Abstract: A staple cartridge assembly for use with a surgical stapling instrument includes a staple cartridge including a plurality of staples and a cartridge deck. The staple cartridge assembly also includes a compressible adjunct positionable against the cartridge deck, wherein the staples are deployable into tissue captured against the compressible adjunct, and wherein the compressible adjunct comprises a first biocompatible layer comprising a first portion, a second biocompatible layer comprising a second portion, and crossed spacer fibers extending between the first portion and the second portion.

Abstract: A group of wells may be located within a region of interest. Multiple scenarios of boundary locations within a group of wells may be obtained. A top-and-base boundary pair, defining a package of interest, may be identified within the individual scenarios of boundary locations. A subsurface property of the package of interest within the individual scenarios of boundary locations may be determined, and the subsurface property of the package of interest within the individual scenarios of boundary locations may be used to determine a subsurface characteristic of the region of interest.

Abstract: An intermediary well may be selected for a group of wells. The intermediary well may be used as an origin point from which branching wells paths are generated to connect the group of wells through the intermediary well. A shortest path between the intermediary well and the group of wells along the branching well paths may be identified, and the group of wells may be aligned along the shortest path. Boundaries of the intermediary well may be propagated to the aligned group of wells to establish correlation between segments of the intermediary well and segments of the aligned group of wells.

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.

Abstract: 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.

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: 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.

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: 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: 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: 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.

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: 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.

Abstract: 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.

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: 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.