...-curve models do not consider the variations in the bottomhole flowing pressure (BHP), which can greatly impact the accuracy of the model’s predictions. This work combines a new technique that incorporates variable BHP conditions into DCA models with Bayesian inference to improve the accuracy...

... artificial intelligence thermal method modeling & simulation enhanced recovery machine learning bayesian inference steam-assisted gravity drainage communication molecular diffusion gas injection posterior distribution fracture conductivity primary depletion conductivity...

... single-phase and scaled two-phase (oil and gas) solutions of the diffusivity equation. First, we perform Bayesian inference to generate probabilistic production forecasts for each model using a Bayesian workflow in which we assess the convergence of our Markov chain Monte Carlo algorithm, calibrate...

... surveillance flow in porous media hydraulic fracturing reservoir simulation fluid modeling production monitoring bayesian inference shale gas complex reservoir history matching upstream oil & gas drillstem/well testing simulation model unconventional resource economics match point emulator...

... bayesian inference reservoir surveillance production control production forecasting artificial intelligence reservoir simulation history matching drillstem/well testing cumulative production acceptance ratio pvt measurement autocorrelation fluid dynamics production data bayesian framework...

... URTeC: 2787 Assisted history matching using Bayesian inference: Application to multi-well simulation of a huff-n-puff pilot test in the Permian Basin Esmail Eltahan*1, Reza Ganjdanesh1, Wei Yu1,2, Kamy Sepehrnoori1, Hunter Drozd3, Raymond Ambrose3. 1. The University of Texas at Austin, 2. SimTech...

... hydraulic fracturing posterior distribution lamination factor hydraulic fracture geometry Bayesian Inference bayesian methodology Metropolis algorithm frac design model Artificial Intelligence Upstream Oil & Gas fracture height application machine learning Quantify Uncertainty...

... the most probable model given the data, and we demonstrate the value of using this approach to consider multiple models in probabilistic DCA for unconventional plays. machine learning Artificial Intelligence production control Bayesian Inference Reservoir Surveillance complex reservoir...

... machine learning Reservoir Surveillance normal score transform geological modeling production monitoring Upstream Oil & Gas Reservoir Characterization Artificial Intelligence information geologic modeling production control Bayesian Inference spatial continuity production...

... and studying complex fluid flow behavior such as well interference. machine learning Artificial Intelligence complex reservoir Simulation simulator subsurface uncertainty Bayesian Inference hydraulic fracturing fracture geometry concentration geomechanics simulation calibration Upstream Oil...

... Drilling Bayesian Inference information trajectory design well planning drilling operation optimization Well position URTeC directional survey well path Wellbore Position Wolfcamp probability distribution geology horizontal well pilot well cloud Bayesian network interpretation formation...

... production control Bayesian Inference machine learning Artificial Intelligence Reservoir Surveillance accuracy prediction Midland Basin Upstream Oil & Gas well performance production data drilling operation production forecast allocation production monitoring algorithm public...

... of perforation shots, limited ray coverage, and identifying / classifying different phase arrivals in the perforation data, building a reasonable velocity model is very challenging. In this paper, we present a field case study, in which we apply the Bayesian inference algorithm to simultaneously invert...

... Characterization Upstream Oil & Gas posterior machine learning hydraulic fracturing accuracy Bayesian Inference bayes theorem uncertain input parameter microseismic data probability input parameter assumption interpretation Location uncertainty likelihood velocity model probabilistic...

.... Numerous working hypotheses have been proposed in order to explain the causes of such behavior; for example loss of fracture conductivity and fracture pressure interaction (Hamed et al. 2013). machine learning Bayesian Inference complex reservoir shale gas Fluid Dynamics flow in porous media...

... the limitations of incomplete or imprecise data and offers a very accurate prediction of where failures may occur. machine learning Artificial Intelligence complex reservoir information Bayesian Inference unconventional resource economics Upstream Oil & Gas likelihood Bayesian network...

... to define how to drill wells and determine fracture stages spacing or the number of wells. There are mainly three categories of Estimated Ultimate Recovery (EUR) prediction methodologies for unconventionals: flow in porous media Artificial Intelligence Bayesian Inference machine learning...

... machine learning Upstream Oil & Gas geostatistical seismically elastic property probability geological modeling Bayesian Inference seismic data URTeC subsurface geomodel three forks formation Reservoir Characterization thin reservoir information conditioning case study...

... be the first arrival were carefully analyzed. With careful processing, we reveal the presence of high number of head waves in the Marcellus Shale. Head wave and direct arrivals were used instead of the conventional P-wave polarization to estimate microseismic event location. A Bayesian inference program...