چكيده به لاتين
Thermal recovery is one of the most common enhanced oil recovery (EOR) techniques in heavy oil reservoirs. Fast-SAGD technique is a novel SAGD approach in which offset wells are drilled for periodic injection and production which reduces the costs of SAGD pair injection-production wells. In addition, this approach results in more production in a shorter period of time. Since production from heavy oil reservoirs is becoming more and more important, this study focused on thermal recovery of some heavy oil fields in Iran. Optimization of this process was performed using various techniques including genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). The objective function was selected as a combination of recovery factor (RF) and cumulative steam to oil ratio (CSOR). One-cycle continuous steam stimulation (CSS) process was selected for offset wells after comparison to two-cycle approach. In this study, effective parameters of Fast-SAGD process including injection pressure and rate, injection, production, and offset well heights, offset well pressure and its injection and production periods were studied. To speed-up the optimization process effective variables were converted to discrete values, instead of continuous ones. This study represents a novel supplementary technique implemented in optimization algorithms to increase the optimization speed, significantly. In this technique, effective parameters of the process were defined using sensitivity analysis. To discretize the selected variable, 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 6.33 times faster to the optimized point in comparison to discrete optimization procedure without RIA. This was 16.458 times faster in comparison to continuous optimization algorithm. Moreover using RIA led to termination of optimization algorithm 9.67 times faster than continuous mode.
Moreover, results were compared to previously optimization technique applied in this field. Results indicated 6.89% increase in recovery factor for the case of one injection and production cycle from the offset well and 6% recovery factor increase for the two-cycle case in comparison to the previous studies. Sensitivity analysis was performed to analyze the main effective parameters in this process. Then response surface methodology (RSM) was introduced to setup a mathematical basis for the effective variables and their interactions. Optimization results using analysis of variance (ANOVA) denoted that the value of injected steam and recovery factor were 10% and 6% less in comparison to genetic algorithm, respectively. This means that mathematical models could estimate somewhat the optimal conditions of the process.
