One of the major problems facing oilfield operations is the occurrence of scale. In oilfields the mixture of produced water and formation water is unavoidable as produced water can be used to enhance production through water re-injection. The occurrence of scale in oil wells may cause flow restriction resulting in production damage, emergency shutdowns, increased maintenance costs due to frequent work-overs and an overall decrease in the production efficiency. Formation water from field XYZ in the Niger Delta region was analyzed to predict the scaling tendency of the water using the General Solubility Index for Calcium carbonate. A scaling program Scale-Check© was written based on the Stiff-Davis predictive model, Langelier's predictive model and the General Solubility Index; these helped to calculate the scaling Index. Scale-Check© was used to predict the scale formation tendency of calcium carbonate scale for Well XY over three periods within the life of the well. Sensitivity analysis that showed the effect of temperature and pressure on carbonate scaling was also implemented. Scale-Check© presents an easy-to-use program for checking the scaling tendency of formation or produced water and hence putting in place proactive measures that go a long way to reduce the negative effects of scaling in the oilfield.

Gas hydrate deposition is one of the major Flow Assurance problems in petroleum production in the offshore environment. Therefore, is important to accurately predict hydrate formation conditions and avoid these conditions or propose a hydrate management plan. This study compares the effectiveness of Artificial Neural Network (ANN) for predicting hydrate formation temperature to the effectiveness of other hydrate temperature prediction correlations such as: Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation. The ANN was trained using 459 hydrate formation experimental data points from Katz chart and Wilcox et al chart. Pressure (P) and specific gravity (ϒ) were chosen as the inputs in the 4-layer network while temperature was the output. The data points were for gases of specific gravity of 0.5539, 0.6, 0.7, 0.8, 0.9 and 1.0. The experimental pressures considered were from 49 psia to 4000 psia. The Neural Network was built using an excel add-in tool, NEUROXL. ANN accurately predicted the experimental hydrate formation temperature with the regression coefficient greater than 0.98 for the different specific gravities considered. Moreso, the error analysis shows ANN performed better than Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation because it had the least Mean Absolute percentage error, MAPE (3.5) compared to the other correlations. ANN is a viable tool for hydrate prediction and the current model can be improved upon by including more experimental data in the ANN.