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Neural Network Prediction of Porosity and Permeability of Heterogeneous Gas Sand Reservoirs

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf01593790openalex-snapshot

Restart procedures for the conjugate gradient method

inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-snapshot

Multilayer feedforward networks are universal approximators

inferredcites →https://doi.org/10.1109/72.329697openalex-snapshot

Training feedforward networks with the Marquardt algorithm

inferredcites →https://doi.org/10.2118/105337-msopenalex-snapshot

Permeability Modeling Using Neural-Network Approach for Complex Mauddud-Burgan Carbonate Reservoir

inferredcites →https://doi.org/10.2118/108068-msopenalex-snapshot

Pore-Scale Characterization and Productivity Analysis by Integration of NMR and Openhole Logs–A Verification Study

inferredcites →https://doi.org/10.2118/110959-msopenalex-snapshot

Neural Network Identification of Hydrocarbon Potential of Shaly Sand Reservoirs

inferredcites →https://doi.org/10.2118/30978-msopenalex-snapshot

State-Of-The-Art in Permeability Determination From Well Log Data: Part 1- A Comparative Study, Model Development

inferredcites →https://doi.org/10.2118/38134-msopenalex-snapshot

Use of Neural Networks to Predict the Permeability and Porosity of Zone "C" of the Cantagallo Field in Colombia

inferredcites →https://doi.org/10.2118/38736-msopenalex-snapshot

Applying NMR Total and Effective Porosity to Formation Evaluation

inferredcites →https://doi.org/10.2118/39703-msopenalex-snapshot

An Artificial Neural Network Model for Estimating Tight Gas Sand Permeability

inferredcites →https://doi.org/10.2118/53277-msopenalex-snapshot

Experiences in Permeability Calculations in the Obayied Field (Egypt) Using Magnetic Resonance Imaging

inferredcites →https://doi.org/10.2118/88476-msopenalex-snapshot

Reservoir Porosity and Permeability Estimation from Well Logs Using Fuzzy Logic and Neural Networks

inferredcites →https://doi.org/10.2516/ogst:1992037openalex-snapshot

Principle and Potential of Nuclear Magnetic Resonance Applied to the Study of Fluids in Porous Media

inferredcites →https://doi.org/10.2523/105337-msopenalex-snapshot

Permeability Modeling Using Neural-Network Approach for Complex Mauddud-Burgan Carbonate Reservoir

inferredcites →https://doi.org/10.2523/110959-msopenalex-snapshot

Neural Network Identification of Hydrocarbon Potential of Shaly Sand Reservoirs

inferredcites →https://doi.org/10.2523/88476-msopenalex-snapshot

Reservoir Porosity and Permeability Estimation from Well Logs Using Fuzzy Logic and Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3349570openalex-extracted

Estimation of Permeability of a Reservoir using Deep Learning Algorithms on Well Logs

inferred← citeshttps://doi.org/10.1007/978-3-031-72509-8_32openalex-snapshot

Porosity Prediction of Dense Sandstone Reservoir Based on Deep Neural Network

inferred← citeshttps://doi.org/10.1007/s00521-016-2729-2openalex-snapshot

Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study

inferred← citeshttps://doi.org/10.1007/s12145-024-01593-zopenalex-snapshot

Improved model development and feature ranking for rock permeability prediction by coupling petrophysical log data and ensemble machine learning techniques

inferred← citeshttps://doi.org/10.1007/s13146-018-0435-0openalex-snapshot

Hybrid clustering-estimation for characterization of thin bed heterogeneous reservoirs

inferred← citeshttps://doi.org/10.1007/s13202-026-02182-0openalex-snapshot

Application of supervised machine learning for predicting and classifying the lithology type of reservoir rock: the case study of Iraq

inferred← citeshttps://doi.org/10.1007/s13369-024-09180-6openalex-snapshot

Towards Rapid Prediction of Nuclear Magnetic Resonance-Based Bimodal Porosities: An Example from the Middle Eastern Carbonate Reservoir

inferred← citeshttps://doi.org/10.1016/j.petlm.2018.04.002openalex-snapshot

New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs

inferred← citeshttps://doi.org/10.1038/s41598-023-30708-7openalex-snapshot

Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir

inferred← citeshttps://doi.org/10.1080/10916466.2010.540611openalex-snapshot

Predicting Log Data by Using Artificial Neural Networks to Approximate Petrophysical Parameters of Formation

inferred← citeshttps://doi.org/10.1080/15567036.2011.610870openalex-snapshot

Using ANFIS and Neural Networks to Predict the Volume Percentage of Matrix and Fluid

inferred← citeshttps://doi.org/10.1109/access.2020.3046698openalex-snapshot

A Comprehensive Review of Soft Computing Models for Permeability Prediction

inferred← citeshttps://doi.org/10.1109/access.2023.3349216openalex-snapshot

Machine Learning in Oil and Gas Exploration: A Review

inferred← citeshttps://doi.org/10.2118/183106-msopenalex-snapshot

Enhancing Rock Property Prediction from Conventional Well Logs Using Machine Learning Technique - Case Studies of Conventional and Unconventional Reservoirs

inferred← citeshttps://doi.org/10.2118/212625-msopenalex-snapshot

Application of Machine Learning for Estimating Petrophysical Properties of Carbonate Rocks Using NMR Core Measurements

inferred← citeshttps://doi.org/10.3390/app12062942openalex-snapshot

A Novel Empirical and Deep Ensemble Super Learning Approach in Predicting Reservoir Wettability via Well Logs

inferred← citeshttps://doi.org/10.3390/en14051513openalex-snapshot

NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock

inferred← citeshttps://doi.org/10.47749/t/unicamp.2022.1375134openalex-snapshot

Carbonate reservoir characterization with integrated rock, special well logs, and machine learning methods

inferred← citeshttps://doi.org/10.62051/ijnres.v3n1.17openalex-snapshot

Reservoir Permeability Prediction Based on Machine Learning

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