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Reservoir facies modeling using seismic derived pseudo Shale volume for improved dynamic simulation – A case study from Aishwariya Field, Rajasthan – Onshore Western India

Nabajyoti Boruah*, Sanjeet Kumar Singh, Chandra Mohan Rautela, Gourav Mukhopadhyay, Aditya Kumar Singh, Akhil Prabhakar, Abhishek Kumar Gupta, Vivek Shankar, Kondal Reddy, V Kothari, Sujoy Mukherjee

Cairn, Oil and Gas Vertical of Vedanta Limited.

*[email protected]

Summary The modeling of upper Fatehgarh reservoir of Aishwariya field, which is a mixture of fluvial and lake margin sand deposits, was challenging due to complex lateral heterogeneities. The vintage seismic data was reprocessed using Beam PSDM and other latest technologies that resulted in significant improvement of the subsurface image. The Acoustic Impedance volume obtained by prestack simultaneous seismic inversion provided good understanding of the gross sand deposition of the reservoir. Multi-attribute and neural network transforms of seismic attributes predicted a pseudo shale volume showing good correlation with the existing well data. The Vshale trends were used as a background secondary trend to populate the reservoir facies in the static model. The dynamic simulations of the static model corroborated field production behavior and exhibited significant improvement in the history matching when compared to earlier models.

Introduction The Aishwariya Field, discovered by Cairn in January 2004, is located in the northern part of Barmer Basin in India (Figure 1). The primary reservoir is the Fatehgarh formation which was deposited during early rift that created the Barmer Basin in the Late Cretaceous to Early Paleocene period (Compton et al., 2009). The Fatehgarh formation is divided into five major units (FA1-FA5). This paper focuses on reservoir characterization of the upper Fatehgarh reservoir (FA1 unit), which contains ~50% of the field stock tank oil initially in place (STOIIP). The FA1 reservoir was deposited in a fluvial-lacustrine environment and is composed of single storey and stacked multi-storey, low energy sinuous fluvial channel sands along with laterally extensive lake margin sands. The average net to gross (NTG) is about 35% in wells and varies significantly across the field with adjacent wells showing varying NTG as evident from core and log data. This poses a challenge to static modeling where the lateral distribution of net sand needs to effectively reflect reservoir and inter-well connectivity so that production behavior is matched during dynamic modeling. Effective reservoir management of the Aishwariya field requires creation of a sub-surface reservoir model with consistent integration of all available data. To meet the objective, we performed seismic inversion followed by multi-variate analysis to predict reservoir facies for seismic conditioning of the reservoir model. Methodology

• Reprocessing of Seismic data The Aishwariya Field is covered by 3D seismic data acquired in 2004, and processed through both PSTM and PSDM in 2010. The vintage PSDM data shows poor reflection coherence and continuity, and

Aishwariya Field

Figure 1: Location map of Aishwariya Field.

Seismic to Simulation- A case study from Aishwariya Field, Rajasthan, Western India

degraded imaging, particularly adjacent to the main trapping fault at the structural crest of the field. In 2014, it was decided to reprocess the seismic data, and an integrated study was undertaken to understand the causes of the poor vintage data quality. Key findings included the need to have an accurate near surface velocity model, and improved depth imaging. Accordingly, the 3D data was reprocessed in 2015 using advanced seismic processing techniques, such as 3D tomographic velocity modeling to characterize the near surface heterogeneity, 3D-noise attenuation, 5D interpolation and regularization, and imaging with Beam PSDM. Results show significant improvement in terms of structural imaging, event continuity and noise attenuation (Figure 2). In addition, preservation of amplitude and phase was validated with improved well-seismic ties compared to vintage data.

• Inversion and multi-variate attribute analysis for reservoir property prediction

Detecting and mapping lateral continuity of individual thin fluvial channel sands (~3-5m) of FA1 is difficult because these sands are below seismic resolution in conventional seismic data. Feasibility studies for pre-stack simultaneous inversion showed that acoustic impedance (AI) can be used to predict gross reservoir trends for the upper Fatehgarh FA1 unit. This unit shows a drop in acoustic impedance in areas of high NTG as sands have relatively lower impedance than shales. Model-based pre-stack simultaneous inversion was carried out using the reprocessed PSDM seismic data. The inverted AI volume captures the gross lateral heterogeneity of FA1 reservoir sands, validated by well data (Figure 3). The Fatehgarh Formation is divided in two major divisions. The Upper Fatehgarh consists of FA1 and non-reservoir FA2 and Lower Fatehgarh encompasses FA3-FA5. The multi-variate study targeted and was optimized only for FA1 Formation, the principal zone of interest for this study (Figure 3). A statistical approach that allows multi-attribute and multi-layer feed forward neural network (MLFN) analysis was used to predict shale volume (Vshale) property from seismic attributes including the inverted AI cube. The resultant pseudo Vshale, with higher resolution than AI and seismic data was used to delineate gross sand fairways for Upper Fatehgarh.

• Static Modeling Static modeling of fluvial reservoirs has always been challenging due to the nature of sand distribution patterns. Detailed knowledge of sand channel geometry, spatial distribution, and connectivity are essential to develop a model that describes fluid flow accurately, predicts the future performance reliably, and helps in decision making. The average well spacing in Aishwariya Field at FA1 level is about 150-200m. Channel widths vary according to individual channel thickness, with maximum channel width of around 150-200m.

Figure 2: Vintage (above) and reprocessed (below) versions of an inline section (depth migrated, stretched to time) showing primary stratigraphic horizons: Base Fatehgarh (green) and top Fatehgarh (yellow). Left panel: conventional Kirchhoff migrated 2010 PSDM, and right panel: Beam migrated 2015 PSDM data. Note the improvement in fault definition.


Due to limited core data availability and very little paleocurrent information, it is very difficult to interpret channel orientations. However, geophysical attributes and sand thickness maps have helped in delineating gross depositional directions and hence, they can be used as secondary guide in capturing details of the reservoir architecture and populating inter-well sand geometries in the current static modeling workflow. As a modeling workflow- the pseudo Vshale volume derived from seismic attribute analysis was depth converted and transformed into a point set with vertical spacing of 9m. The point data set was upscaled and populated in the modeling grid using Sequential Gaussian Simulation (SGS). Geostatistical simulations, defined by spatial variograms were used to reflect the spatial correlation and the small-scale variability, which are not captured in seismic data because of limited resolution (Mukerji et al., 2001). The derived Vshale logs were then compared with the actual Vshale logs for validation (Figure 4).The fine scale pseudo Vshale volume was taken as a ‘soft indicator’ and used as a background trend to populate the reservoir and non-reservoir facies between the wells of FA-1 (Figure 5). The Vshale values were directly used as non-reservoir probabilities and conversely (1-Vshale) values as reservoir probabilities. The facies were populated adopting a pixel based approach using Sequential Indicator simulation (SIS).

• Dynamic Modeling results The static model is being used for simulation and history matching at well and field level. As per simulation results, the model reasonably replicates field dynamic behavior. Inter-well connectivity has improved significantly as compared to earlier static model versions which were modeled using well-derived NTG trends only (Figure 6). The model corroborates the field dynamic and production behavior of upper Fatehgarh to a large extent.

Figure 3: Seismic sections through wells A-17, 33 and 34 at top Fatehgarh level showing Vsand logs in purple color, and the FA1 focus interval highlighted. Top (a): Beam PSD; Middle (b): Prestack inverted AI; Bottom (c): Neural network predicted Vshale. The A-17 well was used (with other calibration wells) to predict Vshale whereas A-33 and A-34 were kept as blind wells for validation. The predicted Vshale captures the anomalous thickness variation between wells A-17 and A-34 (see filtered Vsand logs posted along the corresponding well paths in purple).


Conclusions: Customized seismic reprocessing greatly improved the image quality at the structural crest of Aishwariya field in terms of reflection coherence and continuity. Preservation of amplitude and phase was validated with improved well-seismic ties. Multi-variate, statistical and neural network based analysis of correlation of seismic attributes with reservoir properties provided property-prediction transforms

that enabled capturing of lateral variations of the FA1 reservoir unit. The seismic-guided reservoir model improved modeling of inter-well connectivity, and generated a better history match than prior versions of reservoir models that used only well data. This integrated workflow to build a robust reservoir model is proving its value in optimized reservoir management.

Figure 4: Comparison of predicted Vshale logs (right) and actual Vshale logs (left) after SGS.

Figure 5: Seismic-derived Vshale volume was integrated in the static modeling workflow

as a probability cube for populating facies in the reservoir model.


Acknowledgement: The authors would like to thank our JV partner ONGC, Aishwariya Subsurface team members, and Cairn Geophysical and Geological Function teams for their valuable support during the course of this work.

References: Compton, P., 2009, The geology of the Barmer Basin, Rajasthan, India, and the origins of its major oil reservoir, the Fatehgarh Formation: Petroleum Geoscience, Vol. 15, no. 2, 117-130. Mukerji, T., Avseth, P., Mavko, G., Takahashi, I., Gonzalez, F., E., 2001, Statistical rock physics: Combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization: The Leading Edge, Vol. 20, no. 3, 313-319.

(a) (b) Figure 6: Water cut history match of Upper Fatehgarh comparisons between previous and current model. (a)

Field Plot; (b) Plot for well A-75

reservoir facies modeling using seismic derived pseudo shale ......cairn, oil and gas vertical of...

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