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Predicting the rate of penetration (ROP) is important for optimizing drilling parameters, improving drilling efficiency, and optimizing economic benefits throughout the drilling process. The current prediction model of ROP based on machine learning algorithms does not consider the interference of outliers. Therefore, in this study, we propose a method to predict ROP based on fuzzy support vector regression (FSVR). First, appropriate input parameters were selected from the controllable parameters. Second, based on the local outlier factor, a fuzzy membership degree was assigned to each sample. Finally, the sample with the fuzzy membership value was input into the model for ROP prediction. The results demonstrated that the goodness of fit (R2) of the improved FSVR model is 0.9634, and the mean absolute error is 0.1974. Compared with standard SVR and other models, the improved FSVR model has a stronger anti-interference ability, smaller prediction error for normal samples, and higher accuracy. [ABSTRACT FROM AUTHOR]

Traditional machine learning methods for mechanical rate of penetration (ROP) prediction are affected by complex feature extraction and imitations of human understanding, which make the prediction accuracy difficult to meet the on-site demand. Based on this, a new ROP prediction method combining feature extraction and regression prediction was proposed. Firstly, data in drilling engineering were pre-processed by box-plot method and one-hot encoding to eliminate abnormal data and to make discrete characteristic continuous. Secondly, convolutional neural network (CNN) was applied to extracting data features, and channel attention mechanism squeeze-and-excitation network (SENet) was introduced to construct a SE-CNN model, which could adjust the importance of CNN feature channel. Finally, SE-CNN model was compared with CNN model. The results show that the goodness of fit of SE-CNN model is increased by 2. 1%, the mean absolute error and the root mean squared error are decreased by 1. 1% and 1. 5% . SE-CNN model has a better prediction accuracy, and can forecast the ROP of drilling field and provides a scientific reference for increasing the ROP during drilling. [ABSTRACT FROM AUTHOR]

Rate of drilling bit penetration into the subsurface formations, referred to as rate of penetration (ROP), is considered as the main optimization parameter in the drilling operation. ROP optimization results in a faster and cheaper operation. The rate of bit penetration depends on several factors, including but not limited to rock properties, fluid characteristics and drilling operational parameters. Due to the diversity of affecting parameters, prediction of rate of penetration is a challenging task. There are various mathematical relations in the literature to estimate ROP. However, these relations are developed based on specific conditions, where data from a field or experimental tests are used to develop the relations. In this work, drilling data of three wells from one onshore oil fields are gathered and then the performances of some common ROP models are investigated. Results show that the simplified Bourgoyne and Young model can accurately predict the ROP in the mentioned field, which makes the model applicable for planning of the new offset wells in the future. It should be noted that the model requires fewer input data, which makes it more applicable. The results show the average R2 coefficient for the model is 0.91, which is higher than other models. The results confirmed the applicability of the model. [ABSTRACT FROM AUTHOR]

Polycrystalline diamond compact (PDC) bits, which are widely used in oil and gas well drilling, have begun to be used in geothermal well drilling. Mitsubishi Materials Corporation, K. Maikai Co., Ltd, and National Institute of Advanced Industrial Science and Technology started collaborative research project commissioned by Japan Organization for Metals and Energy Security with the aim of development of PDC bits that have satisfying efficiency to drill hard, inhomogeneous and fractured formations as is common in the geothermal fields. In the present report, the outline of the project is provided, including the development of PDC cutters and bits and the performance evaluation of PDC bits through laboratory and field drilling tests. Three predetermined numerical goals concerning the drilling performance, namely drilling speed, bit life, and gage loss, were achieved in the project. The ROP model proposed by Miyazaki et al. (2022), which was constructed based on a series of laboratory drilling tests under controlled conditions conducted as part of the project, is also reviewed. The model considers the effect of bit wear quantitatively and has an extremely simple form compared with earlier models. The applicability of the model to field drilling data and several future issues are also discussed.

The comparative analysis of predictive methodologies highlights the original contribution of this study in optimizing the prediction of Rate of Penetration (ROP) in mining drilling operations. The emphasis on employing advanced Artificial Neural Networks (ANN), fuzzy logic, and linear regression models provides new insights into enhancing predictive accuracy and operational efficiency in mining practices. This study aims to quantify the effects of three pivotal drilling parameters: compressive strength, rotational pressure, and thrust pressure on the rate of penetration, a critical performance metric in mining drilling operations. Additionally, the study seeks to develop and evaluate advanced predictive methodologies for predicting ROP. The effects of compressive strength, rotational pressure, and thrust pressure on the rate of penetration were investigated through a Design of Experiments (DOE) approach. Initially, the main effects and two-way interactions among these variables were identified using DOE. Subsequently, three predictive methodologies: linear regression, fuzzy logic, and artificial neural networks, were developed and evaluated to predict ROP based on the identified factors. The evaluation of predictive methodologies revealed that the ANN model demonstrated superior accuracy in predicting the ROP, achieving over 95 % accuracy. Additionally, the fuzzy logic model provided effective handling of nonlinearities in the data, while the linear regression model offered initial insights into the relationships between the variables. The application of advanced predictive methodologies: artificial neural networks, fuzzy logic, and linear regression to optimize the prediction of rate of penetration in mining drilling operations offers precise insights into drilling parameter interactions, enhancing operational efficiency and supporting informed decision making in mining practices. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Polycrystalline diamond compact (PDC) bits, which are widely used in oil and gas well drilling, have begun to be used in geothermal well drilling. Mitsubishi Materials Corporation, K. Maikai Co., Ltd, and National Institute of Advanced Industrial Science and Technology started collaborative research project commissioned by Japan Organization for Metals and Energy Security with the aim of development of PDC bits that have satisfying efficiency to drill hard, inhomogeneous and fractured formations as is common in the geothermal fields. In the present report, the outline of the project is provided, including the development of PDC cutters and bits and the performance evaluation of PDC bits through laboratory and field drilling tests. Three predetermined numerical goals concerning the drilling performance, namely drilling speed, bit life, and gage loss, were achieved in the project. The ROP model proposed by Miyazaki et al. (2022), which was constructed based on a series of laboratory drilling tests under controlled conditions conducted as part of the project, is also reviewed. The model considers the effect of bit wear quantitatively and has an extremely simple form compared with earlier models. The applicability of the model to field drilling data and several future issues are also discussed. [ABSTRACT FROM AUTHOR]

In drilling operations, by choosing the proper tools and also incorporating more accurate and reliable parameters, this operation can be performed in less time and cost manner. Among drilling parameters, Rate of Penetration (ROP) is viewed as the main parameter in drilling operation evaluation. Field data investigations can be considered the most fruitful approaches to evaluate drilling performance, or ROP, as well as development of predictive models although laboratory tests and experimental formulas are vastly used to identify the drilling problems. In this research, intelligent modeling was used to predict the penetration rate of drilling operations through analyses of an established comprehensive data base from drilling operations in one of Iranian oilfields, Shadegan oilfield, in which novel artificial intelligence techniques such as Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Grasshopper Optimization Algorithm (GOA) were applied. Since the database includes 400 data, these techniques were utilized due to their effectiveness on a large set of data. In this research, using drilling data compiled from Shadegan oilfield, a precise model was developed to predict the ROP. Results showed that determination coefficient (R2) and Root mean squared error (RMSE) parameters for Particle Swarm Optimization (PSO) are found to be as R2=0.977 and RMSE=0.036, for Grey Wolf Optimization (GWO) R2=0.996 and RMSE=0.014, for Grasshopper Optimization Algorithm (GOA) R2=0.999 and RMSE=0.003, respectively. Ultimately, it can be concluded that all predictive models lead to acceptable results but GOA yields more precise and realistic outcome.

Knowing the rate of penetration of a drill bit in rocks is among the most important parameters in their behaviour measurement. However, the direct measurement of ROP in rocks is a high-cost and time-intensive process. Therefore, obtaining the ROP parameter through a method other than direct measurement can be very useful and effective. Predictive machine learning methods are among the strong and precise techniques for the indirect measurement of ROP. To this end, 492 samples were tested under different UCS, µ, WOB, and ω conditions to obtain the corresponding ROP. To achieve an accurate model, three methods of linear regression analysis, lasso regression, and ridge regression were compared in terms of prediction accuracy. These models were compared through performance criteria of the prediction process and error-based charts. The performance criteria were measured using three measures: mean absolute percentage error, D-squared pinball score, and mean Poisson deviance error. For the MAPE index, the Lasso and Ridge models performed the best with values of 0.2557. Concerning the D2PS index, the linear regression model and Ridge performed better with values of 0.4083 and 0.4025, respectively. Finally, for the MPDE index, the Ridge model provided a more accurate performance with a value of 0.0105. For a better comparison, an objective function was created and calculated by combining these three indicators. The results showed the best rank for the Ridge model with an estimated value of 659.475. Finally, it was concluded that the Ridge model is a reliable and accurate model for predicting the ROP.

The application of high-frequency vibrations to a drill bit is a promising means of increasing rate of penetration in deep hard formations. The implementation of such drill bit high-frequency vibrations is possible by installing the cavitation hydraulic vibrator in the drill string in front of the rock-cutting tool. Purpose. Evaluation of resonant modes (frequencies of oscillations) for the dynamic interaction of the mud pressure in the drill string and the drill bit longitudinal vibrations in case of contact with the rock being destroyed while drilling using the cavitation hydraulic vibrator; comparative analysis of the effectiveness of using a high-frequency mechanical vibrator and a cavitation hydraulic vibrator in case of drilling in hard rocks. Methodology. The research is based on a comparative analysis of the amplitudes and power spectral density of mud pressure oscillations of the mud and vibration accelerations in the drill bit cross section of drill string. Findings. The results are presented in the form of amplitude spectra and power spectral densities of mud pressure and vibration acceleration, as well as the dependence of the increase in the rate of penetration on the frequency of forced oscillations of the drill bit. Originality. Taking into account the influence of the cavitation hydraulic vibrator on the drill rig ROP made it possible: - to determine the resonant frequencies of pressure oscillations of the mud of the drilling tool, for the effective removal of drilled rock at the point of its contact with the drilling tool, and the longitudinal vibration accelerations of the drilling tool, to speed up rate of penetration during the construction of a well; - to evaluate the effectiveness of using a cavitation hydraulic vibrator in comparison with a high-frequency mechanical vibrator. For the studied modes of operation of the hydraulic vibrator at values of the cavitation parameter τ = 0.19, the well rate of penetration increases by 40 % compared to the traditional rotary method and by 26 % compared to the vibratory hammer. For the cavitation operating mode of the hydraulic vibrator τ = 0.41, the increase in the rate of penetration is 62 and 37 %, respectively. At the same time, the operating efficiency of the hydraulic vibrator was ensured at the resonant frequencies of the mud pressure with a frequency of 1,580 Hz at τ = 0.19 and 1,980 Hz at τ = 0.41. Practical value. For a specific design of the cavitation hydraulic vibrator as part of a drill string, by changing the frequency of the drill bit vibration impact on the rock, resonant frequencies are established that ensure the high ROP of the well. [ABSTRACT FROM AUTHOR]

Accurate prediction of the rate of penetration (ROP) is significant for drilling optimization. While the intelligent ROP prediction model based on fully connected neural networks (FNN) outperforms traditional ROP equations and machine learning algorithms, its lack of interpretability undermines its credibility. This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit (ReLU) activation function. By leveraging the derivative of the ReLU function, the FNN function calculation process is transformed into vector operations. The FNN model is linearly characterized through further simplification, enabling its interpretation and analysis. The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield. The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well. The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity. In the well sections with similar drilling data, averaging the weight parameters enables linear characterization of the FNN ROP prediction model, leading to the establishment of a corresponding linear representation equation. Furthermore, the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section. The established linear characterization equation exhibits high precision, strong stability, and adaptability through the application and validation across multiple well sections. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate prediction of the rate of penetration (ROP) is crucial for optimizing drilling parameters and enhancing drilling efficiency in ultra-deep wells. However, this task is challenging due to the harsh geological conditions, complex drilling processes, voluminous drilling data, and nonlinear relationships between drilling parameters and rock-breaking. In this study, a comprehensive intelligent model is proposed that combines clustering and deep residual neural network to address these challenges. Specifically, relevant feature parameters are selected for ROP prediction and the Savitzky–Golay filter is employed to reduce noise in the field data. Formations with similar rock characteristics are clustered using well logging parameters, including sonic logging and natural gamma ray logging, which indicate the formation rock properties. A deep residual neural network is then used to develop the prediction model, with the clustering results and 13 mud logging parameters serving as inputs. The model is trained and tested using field data from an ultra-deep reservoir in northwest China, and its performance is evaluated. The impact of data noise reduction, formation clustering, and deep residual neural network on the prediction accuracy is analyzed through ablation experiments. The proposed model achieves high accuracy in predicting ROP, with relative errors ranging from 11.34 to 11.44% and R2 values from 0.92 to 0.94. Compared to traditional machine learning models, the approach demonstrates superior performance and is suitable for real-time drilling applications. This study provides a promising solution for accurate ROP prediction in ultra-deep wells, helping to optimize drilling parameters and improve drilling efficiency. [ABSTRACT FROM AUTHOR]

The prediction and optimization of the rate of penetration (ROP) for horizontal wells are more complicated than for vertical wells, but most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. To this end, this paper proposes a data knowledge dual-driven horizontal well ROP prediction method. Firstly, the drilling characteristics of horizontal wells are analyzed, showing that the horizontal wells ROP prediction model cannot be modeled using surface measurement data; secondly, based on the analysis of horizontal well drilling characteristics, a physical model-based horizontal well ROP modeling data pre-processing method is proposed by introducing the drag and torque model. Finally, a data knowledge dual-driven horizontal well ROP prediction method is proposed in conjunction with data-driven algorithms. The proposed horizontal well ROP prediction method is applied to the A1~A4 wells in the Sichuan area. Compared with the conventional data-driven ROP prediction method, the prediction accuracy of this method is improved by 30%. The proposed method can provide a basis for the intelligent optimization and management of ROP during the drilling of horizontal wells. [ABSTRACT FROM AUTHOR]

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.

Abstract The rate of penetration (ROP) is an influential parameter in the optimization of oil well drilling because it has a huge impact on the total drilling cost. This study aims to optimize four machine learning models for real-time evaluation of the ROP based on drilling parameters during horizontal drilling of sandstone formations. Two well data sets were implemented for the model training–testing (Well-X) and validation (Well-Y). A total of 1224 and 524 datasets were implemented for training and testing the model, respectively. A correlation for ROP assessment was suggested based on the optimized artificial neural network (ANN) model. The precision of this equation and the optimized models were tested (524 datapoints) and validated (2213 datapoints), and their accuracy was compared to available ROP correlations. The developed ANN-based equation predicted the ROP with average absolute percentage errors (AAPE) of 0.3% and 1.0% for the testing and validation data, respectively. The new empirical equation and the optimized fuzzy logic and functional neural network models outperformed the available correlations in assessing the ROP. The support vector regression accuracy performance showed AAPE of 26.5%, and the correlation coefficient for the estimated ROP was 0.50 for the validation phase. The outcomes of this work could help in modeling the ROP prediction in real time during the drilling process.

Well path design is an important factor in horizontal drilling. A reasonable well profile scheme can significantly reduce drilling drag and torque, which also can enhance drilling rate. This study proposes an approach to faster drill a three-dimensional horizontal well by integrating well path and bottomhole assembly (BHA) design. The shortcomings of two classic well path design principles of shortest well length and minimum drag are investigated and a method integrating well path and BHA design is developed by altering inclination holding section to a slightly build up section and predicting build rate of BHA. For two conventional design principles, the design processes are developed by minimum curvature model and genetic optimizing algorithm. Case studies show that the optimized well depth or drag of two conventional methods have little difference compared to the unoptimized ones, which indicates that these two optimization methods cannot significantly improve drilling efficiency. For proposed design method, the well path design is developed by minimum curvature model, and the drilling tendency prediction method is established by three-dimensional beam-column theory and the rock-bit interaction model. Besides, a set of bent-housing mud motor with double stabilizers (BH-PDM-D) BHA is chosen to drill the well, according to the build rate prediction of both slide drilling and rotary drilling. The predicted result of drilling cycle shows that for the six-section well profile, the proposed method can reduce the drilling time by 24.2 days.

This study focuses on optimizing drilling parameters when using Positive Displacement Motors (PDMs). In drilling operations involving mud motors, weight-on-bit (WOB) alterations lead to variations in the system's parasitic pressure drop. Consequently, this affects the optimum flow rate and the hydraulic power of the bit. Also, if the flow rate changes, the bit's rotations per minute (RPM) also change. In other words, using PDMs creates a link between the hydraulic system and the drilling speed, such that changing drilling parameters such as the WOB causes changes in the hydraulic system's performance. Therefore, one possible way to optimize the drilling parameters is to consider the drilling rate and hydraulic system simultaneously using a multi-objective approach. This study used an integrated approach encompassing data mining and mathematical modeling, employing a multi-objective framework to identify optimal parameters. The approach was applied to Dariyan Formation drilling data. The data mining approach revealed a well-distributed data set covering optimal and suboptimal zones suitable for optimization. In data mining, the identification of optimal conditions included a WOB of 11500 lb, a rotation speed of 105.8 rev/min, and a flow rate of 843 gpm, leading to an ROP of 44.23 ft/h. In multi-objective optimization, the optimal parameters consisted of a WOB of 14480 lb, a rotation speed of 115 rev/min, and a flow rate of 920.8 gpm, resulting in an ROP of 40.49 ft/h. Comparing optimal results with the drilling data shows a substantial MSE reduction of over 35 %. The results show the good performance of this approach in detecting the optimal and non-optimal drilling variables.

Prediction of tunnel boring machine (TBM) performance is still a challenging research subject in engineering geology, geotechnical engineering, and tunnel engineering communities. The longest railway tunnel with approximately 10 km, the Bahce-Nurdagi tunnel, was projected as twin tubes and TBM excavation. One of these tubes was successfully completed and the other is under construction. In this study, the geological and geotechnical parameters of the tunnel route and basic TBM parameters were used to predict the TBM performance. For the purpose of the study, a data set including 5334 cases was compiled. The analyses were performed in two phases, the first phase was performed employing only geological and geotechnical parameters while the basic TBM parameters were considered in the second phase analyses. Although the ANN and ANN-fuzzy models yielded acceptable results, the results clearly showed that the random forest algorithm was superior among all other methods for the data used. The results also revealed that the basic TBM parameters should be considered with advanced modeling techniques needed for a successful prediction model for TBM performance. [ABSTRACT FROM AUTHOR]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drill bits are the main rock-breaking tools in the petroleum and gas industry. Their performance directly affects the quality, efficiency, and cost of drilling. Drill bit manufacturing mainly employs traditional mold forming processes such as milling molding and press molding, which have low production efficiency and long processing cycles and are not conducive to rapid responses to field requirements. Inadequate production accuracy makes it difficult to produce drill bits with complex structures. Three-dimensional (3D) printing technology has fast molding speeds and high molding accuracy. In this paper, 3D printing was applied for the first time to the manufacture of molds for carcass polycrystalline diamond compact (PDC) drill bits and PDC–cone hybrid drill bits. In comparison with forging and milling molding, 3D printing improved production efficiency. The manufactured molds had higher machining accuracy. The ability of 3D printing to make molds with complex surfaces enables the development of drill bits with complex structures. A field experiment was conducted on a PDC drill bit produced by 3D printing, which had a higher rate of penetration and was more efficient in breaking rocks than bits manufactured by traditional processes. The ROP of the drill bit increased by 20.1–25.8%, and the drilling depth increased by 7.7–29.5%. It is therefore feasible to apply 3D printing to the manufacture of petroleum drill bits. [ABSTRACT FROM AUTHOR]

Prediction of the rate of penetration (ROP) is integral to drilling optimization. Many scholars have established intelligent prediction models of the ROP. However, these models face challenges in adapting to different formation properties across well sections or regions, limiting their applicability. In this paper, we explore a novel prediction framework combining feature construction and incremental updating. The framework fine-tunes the model using a pre-trained ROP representation. Our method adopts genetic programming to construct interpretable features, which fuse bit properties with engineering and hydraulic parameters. The model is incrementally updated with constant data streams, enabling it to learn the static and dynamic data. We conduct ablation experiments to analyze the impact of interpretable features' construction and incremental updating. The results on field drilling datasets demonstrate that the proposed model achieves robustness against forgetting while maintaining high accuracy in ROP prediction. The model effectively extracts information from data streams and constructs interpretable representational features, which influence the current ROP, with a mean absolute percentage error of 7.5% on the new dataset, 40% lower than the static-trained model. This work provides a theoretical reference for the interpretability and transferability of ROP intelligent prediction models. [ABSTRACT FROM AUTHOR]

The combination of the hydraulic impactor and positive displacement motor (PDM) is becoming more and more popular in drilling engineering. However, the negatives of the PDM on the impactor cannot be ignored. To improve the performance of the technique, the influence of fluid pulsation from the PDM on the hydraulic impactor was studied first; then, the structure of the impactor was optimized to improve its impact force; finally, field tests were carried out in 2 wells in the shallow formation. The results indicate that the fluid fluctuation generated in the PDM can restrain the performance of the impactor, and that the impact force can be increased by 24.4% to 28.6% through the optimization of design. Field tests show that this technique can further improve the drilling efficiency and rotating stability of the polycrystalline diamond compact bit, and that the rate of penetration and bit footage increase by 32.5% and 34.6%, respectively. In the study, the effect of inlet fluid fluctuation on the performance of the hydraulic impactor was studied using the computational fluid dynamics method. This is unlike other studies that have mostly considered the inlet fluid as a steady flow. Furthermore, the performance of the combine used of the impactor and PDM can be improved through structural optimization. [ABSTRACT FROM AUTHOR]

In this study, the reservoir drill-in fluid (RDF) was modified and optimized to improve the rheological properties and reduce the filtration properties of the drilling fluid used for drilling the oil-bearing zone horizontally. In polymer science, degradation generally refers to a complex process, by which a polymeric material exposed to the environment and workload loses its original properties. Degradation is usually an unwanted process. In certain cases, however, controlled polymer degradation is useful. For instance, it can improve the processability of the polymer or can be used in recycling or natural decomposition of waste polymer. Thus, the drilling fluid and parameter data of 30 horizontal wells that were drilled in the south of Iraq were collected using several reservoir drill-in fluids (RDFs), including FLOPRO, salt polymer mud (SPM), non-damaged fluid (NDF), and FLOPRO_PTS-200 (including the polymer thermal stabilizer). The obtained results showed that the polymer temperature stabilizer (PTS-200) enabled reducing the filtration rate by 44.33% and improved the rheological properties by 19.31% as compared with FLOPRO. Additionally, the average cost of NDF and SPM drilling fluids for drilling the horizontal section of the selected wells is around USD 96,000 and USD 91,000, respectively. However, FLOPRO-based drilling fluid showed less cost for drilling the horizontal section, which is USD 45,000. [ABSTRACT FROM AUTHOR]

The rate of penetration (ROP) is an influential parameter in the optimization of oil well drilling because it has a huge impact on the total drilling cost. This study aims to optimize four machine learning models for real-time evaluation of the ROP based on drilling parameters during horizontal drilling of sandstone formations. Two well data sets were implemented for the model training–testing (Well-X) and validation (Well-Y). A total of 1224 and 524 datasets were implemented for training and testing the model, respectively. A correlation for ROP assessment was suggested based on the optimized artificial neural network (ANN) model. The precision of this equation and the optimized models were tested (524 datapoints) and validated (2213 datapoints), and their accuracy was compared to available ROP correlations. The developed ANN-based equation predicted the ROP with average absolute percentage errors (AAPE) of 0.3% and 1.0% for the testing and validation data, respectively. The new empirical equation and the optimized fuzzy logic and functional neural network models outperformed the available correlations in assessing the ROP. The support vector regression accuracy performance showed AAPE of 26.5%, and the correlation coefficient for the estimated ROP was 0.50 for the validation phase. The outcomes of this work could help in modeling the ROP prediction in real time during the drilling process. [ABSTRACT FROM AUTHOR]

Performance of TBM is significantly influenced by the ground conditions and machine variables. To achieve an optimum rate of penetration (ROP) during TBM excavation, it is important to assess the interaction between rock mass properties and machine operational/performance variables. This paper presents a systematic analysis of TBM performance based on the data collected from the MetroLine-3 UGC-01 project in Mumbai, India, and proposes a few performance prediction models for the Deccan Traps. The current work attempted to bring out the combined effect of RQD × Js as a single predictor variable while suggesting a reliable RSA modeling technique which considers the simultaneous interaction of variables. The database consisted of engineering-geological and machine variables; the selected variables were analyzed using artificial neural networks (ANN) for identifying the significant variables. Subsequently, multivariate regression (MVRA) and response surface analysis (RSA) were utilized to develop a model for predicting TBM ROP. The first model developed using MVRA as a function of rock mass variables yielded a coefficient of determination (R2) of 0.80, whereas the second composite model developed as a function of geological and machine variables yielded an R2 of 0.85. The third model was developed utilizing RSA which resulted in 2FI (two-factor interaction) model with improved R2 of 0.88. Further, the best-performing RSA model accuracy is compared with the existing models and subsequently validated using new datasets and yielded an R2 of 0.79. The developed model equation indicates that UCS, RQD × Js, and thrust variables show significant influence on the TBM ROP. [ABSTRACT FROM AUTHOR]

Drilling Oil and Gas wells is an expensive operation, where the cost for drilling a single shallow offshore HP-UHT well may exceed 30 million USD easily. Due to the huge cost involved, companies are eager to accomplish the drilling operations in a minimal time frame, by increasing the drilling rate or rate of penetration (ROP) and reducing the downhole tool failures. The Mechanical Specific Energy (MSE) concept addresses these two drilling performance measures in totality as it provides maximum ROP which can be achieved with the existing drilling system (i.e., Drill Bits, Drilling Tubular, Well Profile, and Mud weight) without causing downhole tool failures. ROP is a function of Weight on Bit, Rotation per Minute, and Flow rate for an existing drilling system, optimization of these parameters results in increased ROP, whereas unharmonious values lead to shock and vibration in drilling assembly. Machine Learning algorithms facilitate this optimization of drilling parameters for enhancing drilling performance, by the development of an accurate ROP prediction and optimization model. The ROP prediction model was developed using Artificial Neural Network and Random Forest, and the optimization model was developed using evolutionary optimization algorithms like particle swarm optimization and genetic algorithms. The result of the optimization model depends upon the accuracy of the ROP prediction model and the upper and lower limits of drilling parameters defined in the optimization model. Machine learning applications in improving the Drilling Performance can be easily quantified in terms of saving days, where one day of operational cost for the company is approximately 0.2 million USD per Offshore – HP-UHT well. [ABSTRACT FROM AUTHOR]

The prediction and optimization of the rate of penetration (ROP) for horizontal wells are more complicated than for vertical wells, but most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. To this end, this paper proposes a data knowledge dual-driven horizontal well ROP prediction method. Firstly, the drilling characteristics of horizontal wells are analyzed, showing that the horizontal wells ROP prediction model cannot be modeled using surface measurement data; secondly, based on the analysis of horizontal well drilling characteristics, a physical model-based horizontal well ROP modeling data pre-processing method is proposed by introducing the drag and torque model. Finally, a data knowledge dual-driven horizontal well ROP prediction method is proposed in conjunction with data-driven algorithms. The proposed horizontal well ROP prediction method is applied to the A1~A4 wells in the Sichuan area. Compared with the conventional data-driven ROP prediction method, the prediction accuracy of this method is improved by 30%. The proposed method can provide a basis for the intelligent optimization and management of ROP during the drilling of horizontal wells.

Drilling costs and time are directly affected by the rate of penetration (ROP). Gas/oil well drilling in challenging environments is a costly operation due to the high cost of wellbore construction, and drilling optimization requires real-time decision-making. Therefore, it is necessary to estimate ROP accurately and thus assist in optimizing the drilling process. In this study, we used total depth, weight on hook, bit, revolutions per minute, torque, standpipe pressure, flow rates, mud weight, mud viscosity, hole size, and formation hardness as input parameters to forecast ROP. Nine machine learning algorithms, i.e., multiple linear regression (MLR), lasso regression, ridge regression, decision tree (DT), bootstrap aggregating (Bagging), random forest (RF), gradient boosting machines (GBM), symbolic regression (SR), and artificial neural networks (ANN) were performed using a dataset consists of 3484 observations. Moreover, several network hyperparameters were adjusted, indicating that using a deep artificial feed-forward network, i.e., [9–11-1] neurons with logarithmic sigmoid transfer function as a predictor model, provided the most accurate model. Results showed that the random forest and artificial neural network models have the highest accuracy, and it estimates ROP with correlation coefficients of 0.93. Overall, the methodology employed in this paper performed well in predicting ROP in the absence of some unavailable parameters such as UCS. Highlights: • Effect of weight on hook (WOH) on rate of penetration (ROP) investigated for the first time by means of multiple (nine) robust machine-learning algorithms. • Multiple linear regression (MLR), lasso regression, ridge regression, decision tree (DT), bootstrap aggregating (Bagging), random forest (RF), gradient boosting machines (GBM), symbolic regression (SR), and artificial neural networks (ANN) intelligent models were proposed for estimating drilling rate of penetration. • The accuracy of the models was ranked as: ANN > RF > GBM > BA > DT > SR > Lasso > MLR > Ridge. • A comprehensive databank of 3484 data points from a gas field in the Zagros basin was used. The predicted rate of penetration is highly influenced by the values of depth, WOH and RPM. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Prediction of the rate of penetration (ROP) is integral to drilling optimization. Many scholars have established intelligent prediction models of the ROP. However, these models face challenges in adapting to different formation properties across well sections or regions, limiting their applicability. In this paper, we explore a novel prediction framework combining feature construction and incremental updating. The framework fine-tunes the model using a pre-trained ROP representation. Our method adopts genetic programming to construct interpretable features, which fuse bit properties with engineering and hydraulic parameters. The model is incrementally updated with constant data streams, enabling it to learn the static and dynamic data. We conduct ablation experiments to analyze the impact of interpretable features’ construction and incremental updating. The results on field drilling datasets demonstrate that the proposed model achieves robustness against forgetting while maintaining high accuracy in ROP prediction. The model effectively extracts information from data streams and constructs interpretable representational features, which influence the current ROP, with a mean absolute percentage error of 7.5% on the new dataset, 40% lower than the static-trained model. This work provides a theoretical reference for the interpretability and transferability of ROP intelligent prediction models.

Abstract One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

The drilling process is complex, especially for ultra-deep wells, which face the problems of high temperature, high pressure and poor drilling resistance in their formation. In order to establish an ROP (the Rate of Penetration) prediction model for ultra-deep wells, the characteristics of ultra-deep well drilling operations, such as formation temperature and formation pressure, are fully considered in the process of parameter optimization. Combined with the drilling mechanism and mutual information correlation coefficient, the final input parameters are determined. The powerful nonlinear fitting ability of the artificial intelligence method is very suitable for predicting the ROP. Considering the time sequence of multi-source data, this paper combines the powerful timing information-based mining ability of the LSTM (Long Short-Term Memory Neural Network) with the nonlinear fitting ability of FNN (Fully Connected Neural Network), and establishes an intelligent prediction model of the ROP based on a LSTM–FNN. The results show that the average relative error and R 2 of the LSTM–FNN model on the data of well 1 and well 2 are better than the FNN and LSTM models. In addition, the accuracy of the LSTM–FNN model on the data of adjacent wells is reduced by only 5%, which further verifies the good mobility of the model. [ABSTRACT FROM AUTHOR]

Oil and gas reservoirs are of the main assets of countries possessing them. Production from these reservoirs is one of the main concerns of engineers, which can be achieved by drilling oil and gas reservoirs. Construction of hydrocarbon wells is one of the most expensive operations in the oil industry. One of the most important parameters affecting drilling cost is the rate of penetration (ROP). This paper predicts the rate of penetration using artificial intelligence and hybrid models on Kaboud oil field well #7 in the directional stage. In this study, different models were constructed through various approaches based on 1878 dataset obtained from drilling operation in the well#7. Then, the accuracy of the constructed models was compared with each other. It was found that the MLP-ABC algorithm predicts the rate of penetration more accurately, by far, as compared with other methods. The MLP-ABC algorithm achieves impressive ROP prediction accuracy (RMSE = 0.007211 m/h; AAPD = 0.1871%; R2 = 1.000 for the testing subset). Consequently, it can be concluded that this method is applicable to predict the drilling rate in that oilfield.

In this study, the reservoir drill-in fluid (RDF) was modified and optimized to improve the rheological properties and reduce the filtration properties of the drilling fluid used for drilling the oil-bearing zone horizontally. In polymer science, degradation generally refers to a complex process, by which a polymeric material exposed to the environment and workload loses its original properties. Degradation is usually an unwanted process. In certain cases, however, controlled polymer degradation is useful. For instance, it can improve the processability of the polymer or can be used in recycling or natural decomposition of waste polymer. Thus, the drilling fluid and parameter data of 30 horizontal wells that were drilled in the south of Iraq were collected using several reservoir drill-in fluids (RDFs), including FLOPRO, salt polymer mud (SPM), non-damaged fluid (NDF), and FLOPRO_PTS-200 (including the polymer thermal stabilizer). The obtained results showed that the polymer temperature stabilizer (PTS-200) enabled reducing the filtration rate by 44.33% and improved the rheological properties by 19.31% as compared with FLOPRO. Additionally, the average cost of NDF and SPM drilling fluids for drilling the horizontal section of the selected wells is around USD 96,000 and USD 91,000, respectively. However, FLOPRO-based drilling fluid showed less cost for drilling the horizontal section, which is USD 45,000.