Average Penetration Rate Research Articles

Exposure behavior and drilling efficiency of basalt fiber composite impregnated diamond bits in hard granite

Impregnated diamond bits (IDBs) are widely used for drilling in hard formations. To improve the drilling efficiency and exposure behavior of IDBs in granite, three types of Cu-based basalt fiber (BF) composite IDBs were designed and prepared by using the medium-frequency induction hot-pressing and sintering method, in which 0 wt.% BF and 25 vol.% diamond were used in 0BF25D IDB, 1 wt.% BF and 25 vol.% diamond were used in 1BF25D IDB, 1 wt.% BF and 20 vol.% diamond were used in 1BF20D IDB. The drilling efficiency of each IDB was tested under different drilling pressures (WOB), and the exposure behavior of IDBs was investigated by scanning electron microscopy and ultra field microstructural characterization. Results show that drilling granite with grade 9 drillability, low drilling pressure can drill successfully with the addition of BF. The average rate of penetration (ROP) of 1BF20D IBD under 6 kN WOB was 4.78 m/h, which was improved by 60%∼80%, energy consumption decreased by 66% for each meter, torque (TOB) decreased, and rotational speed (RPM) was more stable during the drilling process. The addition of BF and reasonable diamond concentration enhanced the holding power of the diamond which promoted the exposure of the diamond. The average exposed height of the diamond in 1BF20D IDB reached 121.2 μm with 22% to 29% of the whole diamond.

Optimization of Rock Breaking Parameters by Combing Mechanical Specific Energy Theory and Random Forest Algorithm

Abstract As the key indicators to evaluate drilling efficiency, mechanical rate of penetration (ROP) and mechanical specific energy (MSE) are affected by many uncertain factors. In the actual drilling process, it is often necessary to adjust the parameters to achieve speed and efficiency. A dual-objective model is established considering the maximization of ROP and the minimization of MSE. Which combined with the theory of mechanical specific energy and the principle of multi-objective optimization, the model parameter weights are determined by the feature importance in the random forest regression model, and the value range of rock breaking parameter is optimized, realize the fusion of physical model and data model. The results show that, in the four strata of well X, the average ROP of T2k1 stratum is increased by 48.6% and the average MSE is decreased by 26.6%, the average ROP of T1b3 stratum is increased by 89.9% and the average MSE is decreased by 33.8%, the average ROP of the T1b2 stratum was increased by 41.3% and the average MSE was decreased by 39.0%, the average ROP of the T1b1 stratum was increased by 29.2% and the average MSE was decreased by 37.3%, which met the dual objectives of maximizing the ROP and minimizing the MSE. Compared with the traditional single-objective optimization method, the established dual-objective model is more in line with the needs of complex drilling engineering, and the fusion of physical model and data model can reduce certain human error.

Is there any relationship between the effectiveness of some Heterorhabditis bacteriophora isolates on the host and their average penetration rate?

Entomopathogenic nematodes, such as Heterorhabditis bacteriophora, have been widely recognized for their potential in biological control strategies to manage agricultural pests effectively. This study aimed to evaluate the penetration rate and efficacy of different H. bacteriophora isolates, including two isolates (HB4 and HBNL) and a hybrid strain (HBH), against Tenebrio molitor L. (Col: Tenebrionidae) adults. The research also aimed to determine whether there was a correlation between the mortality caused by these isolates and their penetration. These isolates were applied to T. molitor adults at different concentrations 0.78 IJs (infective juveniles)/cm2 (50 IJs/Petri dishes), 1.57 IJs/cm2 (100 IJs/Petri dishes), 2.36 IJs/cm2 (150 IJs/Petri dishes), 3.14 IJs/cm2 (200 IJs/Petri dishes), 3.93 IJs/cm2 (250 IJs/Petri dishes), 7.86 IJs/cm2 (500 IJs/Petri dishes), 11.8 IJs/cm2 (750 IJs/Petri dishes), 15.72 IJs/cm2 (1000 IJs/Petri dishes), 19.66 IJs/cm2 (1250 IJs/Petri dishes). Another objective of the study was to observe the penetration rate means of these nematodes at consistent concentrations and record the resulting mortality rates. The findings revealed that the highest mortality rate of 100% was achieved at concentrations of 250, 500, 750, 1000, and 1250 IJs/Petri dishes. The HBH hybrid strain also exhibited the highest penetration rate mean of 20.33 IJs at a concentration of 1250 IJs/Petri dishes. These results highlight the efficacy of H. bacteriophora isolates and the hybrid strain in controlling T. molitor adults, with significant mortality rates observed at higher nematode concentrations. The study's emphasis on penetration rate means and the relationship between nematode concentration and pest mortality underscores the importance of optimizing entomopathogenic nematode applications for effective pest management.

Experimental study on shale-breaking of special-shaped cutter PDC bit

To enhance the drilling efficiency and extend the service life of PDC (Polycrystalline Diamond Composite) bits in shale formations, this study delves into the rock-breaking mechanisms of special-shaped cutters through a comprehensive experimental approach. This involves optimizing the cutter designs, conducting laboratory experiment and field testing. Among the various cutter geometries considered, concave, axe, planar, and triangular cutters are chosen as the focal points for unit rock-breaking experiments. These tests aim to assess their cutting loads and cutting specific energy to gain a deeper understanding of their performance characteristics. Based on experimental, the debris characteristics are analyzed. Based on the understanding of the shale-breaking characteristics of special-shaped cutters, field testing is performed using a novel PDC bit with a special-shaped cutter. Compared with planar cutters, the concave cutter and the triangular cutter generate lower cutting loads and cutting specific energy. Under identical conditions, the average cutting force and cutting specific energy of concave cutter at different cutting depths are reduced by 16.1% and 19.6% Specifically, the concave cutter generates the largest debris when operated under similar conditions, which is beneficial for increasing rock-breaking efficiency. Laboratory experiment indicate that compared to conventional drill bits, the novel drill bit experiences an increase in torque of approximately 9.8% with increasing WOB (weight on bit). Under high WOB, the ROP (rate of penetration) increases by about 75.4%, while the mechanical specific energy decreases by nearly 40%. Additionally, the novel bit vibration characteristics remain superior to conventional drill bits. Field testing shows that the average ROP of the novel bit and total footage drilled increase by up to 13.3% and 27.2%, respectively, in comparison with those for the conventional bit. The research results are helpful to speed up the efficiency of shale gas drilling.

Femoral Head Penetration Rates and Oxidation of Highly Cross-Linked Polyethylene Hip Liners Implanted More Than 10 Years

BackgroundHighly cross-linked polyethylene (HXLPE) was introduced to improve wear in total hip arthroplasty, with manufacturers implementing different thermal treatments to reduce oxidation. It is important to understand how long-term time in vivo affects the wear of these materials. The purpose of this study was to investigate the wear and oxidative performance of first-generation HXLPE hip inserts implanted for greater than 10 years and compare annealed and remelted HXLPE formulations. MethodsThere were 49 total hip arthroplasty liners retrieved during routine revision surgery as part of an institutional review board–approved implant retrieval program. Penetration rates for the liners were calculated as the difference between the thickness of the unloaded and loaded regions divided by implantation time. Oxidation indices for the rim, locking mechanism, articulating surface, and backside regions were measured using Fourier-transform infrared spectroscopy according to American Society for Testing and Materials 2102. Mann–Whitney U tests were used to determine the statistical difference between annealed and remelted components. ResultsThe cohort had an average implantation time of 13.1 ± 2.6 years for annealed and 12.1 ± 1.7 years for remelted components. The components were revised most often for polyethylene wear, instability, and loosening. The penetration rate averaged 0.0177 ± 0.014 mm/year for annealed components and 0.015 ± 0.022 mm/year for remelted components. Penetration rates did not differ between the remelted and annealed cohorts (P = .28). Oxidation indices were found to be significantly higher in the annealed cohort for all regions of interest (P < .001). ConclusionsOxidation was found to be higher in the annealed HXLPE; however, this does not seem to be associated with greater wear as we found the average penetration rates for the cohorts were low, and the penetration rates were similar between the annealed and remelted cohorts.

Acceptance model of new energy vehicles based on PLS-SEM model

The current energy crisis is worsening worldwide, and in China, urban expansion and per capita vehicle ownership have created a growing energy imbalance and increased pressure to reduce carbon emissions.The popularization of new energy vehicles (NEVs) can provide a step forward to solving energy shortage problems, environmental pollution, and global warming. In 2022, the average penetration rate, which is ratio of new energy vehicle sales to vehicle sales, is just 19.1 %. This paper analysed the reasons for the differences in the penetration rates of new energy vehicles in China's 269 prefecture-level cities, using a Geo Detector approach, and the results showed that the level of economic development, the average annual temperature difference, the density of charging piles, the charging price and the number of population all had significant effects(q＞0.12) on the penetration rate. Based on the above studies, a questionnaire was used to investigate the public's acceptance of new energy vehicles in Xinjiang Uygur Autonomous Region, and a PLS-SEM regression analysis was conducted. The results showed that men, young people and people with a certain level of basic education were 5 % more likely to accept new energy vehicles.Unlike previous studies, perceived cost had no significant correlation with the acceptance of new energy vehicles. Perceived risk had a significant negative correlation with the acceptance of new energy vehicles,the path coefficient is −0.1.The acceptance of new energy vehicles was significantly and positively correlated with vehicle quality and service, the public's understanding of new energy vehicles, and subjective norms, their average path coefficients are above 0.1. We argues that the government should maintain a certain level of promotion of new energy vehicles and accelerate the construction of charging piles, based on the aforementioned results.

Deepwater PDC Jetting Bit-Drilling Technology Based on Well Structure Slimming

Growing global energy demand and limited reserves of traditional energy resources are causing a growing energy shortage. In order to meet future energy needs, new energy resources must be continuously explored. Deepwater drilling research has emerged as one of the key ways to address this issue, and well structure slimming is an effective way to increase drilling speed and reduce costs. The hole size of the second section of deepwater wells decreases from a conventional 660.4 mm to 444.5 mm and increases from 500–800 m to 800–1200 m, creating problems where the conventional 660.4 mm cone bit cannot be used, the rate of penetration (ROP) of the cone bit is low, and the service life is short. To solve these problems, a 444.5 mm artificial polycrystalline diamond compact (PDC) was designed for the first time for use at home or abroad, and according to the characteristics and operation requirements of the 914.4 mm conductor jetting process, a unique anti-collision gauge protector was designed, an innovative bypass nozzle was configured, and a hydraulic design to prevent bit balling in shallow soft mudstone was formulated. PDC jetting bit-drilling technology based on well structure slimming was successfully applied to eight deepwater wells in the eastern South China Sea, which successfully jetted a 914 mm conductor and greatly improved the ROP of their second-section holes. When the below-mudline depth of the second-section hole increased by 37.01%, the average ROP increased by 227.84%. These technical achievements have successfully realized deep drilling with seawater, increased speed and efficiency, achieved good application results, and accumulated valuable experiences that can be used for reducing the cost and increasing the efficiency of offshore drilling operations.

Hydraulic parameters optimization of two-stage PDC bit in deep formation applications

Two-stage PDC (Polycrystalline diamond compact) bit is a special drilling bit consisting of a small-diameter pilot and a large-diameter reamer. The unique rock-breaking structure is designed to improve drilling efficiency and reduce drilling costs in deep and abrasive formations. According to previous studies, the hydraulic structure of the two-stage PDC bit can significantly affect the drilling performance of removing cuttings near the bottom of the wellbore. Therefore, this research has proposed to achieve high efficiency of cuttings removal by optimizing the hydraulic structure. In this paper, the flow field at the bottom of the well near the two-stage PDC bit (Φ171.45 mm pilot + Φ215.9 mm reamer) has been numerically studied using the finite element method. As shown in the analysis results, both the flow rate of the pilot and the flow rate ratio of the pilot to the reamer increase significantly with the increase of the area ratio of the pilot nozzles to the total nozzles. The numerical data supports that the cuttings can be removed effectively from the bottom of the wellbore when the area ratio is between 0.75 and 0.80. Moreover, the cuttings can be removed effectively from the bottom of the wellbore when the mud viscosity is about 10 mPa s, the rate of penetration is between 4 and 8 m/h, the drill speed is 90 rpm, and the cuttings particle sizes is between 1 and 2 mm. Besides, increasing the pump rate can improve the cutting-carrying efficiency through the wellbore bottom. Field test results indicate that the average penetration rate of the optimized bit is increased by 47.17% and the footage of the optimized bit is increased by 95.40% compared to the equivalent formation in adjacent wells. This study can provide guidance for the optimal design of the hydraulic structure of such two-stage PDC bits.

Drilling Conditions Classification Based on Improved Stacking Ensemble Learning

The classification of drilling conditions is a crucial task in the drilling process, playing a vital role in improving drilling efficiency and reducing costs. In this study, we propose an improved stacking ensemble learning algorithm with the objective of enhancing the performance of drilling conditions classification. Additionally, this algorithm aims to have a positive impact on automated drilling time estimation and the continuous improvement of efficiency. In our experimental setup, we employed various base learners, such as random forests, support vector machine, and the K-nearest neighbors algorithm, as initial models for the task of drilling conditions classification. To improve the model’s expressive power and feature relevance specifically for this task, we enhanced the meta-model component of the stacking algorithm by incorporating feature engineering techniques. The experimental results show that the improved ensemble learning algorithm achieves an accuracy and recall rate of 97% and 98%, respectively. Through continuous improvement in drilling operations, the average sliding time is reduced by 21.1%, and the average Rate of Penetration (ROP) is increased by 15.65%. This research holds significant importance for engineering practice in the drilling industry, providing robust support for optimizing and enhancing the drilling process.

Brief communication: New sonde to unravel the mystery of polar subglacial lakes

Abstract. The newly developed RECoverable Autonomous Sonde (RECAS) allows sampling and analysis of subglacial water while the subglacial lake remains isolated from the surface. The sonde was successfully tested in East Antarctica during the 2021–2022 field season: it reached the ice-sheet base at a depth of 200.3 m, sampled basal meltwater and measured its pressure, temperature, pH and conductivity and returned to the ice surface. The average downward penetration rate was 1.85 m h−1, and the average upward penetration rate was 2.94 m h−1. The successful test of a self-melting, recoverable probe in Antarctica marks further progress towards the clean exploration of subglacial water bodies.

Numerical Simulation and Field Test Research on Vibration Reduction of PDC Cutting of Pebbled Sandstone under Composite Impact Load

Downhole vibrations caused by rock breaking when drilling through pebbled sandstone formations negatively affect the rate of penetration (ROP) and the safety of downhole tools. Therefore, it is of great significance to study the cutting characteristics of pebbled sandstone and find a method of reducing the drilling vibrations of pebbled sandstone formations. Based on the DEM (discrete element method), a simulation model of pebbled sandstone considering the random filling of high-strength gravels was established by using the random polygon distribution method. The influence of gravel content on the strength parameters and the breaking state of the pebbled sandstone samples was analyzed. Additionally, a DEM model of PDC cutting rocks loaded by a spring–mass system was established, and the Stribeck effect of contact friction between the PDC cutter and the rock was analyzed. The periodic vibration and the stick–slip phenomenon of the cutting system during the drilling process were presented by this model. The model was employed to simulate and explore the influence of composite impact load on stick–slip vibration during PDC cutting of pebbled sandstone. The simulation results showed that the composite impact load had a more obvious effect on mitigating the vibration of PDC cutting of pebbled sandstone under the condition of a higher horizontal impact amplitude coefficient (qh = 40%). Based on the simulation results, a composite impactor with a large impact angle α = 70° was selected to conduct the field tests in the pebbled sandstone formation of Well T1. The results showed that, compared to conventional drilling, the average WOB (weight on bit) of the section drilled with the composite impactor decreased by 57.13%, the standard deviation of the WOB decreased by 57.29%, and the average ROP increased by 98.31%. The employing of composite impactors in pebbled sandstone formations can significantly reduce drilling vibration, improve ROP, and protect bits and downhole instruments.

Design and Experimental Study of Core Bit for Hard Rock Drilling in Deep-Sea

The hard rock stratum will seriously affect the efficiency of deep-sea drilling and greatly increase the cost of drilling operations due to its complexity. In ocean drilling operations, the design of the bit is critical. Therefore, the core bit should be able to drill in a variety of formations, especially in hard rock stratum, with the function of reducing the need to change the bit due to formation changes and improving the drilling efficiency. In this paper, three different core bits were designed according to the hard rock stratum of the seabed: the roller bit, diamond bit and bionic bit. According to the test results, it was observed that: (1) It is fully proven that the roller core bit has the characteristics of stable drilling in hard strata, small torque and high drilling efficiency. The same method can realize the core in shallow soft strata through the setting of drilling parameters. (2) For the diamond bit, the field test data show that the new formula diamond bit has a good adaptability to the changing hard rock strata in this area, with an average rate of penetration (ROP) of 4.4 m/h and a bit life of 137.75 m. (3) For the bionic bit, field experiment data showed that drilling in the formation lithology was gray green tuff, which had a core recovery up to 100%. The average ROP was 1.35 m/h, and the highest ROP was 1.88 m/h. The design and experiment of three kinds of drill bits have effectively solved the technical problem of efficient drilling in deep-sea hard-rock stratum.

Machine-Learning Model Developed for Rate-of-Penetration Optimization

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31680, “Successful Development and Deployment of a Global ROP-Optimization Machine-Learning Model,” by Timothy S. Robinson, SPE, Exebenus, Peter Batruny, SPE, Petronas, and Dalila Gomes, SPE, Exebenus, et al. The paper has not been peer reviewed. Copyright 2022 Offshore Technology Conference. Reproduced by permission. _ Drilling rate of penetration (ROP) is influenced by many factors, both controllable and uncontrollable, difficult to distinguish with the naked eye. Thus, machine-learning (ML) models such as neural networks have gained momentum in the drilling industry. Previous models were field-based or tool-based, which affected accuracy outside of the trained field. The authors of the complete paper aim to develop one generally applicable global ROP model, reducing the effort needed to redevelop models for every application. Introduction The authors have identified a need for an ROP model that can recommend parameters in real time, which ideally requires a general ROP model that can be applied with low prediction errors. Formation properties are available in real time from logging tools; however, incorporating logging data into a global ROP model is challenging. A distance usually exists between the bit and logging tools where the properties of the formation drilled in real time at the bit are not available. While strides have been made in ML interpretation of logs, human interpretation is still needed. Equipment and Processes A deep neural network was used for modeling ROP. This model was queried as a black box within the scope of a constrained optimization algorithm tasked with identifying combinations of controllable drilling parameters expected to increase ROP. An overview of the ROP optimization system powered by the ROP estimation model is shown in Fig. 1. The ROP model was fitted using training and validation data sets composed of data from independent wells. Four additional wells were held in reserve for final testing purposes, most importantly for assessing the model’s ability to generalize to completely unseen data. For optimization, a three-stage conditional parameter sweep was used where the value expected to optimize ROP was found sequentially for each of the controllable parameters while fixing the values of the other variables. The optimizing value for each parameter was used as a fixed value for the subsequent optimization steps. This approach maintains the simplicity of a grid search and converges to a local optimum quickly because of the relatively small number of search iterations required under this formulation. A variety of well types with global geographical diversity was present in the data, using different downhole drives and bottomhole assemblies. Twenty-nine wells were used for training the ROP model, with an additional five wells used for validation and a further four wells reserved for holdout testing. No overlap existed between the wells constituting the training, validation, and holdout data sets. The drilling data sets were preprocessed by methods detailed in the complete paper. To ensure consistency across all operations, an average ROP was calculated independently based on depth measurements and their associated timestamps. This ROP calculation was used as the target variable for the ROP estimation model and for assessing its predictive performance. Training and validating the neural network for ROP estimation using data from a variety of independent wells is very useful for assessing model generalization. This technique is superior to the common approach of randomly splitting data sets because it minimizes information leakage between training and validation data.

Optimization of drilling parameters based on Copula function method for the Chepai area

In areas with complex stratigraphic lithology, the relationship between the penetration rate and drilling parameters should be fully considered to optimize the drilling process and improve drilling efficiency. The most frequently utilized methods for performing parameter optimization through correlation analysis are the correlation coefficient, principal component analysis, and grey correlation. The correlation coefficient method solely evaluates the extent of linear correlation between two variables, it cannot be applied to the non-linear connection between penetration rate and drilling parameters. The application of principal component analysis may produce inaccurate experimental findings due to the intricate and poorly co-varying nature of drilling parameters. The grey correlation method can lead to the substantial bias in the results because of the vast quantity of data analysed. Based on the vast quantity of data, using the copula function, the big data analysis method analyses the nonlinear relationship between penetration rate and drilling parameters. It constructs a united distribution function expression to determine the optimal parameter selection criteria. The in-situ drilling data from dozens of wells in the Chepaizi area are collected and optimized six types of parameters. The optimal parameter combination is determined. Following field investigation, there was a noteworthy increase of 34.83% in the average penetration rate.

Performance improvement of PDC and PDC bits by cryogenic treatment

Polycrystalline diamond compact (PDC) is one of the most widely used cutting tools in the field of oil and gas drilling. In this paper, PDC was cryogenically treated at temperature of around −190 °C and then the mechanical properties such as microhardness, wear ratio, and impact toughness after cryogenic treatment were tested. The morphology, elemental composition and crystal structure of the PDC was investigated by scanning electron microscope (SEM), Energy Dispersive Spectrometer (EDS) and X-ray diffraction (XRD). Meanwhile, the residual stress of the PCD layer was analyzed using Raman spectroscopy. Untreated and cryogenically treated PDC were respectively fabricated into drill bits and the drilling performance were tested. The results showed that the average microhardness of the PDC after cryogenic treatment was increased by 5.3GPa/10.4%, the maximum wear ratio was increased by 11.8%, and the impact toughness was greatly improved to reach 420 J and increased by 79.4% on average. The average rate of penetration (ROP) of the bit equipped with untreated PDC was 1.03 m/h, while the ROP of the bit equipped with cryogenically treated PDC was 1.56 m/h, increased by 51.4%. Microscopic characterization revealed that the change of fracture mode and the evolution of residual stress in PDC was the main mechanism for the improvement of mechanical properties.

Machine Learning Assists Bit Selection and Optimization

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 21299, “Drilling in the Digital Age: Machine-Learning-Assisted Bit Selection and Optimization,” by Peter Batruny, SPE, Hafiz Zubir, and Pete Slagel, SPE, Petronas, et al. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. _ The project outlined in the complete paper describes machine learning as a powerful tool for bit selection and parameter optimization to improve drilling performance. Machine learning will become a significant part of well planning, design, and operations in the future. The study demonstrates how artificial neural networks (ANNs) can be used to learn from previous operations and influence planning decisions to improve bit performance. Introduction Multiple wells have been drilled in an onshore field in Iraq using different bit designs and with a variety of downhole conditions. To improve the rate of penetration (ROP) in a significant manner, a radical shift in how drill bits are selected, as well as a closer look at bit characteristics, is needed. This work aims to combine analytical and physical models to examine aspects of bit designs previously overlooked. Two mathematical models traditionally have been used for bit selection, the cost per foot (CPF) model and the mechanical specific energy (MSE) model. This work concludes that MSE is more effective than CPF in optimizing ROP and is a valid benchmark for comparing bits. MSE can be used to compare previous bit runs but is limited in predicting ROP for future bit runs. Equipment and Processes Data governance consists of data collection, processing, cleaning, and compilation. Data are collected from American Standard Code for Information Interchange logs in real time and then are processed by removing missing values. The data sets are split by individual bits, with each bit characterized by data compiled from multiple wells in which the bit was run. Throughout this paper, ROP will refer to instantaneous on-bottom ROP. Given the success that ANNs have had, and continue to have, in predicting ROP in the literature, they are used for this work. Once a model is established based on statistical and physical performance, it is used as a base model to run optimization analysis for each bit. This paper examines two methods of optimization, the first being sensitivity analysis based on average ROP. The second optimization method used in this study is a Monte Carlo-style optimization. Once optimal parameters are generated from the sensitivity analysis and Monte Carlo models, they are plotted against the modeled values of offset ROP. This acts as a further verification of the optimal parameters to ensure they are within range of actual historical parameters. The optimal values are then implemented in future wells.

Investigation on the cuttings carrying capacity of a novel retractable drill bit used in casing while drilling with air reverse circulation

An orthogonal experimental design method is employed to investigate the effect of the structural parameters of the drill bit used in the Casing-while-Drilling (CwD) technology on its rock cuttings carrying capacity and reverse circulation performance. The results of the range analysis indicate that diameter of inner jet nozzle has the most significant impact on the cuttings carrying capacity of the drill bit, followed by the diameter of flushing nozzle. While for the reverse circulation ability, the diameters of the flushing nozzles have a dominant impact, followed by the diameters of the inner jet nozzles. In addition, the cuttings carrying capacity and the reverse circulation effect of the drill bit can be improved by shrinking the inner jet nozzle or appropriately enlarging the flushing nozzle. Accordingly, the optimal structural parameters of the drill bit are determined. Through further experiments, the cuttings carrying efficiency and the entrainment ratio of the optimal drill bit are 99.57% and 20.36%, respectively. Moreover, the rotation speed of drill bit has a trivial effect on its performance. At any tested rotation speed, the entrainment ratio is greater than 20%, and all the rock cuttings can be carried out of the borehole bottom using the proposed drill bit with the optimal structure. To test its real performance, a large-diameter retractable drill bit with a diameter of 600 mm was manufactured and tested in field. The results demonstrated that this retractable drill bit had excellent cuttings carrying and reverse circulation ability. The average penetration rate of the drill bit reached about 1.7 m/h in the formation of weathered sandstone with argillaceous composition, which was 1.7 times greater than conventional mud drilling in the same area.

Success and challenges in predicting TBM penetration rate using recurrent neural networks

Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro lines, with totally different geological conditions. In the experiments, the one-step forecast of TBM penetration rate by the traditional recurrent neural network (RNN) or long short-term memory (LSTM) is relatively accurate, irrespective of the different geological conditions used in training and evaluation. Predicting the next Nth step penetration rate proves to be more challenging and depends on the time to the future or the distance ahead of the TBM cutterhead. There are generally time lags between measured and predicted results. The recursive RNN is then developed to address the lag problems, but to no avail. Alternative methods for predicting future penetration rates are studied, including the penetration rate at the Nth step in the future and the average penetration rate of the next N steps, with the latter being trained by long-input or short-input methods. The average N-step forecast using short inputs provides the best results, and its performance over other alternatives becomes more distinct as the number N increases. We also discuss the possibility of the forecast problem as a quasi-random walk, which means that forecasting penetration rate cannot easily be achieved using low-frequency data with RNNs, and that the accuracy depends on the correlation between the last and predicted steps in the data.

Development and Verification of Triple-Ridge-Shaped Cutter for PDC Bits

Summary Polycrystalline diamond compact (PDC) cutter is the key component of PDC bits, whose rock-breaking characteristics are particularly crucial for the bit performance. Based on the understanding on rock-breaking characteristics of various shaped cutters, the multiridge-shaped cutter has been developed to improve the cutting efficiency and durability of PDC cutters. The design of multiple ridges combines the advantages of both the shaped cutter and the reasonable narrow cutter spacing of the bit cutting structure. More specifically, a triple-ridge-shaped cutter (TSC) was compared with the widely used axe-shaped cutter (ASC) in this work to recognize the superiority of the novel multiridge cutter shape. Numerical simulation, laboratory experiments, and field testing were performed to investigate the cutter-rock interactions of TSC and ASC. Numerical simulations were carried out using nonlinear finite element software LS-DYNA and verified by the single cutter testing. Both numerical and experimental results proved that the stress concentration caused by the triple-ridge cutter shape of TSC is significantly greater than that of ASC during the cutter-rock interaction. And the tangential force of TSC is much less than that of ASC under the same cutting conditions, indicating that the PDC bit equipped with TSCs requires less torque while drilling. According to the results of the fractal analysis model of cuttings and the mechanical specific energy (MSE), TSC consumed less drilling energy than ASC to break the same volume of rock, which demonstrated that the TSC has greater cutting efficiency than ASC. Beneficially, the greater cutting aggressivity of TSC in turn improves the wear resistance. The laboratory wear and impact tests presented that TSC outperforms ASC in both wear resistance and impact resistance, indicating better durability under downhole. Although only one controlled experiment was conducted in the field, the field test result showed over 100% improvement in average rate of penetration. In short, the numerical, laboratory, and field testing have confirmed a great potential of TSC in improving the cutting efficiency and durability of PDC bits. The results of this work will provide valuable guidance for the development of shaped PDC cutters to overcome engineering limitations.

O–D matrix estimation based on data-driven network assignment

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem. Abbreviations APR: average penetration rate; DDNA: data driven network assignment; DDNL: data driven network loading; DODME: dynamic OD matrix estimation; DTA:dynamic traffic assignment; FCD: floating-car data; ITS: intelligent transportationsystems; NNLSQ: non-negative least squares; OD: origin destination; ODME: ODmatrix estimation; PV: probe vehicle; RUM: random utility model