Optimization Of Drilling Parameters Research Articles

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 string vibrational analysis and parametric optimization for a portable water well rig development

The vibration of a drill string is a key topic of study in drilling and mining engineering. Excessive Drill String (DS) vibrations can lead to early failure of bits due to fatigue, Drill Pipe (DP) fractures, bearing damage, and a decrease in the Rate of Penetration (ROP). This occurs when the drill string rotates at its natural frequency, which is known as resonance. A modal and harmonic vibrational analysis of a drill string up to 60-65 meters long was conducted in a vertical water well through parametric studies. This project investigates the vibration of a drill string under various parameters, including drill string lengths, Weight on Bit (WOB), and drill string rotational speeds. The Rotational Speed (RS) in the drill string induces lateral vibrations and stick-slip in torsional vibrations. Finite element modeling of the drill string was performed using ANSYS Workbench (Mechanical). Modal and harmonic frequencies were determined to obtain the optimal drill string length and rotational speeds, using Campbell diagrams and varying weights on bits. Different weights on the bit were assumed to simulate the drilling environment. One of the modern methods used to simulate dynamic behavior in drilling operations is random vibrational analysis. A random vibrational analysis of the drill string was performed in a vertical water well using ANSYS Workbench 2020. This analysis aimed to identify the frequency response of the drill string, with the probability of deformation for each sigma under random vibration being evaluated. The analysis indicated a higher probability approximately 99.7% of failure along the axis of the supplied PSD acceleration. The response power spectral density (RPSD) was analyzed in the x, y, and z directions, focusing on any point in the bottom hole assembly, primarily the bit. The response PSD results indicated that as the Drill Pipe (DP) increased, resonance spikes rose across all axes, leading to lateral and longitudinal vibrations in the drill string. These findings highlight lateral movement as a primary cause of vibration, offering insights for optimizing drilling parameters.

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.

Analyzing the performance of single-cutter cutting rock under bottom-hole pressure conditions

Addressing the ambiguity surrounding the process of crack initiation and propagation in rock subjected to confining pressure and hydrostatic pressure, this study integrates laboratory experiments with numerical simulations. First, an analysis is conducted on the cutting force, rock fragmentation energy consumption, rock chip size, and distribution of cutting stress in rocks simultaneously exposed to confining pressure and hydrostatic pressure. Subsequently, an internal crack propagation analysis model for rock is established using the finite discrete element method, and the accuracy of this model is verified through experimentation. The process and characteristics of internal crack propagation during the cutting process are analyzed. Lastly, the variation characteristics of cutting forces under different cutting parameter conditions are compared. The findings indicate that, under identical cutting conditions, confining pressure reduces the degree of granite rock fragmentation, increases the presence of powdery rock debris, and mitigates force fluctuation characteristics, leading to an approximate 17% increase in energy consumption of rock breaking. The presence of confining pressure promotes the generation of micro-cracks within the rock, but restricts the expansion of primary cracks. Notably, shear cracks ahead of the cutting cutter and tensile cracks beneath the cutting path are conspicuously diminished, thereby augmenting the difficulty of rock failure. The elevation of confining pressure and cutting depth emerges as the principal factors contributing to elevated cutting forces, with cutting forces exhibiting exponential growth with increasing confining pressure. At a hydrostatic pressure of 9 MPa, cutting forces escalate by 50 %. Moreover, substantial cutting depth induces discontinuity in the rock failure mechanism. Stress during cutting predominantly concentrates in the vicinity of the PDC cutter front and beneath the cutting trajectory, extending along the advancement direction of the PDC cutter and below the cutting path. These results offer valuable insights for optimizing drilling parameters under confining pressure conditions.

A novel real time sensing framework for assessment of thrust force in drilling of composites using Taguchi and NSGA-II

Monitoring thrust during drilling operations is critical to optimizing drilling parameters and ensuring safe and efficient drilling. In this study, we will monitor variations in thrust force during the drilling process using a sensor coated with carbon black. These sensors measure changes in electrical resistance when the sensor is subjected to a mechanical load, such as drilling force. Carbon black at 5 wt% was used, which was embedded in a fiberglass composite. For this study, an orthogonal arrangement of Taguchi’s L27 sequence was used, with drill diameter D, feed rate F, and spindle speed S as machining parameters. The thrust force was measured with a force dynamometer and the resistance change was measured simultaneously with the developed new system. Analysis of variance was used to find the optimal parameters, and in this study, a mathematical model was proposed to measure the thrust force directly using the electrical resistance detected. NSGA-II algorithm was used to predict solutions for the given parameters for comparison. A comprehensive way of drilling process and data-driven decisions to improve drilling performance and safety was studied, and a correlation between actual thrust and calculated thrust of 0.91 was found. The proposed system was able to monitor and continuously record thrust force in real time during drilling, with electrical resistance as the most important factor with an error percentage of less than 8% compared to the NSGA-II predictions of less than 5%.

Investigation of the drilling behavior of the Ti6Al4V alloy and developing prediction model using artificial neural network

This study investigates the influence of cutting parameters on drilling performance for Ti6Al4V alloy using TiN-coated carbide drill bits in a dry drilling environment. The surface roughness, feed force, cutting moment, cutting temperature, tool performance, hole diameter, circularity, and cylindricity values of the hole were studied. The optimal drilling parameters were determined using ANOVA to achieve the desired hole sizes, and prediction models were developed using artificial neural networks (ANN). The experimental results revealed that the feed rate was the most influential parameter for feed forces (92%), whereas the cutting speed had the greatest effect on tool performance (98%). It was determined as a result of the analyses that the optimal feed rate for tool performance, cutting moment, surface roughness, cutting temperature, and feed forces was 0.05 mm/rev. The diameter of the hole was positively affected at high cutting speeds and feed rates. The circularity and cylindricity of the hole were adversely affected by these cutting conditions. Adhesive wear was seen on the drill bits in all cutting conditions, and surface roughness was negatively affected by this adhesive wear. Artificial neural networks (ANN) were used to predict operating cost outputs (feed force, tool performance) and hole quality outputs (surface roughness, deviation of hole diameter). The compatibility of the experimental results and the ANN model was examined in the control experiment. ANN was able to predict the surface roughness and hole diameter with deviation values of 3% and 7%, respectively. Similarly, the deviation values for feed force and tool performance were determined at 3% and 10%, respectively.

An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation

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.

Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM

The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations.

Interpretation and characterization of rate of penetration intelligent prediction model

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.

Stick–Slip Characteristics of Drill Strings and the Related Drilling Parameters Optimization

To eliminate or reduce stick–slip vibration in torsional vibration of the drilling string and improve the rate of penetration (ROP), a stick–slip vibration model of the drilling string considering the ROP was established based on the multidimensional torsional vibration model of the drilling string. The model was verified by simulation analysis. The characteristics of the drilling string stick–slip vibration in the three stages of stationary, slip, and stick were analyzed. This paper investigated the influence of rotary torque, rotary speed, and weight on bit (WOB) on stick–slip vibrations in the drill string. Based on this, the relationship between the drilling parameters and ROP was established. Drilling parameter optimization was completed for soft, medium-hard, and hard formations. Results showed that appropriately increasing torque and decreasing WOB can reduce or even eliminate stick–slip vibrations in the drill string and increase the ROP. The parameter optimization increased the ROP by 11.5% for the soft formation, 13.7% for the medium-hard formation, and 14.3% for the hard formation. The established drill string stick–slip vibration model provides theoretical guidance for optimizing drilling parameters in different formations.

Modelling and mechanical characteristics of PDC cutter-rock interaction by combining mixed fragmentation modes with dynamic rock strength

Polycrystalline diamond compact (PDC) bit is one of the most widely used drill bits for improving the rate of penetration in deep oil and gas well and geothermal well. However, the dynamic rock fragmentation mechanics characteristics of PDC bits are still unclearly. A coupled fragmentation mechanics model of PDC cutter-rock interaction is established by combining the mixed fragmentation modes with dynamic strength. The coupling influence laws of cutter angle, cutting depth, dynamic strength ratio, breaking modes on the horizontal force coefficient (HFC), vertical force coefficient (VFC) and specific energy are analyzed. The model of this paper can optimize cutter inclination angle, cutting depth and minimum specific energy. With the increase of the cutter inclination angle, the dynamic VFC changes into two modes. The definition of the dynamic modes depends on the dynamic strength ratio. As the cutting angle increases, the cutting force increases. The cutting force increases nonlinearly with increasing cutting depth. The specific energy of rock fragmentation increases nonlinearly with increasing cutting depth. With the increase of dynamic strength, the specific energy of rock fragmentation increases nonlinearly. When the input-energy increases, the rate of penetration response is divided into three stages. The results have important guiding significance for the PDC bit design and drilling parameters optimization to increase the rate of penetration and the efficiency of exploration and development.

Establishment of data-driven multi-objective model to optimize drilling performance

Drilling parameters optimization has consistently generated research interest over the years because of the cost-saving benefits associated to improve drilling efficiency. However, several physics-based and data-driven models have been developed for drilling parameters optimization, and the majority of the data-driven models are based on regression methods. Obtaining highly accurate and optimized drilling parameters with rapid as well as cost-effective simulation runs is difficult to achieve. To accurately and rapidly predict drilling parameters, a multi-objective optimization model was proposed in this study. In the proposed model, the rate of penetration (ROP), unit drilling cost (UDC), and mechanical specific energy (MSE) were considered as the objective functions, while the weight on bit (WOB) and rotations per minute (RPM) were chosen as the optimization variables. Meanwhile, a method for ROP prediction based on improved back propagation neural network (BP) is also presented. Field data presented in this study indicate that when drilling is free of drilling complications, this multi-objective optimization model could optimize WOB and RPM with higher ROP and lower MSE, and UDC.

Enhancing the drilling efficiency through the application of machine learning and optimization algorithm

This article presents a novel Artificial Intelligence (AI) workflow to enhance drilling performance by mitigating the adverse impact of drill-string vibrations on drilling efficiency. The study employs three supervised machine learning (ML) algorithms, namely the Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and Regression Decision Tree (DTR), to train models for bit rotation (Bit RPM), rate of penetration (ROP), and torque. These models combine to form a digital twin for a drilling system and are validated through extensive cross-validation procedures against actual drilling parameters using field data.The combined SVR - Bit RPM model is then used to categorize torsional vibrations and constrain optimized parameter selection using the Particle Swarm Optimization block (PSO). The SVR-ROP model is integrated with a PSO under two constraints: Stick Slip Index (SSI<0.05) and Depth of Cut (DOC<5 mm) to further improve torsional stability. Simulations predict a 43% increase in ROP and torsional stability on average when the optimized parameters WOB and RPM are applied. This would avoid the need to trip in/out to change the bit, and the drilling time can be reduced from 66 to 31 h.The findings of this study illustrate the system's competency in determining optimal drilling parameters and boosting drilling efficiency. Integrating AI techniques offers valuable insights and practical solutions for drilling optimization, particularly in terms of saving drilling time and improving the ROP, which increases potential savings.

Double-layer pipe dual-gradient drilling wellbore pressure calculation model and parameters optimization

Deepwater oil and gas extraction face the problem of narrow safety density window, and double-layer pipe dual-gradient drilling is one of the effective ways to solve the problem of narrow safety density window, but the calculation model of borehole pressure and optimization method of drilling parameters for double-layer pipe dual-gradient drilling has not been thoroughly investigated. Based on the fluid dynamics theory, a dual-gradient drilling wellbore pressure calculation model was established, considering the influence of lift pump head size and installation location on the bottom hole pressure and the dynamic changes of wellbore pressure during drilling. Based on the wellbore pressure model and step-out strategy, the optimization model and algorithm of double-layer pipe dual-gradient drilling parameters were established with the minimum bottom hole pressure difference as the objective function and drilling fluid density, displacement, lift pump to bit distance and back pressure as the optimization parameters. Simulation analysis of the optimized drilling parameters (drilling fluid displacement and wellhead return pressure) was carried out using the optimization algorithm. It was found that the optimized drilling parameters could increase the drilling depth, reduce the bottom hole pressure differential and improve the mechanical drilling speed. The above optimization model and method can be used to optimize the drilling parameters for double-layer tubular dual-gradient drilling, which is beneficial to increase the drilling depth and reduce the bottom hole differential pressure.

Multi-Response Optimization of Drilling Parameters for Aluminum Metal Matrix Composite Using Entropy Weighted Grey Relational Analysis

The present work aims to understand the effect of drilling parameters (drill speed and feed rate) during the drilling of saffil fiber-reinforced Al metal matrix composite (MMC) under dry conditions. The effect of drilling parameters on individual response characteristics is evaluated and the optimum drilling parameters are also investigated using a multi-response optimization technique known as Entropy Weighted Grey Relational Analysis (EWGRA). The drilling parameter optimization has been performed with aim of minimizing the surface roughness of drilled hole, roundness error in drilled hole and feed force during drilling. Weights were assigned to individual responses with the Entropy weight method and Grey relational grades were calculated to obtain the optimal level for drilling parameters. To achieve the minimum values for all output responses the optimal value of drill speed is 22 m/min and the feed rate is 0.075 mm/rev.