Results 1 to 10 of about 507 (124)

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

Applied Sciences, 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.
Touhid Mohammad Hossain, Junzo Watada, Izzatdin A. Aziz, Maman Hermana   +3 more
doaj   +4 more sources

Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques [PDF]

Jurnal Natural
Many scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and ...
MUHAMMAD RIDHA ADHARI, FREDDY SAPTA WIRANDHA, MUHAMMAD YANIS, MUHAMMAD YUSUF KARDAWI   +3 more
doaj   +4 more sources

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

PLoS 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, Azeem T, Rehman F, Amin Y, Ahmad QA, Hussain M.   +5 more
europepmc   +2 more sources

Prediction of Electrofacies Based on Flow Units Using NMR Data and SVM Method: a Case Study in Cheshmeh Khush Field, Southern Iran [PDF]

Journal of Petroleum Science and Technology, 2017
The classification of well-log responses into separate flow units for generating local permeability models is often used to predict the spatial distribution of permeability in heterogeneous reservoirs.
Mahdi Rastegarnia, Mehdi Talebpour, Ali Sanati, seyed hassan hajiabadi   +3 more
doaj   +3 more sources

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

Sci 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, Kadkhodaie A, Arian M, Aleali M, Maleki Z, Kianoush P.   +5 more
europepmc   +2 more sources

Enhancing formation resistivity factor estimation in carbonate reservoirs using electrical zone indicator and multi-resolution graph-based clustering methods. [PDF]

Sci Rep
The complex pore structure of carbonate rocks often results in scattered data in the relationship between formation resistivity factor (FRF) and porosity, posing significant challenges for accurate reservoir characterization. Although traditional methods
Mohammadi M, Niri ME, Bahroudi A, Soleymanzadeh A, Kord S.   +4 more
europepmc   +2 more sources

Applying of the Artificial Neural Networks (ANN) to Identify and Characterize Sweet Spots in Shale Gas Formations [PDF]

E3S Web of Conferences, 2018
The main goal of the study was to enhance and improve information about the Ordovician and Silurian gas-saturated shale formations. Author focused on: firstly, identification of the shale gas formations, especially the sweet spots horizons, secondly ...
Puskarczyk Edyta
doaj   +3 more sources

Stratigraphic architecture of the Mississippian limestone through integrated electrofacies classification, Hardtner field area, Kansas and Oklahoma [PDF]

Interpretation, 2018
The Mississippian Limestone formed through complex structural, stratigraphic, and diagenetic processes involving subsidence, tectonic uplift leading to periodic subaerial exposure, changes in ocean chemistry, variability inherent with carbonate cyclicity, as well as postdepositional alteration.
Niles W. Wethington, Matthew J. Pranter
openaire   +3 more sources

K-Mean Clustering Analysis and Logistic Boosting Regression for Rock Facies Characterization and Classification in Zubair Reservoir in Luhais Oil Field, Southern Iraq

Iraqi Geological Journal, 2021
Identifying rock facies from petrophysical logs is a crucial step in the evaluation and characterization of hydrocarbon reservoirs. The rock facies can be obtained either from core analysis (lithofacies) or from well logging data (electrofacies). In this
Mohammed Albuslimi
doaj   +1 more source

Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio [PDF]

, 2021
   While the well log-based lithology classification has been extensively utilized in reservoir characterization, the classification of carbonate sub-facies remains challenging due to the subtle nuances in conventional well-logs.
Zhang, Chi, Zhang, Fan
core   +1 more source