... reservoir property application sand layer production data enhanced recovery observation data hm result error range artificial intelligence gpsnet model flow network pseudo well node uncertainty parameter Introduction Insights from reservoir simulation models have often been used...

... becomes Eq. 5 . Abstract Time-Lapse (4D) seismic data offers a comprehensive spatial view of reservoir, while production data provides localized insights from individual wells. Both of them are essential for understanding reservoir heterogeneity, monitoring fluid movements, and improving reservoir...

... Eq. 1 indicates that all production data are required for an inversion process known as amplitude matching. This approach results in a highly nonlinear problem and requires significant computational costs ( Tanaka et al., 2015 ). To address these challenges, travel-time inversion has been...

... management of water breakthrough risks. By aligning geological models with production data, the study demonstrates the potential of data assimilation to uncover non-intuitive solutions, offering a robust framework for addressing uncertainties in complex reservoirs. This approach can be particularly...

... production data without history matching . Computational Geosciences , 21 ( 2 ), 315 – 333 . https://doi.org/10.1007/s10596-017-9614-7 Jiang , S. , Sun , W. , and Durlofsky , L.J. ( 2019 ) A data-space inversion procedure for well control optimization and closed-loop reservoir...

... forecasting at scale. At its core is a simplified reservoir simulation model designed to represent a single unconventional well, which is calibrated to historical production data using automated history-matching for producing wells. For new wells that lack production history, a data-driven model generation...

... Geoscience Reservoir Characterization history matching localization machine learning reservoir simulation information production data model parameter conductivity field inversion conductivity Standard Deviation ensemble initial ensemble ensemble Kalman filter application EM data...

... Artificial Intelligence Reservoir Characterization History production assimilation modeling development strategy NPV development well workflow information saturation map ensemble acquisition well location saturation assimilation data assimilation seismic data production data simulation...

... learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single and two-phase coupled flow/geomechanics simulations and a real production dataset from Vaca...

... fracture network (CFN) which is challenging to characterize by conventional methods. In this work, we develop a fracture characterization workflow to estimate the geometric configuration and fracture properties of a CFN by assimilating microseismic event data and production data, sequentially. A novel...

... (DGN) method with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by finding a large ensemble of maximum posteriori points, from a distribution function in which the data and prior mean values have been...

... to obtain satisfactory matching of production data, tuning facies indicator in each gridblock must be allowed during the history matching process. Abstract Assisted history matching (AHM) a channelized model is a very challenging task because it is very difficult to gradually deform the discrete...

... geological features of the true model. Reservoir Characterization Upstream Oil & Gas production data machine learning history matching facies procedure soft data geologic modeling Artificial Intelligence gridblock facies distribution facies probability map permeability distribution...

... and collecting hard data and production data, and updating multiple geological models based on all of the available data. In the optimization step, the number, type, locations and controls for new wells (and future controls for existing wells) are optimized using a hybrid Particle Swarm Optimization - Mesh...

...) to describe field production data from Barnett and Fayetteville Plays. We start describing the main assumptions and significant consequences of the model. First, we discuss the complex hierarchy of structural features encountered in shale formations after multi-stage hydraulic fracturing. Then, we present...

... reservoir models. The presented computational results show significant improvements in terms of preserving channelized features and in terms of reliability of predictions compared to the standard implementation of ensemble-based algorithms. machine learning Upstream Oil & Gas production data...

... Abstract Inference of spatially distributed reservoir properties from production data in scattered wells poses an under-constrained inverse problem that has nonunique solutions. One major contributor to problem ill-posedness is over-parameterization of spatially distributed reservoir properties...

... implement an adaptive sparse reconstruction algorithm to automatically identify incorrect models of reservoir continuity (variogram parameters) during history matching iterations and replace them with more consistent prior model components to improve the production data match. The novel sparse signal...

... but not to essentially different conclusions. Assimilation of production data to update the bottom depth of prior #2 showed that production data do not provide sufficient information for reliable updates away from the wells whereas time lapse seismics provides much more spatially distributed information, leading...

... saturation change optimal solution information production data application history matching parameter space saturation change objective coefficient workflow transform coefficient Introduction Reservoir history matching commonly involves the use of multiple objectives such as matching...