The TRU-Vision system,developed by Baker Hughes,analyzes the gas extracted from drilling mud to estimate the hydrocarbons composition in drilled rock formations.Several separation processes had been surveyed in order ...The TRU-Vision system,developed by Baker Hughes,analyzes the gas extracted from drilling mud to estimate the hydrocarbons composition in drilled rock formations.Several separation processes had been surveyed in order to enhance the gas extraction at the gas trap,namely,mechanical stirring,vacuum,air sparging,membrane separation processes,ultrasounds,and cyclones.Mechanical stirring devices(one propeller,one flat-blade turbine,and two baffles sets),a vacuum generator,and an air bubble generator were designed and assembled to increase the efficiency and the response stability of TRU-Vision system.展开更多
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
文摘The TRU-Vision system,developed by Baker Hughes,analyzes the gas extracted from drilling mud to estimate the hydrocarbons composition in drilled rock formations.Several separation processes had been surveyed in order to enhance the gas extraction at the gas trap,namely,mechanical stirring,vacuum,air sparging,membrane separation processes,ultrasounds,and cyclones.Mechanical stirring devices(one propeller,one flat-blade turbine,and two baffles sets),a vacuum generator,and an air bubble generator were designed and assembled to increase the efficiency and the response stability of TRU-Vision system.
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
