... unconventional play complex reservoir petroleum play type machine learning water production production data geologist deep learning rock type prediction sequence engineering clastic rock neural network shale gas play mudstone production forecasting leung stage 0 information gas production...

... the strengths, limitations, and capabilities of different machine learning techniques for along-well estimation of mineralogical composition to assist with reservoir characterization. geologist neural network deep learning shale gas structural geology complex reservoir reservoir simulation...

... utilizing Physics-Informed Neural Networks (PINNs) to predict adsorption isotherms across diverse shale cores, integrating Langmuir adsorption theory into a data-driven model. By collecting a limited core dataset and leveraging automatic differentiation techniques, the PINN systematically incorporates...

... neural network reservoir surveillance machine learning prediction fruhwirth gradient deviated oil-producing well Introduction Challenge and solution Production optimization of oil-producing wells comprises two main activities: maximizing the well productivity index (PI) and minimizing...

... investigation. This new HDP architecture seamlessly integrates a physics-based equation into the framework of a deep neural network model. The training dataset encompasses a wide array of influencing factors on production rates, encompassing information that may not readily conform to conventional physical...

.... geologist clastic rock neural network hydraulic fracturing deep learning rock type multistage fracturing complex reservoir shale gas sedimentary rock geology artificial intelligence sequence fracture china gru algorithm machine learning optimization forecast journal mudstone...

... and provides valuable insights into consideration of the geological uncertainty in CO 2 storage modeling and design of MMV program for CO 2 storage projects. structural geology subsurface storage modeling & simulation co 2 sustainability neural network risk and uncertainty assessment well...

.... upstream oil & gas waterflooding fluid dynamics united states government neural network gas injection method sustainability asia government equation of state flow in porous media co 2 reservoir characterization enriched co 2 pvt measurement reservoir simulation subsurface storage...

... literature. These steps include sequential sampling, average feature ranking, convolutional neural network (CNN) deep learning modeling, and feature engineering. SPM is a novel methodology that generates results faster than numerical simulators. SPM decouples the mathematical equations of the problem...

... shale gas canada government machine learning validation aguilera numerical simulation information shale reservoir seq2seq multiporosity numerical simulator porosity gfree-sim mechanism neural network sequence artificial lift system numerical simulator hyperparameter dataset injection...

... reservoir surveillance machine learning rmse reserves evaluation neural network history ensemble automated production forecasting paper united states government artificial intelligence production control architecture novel machine learning utilization technology conference production history...

..., the research about comparisons of different deep learning algorithms lacks. In this work, three different deep learning algorithms, including the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Transformer, are applied to forecast the productivities of a three-horizontal-well EGS...

... of these samples, a corresponding oil production rate time series is obtained using a reservoir simulation model; this model was built using publicly available data from Norther Alberta SAGD implementations. Afterwards, the base model and correction term are identified using Long-Short Term Memory neural networks...

... sand artificial intelligence bayesian inference neural network upstream oil & gas thermal method bottom-hole pressure workflow geologic data production data prediction application production monitoring bitumen sagd steam-assisted gravity drainage forecast profile k-mean prediction...

... an artificial neural network (ANN) to predict the anisotropic shear strength of heterogeneous oil sands embedded with shale beddings. The trained model improves accuracy by 12%-76% compared to traditional methods such as response surface methodology (RSM). MLEU provides a reasonable estimate of anisotropic...