Non-linear Heterogeneous Ensemble Model for Permeability Prediction of Oil Reservoirs

Helmy, Tarek; Rahman, Syed Masiur; Hossain, Muhammad Imtiaz; Abdelraheem, Abdulaziz

doi:10.1007/s13369-013-0588-z

Cited by 29 publications

(8 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It involves directly averaging the result of the individual model to provide the final outputs. 52 The simple averaging ensemble technique (SAE) is achieved through two steps: in the first step, each model is trained and tested separately, 53,54 and in the second step, the average of the model output was tested and compared with the observed tested values as illustrated in Fig. 5.…”

Section: Ensemble Learning Conceptmentioning

confidence: 99%

Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

Baig,

Abba,

Usman

et al. 2023

Environ. Sci.: Adv.

View full text Add to dashboard Cite

show abstract

Section: Ensemble Learning Conceptmentioning

confidence: 99%

Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

Baig,

Abba,

Usman

et al. 2023

Environ. Sci.: Adv.

View full text Add to dashboard Cite

show abstract

“…More datasets maybe utilized for the verification of results. In [23] Helmy et al proposed an ensemble based approach using SVM, ANN and ANFIS for more accurate prediction of heart disease. Bagging algorithm has been used to train the individual classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

Bashir¹,

Almazroi

Ashfaq

et al. 2021

IEEE Access

View full text Add to dashboard Cite

A massive amount of medical data is available in healthcare industry, which can be utilized to extract useful knowledge. A Clinical Decision Support System (CDSS) is used to improve patient"s safety by minimizing medical errors. Heart disease is one of the major chronic maladies even in todays" world. Many researchers have employed different data mining techniques to predict heart disease. The objective of proposed framework is to improve the accuracy of heart disease prediction. In this paper, an ensemble based voting scheme is proposed to efficiently predict heart disease. Four benchmark heart disease datasets from UCI repository have been utilized for experimentation and evaluation. The performance of the proposed ensemble is compared with individual classifiers as well as with five different ensemble schemes using various parameters in order to show the effectiveness of the proposed ensemble scheme. The evaluation of results shows that the proposed ensemble scheme has better average accuracy (83%) as compared to other ensemble schemes as well as individual classifiers.

show abstract

“…For many physics-related fields (e.g., climate [23] and ocean [24] modeling), the application hybrid models that combine the data-driven and physicsbased components allow to obtain the better results in comparison with the The other forecasting technique that can be used for oil field forecasting is an ensemble modeling [25]. The AutoML-based approaches can be used to identify the optimal structure of the ensemble or hybrid model.…”

Section: Hybrid Modellingmentioning

confidence: 99%

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Nikitin,

Revin,

Hvatov

et al. 2021

Preprint

View full text Add to dashboard Cite

The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods. For oil production estimation, we implemented and investigated the quality of forecasting models: physics-based, pure data-driven, and hybrid one. The CRMIP model was chosen as a physics-based approach. We compare it with the machine learning and hybrid methods in a frame of oil production forecasting task. In the investigation of reservoir characteristics for wells location choice, we automated the seismic analysis using evolutionary identification of convolutional neural network for the reservoir detection. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.

show abstract

Non-linear Heterogeneous Ensemble Model for Permeability Prediction of Oil Reservoirs

Cited by 29 publications

References 51 publications

Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

A Knowledge-Based Clinical Decision Support System Utilizing an Intelligent Ensemble Voting Scheme for Improved Cardiovascular Disease Prediction

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Contact Info

Product

Resources

About