David A. Wood
Orcid: 0000-0003-3202-4069Affiliations:
- DWA Energy Ltd., Lincoln, UK
According to our database1,
David A. Wood
authored at least 14 papers
between 2018 and 2024.
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Bibliography
2024
Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids.
J. Ind. Inf. Integr., 2024
Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern - a theoretical study.
Expert Syst. Appl., 2024
2023
Improving permeability prediction in carbonate reservoirs through gradient boosting hyperparameter tuning.
Earth Sci. Informatics, December, 2023
Eng. Appl. Artif. Intell., November, 2023
Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations.
Appl. Soft Comput., August, 2023
Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites.
Expert Syst. Appl., July, 2023
Weekly carbon dioxide exchange trend predictions in deciduous broadleaf forests from site-specific influencing variables.
Ecol. Informatics, July, 2023
Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.
SN Comput. Sci., March, 2023
Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset.
Algorithms, January, 2023
Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids.
Eng. Appl. Artif. Intell., 2023
2022
Carbonate / siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning.
Earth Sci. Informatics, 2022
2020
Applying separately cost-sensitive learning and Fisher's discriminant analysis to address the class imbalance problem: A case study involving a virtual gas pipeline SCADA system.
Int. J. Crit. Infrastructure Prot., 2020
2019
A machine learning approach to predict drilling rate using petrophysical and mud logging data.
Earth Sci. Informatics, 2019
2018
A critical-path focus for earned duration increases its sensitivity for project-duration monitoring and forecasting in deterministic, fuzzy and stochastic network analysis.
J. Comput. Methods Sci. Eng., 2018