Detection of obstructions in oil and gas pipelines: machine learning techniques for hydrate classification

• URL: http://arxiv.org/abs/2506.11220v1
• Date: Thu, 12 Jun 2025 18:30:14 GMT
• Title: Detection of obstructions in oil and gas pipelines: machine learning techniques for hydrate classification
• Authors: Hellockston Gomes de Brito, Carla Wilza Souza de Paula Maitelli, Osvaldo Chiavone-Filho,
• Abstract summary: This study employs supervised machine learning techniques to detect and mitigate flow assurance challenges.<n>The primary focus is on preventing gas hydrate formation in oil production systems.<n>The proposed methodology effectively classifies hydrate formation under operational conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Oil and gas reserves are vital resources for the global economy, serving as key components in transportation, energy production, and industrial processes. However, oil and gas extraction and production operations may encounter several challenges, such as pipeline and production line blockages, caused by factors including sediment accumulation, wax deposition, mineral scaling, and corrosion. This study addresses these challenges by employing supervised machine learning techniques, specifically decision trees, the k-Nearest Neighbors (k-NN) algorithm (k-NN), and the Naive Bayes classifier method, to detect and mitigate flow assurance challenges, ensuring efficient fluid transport. The primary focus is on preventing gas hydrate formation in oil production systems. To achieve this, data preprocessing and cleaning were conducted to ensure the quality and consistency of the dataset, which was sourced from Petrobras publicly available 3W project repository on GitHub. The scikit-learn Python library, a widely recognized open-source tool for supervised machine learning techniques, was utilized for classification tasks due to its robustness and versatility. The results demonstrate that the proposed methodology effectively classifies hydrate formation under operational conditions, with the decision tree algorithm exhibiting the highest predictive accuracy (99.99 percent). Consequently, this approach provides a reliable solution for optimizing production efficiency.

Related papers

• Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking [61.44701715285463]
  This paper tackles the problem of passive gaze estimation using both event and frame data. We reformulate gaze estimation as the quantification of the state shifting from the current state to several prior registered anchor states. To improve the generalization ability, instead of learning a large gaze estimation network directly, we align a group of local experts with a student network.
  arXiv  Detail & Related papers  (2024-03-31T03:30:37Z)
• Gas trap prediction from 3D seismic and well test data using machine learning [0.0]
  The aim of this work is to create and apply a methodological approach for predicting gas traps from 3D seismic data and gas well testing. The paper formalizes the approach to creating a training dataset by selecting volumes with established gas saturation and filtration properties within the seismic wavefield. As a result, a cube of calibrated probabilities of belonging to the study space to gas reservoirs was obtained.
  arXiv  Detail & Related papers  (2024-01-23T12:39:15Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection [0.0]
  Application of computer vision for anomaly detection has been under attention in several industrial fields. This work focuses on the research of the Magnetic Flux Leakage data and the preprocessing techniques. In doing so, we exploited the recent convolutional neural network structures and proposed robust approaches.
  arXiv  Detail & Related papers  (2023-09-30T10:37:12Z)
• Detecting broken Absorber Tubes in CSP plants using intelligent sampling and dual loss [0.0]
  Concentrated solar power (CSP) is one of the growing technologies that is leading the process of changing from fossil fuels to renewable energies. Currently, automatic fault detection in CSP plants using Parabolic Trough Collector systems evidences two main drawbacks. We address both gaps by combining the data extracted with the use of an Unmaned Aerial Vehicle, and the data provided by sensors placed within 7 real plants.
  arXiv  Detail & Related papers  (2022-11-25T12:53:52Z)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines [77.45213180689952]
  Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. We introduce a new perspective on efficiently preparing datasets for end-to-end deep learning pipelines. We obtain an increased throughput of 3x to 13x compared to an untuned system.
  arXiv  Detail & Related papers  (2022-02-17T14:31:58Z)
• Fuzzy inference system application for oil-water flow patterns identification [6.060020806741279]
  fuzzy inference system is used to predict the flow pattern of the fluid. The fuzzy inference system is more accurate and reliable than the prediction results of the BP neural network. In the entire production logging process of non-vertical wells, the use of a fuzzy inference system to predict fluid flow patterns can greatly save production costs.
  arXiv  Detail & Related papers  (2021-05-24T10:08:02Z)
• Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea [58.720142291102135]
  The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focus on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.
  arXiv  Detail & Related papers  (2021-03-03T18:51:46Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.