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ISSN: 2666-5441

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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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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LatentPINNs: Generative physics-informed neural networks via a latent representation learning

Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs...

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Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield

Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development....

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Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters

Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF),...

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Automatic description of rock thin sections: A web application

The identification and characterization of rock types is a core activity in geology and related fields, including mining, petroleum, environmental science, industry, and construction. Traditionally,...

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Soil liquefaction assessment using machine learning

Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature...

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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental...

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Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping

The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy...

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A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for...

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Self-supervised multi-stage deep learning network for seismic data denoising

Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow, as our ability to mitigate noise in seismic data affects the quality of our subsequent...

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Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models

Convolutional neural networks have been widely used for analyzing image data in industry, especially in the oil and gas area. Brazil has an extensive hydrocarbon reserve on its coast and has also benefited...

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Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces

This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset...

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Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals

Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely...

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A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India

In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved...

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Reconstruction of lithofacies using a supervised Self-Organizing Map: Application in pseudo-wells based on a synthetic geologic cross-section

Recently, machine learning (ML) has been considered a powerful technological element of different society areas. To transform the computer into a decision maker, several sophisticated methods and algorithms...

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Enhancing economic sustainability in mature oil fields: Insights from the clustering approach to select candidate wells for extended shut-in

Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, companies...

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Exploring emerald global geochemical provenance through fingerprinting and machine learning methods

Emeralds – the green colored variety of beryl – occur as gem-quality specimens in over fifty deposits globally. While digital traceability methods for emerald have limitations, sample-based approaches...

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When linear inversion fails: Neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice

In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation...

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Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania

This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray...

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A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images

Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and geochemical...

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Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids. Traditional methods for predicting pore size distribution (PSD), relying...

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