In an offshore system, hydrocarbon fluids are produced at deeper depths in the oceans, and extended pipelines delivering fluids over long distances are common. Subsequently, these practices increase the tendency of unprocessed... more
In an offshore system, hydrocarbon fluids are produced at deeper depths in the oceans, and extended pipelines delivering fluids over long distances are common. Subsequently, these practices increase the tendency of unprocessed water-containing hydrocarbon fluid to be exposed to lower temperatures and higher pressures conditions where hydrate formation is favourable. One of the solutions to resolve this problem is by introducing hydrate inhibitors preferably low dosage hydrate inhibitors (LDHIs). The more versatile LDHIs; Kinetic Hydrate Inhibitors (KHIs) could further be optimised in cost and its biodegradation properties. The current study used an ionic, neutral, hydrophilic, mucoadhesive and highly branched Tamarindus indica L. polysaccharide (TSP). TSP as a new natural kinetic hydrate inhibitor due to its high methoxyl content. In this study, the polysaccharides were extracted using water-based extraction method which resulted in 61.3% yield. The performance of TSP in delaying th...
This project investigates characteristics of vibration of a drillstring under the action of weight on bit and drillstring rotation. The dominant cause of drillstring and bottom hole assembly's failures, shocks and severe damages to... more
This project investigates characteristics of vibration of a drillstring under the action of weight on bit and drillstring rotation. The dominant cause of drillstring and bottom hole assembly's failures, shocks and severe damages to borehole wall is recognized to be the lateral vibration. Thus, lateral vibration is chosen to be the only factor of interest and focused in this project. Lateral vibration manifests itself from the increased speed of rotary drilling. This study presents a finite element model using ANSYS software to investigate the lateral vibration of drillstring in a vertical well. The analysis proceeds in two stages. Firstly, modal analysis is performed to determine the natural frequencies of the drillstring and the second stage is to carry out harmonic analysis to obtain the frequency response at a varying length of drill pipe. Simulation is first carried out by simulating benchmark problem before proceeding to deal with the actual case studies by carrying out parametric study (drillstring length, weight on bit, rotational speed) and the results obtained are satisfying.
This project investigates lateral vibration of a drillstring under the action of weight on bit and drillstring rotation. The dominant cause of drillstring and bottom hole assembly’s failures, shocks and severe damages to borehole wall is... more
This project investigates lateral vibration of a drillstring under the action of weight on bit and drillstring rotation. The dominant cause of drillstring and bottom hole assembly’s failures, shocks and severe damages to borehole wall is recognized to be the lateral vibration. Thus, lateral vibration is chosen to be the only factor of interest and focused in this project. Lateral vibration manifests itself from the increased speed of rotary drilling. This study presents a finite element model using ANSYS software to investigate the lateral vibration of drillstring in a vertical well. The analysis proceeds in two stages. Firstly, modal analysis is performed to determine the natural frequencies of the drillstring and the second stage is to carry out harmonic analysis to obtain the frequency response at a varying length of drill pipe. Simulation is first carried out by simulating benchmark problem before proceeding to deal with the actual case studies by carrying out parametric study (...
In this work, two different artificial neural network (ANN) models — back-propagation neural network (BPN) and radial basis function neural network (RBFN) — are presented for the prediction of surface roughness in die sinking electrical... more
In this work, two different artificial neural network (ANN) models — back-propagation neural network (BPN) and radial basis function neural network (RBFN) — are presented for the prediction of surface roughness in die sinking electrical discharge machining (EDM). The pulse current (Ip), the pulse duration (Ton), and duty cycle (τ) are chosen as input variables with a constant voltage of 50 volt, and surface roughness is the output parameters of the model. A widespread series of EDM experiments was conducted on AISI D2 steel to acquire the data for training and testing and it was found that the neural models could predict the process performance with reasonable accuracy, under varying machining conditions. However, RBFN is faster than the BPNs and the BPN is reasonably more accurate. Moreover, they can be considered as valuable tools for EDM, by giving reliable predictions and provide a possible way to avoid time-and money-consuming experiments.
Abstract: In this analysis, the optimisation of multiple responses of Electric discharge machining (EDM) using Fuzzy logic coupled with Taguchi method is attempted. The work piece material was AISI P20 tool steel and a cylindrical copper... more
Abstract: In this analysis, the optimisation of multiple responses of Electric discharge machining (EDM) using Fuzzy logic coupled with Taguchi method is attempted. The work piece material was AISI P20 tool steel and a cylindrical copper electrode was used with side impulse flushing. The influence of machining parameters, ie, pulse current (Ip), pulse duration (Ton), work time (Tw), lift time (Tup) and Inter Electrode Gap (IEG) on the Material Removal Rate (MRR) and Surface Roughness (SR) in EDM are examined. L27 ...
