Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

Goliatt, Leonardo; Saporetti, Camila Martins; Oliveira, Luiz Carlos Veiga de; Pereira, Egberto

doi:10.1016/j.petlm.2023.05.005

Cited by 7 publications

(4 citation statements)

References 79 publications

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“…In addition to the size of the dataset, the results are also related to the structure of the dataset. Therefore, according to Goliatt et al [51] and Altin et al [52], it is fundamental to choose the correct algorithm for a specific problem.…”

Section: Discussionmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.

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Section: Discussionmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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“…The Extreme Learning Machine (ELM), Elastic Net Linear, Linear Support Vector Regression (Linear-SVR), Multivariate Adaptive Regression Spline, Artificial Bee Colony, Particle Swarm Optimization (PSO), Differential Evolution, Simple Genetic Algorithm, Grey Wolf Optimizer (GWO), and Exponential Natural Evolution Strategies (xNES) are some of the models that Goliatt et al [ 57 ] used in the temporal domain of shale gas exploration within the YuDong-Nan shale gas field. To estimate total organic carbon, the DE+ELM hybrid model produced an acceptable RMSE of 0.497 when predicting factors such as clay, K-feldspar, pyrite, and other elements.…”

Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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“…Moreover, [10] highlight the significance of optimization in the context of petroleum engineering, specifically in the modelling of shale gas fields. Their research showcases how optimization techniques contribute to the precise evaluation of available resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Recent Advances in Machine Learning-based Optimization Techniques: A Systematic Review

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

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Exploring the convergence of machine learning and optimization techniques, this paper delves into the recent progress and wide-ranging applications of these methods in various domains, including chemical engineering, pharmaceuticals, and environmental management. Through a detailed analysis of pivotal studies published, the paper evaluates the effectiveness, efficiency, applicability, and limitations of different machine learning strategies in optimization scenarios. It reveals how the integration of machine learning in optimization transcends disciplinary boundaries, significantly impacting sectors from healthcare to environmental sustainability. The paper highlights significant developments such as the emergence of hybrid and evolutionary models, advancements in personalized healthcare, and breakthroughs in sustainable resource management and security. Additionally, it discusses the challenges and ethical implications inherent in these advancements, particularly the need for comprehensive datasets and substantial computational resources. Ultimately, the insights gathered suggest that machine learning-based optimization is crucial for fostering innovation and addressing complex challenges in various fields, emphasizing the need for its responsible and ethical use in the ever-changing technological landscape.

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Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

Cited by 7 publications

References 79 publications

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

A Review of Predictive Analytics Models in the Oil and Gas Industries

Recent Advances in Machine Learning-based Optimization Techniques: A Systematic Review

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