Prediction Of 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.

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.

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.

Stop Using Black-Box Models: Application of Explainable Artificial Intelligence for Rate of Penetration Prediction

Summary Rate of penetration (ROP) prediction plays a crucial role in optimizing drilling efficiency and reducing overall costs in the petroleum industry. Although modern artificial intelligence (AI) models have shown promising performance in this task, their lack of interpretability hinders their practical applications. This study introduces the neural basis model (NBM), a self-explainable model, for ROP prediction. The adopted NBM is benchmarked against some well-known methods using a publicly available data set, demonstrating its promising performance. The key advantage of the NBM lies in its ability to provide clear explanations, where the influence of the input drilling parameters on the predicted ROP can be clearly visualized and analyzed. The study also compares the models’ performance under two scenarios—continuous learning and all-for-one. The results indicate that the models’ performance under the continuous learning scenario, in which the models are iteratively updated with new data from the same well, outperforms that under the all-for-one scenario, where models are trained on data from previously drilled wells and applied to new wells. The discrepancy is probably due to the absence of detailed formation characteristics from the data set. Therefore, the model trained in other wells could not generalize well on new wells. Currently, in petroleum engineering, AI is gradually playing an increasingly important role; however, the majority of AI-related works often directly employ black-box models, which lack interpretability and might cause serious risks if deployed in practice. This work introduces an approach to using neural networks to build self-explainable AI (XAI), with the aim of promoting the application of XAI in the petroleum industry.

A highly accurate and robust prediction framework for drilling rate of penetration based on machine learning ensemble algorithm

The rate of penetration (ROP) is a key indicator of drilling efficiency. Many researchers have explored the application of machine learning in ROP prediction. However, few studies have focused on the robustness of the constructed models, and developing a ROP prediction model that can achieve both high accuracy and strong robustness remains a challenge. This paper introduces a novel machine learning approach to constructing a ROP prediction model through ensemble learning algorithms. The model is based on field data from oilfields, incorporating 16 input parameters that influence ROP. First, the feasibility of the collected dataset is verified using correlation analysis. Then, ROP prediction models are developed based on various machine learning algorithms, including Decision Tree Regression (DTR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). By comparing the performance of these models under noise levels of 0%, 1.7%, and 5.1%, RF, LGBM, XGB, and SVR are selected as base learners. These base learners are then combined to construct multiple ensemble models, and the performance of the optimal ensemble model is evaluated under varying noise levels. The results show that the prediction error of the optimal model remains within 10%, and R2 is greater than 0.96. Finally, the Shapley Additive Explanations (SHAP) method is applied to perform interpretability analysis on the optimal ROP prediction model, examining the impact of different input factors on the model's predictive performance. Compared to single models and other ensemble models, the proposed ensemble model not only achieves higher accuracy but also demonstrates strong robustness and generalization capability.

Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach.

Prediction of the ROP based on GA-LightGBM and drilling data

ABSTRACT Constructing oil and gas wells is one of the most expensive activities in the petroleum sector, and increasing drilling speed is crucial for reducing costs, minimizing non-productive time (NPT), and improving efficiency. To address this challenge, a data-driven predictive model for mechanical drilling speed was developed using an artificial intelligence approach. Several machine learning algorithms – including decision trees, support vector regression, k-nearest neighbors, neural networks, XGBoost, and LightGBM – were selected to thoroughly investigate the nonlinear relationship between drilling data and the rate of penetration (ROP). These models were trained, evaluated, and compared, with results indicating that the LightGBM algorithm provided the most accurate ROP predictions. Building on this, the LightGBM model was further refined using cross-validation techniques and a genetic algorithm (GA) to optimize its hyperparameters, resulting in even greater accuracy. The research findings demonstrate that the GA-LightGBM algorithm achieves high precision in ROP prediction on the test set (MAE = 2.39, MSE = 10.53, R2 = 0.93). This model effectively predicts mechanical drilling speed in various well sections, thereby aiding in optimizing the drilling process, improving production efficiency, and reducing costs.

Research on adaptive feature optimization and drilling rate prediction based on real-time data

In the drilling process, the rate of penetration (ROP) is an essential indicator of drilling efficiency. Accurate prediction of the rate of penetration (ROP) is crucial for improving drilling efficiency and minimizing costs. Conventional ROP predictions are typically made post-drilling, often resulting in low accuracy and limited applicability. To predict ROP more efficiently and accurately, an adaptive feature-optimized ROP prediction method based on real-time data was proposed. Initially, a fusion feature selection algorithm based on historical drilling data was designed and optimized using the Particle Swarm Optimization algorithm (PSO). On this basis, adaptive feature optimization was performed on real-time drilling datasets, and four different machine learning ROP prediction models were established using the selected feature parameters. The result shows that compared with the non-adaptive methods, the accuracy of the ROP prediction model using adaptive feature optimization is improved. The improvements are 27%, 44%, 47% and 47%, respectively, it demonstrates the significant advantages of using the designed adaptive feature optimization.

Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks

Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks

Combining Bourgoyne and Young Equations by Bagging Tree Regression to Predict Rate of Penetration in a Southern Iraqi Field, Case Study

Abstract Achieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain knowledge and capturing non-linear relationships. Its ensemble nature also mitigates the impact of outliers. This approach combines physics-based equations with machine learning to enable more accurate ROP predictions in drilling operations. It enhances drilling efficiency, reduces expenses, and improves decision-making in the oil and gas sector. Extensive testing on actual drilling datasets has demonstrated outstanding performance compared to the multiple linear regression (MLR) method. With the increased R2 and zero P-value. positive findings show that this tool can benefit precise future ROP prediction in southern Iraqi oil well drilling.

Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block

Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction.

Domain adaptation neural network based prediction of real-time drill bit tooth wear

Drill bit wear reduces drilling efficiency and escalates operational risks. The drill bit tooth wear grading is a measure of lost tooth height. Accurate monitoring of drill bit tooth wear grade is crucial to adjust drilling parameters and plan timely bit replacement, significantly enhancing drilling efficiency and safety. Current real-time monitoring methods for drill bit wear exhibit limitations, particularly in accounting for variations in the geological formation and drilling parameters, resulting in poor model stability. This paper proposes an intelligent modeling approach that utilizes a Domain Adaptation Neural Network (DANN) to establish an ideal drilling rate of penetration (ROP) prediction model. By incorporating measured ROP, wear coefficients are calculated, and a relationship between wear coefficient and tooth wear grade is established, facilitating real-time monitoring of drill bit tooth wear grade. Test results on ten drill bits demonstrate that the DANN ideal ROP prediction model, leveraging data from new drill bits, enhances the model's adaptability to varying working conditions for new drill bits, thereby improving the accuracy of ideal ROP predictions. We establish relationship between calculated wear coefficients and tooth wear grades. The results indicate that the average error in inner wear grade is 0.6 (7.5%) and the average error in outer wear grade is 1.3 (16%) on an 8-point scale which demonstrates the model's effectiveness in monitoring drill bit tooth wear.

Deep Learning Method for Improving Rate of Penetration Prediction in Drilling

Summary The urgent global need to reduce CO2 emissions necessitates the development of sustainable power generation sources. Geothermal power emerges as a renewable and dependable energy option, harnessing the Earth’s natural heat sources for electricity generation. Unlike other renewables, geothermal energy offers uninterrupted power, immune to weather conditions. However, its efficiency hinges on technological innovation, particularly in the challenging realm of geothermal drilling. Rate of penetration (ROP) is a crucial drilling performance metric, and this study explores how deep learning models, particularly transformers, can optimize ROP prediction. Leveraging data from Utah Frontier Observatory for Research in Geothermal Energy (FORGE), we analyze the relationship between drilling parameters and ROP. Traditional drilling optimization methods face limitations, as drilling dysfunctions can disrupt the linear relationship between ROP and weight on bit (WOB). We propose a dynamic approach that allows adapting drilling parameters in real time to optimize ROP. Our experiments investigate optimal sampling intervals and forecast horizons for ROP prediction. We find that a 60-second sampling interval maximizes the transformer model’s forecasting accuracy. Additionally, we explore retraining to fine-tune models for specific wells, improving forecasting performance. Our transformer-based ROP forecaster outperforms deep learning models, achieving a low overall 5.22% symmetrical mean average percentage error (SMAPE) over a forecast horizon of 10 minutes. This model offers opportunities for cost-effective drilling optimization, with real-time accuracy, speed, and scalability. Future work will focus on larger data sets and integration with drilling automation systems to further enhance the model’s practicality and effectiveness in the field.

Prediction of penetration rate and optimization of weight on a bit using artificial neural networks

Relevance. Achieving the greatest rate of penetration is the aim of every drilling engineer because it is one of the most significant factors influencing drilling costs. However, a variety of drilling conditions could have an impact on rate of penetration, complicating its forecast. Aim. To suggest a novel strategy to accurately predict rate of penetration and optimize drilling parameters. Objects. Real-time drilling data of a few wells in the Ca Tam oil field, Vietnam, with more than 900 datasets including significant parameters like rotary speed, weight on bit, standpipe pressure, flow rate, weight of mud, torque. Methods. Various methods using Artificial Neural Network was proposed to estimate rate of penetration. Results. The number of neurons in a hidden layer was varied then the results of different Artificial Neural Network models were compared in order to obtain the optimal model. The final Artificial Neural Network model shows high exactness when contrasted with actual rate of penetration, in this manner, it tends to be suggested as a successful and reasonable approach to predict the rate of penetration of different wells in the Ca Tam oil field. Based on the proposed Artificial Neural Network model, the optimal weight on bit was determined for the drilling interval from 1800 to 2300 m of oil wells in the research region.

Research on adaptive prediction model of rate of penetration under dynamic formation conditions

Rate of Penetration (ROP) prediction is a crucial for optimizing drilling processes and enhancing drilling efficiency. However, most existing ROP prediction models are static, relying on fixed neighboring well datasets and incapable of adapting to real-time changes in formation conditions during drilling. The dynamic and uncertain nature of geological conditions and formation heterogeneity further limits the extension and application of current ROP prediction models to target wells. To address these challenges, this study introduces an Adaptive Random Forest (ARF) algorithm to establish an ROP adaptive prediction model under dynamic formation conditions during real-time drilling operations. The performance of the ARF model is compared with the Moving-Window Random Forest (MW-RF) ROP dynamic prediction model, using data from the W1 well in the Sichuan basin as an example. Experimental results demonstrate that the ARF model outperforms the MW-RF model, exhibiting smaller overall error and reduced error fluctuation during formation condition changes. The ARF model's dynamic adaptability allows it to respond to the evolving trends of the ROP prediction model, rapidly adjusting to changes in formation conditions based on real-time drilling data flow. In contrast, the MW-RF model exhibits slower adaptability and delays in updating, reacting passively to window movements. The proposed ARF model overcomes limitations associated with static ROP prediction models, making it applicable to practical drilling scenarios and providing a theoretical foundation for real-time drilling parameter optimization and intelligent drilling strategies.

Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin

Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models’ advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R2 of 0.9936, had the best performance, followed by the simple average model and bagging with R2 values of 0.9394 and 0.5998, respectively. From the view of prediction accuracy, and model interpretability, the hybrid physics-ML model with residual modeling is the optimal method for ROP prediction.

Penetration rate prediction for drilling wells in the Oligocene formation

The main role of drilling optimization is a decrease in the drilling cost and non-productive time (NPT) for drilling operations. The penetration rate directly influences the overall cost and cost per foot of drilling operation. Thus, the penetration rate prediction and optimization for drilling wells is one of the most crucial parameters to enhance drilling efficiency. Normally, physics-based ROP modeling is widely used to predict bit response or investigate ROP by using nearby offset data. Due to the complexity and nonlinear of ROP, and the confidence level of ROP models with low R squares, data-driven modeling such as machine learning (ML) has become a more attractive study. This paper has been developed on ROP models using artificial neural network (ANN) and compares the results of physics-based ROP models such as the Maurer model, Bingham model, Warren model for perfect cleaning model, Warren model for imperfect cleaning model, and multiple regression based on the significant level of correlation coefficients of R square from models. Drilling Oligocene formations on 8-1/2’’ hole sections have been collected from six drilled wells in the continental shelf of offshore Vietnam. The ROP prediction results were obtained from the ANN model compared with physics-based models. This comparison has shown that the predictive ROP of the power ANN model with an R square confidence level is higher than that of physics-based models.

Real-time prediction of ROP based on GRU-Informer

Accurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs. ROP prediction is influenced by multiple factors, making accurate prediction challenging. Current research primarily relies on historical data for training and modeling, lacking methods for real-time ROP prediction. This paper introduces a GRU-Informer model for real-time ROP prediction. The model employs GRU (Gated Recurrent Unit) neural networks at the lower level to capture short-term correlations in drilling parameters and uses the Informer model at the top to address long-term dependencies among drilling parameters. Thus, the GRU-Informer can capture both short-term and long-term time dependencies, providing better ROP predictions. This paper constructs a dataset using historical data from a southwestern Chinese oil field for experimentation. RMSE (Root Mean Square Error), MAE (mean absolute error) and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} (Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural networks and Informer in real-time ROP prediction, indicating its practical value.

A study of rotary cutting machine (RCM) performance on Korean granite

PurposeDespite the many advantages this type of equipment offers, there are still some major drawbacks. Linear cutting machine (LCM) cannot accurately simulate the true rock-cutting process as 1. it does not account for the circular path along which tunnel boring machine (TBM) disk cutters cut the tunnel face, 2. it does not accurately model the position of a disk cutter on the cutterhead, 3. it cannot perfectly replicate the rotational speed of a TBM. To enhance the knowledge of these issues and in order to mimic the real rock-cutting process, a new lab testing equipment was developed by Hyundai Engineering and Construction.Design/methodology/approachA new testing machine called rotary cutting machine (RCM) is designed to simulate the excavation process of hard-rock TBMs and includes features such as TBM cutterhead, RPM simulation, constant normal force mode and constant penetration rate mode. Two sets of tests were conducted on Hwandeung granite using different disk cutter sizes to analyze the cutting forces in various excavation modes. The results are analyzed using statistical analysis and dimensional analysis. A new model is generated using dimensional analysis, and its results are compared against the results of actual cases.FindingsThe effectiveness of the new RCM test was demonstrated in its ability to apply various modes of excavation. Initial analysis of chip size revealed that the thickness of the chips is largely dependent on the cutter spacing. Tests with varying RPM showed that an increase in RPM results in an increase in the normal force and rolling force. The cutting coefficient (CC) demonstrated a linear correlation with penetration. The optimal specific energy is achieved at an S/p ratio of around 15. However, a slightly lower S/p ratio can also be used in the design if the cutter specifications permit. A dimensional analysis was utilized to develop a new RCM model based on the results from approximately 1200 tests. The model's applicability was demonstrated through a comparison of TBM penetration data from 26 tunnel projects globally. Results indicated that the predicted penetration rates by the RCM test model were in good agreement with actual rates for the majority of cases. However, further investigation is necessary for softer rock types, which will be conducted in the future using concrete blocks.Originality/valueThe originality of the research lies in the development of Hyundai Engineering and Construction’s advanced full-scale laboratory rotary cutting machine (RCM), which accurately replicates the excavation process of hard-rock tunnel boring machines (TBMs). The study provides valuable insights into cutting forces, chip size, specific energy, RPM and excavation modes, enhancing understanding and decision-making in hard-rock excavation processes. The research also presents a new RCM model validated against TBM penetration data, demonstrating its practical applicability and predictive accuracy.

Rate of penetration prediction with uncertainty assessment: Case study of a middle-east oil field

To increase drilling operation efficiency, attempts have been made to increase the rate of bit penetration into the formations, which is a function of controllable and uncontrollable parameters. Several mathematical models are available for penetration rate estimation. However, these models are not applicable in all circumstances. The most used model for rate of penetration is the one proposed by Bourgoyne and Young and the model has shown reliable results in different oil and gas provinces. However, the model performance significantly depends on the quantity and quality of input data. When the input data are subjected to different types of errors, some extent of uncertainty may propagate to the model. In this work, a Monte Carlo approach was followed to model the uncertainty in the predicted rates of penetrations. For this purpose, input data from an Iranian oil field were acquired and processed to check their quality. After fitting a probability distribution to each input data, the level of uncertainty in the predicted rates of penetration was quantified. Results showed that with an uncertain space of input data, a probabilistic rate of penetration was predicted. For a specific depth point, i.e. 8158.6 ft, rate of penetration range was narrowed to 1.16–1.23 ft/h with a certainty of 10 % and a mean value of 1.23 ft/h. However, for higher certainty levels a wider range of rate of penetration was obtained. Therefore, at each depth point, the certainty bands related to the predicted rate of penetration should also be stated as the model output.