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Supporting technology for enhanced oil recovery: Chemical flood predictive model

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The Chemical Flood Predictive Model (CFPM) was developed by Scientific Software-Intercomp for the US Department of Energy and was used in the National Petroleum Council's (NPC) 1984 survey of US enhanced oil recovery potential (NPC, 1984). The CFPM models micellar (surfactant)-polymer (MP) floods in reservoirs which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option is available in the model which allows a rough estimate of oil recovery by caustic (alkaline) or caustic-polymer processes. This ''caustic''option, added for the NPC survey, is not modeled as a separate process. Rather, the caustic and caustic-polymer oil recoveries are computed simply as 15% and 40%, respectively, of the MP oil recovery. In the CFPM, an oil rate versus time function for a single pattern is computed and the results are passed to the economic routines. To estimate multi-pattern project behavior, a pattern development schedule must be specified. After-tax cash flow is computed by combining revenues with capital costs for drilling, conversion and upgrading of wells, chemical handling costs, fixed and variable operating costs, injectant costs, depreciation, royalties, severance, state, federal, and windfall profit taxes, cost and price inflation rates, and the discount rate. A lumped parameter uncertainty routine is used to estimate risk, and allows for variation in computed project performance within an 80% confidence interval. The CFPM uses theory and the results of numerical simulation to predict MP oil recovery in five-spot patterns. Oil-bank and surfactant breakthrough and project life are determined from fractional flow theory. A Koval-type factor, based on the Dykstra-Parsons (1950) coefficient, is used to account for the effects of reservoir heterogeneity on surfactant and oil bank velocities. 18 refs., 17 figs., 27 tabs.

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Last Updated September 29, 2016, 16:25 (EST)
Created September 29, 2016, 16:25 (EST)
Citation Ray, R.M. Munoz, J.D. ---- Roy Long, Supporting technology for enhanced oil recovery: Chemical flood predictive model, 2016-09-29, https://edx.netl.doe.gov/dataset/supporting-technology-for-enhanced-oil-recovery-chemical-flood-predictive-model
Netl Product yes
Poc Email Roy.long@netl.doe.gov
Point Of Contact Roy Long
Program Or Project KMD
Publication Date 1986-12-1