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
The precise and real-time prediction of pore pressure is critical for optimizing drilling efficiency and mitigating the potential risks associated with drilling operations.In order to surmount the limitations of empir...The precise and real-time prediction of pore pressure is critical for optimizing drilling efficiency and mitigating the potential risks associated with drilling operations.In order to surmount the limitations of empirical methods and to reduce reliance on logging-while-drilling data,this study proposed a stacking ensemble approach that utilized only conventional mud log data.The workflow involves the preliminary processing of data using isolation forest and wavelet thresholding techniques to effectively eliminate outliers and noise.The Eaton index was estimated using a Bayesian inversion algorithm,and pore pressure was estimated by integrating the dc exponent and Eaton method.A feature selection strategy combining data distribution characteristics and regression-based importance ranking was used to optimize the input parameters.Subsequently,a stacking approach was developed for predicting pore pressure,and the corresponding base learner,meta learner,and hyperparameters were optimized.Finally,the validity of the optimized model was substantiated through field data from three test wells(X1,X5,X3)under different drilling scenarios.The results indicated that the global optimal Eaton index of the block was 0.2449,the maximum percentage error of pore pressure estimation was 3.66%,and the corresponding average error was 1.59%compared to the wireline formation test data.The optimal combination of input features was determined to be R-ROP,WOB,TG,PT,MW,PFI,TVD,H,BR,BT,and SPP.The optimal basic learners were identified as BPNN,CNN,LSTM,and LightGBM,while the optimal meta learners were XGBoost.Prediction accuracy is improved when offset wells are densely distributed,spatially balanced,and proximal to the target well;conversely,sparse or distant offset wells result in reduced prediction performance.The mean absolute percentage errors for test offset well X1,X5,and X3 were 0.4353%,0.4646%,and 0.6856%,respectively,with the corresponding R2 values of 0.9362,0.9078,and 0.8950,respectively.Consequently,this approach has the capacity to accurately and in real-time predict pore pressure using solely conventional mud log data.This capability enables timely adjustments to drilling parameters,thereby enhancing operational efficiency and mitigating drilling risks.展开更多
In the Yingdong Oil/Gas Field of the Qaidam Basin,multiple suites of oil-gas-water systems overlie each other vertically,making it difficult to accurately identify oil layers from gas layers and calculate gas-oil rati...In the Yingdong Oil/Gas Field of the Qaidam Basin,multiple suites of oil-gas-water systems overlie each other vertically,making it difficult to accurately identify oil layers from gas layers and calculate gas-oil ratio(GOR).Therefore,formation testing and production data,together with conventional logging,NMR and mud logging data were integrated to quantitatively calculate GOR.To tell oil layers from gas layers,conventional logging makes use of the excavation effect of compensated neutron log,NMR makes use of the different relaxation mechanisms of light oil and natural gas in large pores,while mud logging makes use of star chart of gas components established based on available charts and mathematical statistics.In terms of the quantitative calculation of GOR,the area ratio of the star chart of gas components was first used in GOR calculation.The study shows that:(1)conventional logging data has a modest performance in distinguishing oil layers from gas layers due to the impacts of formation pressure,hydrogen index(HI),shale content,borehole conditions and invasion of drilling mud;(2)NMR is quite effective in telling oil layers from gas layers,but cannot be widely used due to its high cost;(3)by contrast,the star chart of gas components is the most effective in differentiating oil layers from gas layers;and(4)the GOR calculated by using the area ratio of star chart has been verified by various data such as formation testing data,production data and liquid production profile.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
In the process of exploration and construction of shale gas, drilling construction of parameter wells can help to understand the geological characteristics of the mining area and the reserves and distribution of shale...In the process of exploration and construction of shale gas, drilling construction of parameter wells can help to understand the geological characteristics of the mining area and the reserves and distribution of shale gas, which plays a very important role in formulating scientific exploration and exploitation plan and ensuring the smooth progress of the construction process. The construction of shale gas parameter well ZK201 in Fengshan Mining Area is described below for your reference.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 the National Natural Science Foundation of China(Grant No.52474010)the Sichuan Science and Technology Program(Grant No.2025YFHZ0079)the Program of Introducing Talents of Discipline to Chinese Universities(111 Plan)(Grant No.D18016).
文摘The precise and real-time prediction of pore pressure is critical for optimizing drilling efficiency and mitigating the potential risks associated with drilling operations.In order to surmount the limitations of empirical methods and to reduce reliance on logging-while-drilling data,this study proposed a stacking ensemble approach that utilized only conventional mud log data.The workflow involves the preliminary processing of data using isolation forest and wavelet thresholding techniques to effectively eliminate outliers and noise.The Eaton index was estimated using a Bayesian inversion algorithm,and pore pressure was estimated by integrating the dc exponent and Eaton method.A feature selection strategy combining data distribution characteristics and regression-based importance ranking was used to optimize the input parameters.Subsequently,a stacking approach was developed for predicting pore pressure,and the corresponding base learner,meta learner,and hyperparameters were optimized.Finally,the validity of the optimized model was substantiated through field data from three test wells(X1,X5,X3)under different drilling scenarios.The results indicated that the global optimal Eaton index of the block was 0.2449,the maximum percentage error of pore pressure estimation was 3.66%,and the corresponding average error was 1.59%compared to the wireline formation test data.The optimal combination of input features was determined to be R-ROP,WOB,TG,PT,MW,PFI,TVD,H,BR,BT,and SPP.The optimal basic learners were identified as BPNN,CNN,LSTM,and LightGBM,while the optimal meta learners were XGBoost.Prediction accuracy is improved when offset wells are densely distributed,spatially balanced,and proximal to the target well;conversely,sparse or distant offset wells result in reduced prediction performance.The mean absolute percentage errors for test offset well X1,X5,and X3 were 0.4353%,0.4646%,and 0.6856%,respectively,with the corresponding R2 values of 0.9362,0.9078,and 0.8950,respectively.Consequently,this approach has the capacity to accurately and in real-time predict pore pressure using solely conventional mud log data.This capability enables timely adjustments to drilling parameters,thereby enhancing operational efficiency and mitigating drilling risks.
文摘In the Yingdong Oil/Gas Field of the Qaidam Basin,multiple suites of oil-gas-water systems overlie each other vertically,making it difficult to accurately identify oil layers from gas layers and calculate gas-oil ratio(GOR).Therefore,formation testing and production data,together with conventional logging,NMR and mud logging data were integrated to quantitatively calculate GOR.To tell oil layers from gas layers,conventional logging makes use of the excavation effect of compensated neutron log,NMR makes use of the different relaxation mechanisms of light oil and natural gas in large pores,while mud logging makes use of star chart of gas components established based on available charts and mathematical statistics.In terms of the quantitative calculation of GOR,the area ratio of the star chart of gas components was first used in GOR calculation.The study shows that:(1)conventional logging data has a modest performance in distinguishing oil layers from gas layers due to the impacts of formation pressure,hydrogen index(HI),shale content,borehole conditions and invasion of drilling mud;(2)NMR is quite effective in telling oil layers from gas layers,but cannot be widely used due to its high cost;(3)by contrast,the star chart of gas components is the most effective in differentiating oil layers from gas layers;and(4)the GOR calculated by using the area ratio of star chart has been verified by various data such as formation testing data,production data and liquid production profile.
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.
文摘In the process of exploration and construction of shale gas, drilling construction of parameter wells can help to understand the geological characteristics of the mining area and the reserves and distribution of shale gas, which plays a very important role in formulating scientific exploration and exploitation plan and ensuring the smooth progress of the construction process. The construction of shale gas parameter well ZK201 in Fengshan Mining Area is described below for your reference.
