... ABSTRACT Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are transforming the energy sector by enhancing operational efficiency, predictive maintenance, and grid optimization. However, as AI-driven automation becomes integral to energy infrastructure...

... turner artificial lift system geology artificial intelligence machine learning critical gas velocity model prediction accuracy flt flow rate critical gas flow rate gfr gas production machine learning model droplet dataset Introduction This project focuses on a request initiated...

... providing unique insights for a better planning. geologist reservoir simulation sedimentary geology machine learning constraint asset and portfolio management depositional environment simulation model history fraction algeria sedimentology artificial intelligence injection recovery...

.... reservoir simulation geology fluid dynamics risk and uncertainty assessment geologist risk management machine learning workflow reservoir geomechanics fe model deformation inversion workflow vertical strain map proxy model surface displacement reservoir deformation eage conference...

.../well testing oil shale subsurface storage geologist petroleum play type geology mudrock mudstone shale gas play co 2 clastic rock complex reservoir rock type modeling & simulation machine learning sedimentary rock injection rate sequestration shale reservoir permeability bscf...

... to inaccuracies, time-management issues, and increased operational risks. To address these issues and complement automated rheological property measurements during drilling, this study aims to develop and validate a machine learning-based framework for estimating solids content in drilling fluids from readily...

... Abstract Identifying anomalies in daily time series data can be critical for maintaining the performance and integrity of a petroleum production system. Previously numerous attempts have shown that statistical and machine learning approaches can provide a means to detect and identify the types...

.... geologist artificial intelligence machine learning fluid dynamics reservoir simulation reservoir characterization displacement forward model subsidence bowl compaction displacement measurement eage conference geology enhanced recovery geological subdiscipline deformation model inversion...

..., significant improvements can be made upon the untuned PR EoS. This paper provides a method to improve property prediction which can be rapidly implemented in any simulator of choice. Parameters ready to be directly used are provided, as are the method. reservoir simulation machine learning pr eos...

..., including unauthorized Red Zone entry. artificial intelligence workplace safety deployment machine learning video analytic detection violation red zone monitoring use case ai system operation hse violation compliance drilling operation drill floor supervision Introduction...

..., facilitating informed decision-making processes in surface logging operations. data mining dataset deep learning anomaly error value artificial intelligence detection neural network machine learning anomaly detection hole depth transformation autoencoder standard deviation anomalous point...

... exploration guided by Machine Learning algorithm. Then, a second step for uncertainty study with an integrated history match and prediction at the same time, workflows and new tools look for a simulation setup to save simulation time and reduce costs while achieving cases with good history match tolerances...

..., such as machine learning techniques. Machine learning methods are suitable for recognizing complex patterns in large datasets which enable accurate prediction of ESP degradation or failures by analyzing multiple parameters simultaneously. Recent research by ( Shoeibi Omrani et al. 2025 ) demonstrated how...

... international petroleum technology conference machine learning sem image pore application teacher network Introduction Scanning Electron Microscopy (SEM) uses a focused beam of electrons to provide high-resolution images of a natural and man-made materials ( Zhou et al., 2007 ). In the oil and gas...

... type drillstring design machine learning drilling equipment drilling fluid management & disposal reservoir characterization automation adjustment personnel automatic rig modification drilling connection time Introduction The rig is a highly mobile, trailer-mounted unit...

... and nodal analysis), oil-gathering system, machine learning algorithms, data analysis and solving problems of conditional group optimization of wells; business logic that combines individual functional elements within a single process chain. A system is presented that consists of a set of modules...

... management. Data analytics workflows play a crucial role by supporting descriptive analysis, identifying variable relationships through diagnostic insights, utilizing predictive models through machine learning, and using prescriptive analytics to guide strategic decision-making for oil and gas reservoirs...

... artificial intelligence machine learning geologist oil price risk management reservoir characterization percentile oil price scenario price forecast jafarizadeh future oil price infill well probability price uncertainty mev cumulative production price model eur oil price model swanson rule...

... waterflooding artificial intelligence machine learning scenario reservoir simulation sensitivity analysis reservoir geomechanics probability fracture half-length propagation minimum horizontal stress cdf enhanced recovery geological subdiscipline fracture growth fracture initiation uncertainty...

... machine learning approach that facilitates secure collaboration across geothermal sites without sharing raw data. By aggregating model updates, FL improves anomaly detection, downtime reduction, and cost efficiency while preserving confidentiality. A case study demonstrates its effectiveness in predictive...