:Formation and deposition of scale in porous media due to extensive use of seawater for oil displacement and pressure maintenance is a problem that results in production decline and loss of billions of dollars to the petroleum industry yearly. A variety of models are presently being used in the oil industry for predicting scaling tendency and average scale precipitation inside the reservoir. In this work, the prediction of scale formation was done by developing a computer program using Excel, and reservoir parameters data were imputed into the programmed models to obtain results which were used in plotting graph to analyse what happen along the wellbore during production as a result of injection of seawater which is likely to pose scaling threat to the wellbore at any time interval. Findings from the results and graphs obtained proved that the major threat to scale formation along the well bore (sulphate scale precisely) is pressure drop across the skin, the skin factor and the pore volume of water injected with respect to the amount of the sulphate scale precipitated.
- Page(s): 01-06
- Date of Publication: 28 December 2020
- Madu, Clement O Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos
- Fadayini Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos
- N.A. FolamiDepartment of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos
- S.K. BelloDepartment of Mechanical Engineering, Lagos State Polytechnic Ikorodu, Lagos , Nigeria
References
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Madu, Clement O, Fadayini, N.A. Folami and S.K. Bello "Prediction of Scale Formation in Crude Oil Production along the Well-Bore As A Result of Incompatible Waters" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.9 issue 12, December 2020, pp.01-06 URL: www.ijltemas.in/DigitalLibrary/Vol.9Issue12/01-06.pdf
This work used the ANFIS (Adaptive Neuro-Fuzzy Inference System) as a framework to diagnose faults in the boiler section of a power plant. A backpropagation algorithm was used in modeling the ANFIS network. Industrial boiler data were obtained from the power plant, compiled using Excel, and the ANFIS network was then simulated using Artificial Neural Network Tool in MATLAB 2015a. A GUI (Graphical User Interface) was generated to easily interpret the fault results obtained. After the simulation, the ANFIS network was tested using the industrial data and using the Graphical User Interface, it was able to identify the size, root causes, and location of the faults and gave an explanation as to the corrective measures required for all the five faults that occurred in the power plant to be remedied. When the boiler was operating at its set point/standard value no fault was observed. Boiler overheating was experienced when the temperature of overheated steam and super-heated steam pressure increased from their points. Boiler feed pump failure occurred when there was a deviation from the setpoint value of the feedwater flow rate. The boiler plant used was that of the Egbin power plant in Lagos.
- Page(s): 07-11
- Date of Publication: 23 December 2020
- Madu, CDepartment of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos Nigeria.
- Fadayini, O.Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos Nigeria.
- Folami, N.A.Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos Nigeria.
- Ipaye, T. A Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos Nigeria.
- Ovowarie A. P Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos Nigeria.
References
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Madu, C*., Fadayini, O., Folami, N.A., Ipaye, T. A, and Ovowarie A. P "Use of Adaptive Neuro Fuzzy Inference System for Fault Diagnosis of Power Plant" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.9 issue 12, December 2020, pp.07-11 URL: www.ijltemas.in/DigitalLibrary/Vol.9Issue12/07-11.pdf