Stocastic Modelling of Scaling Index, Fracturing and Parameters Performance of Produced Water Re-Injection in a Hydrocarbon Acquifer Field
A stochastic model has been developed to predict scaling index, fracturing and production rate parameters performance derived from field data of produced water reinjection scheme in a hydrocarbon reservoir field. Thus statistical models were derived from regression analysis, chi-square test and Monte Carlo simulation algorithms and applied to five wells in the Nigerian oil field to simulate reinjection performance based on certain stochastic criteria. The simulation results show that the effect of each input reinjection parameters on the scaling Index SI (output) such that when temperature is increased from 80oC to 189oC, the SI increase by say 0.1 while the next marker increase the pressure output to decrease by 0.1. Thus for a given pH, the SI increases as the temperature increase. Furthermore for each temperature, the SI decreases as the pressure increases and based on field data the regression statistics show R to be 0.998476685, R Square to be 0.99695569 and Adjusted R square is 0.919622802 and Standard error of 0.003468055 for the observations shows a strong agreement with field data.
