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Prediction of oilfield scale formation using artificial neural network (ANN)
Scale formation and deposition is a recurring problem in many oil producing fields leading to operational problems, problems in reservoirs, pumps, valves and topside facilities. Scale is described economically as a menace to an oil-field because its build-up clogs the flow lines and causes loss of m...
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2016-07
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/5335 | ||
| 042 | |a dc | ||
| 720 | |a Falode, O. A. |e author | ||
| 720 | |a Udomboso, C. |e author | ||
| 720 | |a Ebere, F. |e author | ||
| 260 | |c 2016-07 | ||
| 520 | |a Scale formation and deposition is a recurring problem in many oil producing fields leading to operational problems, problems in reservoirs, pumps, valves and topside facilities. Scale is described economically as a menace to an oil-field because its build-up clogs the flow lines and causes loss of millions of dollars yearly. The ability to predict the onset and amount of scale formation has been a major challenge in the oil industry. Previous models for predicting scale formation have focused mainly on thermodynamics and limited solubility data, and can predict only the potential or tendency to form scale. However, no studies have considered the influence of kinetic and transport factors. In this paper, a comprehensive and robust model incorporating other factors that have been ignored in past studies is developed using the technique of artificial neural network (ANN). Field data on two types of scale namely Barium and Calcium sulphate were obtained, processed, trained and tested with Artificial Neural Network. The model obtained was validated with actual data. Results show that at constant pressure, the neural network structure with optimum performance for BaSO(4) was ANN {1,2,1} with the lowest Mean Square Value (MSE) of 0.0025 and the highest correlation determination R(2) of 0.9966 while at constant temperature, it was ANN{1,1,1} with MSE of 0.0017 and R(2) of 0.9956. The neural network structure with optimum performance for CaSO4 precipitation kinetics with temperature and pressure was ANN{2,5,1} with MSE of 8.7745e-005 and R(2) of 0.8206 while at constant flow rate it was ANN{1,4,1} with MSE of 2.3007e-006 and R(2) of 0.9953. This gave a very close agreement with actual data in terms of prediction and performance. The results of this study therefore will greatly help to reduce the amount of risk incurred (such as NORM, etc.) due to the deposition and formation of scale in an oilfieldthe cost of stimulating an oil flow line and also improve the productivity of an oil well, hence, increase revenue to the oil industry. | ||
| 024 | 8 | |a 2348-0394 | |
| 024 | 8 | |a ui_art_falode_prediction_2016 | |
| 024 | 8 | |a Advances in Research 7(6), pp. 1-13 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/5335 | |
| 653 | |a Flow assurance | ||
| 653 | |a Scale | ||
| 653 | |a Artificial neural network | ||
| 653 | |a Oilfield | ||
| 653 | |a Modelling | ||
| 653 | |a Deposition | ||
| 245 | 0 | 0 | |a Prediction of oilfield scale formation using artificial neural network (ANN) |