Abstract
Sulphate and carbonate scale are the most important types of inorganic scales that can be observed in majority of reservoirs which encounter scaling problem during water injection.[8–9–10–11] Precipitation of these scales may ends to dramatic permeability reduction during water injection and due to their relative hardness and low solubility there are limited processes for their removal and prevention measures such as ‘squeeze’ inhibitor treatment has to be taken. In this regard, it is important to have a proper understanding of the kinetics of scale formation and trend of scale precipitation with considering the effective parameters on the phenomenon. This paper presents an experimental and theoretical study of calcium sulphate precipitation. A series of experiments were carried out to investigate the effect of different parameters on precipitation of calcium sulphate such as temperature, concentration of brine and flow rate. In addition, three different sets of experiments with the same procedure and different experimental condition were analyzed, compared and contrasted. The functional form of permeability reduction due to effect of different parameters in all experiments persuaded us to device a method to predict precipitation trend. Finally an artificial neural network was developed based on the data that obtained from experimental works. The network can predict permeability with considering effective parameters in each section of precipitation with very good accuracy and because of wide range of entrance data it can be considered as a reliable substitute for time/expense consuming experimental works in different range of time, flow rate, temperature and brine concentration.
