... development programs, ML models have been developed to account for the relative spatial position of wells in the "wine-rack" cross-sectional (barrel) view. These models also depend on engineered features such as staggered offset and vertical offset. Our novel approach uses 2D convolutional neural networks...

... of deep-learning neural network models were trained on Bakken well data: 70% for training, 20% for validation, and 10% for holdout to predict monthly oil, water, and gas volumes. The best ten models were first selected for low validation errors, then advanced for reservoir-engineering benchmarking...

... uses two artificial neural networks. We train the first network using the refined grid to predict fluid flow at the fracture face. The second network is trained as a reverse proxy on the coarse model to compute transmissibility from flow rate. Our analytical modifications provide accurate results...

... of these complex systems, researchers have turned to data-driven approaches to enhance forecasting accuracy and efficiency. Muther et al. (2021) discusses the application of machine learning methods, including Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Long-Short-Term Memory (LSTM), Support...

... Abstract The oil and gas industry faces dual challenges and opportunities posed by the ever-expanding reservoir monitoring data landscape due to ever-growing volumes of data. Artificial intelligence (AI) offers a powerful solution, with machine learning and neural networks poised...

...-learning-assisted rapid production forecasting method, involving massive geomodel compression (18,000 times) followed by neural-network-based regression. The method first compresses the large, heterogeneous shale geomodel to a low-dimensional representation. Then, a neural network processes the low...

.... The approach focuses on real-time monitoring of slurry distribution uniformity across concurrently stimulated perforation clusters. Leveraging datasets obtained through casing-embedded optical cables, measuring vibrations throughout the stimulated interval, we employ a neural network-based model...

... accuracy neural network dataset drilling sensor scenario synthetically forge dataset mape exposure deep learning approach rmse downhole drilling sensor useful life hyperparameter optimization gradient boosted tree eqn URTeC: 4046687 A Deep Learning Approach to Predicting Remaining Useful...

... and artificial intelligence (AI) considered as promising approaches to address This issue. This research aims to utilize and evaluate the performance of several Artificial Neural Networks (ANNs) architectures to predict lithological units (members) of a Permian-Triassic carbonate succession using integration...

... often struggle to capture the intricate relationships between various drilling parameters, leading to inaccurate predictions. neural network deep learning machine learning drilling operation prediction artificial intelligence architecture transformer prediction depth slb-private complex...

... results were compared with those of existing wells, and the percentage of improvement was presented. The paper focused on modeling the ROP following a data driven approach. 4 models were used to acquire the results, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest...

... well studies. clastic rock geology complex reservoir neural network geologist reservoir simulation sedimentary rock deep learning rock type production monitoring hydraulic fracturing artificial intelligence production control forecasting simulation ensemble mudstone ensemble...

..., acoustics etc. The steps in the unified workflow are implemented using a wide variety of DL models ranging from classical ML-based approaches to autoencoder based neural networks to the more powerful Transformer-based approaches that are very effective at modelling the sequential nature of the wellbore logs...

... by a variety of reservoir types, offering new insights for improving the accuracy and efficiency of production forecasting. deep learning reservoir simulation geologist neural network production forecasting artificial intelligence geology complex reservoir reservoir surveillance machine...

... Abstract This study presents a groundbreaking approach by integrating Artificial Neural Networks (ANNs) with traditional production fit models to enhance predictive modeling in a mature oil field in Texas. This method achieves superior predictive accuracy using extensive datasets from 398...

... and the remaining single subsample is used to validate the surrogate. Surrogate development. Here, three typical approximation techniques are used as surrogates. ANN. An artificial neural network (ANN) contains input, hidden and output layers. The neurons of each layer are connected to the neurons of other layers...

... of formation and fluid data. Besides, frequent manual operations are always ignored in the production history because of their cumbersome processing. To overcome this limitation, a supervised deep neural network (DNN) model is established in this paper to forecast hydrocarbon production that considers...

... tree-based algorithms (random forest and gradient boosting), feed-forward neural networks, and recurrent neural networks (simple RNN, LSTM, and GRU), were implemented and trained using historic data of a number of lateral wells that liquid loaded. The best performing model was selected to predict...

... seg unconventional resource technology conference uncertainty quantification upstream oil & gas neural network urtec data mining algorithm interpretation estimator Abstract We have developed a data driven workflow for forecasting future cumulative oil production...

... is required to estimate methane flux from the measurements made by the drone. We trained a convolutional neural network (CNN) using Large Eddy Simulations (LES) dataset of methane plumes that mimic the real dataset of the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) where wind...