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A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

https://doi.org/10.1109/access.2021.3117891
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Machine learning on Crays to optimize petrophysical workflows in oil and gas exploration
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The role of data management in the Industrial Internet of Things
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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
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Decommissioning of Offshore Oil and Gas Installations
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Decommissioning of Offshore Oil and Gas Installations
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Prediction of industrial debutanizer column compositions using data-driven ANFIS- and ANN-based approaches
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DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
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Drilling performance monitoring and optimization: a data-driven approach
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Data–driven decision–making for lost circulation treatments: A machine learning approach
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Implementing artificial neural networks and support vector machines to predict lost circulation
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Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification
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Well production forecasting based on ARIMA-LSTM model considering manual operations
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Unsupervised and supervised learning in cascade for petroleum geology
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Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations
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Predicting injection profiles using ANFIS
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Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
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A hybrid intelligent model for reservoir production and associated dynamic risks
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A review of multi-attributes decision-making models for offshore oil and gas facilities decommissioning
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On the class overlap problem in imbalanced data classification
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Oil and gas infrastructure decommissioning in marine protected areas: System complexity, analysis and challenges
inferredcites →https://doi.org/10.1016/j.measurement.2018.05.069openalex-snapshot
Predicting mode-I fracture toughness of rocks using soft computing and multiple regression
inferredcites →https://doi.org/10.1016/j.neucom.2013.10.039openalex-snapshot
Composition Prediction of a Debutanizer Column using Equation Based Artificial Neural Network Model
inferredcites →https://doi.org/10.1016/j.neucom.2016.02.026openalex-snapshot
Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
inferredcites →https://doi.org/10.1016/j.neucom.2017.11.077openalex-snapshot
Feature selection in machine learning: A new perspective
inferredcites →https://doi.org/10.1016/j.neucom.2018.09.082openalex-snapshot
Time series forecasting of petroleum production using deep LSTM recurrent networks
inferredcites →https://doi.org/10.1016/j.neucom.2018.11.100openalex-snapshot
Pre-processing approaches for imbalanced distributions in regression
inferredcites →https://doi.org/10.1016/j.neuroimage.2017.06.061openalex-snapshot
Cross-validation failure: Small sample sizes lead to large error bars
inferredcites →https://doi.org/10.1016/j.oceaneng.2016.12.030openalex-snapshot
Engineering and legal considerations for decommissioning of offshore oil and gas infrastructure in Australia
inferredcites →https://doi.org/10.1016/j.ocecoaman.2018.10.024openalex-snapshot
Worldwide oil and gas platform decommissioning: A review of practices and reefing options
inferredcites →https://doi.org/10.1016/j.petrol.2019.01.113openalex-snapshot
Machine learning in oil and gas; a SWOT analysis approach
inferredcites →https://doi.org/10.1016/j.petrol.2019.02.045openalex-snapshot
Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field
inferredcites →https://doi.org/10.1016/j.petrol.2019.106486openalex-snapshot
Dynamic risk assessment of reservoir production using data-driven probabilistic approach
inferredcites →https://doi.org/10.1016/j.petrol.2019.106681openalex-snapshot
Fully coupled end-to-end drilling optimization model using machine learning
inferredcites →https://doi.org/10.1016/j.petrol.2019.106682openalex-snapshot
Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model
inferredcites →https://doi.org/10.1016/j.petrol.2020.107013openalex-snapshot
Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network
inferredcites →https://doi.org/10.1016/j.petrol.2020.107818openalex-snapshot
Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
inferredcites →https://doi.org/10.1016/j.psep.2020.09.038openalex-snapshot
Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.12.193openalex-snapshot
Decommissioning of offshore oil and gas structures – Environmental opportunities and challenges
inferredcites →https://doi.org/10.1016/s0167-9473(99)00095-xopenalex-snapshot
Noisy replication in skewed binary classification
inferredcites →https://doi.org/10.1023/a:1010920819831openalex-snapshot
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
inferredcites →https://doi.org/10.1038/s41598-020-76432-4openalex-snapshot
Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach
inferredcites →https://doi.org/10.1071/aj20024openalex-snapshot
Assessing the impacts of scale residues from offshore oil and gas decommissioning on marine organisms
inferredcites →https://doi.org/10.1071/aj20026openalex-snapshot
Co-creation of knowledge – an effective mechanism for managing decommissioning stakeholders
inferredcites →https://doi.org/10.1093/reep/rey018openalex-snapshot
Policy Brief—Regulating Offshore Oil and Gas Exploration: Insights from the Deepwater Horizon Experience in the Gulf of Mexico
inferredcites →https://doi.org/10.1109/access.2019.2947701openalex-snapshot
Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection
inferredcites →https://doi.org/10.1109/access.2020.2990152openalex-snapshot
AI-Driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments
inferredcites →https://doi.org/10.1109/access.2020.2998723openalex-snapshot
Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges
inferredcites →https://doi.org/10.1109/access.2020.3001149openalex-snapshot
Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
inferredcites →https://doi.org/10.1109/access.2021.3082557openalex-snapshot
Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations
inferredcites →https://doi.org/10.1109/drbsd-549595.2019.00012openalex-snapshot
Using Machine Learning to Reduce Ensembles of Geological Models for Oil and Gas Exploration
inferredcites →https://doi.org/10.1109/tit.1968.1054155openalex-snapshot
The condensed nearest neighbor rule (Corresp.)
inferredcites →https://doi.org/10.1115/omae2019-96363openalex-snapshot
Metocean Criteria for the Fatigue Analysis of Subsea Pipelines
inferredcites →https://doi.org/10.1142/s0129065720500434openalex-snapshot
Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease
inferredcites →https://doi.org/10.1145/3479162.3479179openalex-snapshot
Towards Machine Learning-Driven Practices for Oil and Gas Decommissioning – Introduction of a New Offshore Pipeline Dataset
inferredcites →https://doi.org/10.1190/tle35100906.1openalex-snapshot
Facies classification using machine learning
inferredcites →https://doi.org/10.1198/tech.2003.s144openalex-snapshot
Subset Selection in Regression
inferredcites →https://doi.org/10.1201/9781420035933openalex-snapshot
Subset Selection in Regression
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.18637/jss.v028.i05openalex-snapshot
Building Predictive Models in R Using the caret Package
inferredcites →https://doi.org/10.19139/soic-2310-5070-651openalex-snapshot
Development of Oil Production Forecasting Method based on Deep Learning
inferredcites →https://doi.org/10.2118/199203-msopenalex-snapshot
Stakeholder Engagement in the Decommissioning Process
inferredcites →https://doi.org/10.2118/202477-paopenalex-snapshot
Automated Well-Log Processing and Lithology Classification by Identifying Optimal Features Through Unsupervised and Supervised Machine-Learning Algorithms
inferredcites →https://doi.org/10.2523/iptc-19693-msopenalex-snapshot
Real-Time Well Log Prediction From Drilling Data Using Deep Learning
inferredcites →https://doi.org/10.30632/pjv59n6-2018a1openalex-snapshot
Geological Feature Prediction Using Image-Based Machine Learning
inferredcites →https://doi.org/10.3389/fmars.2021.642539openalex-snapshot
Trash or Treasure? Considerations for Future Ecological Research to Inform Oil and Gas Decommissioning
inferredcites →https://doi.org/10.3390/e19060247openalex-snapshot
Improving the Naive Bayes Classifier via a Quick Variable Selection Method Using Maximum of Entropy
inferredcites →https://doi.org/10.3390/pr9071194openalex-snapshot
Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting
inferredcites →https://doi.org/10.3390/s20236793openalex-snapshot
Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training
inferredcites →https://doi.org/10.3997/2214-4609.201900988openalex-snapshot
Machine-Learning in Oil and Gas Exploration: A New Approach to Geological Risk Assessment
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A Comparison of Stakeholder Engagement Strategies for Offshore Decommissioning Projects in the United Kingdom and Australian Landscape
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The Drivers of Stakeholder Engagement
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Application of Machine Learning Technique to Predict Crude Distillation Column Inlet Temperature / Furnace Coil Outlet Temperature in Order to Maximize Distillate Yield and to Minimize Fuel Firing in Furnaces

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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