... Crosswell EM data Reservoir Characterization history matching localization application conductivity optimization problem Computational Geoscience model parameter production data inversion Standard Deviation EM data assimilation conductivity field ensemble ensemble Kalman filter initial...

... in which production data is assimilated and the case in which saturation is assimilated. The NPVs differences analysis allows to realize the economic value that such information adds to the decision of acquiring seismic information. This simulation case set up allows the theoretical understanding...

... 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...

... configuration and fracture properties of a CFN by assimilating microseismic event data and production data, sequentially. A novel stochastic fractal model, that is consistent with rock physics and outcrop observations, is developed in order to generate realizations of the complex fracture network. In the first...

... (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...

... are, respectively, vectors of gridblock permeabilities and porosity; A is a vector of aquifer strength multipliers to different aquifer area; and T fault is a vector of global fault transmissibility multipliers for different faults. d obs is an N d -dimensional observed production data vector and g(m...

... gas production Artificial Intelligence complex reservoir field data shale gas Fluid Dynamics flow in porous media Lee coefficient dual tube dual-tube model BoDTM production data production rate field measurement fracture network shale formation Upstream Oil & Gas matrix Lunati...

... geological features of the true model. Reservoir Characterization Upstream Oil & Gas production data machine learning history matching facies procedure soft data geological modeling reservoir simulation permeability value iteration geologic modeling algorithm probability map...

... updating) is the second key step in CLFD. The goal here is to generate one or more geological models that are consistent with prior geological information and provide flow simulation results that match (to within some tolerance) observed production data. The procedures used here follow those of, e.g...

...” production data and time-lapse seismic data) of the ensemble members are mapped to a lower dimensional space using MDS. Then, the models are clustered according to their distances from the actual observational data. The closest cluster to the actual observed response is updated using the ES equation...

... ) where Δd j is the vector of misfit between the simulated and observed production data in previous iteration ( j and is expressed as: (7) Δ d t = g ( Φ v j ) − d obs Table 1 contains the mathematical description of the IRLS algorithm used to solve this problem...

... changes to structural updates (layer thicknesses) in the static model. This method is suitable for structural updating of large scale reservoir models using production data and/or time-lapse seismics or other spatially distributed data. The method is tested on a 3D synthetic model, where time-lapse...

... parameter space evolutionary algorithm history matching optimal solution saturation change objective workflow Artificial Intelligence Upstream Oil & Gas information production data application coefficient transform coefficient Introduction Reservoir history matching commonly...

... 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 recovery algorithm...

... framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses...

... intelligence dadashpour discrete cosine transform estimation avo gradient permeability production data reduction principal component analysis permeability field Introduction In this paper we address the combination of the principal component analysis (PCA) and the discrete cosine transform...

... ensemble-based method for history matching. history matching history-matching parameter application artificial intelligence seismic data experiment ensemble reservoir simulation evensen production data van leeuwen reservoir model matrix ensemble kalman filter posterior ensemble machine...

... on Roswinkel, a compaction field was estimated by inverting subsidence measurements. The results indicated several sealing faults in the reservoir, dividing the field into different compartments with independent pressure histories. The post-inversion history match of production data, however...

... constraint kernel function enkf ensemble kalman filter simulation model assimilation step saturation spe 141948 scholkopf feature space machine learning upstream oil & gas random field equation kernel production data kenkf state variable Introduction Integrating various kinds...

... into the reservoir description. We tested our method for updating these variables using production data from a deepwater field whose reservoir model has over 200,000 unknown parameters. The match of reservoir simulator forecasts to real field data using a standard application of EnKF had not been entirely...