准噶尔盆地环玛湖凹陷压力系统复杂,区域压力变化大,漏失现象严重,八道湾组底部砾岩发育,厚度100-350 m 不等,可钻性差,导致机械钻速低,钻井周期长,严重制约了环玛湖地区的勘探进程.根据该区块钻井地质特点,有针对性地开展了钻井提速技...准噶尔盆地环玛湖凹陷压力系统复杂,区域压力变化大,漏失现象严重,八道湾组底部砾岩发育,厚度100-350 m 不等,可钻性差,导致机械钻速低,钻井周期长,严重制约了环玛湖地区的勘探进程.根据该区块钻井地质特点,有针对性地开展了钻井提速技术攻关.通过优化井身结构,优选高效PDC 钻头,优选防漏堵漏钻井液体系,运用提速辅助工具,逐步形成了适合环玛湖地区的钻井提速配套技术.现场应用的20 口攻关井中,平均机械钻速达到6.29 m/h,较攻关前提高63.4%,钻井周期为72.29 d,较攻关前缩短37.2%,复杂时率为1.13%,较攻关前降低72.9%.现场应用情况表明,环玛湖地区的钻井提速配套技术提高了机械钻速,缩短了钻井周期,降低了复杂时率,可进一步推广应用.展开更多
Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accu...Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.展开更多
基金Project(2010CB732004)supported by the National Basic Research Program of ChinaProjects(50934006,41272304)supported by the National Natural Science Foundation of China
文摘Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.
