Full-Stack Machine Learning Development Framework for Energy Industry Applications

Chen, Jie; Yao, Changqing; Devarakota, Pandu; Sid‐Ahmed, Mohamed; Dong, Zexuan; Kanuparthi, Sai; Wei, Jia

doi:10.2118/210986-ms

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…It was developed by [50]. SVR is different from ordinary regression, where it minimizes the generalized error bound that gathers the training error and a regularization term instead of minimizing the error between the output and the original values [51].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Design and Performance Analysis of Dry Gas Fishbone Wells for Lower Carbon Footprint

Ouadi

Laalam

Hassan

et al. 2023

Fuels

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Multilateral well drilling technology has recently assisted the drilling industry in improving borehole contact area and reducing operation time, while maintaining a competitive cost. The most advanced multilateral well drilling method is Fishbone drilling (FbD). This method has been utilized in several hydrocarbon fields worldwide, resulting in high recovery enhancement and reduced carbon emissions from drilling. FbD involves drilling several branches from laterals and can be considered as an alternative method to hydraulic fracturing to increase the stimulated reservoir volume. However, the expected productivity of applying a Fishbone well from one field to another can vary due to various challenges such as Fishbone well design, reservoir lithology, and accessibility. Another challenge is the lack of existing analytical models and the effect of each Fishbone parameter on the cumulative production, as well as the interaction between them. In this paper, analytical and empirical productivity models were modified for FbD in a dry gas reservoir. The modified analytical model showed a higher accuracy with respect to the existing model. It was also compared with the modified empirical model, which proved its higher accuracy. Finally, machine learning algorithms were developed to predict FbD productivity, which showed close results with both analytical and empirical models.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Design and Performance Analysis of Dry Gas Fishbone Wells for Lower Carbon Footprint

Ouadi

Laalam

Hassan

et al. 2023

Fuels

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Optimizing estimated ultimate recovery in horizontal wells through data-driven analytics

Alzahabi,

Trindade,

Kamel

2024

Geoenergy Science and Engineering

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Operational Solution Framework: Leveraging Machine Learning and Natural Language Processing for Automatic Well Test Validation

Gao,

2023

Day 1 Tue, November 14, 2023

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The objective of this study is to summarize a proven solution workflow to address the challenges to handle the high volume of well tests daily incorporating information from operational activities, and especially, potential delays and errors in validation impacting other dependent business processes. The proposed solution aims to reduce processing time, minimize human error, and enhance accuracy in well test analysis. Having up-to-date and reliable well test data, engineers can improve engineering workflows, and optimize production. The solution covers data consumption, data preparation, machine learning (ML) solution, cooperating with dependent business processes, deployment and retrain strategy. The ML solution learns from historical well test data with accepted and rejected flag to build a rule-based deterministic ML model to automatically validate and detect the invalid well test with probability. The solution does not only consume structure data but also textual data with natural language processing (NLP), such as well test comments provided by well testing engineers and operational activities in Daily Operational Reports (DORs). Data consumption, operational activities, dependent workflow control are customizable based on different projects. Retrain strategy is based on model prediction accuracy trend and defined during deployment. The solution triggers insights with confidence scores, suggesting acceptance/rejection or review of new well tests. Early detection of possible rejections enables timely actions, including retesting if necessary. The solution was implemented and significantly reduces well test validation time from weeks to hours, enhancing the accuracy of production analysis and optimizations. The data-driven approach offers flexibility and adaptability to meet operation needs, presenting a robust alternative to rule-based validation. By integrating ML and NLP, the solution provides a comprehensive and efficient framework for well test validation, improving decision-making and ensuring compliance with Standard Operation Procedure (SOP). This study introduces a novel approach to well test validation by leveraging ML and NLP. By considering both historical data and manual operational event inputs from engineers, the solution enhances the accuracy and efficiency of the validation process. It contributes to improved production performance analysis, diagnostics, and issue detection. The solution deployment can be customized and adaptable to different data storage and availability, to automate well test validation process in the oil and gas industry.

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Full-Stack Machine Learning Development Framework for Energy Industry Applications

Cited by 4 publications

References 11 publications

Design and Performance Analysis of Dry Gas Fishbone Wells for Lower Carbon Footprint

Design and Performance Analysis of Dry Gas Fishbone Wells for Lower Carbon Footprint

Optimizing estimated ultimate recovery in horizontal wells through data-driven analytics

Operational Solution Framework: Leveraging Machine Learning and Natural Language Processing for Automatic Well Test Validation

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