Leveraging on Machine Learning Solution for Pioneering Wells Augmented Stuck Pipe Indicator in Real Time Centre

2021 ◽  
Author(s):  
Mohamad Hazwan Yusoff ◽  
Meor Muhammad Hakeem Meor Hashim ◽  
Muhammad Hadi Hamzah ◽  
Muhammad Faris Arriffin ◽  
Azlan Mohamad
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Automation System ◽  
Learning Technology ◽  
Drilling Parameters ◽  
Bottom Hole Assembly ◽  
Signature Pattern ◽  
Monitoring Software ◽  
Productive Time

Abstract Stuck pipe incidents remain as one of the major problems in the drilling industry. The incidents will lead to expensive loss time in daily spread cost, bottom hole assembly cost, sidetracking cost as well as fishing cost. The Wells Augmented Stuck Pipe (WASP) Indicator, a state-of-the-art machine learning technology that seamlessly integrates with PETRONAS existing technologies, is introduced as the stuck pipe prevention detection system for the company. Historical real-time drilling data and stuck pipe incidents reports between 2007 and 2019 are used for the development of machine learning models. The models utilize key drilling parameters such as hookload and equivalent circulating density (ECD) to predict and analyze trends to detect any signature pattern anomalies for various stuck pipe events. The prediction and alarm are displayed in real-time monitoring software to trigger the operation team for prompt intervention. The WASP solution has demonstrated proven outcomes using historical and live well with high confidence in detecting stuck pipe incidents due to differential sticking, hole cleaning, and wellbore geometry. The WASP Indicator is envisaged to provide the company with cutting edge advantages in the industry. It is expected that the system will reduce the identification period and improve the reaction time of the monitoring specialists in recognizing the stuck pipe symptoms and highlighting potential incidents. The system is also bringing value to the company via non-productive time (NPT) cost avoidance and identification of early onset of various stuck pipe events based on distinct mechanisms. With the system, the existing portfolio value can be enhanced via setting dynamic trends and models into historical experiences context. The WASP Indicator is aspired to be the forefront innovation that will leap through the norm and lead the region in a greater plan of drilling automation system.

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.

Case Studies for the Successful Deployment of Wells Augmented Stuck Pipe Indicator in Wells Real Time Centre

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Tengku Ezharuddin Tengku Bidin ◽  
...  
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Drilling Fluid ◽  
Drill String ◽  
Drilling Operation ◽  
Cost Impact ◽  
Drilling Parameters ◽  
The Impact ◽  
Productive Time ◽  
Specific Challenge

Abstract The restriction or inability of the drill string to reciprocate or rotate while in the borehole is commonly known as a stuck pipe. This event is typically accompanied by constraints in drilling fluid flow, except for differential sticking. The stuck pipe can manifest based on three different mechanisms, i.e. pack-off, differential sticking, and wellbore geometry. Despite its infrequent occurrence, non-productive time (NPT) events have a massive cost impact. Nevertheless, stuck pipe incidents can be evaded with proper identification of its unique symptoms which allows an early intervention and remediation action. Over the decades, multiple analytical studies have been attempted to predict stuck pipe occurrences. The latest venture into this drilling operational challenge now utilizes Machine Learning (ML) algorithms in forecasting stuck pipe risk. An ML solution namely, Wells Augmented Stuck Pipe Indicator (WASP), is developed to tackle this specific challenge. The solution leverages on real-time drilling database and supplementary engineering design information to estimate proxy drilling parameters which provide active and impartial pattern recognition of prospective stuck pipe events. The solution is built to assist Wells Real Time Centre (WRTC) personnel in proactively providing a holistic perspective in anticipating potential anomalies and recommending remedial countermeasures before incidents happen. Several case studies are outlined to exhibit the impact of WASP in real-time drilling operation monitoring and intervention where WASP is capable to identify stuck pipe symptoms a few hours earlier and provide warnings for stuck pipe avoidance. The presented case studies were run on various live wells where restrictions are predicted stands ahead of the incidents. Warnings and alarms were generated, allowing further analysis by the personnel to verify and assess the situation before delivering a precautionary procedure to the rig site. The implementation of the WASP will reduce analysis time and provide timely prescriptive action in the proactive real-time drilling operation monitoring and intervention hub, subsequently creating value through cost containment and operational efficiency.

From Science to Practice: Improving ROP by Utilizing a Cloud-Based Machine-Learning Solution in Real-Time Drilling Operations

2021 ◽  
Author(s):  
Kriti Singh ◽  
Sai Yalamarty ◽  
Curtis Cheatham ◽  
Khoa Tran ◽  
Greg McDonald
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Situational Awareness ◽  
Field Tests ◽  
Field Trials ◽  
Permian Basin ◽  
Lateral Vibrations ◽  
Drilling Parameters ◽  
Drilling Conditions ◽  
Performance Results

Abstract This paper is a follow up to the URTeC (2019-343) publication where the training of a Machine Learning (ML) model to predict rate of penetration (ROP) is described. The ML model gathers recent drilling parameters and approximates drilling conditions downhole to predict ROP. In real time, the model is run through an optimization sweep by adjusting parameters which can be controlled by the driller. The optimal drilling parameters and modeled ROP are then displayed for the driller to utilize. The ML model was successfully deployed and tested in real time in collaboration with leading shale operators in the Permian Basin. The testing phase was split in two parts, preliminary field tests and trials of the end-product. The key learnings from preliminary field tests were used to develop an integrated driller's dashboard with optimal drilling parameters recommendations and situational awareness tools for high dysfunction and procedural compliance which was used for designed trials. The results of field trials are discussed where subject well ROP was improved between 19-33% when comparing against observation/control footage. The overall ROP on subject wells was also compared against offset wells with similar target formations, BHAs, and wellbore trajectories. In those comparisons against qualified offsets, ROP was improved by as little as 5% and as much as 33%. In addition to comparing ROP performance, results from post-run data analysis are also presented. Detailed drilling data analytics were performed to check if using the recommendations during the trial caused any detrimental effects such as divergence in directional trends or high lateral or axial vibrations. The results from this analysis indicate that the measured downhole axial and lateral vibrations were in the safe zone. Also, no significant deviations in rotary trends were observed.

scholarly journals DeepBus : Machine learning based real time pothole detection system for smart transportation using IoT

2020 ◽  
Vol 3 (3) ◽  
Author(s):  
Kashish Bansal ◽  
Kashish Mittal ◽  
Gautam Ahuja ◽  
Ashima Singh ◽  
Sukhpal Singh Gill
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Smart Transportation ◽  
Pothole Detection

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals Real-time object locator for cryo-EM data collection --- You only navigate EM once ---

2021 ◽  
Author(s):  
Koji Yonekura ◽  
Saori Maki-Yonekura ◽  
Hisashi Naitow ◽  
Tasuku Hamaguchi ◽  
Kiyofumi Takaba
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Electron Diffraction ◽  
Data Collection ◽  
Real Time ◽  
Single Particle ◽  
Detection System ◽  
Target Object ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is the most error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation showed its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and for locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection.

scholarly journals Machine learning-based real-time object locator/evaluator for cryo-EM data collection

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Koji Yonekura ◽  
Saori Maki-Yonekura ◽  
Hisashi Naitow ◽  
Tasuku Hamaguchi ◽  
Kiyofumi Takaba
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Data Collection ◽  
Real Time ◽  
Detection System ◽  
Target Object ◽  
Accurate Data ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy ◽  
Diffraction Patterns

AbstractIn cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.

Technology Focus: Completions (April 2021)

2021 ◽  
Vol 73 (04) ◽  
pp. 41-41
Author(s):  
Doug Lehr
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Science ◽  
Detection System ◽  
Streaming Data ◽  
Predictive Tool ◽  
Well Completion ◽  
Ensemble Machine Learning ◽  
Casing Damage ◽  
Technology Focus

In the 2020 Completions Technology Focus, I stated that digitization will forever change how the most complex problems in our industry are solved. And, despite another severe downturn in the upstream industry, data science continues to provide solutions for complex unconventional well problems. Casing Damage Casing collapse is an ongoing problem and almost always occurs in the heel of the well. It prevents passage of frac plugs and milling tools. Forcing a frac plug through the collapsed section damages the plug, predisposing it to failure, which leads to more casing damage and poor stimulation. One team has developed a machine-learning (ML) model showing a positive correlation between zones with high fracturing gradients and collapsed casing. The objective is a predictive tool that enables a completion design that avoids these zones. Fracture-Driven Interactions (FDIs) Can Be Avoided in Real Time Pressurized fracturing fluids from one well can communicate with fractures in a nearby well or can intersect that well-bore. Such FDIs can occur while fracturing a child well and can negatively affect production in the parent well. FDIs are caused by well spacing, depletion, or completion design but, until recently, were not quickly diagnosed. Analytics and machine learning now are being used to analyze streaming data sets during a frac job to detect FDIs. A recently piloted detection system alerts the operator in real time, which enables avoidance of FDIs on the fly. Data Science Provides the Tools Analyzing casing damage and FDIs is a complex task involving large amounts of data already available or easily acquired. Tools such as ML perform the data analysis and enable decision making. Data science is enabling the unconventional “onion” to be peeled many layers at a time. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199967 - Artificial Intelligence for Real-Time Monitoring of Fracture-Driven Interactions and Simultaneous Completion Optimization by Hayley Stephenson, Baker Hughes, et al. SPE 201615 - Novel Completion Design To Bypass Damage and Increase Reservoir Contact: A Middle Magdalena, Central Colombian Case History by Rosana Polo, Oxy, et al. SPE 202966 - Well Completion Optimization in Canada Tight Gas Fields Using Ensemble Machine Learning by Lulu Liao, Sinopec, et al.

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