Integrating Pressure Transient and Rate Transient Analysis for EUR Estimation in Tight Gas Volcanic Reservoirs

Abstract

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.