...., 2006). Sahiner establishes an artificial neural network model based on error backpropagation to compute the yields (Sahiner, 2017), but the limitation of neural network is that the elements to be predicted must be included in the training dataset. Wu et al. use the direct demodulation method...

... mineral neural network upstream oil & gas asia government rock type europe government well logging logging while drilling log analysis machine learning spwla-2023-0121 spwla 64 annual logging symposium porosity detector dry-weight fraction spectroscopy detector fraction artificial...

... and few labels on model training and obtain a logging intelligent inversion model with ideal performance in terms of logging reservoir parameters prediction. geology sedimentary rock united states government geologist asia government rock type log analysis neural network well logging china...

... is implemented to satisfy the training requirements for deep learning. The designed auto-encoder network and the attention multi-scale convolutional neural network (ATT-CNN) are trained by the simulated dataset. In addition, core data measured in the laboratory are used to verify the denoising and inversion...

... after the end of drilling to be delivered by the service company, which in some cases is not enough for fast decision making regarding completion. In this work, we tested models based on Generative Adversarial Neural Networks (GANs) to reconstruct the complete memory data based on real-time input...

... QI and well tie, and (7) potentially massive computation time saving from days to minutes. geologist sedimentary rock upstream oil & gas structural geology measurement while drilling well logging drilling measurement clastic rock neural network artificial intelligence united...

... wellbore design production enhancement neural network saudi arabia government log analysis spwla 64 artefact upstream oil & gas asia government th annual logging symposium well logging deformation eccentricity geologist united states government well intervention artificial intelligence...

... that lithology and its relationship with logging parameters are independent of each other in depth, ignoring the spatial sequence correlation of rocks in the process of sedimentation and diagenesis. upstream oil & gas deep learning sedimentary rock structural geology clastic rock neural network...

... or image logs) efforts. geologist united states government sedimentary rock rock type structural geology well logging machine learning neural network spwla-2023-0081 artificial intelligence workflow reservoir characterization th annual logging symposium validation upstream oil...

... using random forests, k-nearest neighbors, artificial neural network, and Timur-Coates's model to estimate the logarithm of permeability. Finally, the uncertainty of the estimated permeability is calculated based on the validation variance function for the test set. Results are compared based...

... production control borehole imaging neural network log analysis wellbore seismic deep learning well logging th annual logging symposium spwla 64 conference automated quality assessment reservoir characterization spwla-2023-0087 misalignment accuracy workflow machine learning application...

... and nonlinear problem, with the solution being possibly nonunique. The models introduced here comprise neural networks with dense hidden layers and nonlinear activation functions. Each model simultaneously provides an estimate of a matrix property value and its uncertainty, which are optimized together...

... classification, we have developed an algorithm using a supervised machine-learning approach. A neural network classifier was trained using a large volume of synthetic dispersions. The training data are generated using randomly sampled input physical parameters covering effective borehole ovality model...

... reservoir characterization production control machine learning drilling data acquisition log analysis neural network artificial intelligence borehole imaging wellbore seismic production monitoring reservoir surveillance deep learning real time system drilling measurement well...

... in lithofacies recognition. Nevertheless, prevailing machine learning models such as CNN (Convolutional Neural Network) and DNN (Deep Neural Network) still show some disadvantages, such as: significant amounts of human interpreted data are required for data labeling, which means the accuracy level of model...

... and in automated workflows, multiple interpretations are seldom used. We describe a deep neural network (DNN) that outputs a selected number of stratigraphic interpretations using a single evaluation of the input log data in two milliseconds. The input data defined prior to training consists of one or several log...

... of machine learning to solve the above problems. A long short-term memory network (LSTM) is used to characterize the time series characteristics of logs varying with depth domain. The kernel of the convolutional neural network (CNN) is used to slide on log curves to characterize their relationships...

... reservoir characterization production control well logging reservoir surveillance machine learning log analysis production monitoring deep learning neural network wellbore seismic spwla-2022-0115 spwla 63 feature map university hydraulic fracturing borehole imaging artificial...

... (A-Sands), algorithm was developed, which employs a trained, Mugrosa (B-Sands) and La Paz (C-Sands), deposited in deep-learning artificial neural network (ANN) to invert the Paleogene period. These sands are typically isolated the large quantity of data acquired. The algorithm by interbedded, impermeable...

... solutions that have been widely adopted within the data science and machine learning domains. To evaluate the impact of missing data on machine learning models, three commonly used algorithms, namely support vector regression, random forests, and artificial neural networks, were adopted for the prediction...