of grains in pore fluid. When they are first deposited in water bottom, the corresponding porosity is termed as critical porosity, Φ0 (Nur, 1992). Upon burial, rock becomes stiffer due to geological processes like compaction and cementation which move the points off the lower bound (Reuss bound) in velocity-porosity space. When diagenesis increases, the rock properties (velocity/modulus) fall along steep trajectories (diagenetic trend) that extend upward from the Reuss bound at critical porosity toward the mineral end point at zero porosity. In this context, Modified H/S upper bound was introduced by Avseth et al. (2005) (green dotted line in Fig. 1) describing a mixture of newly deposited sediments at critical porosity with additional mineral (diagenetic), instead of describing a mixture of minerals and pore fluid. On the other hand, porosity controlled by sedimentation, is generally expected to yield flatter trend (follows lower bound) which often termed as depositional trend. Three different rock physics models namely; friable Sand/ Shale, contact cement and constant cement (Avseth et al., 2005) have been discussed. 2.1. Friable Sand/Shale Model The model was introduced by Dvorkin and Nur in 1996.This model for unconsolidated sediments assumes porosity reduction from the initial sand pack value (Critical porosity) due to the deposition of solid matter away from the grain contacts that result in gradual stiffening of the rock. This porosity reduction for clean sandstone is caused by depositional sorting (follows sorting trend shown in Fig. 1) and packing. Clarke (1979)analyzed the effect of grain sorting on reservoir properties and concluded that, in some clastic rocks, reservoir quality is affected more by bimodality than by diagenesis (Dvorkin and Gutierrez, 2001). 2.2. Contact cement model During burial, sands are likely to become cemented sandstones. The cement may be diagenetic quartz, calcite or other minerals. The contact
Fig. 1. Schematic depiction of bounds. Sediments are initially deposited along the lower bound and then sorting-compaction-digenesis starts after critical porosity. Lines for contact models (diagenetic) and friable trend (sorting) are shown. (After Mavko et al., 1998.)
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2.3. Constant cement model The model (Avseth, 2000) is a combination of the friable-sand/shale model and the contact cement model. It assumes that the sands of varying porosity all have the same amount of contact cement, and porosity reduction is solely due to non-contact pore filling materials. Porosity initially decreases from critical limit, Φ0 to Φb due to cementation. From Φb, porosity decreases as in the case of friable sand model. There is deposition of solid phase away from the grain contacts.
3. Case study
Fig. 2. Geographical map of India showing approximate locations of study wells in East Coast of India. Table of well names (pseudo names) and overburden thickness have been shown. (Image source: www.google.co.in/maps.)
cement model (Dvorkin and Nur, 1996) describes the porosity reduction from initial sand pack due to uniform deposition of cement layers on the surface of grains. This drastically increase stiffness of the sandstone by reinforcing the grain contact.
The wells used in this case study are located in three different sedimentary basins on eastern continental margin of India (ECMI) (Fig. 2). In this study we have considered five wells drilled in different depositional setups. Wells from three basins namely; Mahanadi, K-G and Cauvery have been showcased. All the reservoirs are clastic with dominance of quartz, K-Feldspar and Plagioclase Feldspar. Well A and B are located in shallow water of Mahanadi basin, Well C and D are in deep-water K-G basin. Although all the four reservoirs encountered in Wells A, B, C and D are in Tertiary, there is variability in sediment thickness over the reservoir ranging 1300 m to 2400 m (Fig. 2). This leads to differences in quantum of compaction or diagenesis in the reservoirs. Well E is located in deep-water Cauvery basin where the overburden sedimentary thickness is 2641 m. The reservoirs are in Cretaceous age and there is significant presence of diagenetic cementations. In this case study, few shallow channels or delta bar reservoir has been interpreted as friable
Fig. 3. Vp-Porosity cross-plot for four different depositional environments. Depositional (dashed pink) and diagenetic trends (dashed blue) are put in the cross plots. Q indicates sand points and C indicates clay rich points. (a) Fluvial deposits of the Miocene Guayabo Formation from the Llanos Basin, Colombia (both dispersed and laminated lithofacies). (b) Corresponds to Miocene, mud-rich, deep-water deposits from offshore West Africa (laminated lithofacies). (c) Shows sand-rich deep-water deposits from offshore Gulf of Mexico (blocky lithofacies) and (d) illustrates a shallow marine deposits from the Miocene Leon Formation, in the Llanos Basin (Colombia) which shows the predominance of dispersed sand-clay mixtures and occurrence of some subordinate laminated lithofacies. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (Modified after Mauricio, 2005.)
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Fig. 4. Thomas-Stieber plots of Well A. Laminated shale percentage lines are shown in deep blue lines. Data are colored in volume of shale percentage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (Shale, clean sand, laminated and dispersed cartoon courtesy: after Yared et al., 2010.)
sand deposition which follows depositional trend. Depositional environment has been studied through log responses and other geological data (Unpublished Reliance Internal project report). For deeper channel, lobe or turbidite system, there are presence of diagenetic cements and it follows diagenetic trend. Fig. 3 depicts velocity-porosity cross plots (Mauricio, 2005) of different depositional setups (shallow marine, fluvial, deep water etc.) which showcases examples from various basins. In our case study, similar trend of the data has been observed and interpreted for different depositional setups. To avoid the effect of fluid on elastic
properties, fluid substitution (Gassmann, 1951) of the logs to 100% brine was performed by methodology as suggested by Dejtrakulwong and Mavko (2011). It is worth mentioning that most of the reservoirs studied in this paper are hydrocarbon saturated with minimum water saturation in the range of 15–20%. Gamma ray, compressional and shear velocity, density, resistivity, neutron logs were available for all the reservoirs. Total porosity was calculated from neutron-density logs. Volume of shale (Vsh) logs were generated from gamma ray and sigma logs. Brine substituted points are plotted in velocity- porosity crossplot and
Fig. 5. Velocity-Porosity plot of Well A and Well B. Rock physics model lines (Friable sand in dashed pink with varying in fraction of shale from 0.0–0.7 and friable shale in dashed pink) are shown. Zones plotted are marked in red in volume of shale (Vsh) logs for individual wells. Red arrow shows the direction of increase in effective stress. Modified H/S Upper bound for this combination of minerals has been shown in green dotted line. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 6. Thomas-Stieber plots of Well C. Laminated shale percentage lines are shown in deep blue lines. Data are colored in volume of shale percentage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (After Yared et al., 2010.)
the cement model lines have been established using the parameters (percentage of cement, minerals, fluid type, effective stress) from petrographic report, X-ray diffraction report, modular dynamic tester, formation evaluation report. We have referred Fig. 3 as an analogy for various trends in velocity-porosity crossplot where Fig. 3(a) represents fluvial deposits of the Miocene Guayabo Formation from the Llanos Basin, Colombia (both dispersed and laminated lithofacies). Fig. 3(b) represents Miocene mud-rich, deep-water deposits from offshore West Africa (laminated lithofacies). Fig. 3(c) represents sand-rich deep-water deposits from offshore Gulf of Mexico (blocky lithofacies) and Fig. 3(d) illustrates a shallow marine deposit from the Miocene
Leon Formation, in the Llanos Basin (Colombia) which shows the predominance of dispersed sand-clay mixtures and occurrence of some subordinate laminated lithofacies. Our data trend is then compared with one of the crossplots in the figure with corresponding geologic setup. 4. Shallow marine delta bar deposits Reservoirs of Well A and Well B (marked in red rectangle in volume of shale log in Fig. 5) in Mahanadi basin were deposited in shelf as deltaic bar (Unpublished Reliance Internal report). T-S plot of Well A shows dominant laminated trend (in transition from clean sand to shale) in
Fig. 7. Velocity-Porosity plot of Well C. Rock physics model lines (Constant cement model with 3% quartz cement shown in dashed blue with varying in of shale from 0.0–0.7 and friable shale line in dashed pink) are shown. The reservoir zone plotted are shown in red marker in volume of shale log of Well C. Red arrow shows the direction of grain size uniformity and arrow in magenta represents the direction of poor sorting. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. This trend is also expected in Well B which are evident in other plot (Fig. 5). In velocity-porosity plot as shown in Fig. 5, there is slightly decrease in velocity with decrease in porosity. This is probably the result of clean sands have higher velocity compared to shaly sand. This represents largely laminated shaly-sand reservoir for both of Wells A and B in Fig. 5. The trend of laminated shaly-sand reservoir is analogous to the trend of deep-water laminated reservoir of offshore West Africa as shown in Fig. 3(b). Although the lithology of deep-water reservoir of West Africa is mud rich, but the trend of this laminated reservoir in velocity-porosity plane is similar to the trend as observed in Wells A and B in Fig. 5. In elastic space, the data points follow depositional trend. Well A is having higher effective stress acting on the reservoir compared to the effective stress on reservoir in Well B. The reason is sediment thickness
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above the reservoir in Well A is higher than that of Well B (Fig. 2). The direction of change in effective stress in rock physics cross-plot has been shown in red arrow in this figure (Zimmer, 2003).Green arrow shows the laminated trend and arrow in black depicts the depositional trend. Rock velocities are directly proportional to sediment thickness above the reservoir. More overburden translates into higher rock velocity. Accordingly, the point in the crossplot will move along the red arrow. 5. Deep water slope fan lobe deposits Reservoirs belonging to Well C and D in K-G basin were deposited as fan lobe in slope set up. These wells are positioned in different lobes and have different types of reservoirs in terms of lamination. The stacking
Fig. 8. (a) velocity-porosity plot of Well D. Rock physics model lines (constant cement model with 3% quartz cement shown in dashed blue with varying in fraction of shale from 0.0–0.7) are shown. Reservoir zone plotted are marked in red in volume of shale log of Well D. (b) Thin section petrography image of core within the reservoir does show presence of quartz overgrowth (represented as Qo in cross section and SEM image) which is exclusively shown in SEM image in BW (Inset 1). Blue colored areas indicate primary porosity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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patterns depend on the geological parameters like proximity or distance from axis of the lobe. Well C contains mostly amalgamated blocky sandstone reservoir whereas reservoir in Well D is laminated. T-S plot for reservoir (marked in red rectangle in Vsh log in Fig. 7) in Well C has been shown in Fig. 6. We notice less number of data points in transition zone (shaly-sand) in T-S plot for Well C as compared to T-S plot for Well A (Fig. 4) as Well C reservoir is blocky. In rock physics crossplot (Fig. 7 and 8a), the data clusters for Well C are analogous to sand-rich deep-water deposits from offshore Gulf of Mexico shown in Fig. 3(c). We have performed rock physics modeling using constant cement model. Shales for the reservoirs are same in character and can be modeled with friable shale model. Sands are interpreted with constant cement model where especially Well D shows locally dispersed trend with increase in velocity with minimum increase in porosity (Fig. 8a). The dispersed trend (inverted ‘V’ shaped in velocity-porosity plane) for Well D is similar to the trend of dispersed Miocene Leon Formation shown in Fig. 3(d). Probably, Well C is located along the axis of lobe and hence the reservoir has higher net-to-gross compared to Well D (Unpublished Reliance Internal project report). The cement is basically quartz cement as overgrowth and there are evidences of occasionally occurred calcareous cements. The timings of calcite and quartz cementation are different in geological time scale. Quartz cementation of quartz-rich sandstones initiates in association with smectite-to-illite transition in embedding shales (Avseth et al., 2009). Quartz cement is responsible for much of the porosity and permeability reduction in well sorted sandstones (Bjørlykke and Egeberg, 1993). The thin section petrography and scanning electron microscopy (SEM) images show imprints of quartz overgrowth (Fig. 8b). The magnified view of scanning electron microscopy image in inset shows poorly crystallized mixed-layerillite/smectite that occurs as coatings on grains and has impeded the development of quartz overgrowths (Qo). This was postdated by precipitation of well crystallized authigenic kaolinite (K) that is attached to the surfaces of grain sand quartz overgrowths.
for turbidite depositions, the largest grains in suspension will be concentrated near the bottom of the current and thus both the concentration of sediment in suspension and maximum grain size in suspension, decrease upwards from the bottom (Bjørlykke, 2010). But this type of ideal case can also be changed depending upon the type of turbidite or sediment gravity flow and the sediment support mechanism, viscosity, matrix strength, turbulence etc. what we have experienced in this basin. Well E reservoir consists of both blocky and laminated sequence. The sandstones contain secondary minerals assemblages that include pyrite, siderite, quartz overgrowths, calcite, kaolinite and glauconite. Calcite streaks mainly occur in blocky sand and their responses in elastic domain have been discussed later. T-S plot shown in Fig. 9 depicts laminated reservoir. The selected zone is near top of the reservoir marked red rectangle in Vsh log in Fig. 10(a) and it excludes the blocky sands which contains streaks. Fig. 10(a) shows velocity versus porosity plot of Well E which depicts the reservoir is mainly laminated. Fig. 10(b), displays thin section petrography image showing presence of calcareous sandstones within reservoir interval. The laminated trend is analogous to deep-water laminated reservoir of offshore West Africa which is shown in Fig. 3(b). Rock physics modeling has been carried out using constant cement model. In transition from sand to shale, velocity slightly decreases with decrease in porosity as seen in deepwater laminated reservoir in Fig. 3(b). Fig. 11 represents rock physics crossplot of blocky sand reservoir and the zone in marked with red rectangle in Vsh log in inset. Presence of calcite streaks are very common especially inblocky sand compared to zones characterized by laminated shale-sand sequence toward top of the reservoir. This type of cementation occurs due to the leaching of Plagioclase Feldspar within the reservoir. The data points follow diagenetic trend and modeled using constant cement. Calcareous streaks are having low porosity and higher moduli than the laminated reservoir points. These points can be identified in rock physics crossplots as outliers as far as contact model is concerned.
6. Deep-water canyon filled turbidities deposits 7. Conclusions Reservoirs in Well E located at Cauvery basin, were deposited as canyon filled turbidities in slope setup. Early diagenetic material present in the reservoir are mainly calcareous in nature. Generally,
Different depositional environments show significant changes in rock physical signature which has been captured in this study. T-S plot has
Fig. 9. Thomas-Stieber plots of Well E. Laminated shale percentage lines are shown in deep blue lines. Data are colored in volume of shale percentage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (After Yared et al., 2010.)
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Fig. 10. (a) velocity-porosity plot of Well E. Red marker in volume of shale (Vsh) log does represent zone plotted in crossplot. Rock physics model lines (constant cement model with 3.5% calcite cement shown in dotted blue with varying in fraction of shale from 0.0–0.7) are shown. (b) Thin section petrography image within the reservoir zone shows presence of calcareous sandstones. Blue colored areas indicate primary porosity. The pink stained mineral is Calcite labelled as Ca. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
been used to quantify laminated, structural or dispersed shale present in the reservoir. In rock physics crossplots, the data exhibits change in velocity with change in reservoir porosity. But, the reason of porosity reduction is sorting/depositional, diagenesis or combination of all; has been analyzed further. The depositional and diagenetic trends have been identified in velocity-porosity crossplot, where data have been interpreted using different rock physics models. Further these trends have been interpreted as laminated, blocky, calcareous and locally dispersed. In addition, the impact of effective stress on the data has been shown exclusively for non-diagenetic reservoir. On the basis of lamination or blocky nature of the reservoir, the rock physics crossplots have been integrated with depositional setups like position of the well in complete lobe setup or stacked patterns change along and away from the lobe axis. This represents the variability in geological factors controlling
elastic properties within the same or different setups. Behavior of elastic properties for different depositional setups at drilled location, coupled with elastic properties derived from seismic data will add significant values in risk assessment in undrilled location. Acknowledgement Authors are thankful to Reliance Industries Limited for giving necessary permission to publish this study. We would like to especially thank Mr. Neeraj Sinha, Mr. Ajoy Biswal, Dr. Lalaji Yadav and Dr. Nabarun Pal for their valuable inputs and comments in course of this study. Authors also appreciate entire Quantitative Interpretation Group, Sedimentology Group and Petrophysics Group for their proactive support in carrying out this work.
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Fig. 11. Velocity-porosity plot of Well E. Red marker in volume of shale (Vsh) log does represent zone plotted in crossplot. Rock physics model lines (constant cement model with 3.5% calcite cement shown in dotted blue with varying in volume of shale from 0.0–0.7) are shown. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Further reading Avseth, P., Mavko, G., Dvorkin, J., Rykkje, J., 2000. Rock Physics diagnostics of North Sea sands: link between microstructure and seismic properties. Geophys. Res. Lett. 27 (17):2761–2764. https://doi.org/10.1029/1999GL008468.