Penetration-Rate Performance of Roller-Cone Bits

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 The paper has not been peer reviewed.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

Data-driven models have risen in popularity during the past 10 years, which increase the effectiveness and durability of systems without necessitating a lot of human involvement. Despite all of their advantages, they remain the limitations in terms of model interpretation, data selection, and model evaluation. Sensitivity analysis (SA) is a powerful tool to decipher behaviors of data-driven models to analyze the correlations among inputs and outputs of models, and quantify the severity of inputs’ influence on outputs to effectively interpret these black-box models. Feature selection (FS) is a preprocessing approach used in data-driven modeling to select the crucial parameters as inputs fed to models. For the most of the existing works, the FS is well used to select inputs through the analysis on the drilling data correlations, while SA is seldom employed for data-driven model evaluation and interpretation in drilling applications. Data-driven rate of penetration (ROP) models have consistently outperformed many conventional ROP models, most likely as a result of their strong data analysis capabilities, capacity to learn from data in order to recognize data patterns, and effective policies for making logical decisions automatically. A data-driven ROP model was developed from a benchmarking field drilling dataset in this work. Following the ROP modelling, sensitivity analysis methods were employed to identify the input variables that had the greatest influence on ROP estimations. The FS techniques and the sensitivity analysis were combined during the data preprocessing to identify the most important aspects for modelling. The outcomes demonstrate that using the robust sensitivity analysis techniques to overcome the limits of machine learning models allows for the best interpretation and understanding of the produced data-driven models.

Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application. A drilling dataset was gathered from exploration and development wells in both onshore and offshore operations from a variety of fields and regions. The wells were curated to have different water depths, down hole drive such as Rotary Steerable System (RSS), PDM, Standard Rotary, bit types (Mill Tooth, TCI, PDC) and inclinations (vertical or deviated). A deep neural network was used for modelling the relationship between ROP and inputs taken from real-time surface data, such as Torque, Weight-on-Bit (WOB), rotary speed (RPM), flow and pressure measurements. The performance of the ROP model was analyzed using historical data via summary statistics such as Mean Absolute Percentage Error, as well as graphical results such as residuals distributions, cumulative distribution functions of errors, and plots of ROP vs depth for independent holdout testing wells not included in the model fitting process. Analysis was done both in aggregate, and for each specific well. The ROP model was demonstrated to generalize effectively in all cases, with only minor increases in error metrics for the holdout test wells, where the Mean Absolute Percentage Error averaged across wells was ~20%, compared to 17.5% averaged across training wells. Furthermore, residuals distributions were centered close to zero, indicating low systematic error. This work proves the case for a "global" ROP prediction model applicable "out-of-the-box" to a broad set of drilling operations. A global ROP model has the potential to eliminate learning curves, reducing time and costs associated with having to develop a new model for every field. Furthermore, a model that effectively captures the relationships between parameters controllable by drillers and ROP can be used for automatically identifying drilling parameters that improve ROP. Preliminary field-testing of the ROP optimization system yielded positive results, with many examples of increased ROP realized after following drilling parameter recommendations provided by the software.

SummaryReamers are an integral part of deepwater Gulf of Mexico (GOM) drilling and their performance significantly impacts the economics of well construction. This paper presents a novel programmatic approach to model rate of penetration (ROP) for reamers and improve drilling efficiency. Three field implementations demonstrate value added by the reamer drilling optimization (RDO) methodology.Facilitated by user interface panels, the RDO workflow consists of surface and downhole drilling data filtering and visualization, detection of rock formation boundaries, frictional torque (FTRQ) and aggressiveness estimation, ROP modeling with analytical equations and machine learning (ML) algorithms [regression, random forests, support vector machines (SVMs), and neural networks], and optimization of drilling parameters. ROP model coefficients and bit and reamer aggressiveness are dependent on lithology and computed from offset well data. Subsequently, when planning a nearby well, bottomhole assembly (BHA) designs are evaluated on the basis of drilling performance and weight and torque distributions between cutting structures to avoid early reamer wear and dysfunctions. Geometric programming establishes optimal drilling parameter roadmaps according to operational limits, downhole tool ratings, rig equipment power constraints, and adequate hole cleaning.Separate ROP models are trained for reamer-controlled and bit-controlled ROP zones, defined by the proportion of surface weight on bit (WOB) applied at the reamer, in every rock formation. This novel concept enables ROP prediction with the appropriate model for each well segment depending on which cutting structure limits drilling speed. In the first of the three RDO applications with field data from deepwater GOM wells, optimal bit-reamer distances are determined by analyzing reamer weight load in uniform salt sections. Next, ROP modeling for the addition or removal of a reamer from the BHA is used in contrasting well designs to conceivably alleviate a USD 16 million casing inventory surplus. Finally, active optimization constraints are investigated to reveal drilling performance limiters, justifying equipment upgrades for a future deepwater GOM well.The proposed innovative workflow and methodology apply to any drilling optimization scenario. They benefit the practicing engineer interested in drilling performance optimization by providing insights on how different cutting structure sizes affect ROP behavior and ultimately aiding in the selection of appropriate bit and reamer diameters and optimal operational parameters.

This paper seeks to demonstrate how the use of real-time downhole drilling data can help facilitate the timely, well-informed decision to pull out of hole (POOH) if the rate of penetration (ROP) reduces beyond expectation. The traditional decision process starts with offset ROP comparison. If the ROP is lower than expected, then typically there are two scenarios: either the drill bit has become dull or the formation is harder to drill than expected. If the first scenario is suspected then a cost-benefit analysis is performed whereby the remaining footage is assessed and the time to drill to section TD with the current ROP is calculated and compared to the cost of a new drill bit, rig time and the cost of round tripping to change the bit. However, when the bit is on surface and seen to still have good cutting structure then the round trip has been a waste of time and money and more patience should have been exercised in keeping the drill bit on bottom. With real-time, downhole drilling data it is possible to assess the drillability of the formation and estimate the dull condition of the bit. Primary parameters that are used for this assessment are downhole weight on bit and downhole torque, and their relationship to one another. In addition, the trend of the downhole mechanical specific energy is evaluated. The first case study under discussion was drilled in 12¼-in. section, offshore UAE field. For the initial 4,500ft the average ROP was 80ft/hr, at which point a dramatic decrease in ROP was observed. A reduction in ROP was expected at this point because of formation change, but the magnitude of the reduction was much greater. Because the ROP was expected to reduce, the drilling continued with some short-lived increases in ROP. After a sustained time period of low ROP the decision to POOH was made, when the drill bit was on surface the dull grade was 1-6-HC-S-X-I-RO-PR. Upon evaluation of downhole drilling data with the aid of hindsight regarding eventual dull grade it was seen that there were clear indications that the bit was no longer effective and the decision to POOH should have been made. The time period that elapsed between the drill bit becoming damaged and POOH was 30 hours. This base case lesson learned was utilized on a subsequent well in the drilling campaign on an analogous application. By using this downhole signature a quicker, more effective POOH decision was made. The POOH decision in this case was made after just 2.5 hours compared to the 30 hours on the previous well. Once on surface, the dull grade of the bit was 2-7-WT-S-X-I-RO-PR.

Pre-salt layers has long-term exploration and production possibilities, however the properties of such layers are highly challenging for exploration (i.e. these carbonate layers are highly abrasive, located deeper than 5000 m, have generally low permeability) and production (i.e. pre-salt layers are located in harsh oceanic conditions, hundreds of km offshore). These conditions mean increased exploration technological and economic difficulties. One of the partial solutions to decrease costs is to reduce the drilling operations time. Rate of penetration (ROP) has a significant effect on the overall drilling time, driving ROP modeling and optimization to be a viable solution for reducing drilling operations time in such environment. Several mathematical ROP models were developed in the last five decades in the petroleum industry, departing from rather simple but less reliable R-W-N (drilling-rate, weight-on-bit and rotary-speed) equations until the arrival to a comprehensive and complex approach: Bourgoyne and Young ROP Model (BYM) which was first published in 1974. The paper explains the equation, how it is applied in terms of ROP modeling, identifies the main drilling parameters driving each sub-functions, and introduces how they were developed. The paper base itself on the sub-functions of the equation, explains the normalization factors which have significant influence on the model, and also introduces simulations which aim to understand the approach by applying the equation in a pre-salt layer case study. Knowing that, the original publication was introduced in 1974, this paper also aims to identify rooms for improvement and/or alternate the sub-functions to match actual field data better than with the originally given drillability coefficient recommended boundaries stated in the first publication. This is accomplished through a real-world practical application of this ROP model in a pre-salt layer. The paper also assesses the limitation in terms of the applicability of this complex model for ROP analysis and optimization in such carbonate layers.

Harder formations in geothermal and deeper oil and gas wells typically see lower rates of penetration (ROPs) and higher bit wear. Recent drilling experience in the Utah FORGE observed higher ROPs and longer runs using lighter drilling muds and new cutter technologies. Questions have arisen regarding reasons for the improved ROP and if the higher ROP is due to increased spurt loss or reduced hydrostatic pressure from lighter drilling fluids. A study was performed on an inhouse rig using two polycrystalline diamond compact (PDC) bits (4-bladed vs 5-bladed) to analyze the effect of confinement on ROP in Sierra White Granite (SWG). Tests were performed at confinements of 0, 1000, and 2000 psi and at constant rotational speeds of 80 and 150 RPM. Results show that when confinement is increased, ROPs are similar in the ineffective cutting Phase I. However, results in Phase II indicate that ROP is largely different between the unconfined and higher confinement testing. No significant effect on ROP is observed between 1000 and 2000 psi. At zero confinement, the 4-bladed bit outperformed the 5-bladed bit while at 1000 and 2000 psi confinement, the performance was similar. This paper presents the laboratory results with potential applications to hard rock geothermal drilling. INTRODUCTION Confining pressure in drilling terms is the hydrostatic pressure at the bottom of the well while drilling in impermeable formations. Many studies have outlined the effects of confining pressure on rock deformation but the effect of confinement on drilling efficiency and ROP is yet to be thoroughly investigated. In recent years, hard rock drilling especially in geothermal reservoirs has faced great challenges such as low penetration rates and high bit wear leading to high drilling costs. Advanced bit technology is required to maneuver the unique physical and mechanical properties of hard and abrasive rocks at high confining pressures. Although roller cone bits were predominantly used to drill hard and abrasive formations, PDC bits with enhanced cutter properties and ‘special’ cutter geometries are at the forefront of drilling research and technology. During the past years, new PDC cutter technology has improved cutter wear resistance and its capability to carry higher loads. Recent drilling experience in the Utah FORGE observed higher ROPs and longer bit runs using lighter drilling muds and new cutter technologies including shaped cutters (Dupriest and Noynaert, 2022). Glowka, (1985) discussed the potential for developing a PDC drill bit for hard rock applications such as geothermal drilling. Different cutter geometrical shapes and designs are currently being designed to address heavy impact damage in hard formations, prevent thermal damage and extend drilling runs while maximizing ROP. Zhu et al. (2022) analyzed the loading performance and rock cutting mechanism of different PDC cutters by using single cutter analysis to study geometry, aggressiveness, and stress distribution in granite. Xiong et al. (2022) studied the performance of a stinger PDC cutter in granite to evaluate cutting force and mechanical specific energy (MSE). Barnett et al. (2022) studied the effects of drillstring torsional vibration on ROP with PDC bits in hard rock. Wang et al. (2020) designed a PDC hybrid drill bit to navigate hard and abrasive formations. Akhtarmanesh et al. (2021) designed an ROP model for drilling hard and abrasive formations for PDC bits. It was seen that distinct phases of drilling exist where Phase I is the inefficient drilling phase due to insufficient WOB and Phase II is the efficient drilling phase due to higher WOB which results in a higher ROP. Figure 1 shows the different phases of drilling as described by Akhtarmanesh et al. (2021). Due to the complexity of drilling in hard formations, more testing is crucial to completely understand the effects of temperature, rock behavior, and confining pressure on ROP and bit cutting mechanism. An understanding of bit cutting action in hard rock is crucial for successful ROP optimization and improving drilling efficiency.

Is this the beginning of the end of weighted mud systems? An advanced well control practice called Managed Pressure Drilling (MPD) is staged to challenge the conventional drilling practice of when in doubt "weight it up". Formation overpressures have traditionally forced operators to weight-up mud systems in order to advance drilling operations while preventing formation fluids invasion. A direct consequence of increased mud weight is a dramatic reduction in drilling Rate of Penetration (ROP), requirement for additional casing seats/strings and increased well control risks due to kicks from losses. This drilling hazard has represented a major source of invisible or intangible lost time and hence, cost. A drilling hazard mitigation multi-well trial was carried out to investigate and quantify the reduction in ROP as a result of mud weight increases to overcome troublesome formation overpressures and associated High Pressure Low Volume (HPLV) nuisance gas. Without increasing mud weight to control overpressures, MPD technology was applied to safely and cost effectively drill through overpressures and avoid an intermediate casing string normally used to isolate a loss zone. MPD trial results examined in this paper addresses many of the issues and provides forward-looking statements regarding large-scale introduction of the system to other fields. The multi-well trial results showed a close match to the predicted MPD ROP curve, achieving an economically rewarding ROP gain of at least 2.5 times and gross well cost reductions of over 20%, without recordable troublesome zone Non-Productive Time (NPT). Initial concerns of borehole instabilities using lightweight mud with borehole pressures less than the adjacent pore pressure were not observed (in both vertical and directional well cases).

This paper describes the field experience of a new hybrid bit based on proven polycrystalline diamond compact (PDC) bit designs with rolling cutters on the bit periphery. A hybrid bit can drill shale and other plastically behaving formations up to three or four times faster than a roller-cone bit by being more aggressive and efficient. The penetration rate of a hybrid bit responds linearly to revolutions per minute (RPM) unlike roller-cone bits, which have an exponential response with an exponent of less than one. In other words, the hybrid bit drills significantly faster than a comparable roller-cone bit in motor applications. Another benefit is the effect of the rolling cutters on the bit dynamics. Compared to conventional polycrystalline diamond cutter bits, torsional oscillations are as much as 50% lower; stick/slip is reduced at low RPM, and whirl at high RPM. This gives the hybrid bit a wider operating window and greatly improves toolface control in directional drilling. The hybrid drill bit technology combines roller-cone and PDC bits into a single patented design, employing the best attributes of each bit type to improve reliability and efficiency, while ensuring performance consistency in the most problematic applications. This revolutionary technology has a large spectrum of applications, excelling in typically troublesome formations for PDC and roller-cone bits, such as interbedded hard and soft stringers or hard nodular inclusions. The results of a field test in an inland application will be presented, comparing the performance of hybrid bits with conventional PDC and roller-cone bits used on offset wells. A planning and engineering process was applied, ensuring high-quality support to the operator in the office and at the rigsite. The client saved multiple conventional tungsten carbide insert bit runs and drilled 100% farther, ultimately achieving a rate of penetration (ROP) two times faster than the best tungsten carbide insert (TCI) bit run in the same well.

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

Due to a great deal of information available, this paper has been arranged in two parts. The paper presented here deals with Part I. In this part, theory and laboratory data of the DVG (Downhole Vortex Generator) Sub, which is run immediately on top of the standard bits, is introduced. In our models, we have emphasized changes in drilling strengths and crossflow as a result of vortex action. The theoretical as well as field data indicate an average increase of penetration rate to fall within 10 to 75 percent for 9-7/8″ holes. Furthermore, on the basis of laboratory data, it was concluded that (1) the vortex pressure drop increases as the DVG sub nozzle angle to the horizontal plane decreases, (2) there is a non-linear increase of the vortex pressure drop with flowrate, (3) the DVG sub maximizes the crossflow velocities below the jet bit by suppressing the impact vortex formed as a result of the jet impact near the cones.

A central element to reduce drilling cost is to improve drilling operation by analyzing real-time data. Developing advanced real-time analysis tools is one way to improve the drilling operation. Two approaches which currently are used for optimizing the actual rotary drilling process are mechanical specific energy and inverted rate of penetration models. The mechanical specific energy method is defined as the work needed to destroy a given volume of the rock. It can act as a tool during the drilling operation to detect changes in drilling efficiency thus providing a method to optimize the drilling parameters to enhance instatanious rate of penetration. Rate of penetration models, on the other hand, can be used to calculate formation drillability considering the effects of drilling parameters, bits design and bit wear. Drilling optimization using rate of penetration models is done by changing the drilling parameters and/or bit design to find the optimum drilling scenario for an entire bit run. The mechanical specific energy log and the drillability ratio differ when mud weight is changed and when bits are worn. These two differences are due the fact that mechanical specific energy does not include bit wear as well as the effect of changing mud weight. By combining these methods and modifying the mechanical specific energy equations to incorporate these effects and the mechanical specific energy can be used as a real-time trending tool for bit wear estimations. In this analysis, wells from offshore Middle East and onshore North America are analyzed. The field results are very encouraging in that the bit wear for both roller cone and PDC bits can be predicted. The field validation of this new approach shows that the supplementary information on the bit wear status can in some cases benefit in the decision of when to pull the bit while it still is in the hole and thereby possibly improve overall economics of the drilling operation.

Accurate prediction of Rate of Penetration (ROP) is critical for optimizing drilling efficiency and reducing costs in hydrocarbon exploration. Traditional ROP models often overlook formation temperature, despite its significant influence on rock mechanics and drilling fluid rheology, particularly in high-temperature sandstone reservoirs like those in the Niger Delta. This study employs three machine learning (ML) algorithms—Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)—to evaluate the contribution of formation temperature to ROP prediction. A dataset of 1,200 drilling records from Niger Delta wells was used, incorporating parameters such as weight-on-bit (WOB), rotary speed (RPM), pump pressure, and formation temperature. Model performance was assessed using R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), with ablation studies to isolate temperature’s impact. Inclusion of formation temperature improved ROP prediction accuracy across all models. The ANN achieved the highest performance (R² = 89.3%, RMSE = 0.387, MAE = 0.141), followed by RF (R² = 90.5%, RMSE = 2.737, MAE = 0.900) and SVR (R² = 87.8%, RMSE = 0.553, MAE = 0.169). Temperature omission led to significant performance degradation (R² reductions of 7–13%). Sensitivity analysis ranked temperature among the top three influential features. Formation temperature is a critical but underutilized parameter in ROP modeling. ML techniques, particularly ANN, demonstrate superior capability in capturing nonlinear temperature-dependent effects, offering actionable insights for real-time drilling optimization in thermally complex formations. This study provides a framework for integrating thermal data into predictive models to enhance drilling efficiency in the Niger Delta and analogous basins.

Lost circulation costs are a significant expense in drilling oil and gas wells. Drilling anywhere in the Rumaila field, one the world’s largest oilfields, requires penetrating the Dammam formation, which is notorious for lost circulation issues and thus a great source of information on lost circulation events. This paper presents a new, more precise model to predict lost circulation volumes, equivalent circulation density (ECD), and rate of penetration (ROP) in the Dammam formation. A larger data set, more systematic statistical approach, and a machine-learning algorithm have produced statistical models that give a better prediction of the lost circulation volumes, ECD, and ROP than the previous models for events. This paper presents the new model, validates the key elements impacting lost circulation in the Dammam formation, and compares the predicted outcomes to those from the older model. The work previously presented by Al-Hameedi et al. (http://www.onepetro.org, 2017a; http://www.AADE.org, 2017b) provided a platform for predicting the severity of lost circulation incidents in the Dammam formation. Using the new models, the predictions closely track actual field incidents of lost circulation. When new lost circulation events were compared with predictions from the old and new models, the new model presented a much tighter prediction of events. Three equations for optimizing operations were developed from these models focusing on the elements that have the highest degree of impact. The total flow area of the nozzles was determined to be a significant factor in the ROP model indicating that nozzle size should be chosen carefully to achieve optimal ROP. Good modeling of projected lost circulation events can assist in evaluating the effectiveness of new treatments for lost circulation. The Dammam formation is a significant source of lost circulation in a major oilfield and warrants evaluation of the effectiveness of lost circulation treatments. These techniques can be applied to other fields and formations to better understand the economic impact of lost circulation and evaluate the effectiveness of various lost circulation mitigation efforts.

Most coal seams in China are characterized by high‐gas pressure and high‐gas content, which seriously threaten the safety of coal mines. Gas extraction is an effective measure for preventing gas disasters. However, the drilling process in soft coal seams is inefficient; it is difficult for the boreholes to reach the design length. The rate of penetration (ROP) plays an important role in determining drilling efficiency and borehole length in soft coal seams. In this study, we establish a simplified ROP model for soft coal seams and evaluate the maximum ROP of Hebi No. 6 coal mine. First, the drill cutting transport capacity of the drilling system and drill cutting quantity generated during the drilling process are analyzed. By simplifying drill cutting transport and coal seam properties, the ROP model is established. Second, the Rock Failure Process Analysis software is used to analyze the deformation region of boreholes, which has a great influence on the drill cutting transport space. Finally, a field test is implemented in Hebi No. 6 coal mine in Henan Province, China, to verify the model, and we propose a measure to control the ROP below the maximum value to increase the length of the borehole. The test results indicate that the model has high accuracy, with errors less than 15%, and the lengths of boreholes in air‐return and transportation roadways are increased to 60 and 80 m, respectively, and the increase rate is greater than 50%.