..., significantly reducing both memory requirements and computational complexity compared to conventional methods. In parallel, deep neural networks—employing convolutional and recurrent architectures—are utilized to accurately model and estimate the dynamic, nonlinear mud channel, thereby facilitating effective...

... Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. The Gated Recurrent Unit (GRU) network processes sequence data through cycles of time steps. Unlike traditional feedforward neural networks, GRU uses recurrent layers to capture long...

... Neural Network (SNN) to automate log alignment by learning geological similarity between reference and unsynchronized logs. The SNN eliminates the need for labeled data, minimizes post-processing, and effectively handles irregular depth errors. Tested on gamma-ray (GR) logs from different logging...

.... However, manually analysing data spanning thousands of meters within a well remains a labour-intensive process that relies heavily on technical expertise. A key limitation of conventional convolutional neural networks (CNNs) in addressing this challenge is splitting long borehole images into small parts...

... and field experimental results. In addition, comparisons are made with XRD analysis results. structural geology artificial intelligence reservoir characterization neural network well logging machine learning mineral concentration spwla 66th dimension geologist rock type concentration...

... to time-domain data using convolutional operations. Among the tested machine learning models tested, the convolutional neural network (CNN) with batch normalization demonstrated the highest efficiency and accuracy. The proposed method yields predictions with a mean absolute relative error of 2% for shear...

... urgent resolution. Current limitations in integrating pore-scale fluid behavior with reservoir-scale dynamics hinder predictive accuracy for field applications. geologist pvt measurement geology fluid dynamics reservoir simulation neural network rock type enhanced recovery artificial...

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... sample to obtain the dataset for training, validation, and testing of the deep neural network. Then, we used deep-learning techniques to establish the mapping relation between the voxel of low-resolution X-ray computed tomography images for the plunger core sample (plunger CT images) and the various...

... guide RT image enhancement. In this work, we benchmark the performance of various deep neural network architectures for conditional borehole image enhancement, including conditional variational autoencoders (CVAE), generative adversarial networks (GAN), and deep convolutional feature extractors based...

... aiding in advancing the adoption of ML as a valuable tool in hydrocarbon exploration and production. geologist borehole imaging neural network artificial intelligence production monitoring reservoir characterization image log transformation petroleum geology well logging structural...

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..., and computing the corresponding orientation of the structure. The workflow starts with borehole images and the associated segments provided by the "auto dip picking" algorithm. A convolutional neural network detects bedding features and categorizes them as sinusoidal or non-sinusoidal bedding features...

... of simulated scenarios. An ML algorithm, e.g., a neural network or a decision forest, was then trained to predict these noise levels directly from the measurements without access to the actual scenario. The trained model could then be used to evaluate the noise levels in unseen scenarios and real-world cases...

... and the background, which is crucial for accurate data extraction. Researchers can isolate specific features within an image for further analysis. Advanced techniques such as convolutional neural networks (CNNs) and deep learning frameworks, like DeePore created by Rabbani et al. (2020), have significantly improved...

..., as represented by each color (Kwak et al., 2016). Since MICP PTS and NMR T2 distributions are highdimensional vectors, deep learning (DL) neural network architecture would be the logical choice for mapping their transformations. Note that neural network (NN) is a specific type of ML algorithm comprising layers...

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