基于遗传算法优化双管掺水集油工艺参数

Process Parameter Optimization of Double-pipe Water-blending Oil Gathering Based on Genetic Algorithm

华北油田二连高寒地区某采油区采用双管掺水集油工艺,该工艺的热力损耗主要来源于掺水温度和每口单井的掺水量的调节,由于现场不能定量地判断掺水参数与其他参数之间的关系,导致回液温度往往高于流体凝点10～15℃,超出了经济运行成本。通过建立修正后的舒霍夫管道温降模型,确定掺水参数与其他参数的定量关系,再建立Beggs-Brill两相流压降模型确定边界条件,应用遗传算法优化,得到最优的掺水温度和各单井的掺水量,将某采油区热力日损耗降低了20%～25%。

In Erenhot Alpine Area of Huabei Oilfield, double- pipe water- blending oil gathering process is adopted. The thermal loss of the process mainly comes from the adjustment of water blending temperature and water- blending quantity of each single well. Because it is impossible to quantitatively judge the relationship between water- blending parameters and various parameters, the liquid return temperature is often 10 to 15℃ higher than the condensation point of fluid, which exceeds the economic operation cost. Through the establishment of the modified Schuhoff pipeline temperature drop model,the quantitative relationship between water-blending parameters and other parameters is determined. Then the Beggs- Brill two- phase flow pressure drop model is established to determine the boundary conditions. The genetic algorithm is used to get the optimal water- blending temperature and the water- blending quantity of each single well, and the daily thermal loss in an oil production area is reduced by 20% to 25%.