Optimization Of Drilling Parameters Research Articles

A Study of Drilling Parameter Optimization of Functionally Graded Material Steel–Aluminum Alloy Using 3D Finite Element Analysis

Composite materials, such as aluminum alloy FGMs, provide advantageous weight reduction properties compared to homogenous pure structures while still preserving sufficient stiffness for diverse applications. Despite various research on drilling simulation concepts and ideas for these materials, there still needs to be an agreement on the process modeling. Researchers have looked into a lot of different numerical methods, including Lagrangian, Eulerian, arbitrary Lagrangian–Eulerian (ALE), and coupled Eulerian–Lagrangian (CEL), to find solutions to problems like divergence issues and too much mesh distribution, which become more of a problem at higher speeds. This research provides a global analysis of bottom-up meshing for eleven 1 mm layers using ABAQUS® software. It combines the internal surface contact approach with the Lagrangian domain’s kinematic framework. The model uses the Johnson–Cook constitutive equation to precisely predict cutting forces, stress, and strain distributions, optimizing cutting parameters to improve drilling performance. According to Taguchi analysis, the most favorable parameters for reducing cutting force and improving performance are a rotational speed of 700 rpm, a feed rate of 1 mm/s, and a depth of cut of 3 mm. The findings suggest that increasing the feed rate and depth of cut substantially affects the cutting force, while the rotational speed has a comparatively little effect. These ideal settings serve as a foundation for improving FGM drilling efficiency.

A Novel Geothermal Wellbore Model Based on the Drift-Flux Approach

Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas–liquid two-phase flow drift-flux model and heat transfer model. The key features include handling the heat transfer between the formation and the wellbore, addressing the slip relationship between the gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite difference method, and the highly nonlinear system is solved using the Newton–Raphson method. The numerical simulation, validation, and comparison with existing models demonstrate the enhanced accuracy of this model. In our tests, the model achieved a high accuracy in calculating the bottom-hole pressure and temperature, with mean relative errors (MREs) significantly lower than those of other models. For example, the MREs for the bottom-hole pressure and temperature of the Rongxi area well in Xiongan, calculated by this model, are 1.491% and 1.323%, respectively. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geothermal energy development.

Ex-vivo parametric study of laser ablation-based drilling of cortical bone.

Frequently orthopedic surgeries require mechanical drilling processes especially for inserted biodegradable screws or removing small bone lesions. However mechanical drilling techniques induce large number of forces as well as have substantially lower material removal rates resulting in prolong healing times. This study focuses on analyzing the impact of quasi-continuous laser drilling on the bone's surface as well as optimizing the drilling conditions to achieve high material removal rates. An ex-vivo study was conducted on the cortical region of desiccated bovine bone. The laser-based drilling on the bovine bine specimens was conducted in an argon atmosphere using a number of laser pulses ranging from 100 to 15,000. The morphology of the resulting laser drilled cavities was characterized using Energy dispersive Spectroscopy (EDS) and the width and depth of the drills were measured using a laser based Profilometer. Data from the profilometer was then used to calculate material removal rates. At last, the material removal rates and laser processing parameters were used to develop a statistical model based on Design of Experiments (DOE) approach to predict the optimal laser drilling parameters. The main outcome of the study based on the laser drilled cavities was that as the number of laser pulses increases, the depth and diameter of the cavities progressively increase. However, the material removal rates revealed a decrease in value at a point between 4000 and 6000 laser pulses. Therefore, based on the sequential sum of square method, a polynomial curve to the 6th power was fit to the experimental data. The predicted equation of the curve had a p-value of 0.0010 indicating statistical significance and predicted the maximum material removal rate to be 32.10 mm3/s with 95%CI [28.3,35.9] which was associated with the optimum number of laser pulses of 4820. Whereas the experimental verification of bone drilling with 4820 laser pulses yielded a material removal rate of 33.37 mm3/s. Therefore, this study found that the carbonized layer formed due to laser processing had a decreased carbon content and helped in increasing the material removal rate. Then using the experimental data, a polymetric equation to the sixth power was developed which predicted the optimized material removal rate to occur at 4820 pulses.

An Analysis of Chip Formation and Hole Circularity in Drilling Applications: An Aircraft Components Perspective

Drilling plays a critical role in the production of precise holes for aircraft components. However, drilling aluminum alloys poses challenges, leading to poor hole quality and potential defects in the airframe structure. This study focused on investigating chip formation, hole circularity, and drilling parameters, specifically feed rate and spindle speed. Dry drilling trials are conducted using high-speed steel drill bits and the Al6061-T6 alloy. The CIMCO MDC-MAX software is employed to monitor machine performance and accumulate comprehensive data. The study investigates the effect of varying feed rates on hole circularity, chip development, and chip thickness. Findings indicate that higher feed rates result in increased circularity error and chip thickness. The circularity graph shows inconsistencies due to workpiece vibration during drilling. Chip thickness rises with the feed rate, particularly with an increased number of drilled holes, attributed to factors such as tool rubbing and improper cutting. Additionally, the study highlights the correlation between machine performance and product quality. The CIMCO MDC-MAX data reveals that a feed rate of 0.260 mm/rev corresponds to high machine performance and low circularity. Conversely, Drill 6, operating at a feed rate of 0.230 mm/rev, exhibits poor machine performance and higher average circularity. The research enhances understanding of chip formation and hole circularity in drilling applications for aircraft components. The results emphasize the importance of optimizing drilling parameters to achieve superior hole quality. Practical implications are provided for enhancing the drilling process in aerospace manufacturing.

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

The purpose of this research is to ascertain the optimal abrasive waterjet drilling parameters for making holes on Inconel 718 superalloy coated with yttrium-stabilized zirconia, namely waterjet pressure (JP), standoff distance (SD), abrasive flow rate (FR), and angle of impact (AI). The study explores the predictive modeling of the entry diameter (DN) and exit diameter (DX) of the drilled holes using an amalgamation of experimental analysis, response optimization and machine learning approaches. Eight different ML techniques are used to predict DN and DX. Better values of DN = 1.31352 mm and DX = 1.00515 mm are obtained through Random Forest for the setting of JP = 175 MPa, FR = 250 g min−1, SD = 1.45455 mm, and AI = 0.909091 deg. Further, the tuning of hyperparameters of Random Forest algorithm is performed to study the improvement in measuring DN and DX. The least value of total absolute error = 0.468 is observed while finding the DN and DX using Random Forest algorithm than the traditional response optimization method with reference to the confirmation test results. The work provides important insights for enhancing the machinability of YSZ-coated Inconel 718 superalloy utilizing the AWJ drilling process by bridging the gap between manufacturing research, machine learning, and real-world applications.

Optimizing drilling parameters for fly ash-filled hemp/epoxy composites: investigating drill bit geometries, feed rates, and spindle speeds

This article investigates the drilling behavior of hemp fiber-reinforced epoxy composites, both unfilled and fly ash-filled. Drilling performance tests were conducted at varying feed rates (8, 16, and 24 mm min−1) and spindle speeds (700, 1400, and 2100 RPM) using three drill geometries: parabolic, 8-facet, and dagger. The fly ash-filled hemp/epoxy composite showed significant reductions in maximum thrust force (25.09%, 19.92%, and 21.43%) and torque (80%, 79.87%, and 59.25%) when drilled with parabolic, 8-facet, and dagger drill bits, respectively, compared to the unfilled composite. The maximum drilling temperature reached 90.6 °C during cutting and decreased to 71.7 °C post-drilling. Overall, the fly ash-filled composites demonstrated superior performance in terms of drilling-induced stresses and damage.

Axial transfer characteristics of a drillstring in conjunction with bit penetration behavior in horizontal wells

Nonlinear friction and interaction between bit and rock often result in a lower weight on bit (WOB) than the intended design value. The analysis of the dynamic transmission mechanism of a drillstring in complex horizontal wells is a crucial engineering problem that aims to enhance drilling efficiency. In this paper, a mechanical model was developed, integrating nonlinear bit penetration behavior and variable axial loads, aiming to unveil the nonlinear axial vibration characteristics and elucidate the transmission mechanisms within the drillstring. Notably, the mean value of axial force distributed along the drillstring is dictated by wellhead pulling force, while the amplitude is shaped by the interaction between bit and rock, and rotary speed. In calculated examples for horizontal wells, the analysis reveals that significant losses in power axial transmission, resulting in WOB is only 38% of the intended value. And reducing the wellhead pulling force by 30 kN leads to a maximum increase in WOB only of 9.51 kN. Extended region in drillstring with negative axial forces exhibit maximum values 5–6 times higher than the WOB. Specific zones, such as 20 m–40 m and 0–20 m from the bit, experience axial force fluctuation amplitudes of 40.5% and 138.8% of the mean value respectively, with the latter identified as an intense vibration zone, while the acceleration values 0–10 m from the bit signal a high-risk zone. In prolonged horizontal section, stability issues manifest, with spiral buckling observed in the holding section and sinusoidal buckling in the horizontal section. Mitigating the synchronous vibrations between the bit and the surrounding drillstring can effectively reduce the intensity of axial vibration. To ensure successful drilling to target formation, adjusting the wellhead pulling force to keep the WOB in the range of 60–90 kN and limiting the rotary speed to 90 r/min are recommended practices. This comprehensive analysis offers valuable insights for optimizing drilling parameters, pinpointing high-vibration zones, and mitigating risks associated with drilling, providing a holistic approach to enhancing drilling efficiency.

Application of artificial intelligence to predict rock strength and drilling efficiency using in-cutter sensing data and vibration modes

Drilling is a complex destructive action that induces vibrations due to the rock-bit interaction, which affects the overall drilling efficiency and wellbore quality. This study aims to enhance drilling efficiency by deploying artificial neural networks (ANNs) to integrate in-cutter force sensing and vibration data. Data is collected from experiments conducted with sharp cutters on rock samples of varying mechanical properties, measuring variables such as weight on bit, torque, rotational speed, in-cutter force, and vibration measurements. A scoring system is used to evaluate the drilling efficiency by coupling the mechanical specific energy and vibration modes. An ANN is trained with these variables to predict the rate of penetration and rock strength, which are also measured in the experiments to be used as ground truth. The reliability of the framework is demonstrated by testing the validity of the ANN model on samples with various mechanical properties. It introduces a reliable and swift method for determining optimal drilling parameters, supported by a sensitivity analysis to fine-tune the ANN and assess the influence of each parameter on performance. This study demonstrates that ANN could be successfully implemented to predict the rate of penetration and rock strength on a laboratory-scaled drilling rig. The results show that the ANN model accurately predicts training and testing datasets for scoring while drilling multiple layers with a correlation coefficient of 0.966. Integration of in-cutter sensing technology, vibration data, and ANN can be of significant interest and be used on field applications to provide a reliable and rapid decision about drilling efficiency.

Study on flow pattern transition of cuttings transport in extended-reach drilling

Inadequate hole cleaning is one of the major challenges in extended-reach drilling, especially prominent in large-sized hole sections, such as 121/4-in. section. As the drilling parameters including flow rate, ROP and rotational speed change, the cuttings flow pattern converts between fully suspended flow, two-layer flow, and three-layer flow. However, most existing cuttings transport models only study the problem of cuttings transport under a certain flow pattern and do not consider the dynamic conversion of different cuttings flow patterns. Pipe rotation plays a key role in cuttings transport in rotary drilling, but the existing studies have not fully revealed the effect of pipe rotation on the conversion of cuttings transport patterns. In this paper, a stirring diffusion factor is presented to quantify the effect of pipe rotation on cuttings transport, and the mathematical expression of the stirring diffusion factor is obtained through numerical simulations and the nonlinear regression method. Secondly, the critical conditions for the conversion of cuttings flow patterns are obtained by combining mechanical analysis and numerical simulation results, clarifying the applicable scope of different cuttings flow patterns. According to the comparison with experimental results, the prediction error of the new model considering the conversion of cuttings flow patterns is not higher than 11.1%, and the minimum prediction error is only 3.07%. Thirdly, the flow pattern maps for cuttings transport under different drilling parameters are drawn to provide guidance for the identification of cuttings transport pattern and optimization of drilling parameters. Finally, the flow pattern maps are applied to a case study of an extended-reach drilling. The results show that there is a delay effect in the conversion of cuttings flow patterns due to the nonlinear characteristics of annular geometry. Furthermore, the effects of flow rate, ROP and rotational speed on cuttings transport patterns are not independent, but mutually reinforcing and constraining each other. Inappropriate selection of drilling parameters will result in three-layer flow of cuttings transport, and then pipe sticking is likely to occur. The cuttings transport pattern can be converted from three-layer flow to two-layer flow by optimizing drilling parameters, which can significantly improve the hole cleaning effect.

Evaluating the optimum abrasive water jet machinability for CARALL composites with various fiber orientations

AbstractCarbon Fiber Reinforced Aluminum Laminated (CARALL) composites are widely used in aircraft structures due to their ability to be produced in different shapes with desired properties and their high impact resistance properties. As with other layered composite materials, processing of CARALL composites by conventional manufacturing methods results in many damage mechanisms such as fiber breakage, deformation in the hole region, stress concentration, resin‐fiber separation and microcracks. One of the modern manufacturing methods, Abrasive Water Jet (AWJ), is a processing method in which the material is removed by abrasion and almost any material can be cut without thermal degradation. There are no experimental studies in the literature on the drilling of CARALL composites by modern manufacturing methods. The aim of this study is to investigate the impact of machining parameters on the output variables (kerf taper angle (K), roundness error (Re) and material removal rate (MRR)) as well as the effect of fiber orientation on the drilling of CARALL composites with different fiber orientations on an AWJ machine. PROMETHEE‐GAIA weighted by Entropy Weighting Method were used to ascertain the optimum levels of control factors. CARALL composites with different fiber orientations were drilled with an 8 mm diameter AWJ with three different water pressures, three different nozzle feed rates. With PROMETHEE‐GAIA multi‐criteria optimization method, the optimum levels of the factors that provide both minimum Re and K values and maximum MRR value were obtained with twill woven material, 1680 mm/min feed rate and 1680 bar water pressure.Highlights CARALL composite materials with two different fiber orientations (twill weave and UD) were used. CARALL composite materials were drilled at different machining parameters. Abrasive water jet was used in drilling experiments. Optimum drilling parameters were determined to achieve minimum roundness error, minimum kerf angle and maximum material removal rate. PROMETHEE‐GAIA was used as a multi‐criteria decision‐making method.

Biomechanical Finite Element Simulation of Cochlear Implant Surgical Robot Drilling Through Direct Cochlear Access

AbstractRobotic drilling through direct cochlear access for cochlear implantation (CI) is currently a prominent area in the field of CI surgery. However, the safety of drilling remains an issue due to the limited space within the facial recess. Estimation and control of drilling force are critical to prevent drill‐bit breakthrough, excessive heat generation, and mechanical damage to the facial nerve. The bone drilling process presents a favorable application scenario for finite element (FE) analysis. In this study, a 3D FE model of temporal bone drilling is established for the first time to predict the drilling force. The self‐developed CI surgical robot validates these findings through experimental drilling. The experimental results show that the drilling process can be divided into three stages, and the corresponding force curve can be divided into six zones. The overall trend of temporal bone drilling is two force peaks and one trough in the middle. The drilling force is positively correlated with the feed rate. The FE results found to be in good agreement with the experimental results. Numerical simulation can be utilized as an invaluable tool for assisting in predicting the position of drilling tools and optimizing drilling parameters in the future.

Investigation of the effect of ultrasonic vibration on the performance of the friction drilling by FEM simulation

The friction drilling process is very important in various industries such as automotive, aerospace, and oil and gas industries. Therefore, they have always sought to find methods to improve this process. One of these methods is to optimize the application of ultrasonic vibrations in the process, and limited research has been done in this regard. The current research aims to investigate the effect of ultrasonic vibrations simultaneously with the parameters of drilling on the friction drilling process to optimize this process with the help of finite element simulation. Due to the nature of the complex transformations of the process, a Coupled Eulerian–Lagrangian model of the process was first created in the Abaqus software. Then, the process simulation was carried out in the presence and absence of ultrasonic vibrations. Rotational speed, feed rate, and amplitude of vibrations are the influencing parameters, and sheet temperature and axial force, and torque applied to the tool are the parameters investigated in this research. The results show that higher rotation speed and lower feed rate lead to a reduction of axial force and torque. Also, the application of ultrasonic vibration reduces up to 50.48% the axial force and up to 46.67% torque, and this reduction is improved by increasing the range of vibrations. The optimal mode was selected After analyzing 12 different modes based on the lowest amount of axial force and torque on the pin. It was found that the ideal mode occurs at a feed rate of 4.23 m/s and a rotation speed of 3000 rpm, with a 20-µm range. These findings and optimizing drilling parameters bring new horizons in material bonding techniques in various industries.

Numerical Simulation of Rock Breaking by Diamond Particles Under Ultra Deep Drilling Conditions

In the quest to access deeper fossil energy reserves, the past three decades have witnessed a concerted effort by scientists to develop and refine technologies capable of efficient drilling in environments characterized by high temperatures and pressures. Despite these advancements, our comprehension of the physical phenomena at play remains incomplete. A fundamental challenge pertains to the interaction between the rock and the drilling bit during drilling operations. This study delves into the influence of drilling bit pressure and rotation speed on the rock-breaking efficacy of impregnated diamonds under geostress conditions of 240 MPa and temperatures of 200℃.It juxtaposes these findings with those from rock-bit interaction experiments executed on a physical simulator, which was designed in accordance with similarity principles, in a controlled laboratory setting. Our findings indicate that for ultra-deep formations, a high Weight on Bit (WOB) coupled with a lower rotation speed results in enhanced rock cutting efficiency and a reduced rate of bit wear. Consequently, we recommend optimal drilling parameters of 8.0 to 8.5 kN for WOB and a rotational speed range of 180 to 240 rad/min. Furthermore, the study underscores the profound impact of horizontal differential stress on the rate of penetration (ROP). The insights gleaned from these tests are instrumental in refining the design of rock-breaking tools and optimizing drilling parameters, thereby augmenting the efficiency of rock breaking in challenging drilling environments.

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.

Ultrasonic-assisted drilling of cortical and cancellous bone in a comparative point of view

BackgroundDuring bone drilling, a common procedure in clinical surgeries, excessive heat generation and drilling force can cause damage to bone tissue, potentially leading to failure of implants and fixation screws or delayed healing. With this in mind, the aim of this study was to evaluate the efficiency of ultrasonic-assisted drilling compared to conventional drilling as a potential method for bone drilling. MethodsThis study examined optimal drilling parameters based on previous findings and investigated both cortical and cancellous bone. In addition to evaluating drilling force and temperature elevation, the effects of these factors on osteonecrosis and micro-crack formation were explored in ultrasonic-assisted and conventional drilling through histopathological assessment and microscopic imaging. To this end, three drilling speeds and two drilling feed-rates were considered as variables in the in vitro experiments. Furthermore, numerical modeling provided insight into temperature distribution during the drilling process in both methods and compared three different vibration amplitudes. ResultsAlthough temperature elevations were lower in the conventional drilling, ultrasonic-assisted drilling produced less drilling force. Additionally, the latter method resulted in smaller osteonecrosis regions and did not produce micro-cracks in cortical bone or structural damage in cancellous bone. ConclusionsUltrasonic-assisted drilling, which caused less damage to bone tissue in both cortical and cancellous bone, was comparatively more advantageous. Notably, this study demonstrated that to determine the superiority of one method over the other, we cannot rely solely on temperature variation results. Instead, we must consider the cumulative effect of both temperature elevation and drilling force.

Rig Automation Empowers Well Construction in Ecuador

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216249, “Digital Innovation of Well-Construction Process in Ecuador Through Rig Automation,” by Karen Peña, Kevin Etcheverry, and Hugo Quevedo, SPE, SLB, et al. The paper has not been peer reviewed. _ Artificial intelligence (AI)-based digital drilling technology was implemented in two mature fields in Ecuador that represent 33% of the country’s oil production. The drilling campaign’s main strategy included the deployment of a novel automation solution on two rigs, resulting in the optimization of the well-construction process. In the complete paper, the authors present the results of implementation of a rig-automation solution applied to 20 wells in 2022. Scope of the Digital Solution The authors detail the validation, optimization, and implementation of a drilling software to automate work flows and the installation and commissioning of associated hardware components. The following key indicators and objectives were established: - Validation of drilling-software work flows - Achievement of a 75% average in automation control - Improvement of rate of penetration (ROP) by 5% - Reduction of pre- and post-connection times by at least 30% - Real-time tuning of parameters - Training of all personnel involved in the use of the drilling-software package - Installation and commission of hardware components Drilling-Software Implementation The digital solution is an advanced software system that brings automation capabilities to drilling rigs, analogous to the different levels of automation found in automobiles. By drawing a parallel to car automation, one can better understand the system’s functionality and benefits. Level 1 consists of task automation, comparable to automatic driving features such as cruise control. In this context, the drilling software takes on the role of automating specific tasks within the drilling process. Level 2 involves process automation, similar to a car’s ability to autonomously park itself. The digital solution automates selected processes in drilling operations that enable efficient and precise execution. Level 3 encompasses adaptive automation, akin to a self-driving car that dynamically adapts to its surroundings. The drilling software continuously analyzes the real-time conditions of the wellbore and suggests optimal drilling parameters based on proprietary algorithms that leverage the well-known DeTournay model principle. The drilling software actively controls four key rig machines: the drawworks, topdrive, automated driller, and mud pumps. The solution adjusts drilling parameters intelligently, optimizing the drilling process and improving overall efficiency and drilling stability.

Effect of Choice of Drilling Kinematic System on Cylindricity Deviation, Roundness Deviation, Diameter Error and Surface Roughness of Holes in Brass Alloy

This article presents the results of an experimental study on the effect of the selection of kinematic system for the drilling process on the cylindricity deviation, roundness deviation, diameter error and surface roughness of holes in brass alloy. Three different kinematic systems based on the dependence of the direction of rotation of the workpiece and the drill bit were used. The drill bit was mounted in an axially driven holder that allowed it to be put into motion. Cutting tests were conducted at three different spindle speeds and three different feed rates per revolution (27 tests in total). A static ANOVA analysis was used to evaluate the effect of each input parameter on each output parameter. The results of this work have practical applications in machining. The following input parameters of the drilling process should be used to obtain the smallest values of each output parameter: for CYL, n = 4775 rpm, fn = 0.14 mm/rev and KIN III; for RON, n = 4775 rpm, fn = 0.1 or 0.12 mm/rev and KIN II; for DE, n = 3979 rpm, fn = 0.1 mm/rev and KIN I; and for Rz, n = 4775 rpm, fn = 0.1 mm/rev and KIN II. This research work also used Grey Relational Analysis with which input parameter optimization was derived. The optimal drilling parameters are spindle speeds of 4775 rpm, a feed per revolution of 0.1 mm/rev and the use of the first kinematic system. This paper also includes equations for predicting each parameter that describes the dimensional and shape accuracy and roughness of the hole surface. Using the first kinematic system reduced the roughness of the hole surface by as much as 58%. The correct selection of kinematic system improved its dimensional accuracy by 15%. On the other hand, the roundness deviation of the hole improved by 33% and the cylindricity deviation of the hole by 6%.

Numerical simulation study on impact factors to dynamic filtration loss

During drilling operations, naturally, fractured formations are prone to show serious mud losses, which hinder drilling and increase nonproductive time and costs. The influencing factors of dynamic fluid loss are important for optimizing drilling parameters, reducing drilling fluid loss, and protecting oil and gas reservoirs. In this study, we simulated the dynamic filtration loss of drilling fluid during drilling under formation conditions using commercial software CMG (Computer Modelling Group). The effects of filtration time, filtrate viscosity, pressure difference, internal filter cake permeability, and external filter cake permeability on filtration loss were investigated. The simulation results showed that the permeability of the external mud cake is an important factor to control the fluid loss, and the pressure consumed by the external mud cake with low permeability can account for more than 90% of the total pressure difference. When the permeability of the external mud cake is high, the permeability of the internal mud cake also has a significant influence on the dynamic fluid loss. Under formation conditions, the dynamic fluid loss of radial fluid loss is still proportional to the filtration time and pressure difference, and inversely proportional to the filtrate viscosity of drilling fluid. Under the simulated conditions, the pressure will quickly transfer to the boundary, and the formation pressure at the same position in the formation will gradually increase, while the increase is relatively small with a constant filtration rate. The results of this paper can be used to the real site for drilling optimization. This numerical analysis method can be easily applied to filtrate loss analysis, formation damage area calculation, and other radial flow-related study.

Drill Bit Selection and Drilling Parameter Optimization using Machine Learning

Machine Learning (ML) Algorithms have demonstrated their tremendous application in optimizing and enhancing the performance of various complex operations in the field of science and technology. In this research work, ML is applied to address two of the most critical factors affecting the drilling performance in the Oil and Gas Industry, which are drilling bit selection and drilling parameters optimization. Rate of Penetration is a key performance indicator of drilling efficiency, higher ROP signifies higher drilling efficiency. In this research work, a hyperparameter tuned Random Forest Regressor algorithm with an accuracy of 0.73 based on the coefficient of determination i.e., R2 Score, is used to develop ROP prediction model and subsequently drill bit selection and drilling parameters optimization is performed using Particle Swarm Optimization. The developed model has practical applicability in the selection of drill bit and optimization of drilling parameters in the Oil and Gas field. Higher ROP results in less drilling time, which correspondingly results in less capital expenditure on the project.

A real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on intelligent optimization algorithm and machine learning

Offshore large-scale cluster extended reach wells (ERWs) are widely used to develop offshore oil & gas resources. Due to the complex downhole environments and complicated geological conditions, drilling parameters real-time optimization is challenging in offshore large-scale cluster ERWs drilling. In this paper, a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on an intelligent optimization algorithm and machine learning (IOA-ML) is proposed. The method takes ROP as the objective function, establishes a ROP model based on long short-term memory (LSTM) neurons, and obtains drilling parameters optimization results asynchronously by combining the genetic algorithm, differential evolution algorithm, and particle swarm algorithm. The results show that ROP has been improved by 33.33% on average after the optimization by this real-time intelligent optimization method with an optimization time within 60s, which meets the requirements of real-time optimization of drilling parameters.