Correlation-Based Estimation of Condensate Yield from Downhole Fluid Gradient

Abstract

Evaluation of investment opportunities in gas developments requires comprehensive subsurface data analysis and integration to quantify both value potential and associated risks. A critical element in such evaluations is the estimation of condensate yield, expressed as the Condensate–Gas Ratio (CGR), which supports the determination of condensate initially in place (CIIP) and forecasted condensate recoveries under different development scenarios. Although gas volumes typically dominate in such systems, the associated condensate liquids often provide a significant value upside, particularly under Nigeria's favorable natural gas liquids (NGL) fiscal regimes. During due diligence on a new gas asset, neutron–density log responses exhibited ballooning behavior consistent with a gas phase. This interpretation was corroborated by Repeat Formation Tester (RFT) pressure gradients of less than 0.18 psi/ft, as well as by seismic attribute analysis that also indicated gas-bearing intervals. However, the absence of bottomhole or recombined surface PVT data and Drill Stem Test (DST) results posed a challenge for estimating the fluid's condensate yield, a critical parameter for project evaluation. To address this data gap, a comprehensive corporate database of retrograde gas reservoirs with laboratory PVT analyses and RFT/MDT fluid gradient data was utilized. Empirical correlations were developed by trending measured fluid gradients against known laboratory-determined CGRs. The derived correlation was subsequently applied to the new opportunity, using its measured gradients to estimate the CGRs for the respective reservoirs. The accuracy of the developed correlation was tested against MDT and PVT data from a condensate reservoir in a recently drilled well and the resulting CGR estimate lies within <5% variance from that measured in the lab. These estimates were then integrated into PVT correlations and dynamic material balance models to compute condensate initially in place and evaluate development scenarios. The correlation-based approach provided reliable CGR estimates in the absence of direct PVT measurements and delivered a significant fiscal uplift to the project's overall economics. The study demonstrates that gradient-based empirical correlations, when supported by robust internal datasets, can effectively reduce uncertainty in condensate yield estimation and enhance investment decision-making in gas and condensate projects.