Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 57, P. 1000 - 1009

Published: Jan. 13, 2024

Language: Английский

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Hydraulic flow unit and rock types of the Asmari Formation, an application of flow zone index and fuzzy C-means clustering methods

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 29, 2024

Rock types are the reservoir's most essential properties for special facies modeling in a defined range of porosity and permeability. This study used clustering techniques to identify rock 280 core samples from one wells drilled Asmari reservoir Mansouri field, SW Iran. Four hydraulic flow units (HFUs) were determined studied data utilizing histogram analysis, normal probability sum squared errors (SSE) statistical methods. Then, two zone index (FZI) fuzzy c-means (FCM) methods determine given well according results obtained HFU continuity acts in-depth. The FCM method, with number 3.12, compared FZI, 2.77, shows more depth. relationship between permeability improved considerably by techniques. improvement is achieved using FZI method study. Generally, all increased 0.55 0.81 first finally 0.94 fourth HFU. Similar an characterized samples. In comparison, correlation coefficients less than those general case HFUs. aims flowing fluid porous medium employing c-mean logic. Also, determining units, especially siliceous-clastic log Formation, third have highest quality Results can be nearby wellbores without cores.

Language: Английский

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Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 7, 2024

Abstract Geoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile–quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) qualitative log curve assessment through three wells (X4, X5, X6) complex geological formation to distinguish from tight sand shale. Also, we reservoir rock typing (RRT), gas-bearing non-gas bearing potential zones. Results showed gamma-ray resistivity logs are not reliable tools for identification. Further, highlighted high acoustic (AC) neutron porosity (CNL), low density (DEN), photoelectric, values as compared While, 5–10% values. The SOM clustering provided evidence of good-quality RRT facies, whereas other clusters related shale poor-quality RRT. A t-SNE algorithm accurately distinguished was used make CNL DEN plot that presence low-rank bituminous rank study area. presented strategy shall provide help comprehend coal-tight lithofacies units future mining.

Language: Английский

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Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis

Minerals, Journal Year: 2022, Volume and Issue: 13(1), P. 29 - 29

Published: Dec. 24, 2022

The detailed reservoir characterization was examined for the Central Indus Basin (CIB), Pakistan, across Qadirpur Field Eocene rock units. Various petrophysical parameters were analyzed with integration of various cross-plots, complex water saturation, shale volume, effective porosity, total hydrocarbon neutron porosity and sonic concepts, gas effects, lithology. In total, 8–14% high 45–62% saturation are superbly found in reservoirs Eocene. Sui Upper Limestone is one poorest among all these reservoirs. However, this has few intervals rich hydrocarbons highly values. volume ranges from 30 to 43%. filled porosities along secondary porosities. Fracture–vuggy, chalky, intracrystalline main contributors porosity. produce without gas-emitting carbonates an irreducible rate 38–55%. order evaluate lithotypes, including axial changes characterization, self-organizing maps, isoparametersetric maps parameters, litho-saturation cross-plots constructed. Estimating wells understanding prospects both feasible methods employed study, could be applied anywhere else comparable basins.

Language: Английский

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Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan

Geosystems and Geoenvironment, Journal Year: 2022, Volume and Issue: 2(1), P. 100123 - 100123

Published: Aug. 13, 2022

Understanding geological variance in a proved reservoir requires accurate as well exact characterization of lithological facies. In the Kadanwari gas field, machine learning (ML) classification algorithms have been used to forecast facies on such an accessible dataset. The goal is increase reliability categorization using rigorous application learning. current study identify lithofacies, we self-organizing map (SOM) and crossplot techniques. reservoir, recognition lithofacies main piece work. It expensive with conventional methods from core data, it challenging extend this non-cored wells. This research provides less method for systematic objective through well-log data by Kohonen SOM. SOMs are human-made neural networks that do not need surveillance input space into groups structure topology arranged according changes. results SOM indicates zone interest mainly composed sandstone, shaly shale diminutive amount carbonates. cluster analysis approach has utilized categorize rock Cretaceous field analyzing properties examining log dimensions. Four reservoirs were concluded, each which was internally identical petrophysical but distinct others. sandstone graded excellent while poor reservoir.

Language: Английский

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Seismic driven reservoir classification using advanced machine learning algorithms: A case study from the Lower Ranikot/Khadro sandstone gas reservoir, Kirthar Fold Belt, Lower Indus Basin, Pakistan

Geoenergy Science and Engineering, Journal Year: 2023, Volume and Issue: 222, P. 211451 - 211451

Published: Jan. 9, 2023

Language: Английский

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Machine learning - a novel approach to predict the porosity curve using geophysical logs data: An example from the Lower Goru sand reservoir in the Southern Indus Basin, Pakistan

Journal of Applied Geophysics, Journal Year: 2023, Volume and Issue: 214, P. 105067 - 105067

Published: May 17, 2023

Language: Английский

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Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(2), P. 112210 - 112210

Published: Feb. 14, 2024

Language: Английский

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Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran

Minerals, Journal Year: 2024, Volume and Issue: 14(3), P. 233 - 233

Published: Feb. 25, 2024

The crucial parameters influencing drilling operations, reservoir production behavior, and well completion are lithology rock. This study identified optimal rocks facies in 280 core samples from a drilled the Asmari of Mansouri field SW Iran to determine number hydraulic flow units. Reservoir were prepared, their porosity permeability determined by measuring devices. zone index (FZI) was calculated for each sample using MATLAB software; then, histogram analysis performed on logarithmic data FZI, units based obtained normal distributions. Electrical artificial neural network (ANN) multi-resolution graph-based clustering (MRGC) approaches. Five electrical with dissimilar conditions lithological compositions ultimately specified. Based described lithofacies, shale sandstone zones three five demonstrated elevated quality. aimed reservoir’s porous medium’s flowing fluid according C-mean fuzzy logic method. Furthermore, third fourth Formation have best high quality due determining siliceous–clastic rock log data. Outcomes could be corresponded unit determination further nearby wellbores without cores.

Language: Английский

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Multi-well clustering and inverse modeling-based approaches for exploring geometry, petrophysical, and hydrogeological parameters of the Quaternary aquifer system around Debrecen area, Hungary.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 24, P. 101086 - 101086

Published: Jan. 9, 2024

This research aims to explore the application of an unsupervised machine learning and inverse modeling-based methods map aquifers geometry investigate petrophysical hydrogeological parameters Quaternary aquifer system around Debrecen area, Eastern Hungary. The study utilized a limited geophysical well-logs, including spontaneous potential, natural gamma ray, normal resistivity logs. k-means clustering technique is applied identify distribution lithological facies within formerly identified basin-scale hydrostratigraphical units coarsening upward, alluvial, valley incision, Late Miocene deposits. Based on mathematical geological considerations, analysis revealed three main clusters (C1, C2, C3), representing different lithofacies shale, shaly sand, sand gravel. result cluster further validated with surface survey using vertical electrical sounding (VES) technique. Furthermore, inverse-modeling-based approach Csókás method employed detect horizontal hydraulic conductivity along these units. model empirically modified from Kozeny-Carman equation that suites unconsolidated freshwater-bearing based their formation factor effective grain size. results indicated widely ranged between almost zero in layers more than 21.5 m/d sandy gravely layers. However, incision unit showed uniform conductivity. results, area are classified as moderate highly productive ideal for groundwater development. methodology contributed understanding complexity conditions, providing robust characterize heterogeneous systems.

Language: Английский

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