The study of the thin bed responses and correction methods in cased hole density logging can provide a theoretical basis for research to improve data processing methods. By using the Monte Carlo program MCNP, the chan...The study of the thin bed responses and correction methods in cased hole density logging can provide a theoretical basis for research to improve data processing methods. By using the Monte Carlo program MCNP, the change of detector count from thin beds with the vertical depth was calculated at different casing thicknesses. The calculation showed that with the low density thin bed moving upward, detector count first increased to a maximum then decreased. The responses of a thin bed with a high density were opposite to those of a thin bed with a low density. The change curve was symmetrical, and the maximums or minimums appeared at the midpoint between the detector and source. Besides, detector count increased with increasing thin bed thickness. At a specific thin bed thickness, further increase of thin bed thickness resulted in a slow increase of detector count then the count rate leveled off. In actual logging, the influence of adjacent formations on density log measurements can be ignored. Finally, based on numerical simulation correction methods for the dual influence of casing and thin beds are discussed.展开更多
Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condi...Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condition and application of logging data.With Monte Carlo simulation technique,the paper obtains the relationship between neutron and density logging measurement and borehole di-ameter,porosity or gas saturation for well-balanced and under-balanced logging.The conclusions show that the response trend of under-balanced logging to gas reservoirs agrees with that of well-balanced logging with small borehole,and under-balanced logging data can be used usually as well-balanced logging data.When borehole diameter is large,under-balanced logging data should be corrected for the influences of borehole.展开更多
In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent...In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect data.This study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon volumes.It is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant challenge.To tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm Optimization(PSO),Imperialist Competitive Algorithms(ICA),and Genetic Algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log data.Remarkably,our results highlight that the Genetic Algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of 0.0013.In comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.展开更多
文摘The study of the thin bed responses and correction methods in cased hole density logging can provide a theoretical basis for research to improve data processing methods. By using the Monte Carlo program MCNP, the change of detector count from thin beds with the vertical depth was calculated at different casing thicknesses. The calculation showed that with the low density thin bed moving upward, detector count first increased to a maximum then decreased. The responses of a thin bed with a high density were opposite to those of a thin bed with a low density. The change curve was symmetrical, and the maximums or minimums appeared at the midpoint between the detector and source. Besides, detector count increased with increasing thin bed thickness. At a specific thin bed thickness, further increase of thin bed thickness resulted in a slow increase of detector count then the count rate leveled off. In actual logging, the influence of adjacent formations on density log measurements can be ignored. Finally, based on numerical simulation correction methods for the dual influence of casing and thin beds are discussed.
基金Supported by the Major State Basic Research Development Program of China(973 Program)(Grant No.2006CB202306)
文摘Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condition and application of logging data.With Monte Carlo simulation technique,the paper obtains the relationship between neutron and density logging measurement and borehole di-ameter,porosity or gas saturation for well-balanced and under-balanced logging.The conclusions show that the response trend of under-balanced logging to gas reservoirs agrees with that of well-balanced logging with small borehole,and under-balanced logging data can be used usually as well-balanced logging data.When borehole diameter is large,under-balanced logging data should be corrected for the influences of borehole.
文摘In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect data.This study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon volumes.It is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant challenge.To tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm Optimization(PSO),Imperialist Competitive Algorithms(ICA),and Genetic Algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log data.Remarkably,our results highlight that the Genetic Algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of 0.0013.In comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.
