Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
Disclosed is a method and system for identifying simulated basin results and associated input parameter values for simulation of geographic basins by a stratigraphic forward model simulation program that are most likely to represent the actual basin by treating inputs and outputs of the stratigraphic forward model simulation program in a unified manner. An embodiment may calculate probability distributions for input parameters and validation data, and calculate likelihoods of simulated basins as a combination of the combination of the probabilities of the input parameters used to create the simulated basin and of the combination of the probabilities simulation validation results of the simulated basin. An embodiment may then select most likely simulation model result basins based on the results having a higher calculated likelihood. The most likely simulated basins may be used for analysis of exploration and/or production decisions without the need for additional, expensive testing on the actual basin.
Abstract:
Process-based numerical forward stratigraphic models of different spatiotemporal scales may be nested to address subsurface characterization at different scales. Subsurface representations may be generated using an iterative loop in which subsurface representations are generated using different-scale subsurface models, compared to scale-appropriate data, and used to define boundary conditions/inputs for subsequently run subsurface models. Results from the subsurface models may be compared to one or more standards for quality control and/or for subsurface representation selection. A series of comprehensive subsurface representations may be generated, with the subsurface representations being constrained by different scales of information and physical plausible scenarios.
Abstract:
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.
Abstract:
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).
Abstract:
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.
Abstract:
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.
Abstract:
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.