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ISSN: 2666-5441

On the application of machine learning algorithms in predicting the permeability of oil reservoirs

Permeability is one of the main oil reservoir characteristics. It affects potential oil production, well-completion technologies, the choice of enhanced oil recovery methods, and more. The methods used...

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Interpretable machine learning models for evaluating strength of ternary geopolymers

Ternary geopolymers incorporating multiple solid wastes such as steel slag (SS), fly ash (FA), and granulated blast furnace slag (GBFS) are considered environmentally friendly and exhibit enhanced performance....

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Comparison of processing speed of NRS-ANN hybrid and ANN models for oil production rate estimation of reservoir under waterflooding

This study compared the predictive performance and processing speed of an artificial neural network (ANN) and a hybrid of a numerical reservoir simulation (NRS) and artificial neural network (NRS-ANN)...

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Cellular automata models for simulation and prediction of urban land use change: Development and prospects

Rapid urbanization and land-use changes are placing immense pressure on resources, infrastructure, and environmental sustainability. To address these, accurate urban simulation models are essential...

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Deep learning approaches for estimating maximum wall deflection in excavations with inconsistent clay stratigraphy

This paper presents a deep learning architecture combined with exploratory data analysis to estimate maximum wall deflection in deep excavations. Six major geotechnical parameters were studied. Statistical...

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Machine learning assisted estimation of total solids content of drilling fluids

Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations. In particular, maintaining the optimal levels...

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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning

Capillary pressure plays a crucial role in determining the spatial distribution of oil and gas, particularly in medium-to-low permeability reservoirs, where it is closely linked to the rock's pore structure...

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Generating high-resolution climate data in the Andes using artificial intelligence: A lightweight alternative to the WRF model

In weather forecasting, generating atmospheric variables for regions with complex topography, such as the Andean regions with peaks reaching 6500 m above sea level, poses significant challenges. Traditional...

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Explaining machine learning models trained to predict Copernicus DEM errors in different land cover environments

Machine learning models are increasingly used to correct the vertical biases (mainly due to vegetation and buildings) in global Digital Elevation Models (DEMs), for downstream applications which need...

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Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts

Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed. They are important indicators in many analyses, are used in climate change research, monitoring...

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Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence

Greenhouse gases (GHGs) from agriculture in Africa are among the world's fastest-growing emissions, with the livestock sector as the primary contributor. However, the methods for quantifying these emissions...

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Prediction of groundwater level in Indonesian tropical peatland forest plantations using machine learning

Maintaining high groundwater level (GWL) is important for preventing fires in peatlands. This study proposes GWL prediction using machine learning methods for forest plantations in Indonesian tropical...

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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow

We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations (PDEs) that is based on online/adaptive learning. It is applied in the context of multiphase...

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Convolutional sparse coding network for sparse seismic time-frequency representation

Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently,...

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Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models

Rockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. This study proposes...

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Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO

The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people...

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Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases

The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently. Recently, deep learning algorithms exhibit a powerful capability of detecting...

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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process

Geotechnical parameters derived from an intrusive cone penetration test (CPT) are used to asses mechanical properties to inform the design phase of infrastructure projects. However, local, in situ 1D...

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Robust low frequency seismic bandwidth extension with a U-net and synthetic training data

This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data. Traditional seismic data often lack both high and low frequencies, which are...

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Microseismic moment tensor inversion based on ResNet model

This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events. Taking the great advantages of deep networks in classification and regression tasks, it...

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Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning

This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on...

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Fast 2D forward modeling of electromagnetic propagation well logs using finite element method and data-driven deep learning

We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the near-wellbore environment. The approach integrates the finite element method with deep residual...

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Thank you reviewers!

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An intelligent recognition method of deep shale gas reservoir laminaset based on laminaset clustering and R-L-M algorithm

Lamina structures, as typical sedimentary features in shale formations, determine both the quality of shale reservoirs and fracturing effects. In this study, through electric imaging logging, based...

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Digital core reconstruction of tight carbonate rocks based on SliceGAN

The pore structures of the Majiagou Formation in the Ordos Basin are complex, featuring micro- and nano-scale intra-crystalline and inter-crystalline pores that significantly impact hydrocarbon storage...

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