Prediction of Permeability Using Random Forest and Genetic Algorithm Model

JunhuiWang,; Yan, Wanzi; ZhijunWan,; Wang, Yi; Lv, Jian; Zhou, Aiping

doi:10.32604/cmes.2020.014313

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…The root-mean-square error (RMSE) and coefficient of determination (R 2 ), as the most frequently used ANN model metrics, 32 have been employed in this study for the assessment of the missing DHP prediction.…”

Section: Selection Of Predictionmentioning

confidence: 99%

Affecting Factors on History Matching Field-Level Coal Seam Gas Production from the Surat Basin, Australia

Ren,

Zhou,

Jeffries

et al. 2024

Energy Fuels

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Reservoir history matching (HM) is an effective means for unconventional prospect studies, which enables the calibration of reservoir models and lays a solid foundation for production forecasting. This work presents a comprehensive reservoir simulation model comprising key static properties and dynamic data from the Surat Basin. The simulation model is deployed to discuss the HM experience of coal seam gas (CSG) wells, i.e., the challenges encountered and how these challenges have been overcome during the HM process. The challenges are mainly caused by uncertainties related to reservoir properties, tenement boundaries, and production data set integrity. Sensitivity analysis was conducted on the reservoir properties as well as the model boundary extent to ascertain the range of uncertainty and the relative weight of each parameter. An artificial neural network (ANN), a commonly applied machine learning process, was employed to predict the missing downhole pressure (DHP) data points for the following HM control comparison. The results demonstrate that the source and direction of water influx have played a central role in CSG production in the Surat Basin, especially for the well batches close to the simulation model boundary. Meanwhile, the novelty of ANN application for DHP prediction allows the HM the flexibility to switch from water rate constraint to bottom-hole pressure constraint; this eventually allows the HM simulation to reach the required degree of accuracy despite having wells with incomplete DHP data.

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Section: Selection Of Predictionmentioning

confidence: 99%

Affecting Factors on History Matching Field-Level Coal Seam Gas Production from the Surat Basin, Australia

Ren,

Zhou,

Jeffries

et al. 2024

Energy Fuels

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“…While random forests are typically employed for predictive or classification endeavors, in this context, they serve as a proxy to emulate the genetic algorithm's operational dynamics. This surrogate modeling facilitates a nuanced understanding of the impact of various input variables on the genetic algorithm's output (Jiang Wang et al, 2020; S.A. Naghibi et al, 2017;H Norouzi et al, 2021) [32][33][34].…”

Section: Sensitive Analysismentioning

confidence: 99%

Resource Scheduling Optimization of Fresh Food Delivery Porters Considering Ambient Temperature Variations

Shan,

Yao

2024

Sustainability

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In the context of an evolving socio-economic landscape and rising living standards, the online market for fresh products, encompassing fruits, vegetables, meats, dairy, and eggs, has seen substantial growth, necessitating sophisticated logistics for e-commerce home delivery. This study tackles the distinct challenges of fresh product delivery, which demand rigorous adherence to climate conditions and product mix during transport, significantly influencing the operational strategies and scheduling of delivery platforms. To address these challenges, a comprehensive mathematical model was developed to optimize fresh food home delivery scheduling, focusing on reducing spoilage rates and accommodating the dynamic impact of environmental temperature changes. The model posits assumptions of a consistent and ample supply of fresh goods, standard initial quality loss, and efficient porter assignment for multi-category order combinations. It introduces three objective functions, targeting the minimization of fresh food loss, maximization of customer satisfaction, and reduction of distributor costs. The efficacy of the model and its genetic-algorithm-based solution method was assessed through numerical analysis and case studies, illustrating that the model enhances delivery efficiency and service quality across varying temperature conditions. This substantiates the critical role of environmental temperature management in optimizing fresh food delivery, offering a robust framework for advancing logistical operations in the perishable goods sector and ensuring quality and efficiency in fresh food e-commerce delivery.

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“…The RF algorithm shown in Fig. 4 generates results based on the average result of each tree [38,39].…”

Section: Random Forestmentioning

confidence: 99%

Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

Wong¹,

Al-Ani²,

Hasikin‬³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH 3 -N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal-polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed. The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river. The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features. This work analyzes 17 new input features, namely conductivity (COND), salinity (SAL), turbidity (TUR), dissolved solids (DS), nitrate (NO 3 ), chloride (Cl), phosphate (PO 4 ), arsenic (As), chromium (Cr), zinc (Zn), calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), E. coli, and total coliform, in predicting WQI using machine learning techniques. Five regression algorithms-random forest (RF), AdaBoost, support vector regression (SVR), decision tree regression (DTR), and multilayer perception (MLP)-are applied for preliminary model selection. The results show that the RF algorithm exhibits better prediction performance, with R 2 of 0.974. Then, this work proposes a modified RF by incorporating the synthetic minority oversampling technique (SMOTE) into the conventional RF method. The proposed modified RF method is shown to achieve 77.68%, 74%, 69%, and 71% accuracy, precision, recall, and F1-score, respectively. In addition, the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction. The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation.

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Prediction of Permeability Using Random Forest and Genetic Algorithm Model

Cited by 13 publications

References 8 publications

Affecting Factors on History Matching Field-Level Coal Seam Gas Production from the Surat Basin, Australia

Affecting Factors on History Matching Field-Level Coal Seam Gas Production from the Surat Basin, Australia

Resource Scheduling Optimization of Fresh Food Delivery Porters Considering Ambient Temperature Variations

Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

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