Title page for ETD etd-0927102-125908


Type of Document Master's Thesis
Author Yue, Junpeng
Author's Email Address jyue1@lsu.edu
URN etd-0927102-125908
Title Water-Drive Gas Reservoir: Sensitivity Analysis and Simplified Prediction
Degree Master of Science in Petroleum Engineering (M.S.P.E.)
Department Petroleum Engineering
Advisory Committee
Advisor Name Title
Christopher D. White Committee Chair
Andrew K. Wojtanowicz Committee Member
Zaki Bassiouni Committee Member
Keywords
  • gas recovery
  • aquifer productivity index
  • experimental design
  • response surface methodology
  • water-drive
  • gas production factor
  • sweep efficiency
Date of Defense 2002-09-25
Availability unrestricted
Abstract
Water influx and well completions affect recovery from water-drive gas reservoir. Material balance, aquifer models and well inflow equations are used to examine and predict the pressure depletion, water influx, and production rates of water-drive gas reservoirs. The parameters of these simple, lumped models are estimated from simulation results using response surfaces and experimental designs for eight varying geologic and engineering factors. Eleven simulated responses (including maximum gas rate, aquifer and well constants, and water breakthrough) are analyzed using ANOVA and response models.

A sensitivity analysis of aquifer productivity index, gas production factor, and sweep efficiency reveals that permeability is the dominating factor. In contrast to earlier investigations, this study indicates that water-drive gas recovery is often higher for higher permeability water-drive gas reservoirs. The high gas mobility more than offsets the high aquifer mobility. The other seven factors are statistically significant for many responses, but much less important in determining reservoir behavior.

The proposed approach combines simple analytic expressions with more complete but difficult-to-use reservoir simulation models. The response models can be used to make quick, accurate predictions of water-drive gas reservoirs that include the effects of changing geologic and engineering variables. These simple, approximate models are appropriate for prospect screening, sensitivity analysis and uncertainty analysis.

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