.... structural geology geologist gas injection method waterflooding neural network artificial intelligence reservoir characterization multi-objective optimization endpoint accuracy optimization problem enhanced recovery co 2 optimization algorithm numerical simulator optimization climate change...

... to assess CO2 trapping efficiency. Additionally, Artificial Neural Network (ANN), a deep learning tool was also developed and tested. The developed ML models demonstrated promising results for predicting trapping indices in saline aquifers, with correlation factor R 2 > 0.9. Moreover, partial dependance...

... environmental impact. detection accuracy neural network emission deep learning machine learning footage deep learning image processing model estimation architecture algorithm video footage deployment methane emission artificial intelligence leak leak detection tdlp-ng Introduction...

... it critical to have a method to measure and correct or compensate for the ECC effects in order to obtain a reliable cement bond log. The research study presented in this paper covers a real-time ECC correction algorithm for cement log data using neural networks. The scope covers the design, implementation...

... in the South China Sea and dynamic analysis data from the OrcaFlex software, the study employs an enhanced Backpropagation (BP) neural network and Bayesian Optimization method for multi-objective iterative optimization under specific target functions, achieving an optimal overall design for the ultra-deepwater...

.... This study developed a Dual-Input GRU Neural Network (Di-GRU) capable of end-to-end prediction, thereby circumventing the need to consider complex underlying mechanisms. The model consists of a time series sub-network and a non-time series subnetwork. The time series sub-network inputs features...

... generation offers a sustainable means to reduce carbon footprint while delivering electricity. sedimentary rock clastic rock neural network sedimentary geology co2 co2 plume geothermal power production co2 storage reservoir structural geology sedimentology artificial intelligence co2...

... Artificial Neural Network (ANN) was found to provide the best model to predict the Base Shear Collapse, and hence the RSR for a typical jacket platform. The ANN model was incorporated into the IGUSA tool for deployment within PETRONAS. It is envisaged that IGUSA will be a valuable rapid screening tool...

... behavior assessments of other nearshore floating structures such as Floating Offshore Liquefied Gas Terminals (FLGTs), Floating Storage and Regasification Units (FSRUs), and floating wind turbine structures. simulation of human behavior artificial intelligence liquified natural gas neural network...

... and increased the probability to find good reservoir facies as proven from findings of recently drilled development wells. artificial intelligence upstream oil & gas order sequence neural network sea level variation calibration reservoir simulation milankovitch cycle static model reservoir...

... for economical extraction of blue hydrogen. climate change machine learning thermal method production control sustainable development production logging neural network upstream oil & gas artificial intelligence framework membrane enhanced recovery social responsibility steam-assisted...

... Abstract This study presents a novel neural network model to explore its application in automatically interpreting subsurface faults from seismic images. A Wavelet Convolutional Neural Network (CNN) model that incorporates discrete wavelet decomposition is presented, and its capability...

... are not represented by the developed model. Therefore, the model is expected to detect all types of stuck pipe, despite the various mechanisms. The AE, which is an algorithm employed for dimensionality reduction using neural networks, contains an encoder encoding the input data into a lower dimensional...

... and the digitalization journey. Innovative machine learning tools continue to unlock new values from legacy core data that significantly impact the entire reservoir life cycle including reserves booking, production forecasting, well placement, and completion design. machine learning neural network data mining...

... production data. To answer this question, we leveraged the automatic history match (AHM) scheme based on Neural Network-Markov Chain Monte Carlo (NN-MCMC). We utilized the production data to characterize fractures and reservoir properties and stochastically quantify their uncertainty.The framework is based...

... to an accurate determination of Flow Zone Indicators (FZI) in each cored interval. Subsequently, Neural Networks were used to predict HFU in un-cored intervals using log data, this model was then extrapolated to estimate permeability in un-cored intervals. Microporosity estimations from MICP are relatively...

... of similar AI methods throughout the oil and gas industry. artificial lift system neural network artificial intelligence testing sample esp module accuracy deployment workflow deep learning model data mining machine learning upstream oil & gas deep learning esp critical event...

.... Convolutional neural networks (CNNs) are used in many fields, especially for image processing and features recognition. On the other hand, genetic algorithms (GA) add a valuable benefit to data processing in terms of global search and optimization. This paper demonstrates the integration of ML techniques...

...%. The application of this technology is insightful to promote the IoT and intelligence of oilfield artificial lifting system. sustainable development sustainability real time system social responsibility machine learning neural network artificial intelligence upstream oil & gas energy consumption...

.... In this study, a comprehensive investigation on the CO 2 injectivity change of different injection flow rates and brine salinity was performed using core flooding experiments, permeability change prediction using (Kozeny-Carman and Hagen-Poiseuille models) and artificial neural network model (ANN). Core...