Stuck Pipe Early Detection on Extended Reach Wells Using Ensemble Method of Machine Learning

2021 ◽  
Author(s):  
Rushad Ravilievich Rakhimov ◽  
Oleg Valerievich Zhdaneev ◽  
Konstantin Nikolaevich Frolov ◽  
Maxim Pavlovich Babich
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Historical Data ◽  
Learning Algorithm ◽  
Ensemble Method ◽  
Machine Learning Algorithm ◽  
Time Data ◽  
Machine Learning Model ◽  
Real Time Data ◽  
The Ensemble Method

Abstract The ultimate objective of this paper is to describe the experience of using a machine learning model prepared by the ensemble method to prevent stuck pipe events during well construction process on extended reach wells. The tasks performed include collecting, analyzing and cleaning historical data, selecting and preparing a machine learning model, testing it on real-time data by means of desktop application. The idea is to display the solution at the rig floor, allowing Driller to quickly take actions for prevention of stuck pipe event. Historical data mining and analysis were performed using software for remote monitoring. Preparation, labelling and cleaning of historical and real-time data were executed using programmable scripts and big data techniques. The machine learning algorithm was developed using the ensemble method, which allows to combine several models to improve the final result. On the field of interest, the most common type of stuck pipe are solids induced pack offs. They occur due to insufficient hole cleaning from drilled cuttings and wellbore collapse due to rocks instability. Stuck pipe prevention on extended reach drilling (ERD) wells requires holistic approach meanwhile final role is assigned to the driller. Due to continuously exceeding ERD envelope and increased workloads on both personnel and drilling equipment, the effectiveness of preventing accidents is deteriorating. This leads to severe consequences: Bottom Hole Assembly lost in hole, the necessity to re-drill the bore and eventually to increased Non-Productive Time (NPT). Developed application based on ensemble machine learning algorithm shows prediction accuracy above 94%. Reacting on alarms, driller can quickly take measures to prevent downhole accidents during well construction of ERD wells.

scholarly journals Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Bin Ye ◽  
Kangping Liu ◽  
Siting Cao ◽  
Padmaja Sankaridurg ◽  
Wayne Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Segmentation Method ◽  
Time Data ◽  
Threshold Segmentation ◽  
Svm Model ◽  
Real Time Data

Abstract Background Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches. Methods Real time data on luminance, ultraviolet light levels and number of steps obtained with smart watches from dataset A: 12 adults from 8 scenes and manually recorded true locations. 70% of data was considered training set and support vector machine (SVM) algorithm generated using the variables to create a classification system. Data collected manually by the adults was the reference. The algorithm was used for predicting the location of the remaining 30% of dataset A. Accuracy was defined as the number of correct predictions divided by all. Similarly, data was corrected from dataset B: 172 children from 3 schools and 12 supervisors recorded true locations. Data collected by the supervisors was the reference. SVM model trained from dataset A was used to predict the location of dataset B for validation. Finally, we predicted the location of dataset B using the SVM model self-trained from dataset B. We repeated these three predictions with traditional univariate threshold segmentation method. Results In both datasets, SVM outperformed the univariate threshold segmentation method. In dataset A, the accuracy and AUC of SVM were 99.55% and 0.99 as compared to 95.11% and 0.95 with the univariate threshold segmentation (p < 0.01). In validation, the accuracy and AUC of SVM were 82.67% and 0.90 compared to 80.88% and 0.85 with the univariate threshold segmentation method (p < 0.01). In dataset B, the accuracy and AUC of SVM and AUC were 92.43% and 0.96 compared to 80.88% and 0.85 with the univariate threshold segmentation (p < 0.01). Conclusions Machine learning algorithm allows for discrimination of outdoor versus indoor environments with high accuracy and provides an opportunity to study and determine the role of environmental risk factors in onset and progression of myopia. The accuracy of machine learning algorithm could be improved if the model is trained with the dataset itself.

Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

2021 ◽  
Author(s):  
Rodrigo Chamusca Machado ◽  
Fabbio Leite ◽  
Cristiano Xavier ◽  
Alberto Albuquerque ◽  
Samuel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Analytics ◽  
Historical Data ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Real Time Data ◽  
Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

2020 ◽  
Vol 223 (3) ◽  
pp. 437.e1-437.e15
Author(s):  
Joshua Guedalia ◽  
Michal Lipschuetz ◽  
Michal Novoselsky-Persky ◽  
Sarah M. Cohen ◽  
Amihai Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Machine Learning Model ◽  
Vaginal Deliveries ◽  
Real Time Data

scholarly journals Predictions for Startups

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1038 ◽  
Author(s):  
Eliganti Ramalakshmi ◽  
Sindhuja Reddy Kamidi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Good Working ◽  
Machine Learning Model ◽  
Real Time Data ◽  
Regression Algorithms ◽  
Classification And Regression

In general 90 out of 100 startups fail to gain expected funding. There can be several reasons like bad management, lack of sufficient funds, good working team etc. which leads to failure of startup. This work aims to create a machine learning model for predicting the range of funding for the startups based on many key attributesthat are involved at different stages in the startup functioning. It is very important to predict the range of funding prior to the implementation of project and till today not much work is done in this respect. This paper proposes implementing a model to predict the funding of a startups based on many important factors like idea of the startup, place where the startup established, domain vertical to which the startup belongs, prior investors, type of funding the organization is expecting. A model is developed by working on real time data of startups from 2015 to 2017. Classification and regression algorithms are used to build the model.  

Machine Learning for Real-time Data Reduction in Cloud of Things

2020 ◽  
Author(s):  
Atheer Alahmed ◽  
Amal Alrasheedi ◽  
Maha Alharbi ◽  
Norah Alrebdi ◽  
Marwan Aleasa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Reduction ◽  
Time Data ◽  
Real Time Data

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks

2021 ◽  
Author(s):  
Aria Abubakar ◽  
Mandar Kulkarni ◽  
Anisha Kaul
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Gamma Ray ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Well Log ◽  
Machine Learning Model ◽  
Well Correlation ◽  
Geological Formations

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.

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