... by a variety of reservoir types, offering new insights for improving the accuracy and efficiency of production forecasting. geologist neural network artificial intelligence reservoir simulation modeling & simulation production control transformer module universal production forecasting...

... well studies. sedimentary rock geologist clastic rock neural network hydraulic fracturing production control deep learning reservoir simulation ensemble rock type mudrock information production monitoring reservoir surveillance modeling machine learning urtec physics informed...

... 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...

..., 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...

... often struggle to capture the intricate relationships between various drilling parameters, leading to inaccurate predictions. complex reservoir neural network deep learning artificial intelligence machine learning urtec depth coiled-tubing drilling dynamic architecture transformer...

... 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...

... 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...

... 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...

... 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...

... and, consequently, production behavior (Waite, 2021). sedimentary rock clastic rock unconventional resource economics fluid dynamics unconventional play deep learning reservoir simulation mudrock flow in porous media reservoir surveillance neural network rock type petroleum play type shale gas...

... 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...

.... 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...

...-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...

... and across diverse drilling tools in the industry. This research's implications resonate beyond drilling, offering potential applications for various industrial sensors and predictive maintenance systems. neural network dataset artificial intelligence rul prediction deep learning machine learning...

... 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...

... 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...

... learning ufd simulation drilling operation optimization problem mudstone optimum ufd dimensionless pi upstream oil & gas fracture neural network rock type ufd optimization prop numerical simulation fracture conductivity design pseudosteady condition hydraulic fracture permeability...

... Abstract Application of a Convolutional neural network (CNN) is presented for an unconventional reservoir to simultaneously predict elastic, mineral volumes, geomechanical and reservoir properties from seismic and well data. In the Barnett Shale, success requires identifying brittle...

... 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...

... 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...