... standards so that they meet both the requirements of the AI training system. Abyss has a mature AI training suite which utilizes supervised deep learning models. We may use conventional machine vision techniques as preprocessing steps in training as needed. As we train the first machine learning models, we...

... Abstract This study aims to present a novel approach for estimating relative permeability curves using Machine Learning (ML) and Deep Learning (DL) techniques based on production data. This method aims to overcome the shortage in core data availability, which is much needed for reservoir...

..., having the potential to revolutionize data-driven decision-making by the industry. prediction hindcast artificial intelligence forecasting mexico louisiana offshore oil port coefficient deep learning application resolution engineering offshore technology conference accuracy...

... found a value of 0.810, while for the ones based on the 2D Combined Signals, we found 0.958. neural network deep learning cement and bond evaluation log analysis accuracy well logging casing and cementing artificial intelligence machine learning signal convolutional neural network vdl...

... strength deep learning geology social responsibility machine learning test dataset cone penetration testing artificial intelligence strength algorithm integrating dual-parameter soil classification classification criteria sustainable development investigation undrained shear strength...

... determined both offshore in a field laboratory and onshore in a geotechnical laboratory. Recently, artificial intelligence (AI), machine learning, and deep learning have been the subject of intense media hype and therefore it was deemed appropriate to develop a deep-learning algorithm (DLA) to predict V S...

... and operations fracturing materials neural network production logging data mining reservoir surveillance deep learning proppant artificial intelligence production monitoring concentration perforation fr concentration workflow production control proppant concentration equation hybrid physics...

... property estimation. In this paper, we revisit these challenges from the perspective of pattern recognition and propose implementing deep learning (DL) into automating three essential tasks in windfarm site characterization, including (i) cone-penetration testing (CPT) data conditioning, (ii) ultra-high...

... and domain knowledge. This line of research could ultimately lead to more informed quantification of hydrate plugging risk. deep learning flow assurance accuracy pinn prediction neural network machine learning artificial intelligence classification dnn1 classification accuracy dnn...

... lno triantafyllou offshore technology conference dataset cylinder deep learning real time system algorithm neural operator firstling digimar lstm-modnet firstling-digimar forecasting Introduction Marine risers are vital components in offshore engineering as they serve as conduits...

... Assembly (BHA) and drill bit. This approach not only revolves around ROP prediction but also emphasizes the practical application potential of the predictive model, an aspect that received limited attention in prior research. geologist deep learning reservoir geomechanics rop prediction geology...

... and only works during daylight hours. Recent advances in deep learning algorithms, processing hardware, and training data availability have made the prospect of automated marine mammal lookout systems more feasible. Furthermore, thermal infrared (IR) cameras offer the possibility of maintaining a reliable...

... transform the human-machine interaction in the oil and gas industry, covering specific examples in the fields of artificial lift and power generation. neural network artificial lift system deep learning machine learning generative ai artificial intelligence interaction turbine human-machine...

... that the proposed deep-learning model could process live-stream videos without frame losses. Compared with traditional networks, this model delivered significantly enhanced performance, efficiently classifying cuttings like extra heavy, heavy, light, and none based on criteria used by rig engineers ( Du et al...

... makes it possible to conduct more detailed and accurate forecasts, which can help to improve the safety and efficiency of CO 2 storage projects. geologist air emission neural network petroleum play type deep learning enhanced recovery subsurface storage reservoir surveillance chemical...

... deep learning powered algorithms to solve challenging problems including sparse monitoring seismic data reconstruction, simultaneous source shooting monitoring data deblending, and rapid subsurface property estimation directly from very sparse pre-migration seismic data. These algorithms exploit...

... view that covers multiple worksites. This work will assist developers and regulators to understand a realistic state of technical readiness and how to appropriately scope data products that support data fusion consistently across visualization platforms. marine mammal application deep learning...

... and optimizing field behavior. The goal of this work was to assess potential of deep learning models for slugging forecasting and production optimization. Historical data from several field transmitters which record signatures specific to slugging events was collected and studied. Domain experts classified...

.... This time-efficient and cost-effective solution will detect pipeline leakage and rupture at an early stage and allow operators to make timely and informed decisions to minimize environmental impacts. united states government deep learning midstream oil & gas north america government artificial...

... there may be lags of unknown duration between different drilling parameters, a properly designed time series analysis model may be able to capture their relationships and make reasonable predictions. Recurrent Neural Network with long short-term memory (RNN-LSTM) architecture is a deep learning algorithm...