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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A Comprehensive Study on Optimizing Reservoir Potential: Advanced Geophysical Log Analysis of Zamzama Gas Field, Southern Indus Basin, Pakistan

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 135, P. 103640 - 103640

Published: May 20, 2024

Language: Английский

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Prospect Evaluation of the Cretaceous Yageliemu Clastic Reservoir Based on Geophysical Log Data: A Case Study from the Yakela Gas Condensate Field, Tarim Basin, China

Energies, Journal Year: 2023, Volume and Issue: 16(6), P. 2721 - 2721

Published: March 14, 2023

This paper evaluated the oil and gas potential of Cretaceous Yageliemu clastic reservoir within Yakela condensed field lying in Kuqa Depression, Tarim Basin, China. The petrophysical properties interest zones area were characterized using geophysical logs from five wells. results reveal that gas-bearing are by high resistivity, good permeability (K) effective porosity (Φeff), low water saturation (Sw), shale concentration (Vsh), reflecting clean sand. distribution model showed these shales have no major influence on fluid saturation. average volume, porosity, hydrocarbon indicate Formation studied contains prospective properties. spatial parameters, rock typing (RRT), lithofacies analyzed cross plots litho (volumetric analysis), iso-parametric representations characteristics, cluster analysis, self-organizing feature maps, respectively. southeastern northeastern regions research should be ignored because their concentrations. sediments southwest northwest include most intervals considered for future exploration development fields study area.

Language: Английский

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Quantitative Characterization of Shallow Marine Sediments in Tight Gas Fields of Middle Indus Basin: A Rational Approach of Multiple Rock Physics Diagnostic Models

Processes, Journal Year: 2023, Volume and Issue: 11(2), P. 323 - 323

Published: Jan. 18, 2023

For the successful discovery and development of tight sand gas reserves, it is necessary to locate with certain features. These features must largely include a significant accumulation hydrocarbons, rock physics models, mechanical properties. However, effective representation such reservoir properties using applicable parameters challenging due complicated heterogeneous structural characteristics hydrocarbon sand. Rock modeling sandstone reservoirs from Lower Goru Basin fields represents link between seismic diagnostic models have been utilized describe sands two wells inside this Middle Indus Basin, including contact cement, constant friable The results showed that sorting grain coating cement on grain’s surface both affected cementation process. According levels in ranged 2% more than 6%. established study would improve understanding for relatively high Vp/Vs unconsolidated under study. Integrating prediction elastic estimated data. velocity–porosity moduli-porosity patterns zones are distinct. To generate template (RPT) Early Cretaceous period, an approach based fluid replacement has chosen. ratio P-wave velocity S-wave (Vp/Vs) P-impedance can detect cap shale, brine sand, gas-saturated varying water saturation porosity Rehmat Miano fields, which same shallow marine depositional characteristics. Conventional neutron-density cross-plot analysis matches up quite well RPT’s expected detection sands.

Language: Английский

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A Gamma-ray spectroscopy approach to evaluate clay mineral composition and depositional environment: A case study from the lower Goru Formation, Southern Indus Basin, Pakistan

Journal of Applied Geophysics, Journal Year: 2024, Volume and Issue: 226, P. 105414 - 105414

Published: May 29, 2024

Language: Английский

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An optimized neuro-fuzzy system using advance nature-inspired Aquila and Salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations

Journal of Energy Storage, Journal Year: 2022, Volume and Issue: 56, P. 106150 - 106150

Published: Nov. 17, 2022

Language: Английский

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Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

Frontiers in Earth Science, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 11, 2023

The purpose of this research is to delineate the structures Lower Goru Formation, investigate fluid properties, and clarify hydrocarbon-prone areas through seismic attributes analysis. First, acquired data was matched by interpretation datum. Structural analysis done performing horizon interpretation, fault contour mapping on C-Interval Formation. Hydrocarbon zones were marked with help attribute sections justified petrophysical An integrated approach such as structural attribute, spectral decomposition, analyses used in current better understand geological structure features. This showed that normal faults are present area showing negative flower structure, horst graben, oriented north-west south-east. map shows inclination bound closure near well locations. Variance decomposition verify lineation behavior. Instantaneous amplitude instantaneous phase justify hydrocarbon bearing zones, bright spots at C–Interval Petrophysical available wells a number significant having more than 55% saturation four possible reservoir Sawan-02 well, two Sawan-07 three Sawan-09 identified based interpretation. Based these analyses, interest has very good potential, closure, visible spots. finding will be helpful for future exploration development Sawan area.

Language: Английский

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Interactive machine learning for segmenting pores of sandstone in computed tomography images

Gas Science and Engineering, Journal Year: 2024, Volume and Issue: 126, P. 205343 - 205343

Published: May 9, 2024

Language: Английский

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Machine Learning Based Reservoir Characterization and Numerical Modeling from Integrated Well Log and Core Data

Geoenergy Science and Engineering, Journal Year: 2024, Volume and Issue: 243, P. 213296 - 213296

Published: Sept. 7, 2024

Language: Английский

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Data-driven machine learning approaches for precise lithofacies identification in complex geological environments

Geo-spatial Information Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: Oct. 18, 2024

Reservoir characterization is a vital task within the oil and gas industry, with identification of lithofacies in subsurface formations being fundamental aspect this process. However, complex geological environments high dimensions, such as Lower Indus Basin Pakistan, poses notable challenge, especially when dealing limited data. To address issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), self-organizing map (SOM). We utilized these proposed approaches to assess their performance scenarios varying core sample availability, specifically evaluating effectiveness identifying Goru formation middle Basin. The study reveals that number samples, MRGC preferred choice, while KNN or more suitable for larger datasets. results demonstrate superior specified environment, SOM following closely behind, ANN exhibiting comparatively lower efficacy. accurate from selected model complemented by application truncated Gaussian simulation method facies modeling. Comparative confirm excellent agreement between well logs electro-facies obtained volume. This highlights crucial role selecting right approach precise modeling environments. comparative analysis provides practitioners petroleum industry insights into strengths limitations each method, enhancing existing knowledge. In conclusion, research emphasizes significance comprehensive selection advancing diverse areas, ultimately benefiting broader field industry.

Language: Английский

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