Discussion of reasonable drilling parameters in impregnated diamond bit drilling

The impregnated diamond(ID)bit drilling is one of the main rotary drilling methods in hard rock drilling and it is widely used in mineral exploration,oil and gas exploration,mining,and construction industries.In this study,the quadratic polynomial model in ID bit drilling process was proposed as a function of controllable mechanical operating parameters,such as weight on bit(WOB)and revolutions per minute(RPM).Also,artificial neural networks(ANN)model for predicting the rate of penetration(ROP)was developed using datasets acquired during the drilling operation.The relationships among mechanical operating parameters(WOB and RPM)and ROP in ID bit drilling were analyzed using estimated quadratic polynomial model and trained ANN model.The results show that ROP has an exponential relationship with WOB,whereas ROP has linear relationship with RPM.Finally,the optimal regime of mechanical drilling parameters to achieve high ROP was confirmed using proposed model in combination with rock breaking principal.

Determination of the constant m_(i) in the Hoek-Brown criterion of rock based on drilling parameters

The constant m_(i) in the Hoek-Brown(H-B) criterion is a fundamental parameter required for determining the compressive strength of rock. In this paper, drilling parameters provide a new basis for determining the constant mi. An analytical relationship between the drilling parameters and constant miis established in consideration of the contact response between the drilling bit and the cut rock in the crushed zone.New models are developed to predict the triaxial compressive strength(TCS), internal friction angle φand cohesion c of rock. Drilling tests are carried out on 6 rock types to study the correlation between φ and m_(i). A comparison between the predicted values of rock mechanical properties and the measured values from the laboratory is performed to verify the accuracy of the proposed model(yielding an error less than 10%). The TCSs and constant m_(i) values of fifteen rocks are cited to validate the accuracy of the proposed model. The result shows that the proposed model predicts the TCS and constant m_(i) within a maximum error of 20%. The method can be conveniently applied to the rock mechanical properties.

Analysis of effects of operating parameters on rate of penetration in drilling process with air down-the-hole hammer

Air down-the-hole(DTH)hammer drilling has long been recognized to have the potential of drilling faster than conventional rotary drill,especially in some hard rocks such as granite,sandstone,limestone,dolomite,etc.with the same weight on bit(WOB)and rotations per minute(RPM).So,it has been widely used in many drilling fields including mineral resource exploration drilling,oil and gas drilling and geothermal drilling.In order to reduce drilling cost by selecting optimal drilling parameters,rate of penetration(ROP)should be estimated accurately and the effects of different factors on ROP should be analyzed.In this research,ANN model with several multi-layer perception back propagation(BP)networks for predicting ROP of air DTH hammer drilling was developed using controllable parameters such as impact energy,impact frequency,WOB,RPM and bit operating time for the formations with a certain drillability index of rock.Several BP neural networks with the different neurons in hidden layers were developed and compared for selecting optimal architecture of ANN.The effects of the drilling parameters such as impact energy,impacting frequency,WOB,RPM and bit operating time on the ROP of air DTH hammer drilling were investigated by trained ANN.From the analyses,the optimum range of drilling parameters for providing high ROP were determined and analyzed for a formation with a certain drillability index of rock.The methodology proposed in this study can be used in many mathematical problems for optimization of drilling process with air DTH hammer.

Drill string dynamic characteristics simulation for the ultra-deep well drilling on the south margins of Junggar Basin

Improper drilling parameters may cause severe vibration of drill string which leads to reduce the rate of penetration and drilling tool premature failure accidents in the drilling process of ultra-deep well.The study on dynamic characteristics of drill string plays an important role in increasing the safety of drilling tool and optimizing the drilling parameters.Considering the influences of real borehole trajectory,interaction between bit and formation,contact between drill string and borehole wall,stiction of drilling fluid and other factors,a comprehensive drill string dynamic model was established to simulate the changes of wellhead hook load,torque,equivalent stress of drill string and BHA(bottomhole assembly)section acceleration and motion trajectory with time at different WOBs(weights on bit)and rotary speeds.The safety factor and overpull margin of wellhead drill string were calculated and the strength of drilling tool in ultra-deep well was checked using the fourth strength theory.The analysis results show that,in the drilling process of ultra-deep well,the transverse motion amplitude of the drill string near the wellhead is relatively small and vibration of drill string mainly occurs in the lower well section.As the rotary speed increases,the number of collision between lower drilling tool and borehole wall increases,wellhead transverse stress increases,change in torque is not large and change in wellhead equivalent stress is relatively small.As the WOB increases,wellhead torque will increase,axial load and equivalent stress will decrease and vibration acceleration of BHA will increase sharply.Wellhead overpull margin and safety factor will decrease with the increase of rotary speed and increase with the increase of WOB.Wellhead safety factor of S135 drilling tool in an F190.5 mm ultra-deep well on the south margins of Junggar basin changes around 1.8.The drilling tool is safe and has relatively sufficient ability to deal with the downhole accidents if a large size high steel grade drill string(F139.7 mm S135)is used.However,in view of BHA safety,neither rotary speed shall be too high nor WOB shall be too large.

Intelligent classification model of surrounding rock of tunnel using drilling and blasting method

Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.