Study on Asphaltene Precipitation Using Thin Film Micro-reactor

Asphaltenes are present in most petroleum materials and in all heavy oils and bitumen from oil sands. Asphaltenes are the heaviest and most complex molecules in crude oil and are defined by its solubility class as the constituents of oil which are soluble in toluene but insoluble in n-heptane. Asphaltene precipitation in the petroleum industry is detrimental to production operations since oil flow rate is impaired due to asphaltene precipitation or deposition. To study the effect of temperatures on asphaltene precipitation, a thin film micro-reactor is used to carry out the experiment. The effect of bulk temperature on asphaltene precipitation on heat transfer surface is studied and the result is observed and recorded. In this paper, four types of crude oils were tested to see the asphaltene precipitation

Study on Asphaltene Precipitation Using Thin Film Micro-reactor

Asphaltenes are present in most petroleum materials and in all heavy oils and bitumen from oil sands. Asphaltenes are the heaviest and most complex molecules in crude oil and are defined by its solubility class as the constituents of oil which are soluble in toluene but insoluble in n-heptane. Asphaltene precipitation in the petroleum industry is detrimental to production operations since oil flow rate is impaired due to asphaltene precipitation or deposition. To study the effect of temperatures on asphaltene precipitation, a thin film micro-reactor is used to carry out the experiment. The effect of bulk temperature on asphaltene precipitation on heat transfer surface is studied and the result is observed and recorded. In this paper, four types of crude oils were tested to see the asphaltene precipitation

PREDICTIVE ANALYTICS FOR OIL AND GAS ASSET MAINTENANCE USING XGBOOST ALGORITHM

One of the most important aspect in the oil and gas industry is the asset management at their respective platforms. Without proper asset management, it will lead to various unexpected scenarios including increase in plant deterioration, increased chances of accidents and injuries and breakdown of assets at unexpected times which will lead to poor and hurried maintenance. Accurate prediction of asset maintenance is needed to ensure that all the oil and gas platform could run their respective activities in a cost- effective way. There is a great need for this prediction usage in Malaysia as the oil and gas industry in this country contributes hugely to the economy. In this project dissertation, the parameters which are the factors affecting the asset failure on oil and platform will be interpreted using XGBoost, a gradient boosting model, from machine learning libraries and prediction on the asset lifetime will be made

PREDICTIVE ANALYTICS FOR OIL AND GAS ASSET MAINTENANCE USING XGBOOST ALGORITHM

One of the most important aspect in the oil and gas industry is the asset management at their respective platforms. Without proper asset management, it will lead to various unexpected scenarios including increase in plant deterioration, increased chances of accidents and injuries and breakdown of assets at unexpected times which will lead to poor and hurried maintenance. Accurate prediction of asset maintenance is needed to ensure that all the oil and gas platform could run their respective activities in a cost- effective way. There is a great need for this prediction usage in Malaysia as the oil and gas industry in this country contributes hugely to the economy. In this project dissertation, the parameters which are the factors affecting the asset failure on oil and platform will be interpreted using XGBoost, a gradient boosting model, from machine learning libraries and prediction on the asset lifetime will be made

Predictive Analytics for Oil and Gas Asset Maintenance Using XGBoost Algorithm

One of the most important aspects of the oil and gas industry is asset management at their respective platforms. Without proper asset management, it will lead to various unexpected scenarios including an increase in plant deterioration, increased chances of accidents and injuries, and breakdown of assets at unexpected times which will lead to poor and hurried maintenance. Given the significant economic contribution of the oil and gas sector to oil-producing countries like Malaysia, accurate asset maintenance prediction is essential to ensure that the oil and gas platform can manage its operations profitably. This research identifies the parameters affecting the asset failure on oil and platform that will be interpreted using the XGBoost gradient boosting model from machine learning libraries. The model is used to predict the asset's lifetime based on readings collected from the sensors of each machine. From result, our prediction method using XGBoost for asset maintenance has presented a 6.43% increase in classification accuracy as compared to the Random Forest algorithm