... , A. , 2019 . Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems . 2nd Edition, O'Reilly Media, Inc. , Sebastopol. Goodfellow , I. , Bengio , Y. , Courville , A. , 2016 . Deep learning . Li , H...

... control and optimum gas injection strategies. geologist artificial intelligence gor geology machine learning gas injection method main control factor gas breakthrough enhanced recovery dependent variable producer variation trend different stage bhfp gas injection strategy bhip...

... In addition to processing power, cloud-based analytics enables companies to run complex simulations, interpret seismic data, and make more informed decisions in exploration and production (E&P). With the integration of machine learning algorithms, cloud platforms allow for deploying advanced...

... is a reasonable way to improve recognition accuracy. neural network machine learning deep learning artificial intelligence multivariate time sery accuracy multiplex visibility graph pipeline system operation condition information visibility graph liquified natural gas (lng) time sery attention...

... zone well logging machine learning upper clastic rock layer poisson strata rock mechanic parameter transit time horizontal geostress strength clastic rock section mpa experiment time difference Introduction Geomechanical parameters typically encompass rock mechanical properties...

... characteristic fluid dynamics deep learning machine learning permeability experimental data simulation digital core model porosity china chengdu north petroleum exploration application information development technology co Introduction In the exploration and development of oil and gas fields...

.... The tradeoff between data and physics in different models, as shown in Fig. 1 , demonstrates that leveraging physics-based models can strike a balance between speed and accuracy. Physics-informed machine learning (PIML) techniques and physics-informed neural networks (PINNs) enable the model to function...

... hill in the later stage. fracture characterization neural network hydraulic fracturing reservoir characterization geology complex reservoir artificial intelligence machine learning geologist directional drilling rock type log analysis sedimentary rock drilling operation horizontal...

... production data optimization problem history machine learning permeability presented multiplier structural geology flow in porous media reservoir characterization application mismatch drive mechanism Introduction Reservoir models are key deliverables in sub-surface studies to assist key...

... & disposal prediction relative error optimization drilling data artificial intelligence att-bilstm-lstm model bit selection penetration rop drilling rate eng machine learning bingham model short-term memory modified bourgoyne&young bidirectional-long short-term memory error comparison...

... pressure data gas lift artificial intelligence modeling & simulation objective function mismatch function presented uncertain parameter embracing greenfield uncertainty production data machine learning variant case international petroleum technology conference static model Introduction...

... drilling drilling fluids and materials risk management natural language cognitive system drilling operation volve field expert system drilling fluid management & disposal sequence npt risk and uncertainty assessment artificial intelligence wellbore design text processing machine learning...

... operation while maximizing the return of investment. trajectory design optimization problem well planning artificial intelligence joint point machine learning evolutionary algorithm surface location pad design collision risk drilling operation trajectory constraint severity...

... loading. Finally, for the liquid loaded wells on well cycling operation (intermittent production), a machine learning model is developed to optimize cycling parameters based on features extracted from a sequence of past cycles. The trained models predict the optimum shut-in and production time to maximize...

... in detailed existed-well assessment tasks. agent geology geologist rock type artificial intelligence arxiv natural language application accuracy mechanism large language model manuscript deep learning machine learning interpretation agent framework curve reconstruction specific task...

... deep learning neural network geology co 2 artificial intelligence machine learning climate change scenario saline aquifer modeling & simulation chemical flooding methods inference dataset geologist subsurface storage enhanced recovery dataset solubility residually accuracy...

... and more stable error fluctuation range. deep learning geology neural network reservoir characterization artificial intelligence prediction machine learning algorithm prediction accuracy depth prediction pso-bp prediction result geologist optimization problem evolutionary algorithm...

... underpinned by Artificial Intelligence (AI) / Machine Learning (ML) and the OSDU Data Platform. The approach was conceived with the goal of improving Well Master Data quality and its objective, problem statement, solution architecture, challenges and impact on sustaining Upstream data quality are outlined...

... analytical formulas and machine learning algorithms to analyze well data and recommend the optimal Rigless ESP system. Engineers input key downhole parameters and production forecasts into the software, which then rigorously analyzes the data to provide precise, evidence-based recommendations. The Advisor's...

... to determine hydrocarbon phase behavior during gas recycling. Finally, fluid properties and oil-gas interaction affected by gaseous components were calculated and predicted via phase equilibrium calculation and machine learning algorithm. The crude oil data was obtained from real reservoir. Experiment data...