Application and variance based sensitivity analysis of surfactant-polymer flooding using modified chemical flood predictive model

Abstract We study the performance and behavior of surfactant–polymer (SP or micellar–polymer, MP) flooding enhanced oil recovery (EOR) using an analytical chemical flood predictive model (CFPM). The research has two parts based on the deterministic and the stochastic nature of the problem. In a deterministic study, the SP flood performance (ultimate recovery efficiency and oil-rate vs. time) of TORIS reservoir database (the Tertiary Oil Recovery Information System) was predicted using the modified CFPM. Results helped to determine the best candidates for SP flooding based on each reservoir's rock and fluid properties. From there we can determine the effect of different parameters (reservoir rock and fluid properties, injection design variables) on ultimate recovery efficiency and peak oil rate of an SP flood, which gives good clues about the sensitivity of output results to different input parameters. Stochastic study helps to recognize the behavior of the model under uncertain inputs. We used a variance-based sensitivity analysis (SA) method known as Winding Stairs (WS), which needs much fewer runs than traditional Monte-Carlo (MC) and Latin Hypercube methods. The results of the SA method facilitate identifying the most important sources of uncertainty of SP floods either through direct influences or interactions with other parameters. Based on these results we can reduce the uncertainty of output results of SP flood significantly by reducing the uncertainty of the input parameters that cause the largest uncertainty.

[1]  L. Lake,et al.  Enhanced Oil Recovery , 2017 .

[2]  M. Stein.  Large sample properties of simulations using latin hypercube sampling , 1987 .

[3]  Ngai Hang Chan.  Wiley Series in Probability and Statistics , 2010 .

[4]  I. Sobol.  On the Systematic Search in a Hypercube , 1979 .

[5]  Larry W. Lake,et al.  Field Applications of Capacitance Resistive Models in Waterfloods , 2008 .

[6]  Jarl P. Johnson,et al.  Predicting Waterflood Performance by the Graphical Representation of Porosity and Permeability Distribution , 1965 .

[7]  M. Jansen,et al.  Monte Carlo estimation of uncertainty contributions from several independent multivariate sources. , 1994 .

[8]  K. H. Coats,et al.  Prediction of polymer flood performance , 1971 .

[9]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[10]  Gary A. Pope,et al.  A Simplified Predictive Model for Micellar-Polymer Flooding , 1982 .

[11]  Larry W. Lake,et al.  Validation of a Modified Carman-Kozeny Equation To Model Two-Phase Relative Permeabilities , 1999 .

[12]  R. Seright,et al.  EOR Screening Criteria Revisited - Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Projects , 1997 .

[13]  Rory A. Fisher,et al.  The Arrangement of Field Experiments , 1992 .

[14]  L. W. Lake,et al.  Screening Estimation Of Recovery Efficiency And Chemical Requirements For Chemical Flooding , 1978 .

[15]  Frances Y. Kuo,et al.  Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator , 2003, TOMS.

[16]  Wm. E. Stiles,et al.  Use of Permeability Distribution in Water Flood Calculations , 1949 .

[17]  Larry W. Lake,et al.  A Simplified Predictive Model for CO2 Miscible Flooding , 1984 .

[18]  R. M. Giordano.  Estimating Field-Scale Micellar/Polymer Performance , 1987 .

[19]  E. Koval,et al.  A Method for Predicting the Performance of Unstable Miscible Displacement in Heterogeneous Media , 1963 .

[20]  Morteza Sayarpour,et al.  Development and application of capacitance-resistive models to water/CO₂ floods , 2008 .

[21]  Stefano Tarantola,et al.  Winding Stairs: A sampling tool to compute sensitivity indices , 2000, Stat. Comput..

[22]  A. W. Kemp,et al.  Univariate Discrete Distributions , 1993 .

[23]  L. Dake.  Fundamentals of Reservoir Engineering , 1983 .

[24]  S. Tezuka.  Uniform Random Numbers: Theory and Practice , 1995 .

[25]  G. D. Wyss,et al.  A user`s guide to LHS: Sandia`s Latin Hypercube Sampling Software , 1998 .

[26]  Larry W. Lake,et al.  Sensitivity Analyses of Production and Recovery Forecasts Using Variance-Based Methods , 2008 .

[27]  Andrea Saltelli,et al.  Sensitivity Analysis for Importance Assessment , 2002, Risk analysis : an official publication of the Society for Risk Analysis.

[28]  Jay D. Johnson,et al.  Competing Failure Risk Analysis Using Evidence Theory , 2005, Risk analysis : an official publication of the Society for Risk Analysis.

[29]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[30]  G.H.F. Gardner,et al.  Some Experiments on the Flow of Miscible Fluids of Unequal Density Through Porous Media , 1963 .

[31]  W. J. Whiten,et al.  Computational investigations of low-discrepancy sequences , 1997, TOMS.