Abstract

Material-balance (MB) analysis for in-place volume estimation in gas reservoirs has been in practice for decades. Nonlinear responses from geopressure reservoirs with or without aquifer influx present special interpretation challenges. One of the main challenges of in-place volume estimates involves the estimation of average-reservoir pressure with production. To that end, modern pressure sensors installed at bottomhole and/or surface largely help establish a given well's dynamic performance by way of rate-transient analysis.

This paper explores the applicability and limitations of the standard analytical tools in volumetric, geopressure, and waterdrive systems for a diverse array of fluids, from dry gas to near-critical gas/condensate. The systematic approach presented in this paper attempts to increase accuracy in results by ensuring consistency in solutions from multiple methods used to first assess the average-reservoir pressure from production performance data, followed by in-place volume estimation. In this context, we examined analytical tools, such as the pav/z vs. cumulative gas production (Gp) plot, and cumulative reservoir voidage vs. cumulative total expansion plot. Both pot aquifer and unsteady-state Carter-Tracy aquifer models were considered to account for water influx. Besides the use of Cole and drive indices plots, two diagnostic log-log plots are introduced involving total expansivity and change in average-reservoir pressure. In addition, we sought solution objectivity by introducing a diagnostic tool in the Walsh and Yildiz-McEwen MB plots. Both MB methods involve plotting of cumulative reservoir voidage (F) vs. cumulative total expansion (Et), whereas the diagnostic tool consists of plotting F/Et vs. Et on the same graph.

Initially, synthetic data helped us understand the overall system behavior and instilled confidence in the solutions obtained for various combinations of drive mechanisms. Statistical design of experiments prompted us to explore independent variables, such as aquifer-to-hydrocarbon PV ratio, production rate, degree of overpressure, and the aquifer source. Those learnings were validated with published and new field data encompassing an array of reservoirs with various drive mechanisms and fluid type.

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