Research on time series forecasting method of ion concentration in produced fluid

Abstract

Abstract In the research on strong alkali‐surfactant‐polymer flooding scaling prediction, the variation characteristics of ion concentration in the produced fluid are obviously consistent with those of scale component content. Consequently, studying the variation tendency of ion concentration in produced fluid is helpful for further revealing the scaling tendency. Given the periodicity and chaos characteristics of the ion concentration data of the produced fluid, an echo state network (ESN) is used to realize the relevant time series forecasting. Simultaneously, to cope with the failure of the ESN in selecting suitable reservoir parameters according to different characteristics of time series, a modified discrete particle swarm optimization algorithm based on objective space decomposition is used to optimize the reservoir parameters. The experimental results indicate that the improved ESN presents the lowest error and is the closest to the target value.

Keywords

• echo state network (ESN)
• modified discrete particle swarm optimization (MDPSO)
• scaling prediction
• time series forecasting