A novel optimization technique for Fast-SAGD process in a heterogeneous reservoir using discrete variables and repetition inhibitory algorithm

Abstract

One of the most prevalent methods for enhanced oil recovery in heavy oil reservoirs is thermal techniques. Fast-SAGD process is an advance method of enhance oil recovery in which offset wells are drilled for cyclic steam injection and production. This leads to increasing the production efficiency. Optimization of this process was performed using various techniques including genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). Combination of recovery factor and cumulative steam to oil ratio was introduced as the objective function. This study represents a novel supplementary technique implemented in optimization algorithms to increase its speed, significantly. In this technique, effective parameters were defined using sensitivity analysis. Then they were converted to discrete variables. To discretize the selected parameters, three different functions including logarithmic, square, and linear were applied using Minitab 18. Moreover, repetition inhibitory algorithm (RIA) was implemented in optimization algorithms for the first time to prevent recalculation of duplicate states in optimization for the next generation, and to speed-up the optimization process. The results of sensitivity analysis indicated that maximum and minimum effective parameters were attributed to Fast-SAGD production well height and soak time, respectively. Results indicated that among various optimization algorithms, GA worked 6% better in comparison to other optimization techniques and linear discretization function resulted in better optimized point in a shorter time. Results indicated that optimization process using discrete variables and repetition inhibitory algorithm led to the optimized point 6.33 times faster than discrete optimization procedure without RIA. This was 16.46 times faster in comparison to continuous optimization algorithm. Moreover using RIA led to termination of optimization algorithm 9.67 times faster than continuous mode.