A5045498018- Hydrocarbon exploration and reservoir analysis
- Hydraulic Fracturing and Reservoir Analysis
- Reservoir Engineering and Simulation Methods
- Geochemistry and Geologic Mapping
- Seismic Imaging and Inversion Techniques
- Drilling and Well Engineering
- Methane Hydrates and Related Phenomena
- NMR spectroscopy and applications
- Rock Mechanics and Modeling
- CO2 Sequestration and Geologic Interactions
- Mineral Processing and Grinding
- Water Quality and Pollution Assessment
- Atmospheric and Environmental Gas Dynamics
- Seismic Performance and Analysis
- earthquake and tectonic studies
- Geological and Geochemical Analysis
- Enhanced Oil Recovery Techniques
- Geophysical Methods and Applications
- Child Nutrition and Water Access
- Geochemistry and Elemental Analysis
- Groundwater and Watershed Analysis
- Advanced Fiber Optic Sensors
- Geophysical and Geoelectrical Methods
- Remote Sensing and LiDAR Applications
- Geomagnetism and Paleomagnetism Studies
China University of Geosciences
2020-2024
Abstract Lithofacies identification plays a pivotal role in understanding reservoir heterogeneity and optimizing production tight sandstone reservoirs. In this study, we propose novel supervised workflow aimed at accurately predicting lithofacies complex heterogeneous reservoirs with intercalated facies. The objectives of study are to utilize advanced clustering techniques for facies evaluate the performance various classification models prediction. Our methodology involves two-information...
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...
Nowadays, there are significant issues in the classification of lithofacies and identification rock types particular. Zamzama gas field demonstrates complex nature due to heterogeneous reservoir formation, while it is quite challenging identify lithofacies. Using our machine learning approach cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects provide an accurate result even when user inexperienced. To constrain models, type a critical...
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...
Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, with technological advancement, reliance on conventional methods has decreased. In this regard, research aims to reduce well logging, purposing successive machine learning (ML) techniques precise porosity measurement. So, examines prediction of curves Sui main upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) fuzzy logic (FL). Thus, input dataset includes...
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...
This study involved an interpretation of wire-line logs in assessing the hydrocarbon capacity sandstone reservoir within gas fields Kadanwari, Sindh, Pakistan. Wire-line from four wells (KD-01, KD-03, KD-10 and KD-11) were used our research provided for thirteen zones. Reservoir analyses done effective porosity (ϕeff), shale content (Vsh), net pay thickness variations, water saturation, (Sw Shc). Hydrocarbon-bearing zones consist high values resistivity, permeability, porosity, low...
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),...
Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of machine learning algorithms XGBoost, SVM, RF using data from Sentinel-2 Landsat-9 satellites. The study influence significant spectral bands vegetation indices on accuracy AGB estimate. results presented in paper indicate that were more effective than data. mainly because it had higher spatial resolution, which enabled model...
The present study based on petrophysical computations assess the conventional fuel resources of Yageliemu Formation. Formation was evaluated using log data from four wells (YK19, YK20, YK21, and YK32) observed over entire reservoir interval. originates Lower Cretaceous reservoir, which is located in Yakela gas condensate field, Kuqa Depression Tarim Basin China. studied bearing zones are primarily composed sandstone, with small amounts shale clay contents. properties gas-bearing were...