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Volume 30 – Issue 5 – May 2012

Rock Physics & Formation Evaluation Special Topic

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special topicfirst break volume 30, May 2012

Rock Physics & Formation Evaluation

1 Oil India Limited2 Schlumberger Asia Services* Corresponding author, E-mail: [email protected]

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 Gupta2 and Surender Singh2 explain how the search for heavy oil in the Baghewala area, western India was pro-gressed 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 hydrocar-bons in sedimentary rocks of this age in India. This paper summarizes the results of rock physics modelling and simul-taneous inversion studies aimed at advancing that search in the Baghewala area.

Key challenges and study objectivesThe 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 reser-voir complexity and data quality issues.

Jodhpur Sands The Jodhpur sandstone is a complex hydrocarbon habitat that exhibits poor porosity (<10%), high fluid viscosity (spe-cific gravity 14-18˚ API), and significant vertical and lateral heterogeneity. Post-stack inversion studies had proved inad-equate 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, acous-tic 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.

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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 ith constituent, the volume fraction is given

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 CarbonatesThe 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 tar-geting the Bilara; all four were targeting the Jodhpur forma-tion. 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 heterogene-ity 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 explor-ing for heavy oil using seismic data.

Characterizing heavy oil bearing facies in the Baghewala areaInput data used for the initial study included four wells and 50 km2 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 modellingn Seismic conditioning and AVO modellingn Simultaneous inversionn Reservoir facies prediction

Rock physics modellingRock physics analysis and modelling was necessary to cal-culate 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 back-ground 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.




AVO modelling and inversion are highly sensitive to flat-tening 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 8–400, each with an 80 angle band, and applied NRM to reduce residual misalignment of major seismic reflection events through the five angle stacks.

by Xi and Ni represents the order of best fitting polynomial. aij are the corresponding regression coefficients. Vp and Vs

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 veloci-ties 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 modellingAVO 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 exces-sive noise, prestack migration gathers from previous studies were not suitable either for AVO analysis or prestack inver-sion. 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 off-sets

n Residual amplitude analysis and compensation (RAAC) in the offset domain to preserve relative amplitude while allowing data scaling

n Radon to remove multiplesn Random noise attenuation (RNA) in the offset domain to

remove random noisen Spatially continuous velocity analysis (SCVA) to refine

the velocity analysisn AVO angle decomposition for prestack inversionn Non-rigid matching (NRM) to align events on gathers

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.

Figure 5 Extensive conditioning processes made existing 3D seismic data suit-able for AVO analysis and prestack inversion.

FormationGreenberg-Castagna model coefficients

Coefficient Shale Sand Dolomite

Jodhpur a0 -1.00 -0.85588 -0.07775

a1 0.56 0.62000 0.60000

Bilara a0 -1.00 -0.85588 -0.07775

a1 0.50 0.62000 0.60000

Table 1 Parameters used for Greenberg-Castagna rock physics model.

Mineral Bulk Modulus

(GPa)

Shear Modulus

(GPa)

Bulk Density(g/cc)

Dolomite 94.9 45 2.87

Shale 22 10 2.62

Brine 2.7 0 1.01

Table 2 Elastic parameters for Bilara rock physics modeling using the Kuster-Toksoz equations.




Reservoir facies predictionThe inverted data was used to derive good and poor qual-ity 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 poros-ity >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 sur-rounding Baghewala A-1 – the original discovery well – near the crest of the structure, which is the most promising location for development of heavy oil.

UncertaintiesIn this initial study, rock physics modelling and simultaneous inversion included the Bilara carbonates. Because these car-bonates 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

Good well-seismic ties were obtained at the target inter-vals 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 syn-thetic 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 inversionThe 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.

Figure 6 Results of simultaneous inversion showed a good match between acoustic impedance, shear impedance, and measured log data.




Rock physics modellingWhile the Greenberg-Castagna rock physics model is appro-priate 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 con-stituent 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)

significantly greater in the Bilara than in the Jodhpur forma-tion due to lower porosity and permeability, and a larger over-lap 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 prop-erties in the Bilara carbonates, and identify a suitable strat-egy 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 carbonatesBoth 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 signifi-cantly with respect to small variations in pore geometry. To better explain these variations, a second rock physics mod-eling study was carried out.

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 delinea-tion of heavy oil-bearing sands.




n To determine saturated rock moduli the Gassmann meth-od (1951) was used to incorporate the fluid modulus in the rock.

ResultsInversion 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

(2)

where KKT and μKT are the bulk and shear moduli of effec-tive media and Km and μm are the bulk and shear moduli of minerals. Ki and μi and are the bulk and shear moduli of an inclusion xi and represents the volume fractions of inclusions. Pmi and Qmi 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 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 0.04.

Figure 10 Elastic moduli versus porosity for measured data (circles) and mod-eled data (lines). A and B show results for clean Bilara reservoir (vcl<0.15). C and D show results for entire Bilara formation.

Figure 11 Variations in Vp/Vs with porosity for different pore aspect ratios (left) and clay volumes (right).




AcknowledgmentsThe authors thank the management of Oil India Limited for their encouragement and support to carry out this work and permission to publish this paper.

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Natur Gesellshaft in Zuricj, 96, 1–23.

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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 conjunc-tion 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.

ConclusionsIn 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 dif-ferent 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.

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