Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters
Authors
Keywords
-
Journal
ACS Omega
Volume -, Issue -, Pages -
Publisher
American Chemical Society (ACS)
Online
2021-05-20
DOI
10.1021/acsomega.1c01340
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formation
- (2020) Ahmed Abdulhamid Mahmoud et al. Sustainability
- Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System
- (2020) Ahmed Alsabaa et al. SENSORS
- Application of artificial neural network to predict the rate of penetration for S-shape well profile
- (2020) Ahmad Al-Abduljabbar et al. Arabian Journal of Geosciences
- Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study
- (2020) Ahmed Alsaihati et al. JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
- Comparative analysis of artificial intelligence techniques for formation pressure prediction while drilling
- (2019) Abdulmalek Ahmed et al. Arabian Journal of Geosciences
- Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks
- (2019) Ahmed Abdulhamid Mahmoud et al. Energies
- Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence
- (2019) Ahmed Abdulhamid Mahmoud et al. Energies
- Lithology identification using well logs: A method by integrating artificial neural networks and sedimentary patterns
- (2019) Xiaoxu Ren et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks
- (2018) Adil Ahmed et al. APPLIED ENERGY
- Real-Time Determination of Rheological Properties of Spud Drilling Fluids Using a Hybrid Artificial Intelligence Technique
- (2018) Khaled Abdelgawad et al. JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
- Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network
- (2017) Ahmed Abdulhamid A. Mahmoud et al. INTERNATIONAL JOURNAL OF COAL GEOLOGY
- Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran
- (2017) Moslem Fatehi et al. JOURNAL OF AFRICAN EARTH SCIENCES
- Use of machine learning and data analytics to increase drilling efficiency for nearby wells
- (2017) Chiranth Hegde et al. Journal of Natural Gas Science and Engineering
- Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)
- (2016) Salaheldin Elkatatny et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields
- (2013) R. Keshavarzi et al. European Journal of Environmental and Civil Engineering
- Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, Algeria
- (2013) Réda Samy Zazoun JOURNAL OF AFRICAN EARTH SCIENCES
- A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction
- (2013) Fatai Anifowose et al. NEURAL COMPUTING & APPLICATIONS
- An Innovative Approach for Pore Pressure Prediction and Drilling Optimization in an Abnormally Subpressured Basin
- (2012) Oscar Contreras et al. SPE DRILLING & COMPLETION
- Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization
- (2011) Fatai Anifowose et al. Journal of Natural Gas Science and Engineering
- Origin of overpressure and pore-pressure prediction in the Baram province, Brunei
- (2008) Mark R. P. Tingay et al. AAPG BULLETIN
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started