Directional Drilling Automation Using a Laboratory-Scale Drilling Rig: SPE University Competition

2020 ◽  
pp. 1-10
Author(s):  
Emmanuel Akita ◽  
Forrest Dyer ◽  
Savanna Drummond ◽  
Monica Elkins ◽  
Payton Duggan ◽  
...  
Keyword(s):  
Steering System ◽  
Lessons Learned ◽  
Laboratory Scale ◽  
Small Scale ◽  
Directional Drilling ◽  
Design Development ◽  
Rock Interaction ◽  
Precise Control ◽  
Drilling Rig ◽  
Downhole Sensors

Summary The use of drilling automation is accelerating, mostly in the area of rate of penetration (ROP) enhancement. Autonomous directional drilling is now a high focus area for automating drilling operations. The potential impact is immense because 93% of the active rigs in the US are drilling directional or horizontal wells. The 2018–2019 Drilling Systems Automation Technical Section (DSATS)-led international Drillbotics® Student Competition includes automated directional drilling. In this paper, we discuss the detailed design of the winning team. We present the surface equipment, downhole tools, data and control systems, and lessons learned. SPE DSATS organizes the annual Drillbotics competition for university teams to design and develop laboratory-scale drilling rigs. The competition requires each team to create unique downhole sensors to allow automated navigation to drill a directional hole. Student teams have developed new rig configurations to enable several steering methods that include a rotary steering system and small-scale downhole motors with a bent-sub. The most significant challenge was creating a functional downhole motor to fit within a 1.25-in. (3.18 cm) diameter wellbore. Besides technical issues, teams must demonstrate what they have learned about bit-rock interaction and the physics of steering. In addition, they must deal with budgets and funding, procurement and delivery delays, and overall project management. This required an integrated multidisciplinary approach and a major redesign of the rig components. The University of Oklahoma (OU) team made significant changes to its existing rig to drill directional holes. The design change was introduced to optimize the performance of the bottomhole assembly (BHA) and allow directional drilling. The criteria for selecting the BHA was hole size, BHA dynamics, a favorable condition for downhole sensors, precise control of drilling parameters, rig mobility, safety, time constraints, and economic practicality. The result is an autonomous drilling rig that drills a deviated hole toward a defined target through a 2 × 2 × 1-ft (60.96 × 60.96 × 30.48 cm) sandstone block (i.e., rock sample) without human intervention. The rig currently uses a combination of discrete and dynamic modeling from experimentally determined control parameters and closed-loop feedback for well-trajectorycontrol. The novelty of our winning design is in the use of a small-scale cable-driven downhole motor with a bent-sub and quick-connect-type swivel system. This is intended to replicate the action of a mud motor within the limits of the borehole diameter. In this paper, we present details of the rig components, their specifications, and the problems faced during the design, development, and testing. We demonstrate how a laboratory-scale rig can be used to study drilling dysfunctions and challenges. Building a downhole tool to withstand vibrations, water intrusion, magnetic interference, and electromagnetic noise are common difficulties faced by major equipment manufacturers.

Modelling of the Dynamic Behavior of the Power Transmission of an Automatic Small Scale Drilling Rig

2017 ◽  
Author(s):  
Eric Cayeux ◽  
Hans Joakim Skadsem
Keyword(s):  
Mathematical Models ◽  
Physical System ◽  
Power Transmission ◽  
Drill String ◽  
Laboratory Scale ◽  
Small Scale ◽  
Drilling Process ◽  
Drilling Rig ◽  
Contact Points ◽  
Drilling Conditions

The automatization of the drilling process opens the opportunity to faster reactions in case of unexpected drilling conditions, therefore reducing the risk that a drilling incident escalates to a serious situation. It also allows to push the drilling performance to be as close as possible to the limits of drillability as a function of the varying drilling conditions. But to achieve high level of drilling process automation, it is necessary to have access to accurate mathematical models of the complex physical system that is composed of the drilling rig, the drill-string, the drilling fluid and the borehole itself. As the development of accurate heat transfer, mechanical and hydraulic models and their utilization in full scale drilling applications is a huge and complex task, it is tempting to recreate drilling automatization problems in a laboratory scale setup. Because of sudden variations of the downhole drilling conditions, like when transitioning from soft to hard rock or when the bit is subject to large torque variations induced by interbedded rock layers, the boundary conditions at the bit change suddenly and require quick response from the automatic top-drive and hoisting system controllers. At a small laboratory scale, the necessary reaction times are of the order of milliseconds and therefore exclude any manual intervention. It is therefore crucial that the automatic control methods utilize precise mathematical models of the physical system to accurately estimate the limits by which the drilling process can be managed under safe conditions. For that reason, a general purpose mathematical model of small-scale drilling rigs has been developed. First, the Rayleigh-Ritz method is used to determine the deflection of the drill-string and to estimate the side forces at the contact points along the drill-string and BHA (Bottom Hole Assembly). Then the dynamic response of the power transmission system is modelled for both variable frequency drive controlled tri-phase motors and for stepper motors, including friction effects at the contact points. Friction is modelled using Stribeck theory rather than the classical Coulomb laws of friction. Finally, the expected response of 3D accelerometers, that could be placed on the outside of a BHA component, is modelled to retrieve possible inclination variations and potential vibration modes such as torsional oscillations, forward or backward whirl. The generality of the model is such that it can be used for many small-scale drilling rig designs.

Development of Physics Learning Module Based on Guided Inquiry with Light Wave Material in Class XI of Senior High School

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Evi Rahmawati ◽  
Irnin Agustina Dwi Astuti ◽  
N Nurhayati
Keyword(s):  
Large Scale ◽  
Learning Strategy ◽  
Student Response ◽  
Average Score ◽  
Small Scale ◽  
Design Development ◽  
Teacher Response ◽  
Student Responses ◽  
Analysis Design ◽  
Scientific Skills

IPA Integrated is a place for students to study themselves and the surrounding environment applied in daily life. Integrated IPA Learning provides a direct experience to students through the use and development of scientific skills and attitudes. The importance of integrated IPA requires to pack learning well, integrated IPA integration with the preparation of modules combined with learning strategy can maximize the learning process in school. In SMP 209 Jakarta, the value of the integrated IPA is obtained from 34 students there are 10 students completed and 24 students are not complete because they get the value below the KKM of 68. This research is a development study with the development model of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The use of KPS-based integrated IPA modules (Science Process sSkills) on the theme of rainbow phenomenon obtained by media expert validation results with an average score of 84.38%, average material expert 82.18%, average linguist 75.37%. So the average of all aspects obtained by 80.55% is worth using and tested to students. The results of the teacher response obtained 88.69% value with excellent criteria. Student responses on a small scale acquired an average score of 85.19% with highly agreed criteria and on the large-scale student response gained a yield of 86.44% with very agreed criteria. So the module can be concluded receiving a good response by the teacher and students.

Lessons Learned from Small Scale Field Test Demonstrating CO2 EOR and Geologic Storage at Wellington Field in Southern Kansas

2019 ◽  
Author(s):  
Yevhen Holubnyak ◽  
Willard Watney ◽  
Tiraz Birdie ◽  
Dana Wreath ◽  
George Tsoflias ◽  
...  
Keyword(s):  
Field Test ◽  
Lessons Learned ◽  
Small Scale ◽  
Geologic Storage ◽  
Scale Field ◽  
Co2 Eor

scholarly journals The Brazilian electronic theses and dissertations digital library: providing open access for scholarly information

2006 ◽  
Vol 35 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Sílvia Barcellos Southwick
Keyword(s):  
Digital Library ◽  
Lessons Learned ◽  
Research Centers ◽  
Design Development ◽  
Metadata Standard ◽  
Electronic Theses And Dissertations ◽  
Development And Management ◽  
Open Archives ◽  
Theses And Dissertations ◽  
Paper Address

This paper describes a project led by the Instituto Brasileiro de Informações em Ciência e Tecnologia (Ibict), a government institution, to build a national digital library for electronic theses and dissertations - Bibliteca Digital de Teses e Dissertações (BDTD). The project has been a collaborative effort among Ibict, universities and other research centers in Brazil. The developers adopted a system architecture based on the Open Archives Initiative (OAI) in which universities and research centers act as data providers and Ibict as a service provider. A Brazilian metadata standard for electronic theses and dissertations was developed for the digital library. A toolkit including open source package was also developed by Ibict to be distributed to potential data providers. BDTD has been integrated with the international initiative: the Networked Digital Library of Thesis and Dissertation (NDLTD). Discussions in the paper address various issues related to project design, development and management as well as the role played by Ibict. Conclusions highlight some important lessons learned to date and challenges for the future in expanding the BDTD project.

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

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