Research on prediction model of scaling in ASP flooding based on data mining

Abstract

As a result of alkali ASP flooding in oil and gas fields, strata and pipelines become seriously scaled, which poses a threat to the normal operation of crude oil production. We propose an intelligent knowledge reasoning model for dynamic scaling prediction in order to address the problems of high directivity, poor generalization ability, and poor application effect of existing scaling prediction methods. The model framework includes the knowledge acquisition layer which mainly relates to the manual acquisition of scaling prediction knowledge and the intelligent training of the knowledge base, and it includes the knowledge modeling layer that provides a set of standard domain common ontology and knowledge organization system using the ontology modeling technology, it also includes the knowledge inference layer which is the application layer of the model. The three layers collaborate and finally complete the scaling prediction through inference and expression. A total of 238 wells were selected for experimentation in the northern development area of the Xingshugang Oilfield. Experimental results indicate that the model has the highest accuracy of 91.87%. Additionally, the time series prediction trend for the six ions matches the trend of change in ion concentration in the scaling state, verifying the accuracy of the model’s predictions.