Application of artificial intelligence techniques in the petroleum industry: a review

Rahmanifard, Hamid; Plaksina, Tatyana

doi:10.1007/s10462-018-9612-8

Cited by 132 publications

(55 citation statements)

References 82 publications

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“…What makes artificial intelligence unique is that it is mostly proposed for data analyses by academics in the fields of social science and arts apart from its usefulness in science and engineering [12] , because of its wide applications. For example, in recent times, artificial intelligence (AI) has been extensively applied to optimization issues in diverse areas like industrial production and petroleum exploration [13] and business [14] setting.…”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,149

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,149

785

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“…ANN offers many advantages over traditional mathematical models such as; requiring less time for the model development, avoiding extensive experimental work to formulate a non-linear relationship and the ability to learning complex relationships regardless of requiring to understand the model structure [55] . In the past decades, ANN techniques have been extensively implemented for solving problems and for prediction tasks in the petroleum industry [56] . For instance, Jiyuan et al [57] studied the natural gas/water interfacial tension (IFT) using ANN.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

116

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“…Genetic algorithm is a parallel stochastic-search optimization method through simulating the natural genetic mechanism and biological evolution theory [11]. According to the selected fitness function, individuals are screened through selection, crossover and mutation in inheritance.…”

Section: Genetic Algorithm To Optimize Bp Neural Networkmentioning

confidence: 99%

Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm

Li¹

2018

OGCE

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The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.

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Application of artificial intelligence techniques in the petroleum industry: a review

Cited by 132 publications

References 82 publications

State-of-the-art in artificial neural network applications: A survey

State-of-the-art in artificial neural network applications: A survey

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm

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