...) condition is presented. A nested optimization based on the PI-performance model is provided. In the-inner shell, the optimum fracture parameters considering time dependency are achieved by searching the maximum PI based on unified fracture design (UFD). Coupling with deep learning and UFD practices...

... neural network deep learning upstream oil & gas complex reservoir united states government drillstem testing europe government production monitoring gas well deliquification liquid loading canada government reservoir surveillance artificial lift system simulation production control...

... in the national greenhouse-gas inventory (Zavala-Araiza et al., 2015). Both anthropogenic and natural emissions of CH 4 are likely to increase if no mitigation plans are deployed. upstream oil & gas emission neural network united states government air emission deep learning ch 4 methane...

... sufficient information to aid in interpreting images for mechanical properties. With this work, we demonstrate that images contain a wealth of information that can easily be exploited for screening or quicklook analyses using machine learning. upstream oil & gas geologist deep learning...

..., the traditional thin-section identification method is subject to strong subjectivity, high reliance on experience, heavy workload, long identification cycle, and inability to achieve complete and accurate quantification. This article uses generalized, robust, and parameter-efficient deep-learning architectures...

... in well fracturing, and help promote the development of a fully automated real-time fracturing analysis system. multistage fracturing deep learning upstream oil & gas asia government neural network geology drilling operation hydraulic fracturing complex reservoir geologist united...

... intelligence methods to capture the complex relationship between static and dynamic parameters and early-stage shale gas production, and quantitatively analyze the key factors affecting long-term production. First, a deep learning algorithm named temporal fusion transformer (TFT) is implemented to predict...

... the nonlinearity as well as the impact of manual operations. Production history was obtained from 3 wells of the Eagle Ford shale (Frio, La Salle, and Zavala County) where a single well data was split into a 75:25 ratio for training and testing. Deep learning (DL) algorithms, long short-term memory (LSTM) and Bi...

.... Introduction The objectives of this study is to accurately predict unconventional reservoir properties from seismic and well data using convolutional neural networks (CNNs). There is a great interest in the use of Deep Learning (Goodfellow et al.,2016) to automate complex workflows and reduce project times...

... for prediction accuracy. After several rounds of model refinement we then selected a deep-learning neural network architecture as the model that offered the best combination of predictive accuracy, prediction speed, training efficiency, and model portability. The accuracy of the selected machine learning model...

... this promising deep learning model utilizing it as an automated handy tool to audit fracture interpretations quickly where datasets are incomplete. reservoir geomechanics reservoir characterization complex reservoir artificial intelligence accuracy upstream oil & gas machine learning well...

... descriptions or uncaptured physical phenomena. In this paper, the integration of residual learning and physics-constrained models constitute the Physics- Guided Deep Learning (PGDL) model. The PGDL model augments the predictions from the residual learning model and the physics-constrained model resulting...

... Abstract Recently, machine and deep learning algorithms have been proposed as alternatives to statistical methods for production time series forecasting of unconventional reservoirs. Although most efforts provide timeseries forecasts using machine learning (ML) algorithms for unconventional...

...: a medium to high permeability, a pseudo-boundary-dominated flow, a constant skin factor, and a fixed bottom hole pressure (Lee et al., 2019). production monitoring production control shale gas upstream oil & gas production forecasting deep learning seq2seq lstm model information...

... Abstract Accurate production forecasting is crucial to the decision making process and evaluation of investment scenario both in conventional and unconventional reservoirs. Deep Learning (DL), which is a subset of Machine Learning (ML), show promising results in improving forecasting accuracy...

... system to update the ML model and adjust the resulting insights based on the most recent system status and newest data (Figure 2, top). The ML model combines neural network, decision tree, and advanced deep learning systems that aggregate the available datasets and applications to provide a complete...

... Abstract Objective In this paper we present recent work leveraging the advances in deep learning to propose a novel deep convolutional neural network Focal Mechanism Network (FMNet) for estimating the location, magnitude and source focal mechanisms of earthquakes rapidly, using full...

... production control machine learning production forecasting production monitoring hydraulic fracturing deep learning artificial intelligence neural network reservoir surveillance complex reservoir water rate history input forecaster completion characteristic architecture history...

... uncertainties. artificial intelligence flow in porous media deep learning model selection quality result workflow reservoir simulation cnn-based approach representative model subsurface uncertainty complex reservoir upstream oil & gas machine learning probabilistic model neural network...

... artificial intelligence to propose a much faster fit-for-purpose alternative to full-physics modeling to accelerate the study very significantly for large number of wells and geological scenarios. A deep-learning-based model (Artificial Learning Fracture, or ALF) was developed to accelerate the hydraulic...