Optimization of drilling parameters using improved play-back methodology

2021 ◽  
pp. 108991
Author(s):  
Viswanth Ramba ◽  
Senthil Selvaraju ◽  
Senthilmurugan Subbiah ◽  
Muthukumar Palanisamy ◽  
Aman Srivastava
Keyword(s):  
Drilling Parameters

scholarly journals Effect of Twist Drill Geometry and Drilling Parameters on Hole Quality in Single-Shot Drilling of CFRP/Al7075-T6 Composite Stack

2021 ◽  
Vol 5 (7) ◽  
pp. 189
Author(s):  
Muhammad Hafiz Hassan ◽  
Jamaluddin Abdullah ◽  
Gérald Franz ◽  
Chim Yi Shen ◽  
Reza Mahmoodian
Keyword(s):  
Feed Rate ◽  
Thrust Force ◽  
Critical Dimension ◽  
Helix Angle ◽  
Single Shot ◽  
Twist Drill ◽  
Hole Quality ◽  
Drilling Parameters ◽  
Industry Practice ◽  
Maximum Thrust

Drilling two different materials in a layer, or stack-up, is being practiced widely in the aerospace industry to minimize critical dimension mismatch and error in the subsequent assembly process, but the compatibility of the drill to compensate the widely differing properties of composite is still a major challenge to the industry. In this paper, the effect of customized twist drill geometry and drilling parameters are being investigated based on the thrust force signature generated during the drilling of CFRP/Al7075-T6. Based on ANOVA, it is found that the maximum thrust force for both CFRP and Al7075-T6 are highly dependent on the feed rate. Through the analysis of maximum thrust force, supported by hole diameter error, hole surface roughness, and chip formation, it is found that the optimum tool parameters selection includes a helix angle of 30°, primary clearance angle of 6°, point angle of 130°, chisel edge angle of 30°, speed of 2600 rev/min and feed rate of 0.05 mm/rev. The optimum parameters obtained in this study are benchmarked against existing industry practice of the capability to produce higher hole quality and efficiency, which is set at 2600 rev/min for speed and 0.1 mm/rev for feed rate.

scholarly journals A Force Sensorless Method for CFRP/Ti Stack Interface Detection during Robotic Orbital Drilling Operations

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Fang ◽  
Ze-Min Pan ◽  
Bing Han ◽  
Shao-Hua Fei ◽  
Guan-Hua Xu ◽  
...  
Keyword(s):  
Cutting Force ◽  
Thrust Force ◽  
Cutting Parameters ◽  
Force Model ◽  
Drilling Process ◽  
Reinforced Plastics ◽  
Orbital Drilling ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Machining Properties

Drilling carbon fiber reinforced plastics and titanium (CFRP/Ti) stacks is one of the most important activities in aircraft assembly. It is favorable to use different drilling parameters for each layer due to their dissimilar machining properties. However, large aircraft parts with changing profiles lead to variation of thickness along the profiles, which makes it challenging to adapt the cutting parameters for different materials being drilled. This paper proposes a force sensorless method based on cutting force observer for monitoring the thrust force and identifying the drilling material during the drilling process. The cutting force observer, which is the combination of an adaptive disturbance observer and friction force model, is used to estimate the thrust force. An in-process algorithm is developed to monitor the variation of the thrust force for detecting the stack interface between the CFRP and titanium materials. Robotic orbital drilling experiments have been conducted on CFRP/Ti stacks. The estimate error of the cutting force observer was less than 13%, and the stack interface was detected in 0.25 s (or 0.05 mm) before or after the tool transited it. The results show that the proposed method can successfully detect the CFRP/Ti stack interface for the cutting parameters adaptation.

Horizontal Well Penetration Rate Increasing Technology in Sulige Gas Field

2015 ◽  
Vol 733 ◽  
pp. 17-22
Author(s):  
Yang Liu ◽  
Zhuo Pu He ◽  
Qi Ma ◽  
Yu Hang Yu
Keyword(s):  
Horizontal Well ◽  
Economies Of Scale ◽  
Reference Value ◽  
Drilling Speed ◽  
Underground Engineering ◽  
Gas Field ◽  
Horizontal Section ◽  
Well Drilling ◽  
Drilling Parameters ◽  
Key Techniques

In order to improve the drilling speed, lower the costs of development and solve the challenge of economies of scale development in sulige gas field, the key techniques research on long horizontal section of horizontal well drilling speed are carried out. Through analyzing the well drilling and geological data in study area, and supplemented by the feedback of measured bottom hole parameters provided by underground engineering parameters measuring instrument, the key factors restricting the drilling speed are found out and finally developed a series of optimum fast drilling technologies of horizontal wells, including exploitation geology engineering technique, strengthen the control of wellbore trajectory, optimize the design of the drill bit and BHA and intensify the drilling parameters. These technologies have a high reference value to improve the ROP of horizontal well in sulige gas field.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Connection Recipes: Exploring Optimum Connection Practices through a Data-Driven Approach

2021 ◽  
Author(s):  
Rahmat Ashari ◽  
Owen Sorensen ◽  
Pradeepkumar Ashok ◽  
Eric van Oort ◽  
Matthew Isbell ◽  
...  
Keyword(s):  
Rotational Speed ◽  
Data Driven ◽  
Knowledge Gap ◽  
Radial Acceleration ◽  
Stick Slip ◽  
Drilling Parameters ◽  
Quality Checks ◽  
Data Driven Approach ◽  
Hole Making ◽  
Appreciable Variation

Abstract Although numerous studies have investigated how shocks and vibrations contribute to bottomhole assembly (BHA) failures during hole-making, very few have explicitly focused on shock and vibrational behaviors during drillpipe connections. This study adopts a data-driven approach to explore various connection practices and their associated shocks and vibrations, aiming to propose optimum "connection recipes" that minimize negative drillstring impacts during connections. This study utilized data from surface sensors as well as downhole accelerometers and gyroscopes installed both at a downhole sub and the bit. In total, 520 connections from 5 lateral sections were studied. Several quality checks and corrections were performed to ensure synchronization between surface and downhole data. The analyses focused on two connection phases specifically: going off-bottom and going back to bottom. The presence of stick-slip events and high magnitudes of both maximum and root mean squared (RMS) radial accelerations were examined together with the associated surface drilling parameters. Various visualization approaches were performed to help demonstrate the vibration and shock behaviors resulting from different going off-bottom and going back to bottom practices. The analyses showed that restarting surface rotational speed at low values (≤ 40 RPM) risks inducing stick-slip events when going back to bottom. When the surface RPM was increased sufficiently, a notable reduction in RMS radial acceleration was observed. Maximum radial acceleration magnitude was highest before WOB application, which could be mitigated by immediate WOB re-application. Appreciable variation in the maximum radial acceleration was apparent when restarting at low (≤ 15 klbf) WOB values. When going off-bottom, drilling off should be accompanied by a reduction in the surface rotational speed to avoid a jump in the maximum radial acceleration values. This work provides suggestions on how to execute better connections. Since the impacts of shocks and vibrations during connections have previously been largely overlooked, this study fills a knowledge gap to help establish better practices and automation routines to improve the lifespan of the bit and downhole tools.

Application of Machine Learning for Lithology-on-Bit Prediction using Drilling Data in Real-Time

2021 ◽  
Author(s):  
Temirlan Zhekenov ◽  
Artem Nechaev ◽  
Kamilla Chettykbayeva ◽  
Alexey Zinovyev ◽  
German Sardarov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Hybrid Modeling ◽  
Data Set ◽  
Drilling Parameters ◽  
Drilling Data ◽  
Stable Solutions ◽  
Mud Logging

SUMMARY Researchers base their analysis on basic drilling parameters obtained during mud logging and demonstrate impressive results. However, due to limitations imposed by data quality often present during drilling, those solutions often tend to lose their stability and high levels of predictivity. In this work, the concept of hybrid modeling was introduced which allows to integrate the analytical correlations with algorithms of machine learning for obtaining stable solutions consistent from one data set to another.

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