David A. Wood

发表

Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms

Jamshid Moghadasi, Hamzeh Ghorbani, Mohammad Mehrad, 2021, Journal of Petroleum Science and Engineering.

Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

Khalil Shahbazi, Hamzeh Ghorbani, David A. Wood, 2021 .

The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets

David A. Wood, Sajjad Mardanirad, Hassan Zakeri, 2021, SN Applied Sciences.

Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight

David A. Wood, 2021 .

Prediction of oil flow rate through orifice flow meters: Optimized machine-learning techniques

Mohammad Farsi, Hamzeh Ghorbani, David A. Wood, 2021 .

Net ecosystem carbon exchange prediction and insightful data mining with an optimized data-matching algorithm

David A. Wood, 2021 .

Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

David A. Wood, Hamzeh Ghorbani حمزه قربانی, A. Choubineh, 2019, Experimental and Computational Multiphase Flow.

Hourly-averaged solar plus wind power generation for Germany 2016: Long-term prediction, short-term forecasting, data mining and outlier analysis

David A. Wood, 2020 .

Shear modulus prediction of embedded pressurized salt layers and pinpointing zones at risk of casing collapse in oil and gas wells

David A. Wood, Mehdi Ahmadi Alvar, K. Shahbazi, 2020 .

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

David A. Wood, Hamzeh Ghorbani حمزه قربانی, A. Choubineh, 2020 .

ANN-Based Prediction of Laboratory-Scale Performance of CO2-Foam Flooding for Improving Oil Recovery

Mohammad Ali Ahmadi, Abouzar Choubineh, Seyedeh Raha Moosavi, 2019, Natural Resources Research.

Wellbore stability analysis to determine the safe mud weight window for sandstone layers

David A. Wood, Hamzeh Ghorbani حمزه قربانی, M. Cheraghi Seifabad, 2019, Petroleum Exploration and Development.

Total Organic Carbon Predictions from Lower Barnett Shale Well-log Data Applying an Optimized Data Matching Algorithm at Various Sampling Densities

David A. Wood, 2020, Pure and Applied Geophysics.

Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data

David A. Wood, 2020 .

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

David A. Wood, 2020 .

Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm

Jamshid Moghadasi, Hamzeh Ghorbani, David A. Wood, 2017 .

Estimating Organic-Rich Shale Fractal Dimensions from Gas Adsorption Isotherms: Combining Different Methods Leads to More Reliable Values and Insight

David A. Wood, 2021, Natural Resources Research.

Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization

Abouzar Choubineh, Elias Khalafi, Hamzeh Ghorbani, 2017 .

A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning

David A. Wood, Hamzeh Ghorbani حمزه قربانی, N. Mohamadian, 2021 .

Rheological and ﬁltration characteristics of drilling ﬂuids enhanced by nanoparticles with selected additives: an experimental study

Hamzeh Ghorbani, David A. Wood, Nima Mohamadian, 2018 .

Transformation of associated natural gas into valuable products to avoid gas wastage in the form of flaring

David A. Wood, S. Jokar, P. Parvasi, 2021 .

Reinforcement of oil and gas wellbore cements with a methyl methacrylate/carbon-nanotube polymer nanocomposite additive

David A. Wood, R. Ashena, N. Mohamadian, 2020 .

Estimation of minimum miscibility pressure of varied gas compositions and reservoir crude oil over a wide range of conditions using an artificial neural network model

David A. Wood, A. Choubineh, A. Helalizadeh, 2018, Advances in Geo-Energy Research.

Lithofacies and stratigraphy prediction methodology exploiting an optimized nearest-neighbour algorithm to mine well-log data

David A. Wood, 2019 .

Bakken Stratigraphic and Type Well-Log Learning Network for Transparent Prediction and Rigorous Data Mining

David A. Wood, 2019, Natural Resources Research.

Transparent machine learning provides insightful estimates of natural gas density based on pressure, temperature and compositional variables

David A. Wood, A. Choubineh, 2020 .

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inﬂow control devices

David A. Wood, B. Aadnøy, Rasool Khosravanian, 2021, Advances in Geo-Energy Research.

Machine Learning and Regression Analysis Reveal Different Patterns of Influence on Net Ecosystem Exchange at Two Conifer Woodland Sites

David A. Wood, 2022, Research in Ecology.

Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

David A. Wood, Hamzeh Ghorbani حمزه قربانی, A. Choubineh, 2018, Journal of Petroleum Exploration and Production Technology.

Artificial Intelligence-based Modeling of Interfacial Tension for Carbon Dioxide Storage

David A. Wood, A. Choubineh, A. Hosseini, 2020 .

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

David A. Wood, Pezhman Soltani Tehrani, Hamzeh Ghorbani حمزه قربانی, 2022, Journal of Petroleum Exploration and Production Technology.

Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early‐onset type 2 diabetes

David A. Wood, 2022, Chronic diseases and translational medicine.

Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs

David A. Wood, W. Al-Mudhafar, Mohammed A. Abbas, 2022, Marine and Petroleum Geology.

Country-wide solar power load profile for Germany 2015 to 2019: The impact of system curtailments on prediction models

David A. Wood, 2022, Energy Conversion and Management.

Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis

David A. Wood, 2022, Advances in Geo-Energy Research.

A support vector machine analysis to predict density of mixtures of methanol and six ionic liquids

David A. Wood, A. Choubineh, A. Golparvar, 2018, Monatshefte für Chemie - Chemical Monthly.

Geomechanical brittleness index prediction for the Marcellus shale exploiting well-log attributes

David A. Wood, 2023, Results in Engineering.

Real-time porosity prediction using gas-while-drilling data and machine learning with reservoir associated gas: Case study for Hassi Messaoud field, Algeria

David A. Wood, S. Heddam, Ouafi Ameur-zaimeche, 2022, Marine and Petroleum Geology.

Predicting saturated vapor pressure of LNG from density and temperature data with a view to improving tank pressure management

David A. Wood, David A. Wood, 2020 .

Stratigraphic influences on reservoir heterogeneities of the Mid-Cretaceous carbonates in southwest Iran: Insight from an integrated stratigraphic, diagenetic and seismic attribute study

David A. Wood, A. Kadkhodaie, R. Moussavi-Harami, 2022, Journal of Asian Earth Sciences.

Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling

Shahab S. Band, David A. Wood, N. Mohamadian, 2022, Marine and Petroleum Geology.

A hybrid nanocomposite of poly(styrene-methyl methacrylate- acrylic acid) /clay as a novel rheology-improvement additive for drilling fluids

David A. Wood, N. Mohamadian, Hamzeh Ghorbani, 2019, Journal of Polymer Research.

Hydrocarbon generation and kinetics: A case study of Permian shales, India

David A. Wood, B. Hazra, P. Singh, 2021, Journal of Asian Earth Sciences.

The impacts of gas impurities on the minimum miscibility pressure of injected CO2-rich gas–crude oil systems and enhanced oil recovery potential

David A. Wood, A. Choubineh, Abbas Helalizadeh, 2018, Petroleum Science.

Effective prediction of lost circulation from multiple drilling variables: a class imbalance problem for machine and deep learning algorithms

David A. Wood, Sajjad Mardanirad, H. Zakeri, 2021, Journal of Petroleum Exploration and Production Technology.

Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response

David A. Wood, N. Mohamadian, M. Abdali, 2021, Journal of Construction Materials.

Discrete fracture modeling by integrating image logs, seismic attributes, and production data: a case study from Ilam and Sarvak Formations, Danan Oilfield, southwest of Iran

David A. Wood, R. Rezaee, A. Kadkhodaie, 2022, Journal of Petroleum Exploration and Production Technology.

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

David A. Wood, Abouzar Choubineh, Zahak Choubineh, 2020, Int. J. Crit. Infrastructure Prot..

A transparent Open-Box learning network provides insight to complex systems and a performance benchmark for more-opaque machine learning algorithms

David A. Wood, David A. Wood, 2018 .

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

David A. Wood, Pezhman Soltani Tehrani, N. Mohamadian, 2022, Journal of Petroleum Exploration and Production Technology.

Robust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir

Shahab S. Band, David A. Wood, Mehdi Ahmadi Alvar, 2022, Marine and Petroleum Geology.

Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

David A. Wood, Z. Dai, Mohammad Mehrad, 2023, Expert Systems with Applications.

Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes

David A. Wood, N. Mohamadian, J. Moghadasi, 2021, Journal of Petroleum Exploration and Production.

Auto-detection interpretation model for horizontal oil wells using pressure transient responses

David A. Wood, B. Vaferi, S. R. Moosavi, 2020, Advances in Geo-Energy Research.

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

David A. Wood, David A. Wood, 2020, Technology and Economics of Smart Grids and Sustainable Energy.

Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables

David A. Wood, Mohammad Mehrad, S. Davoodi, 2023, Separation and Purification Technology.

Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

David A. Wood, N. Mohamadian, M. Farsi, 2021, Natural Resources Research.

Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand

Ahmed E. Radwan, David A. Wood, 2022, Journal of Rock Mechanics and Geotechnical Engineering.

Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes

David A. Wood, 2022, Advances in Geo-Energy Research.

Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone Reservoirs. Case study: Rhourd Chegga oilfield, Algeria

David A. Wood, S. Heddam, Ouafi Ameur-zaimeche, 2023, Geoenergy Science and Engineering.

Solar plus wind country-wide electrical power forecasts across successive years by optimized data matching

David A. Wood, David A. Wood, 2020 .

Solar plus wind country-wide electrical power forecasts across successive years by optimized data matching

David A. Wood, 2020, International Journal of Energy and Environmental Engineering.

German country-wide renewable power generation from solar plus wind mined with an optimized data matching algorithm utilizing diverse variables

David A. Wood, David A. Wood, 2020, Energy Systems.

German solar power generation data mining and prediction with transparent open box learning network integrating weather, environmental and market variables

David A. Wood, David A. Wood, 2019, Energy Conversion and Management.

German country-wide renewable power generation from solar plus wind mined with an optimized data matching algorithm utilizing diverse variables

David A. Wood, 2019, Energy Systems.

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

David A. Wood, N. Mohamadian, S. Rashidi, 2020 .

Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations

David A. Wood, Mohammad Mehrad, S. Davoodi, 2023, Appl. Soft Comput..

Feasibility stage screening for sustainable energy alternatives with a fuzzy multi-criteria decision analysis protocol

David A. Wood, 2021, Modeling Earth Systems and Environment.

Long-term atmospheric pollutant emissions from a combined cycle gas turbine: Trend monitoring and prediction applying machine learning

David A. Wood, 2023, Fuel.

Sensitivity analysis and optimization capabilities of the transparent open-box learning network in predicting coal gross calorific value from underlying compositional variables

David A. Wood, David A. Wood, 2019, Modeling Earth Systems and Environment.

Transparent open-box learning network provides auditable predictions for coal gross calorific value

David A. Wood, David A. Wood, 2018, Modeling Earth Systems and Environment.

Ozone air concentration trend attributes assist hours-ahead forecasts from univariate recorded data avoiding exogenous data inputs

David A. Wood, 2023, Urban Climate.

Feature averaging of historical meteorological data with machine and deep learning assist wind farm power performance analysis and forecasts

David A. Wood, 2022, Energy Systems.

Country-wide German hourly wind power dataset mined to provide insight to predictions and forecasts with optimized data-matching machine learning

David A. Wood, David A. Wood, 2020 .

Local Integrated Air Quality Predictions from Meteorology (2015 to 2020) with Machine and Deep Learning Assisted by Data Mining

David A. Wood, 2022, Sustainability Analytics and Modeling.

Near-term, national solar capacity factor forecasts aided by trend attributes and artificial intelligence

David A. Wood, 2022, International Journal of Energy and Environmental Engineering.

Carbonate / siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning

David A. Wood, 2022, Earth Science Informatics.

Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset

David A. Wood, Frans Coenen, A. Choubineh, 2023, Algorithms.

Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids

David A. Wood, Mohammad Mehrad, S. Davoodi, 2023, Eng. Appl. Artif. Intell..

Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

David A. Wood, 2021, Research in Ecology.

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

David A. Wood, 2020, Technology and Economics of Smart Grids and Sustainable Energy.

Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs

David A. Wood, 2022, Artificial Intelligence in Geosciences.

Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence

David A. Wood, 2022, Artificial Intelligence in Geosciences.

Optimized machine learning models for natural fractures prediction using conventional well logs

David A. Wood, N. Mohamadian, S. Tabasi, 2022, Fuel.

Machine‐learning predictions of caffeine co‐crystal formation accompanying experimental and molecular validations

David A. Wood, M. A. Al Mesfer, Mohd Shariq Khan, 2022, Journal of Food Process Engineering.

Transparent open-box learning network provides auditable predictions

David A. Wood, Behzad Vaferi, Abouzar Choubineh, 2018, Journal of Thermal Analysis and Calorimetry.

Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning

David A. Wood, M. Bajolvand, Mohammad Mehrad, 2023, International Journal of Rock Mechanics and Mining Sciences.

Bursting into the Public Eye: Analyzing the Development of Renewable Energy Research Interests

David A. Wood, Soroush Ghazinoori, Saeed Roshani, 2023, Renewable Energy Focus.

Trend decomposition aids forecasts of air particulate matter (PM2.5) assisted by machine and deep learning without recourse to exogenous data

David A. Wood, 2022, Atmospheric Pollution Research.

Reliable predictions of oil formation volume factor based on transparent and auditable machine learning approaches

David A. Wood, Abouzar Choubineh, David A. Wood, 2019, Advances in Geo-Energy Research.

Kerogen Kinetic Distributions and Simulations Provide Insights into Petroleum Transformation Fraction (TF) Profiles of Organic-Rich Shales

David A. Wood, 2024, Journal of Earth Science.

Machine learning to improve natural gas reservoir simulations

David A. Wood, Frans Coenen, A. Choubineh, 2022, Sustainable Natural Gas Reservoir and Production Engineering.

Evolutionary Algorithms for Controller Tuning of Tert-Amyl-Methyl-Ether Reactive Distillation

David A. Wood, Alireza Behroozsarand, 2020, Journal of Systems Science and Systems Engineering.

Machine learning models to predict rare earth elements distribution in Tethyan phosphate ore deposits: Geochemical and depositional environment implications

David A. Wood, S. Heddam, Ouafi Ameur-zaimeche, 2024, Geochemistry.