Application of neural network and fuzzy mathematic theory in evaluating the adaptability of inflow control device in horizontal well

Chen, Feifei; Duan, Yunlong; Junbin, Zhang; Wangkun,; Wang, Weifeng

doi:10.1016/j.petrol.2015.07.020

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…They reported that the developed AI models provided very good perditions and outperformed the industry's well-known correlations. Chen et al (2015) and Feifei et al (2015) determined the productivity index for horizontal wells using AFL, ANN, and FN. They mentioned that the developed models investigate the influences of reservoir parameters (such as reservoir size, thickness, and reservoir permeability) on well performance.…”

Section: Production In the Reservoirmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

101

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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Section: Production In the Reservoirmentioning

confidence: 99%