Results 1 to 10 of about 124 (90)

Facies evaluation and sedimentary environments of the Yamama Formation in the Ratawi oil field, South Iraq. [PDF]

open access: yesSci Rep, 2023
Microfacies and environmental analyses of the Yamama Formation were conducted in the Tithonian–Hautervian sequence in the Ratawi oil field of Basra city in southern Iraq. The study includes petrographic, facies, and depositional models for the study area.
Al-Iessa IA, Zhang WZ.
europepmc   +2 more sources

The Application of Pattern Recognition in Electrofacies Analysis

open access: yesJournal of Applied Mathematics, 2014
Pattern recognition is an important analytical tool in electrofacies analysis. In this paper, we study several commonly used clustering and classification algorithms.
Huan Li, Xiao Yang, Wenhong Wei
doaj   +2 more sources

Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach

open access: yesApplied Sciences (Switzerland), 2020
Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs.
Maman Hermana   +2 more
exaly   +3 more sources

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods. [PDF]

open access: yesSci Rep
Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships.
Eftekhari SH   +6 more
europepmc   +2 more sources

A case study of reservoir characterization and modeling for tidal-dominated estuary reservoir in Ecuador. [PDF]

open access: yesSci Rep
The Oriente Basin, as part of the retro-arc foreland basin system, develops tidal-dominated estuary, where the LU layer exhibits complex sedimentary characteristics in lateral that brings great uncertainty to reservoir characterization using only wells ...
Jiwei C   +7 more
europepmc   +2 more sources

Analyzing the impact of clay minerals on the reservoir quality of the Lower Goru Formation using Unsupervised Machine Learning. [PDF]

open access: yesPLoS One
The reservoir quality of the Lower Goru Formation is highly variable due to its heterogeneous nature influenced by sea level fluctuations during the Early Cretaceous period.
Noreen K   +5 more
europepmc   +2 more sources

A comparative study of 3D FZI and electrofacies modeling using seismic attribute analysis and neural network technique: A case study of Cheshmeh-Khosh Oil field in Iran

open access: yesPetroleum, 2016
Electrofacies are used to determine reservoir rock properties, especially permeability, to simulate fluid flow in porous media. These are determined based on classification of similar logs among different groups of logging data.
Mahdi Rastegarnia   +2 more
exaly   +3 more sources

Integrating NMR and machine learning for pore-type driven rock classification in the heterogeneous Asmari carbonate reservoirs. [PDF]

open access: yesSci Rep
The Asmari Formation’s complex heterogeneity presents fundamental challenges for reservoir characterization, where conventional lithology-based methods inadequately capture dynamic fluid behavior and pore-scale productivity controls.
Veysi M   +5 more
europepmc   +2 more sources

Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization

open access: yesApplied Sciences (Switzerland)
Electrofacies are log-related signatures that reflect specific physical and compositional characteristics of rock units. The concept was developed to encapsulate a collection of recorded well-log responses, enabling the characterization and ...
najmudeen Sibaweihi   +2 more
exaly   +3 more sources

A new approach to predict carbonate lithology from well logs: A case study of the Kometan formation in northern Iraq. [PDF]

open access: yesHeliyon
Understanding the spatial variation in lithology is crucial for characterizing reservoirs, as it governs the distribution of petrophysical characteristics.
Hussein HS, Mansurbeg H, Bábek O.
europepmc   +2 more sources

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