Research on calculation model of bottom of the well pressure based on machine learning

Liang, Haibo; Liu, Gang; Zou, Jialing; Bai, Jing; Jiang, Yingjun

doi:10.1016/j.future.2021.05.011

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…This style recognition method also needs to be based on the assumption that the market style at the current time point can find the same situation in the past period of time. If there is a fundamental and permanent change in the market, starting from the current time and completely entering a new market style that cannot be found in the past, then the model cannot be effective [10].…”

Section: Market Style Recognition Methodsmentioning

confidence: 99%

Stock Quantitative Intelligent Investment Model Based on Machine Learning Algorithms

Wang

2024

SCPE

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In order to cope with sudden changes in market style, the author designed and implemented an investment system. In response to the inability to cope with sudden changes in market style, the author proposed an improved scheme when using a classifier for training, using a hidden Markov model to select time points in the same market state as the training samples for the classifier. The results of backtesting and real market testing show that the improved investment system can capture changes in market style, and during the testing period, the test results obtained higher returns compared to the pre improved classifier. At present, the investment system implemented by the author has become the core backend module of the officially launched intelligent investment advisory product Zhiyu Liangtou APP, providing strong investment decision-making suggestions for small and medium-sized investors. In the future, we can provide backend support for more user end products.

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Section: Market Style Recognition Methodsmentioning

confidence: 99%

Stock Quantitative Intelligent Investment Model Based on Machine Learning Algorithms

Wang

2024

SCPE

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“…The selection of slope, however, necessitated expert logging interpretation skills and was frequently prone to error. Currently, machine learning methods were frequently used to understand exploration log data [17][18][19][20] and production log output profiles [21]. As a form of machine learning technique, random forests provide predictions by constructing several decision trees.…”

Section: Introductionmentioning

confidence: 99%

A Model Based on the Random Forest Algorithm That Predicts the Total Oil–Water Two-Phase Flow Rate in Horizontal Shale Oil Wells

Zhou

Liu

Fei³

et al. 2023

Processes

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Due to variables like wellbore deviation variation and flow rate, the local flow velocity in the output wellbore of horizontal shale oil wells varied significantly at various points in the wellbore cross-section, making it challenging to calculate the total single-layer production with accuracy. The oil–water two-phase flow rate calculation techniques for horizontal wells developed based on particular flow patterns and array spinners had excellent applicability in their respective niches but suffered from poor generalizability and demanding experience levels for logging interpreters. In this study, we employed five spinners in a triangular walled array instrument to create the multi-decision tree after figuring out how many leaf nodes there were and examining the defining characteristics of the observed values gathered under various experimental setups. The construction of the entire oil–water two-phase flow prediction model was made possible when the random forest regression approach was used with it. The total oil–water flow rate at each perforated layer was predicted using the model in sample wells, and the mean square error with the third party’s interpretation conclusion was 1.42, indicating that the model had an excellent application effect. The approach, which offered a new interpretation method for calculating the oil–water two-phase flow rate of horizontal wells based on multi-location local flow rate, required less interpretation knowledge from the interpreter and had a stronger generalization capacity.

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Heave attenuation in offshore managed pressure drilling with an integral sliding mode controller

Pazoki,

Nikoofard,

Sedigh

2024

Applied Ocean Research

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Research on calculation model of bottom of the well pressure based on machine learning

Cited by 10 publications

References 26 publications

Stock Quantitative Intelligent Investment Model Based on Machine Learning Algorithms

Stock Quantitative Intelligent Investment Model Based on Machine Learning Algorithms

A Model Based on the Random Forest Algorithm That Predicts the Total Oil–Water Two-Phase Flow Rate in Horizontal Shale Oil Wells

Heave attenuation in offshore managed pressure drilling with an integral sliding mode controller

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