The Raageshwari Deep Gas (RDG) Field, situated in the southern part of Barmer Basin, is a tight gascondensate reservoir comprising of Volcanics with basic lava flows (basalts) and stacked silicic pyroclastic flows (felsic) interbedded with basalts, and overlying clastic Fatehgarh Formation. The field is currently being developed using deviated wells with multi-stage hydraulic fracturing. The volcanic rocks pose a significant challenge in reservoir zone identification and trend prediction. Variability in mineralogy, lithofacies, thickness of reservoir subunits and areal distributions of pores/vesicles and fractures results in marked reservoir heterogeneity. This paper demonstrates a comprehensive facies characterization for pay zone identification, building a robust reservoir model and execution of multistage hydro-fracturing.

The facies characterization methodology integrates cores, mudlogs (gas shows and chromatographs), wireline logs, hydraulic fracturing and production data. Conventional (sand-shale) petrophysical workflows are not applicable to volcanic rocks that are fundamentally different in nature. Hence a new unconventional work flow was established and validated in pilot wells. It was evident that the key parameter to address would be permeability given the tight nature of the formation (micro-pores). An initial facies classification was conceptualized integrating basic suite of logs and core data. New learnings on well performance behavior were assimilated with NMR log data in further refining the facies model. Higher gas counts and higher productivity was found to be associated with higher NMR bins indicative of larger pores and hence better facies.

The pay zones identified based on refined facies model helped in optimizing hydraulic fracturing of around 100 zones in 15 wells in recently concluded Hydro-frac campaign. The pin pointing of better producible zones in an approximately 700 m thick volcanic package facilitated reduction in operational costs. Multiple perforations (clusters) were combined in each fracturing stage; injectivity of individual cluster was checked during mini-frac and post fracture temperature analysis resulting in an optimized hydro-frac job. Production logging was carried out to confirm contribution from stimulated intervals. It was observed that almost all fractured intervals were contributing to production validating the petrophysical work. Improved facies classification was also built into the reservoir model thus improving the property distribution and reservoir predictability away from the wellbore. This study facilitated in building a robust history matched reservoir simulation model for realistic production forecasting.

This case study from an unconventional volcanic reservoir emphasizes the importance of integrating different datasets, in unraveling reservoir complexity leading to increased confidence in effective reservoir management. The volcanic reservoirs pose a huge technical challenge for sustained production performance and reservoir management; calling for continuous upgrading of the facies model by aggregating data from hydro-fracturing and newly drilled wells.

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