Rock Physics & Formation Evaluation. Special Topic

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1 Volume 30 Issue 5 May 2012 Special Topic Technical Articles Dual representation of multiscale fracture network modelling for UAE carbonate field AVO and spectral decomposition for derisking Palaeogene prospects in UK North Sea EAGE News Link-up with UK Onshore Geophysical Library Countdown to Copenhagen Annual Meeting Workshop reports on Carbonates and Unconventionals

2 first break volume 30, May 2012 special topic Rock physics modelling and simultaneous inversion for heavy oil reservoirs: a case study in western India S.K. Basha, 1 Anup Kumar, 1 J.K. Borgohain, 1 Ranjit Shaw, 2 Mukesh Gupta 2 and Surender Singh 2 explain how the search for heavy oil in the Baghewala area, western India was progressed using rock physics modelling and simultaneous inversion. T he Bikaner-Nagaur basin is a Late Proterozoic- Cambrian basin located in Rajasthan, India (Figure 1). The Baghewala anticlinal structure in this basin was first identified using 2D seismic surveys in the late 1980s. In 1991, the first well drilled in the structure the Baghewala A-1 well resulted in the discovery of heavy oil in clastics of the early Cambrian Jodhpur formation, which is part of a 1500 m sedimentary cover overlying Precambrian volcanics and basement rock. This discovery has opened new opportunities for exploration in western India. Infra- Cambrian rocks have not been explored widely, despite proven hydrocarbon plays in such reservoirs elsewhere in the world. Recently Singh and Tewari (2011) have argued strongly in favour of a more aggressive search for hydrocarbons in sedimentary rocks of this age in India. This paper summarizes the results of rock physics modelling and simultaneous inversion studies aimed at advancing that search in the Baghewala area. Key challenges and study objectives The Baghewala anticline provides favourable conditions for the entrapment of oil migrating from shales interbedded with carbonates of the Bilara formation, which also provides the top seal for the Jodhpur reservoir (Figure 2). Six wells have been drilled on the structural high to date. Several have flowed oil of high viscosity from Jodhpur shaly sands. However, assessing the distribution of heavy oil within the reservoir has been particularly challenging due both to reservoir complexity and data quality issues. Jodhpur Sands The Jodhpur sandstone is a complex hydrocarbon habitat that exhibits poor porosity (<10%), high fluid viscosity (specific gravity API), and significant vertical and lateral heterogeneity. Post-stack inversion studies had proved inadequate for characterizing reservoir heterogeneities, primarily because no measured shear velocity (Vs) logs were available in any of the four wells in the study area. Although analysis of petrophysical data indicated that heavy oil occurs in sands of relatively higher porosity, acoustic impedance (AI) derived from post-stack inversion could not definitively map the distribution of those zones because AI values for oil sands overlapped those of brine sands. It was essential, therefore, to derive additional rock properties from prestack inversion shear impedance (SI) or the ratio of compressional velocity to shear velocity (Vp/Vs) to reduce uncertainties in mapping the lateral heterogeneity of seismic reservoir facies. In addition to lacking shear velocity data in the wells, existing 3D seismic data was of insufficient quality for Figure 1 The Baghewala heavy oil field is located in the Bikaner-Nagaur basin in Rajasthan, western India. 1 Oil India Limited 2 Schlumberger Asia Services * Corresponding author, Mgupta5@slb.com 2012 EAGE 69

3 special topic first break volume 30, May 2012 quantitative interpretation. The final gathers from previous processing were not optimal for prestack studies due both to excessive noise in the near offsets and to post-migration filtering processes that had failed to preserve necessary amplitudes. Bilara Carbonates The presence of heavy oil has also been detected in the overlying Bilara formation, which consists of a mixture of carbonates and shales. However, none of the wells were targeting the Bilara; all four were targeting the Jodhpur formation. Due to even lower porosity and permeability, the Bilara has not flowed heavy oil. Again, without shear velocity data in any of the wells, reservoir properties of oil-bearing Bilara carbonates could not be adequately characterized. To address the challenges of mapping and exploring for heavy oil in the Baghewala area, Oil India partnered with the Schlumberger Data & Consulting Services (DCS) team in Mumbai and WesternGeco to conduct prestack simultaneous inversion and rock physics modelling studies. Objectives for the initial study were to better understand lateral heterogeneity and delineate the limits of heavy oil within the Jodhpur and Bilara formations using reservoir properties derived from simultaneous inversion. Rock physics modelling was essential to predict shear data in all wells, based on shear measurements from two offset wells (Figure 3). Objectives of a subsequent study were to further refine the relationship between elastic and petrophysical properties in the Bilara carbonates, and identify a more suitable strategy for exploring for heavy oil using seismic data. Characterizing heavy oil bearing facies in the Baghewala area Input data used for the initial study included four wells and 50 km 2 of 3D seismic with interpreted horizons and faults. The following workflow provided rigorous in-depth analysis and interpretation of seismic and well data in the Baghewala area: n Rock physics modelling n Seismic conditioning and AVO modelling n Simultaneous inversion n Reservoir facies prediction Rock physics modelling Rock physics analysis and modelling was necessary to calculate shear sonic logs from petrophysical logs for prestack inversion. In quantitative reservoir characterization studies using simultaneous inversion, Vs logs are critical to estimate wavelets for multiple angle/offset stacks and to build a low frequency shear impedance or Vp/Vs model to serve as background for inversion. None of the four wells that penetrate the target formations had measured shear velocity data, and one well also lacked compressional velocity data. Multi-linear regression with resistivity, clay volume, and bulk density as Figure 2 Acoustic impedance showing relationship between Bilara carbonates and Jodhpur sandstones, and location of discovery well A-1. Figure 3 None of the four wells on the Baghewala structure had shear sonic logs. Rock physics modelling predict shear velocity based on nearby offset wells. independent variables were used to predict Vp for that well. Using Techlog petrophysical analysis software, a rock physics model was built using regression coefficients derived from measured Vp and Vs logs in a nearby offset well. The Greenberg-Castagna (1992) rock physics model predicts shear velocity in a sedimentary rock from measured compressional velocity and volume fractions of constituent lithologies (Table 1) using the following equation (Mavko et al., 1998): with the constraint: Where L is the number of pure mono-mineralic lithology constituents. For i th constituent, the volume fraction is given EAGE

4 first break volume 30, May 2012 special topic Figure 4 Measured logs (black) and predicted logs (red) in the two offset wells show a good match, enabling prediction of Vs in well A-1. by X i and N i represents the order of best fitting polynomial. a ij are the corresponding regression coefficients. V p and V s represent the P- and S-wave velocities (km/s), respectively, in composite brine-saturated multi-mineralic rock. Observed and predicted shear velocities in the Jodhpur and Bilara formations exhibited a good match in the offset well. Next, the consistency of the rock physics model was validated by predicting shear velocity in a second offset well that had existing shear sonic measurements. Again, observed and predicted Vs matched closely in both formations. Finally, the validated model was used to estimate shear wave velocities in the four study wells (Figure 4). This made it possible to calculate elastic properties, such as Vp/Vs, which were critical to distinguishing between brine- and oil-bearing reservoir facies. Seismic conditioning and AVO modelling AVO modelling was essential to quality control the gathers and evaluate the response of reservoir rock bodies extracted from inversion. However, due to amplitude issues and excessive noise, prestack migration gathers from previous studies were not suitable either for AVO analysis or prestack inversion. To compensate for these shortcomings, WesternGeco used the Omega seismic data processing software to apply a series of data preconditioning processes, including: n Anomalous amplitude attenuation (AAA) to remove anomalous high amplitude and noise bursts in near offsets n Residual amplitude analysis and compensation (RAAC) in the offset domain to preserve relative amplitude while allowing data scaling n Radon to remove multiples n Random noise attenuation (RNA) in the offset domain to remove random noise n Spatially continuous velocity analysis (SCVA) to refine the velocity analysis n AVO angle decomposition for prestack inversion n Non-rigid matching (NRM) to align events on gathers Figure 5 Extensive conditioning processes made existing 3D seismic data suitable for AVO analysis and prestack inversion. Formation Greenberg-Castagna model coefficients Coefficient Shale Sand Dolomite Jodhpur a a Bilara a a Table 1 Parameters used for Greenberg-Castagna rock physics model. Mineral Bulk Modulus (GPa) Shear Modulus (GPa) Bulk Density (g/cc) Dolomite Shale Brine Table 2 Elastic parameters for Bilara rock physics modeling using the Kuster- Toksoz equations. AVO modelling and inversion are highly sensitive to flattening or alignment of events from near to far offsets. Therefore, the NRM process a proprietary technique was particularly important to bring out weak AVO information in the study area. Five angle stacks were generated ranging from , each with an 8 0 angle band, and applied NRM to reduce residual misalignment of major seismic reflection events through the five angle stacks EAGE 71

5 special topic first break volume 30, May 2012 Good well-seismic ties were obtained at the target intervals in all four wells. A multi-well wavelet was estimated and found to be most representative of all wells in the study area. The estimated wavelet was used to generate synthetics for the offset and angle stack gather domains at each well location for AVO modelling. The Jodhpur sandstone overlaid by the Bilara formation exhibited class-iv AVO behaviour, a type controlled primarily by lithology. The AVO behaviours of synthetic and seismic gathers were quite similar, indicating that post-migration data conditioning and shear prediction from rock physics modelling were, in fact, consistent. As such, the data (Figure 5) were now suitable for simultaneous inversion. Simultaneous inversion The purpose of inversion is to reduce discrepancies between observed and modelled seismic data via an iterative process, in order to derive rock properties from the seismic signal. After preconditioning, the data were loaded into the ISIS suite of reservoir characterization technology inversion engine, which utilizes a global optimization algorithm with a non-linear cost function to simultaneously invert numerous input angles/offset stacks to an earth model. The inversion workflow began with conversion of well data from depth to time, and the use of calibrated wells and different angle stacks to derive wavelets. To guide and constrain the interpolation of reservoir properties during simultaneous inversion, a prior low frequency model was generated by laterally extrapolating filtered well log data, using seismic horizons and interval velocities as constraints. Then the inversion was executed, which produced three outputs: acoustic impedance, shear impedance, and density. The quality of the simultaneous inversion was reasonably good. Observed and inverted AI and SI exhibited a good match with measured logs (Figure 6) at all wells, and the density match was fair to good. Reservoir facies prediction The inverted data was used to derive good and poor quality reservoir facies and associated uncertainties over the Baghewala structure under a Bayesian framework (Sengupta and Bachrach, 2007). Both rock physics and AVO trends are dependent on lithology and fluid properties in particular, clay volume, effective porosity, and water saturation. Seismic facies analysis generates a spatial distribution of facies defined by these properties, along with sonic and density logs, and seismic response based on elastic attributes. Analysis of the Jodhpur and Bilara formations produced two sets of probability density functions (PDFs) to estimate clay volume and porosity. It was possible to distinguish good quality reservoir in the Jodhpur through statistical analysis of acoustic impedance and shear impedances (Figure 7). Good facies were defined by low AI and low SI, clay volume <20%, effective porosity >7%, and water saturation <90%. As a result, reservoir facies favouring heavy oil in the Jodhpur sandstone over the structure, along with associated uncertainties (Figure 8) were successfully delineated and mapped. This has provided a reliable platform for future studies and drilling decisions. In particular, higher porosities and cleaner rocks were found surrounding Baghewala A-1 the original discovery well near the crest of the structure, which is the most promising location for development of heavy oil. Uncertainties In this initial study, rock physics modelling and simultaneous inversion included the Bilara carbonates. Because these carbonates are relatively clean, the main influence on impedance variations was formation porosity. The crest of the structure showed a greater probability of hard carbonates. However, uncertainties associated with seismically-derived facies were Figure 6 Results of simultaneous inversion showed a good match between acoustic impedance, shear impedance, and measured log data EAGE

6 first break volume 30, May 2012 special topic Figure 7 Inverted data were used to distinguish good and poor quality reservoir facies. Good facies were characterized by low AI and low SI. Figure 8 Inverted impedance and reservoir facies maps of the Jodhpur formation enabled delineation of heavy oil-bearing sands. significantly greater in the Bilara than in the Jodhpur formation due to lower porosity and permeability, and a larger overlap of elastic properties between good, poor, and non-reservoir facies. For this reason, a subsequent study was conducted to refine the relationship between elastic and petrophysical properties in the Bilara carbonates, and identify a suitable strategy for exploring for heavy oil using seismic data. Optimal locations to develop heavy oil from the primary target the Jodhpur sandstone might not be the best locations to target heavy oil within the Bilara. Mapping high fracture densities in the Bilara carbonates Both mineral content and pore shape influence the elastic properties of reservoir rocks. The initial study showed that elastic moduli of very tight Bilara carbonates varied significantly with respect to small variations in pore geometry. To better explain these variations, a second rock physics modeling study was carried out. Rock physics modelling While the Greenberg-Castagna rock physics model is appropriate for clastics of the Jodhpur formation, it may not be the optimum model for carbonates. Differential effective medium (DEM) theories (Ruiz and Dvorkin, 2010) have been used successfully to model elastic properties of rocks that exhibit various pore geometries (Figure 9) and constituent mineral volumes. Assuming the Bilara formation is a mixture of recrystallized carbonates and shales with different embedded pore geometries, the decision was made to use a set of DEM equations from Kuster and Toksoz (1974), which have been used extensively to explain elastic properties of rocks at many depths (Xu and White, 1995). According to Kuster and Toksoz, the effective elastic properties of a medium with embedded inclusions of a specific geometrical shape can be written as (1) 2012 EAGE 73

7 special topic first break volume 30, May 2012 Figure 9 Conceptual model used for carbonates in the Bilara rock physics model assumed two types of pores: inter-granular (spherical) and fractures with aspect ratio (2) where K KT and μ KT are the bulk and shear moduli of effective media and K m and μ m are the bulk and shear moduli of minerals. K i and μ i and are the bulk and shear moduli of an inclusion x i and represents the volume fractions of inclusions. P mi and Q mi can be written as (Mavko et al., 1998) where,,, and a is the aspect ratio (ratio of minor to major axis) of elliptical inclusions. For spherical inclusions, these terms are given by, with To build the rock physics model, a wet Bilara carbonate was selected. It was composed of two different pore types: rounded, primarily inter-granular pores, and pores with an aspect ratio of approximately 0.04, representing fractures. The modelling workflow had three parts: n Reuss (1929) mixing method was used to incorporate grain properties of the carbonate and shale fractions. n Kuster-Toksoz equations were used to obtain effective elastic properties of the dry rock framework. Figure 10 Elastic moduli versus porosity for measured data (circles) and modeled data (lines). A and B show results for clean Bilara reservoir (vcl<0.15). C and D show results for entire Bilara formation. n To determine saturated rock moduli the Gassmann method (1951) was used to incorporate the fluid modulus in the rock. Results Inversion of measured sonic velocities determined the overall distribution of pore geometries of different aspect ratios in clean Bilara carbonate. As it turned out, fractures of low aspect ratio less than 0.04 were the dominant type. Modelling of various pore shapes and clay volumes showed that, for a specific porosity, as the aspect ratio of the fractures increased, bulk and shear modulii increased. By constrast, as clay volume increased, elastic modulii decreased. Also, both AI and SI decreased with both aspect ratio and clay volume (Figure 10). Estimating the ratio of compressional to shear velocity (Vp/Vs) in clean Bilara carbonate showed that Vp/Vs decreased with aspect ratio and increased with clay volume. As porosity increased, the difference increased (Figure 11). Thus the effects of fractures and clay can be discriminated using Vp/Vs with shaly carbonates having higher Vp/Vs, and clean, fractured reservoir having lower Vp/Vs. Finally, to determine the sensitivity of elastic properties to the presence of heavy oil Figure 11 Variations in Vp/Vs with porosity for different pore aspect ratios (left) and clay volumes (right) EAGE

8 first break volume 30, May 2012 special topic in the Bilara formation, the fluid modulus was included in the rock physics model. This showed that replacement of brine by heavy oil in the pores had an insignificant effect on elastic modulii. The primary findings of this second study, therefore, were that the elastic properties of Bilara carbonates are more sensitive to pore geometry than to fluid properties, that areas of high fracture density can be identified using AI in conjunction with Vp/Vs, and that mapping areas of high fracture density would serve as an effective strategy to explore for heavy oil in this formation. Conclusions In the initial rock physics modelling and inversion study, the primary challenge to overcome was the lack of measured shear velocity data in the four wells of the study area. Such data is essential for prestack simultaneous inversion. This limitation was overcome by predicting shear data using two nearby offset wells. Inverted data successfully identified and delineated the lateral extent of good reservoir facies, with associated uncertainties, in the Baghewala area. However, uncertainties in the Bilara carbonates were substantially greater than in the Jodhpur sandstones. Subsequent rock physics modelling of the Bilara formation alone using a different set of equations provided a viable method of locating heavy oil by mapping zones of higher fracture density. The results of the study were useful in proposing exploratory and development drilling locations for exploitation of heavy oil in Baghewala area. Acknowledgments The authors thank the management of Oil India Limited for their encouragement and support to carry out this work and permission to publish this paper. References Gassmann, F. [1951] Uber die Elastizitat poroser Medien. Vierteil Der Natur Gesellshaft in Zuricj, 96, Greenberg, M.L. and Castagna, J.P. [1992] Shear-wave velocity estimation in porous rocks: Theoretical formulation, preliminary verification and application. Geophysical Prospecting, 40, Kuster, G.T. and Toksoz, M.N. [1974] Velocity and attenuation of seismic waves in two-phase media. Geophysics, 39, Mavko, G., Mukhergi, T. and Dovorkin, J. [1998] The Rock Physics Handbook. Cambridge University Press. Reuss, A. [1929] Berechnung der Fliessgrenzen von Mischkridtallen auf Grund der Plastizitatsbedingung fur Einkristalle fur Angewandte. Mathematik und Mechanik, 9, Ruiz, F. and Dvorkin, J. [2010] Predicting elasticity in non clastic rocks with differential effective medium model. Geophysics, 75, E41 E53. Sengupta, M. and Bachrach, R. [2007] Uncertainty in seismic-based pay volume estimation: Analysis using rock physics and Bayesian statistics. The Leading Edge, 26, Singh, A.K. and Tewari, P.K. [2011] InfraCambrian hydrocarbon systems and emerging hydrocarbon potential in Bikaner-Naguar and Jaisalmer basins (Miajlar sub basin) of Rajasthan. GeoIndia, 2 nd South Asian Geoscience Conference and Exhibition. Xu, S., and White, R.E. [1995] A new velocity model for clay-sand mixtures. Geophysical Prospecting, 43, EAGE 75