Drilling optimization requires accurate drill bit rate-of-penetration(ROP)predictions.ROP decreases drilling time and costs and increases rig productivity.This study employs random forest(RF),gradient boosting modelin...Drilling optimization requires accurate drill bit rate-of-penetration(ROP)predictions.ROP decreases drilling time and costs and increases rig productivity.This study employs random forest(RF),gradient boosting modeling(GBM),extreme gradient boosting(XGBoost),and adaptive boosting(Adaboost)models to generate ROP pre-dictions.The models use well data from a 3200-m segment across the stratigraphic column(Dibdibba to Zubair formations)of the large West Qurna oil field in Southern Iraq,penetrating 19 formations and four oil reservoirs.The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies.The ROP predictive models were developed using 14 operational parameters:TVD,weight on bit(WOB),torque,effective circulating density(ECD),drilling rotation per minute(RPM),flow rate,standpipe pressure(SPP),bit size,total RPM,D exponent,gamma ray(GR),density,neutron,caliper,and discrete lithology distribution.Training and validation of the ROP models involves data compiled from three development wells.Applying Random subsampling,the compiled dataset was split into 85%for training and 15%for validation and testing.The test subgroup’s measured and predicted ROP mismatch was assessed using root mean square error(RMSE)and coefficient of correlation(R^(2)).The RF,GBM,and XGBoost models provide ROP predictions versus depth with low errors.Models with cross-validation that integrate data from three wells deliver more accurate ROP pre-dictions than datasets from single well.The input variables’influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.展开更多
Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent an...Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.展开更多
文摘Drilling optimization requires accurate drill bit rate-of-penetration(ROP)predictions.ROP decreases drilling time and costs and increases rig productivity.This study employs random forest(RF),gradient boosting modeling(GBM),extreme gradient boosting(XGBoost),and adaptive boosting(Adaboost)models to generate ROP pre-dictions.The models use well data from a 3200-m segment across the stratigraphic column(Dibdibba to Zubair formations)of the large West Qurna oil field in Southern Iraq,penetrating 19 formations and four oil reservoirs.The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies.The ROP predictive models were developed using 14 operational parameters:TVD,weight on bit(WOB),torque,effective circulating density(ECD),drilling rotation per minute(RPM),flow rate,standpipe pressure(SPP),bit size,total RPM,D exponent,gamma ray(GR),density,neutron,caliper,and discrete lithology distribution.Training and validation of the ROP models involves data compiled from three development wells.Applying Random subsampling,the compiled dataset was split into 85%for training and 15%for validation and testing.The test subgroup’s measured and predicted ROP mismatch was assessed using root mean square error(RMSE)and coefficient of correlation(R^(2)).The RF,GBM,and XGBoost models provide ROP predictions versus depth with low errors.Models with cross-validation that integrate data from three wells deliver more accurate ROP pre-dictions than datasets from single well.The input variables’influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.
文摘Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.
