Penetration Rate Research Articles

An Automated Approach for Discriminating Hole Cleaning Efficiency While Predicting Penetration Rate in Egyptian Western Desert Wells

AbstractHigher rate of penetration (ROP) indicates successful drilling operation but is not the only drilling success measure. However, Conventional ROP prediction methods focus on increasing ROP and neglect the hole cleaning state, which can be altered by ROP changes. Higher ROP in vertical and deviated wells may increase cutting concentration, leading to hole cleaning problems such as overpulling and stuck pipe. With this problem in mind, this paper utilized geological, rheological, and drilling data of 31 vertical wells across four oil fields located in the Egyptian Western Desert, developed intelligent ROP prediction model through back propagation neural network (BPNN), and compared the proposed BPNN results with an empirical model. Finally, the pattern recognition algorithms including discriminant analysis, support vector machines, and neural network pattern recognition (NNPR) were implemented to discriminate hole cleaning efficiency following the ROP prediction process. Recognition models were developed based on predicted ROP, bit wear rate, specific energy, and drilling fluid carrying capacity index to evaluate hole cleaning. The accuracy of the multi-strategy classifier was evaluated using area under curve, confusion matrix, and receiver operating characteristic. The BPNN model outperformed the empirical model in terms of linear correlation coefficient (R = 98.6%) and average absolute error (AAE = 5.5%). Additionally, the best classification performance was achieved using the NNPR algorithm with 91% accuracy and a cross-validation error equal to zero. For validity, the proposed approach predicted ROP and classified hole cleaning efficiency for new vertical well in adjacent oil field, resulting in a 6% improvement in ROP.

WOB fuzzy control and experimental study on horizontal well experimental setup

This paper developed a horizontal drillstring-wellbore-bit experimental setup based on the similarity of physical behaviors. A data acquisition and control system was established to achieve real-time control over the Rotations Per Minute (RPM) and Rate of Penetration (ROP). Combining experts’ experience and practical drilling experiments, a constant Weight on Bit (WOB) fuzzy controller was designed for rock-breaking experiment. To validate the reliability of the control system, control tests were conducted with different WOB settings at varying RPM. The results indicated that the WOB control error was within 60 N, with an error rate below 3%. Rock drilling experiments were conducted on different rocks. In sandstone drilling experiments, as WOB increased from 600N to 900 N, the ROP, Torque on Bit (TOB), and Stick-slip Vibration Intensity (SSVI) increased significantly, reaching 114.12, 3.6, and 2.97 times of the original values, respectively. When the RPM increased from 21 to 30 RPM, TOB, SSVI, and ROP changed to 1.43, 0.89, and 3.84 times of the original values. The increase in ROP during sandstone drilling was evident, suggesting a moderate increase in drilling parameters, with caution against stick-slip vibrations, which can be mitigated by increasing RPM. In limestone drilling experiments, as WOB increased from 900 to 3300 N, TOB, SSVI, and ROP increased by 2.45, 3.17, and 56.34 times, respectively. When RPM was increased from 45 to 63 RPM, TOB, SSVI, and ROP changed by 0.88, 0.74, and 1.68 times, respectively. Under conditions of high WOB and RPM, the limestone drilling speed increased more significantly.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

A Computational Fluid Dynamics Study on the Effect of Drilling Parameters on Wellbore Cleaning in Oil Wells

Poor wellbore cleaning is a significant challenge in oil drilling, primarily due to the accumulation of cuttings at the bottom of the well, particularly in deviated and horizontal wells. This study addresses this issue by employing Computational Fluid Dynamics (CFD) with the commercial software ANSYS FLUENT (2023-R1) to simulate a solid–liquid multiphase flow in an annulus. The primary objective is to analyze the cuttings concentration, pressure loss, and solid velocity profiles across various drilling parameters, including drill pipe rotation, the flow rate, rate of penetration, inclination angle, and fluid rheology. Our results underscore the critical role of these parameters in enhancing cuttings transport efficiency. Specifically, the drill pipe rotation, flow rate, and rate of penetration emerge as the most influential factors affecting the wellbore cleaning performance. With a validated model exhibiting an average error of 4.24%, this study provides insights into optimizing drilling operations to improve wellbore cleaning and increase hydrocarbon recovery.

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.

Fast and Efficient Mitigation of Coupled Axial-Torsional Drillstring Vibrations through State-Feedback Linear-Quadratic-Integral (LQI) Control

Summary In the exploitation of subsurface hydrocarbon and geothermal energy, more than 50% of total expenses are typically attributed to drilling and well completion. Coupled triaxial vibrations in the drilling system, excited by various mechanical, hydraulic, and geological sources, are major causes of premature downhole tool failures, excessive bit damage, compromised hole quality, reduced rate of penetration (ROP), and increased nonproductive time. Drillstring dynamic models, shock absorption tools, and controllers have been developed since the 1960s to understand and mitigate detrimental vibrations in the drilling system, aiming for smoother and more efficient operations. With advancements in measurement/logging while drilling technologies, rig equipment, mud pulse/wired pipe telemetry, and the digitalization of drilling engineering, a foundation has been established for proactive drilling vibration detection, analysis, and control to achieve efficient and safe drilling. In this study, a reduced-order axial-torsional drillstring dynamic model with soft string lateral contacts is first developed. Nonlinear drilling boundary conditions and inputs, such as bit-rock interactions (BRIs) with torque-on-bit (TOB) velocity-weakening effects, wellbore-drillstring contacts/friction, drillstring gravity, mud buoyancy, and more, are defined within the modeling framework. Equilibrium states and linearization of the model are derived for any given weight-on-bit (WOB) and rotational speed (RPM) setpoints, 3D well trajectories, and drillstring dimensions. A linear-quadratic-integral (LQI) controller is developed and tested for drillstring vibration suppression using a 6,562 ft (2000 m) drillstring model. In comparison to commercially available controllers for stick/slip mitigation, which are designed based on impedance matching and only address the decoupled drillstring torsional dynamics, the proposed state-feedback LQI controller emphasizes the coupling between the axial and torsional drillstring dynamics by simultaneously controlling WOB and RPM. Simulation results demonstrate that the LQI controller can suppress stick/slip and stabilize downhole WOB within 7 seconds, while a commercial drilling controller takes more than 35 seconds to achieve the same. Additionally, valuable insights and potential future directions for nonlinear drillstring vibration mitigation and drilling controller design are provided. With its fast drilling dysfunction stabilization time, small downhole RPM/WOB overshoots, minimal rig control inputs, robustness against drilling nonlinearities, and high efficiency, the proposed multi-input-multi-output state-feedback drilling controller holds great potential for application in proactive drilling vibration control, drilling automation, and real-time drilling optimization.

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky–Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model’s robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model’s predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

Assessment of the Viability of Community-Based Health Insurance Schemes in the Ziguinchor Department from 2016 to 2018 in the Context of Universal Health Coverage (Senegal)

<i>Introduction</i>: This study examines the viability of community-based health insurance schemes in the Ziguinchor department of Senegal from 2016 to 2018, within the context of universal health coverage. Health insurance schemes have emerged as a promising solution to extend social health protection to informal sector workers and rural populations. However, despite political support, these structures face persistent challenges in terms of enrollment, member retention, and management. The study aims to assess the viability of these schemes, as well as the perceptions of various stakeholders on mutual health insurance. <i>Methodology</i>: The study adopted a mixed approach, combining quantitative and qualitative methods, based on the theoretical framework developed with the support of the International Labor Office (ILO). This framework evaluates the viability of health insurance schemes according to four dimensions: institutional, technical, functional, and financial/economic. A questionnaire with 87 questions was used to collect quantitative data on these aspects from the seven health insurance schemes in the Ziguinchor department. For the qualitative component, focus groups and individual interviews were conducted with various actors, including beneficiaries, non-beneficiaries, community leaders, scheme managers, and healthcare providers. <i>Results</i>: The study's findings reveal a complex situation for the health insurance schemes in the Ziguinchor department. Institutionally, the schemes have a solid foundation with legal status, internal regulations, and affiliation to a departmental union. Technically, membership is voluntary and family-based, with control mechanisms in place, although their effectiveness is limited. Functional viability shows positive signs with a membership growth rate of 20.18%, but the overall penetration rate remains low at 16.99%, and the premium collection rate is very low, averaging 26.78%. The financial situation is particularly of concern: no scheme can meet its short or long-term debts, and the average claims ratio is excessively high at 282%. As for stakeholder perceptions, they are mixed: while the schemes are generally considered beneficial, significant practical difficulties are identified, including lack of information, funding problems, and procedural complexity. <i>Conclusion</i>: The study highlights the complexity of the role of health insurance schemes in the quest for universal health coverage. Despite undeniable strengths, the schemes face considerable challenges in terms of functional and financial viability. Corrective measures are necessary, including strengthening management capacities, innovating premium collection methods, and improving communication. An evolution of the model is also suggested, potentially towards greater integration into a national social protection system or the development of innovative public-private partnerships.

Research on the Influencing Factors of High-quality Development of Export Enterprises

The Internet and big data have a profound impact on export enterprises, and their high-quality development is also facing unprecedented opportunities and challenges. This paper takes small and medium-sized enterprises of sales oriented export trade as the research sample, and uses the BP neural network model to conduct empirical research on the factors affecting the high-quality development of enterprises. Research conclusions: (1) A influencing factor system for high-quality development of export enterprises has been constructed, which includes 26 specific factors; (2) The factors in order of impacts: convenience of shopping operations, website content, comprehensive quality of employees, product awareness, consumer penetration rate, internationalization level, enterprise scale and reputation; (3) Factors such as overall employee quality, internationalization level, employee training system and effectiveness, network security, internationalization style of website design, convenience of shopping operations, sales growth rate of the enterprise, and profit growth rate do not have a direct, absolute positive relationship with the high-quality development of the enterprise.

A rate of penetration (ROP) prediction method based on improved dung beetle optimization algorithm and BiLSTM-SA

In the field of oil drilling, accurately predicting the Rate of Penetration (ROP) is crucial for improving drilling efficiency and reducing costs. Traditional prediction methods and existing machine learning approaches often lack accuracy and generalization capabilities, leading to suboptimal results in practical applications. This study proposes an end-to-end ROP prediction model based on BiLSTM-SA-IDBO, which integrates Bidirectional Long Short-Term Memory (BiLSTM), a Self-Attention mechanism (SA), and an Improved Dung Beetle Optimization algorithm (IDBO), incorporating the Bingham physical equation.We enhanced the DBO algorithm by using Sobol sequences for population initialization and integrating the Golden Sine algorithm and dynamic subtraction factors to develop a more robust IDBO. This optimized the BiLSTM-SA model, resulting in a BiLSTM-SA-IDBO model with an RMSE of 0.065, an R² of 0.963, and an MAE of 0.05 on the test set. Compared to the original BiLSTM-SA model, these metrics improved by 78%, 21%, and 83%, respectively. Additionally, we compared this model with BP Neural Network, Random Forest, XGBoost, and LSTM models, and found that our proposed model significantly outperformed these traditional models. Finally, through practical testing, the model’s excellent predictive ability and generalization were verified, demonstrating its great potential for practical applications.

Asymmetric effects of life and non-life insurance on economic growth in Saudi Arabia: A nonlinear analysis

This paper explores the asymmetric effects of life and non-life insurance on economic growth in Saudi Arabia. Quarterly data on insurance penetration rates and GDP growth from 2009 to 2022, obtained from the Global Economy, Saudi Arabian Monetary Authority, and World Bank databases, are utilized. A nonlinear autoregressive distributed lag (NARDL) model is employed to examine the relationships between insurance and growth. The results reveal a significant and asymmetric relationship between life insurance penetration and GDP growth. Specifically, a 1% increase in life insurance penetration is associated with a 0.3% increase in GDP growth in the long run, while a 1% decrease shows no significant effect. For non-life insurance, both increases and decreases demonstrate significant but asymmetric impacts on growth. A 1% increase in non-life insurance penetration corresponds to a 0.2% increase in GDP growth, whereas a 1% decrease is linked to a 0.15% decrease in GDP growth. These findings support the ‘supply-leading’ hypothesis, suggesting that the insurance sector can play a leading role in promoting economic growth. The results provide new quantitative insights into the relationship between insurance and economic growth in Saudi Arabia, offering valuable implications for policymakers. It is suggested that the insurance industry can be leveraged to foster economic growth by promoting life insurance and managing the asymmetric impacts of non-life insurance.

Experimental and numerical investigation on the relationship between stick–slip vibration and rate of penetration

Experimental and numerical investigation on the relationship between stick–slip vibration and rate of penetration

Liquid Water Transport and Distribution in the Gas Diffusion Layer of a Proton Exchange Membrane Fuel Cell Considering Interfacial Cracks

The proton exchange membrane fuel cell (PEMFC), with a high energy conversion efficiency, has become an important means of hydrogen energy utilization. However, water condensation is unavoidable in the PEMFC because of low operating temperatures. The impact of liquid water on PEMFC performance and stability is significant. The gas diffusion layer (GDL) provides a critical transport path for liquid water in the PEMFC. Liquid water saturation and distribution in the GDL determine water flooding and mass transfer efficiency in the PEMFC. In this study, focusing on the effects of the water introduction method, osmotic pressure, and contact angle, the liquid water transport in the GDL was numerically investigated based on a pore-scale model using the volume of fluid (VOF) method. The results showed that compared with the conventional water introduction method without cracks, the saturation and spatial distribution of water inside the GDL obtained in the simulation were more consistent with the experimental results when the water was introduced through the microporous layer (MPL) crack. It was found that increasing the osmotic pressure resulted in a faster rate of water penetration, faster approaching the steady-state performance, and higher saturation. The ultra-high osmotic pressure contributed to the secondary breakthrough with a significant increase in saturation. Increasing the contact angle caused higher capillary resistance, especially in the region with small pore sizes. At a constant osmotic pressure, as the contact angle increased, the liquid water gradually failed to penetrate into the small pores around the transport path, causing saturation reduction and an ultimate failure to break through the GDL. Increasing the contact angle contributed to a higher breakthrough pressure and secondary breakthrough pressure.

Modeling and Estimating the Effect of a Mix of Varying Levels of Automated Vehicles on Operational Performance of Urban Freeways

Automated vehicles (AVs) will likely influence various aspects of the existing transportation system in the next few decades. Existing literature documents the operational benefits of AVs at different penetration rates. However, estimating the effect of the mix of different levels of AVs on the operational performance of traffic flow has not been explored in the past. This study employs a microsimulation approach to model and estimate the potential effect of varying levels of AVs on the operational performance of freeways. The proposed approach involves a two-step process for calibrating the simulation model. In the first step, the model is calibrated from an operational perspective. In the second step, the calibration is performed from a safety perspective. Thirteen scenarios were generated considering varying penetration rates of different levels of AVs. Congestion index (CI), buffer time (BT), travel time uncertainty (TTU), and delay were computed for each scenario and segment type. The estimations indicate that increasing the penetration of level 3 to level 5 AVs significantly reduces CI, BT, TTU, and delay. In contrast, the effect of level 1 and 2 AVs on freeway operational performance is negligible. Notably, the operational benefits are more pronounced with higher penetration of level 3 and higher AVs. Additionally, the reduction in delay is greater on interchange segments compared with basic freeway segments. The study findings are valuable insights concerning the operational performance of freeways under varying penetration of different levels of AVs, assisting practitioners in formulating AVs-inclusive policies.

Modeling rate of penetration using hybridization of Artificial Neural Network and Artificial Fish Swarm algorithm

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the Artificial Fish Swarm Algorithm (AFSA) with the Back Propagation (BP) algorithm in an Artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young’s model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model’s applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure

This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers a cost-effective, rapid, and adaptable method for evaluating soil resistance, making it suitable for liquefaction assessment across diverse soil conditions. This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. The SVM-PSO model demonstrated superior performance, with high R2 values of 0.999 and 0.989 in the training and testing phases, respectively. The proposed methodology offers a sustainable and accurate approach for liquefaction assessment, reducing the environmental impact of geotechnical investigations, while ensuring reliable predictions. This study bridges the gap between field testing and advanced computational techniques, providing a powerful tool for geotechnical engineers to assess liquefaction risks and design resilient infrastructures.

Reproducibility of Cardiac Multifrequency MR Elastography in Assessing Left Ventricular Stiffness and Viscosity.

Cardiac magnetic resonance elastography (MRE) shows promise in assessing the mechanofunctional properties of the heart but faces clinical challenges, mainly synchronization with cardiac cycle, breathing, and external harmonic stimulation. To determine the reproducibility of in vivo cardiac multifrequency MRE (MMRE) for assessing diastolic left ventricular (LV) stiffness and viscosity. Prospective. This single-center study included a total of 28 participants (mean age, 56.6 ± 23.0 years; 16 male) consisting of randomly selected healthy participants (mean age, 44.6 ± 20.1 years; 9 male) and patients with aortic stenosis (mean age, 78.3 ± 3.8 years; 7 male). 3 T, 3D multifrequency MRE with a single-shot spin-echo planar imaging sequence. Each participant underwent two cardiac MMRE examinations on the same day. Full 3D wave fields were acquired in diastole at frequencies of 80, 90, and 100 Hz during a total of three breath-holds. Shear wave speed (SWS) and penetration rate (PR) were reconstructed as a surrogate for tissue stiffness and inverse viscous loss. Epicardial and endocardial ROIs were manually drawn by two independent readers to segment the LV myocardium. Shapiro-Wilk test, Bland-Altman analysis and intraclass correlation coefficient (ICC). P-value <0.05 were considered statistically significant. Bland-Altman analyses and intraclass correlation coefficients (ICC = 0.96 for myocardial stiffness and ICC = 0.93 for viscosity) indicated near-perfect test-retest repeatability among examinations on the same day. The mean SWS for scan and re-scan diastolic LV myocardium were 2.42 ± 0.24 m/s and 2.39 ± 0.23 m/s; the mean PR were 1.24 ± 0.17 m/s and 1.22 ± 0.14 m/s. Inter-reader variability showed good to excellent agreement for myocardial stiffness (ICC = 0.92) and viscosity (ICC = 0.85). Cardiac MMRE is a promising and reproducible method for noninvasive assessment of diastolic LV stiffness and viscosity. 2 TECHNICAL EFFICACY: 1.

Biochar influences phytoremediation of heavy metals in contaminated soils: an overview and perspectives.

Heavy metals (HMs) contamination has gained much attention due to its high degree of toxicity for living organisms. Therefore, different techniques are underway to eradicate HMs from the environment. Among the biological techniques, phytoremediation is a suitable method, but owing to the slow rate and chance of HMs penetration into the food chain, alternative techniques are needed to reduce their phytotoxicity, and biochar is one of them. Due to the diverse characteristics, biochar immobilizes HMs in the soil by improving soil pH, ion exchange, electrostatic interactions, complexation, precipitation, surface adsorption, and microbial activation. Thereby, amendment of biochar in the HMs-contaminated soils reduces HMs toxicity to plants and limits their penetration into the food chain. In contrast, some biochars have also been studied to induce metal availability in soils and subsequently its uptake by plants. This dual role of biochar depends on the feedstock of biochar, incineration temperature, and the rate of application. Moreover, biochar treatments enhance plant growth under HMs stress by improving nutrient availability, water retention capacity, scavenging of reactive oxygen species, and photosynthetic efficiency. Owing to the beneficial characteristics of biochar in HMs-contaminated sites, the number of publications has tremendously increased. Additionally, the plant species and the types of HMs that have been tested frequently under biochar treatments in these articles have been studied. Overall, the current study would help in understanding the mechanisms of how biochar influences phytoremediation of HMs and improves plant growth in HMs-polluted soils and the current scenario of the available literature.

Prediction of TBM cutter wear in heterogeneous ground under high ambient pressure

To prevent tunnel face collapse in heterogeneous ground, the applied face pressure in the excavation chamber can be increased to stabilise the tunnel face, which may hinder cutter rotation during tunnel boring machine (TBM) tunnelling. This study proposes a TBM cutter wear prediction model that considers the impact of the variation in the cutter normal force on the cutterhead owing to the high applied face pressure and low penetration rate. First, the cutter motion mode in heterogeneous ground is investigated. The cutter is found to slide when cutting soft rock based on an analysis of the field data. The evolution process of the cutter wear morphology under heterogeneous ground conditions is summarised. A method for calculating the cutter normal force when the cutter slides on the tunnel face is proposed by referring to the cutting mechanism of the drag bits. The cutter hardness is measured on-site using a portable hardness tester. A semi-empirical model considering the variation in the cutter normal force on the cutterhead is proposed to predict the cutter wear in heterogeneous ground. Wear prediction is compared with other predictive models and field data for validation, and the discrepancies between the different models are discussed. The results show that the proposed model is effective for predicting TBM cutter wear under complex geological conditions.

Evaluation and optimization of drilling efficiency while drilling based on improved rock-breaking specific energy model of bit

Rock-breaking specific energy model of bit is the key foundation of evaluation and optimization of downhole drilling condition, while some necessary parameters for the existing models is difficult to measure directly while drilling. Aiming to improve the accessibility and accuracy of the model, this study proposes and illustrates an improved method by combining field tests and logging while drilling. The improved model was used to distinguish the inefficient drilling conditions and then to optimize drilling parameters. The results of application cases show that, the necessary parameters for the improved model could be obtained in time and the improved model helps to distinguish the reasons of stick–slip, whirling, load transfer difficulties in different horizontal wells. Based on the proposed model, the drilling parameters were evaluated and optimized, and then the rate of penetration get improved by 90.5% with higher energy efficiency. The proposed model and method could facilitate to realize automated and intelligent drilling.