Chevron U.s.a. Inc.

Exxonmobil Upstream Research Company

Other

Tao Sun

Dachang Li

Ashley D Harris

Max Deffenbaugh

Morgan David Sullivan

John Van Wagoner

Chun Huh

Michael James Pyrcz

Maisha Lara Amaru

Lewis Li

Kaveh Ghayour

Fabien J Laugier

David Hoyal

Martin Perlmutter

Brett M Hern

Brian J Willis

Timothy A Chartrand

Xiao-Hui Wu

Sebastien B Strebelle

Chris J Donofrio

Matthias Imhof

Jacob Covault

John C Van Wagoner

Neal L Adair

Xiaohui Wu

Paul A Dunn

Stephen L Lyons

Alicia Downard

David N Awwiller

Hongmei Li

William B Heard

Cory J Ramage

Robert M Stuart

Frank J Goulding

Sebastien Strebelle

David C Hoyal

Martin A Perlmutter

David N Awwieller

A computational stratigraphy model may be run for M mini-steps to simulate changes in a subsurface representation across M mini-steps (from 0-th subsurface representation to M-th subsurface representation), with a mini-step corresponding to a mini-time duration. The subsurface representation after individual steps may be characterized by a set of computational stratigraphy model variables. Some or all of the computational stratigraphy model variables from running of the computational stratigraphy model may be provided as input to a machine learning model. The machine learning model may predict changes to the subsurface representation over a step corresponding to a time duration longer than the mini-time duration and output a predicted subsurface representation. The subsurface representation may be updated based on the predicted subsurface representation outputted by the machine learning model. Running of the computational stratigraphy model and usage of the machine learning model may be iterated until the end of the simulation.

System and method for accelerated computation of subsurface representations

A method is described for analysis of subsurface data including the use of physics-based modeling and experimental design that allows calculation of probabilities of physical subsurface properties. The method may include calculations of key controlling parameters. The method may include using multiple dimension scaling. The method may be executed by a computer system.

System and method for analysis of subsurface data

Well information may define subsurface configuration of different wells. Marker information defining marker positions within the wells may be obtained. A dissimilarity matrix for the wells may generated, with the element values of the dissimilarity matrix determined based on comparison of corresponding subsurface configuration of the wells. A gated dissimilarity matrix may be generated from the dissimilarity matrix based on the marker positions within the wells. The elements values of the gated dissimilarity matrix corresponding to one set of marker positions and not corresponding to the other set of marker positions may be changed. Correlation between the wells may be determined based on the gated dissimilarity matrix such that correlation exists between a marker position in one well and a marker position in another well.

Comparison of wells using a dissimilarity matrix

A subsurface representation may define simulated subsurface configuration of a simulated subsurface region. The simulated subsurface region may include simulated wells, and the simulated subsurface configuration may define simulated correlation between the simulated wells. Subsurface configuration of wells may be compared with the simulated subsurface configuration to generate similarity maps for the wells. The similarity maps may be arranged based on spatial arrangements of the wells such that the similarity maps overlap with each other within an overlap area. Locations within the overlap area may correspond to groupings of matched simulated wells. Correlation between the wells may be determined based on the simulated correlation between the matched simulated wells.

Multiple well matching within subsurface representation

A subsurface representation may define simulated subsurface configuration of a simulated subsurface region. The simulated subsurface region may include simulated wells, and the simulated subsurface configuration may define simulated correlation between the simulated wells. Subsurface configuration of wells may be compared with the simulated subsurface configuration to generate similarity maps for the wells. Simulated wells may be matched to the wells based on the similarity maps and the arrangement of the wells. Correlation between the wells may be determined based on the simulated correlation between the matched simulated wells.

Correlation of multiple wells using subsurface representation

Correlation matrices may be used to simultaneously correlate multiple wells. A correlation matrix may be generated for individual pairs of multiple wells. The values of elements of the correlation matrices may be determined based on matching between segments of the multiple wells and segments of one or more computational stratigraphic models. An N-dimensional space including an axis for individual wells may be generated. Directed walk may be performed within the N-dimensional space to generate paths representing scenarios of correlations for segments of the multiple wells.

Correlation matrix for simultaneously correlating multiple wells

Unsegmented continuous subsurface data may be analyzed using one or more moving windows to characterize a subsurface region. Unsegmented continuous subsurface data may be scanned using the moving window(s). Probabilities that portions of the subsurface region include a subsurface feature may be determined based on analysis of the portions of the unsegmented continuous subsurface data within the moving window(s).

Characterization of subsurface regions using moving-window based analysis of unsegmented continuous data

Data in physical space may be converted to layer space before performing modeling to generate one or more subsurface representations. Computational stratigraphy model representations that define subsurface configurations as a function of depth in the physical space may be converted to the layer space so that the subsurface configurations are defined as a function of layers. Conditioning information that defines conditioning characteristics as the function of depth in the physical space may be converted to the layer space so that the conditioning characteristics are defined as the function of layers. Modeling may be performed in the layer space to generate subsurface representations within layer space, and the subsurface representations may be converted into the physical space.

Generation of subsurface representations using layer-space

Depth-averaged flow simulation systems and methods provided herein employ parameterized templates for dynamical depth profiling for at least one step of a simulation. In one illustrative computer-based embodiment, the simulation method includes, for each map point at one given time step: determining a flow template and a sediment concentration template based on depth-averaged flow velocity and depth-averaged sediment concentrations of different classes of grain size for that map point; employing the templates to construct a vertically-distributed flow velocity profile and vertically-distributed sediment concentration profiles for associated classes of grain size for that map point, thereby obtaining 3D flow velocity and 3D sediment concentration fields; using the 3D fields to calculate fluid and sediment fluxes; updating the flow velocity and sediment concentration profiles based on the divergence of the fluxes; integrating the profiles to compute updated depth-averaged flow velocity and sediment concentrations and center of gravity; and solving the depth-averaged flow equations for the next time step.

Sediment transport simulation with parameterized templates for depth profiling

A method of simulating a hydrocarbon reservoir is disclosed. A process-based model is generated that mimics a depositional process of the reservoir. The process-based model is analyzed to extract statistics of geometries of a body that forms part of the reservoir, and depositional rules of the body. An object-based modeling method is applied to construct multiple unconditional geologic models of the body using the statistics and the depositional rules. Training images are constructed using the multiple unconditional geologic models. Well data and gross thickness data are assigned into a simulation grid. A single multiple-point geostatistical simulation is performed using the training images. A three-dimensional (3D) reservoir model is constructed using results of the multiple-point geostatistical simulation.

Conditional process-aided multiple-points statistics modeling

A new gridding method is disclosed for forward stratigraphic modeling that allows for syndepositional and/or postdepositional fault movement. The new gridding algorithm may represent both the lateral move of structure block, and provide efficiency that is comparable to the structured grid for forward stratigraphy model accessing previous deposited sediments stored in the grid. Embodiments of the disclosed methods allow for structural moves by performing a set of simple operations on the grid. The operations are generally simple, and do not change the overall topology of the grid. Therefore the operation can be easily repeated and the overall topological structure of the grid remains largely unchanged for simple access by the forward stratigraphic model. Further details and advantages of various embodiments of the method are described in more herein.

Method of stratigraphic modeling of faults

A computer-based method of conditioning reservoir model data includes performing a modeling process within a 3D stratigraphic grid to generate an initial model including one or more facies objects within the model volume, the modeling process including parametric distributions, initial and boundary conditions as well as depositional and erosional events to define the facies objects within the model volume. The mismatch between this initial model and the conditioning well data and potential input trend model is applied to compute a locally variable constraint model. The method further includes executing a multiple point statistics simulation with this constraint model that varies between completely constrained by the initial model at locations where the initial model is consistent with known well data and potential input trend models, and unconstrained by the initial model at locations where the initial model does not match known well data or potential input trend models to allow conformance to the known data.

Conditioning of object or event based reservior models using local multiple-point statistics simulations

A method is described for a method for depositional modeling dependent on geological boundary conditions including receiving a process-based depositional model of a siliciclastic formation; determining a type of boundary condition at each boundary of the process-based depositional model wherein the boundary condition on at least one boundary is one of a flux-preserving boundary condition or a discrete boundary condition and wherein the boundary condition accounts for at least one of time-varying inflow of water and sediments into the process-based depositional model, time-varying outflow of water and sediments out of the process-based depositional model, and downstream controls; modeling rates of sediment and water flow over time, dependent on the boundary condition, to create a modeled depositional system; and analyzing the modeled depositional system to identify potential hydrocarbon reservoirs. The method may be executed by a computer system.

System and method for modeling coupled systems of hydrodynamics and sediment transport

A method of enhancing a geologic model of a subsurface region is provided. A bed topography of the subsurface region is obtained. The bed topography is defined by a plurality of cells with an elevation associated with each cell center. The bed topography is represented as a cell-centered piecewise constant representation based on the elevations associated with the cells. The bed topography is reconstructed to produce a spatially continuous surface. Flux and gravitational force-related source terms are calculated based on the reconstructed bed topography. Fluxes are calculated between at least two of the cells. Fluid flow, deposition of sediments onto the bed, and/or erosion of sediments from the bed are predicted using the fluxes and gravitational force-related source terms. The predictions are inputted into the geologic model to predict characteristics of the subsurface region, and the predicted characteristics are outputted.

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Missouri City, TX, United States of America

Tao Sun