... reservoir upstream oil & gas drilling operation production control enhanced recovery modeling & simulation neural network deep learning machine learning chemical flooding methods training dataset chamber development heterogeneity artificial intelligence sagd reservoir simulation...

... dynamics irani machine learning reservoir surveillance sagd svrcek bitumen oil rate log-log model correlation butler 5-line model variation coalescence farouq ali steam chamber steam-assisted gravity-drainage oil rate prediction sagd process Introduction Steam-assisted gravity...

... of which fall into the categories mentioned above. Based on their recommendation, the application of bare fiber in the free fall mechanism is the best method due to the lowest risk and cost involved. Introduction machine learning completion monitoring systems/intelligent wells production control...

... models, the same question would commonly arise: are there ways to quickly screen a large number of heterogeneous SAGD models? This question is particularly relevant in many data-driven modeling workflows. With the increasing popularity of data analytics and machine learning (ML), many studies have...

.... It is computationally inexpensive as no numerical simulations are required. Overall, it can potentially reduce the energy required to extract heavy oil and increase the revenue while inflicting no additional capital cost and reducing greenhouse gas emissions. thermal method machine learning bitumen complex...

... period drilling operation esp pump failure machine learning regression model detection artificial lift system Upstream Oil & Gas SAGD operation scale analysis geometry non-failure event statistics probability production period geology well head parameter wellhead data society...

... to support the use of data-driven models as complementary and computationally-efficient tools for inference of shale barriers. machine learning enhanced recovery steam-assisted gravity drainage thermal method SAGD neural network Simulation time-sery data Artificial Intelligence Upstream Oil...

... consideration when 3D models are utilized. Furthermore, the ability to visualize and select representative models or scenarios from individual clusters has important potential for facilitating improvements in operations design in the presence of reservoir heterogeneities. machine learning steam-assisted...

... Abstract A novel modeling technology termed " Data Physics " that combines reservoir physics with machine learning is validated against a conventional reservoir simulator for thermal recovery problems, i.e., steam flooding and steam assisted gravity drainage (SAGD). The novelty of the new model...

... reservoir simulation geological realization probabilistic forecasting forecasting simulation result Thickness shale barrier machine learning SAGD Upstream Oil & Gas oil recovery factor shale proportion permeability SAGD performance reference model water saturation workflow SAGD process...

... of abrasive material inherent to in-situ production, and the sampling of gas streams with heavy condensate for the accounting of solvent production through casing vent gas. enhanced recovery thermal method data quality oil sand machine learning bitumen Upstream Oil & Gas calibration curve...

.... machine learning thermal method Artificial Intelligence SAGD Modeling & Simulation enhanced recovery steam-assisted gravity drainage radarsat-2 satellite cyclic steam stimulation Upstream Oil & Gas insar observation operation InSAR measurement InSAR ground deformation injection...

...-assisted gravity drainage objective function model validation machine learning flow in porous media Fluid Dynamics oil production Eocene reservoir primary production enhanced recovery producer data analysis SAGD validated model viscosity Kuwait pilot area st Eocene reservoir influx...

... of the barriers. enhanced recovery machine learning thermal method reservoir simulation steam-assisted gravity drainage training dataset heterogeneity SAGD Engineering Artificial Intelligence Upstream Oil & Gas basic shale unit History Reservoir Heterogeneity production profile numerical...

... characterized any changes to ICD flow relationships before and after the trial. Inspections of the devices post-operation provided crucial learnings on the mechanical integrity of such systems. Reservoir Surveillance injector completion production control production logging inspection machine...

... determination society of petroleum engineers machine learning bitumen abram PSD class minor class psd curve reservoir characterization Introduction Particle Size Distribution (PSD) is an important characteristic of granular media. The PSD is also an essential information in the design...

..., a major progress. It can also serve as a critical guideline to estimate and predict the water/polymer breakthrough and the oil production in viscous-oil benchscale experiments and field projects. machine learning flow in porous media Upstream Oil & Gas experiment enhanced recovery Fluid...

... of heterogeneous reservoir features from the profiles of SAGD field production data. Artificial Intelligence thermal method SAGD machine learning neural network enhanced recovery steam-assisted gravity drainage oil production prediction Reservoir Heterogeneity proxy model SAGD production basic...

... simulation and history-matching workflows, but it rather offers a complementary tool for extracting additional information from field data and incorporating AI-based models into practical reservoir modeling workflows. enhanced recovery machine learning Artificial Intelligence steam-assisted gravity...

... & Gas reward matrix Artificial Intelligence pipeline monitoring information Upstream Oil & Gas leak detection detection continuous monitoring satellite Game Theory application oil spill society of petroleum engineers pipeline monitoring probability machine learning node imagery...