... by revisiting the data set. Overall, the proposed workflow demonstrates successful application in the Eagle Ford formation such that it can be directly implemented for other unconventional resource plays. machine learning geological modeling geologic modeling reservoir characterization energy...

... for the upper and lower reservoir intervals, respectively. This study demonstrated the potential for significant additional hydrocarbon production from the Meghil field as well as the potential impact of heterogeneity on well placement and spacing. geologic modeling geological modeling well logging...

... coarsening that naturally avoids the issues that arise in the use of the harmonic average for the vertical transmissibility. Starting from a high-resolution fine scale 3D geologic model, sequential layer grouping provides us with a series of increasingly coarsened reservoir models. Each model in turn...

... for geologic uncertainty. The parsimonious representation of geologic heterogeneity and the use of ML for forward modeling makes the approach highly scalable and well-suited for full field applications. geologic modeling reservoir characterization machine learning reservoir surveillance enhanced...

... Geological modelling process comprises several steps; see Fig. 2 , which are required to be put under quality control in order to create correct representation of reservoir structure and properties. There are several factors that may impact geomodel quality: Volume and quality of input...

.... geological modeling machine learning Upstream Oil & Gas Libya geologic modeling Artificial Intelligence Reservoir Characterization semivariogram zelten reservoir interpretation carbonate reservoir society of petroleum engineers reservoir quality petroleum system paleocene zelten carbonate...

... machine learning Upstream Oil & Gas geological modeling flow in porous media geologic modeling Artificial Intelligence oilfield facies-constrained modeling Reservoir Characterization Fluid Dynamics formula sandstone rocktype model sedimentary microfacies Simulation...

... study of a real oil field model and demonstrate practical applicability of the spectral method and its advantages when applied to the real case. The Spectral Approach Artificial Intelligence spectral approach simulation result geologic modeling Modeling & Simulation Simulation...

... simulation runs. In addition, the training of CNN is implemented using only geologic realizations without requiring additional reservoir simulation. The performance of the workflow is evaluated using tomographic inversion and model calibration in fluvial reservoirs. geologic modeling artificial...

... modeling geologic modeling eastern siberia Salinization Reservoir Characterization reservoir property conductivity saturation Drilling seismic survey reservoir survey data inversion resistivity sediment Upstream Oil & Gas upper part geoelectrical model society of petroleum engineers...

... surface, defined above, as proxy for the assessment of the P10, P50, and P90 values of PI using the Monte-Carlo method. Geological Modeling The geological modeling encapsulates the structural modeling part which corresponds to the building of the numerical framework, defined by the horizons...

... on computing the conditional probability P ( A | B , C ) at the first few grid cells along the random path { x 1 , x 2 , …, x N g }. In this section, a series of experiments are used to reveal such behavior of MPS facies simulation, followed by a detailed discussion. geological modeling reservoir...

... outcrop models established by reservoir outcrop information from various approaches. The information of virtual outcrops is comprehensive, objective and easy to obtain. In this paper, we establish three dimensional geologic model of study area based on the virtual outcrops and prior geological cognition...

... coherent with expected geological contexts and physical behaviors of the basin. geologic modeling Artificial Intelligence machine learning Upstream Oil & Gas geological modeling optimization problem parametrization workflow variation basin modeling Simulation cenomanian source-rock...

... "containers" or model regions appears to have only a small impact on forecast recovery. geologic modeling Upstream Oil & Gas porosity Modeling & Simulation spatial continuity workflow stratigraphic layer forecast Meddaugh grid Reservoir Characterization stratigraphic detail...

... ) C ( x i , x j ) f ( x j ) d x i d x j n Finally, following similar procedure, the variance of the objective function J , denoted by σ J 2 , can be calculated as follows geologic modeling Artificial Intelligence Reservoir Characterization...

... of the permeability field in comparison to methods like ‘Ensemble Kalman Filter’ (EnKF) that are sub-optimal in such cases. Reservoir Characterization geologic modeling Artificial Intelligence machine learning Upstream Oil & Gas liquid rate Inda new well ensemble permeability distribution history...

... ( Meddaugh, 2015 ) suggested that other sources of potential bias such as stochastic geological model parameters (e.g. the semivariogram model) and upscaling are generally small with the possible exception of static/dynamic model grid parameters (particularly dynamic model cell size) and well location...

... of sediment deposits. The hexahedrons in the immediate vicinity of the faults are discarded to be replaced by tetrahedrons thereafter. Construction of a geologically consistent Hybrid Grid, depends first and foremost on the construction of an accurate geological model. The geological model is represented...

... study, seismic interpretation, well logging analysis, paleotectonic research, reservoir quality characterization and diagenetic alternations inspection. The workflow for CGM creation applied in this work included the following steps: Conceptual geological modelling method can...