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A novel selection method of seismic attributes based on gray relational degree and support vector machine
2018
PLoS ONE
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This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). ...
This paper presents a novel selection method of seismic attributes for reservoir prediction based on the gray relational degree (GRD) and support vector machine (SVM). ...
Acknowledgments We acknowledge the financial support provided by the Natural Science Foundation of China (Grant No. ...
doi:10.1371/journal.pone.0192407
pmid:29394297
pmcid:PMC5796712
fatcat:xj6vi7rv6rcircmgta55k7trgu
Intelligent prediction and integral analysis of shale oil and gas sweet spots
2018
Petroleum Science
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Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of ...
The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions ...
Acknowledgements The authors would like to thank the editors and reviewers for their valuable comments. ...
doi:10.1007/s12182-018-0261-y
fatcat:vehsaylxtvetdgfvrb4ottfppm
A new method of predicting the saturation pressure of oil reservoir and its application
2020
International journal of hydrogen energy
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h i g h l i g h t s Machine learning could be used to determine oil saturation pressure. The new method is simple and accurate for rapid calculation. ...
The new method lays a foundation for fossil hydrogen energy development. ...
Acknowledgements This work was jointly supported by the Major Project of China National Petroleum Corporation (Grant No. 2016D-4402). ...
doi:10.1016/j.ijhydene.2020.08.042
fatcat:zamxb7c2mvcdfopp6nhmnyy5gq
A Hybrid of Functional Networks and Support Vector Machine models for the prediction of petroleum reservoir properties
2011
2011 11th International Conference on Hybrid Intelligent Systems (HIS)
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Keywords-hybrid models, computational intelligence, porosity, permeability, functional networks, support vector machines. ...
This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model ...
INTRODUCTION Computational Intelligence (CI) techniques such as Functional Networks (FN) and Support Vector Machines (SVM) have shown to be effective for a wide range of realworld applications. ...
doi:10.1109/his.2011.6122085
dblp:conf/his/FataiLA11
fatcat:2yewz25v6rektotmxedkydweaa
Permeability prediction and its impact on reservoir modeling at Postle Field, Oklahoma
2011
The Leading Edge
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A permeability model was developed for a thin reservoir located at Postle Field, Oklahoma, with the objective of accurately predict fluid flow under a CO 2 injection framework. ...
This is not uncommon in the oil and gas industry since the average reservoir thickness in North America is 17ft (5m). ...
A multiclass classification using Support Vector Machines (SVM) was performed to breakdown the Morrow A sandstone into sandstone petrofacies data. ...
doi:10.1190/1.3535436
fatcat:xvq2lpsvvbgf7l7oyxcj5tdbb4
A Big Data Method Based on Random BP Neural Network and Its Application for Analyzing Influencing Factors on Productivity of Shale Gas Wells
2022
Energies
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of 1.6, Type I reservoir of 18 m thick, optimal horizontal section of 1600 m long, and 20 fractured sections. ...
the influencing factors for a whole development block. ...
We selected the following models for comparison, including machine learning models: Logistic Regression (LR) [33] and Support Vector Regression (SVR) [34] , and deep learning models: Long Short-Term ...
doi:10.3390/en15072526
fatcat:s346p3w25fhh7dcw2vjwj2tst4
Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation
2021
Geofluids
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Optimization measures are proposed for the development of this kind of sandstone reservoir based on main controlling factor analysis. ...
This study proposes a reference case for oil saturation quantitative analysis based on machine learning methods that will help reservoir engineers make better decision. ...
Acknowledgments The authors thank the support from the National Natural Science Foundation of China (Grant No. 51974357). ...
doi:10.1155/2021/6515846
fatcat:ydzgjbpln5dtjgqe562zm3sdzy
Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation
2021
Energies
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The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. ...
Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). ...
Figure 2 . 2 Support vector machine regression diagram. ...
doi:10.3390/en14175509
fatcat:divdt3grazanzdziuchidr5d2i
Reservoir prediction using multi-wave seismic attributes
2011
Earthquake Science
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In order to solve these problems, we study methods of principal component analysis (PCA), independent component analysis (ICA) for attribute optimization and support vector machine (SVM) for reservoir ...
the prediction accuracy. ...
Acknowledgements This study was supported by China Important National Science & Technology Specific Projects (No. 2011ZX05019-008) and National Natural Science Foundation of China (No. 40839901). ...
doi:10.1007/s11589-011-0800-8
fatcat:7fy5ajwpfbhjnpgr7qhpu2k7gy
Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence
2019
Energies
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The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. ...
These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability ...
Figure 5 . 5 Predicted vs. actual recovery factor based on the support vector machine (SVM) model (a) training dataset, (b) testing dataset. ...
doi:10.3390/en12193671
fatcat:bhcgddmbk5f4jbstljthzvo3my
Quantitative prediction of fluvial sandbodies by combining seismic attributes of neighboring zones
2020
Journal of Petroleum Science and Engineering
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neighboring zones, implemented by a supervised machine learning algorithm using support vector regression (SVR). ...
resolution: this can produce significant error in the prediction of sand location and thickness using seismic attributes. ...
We thank the Shengli Oil Field Company for providing and permitting publication of the subsurface data. ...
doi:10.1016/j.petrol.2020.107749
fatcat:v7soxdtwzfdqher52k7xismzlm
New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs
2018
Open Journal of Yangtze Oil and Gas
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reliable predictive models for the calculation of water saturation of shale and tight reservoirs. ...
The determination of water saturation is a key step for the reservoir characterization and prediction of future reservoir performance in terms of production. ...
Development of Models
Least Squares Support Vector Machine Model Least squares support vector machines are least squares forms of support vector machines (SVM), which are a set of associated supervised ...
doi:10.4236/ojogas.2018.33019
fatcat:wvmmn6koq5fzrn6qywunx5jcca
Casing life prediction using Borda and support vector machine methods
2010
Petroleum Science
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The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine (SVM), a casing life prediction model is established. ...
In the prediction model, eight risk indexes are defi ned as input vectors and casing life is defined as the output vector. ...
Acknowledgments The authors are grateful for financial support from "973 Project" (Contract No. 2010CB226706). ...
doi:10.1007/s12182-010-0087-8
fatcat:4dtfgqtxsfdd7eqbzso46bulsq
Using machine learning for model benchmarking and forecasting of depletion-induced seismicity in the Groningen gas field
2021
Computational Geosciences
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Results show that seismicity forecasts generated using Support Vector Machines significantly outperform beforementioned baselines. ...
To investigate whether machine learning techniques such as Random Forests and Support Vector Machines bring new insights into forecasts of induced seismicity rates in space and time, a pipeline is designed ...
We thank NAM for funding this work and allowing for its publication. We thank the three anonymous reviewers for their useful comments that greatly improved this paper. ...
doi:10.1007/s10596-020-10023-0
fatcat:6p5n6e33djaste7lzoirlylhlm
Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs
[article]
2020
arXiv
pre-print
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Concentrating on reservoir production prediction, a specific HDNN model is configured and applied to an oil development block. ...
We develop a general architecture of hybrid deep neural networks (HDNNs) to support mixed inputs. ...
Qiu for valuable discussions on production characterization of the adopted oil block. ...
arXiv:2005.08419v1
fatcat:jqi3jbsukve6laa365jzfexcea
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