Sensitivity Analysis and Prediction of Gas Reservoirs Performance Supported by an Aquifer Based using Box-Behnken Design and Simulation Studies

Document Type : Research Paper


Petroleum Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran


Prediction of gas reservoir performance in some industrial cases requires costly and time-consuming simulation runs and a strong CPU must be involved in the simulation procedure. Many reservoir parameters conform to a strong aquifer behavior on gas reservoir performance. Effects of parameters, including reservoir permeability, aquifer permeability, initial reservoir pressure, brine water salinity, gas zone thickness, water zone thickness, temperature, tubing diameter, reservoir inclination, the effective intruding angle of the aquifer, and porosity were investigated using Tornado chart, and seven parameters were filtered. Response functions of aquifer productivity index, gas recovery factor, initial maximum gas production, water sweep efficiency, gas production rate, water breakthrough time, and water production were defined statistically, using Eclipse E100 and Box-Behnken design (BBD). According to the formulae generated by the BBD based on simulation runs, reservoir permeability, aquifer permeability, well-head pressure, and gas zone thickness are the most influencing parameters on the gas reservoir performance supported by the strong aquifer. The aquifer was found to be important especially due to its productivity index and sweep water efficiency. Validation of results given by the BBD through simulation runs showed response functions of aquifer productivity index, sweep water efficiency, maximum gas production, and recovery factor are of deviation percentages in the ranges of 10.61%, 6.302%, 3.958%, and 2.04%, respectively.


[1] Karami, S., A.H.S. Dehaghani, and S.A.H.S. Mousavi, Condensate blockage removal using Microwave and Ultrasonic waves: discussion on rock mechanical and electrical properties. Journal of Petroleum Science and Engineering, 2020. 193.
[2] Suzanne, K., et al. Distribution of trapped gas saturation in heterogeneous sandstone reservoirs. in Proceedings of the Annual Symposium of the Society of Core Analysts. 2011.
[3] Ahmed, T., reservoir engineering handbook. 2006: Elsevier.
[4] Telishev, A., et al. Hybrid Approach to Reservoir Modeling Based on Modern CPU and GPU Computational Platforms in SPE Russian Petroleum Technology Conference. 2017. Society of Petroleum Engineers.
[5] Li, Y., et al., New methodology for aquifer influx status classification for single wells in a gas reservoir with aquifer support. Journal of Natural Gas Geoscience, 2016. 1(5): p. 407-411.
[6] Huang, X., et al., Mathematical model study on the damage of the liquid phase to productivity in the gas reservoir with a bottom water zon. petroleum, 2018. 4(2): p. 209-214.
[7] Al-Hashim and J. Bass, Effect of aquifer size on the performance of partial waterdrive gas reservoirs. SPE reservoir engineering, 1988. 3(2): p. 380-386.
[8] Geffen, T., et al., Efficiency of gas displacement from porous media by liquid flooding. Journal of Petroleum Technology, 1952. 4(2): p. 29-38.
[9]. Saleh, S. A Model for Development and Analysis of Gas Reservoirs With Partial Water Drive. in SPE Annual Technical Conference and Exhibition. 1988.
[10] Armenta, M. and A. Wojtanowicz. Incremental recovery using dual-completed wells in gas reservoirs with bottom water drive: a feasibility study. in Canadian International Petroleum Conference. 2003.
[11] Li, M., H.R. Zhang, and W.J. Yang. Determination of the aquifer activity level and the recovery of water drive gas reservoir. in North Africa Technical Conference and Exhibition. 2010.
[12] Wang, J., et al. The Prediction Methods of the Water Influx Intensity of the Non-homogeneous Aquifer Gas Reservoir. in Fourth International Conference on Computational and Information Sciences (ICCIS). 2012.
[13] Tran, T.V., et al., A case study of gas-condensate reservoir performance under bottom water drive mechanism. Journal of Petroleum Exploration Production Technology, 2018: p. 1-17.
[14] Stright Jr, D., et al., Characterization of the Pliocene gas reservoir aquifers for predicting subsidence on the Ravenna coast. Petroleum Science Technology, 2008. 56(10-11): p. 1267-1281.
[15] Yu, Q., et al., Researches on calculation methods of aquifer influx for gas reservoirs with aquifer support. Journal of Petroleum Science and Engineering, 2019. 177: p. 889-898.
[16]. Kim, S., et al., Aquifer characterization of gas reservoirs using ensemble Kalman filter and covariance localization. Journal of Petroleum Science and Engineering, 2016. 146: p. 446-456.
[17] Yue, J., Water-drive gas reservoir: sensitivity analysis and simplified prediction. 2002.
[18] Lee, A.L., M.H. Gonzalez, and B.E. Eakin, The viscosity of natural gases. Journal of Petroleum Technology, 1966. 18(8): p. 997-1,000.
[19] Dranchuk, P., R. Purvis, and D. Robinson. Computer calculation of natural gas compressibility factors using the Standing and Katz correlation. in Annual Technical Meeting. 1973.
[20] Long, L.C. Experimental research on gas saturation behind the water front in gas reservoirs subjected to water drive. in 6th World Petroleum Congress. 1963.
[21] Myers, R.H., D.C. Montgomery, and C.M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments. 2016: John Wiley & Sons.