AIMC Topic: Oil and Gas Fields

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Identification and classification of oil and gas pipeline intru-sion events based on 1-D CNN network.

PloS one
Oil and gas pipeline security is critical to national infrastructure, yet existing monitoring systems often lack the sensitivity and real-time responsiveness required to detect subtle intrusion events. This study presents a novel multimodal sensing a...

Automated cementing quality detection using a domain-specific, multi-scale convolutional neural network.

PloS one
Cementing quality is a key factor in ensuring the long-term safe production of oil and gas wells and preventing defects. Traditional cementing quality evaluation mainly relies on logging interpreters manually analyzing acoustic logging data, such as ...

Porosity prediction from well logging data via a hybrid MABC-LSSVM model.

PloS one
Porosity is a key parameter for evaluating reservoir performance, but high-precision prediction is highly challenging in complex shale reservoirs due to the strong heterogeneity of the formation and the highly nonlinear relationship between logging p...

Transfer learning-driven prediction of oil and gaspipeline corrosion rates in small sample scenarios.

PloS one
To ensure the safe operation of oil and gas pipeline systems in complex environments, accurately predicting the corrosion rate of natural gas well pipes is of paramount importance. Given the widespread challenge of pipe corrosion in the oil and gas i...

Ecological risk assessment of oilfield soil through the use of machine learning combining with spatial interaction effects.

Ecotoxicology and environmental safety
With the intensification of oil extraction activities, total petroleum hydrocarbons (TPHs) and toxic elements contamination in soil around oil wells have become severe environmental problems. This paper proposed a novel method based on machine learni...

Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems.

Neural networks : the official journal of the International Neural Network Society
Fault diagnosis is of great importance to the reliability and security of Sucker Rod Pumping (SRP) oil production system. With the development of digital oilfield, data-driven deep learning SRP fault diagnosis has become the development trend of oilf...

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

PloS one
Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to n...

A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma.

Environmental science & technology
Undocumented Orphaned Wells (UOWs) are wells without an operator that have limited or no documentation with regulatory authorities. An estimated 310,000 to 800,000 UOWs exist in the United States (US), whose locations are largely unknown. These wells...

Machine learning-based classification of petrofacies in fine laminated limestones.

Anais da Academia Brasileira de Ciencias
Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a tes...

Shale gas geological "sweet spot" parameter prediction method and its application based on convolutional neural network.

Scientific reports
Parameters such as gas content (GAS), porosity (PHI) and total organic carbon (TOC) are key parameters that reveal the shale gas geological "sweet spot" of reservoirs. However, the lack of a three-dimensional high-precision prediction method is not c...