AVA classification as an unsupervised machine-learning problem

Bougher, Ben; Herrmann, Felix J.

doi:10.1190/segam2016-13874419.1

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Successful ML algorithms that have been effectively applied in the oil and gas industry include SVM, artificial neural networks (ANNs), and DL which contributed to provide a safer environment for the workers in this industry. There are several reports of machine learning algorithms used in the exploration of oil and gas [ 126 ], and drilling [ 127 ], reservoir engineering [ 128 ], production operations [ 129 ], in the oil and gas industry.…”

Section: Resultsmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

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Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.

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Section: Resultsmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

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“…Several utilizations of unsupervised learning are clustering, dimensionality reduction, and anomaly detection. In the geosciences field of study, unsupervised learning has been applied to aid interpretation, such as multi-attribute analysis [6][7][8]16], AVO cross-plotting, and classification [3,17].…”

Section: Unsupervised Learningmentioning

confidence: 99%

Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible.

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“…The facies classification from seismic in hydrocarbon reservoirs is a challenging problem since seismic data is an indirect measurement, with limited resolution and usually low signal to noise ratio. Some authors have approached the problem from a Bayesian inverse theory point of view [1][2], others with geostatistical tools [3][4][5][6][7][8][9] and yet other by means of machine learning [10][11].…”

Section: Introductionmentioning

confidence: 99%