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.

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.

Method for predicting cuttings transport using artificial neural networks in foam drilling

Foam is used widely in underbalanced drilling for oil and gas exploration to improve well perfor-mance.Accurate prediction of the cutting transport and pressure loss in the foam drilling is an important way to prevent stuck pipe,lost circulation and to increase the rate of penetration(ROP).In foam drilling,the cuttings transport quality may be defined in terms of cuttings consistency and downhole pressure loss,which are controlled by many factors.Therefore,it is very difficult to establish the mathematical equation that reflects nonlinear relationship among various factors.The field and experimental measurements of these parameters are time consuming and costly.In this study,the authors suggest a cuttings transport mathematical modeling using BPN(back propagation network),RBFN(radial basis function network)and GRNN(general regression neural network)based on various experiment data of cuttings transport of previous researchers and compared the result with experiment data.Results of this study show that the GRNN has a correlation coefficient of 0.99962 and an average error of 0.15 in training datasets,and a correlation coefficient of 0.99881 and an average error of 0.612 in testing datasets,which has higher accuracy and faster training velocity than the BP network or RBFN network.GRNN can be used in many mathematical problems for accurate estimation of cuttings consistency and downhole pressure loss instead of field and experimental measurements for hydraulic design in foam drilling operation.