First Real Time Measurements of Downhole Vibrations, Forces, and Pressures Used To Monitor Directional Drilling Operations

1989 ◽  
Author(s):  
R.L. Cook ◽  
J.W. Nicholson ◽  
M.C. Sheppard ◽  
W. Westlake
Keyword(s):  
Real Time ◽  
Directional Drilling ◽  
Drilling Operations

A Real-Time Probabilistic Slide Drilling Dysfunction Advisory to Assist Remote Directional Drilling Operations

2021 ◽  
Author(s):  
Dawson Ramos ◽  
Pradeepkumar Ashok ◽  
Michael Yi ◽  
John D’ Angelo ◽  
Ian Rostagno ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Real Time ◽  
Complex Analysis ◽  
Directional Drilling ◽  
Rotary Drilling ◽  
Stick Slip ◽  
Well Drilling ◽  
Drilling Data ◽  
Drilling Operations ◽  
Corrective Actions

Abstract Current slide drilling practices rely heavily on the intuition of the directional drillers to identify and correct drilling dysfunctions. Monitoring numerous dysfunctions simultaneously requires more complex analysis than can be done manually in real-time. There is also currently a big shift towards remote directional drilling. And as such, there is the need for a tool that can, in real-time, diagnose slide drilling dysfunctions accurately and provide advisory to both the remote directional drillers and rig crew. This paper proposes a method for a real-time slide drilling advisory system consisting of a probabilistic model which computes the likelihood that various slide drilling dysfunctions are occurring and an algorithm that determines what corrective action, if any, should be taken as a result. The dysfunction types monitored include buckling, high friction, poor toolface control, stick slip, and bit bounce. The model employs a Bayesian network which uses evidence derived from transient drilling data trends to infer the probability that any of the five considered dysfunctions are taking place. Data trends known to correlate with each dysfunction type are considered simultaneously to ensure that all dysfunction types are monitored continuously. As dysfunction probabilities are calculated, the algorithm cross references them with current drilling parameters and contextual data to determine necessary corrective actions. Corrective actions are output in the form of simple drilling parameter changes shown on a customizable graphical display. The dysfunction beliefs calculated were validated using historical data gathered from North America land drilling operations. For high friction and poor toolface control, known instances of dysfunction were identified using information in drilling logs and expert opinion and used for validation. The validation process resulted in a further refinement of the model. The proposed model along with graphical advisory displays were deployed on rigs in several North American land well drilling operations, as well as in the remote directional drilling center. While there is a lot of prior work that enables identification of rotary drilling dysfunctions in real-time, this is the first method that diagnoses slide drilling dysfunctions in real-time. The approach combines physics based models with a Bayesian network to improve accuracy and robustness in dysfunction detection. Additionally, it considers both real-time drilling data as well as drilling data from the past when diagnosing dysfunctions and facilitates remote directional drilling.

scholarly journals Rotary steerable systems: mathematical modeling and their case study

2021 ◽  
Vol 11 (6) ◽  
pp. 2743-2761
Author(s):  
Caetano P. S. Andrade ◽  
J. Luis Saavedra ◽  
Andrzej Tunkiel ◽  
Dan Sui
Keyword(s):  
Directional Drilling ◽  
Steering Control ◽  
Fundamental Physics ◽  
Drilling Tools ◽  
Drilling Operations ◽  
Beam Bending ◽  
Extended Reach Drilling ◽  
And Control ◽  
2D And 3D

AbstractDirectional drilling is a common and essential procedure of major extended reach drilling operations. With the development of directional drilling technologies, the percentage of recoverable oil production has increased. However, its challenges, like real-time bit steering, directional drilling tools selection and control, are main barriers leading to low drilling efficiency and high nonproductive time. The fact inspires this study. Our work aims to contribute to the better understanding of directional drilling, more specifically regarding rotary steerable system (RSS) technology. For instance, finding the solutions of the technological challenges involved in RSSs, such as bit steering control, bit position calculation and bit speed estimation, is the main considerations of our study. Classical definitions from fundamental physics including Newton’s third law, beam bending analysis, bit force analysis, rate of penetration (ROP) modeling are employed to estimate bit position and then conduct RSS control to steer the bit accordingly. The results are illustrated in case study with the consideration of the 2D and 3D wellbore scenarios.

Turbodrill Bottomhole Assembly and Real-Time Data Improve Through-Tubing Coiled-Tubing Drilling Operations in Sour Gas Well

2015 ◽  
Author(s):  
A. Ebrahimi ◽  
P. J. Schermer ◽  
W. Jelinek ◽  
D. Pommier ◽  
S. Pfeil ◽  
...  
Keyword(s):  
Real Time ◽  
Time Data ◽  
Gas Well ◽  
Coiled Tubing ◽  
Real Time Data ◽  
Drilling Operations ◽  
Sour Gas

Lean Optimization on Well Placement: Directional Drilling Operations in South of Oman Wells: A Case Study

2016 ◽  
Author(s):  
Nasser Al Kindi ◽  
Qasim Al Shehhi ◽  
Abdullah Al Adwani ◽  
Saud Al Habsi ◽  
Roy Emanuel
Keyword(s):  
Directional Drilling ◽  
Well Placement ◽  
Drilling Operations

Pushing the Limits in Offshore Mexico

2021 ◽  
Author(s):  
Hector Hugo Vizcarra Marin ◽  
Alex Ngan ◽  
Roberto Pineda ◽  
Juan Carlos Gomez ◽  
Jose Antonio Becerra
Keyword(s):  
Gulf Of Mexico ◽  
Real Time ◽  
Lessons Learned ◽  
Clear Understanding ◽  
Directional Drilling ◽  
Case Histories ◽  
Control Plan ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Drilling Engineering

Abstract Given the increased demands on the production of hydrocarbons and cost-effectiveness for the Operator's development wells, the industry is challenged to continually explore new technology and methodology to improve drilling performance and operational efficiency. In this paper, two recent case histories showcase the technology, drilling engineering, and real-time optimization that resulted in record drilling times. The wells are located on shallow water in the Gulf of Mexico, with numerous drilling challenges, which typically resulted in significant Non-Productive Time (NPT). Through close collaboration with the Operator, early planning with a clear understanding of offset wells challenges, well plan that minimize drilling in the Upper Cretaceous "Brecha" Formation were formulated. The well plan was also designed to reduce the risk of stuck pipe while meeting the requirements to penetrate the geological targets laterally to increase the area of contact in the reservoir section. This project encapsulates the successful application of the latest Push-the-Bit Rotary Steerable System (RSS) with borehole enlargement technology through a proven drilling engineering process to optimize the drilling bottomhole assembly, bit selection, drilling parameters, and real-time monitoring & optimization The records drilling times in the two case histories can be replicated and further improved. A list of lessons learned and recommendations for the future wells are discussed. These include the well trajectory planning, directional drilling BHA optimization, directional control plan, drilling parameters to optimize hole cleaning, and downhole shocks & vibrations management during drilling and underreaming operation to increase the drilling performance ultimately. Also, it includes a proposed drilling blueprint to continually push the limit of incremental drilling performance through the use of RSS with hydraulics drilling reamers through the Jurassic-age formations in shallow waters, Gulf of Mexico.

Pareto optimal directional drilling advisory for improved real-time decision making

2021 ◽  
pp. 110031
Author(s):  
John D'Angelo ◽  
Mohamed Shafik Khaled ◽  
Pradeepkumar Ashok ◽  
Eric van Oort
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Pareto Optimal ◽  
Directional Drilling

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

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