Cairn India operated Ravva Field is located offshore Krishna-Godavari (KG) Basin on the east coast of India. The field was discovered in 1987 and was brought into production in 1993 from the Middle Miocene sandstones. Oil production rate reached its plateau of ~50,000 bopd by 1999. The field produced at the plateau rate for ~ 9 years before starting to decline by the end of 2007. Two infill drilling campaigns were planned and executed in 2007 and 2011 to arrest the production decline and add incremental reserves to the field. New infill wells increased production for a short while but failed to arrest the prevailing declining trend. By the end of 2013, Ravva oil production dropped to ~21,000 bopd and was declining by 35–40% per year. Arresting the production decline by finding additional reserves in a mature waterflooded field which had already achieved ~49% oil recovery was a challenge with significant risks. Planning for additional infill drilling targeting areas of undrained oil required an integrated approach with adoption of advanced technologies to minimize risks. A 4-D monitor seismic survey was acquired in 2010 over the Ravva field and co-processed with a vintage baseline survey to identify areas of undrained oil. Separate processing of the 2010 high density 3-D survey provided improved seismic imaging of the subsurface architecture. A high resolution 3-D model was created by using sequence stratigraphic concepts, fault seal analysis studies, detailed property modeling through integration of seismic attributes, log and core data. An integrated subsurface study was initiated in 2011 to identify infill well opportunities in the field. A full cycle of integrated reservoir modeling from seismic to simulation was carried out using advanced technologies and workflows. The integrated study resulted in the identification of several infill opportunities as well as near field exploration and appraisal targets. The Ravva Phase-5 drilling campaign executed in 2014-15 comprising 8 infill, 1 water injector, and 2 exploration/appraisal wells was the most extensive infill campaign in the field's history. Well results from the drilling campaign have been very encouraging. The ten producers drilled in the campaign together added ~18,000 bopd to the field production and they are estimated to improve the field oil recovery by ~3%. By March 2015, when all new wells were brought online, Ravva field production had once again reached 28,000 bopd. The success in near field exploration and appraisal has extended the producing field boundary and has identified upsides that could support future drilling campaigns. The Ravva Phase-5 drilling campaign has showcased the first successful application of a 4-D OBC seismic project in India. New technologies such as 4-D seismic combined with integrated subsurface reservoir modeling can demonstrably arrest production decline in comparable mature oil fields and drive recovery factors closer to the technical limit.

Raageshwari Deep Gas (RDG) Field in the Southern part of Barmer Basin is a tight gas-condensate reservoir composed of a thick volcanic unit overlain by volcanogenically-derived clastic Fatehgarh formation. This tight reservoir hosts significant gas reserves and is being successfully exploited with the implementation of multi-stage hydraulic fracturing. For optimum hydraulic fracture stimulation, a clear understanding of the geomechanical properties of the reservoir and its seamless integration with petrophysical interpretation is of paramount importance to achieving long-term sustainable well performance. The key geomechanical factors in hydraulic fracturing of deep volcanic reservoirs form a niche subject as opposed to the widely published unconventional shale plays. This paper illustrates the workflow developed for construction of 1D-Geomechanical model in tight volcanics and its application for selecting perforation intervals and designing of frac jobs; its validation through diagnostic fluid injection, execution of hydraulic fracturing jobs and associated challenges. The one dimensional Geomechanical model integrates basic petrophysical logs, dipole sonic data, rock mechanical tests on core, processed image log data with break out analysis, regional tectonic history, existing natural fracture evidences and drilling data. Most importantly, the model is calibrated with field test data such as diagnostic fluid injectivity test (DFIT), step rate test (SRT) and mini-frac data. The workflow involves estimation of rock mechanical properties (Young's modulus, Poisson's ratio, uniaxial compressive strength) based on logs and calibration with core data and documented analogues. The next step is modelling of stresses in the field for identification of current stress regime. Integration of failure models with wellbore image data provides the understanding of maximum horizontal stress. Basic log data is used for estimation of over burden and pore pressure. Calibration of pore pressure is carried out from the DFIT data. The third step involves the assimilation of rock strength model with stress model to estimate minimum horizontal stress. In a geologically complex setting with multiple histories of tilting and faulting, tectonics plays an important role in the existing stresses. All these variables are captured and validated with field test data to construct a useful geomechanical model. As part of the recently concluded hydraulic-fracturing campaign, the 1D-Geomechanical model was successfully applied to identify approximately 125 fracture stages in 20 wells for multi-cluster hydro-fracturing in the field. An effective geomechanical model, along with petrophysical interpretation has proved to be helpful in enhancing recovery, improving frac success rate and ultimately, reducing cost on operations. The approach emphasizes the importance of continuous update of the model to deal with variation within the field area and heterogeneity in volcanic rocks.

Raageshwari Deep Gas (RDG) is a clastic-volcanic reservoir located in the southern Barmer basin, India. RDG is a tight retrograde gas-condensate reservoir of permeability in the range of 0.01-1 md with a condensate gas ratio (CGR) of ~65 stb/mmscf. RDG is composed of a poorly sorted sandstone interval (Fatehgarh formation) overlying low net-to-gross (NTG) stacked succession of thick cycles of volcanic units (Basalt and Felsic) of ~700m gross thickness at a depth of 2800 m. RDG field is being developed using pad-drilled deviated wells, with multi-stage hydraulic fractures. In tight gas fields, one of the major challenges is obtaining the right set of parameters to accurately forecast the estimated ultimate recovery (EUR) per well. EUR per well depends on fracture parameters such as fracture half-length (Xf), fracture height (Hf), fracture conductivity (Fc) and reservoir characteristics like matrix porosity (Φ), matrix permeability (k), net pay thickness (h), drainage area, reservoir pressure, reservoir fluid and operating conditions. EUR may be estimated using decline curve analysis (DCA), rate transient analysis (RTA), and reservoir simulation. DCA is the simplest method but has high uncertainty early in a well’s production history, reservoir simulation is complex and requires detailed reservoir characterisation. RTA is easier compared to reservoir simulation and gives reasonable estimations of fracture and reservoir parameters. Since RTA is performance based it provides continuous evolution of high confidence EUR, even with limited production history. To characterize tight fields, estimating kh of various layers through pressure transient analysis (PTA) requires long shut-in data. Thus PTA is generally only available for analysing early time effects (like fracture parameters). Thus, in low permeability reservoirs, RTA becomes preferred tool since it does not require shut-in data. RTA models and type curves generate non-unique solutions. Hence, integrating the petrophysical database with production logs, PTA results and RTA results is utilized to reduce uncertainty in k, h, Fc, and Xf. By utilizing all these data, the uncertainty in EUR estimation per well is reduced. These parameters are used as input for history matching to validate the interpretation and to optimize the RTA solutions. It was observed that history matches in RTA were improved when Fc and Xf from PTA were available. Flowing material balance (FMB) was then used to estimate drainage area, GIIP and EUR per well. This paper demonstrates the workflow to use PTA, RTA, production logs, and petrophysical data to obtain the right set of parameters to get high confidence in EUR per well. The finalized EUR per well for different well types can then be used for field development and deciding well spacing. Full field production forecasting based on RTA provides additional validation or an alternative to the estimates done through reservoir simulation.

Ravva is a mature field located offshore east coast of India with over 20 years of production history from Middle Miocene sandstone reservoirs. During the development phase of the field, Late Miocene (LM) sands were intersected in few wells at shallow depths. Due to the presence of more promising and critical zones below, these sands were not completed and fell above the production packer and behind the production casing. The marginal reserves in these sands did not justify workover operation to complete it. Rigless options were studied and a shut-in well was selected for implementation. In order to safely complete and access the bypassed shallow sands, a vessel based pumping operation was planned to place a cement packer in the tubing – casing annulus. The slurry was circulated into the production casing/tubing annulus through a circulation SSD installed above the production packer.The cement packer was thus placed across the zone of interest. Designing of the cement slurry was based on reservoir parameters and the setting time was optimized to prevent reversing of the slurry back into the tubing. The cement mix had a bonding agent to exhibit good metal-cement bonding providing prolonged endurance of the cement with the capability of holding the expected pressure differential. The cement packer emulated a production packer providing zonal isolation for the new completion. Size of the platform precluded spotting of the complete Coil Tubing spread on the platform. High cost prohibited catenary coil tubing operations. As an alternative, the pump spread was placed on a DP vessel and the coil tubing unit was spotted on the platform. A high pressure hose running up from the vessel to a standpipe on the main deck of the platform formed the main conduit for fluids being pumped from the vessel into the CT or wells. Onsite mixing of the cement slurry on the vessel required detailed planning and execution. The well was activated & tested at rates of 1500 bopd with no integrity concerns till date. This paper will emphasize on the operational procedure and challenges of successfully completing the zone and bringing the shut-in well online. The execution of this operation was done at 1/10th of the cost of a rig based workover. This has also opened up new opportunities to access similar bypassed reserves resulting in incremental production from reservoirs which would have otherwise been left untapped.

The Raageshwari Deep Gas Field (RDG), Barmer Basin, India, is a thick (~900m gross), low permeability (0.01-1md) gas condensate field (CGR: ~70 STB/MMscf). The pay-zone consists of a poorly sorted sandstone interval on top of stacked succession of volcanic lava flow cycles. The field is being developed through multi-stage hydraulic fracture stimulations to allow production at economical rates. Recently two hydraulic fracturing campaigns comprising of 125 stages in 20 wells were successfully completed which have resulted in significant improvement in well productivity. This paper highlights the integrated workflow applied in a volcanic reservoir to reduce subsurface uncertainty and value addition in optimizing hydraulic fracturing operation. To account for the complexity associated with volcanic rocks, a fit for purpose integrated petrophysical and geomechanical workflow was developed using a basic wireline logs, processed NMR, dipole sonic and image log analysis. The model was calibrated with dynamic data especially diagnostic fracture injection test (DFIT), pressure build up and production logging tool (PLT) analysis. The final calibrated model was used to identify the net reservoir packets along the well length, which further helped to identify the optimum locations to create hydraulic fractures. A typical log motif generated using the integrated model is given in Figure-1. As seen on the generic log in Figure-1, net reservoir intervals are in multiple packets of varying thickness contained throughout the thick gross reservoir package. Connecting maximum net reservoir packets through hydraulic fractures is critical for maximizing the well productivity and EUR. The limited entry fracturing technique (Lagrone and Rasmussen, 1963) was used to connect the maximum net reservoir with a fewer number of stages and a lower cost. Extensive data acquisition (extended DFIT, formation wise testing, PVT sampling, PLT and pressure build-up) was carried out to increase reservoir understanding followed by detailed subsurface modeling. The extensive data acquisition programme was successful in reducing a number of uncertainties, including: reservoir information, effective fracture parameters and presence of natural fractures. The key outcomes from the two frac/data acquisition campaigns are as follows: The integrated petrophysical and geomechanical model enabled us to improve the net connected reservoir by 65% in the volcanic sectionsThe optimisation of the number of frac stages and their costAn improved subsurface understanding in well deliverability, frac conductivity, half-length and frac heightConfirmation of gas contribution from each perforation and the establishment of deeper gas which validated petrophysical payA new, more accurate, poro-perm transform calibrated with permeability estimation using DFIT and pressure build upA better subsurface understanding which supports higher estimates of GIIP and EUR