Novel Scaling Prediction Model for Gathering and Transportation Station in Changqing Oilfield

Abstract

Scaling is a significant challenge in oilfield production gathering and transportation stations, and it not only constrains the economic efficiency but also affects the development of oil and natural gas. This study proposes a scaling prediction model based on chemical experimental analysis and reservoir dynamic analysis methods for the gathering and transportation stations in the Changqing Oilfield. The objective of this study is to provide technical support for the oilfield to advance precise management and achieve cost reduction and efficiency enhancement. Initially, the water quality and scale samples of the oilfield were tested and analyzed using Inductively Coupled Plasma (ICP), Ion Chromatography (IC), and Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), and the distribution and patterns of scaling in the gathering and transportation pipelines were studied. Based on this, using the test data and the production liquid ratio of each development layer at the gathering and transportation stations, a reservoir dynamic correlation method was employed to construct a prediction model for the development layer with the highest similarity to the tested water samples at the stations and the types of scale samples. The results indicate that this prediction method can effectively reduce the scaling rate and provide guidance for the anti-scaling process in the Changqing Oilfield.