Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty.Though,attaining these parameters,for instance total organic carbon(TOC),volatile and residual hydrocarbon(S1&S2)is a chall...Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty.Though,attaining these parameters,for instance total organic carbon(TOC),volatile and residual hydrocarbon(S1&S2)is a challenge for geologists due to the high cost and time consumption.Therefore,addressing this issue has become an interesting subject for many researchers.As a result,on the ground of conventional well logs,vast kinds of methods,for example,back propagation artificial neural network(BPANN),have been introduced to solve this problem.Implementing these kinds of methods brings scientists tremendous amounts of information related to the richness of organic matter in a meantime.However,the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum.Therefore,current study cope with this issue and alleviate the uncertainty,Least Squares Support Vector Machine(LSSVM)and Adaptive-Neuro Fuzzy Inference System(ANFIS)algorithms cooperating with the particle swarm optimization(PSO)were suggested as a suitable method to increase the precision of estimating geochemical factors.The data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China,which consists of geochemical and well logging information.Outputs of this study illustrated that ANFIS-PSO and LSSVMPSO have a great ability to estimate geochemical parameters.The values of R^(2) obtained for these two models in order to predict the output parameters of TOC,S_(1) and S_(2) are equal to 0.6846&0.785,0.6864&0.778,and 0.7343&0.8128,respectively.The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model.Also,a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters.展开更多
文摘Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty.Though,attaining these parameters,for instance total organic carbon(TOC),volatile and residual hydrocarbon(S1&S2)is a challenge for geologists due to the high cost and time consumption.Therefore,addressing this issue has become an interesting subject for many researchers.As a result,on the ground of conventional well logs,vast kinds of methods,for example,back propagation artificial neural network(BPANN),have been introduced to solve this problem.Implementing these kinds of methods brings scientists tremendous amounts of information related to the richness of organic matter in a meantime.However,the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum.Therefore,current study cope with this issue and alleviate the uncertainty,Least Squares Support Vector Machine(LSSVM)and Adaptive-Neuro Fuzzy Inference System(ANFIS)algorithms cooperating with the particle swarm optimization(PSO)were suggested as a suitable method to increase the precision of estimating geochemical factors.The data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China,which consists of geochemical and well logging information.Outputs of this study illustrated that ANFIS-PSO and LSSVMPSO have a great ability to estimate geochemical parameters.The values of R^(2) obtained for these two models in order to predict the output parameters of TOC,S_(1) and S_(2) are equal to 0.6846&0.785,0.6864&0.778,and 0.7343&0.8128,respectively.The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model.Also,a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters.
