Abstract: The present disclosure describes a computer-implemented method that includes: receiving a seismic dataset of a surveyed subsurface of a reservoir, the seismic dataset comprising observed pressure and production data of the reservoir as well as a set of geological and geo-mechanical parameters representing physical features of the surveyed subsurface; generating multiple realizations of a discrete fracture network (DFN) based on a subset of the set of geological and geo-mechanical parameters; selecting, from the multiple realizations, one or more realizations based on a parameter with a value under a 10% quantile of a full range of likely values; performing a forward simulation for the reservoir based on the selected one or more realizations and the observed pressure and production data; determining that a misfit of the forward simulation is below a threshold based on evaluating an objective function; and producing a model of the reservoir based on the forward simulation.

Abstract: A method for generating history matched grid data. The method includes obtaining observed production data and obtaining latent grid data. The method further includes processing the observed production data to form observed latent production data and generating denoised latent grid data by removing noise from the latent grid data using a diffusion model guided by the observed latent production data and a time encoding. The method further includes generating history matched grid data by decoding the denoised latent grid data and optimizing production of a well using the history matched grid data.

Abstract: Systems and methods include a method for aggregating source data to form ontological frameworks. Aggregation functions are defined for ontological frameworks modeling categories of components of a facility. Each aggregation function defines a target component selected from a Things category, an Events category, and a Methods category. Defining the target component includes aggregating information from one or more components selected from one or more of the Things category, the Events category, and the Methods category. Source data is received in real-time from disparate sources and in disparate formats. The source data provides information about the components of the facility and external systems with which the facility interacts. Using the aggregation functions, the source data is aggregated to form the ontological frameworks. Each ontological framework models a component of the Things category, a component of the a Events category, or a component of the Methods category.

Abstract: A method for reservoir simulation involves examining a knowledge graph logic associated with a reservoir simulation model for completeness. The knowledge graph logic contains decision information that governs an execution of the reservoir simulation model. The method further involves making a determination, based on the examination, that the knowledge graph logic is incomplete, based on the determination, generating an updated knowledge graph logic, obtaining the decision information from the updated knowledge graph, and executing the reservoir simulation model as instructed by the decision information.

Abstract: Methods and systems are provided for determining a sweep efficiency within a hydrocarbon reservoir. The method includes obtaining a well log for each of a plurality of wellbores penetrating the hydrocarbon reservoir, a deep sensing dataset for the hydrocarbon reservoir and determining a plurality of classified well logs, one from each well log using a first machine learning (ML) network. The method further includes determining a classified deep sensing dataset from the deep sensing dataset using a second ML network, training a third ML network to predict the sweep efficiency based, at least in part on the plurality of classified well logs and the classified deep sensing dataset at a location of each of the wellbores, and determining the sweep efficiency within the hydrocarbon reservoir using the trained third machine learning network based, at least in part, on the classified deep sensing dataset.

Abstract: Systems and methods include a computer-implemented method for optimizing variogram ranges uncertainties. Variogram modeling is performed using variogram models on wells in parallel (major), normal (minor), and vertical directions for continuous log porosity to select a best-fit variogram model using large uncertainty ranges and a preferred-normal distribution. A distribution of geological properties is determined onto the best-fit variogram model. Multiple realizations are executed to determine predicted porosities over the best-fit variogram model. Correlation coefficients of actual porosity versus predicted porosity are generated using the multiple realizations. The process is repeated until a correlation meets a predetermined acceptance criteria. A variogram range for the best-fit variogram model is optimized using a high correlation realization. Correlations are determined for the subset of wells by executing multiple realizations using a same seed number.

Abstract: Systems, methods, and computer readable media for the identification of fluid flow paths from the combination of aperture determined from microresistivity logs and mechanical properties from a mechanical earth model for a strike-slip fault regime. Fluid flow paths may be identified from a combination of apertures, shear stress, and normal stress for fractures in a naturally fractured hydrocarbon reservoir.

Abstract: Systems and methods for developing hydrocarbon reservoirs based on constrained natural fracture parameters. A natural fracture modeling is generated for a reservoir, an initial set of fracture model parameters is determined, and a fracture model optimization is conducted to determine an optimized set of fracture model parameters. The optimized set of fracture model parameters are used as a basis for modeling the reservoir, and the modeling is used to generate a simulation of the reservoir.

Abstract: Systems and methods for developing hydrocarbon reservoirs based on constrained natural fracture parameters. A natural fracture modeling is generated for a reservoir, an initial set of fracture model parameters is determined, and a fracture model optimization is conducted to determine an optimized set of fracture model parameters. The optimized set of fracture model parameters are used as a basis for modeling the reservoir, and the modeling is used to generate a simulation of the reservoir.

Abstract: Systems and methods include a method for providing recommendations and advisories associated with a facility. Source data is received in real-time from disparate sources and in disparate formats. The source data provides information about a facility and external systems with which the facility interacts. The source data is aggregated to form ontological frameworks. Each ontological framework models a category of components selected from components of a Things category, components of an Events category, and components of a Methods category. An abstraction layer is created based on the ontological frameworks. The abstraction layer includes abstractions that support queries, ontologies, metadata, and data mapping. A knowledge discovery layer for discovering knowledge from the abstraction layers is provided. Discovering the knowledge includes graph/network computation, graph/network training and validation, and graph representation learning.

Abstract: One or more methods for validating reservoir simulation models. At least one of the methods include determining one or more time segments of fluid recovery of a reservoir by analyzing a production history of the reservoir; running a simulation model, for the first time segment, to generate one or more drainage volumes; generating, for a first time segment, a plurality of grid regions along one or more no-flow boundaries of the one or more drainage volumes; generating, for the first time segment, a plurality of sector models corresponding to the plurality of grid regions; and performing, for the first time segment, a history matching process corresponding to a time phase simultaneously on each of the plurality of sector models to generate, for each of the sector models, a history matching output.

Abstract: Methods for validating reservoir simulation models can include determining one or more time segments of fluid recovery of a reservoir; generating, for a first time segment, one or more streamlines on a full simulation grid corresponding to the reservoir by performing one or more reservoir simulations; generating, for the first time segment, one or more drainage volumes; generating, for the first time segment, grid regions along one or more no-flow boundaries of the one or more drainage volumes; generating, for the first time segment, sector models corresponding to the grid regions; performing, for the first time segment, a history matching process corresponding to a time phase simultaneously on each of the sector models to generate, for each sector model, a history matching output; and comparing, for the first time segment and for each sector model, the history matching output for that sector model to a tolerance threshold.

Abstract: A method may include obtaining dynamic response data regarding a geological region of interest. The dynamic response data may include various transmissibility values. The method may further include determining a reservoir graph network based on the dynamic response data and a reservoir grid model. The reservoir graph network may include various grid cells, various wells, and various graph edges. The method may further include generating a graph neural network based on the reservoir graph network. The method may further include updating the graph neural network using a machine-learning algorithm to produce an updated graph neural network for the geological region of interest. The method may further include simulating a well within the geological region of interest using the updated graph neural network.

Abstract: The present disclosure describes a computer-implemented method that includes: receiving a seismic dataset of a surveyed subsurface of a reservoir, the seismic dataset comprising observed pressure and production data of the reservoir as well as a set of geological and geo-mechanical parameters representing physical features of the surveyed subsurface; generating multiple realizations of a discrete fracture network (DFN) based on a subset of the set of geological and geo-mechanical parameters; selecting, from the multiple realizations, one or more realizations based on a parameter with a value under a 10% quantile of a full range of likely values; performing a forward simulation for the reservoir based on the selected one or more realizations and the observed pressure and production data; determining that a misfit of the forward simulation is below a threshold based on evaluating an objective function; and producing a model of the reservoir based on the forward simulation.

Abstract: A method may include obtaining dynamic response data regarding a geological region of interest. The dynamic response data may include various transmissibility values. The method may further include determining a reservoir graph network based on the dynamic response data and a reservoir grid model. The reservoir graph network may include various grid cells, various wells, and various graph edges. The method may further include generating a graph neural network based on the reservoir graph network. The method may further include updating the graph neural network using a machine-learning algorithm to produce an updated graph neural network for the geological region of interest. The method may further include simulating a well within the geological region of interest using the updated graph neural network.

Abstract: In some embodiments, an apparatus and a system, as well as a method and an article, may operate to receive parameters for a first node of a network, the network including a plurality of segments through which an object is to be routed and a plurality of nodes, including the first node, representative of intersections of two or more segments of the plurality of segments; to generate segment lengths for segments between the first node and neighboring nodes to identify a nearest neighboring node with a shortest segment length relative to the first node; and to repeat operations of receiving parameters, generating segment lengths, and identifying nearest neighboring nodes until a route has been identified between a first endpoint and a second endpoint of the network. Additional apparatus, systems, and methods are disclosed.

Abstract: Methods for validating reservoir simulation models can include determining one or more time segments of fluid recovery of a reservoir; generating, for a first time segment, one or more streamlines on a full simulation grid corresponding to the reservoir by performing one or more reservoir simulations; generating, for the first time segment, one or more drainage volumes; generating, for the first time segment, grid regions along one or more no-flow boundaries of the one or more drainage volumes; generating, for the first time segment, sector models corresponding to the grid regions; performing, for the first time segment, a history matching process corresponding to a time phase simultaneously on each of the sector models to generate, for each sector model, a history matching output; and comparing, for the first time segment and for each sector model, the history matching output for that sector model to a tolerance threshold.

Abstract: One or more methods for validating reservoir simulation models. At least one of the methods include determining one or more time segments of fluid recovery of a reservoir by analyzing a production history of the reservoir; running a simulation model, for the first time segment, to generate one or more drainage volumes; generating, for a first time segment, a plurality of grid regions along one or more no-flow boundaries of the one or more drainage volumes; generating, for the first time segment, a plurality of sector models corresponding to the plurality of grid regions; and performing, for the first time segment, a history matching process corresponding to a time phase simultaneously on each of the plurality of sector models to generate, for each of the sector models, a history matching output.

Abstract: Systems and methods for generating a fractured reservoir model include: receiving a seismic dataset of a surveyed subsurface; identifying a dynamic response of each parameter; selecting a subset of parameters from the set of parameters based on the dynamic response of each parameter; sampling an outer boundary of a parameter uncertainty domain; adjusting the range of values associated with each parameter of the subset based on the sampling; generating a geo-model based on the adjusted range of values associated with each parameter of the subset; generating a discrete fracture network model based on the geo-model; generating a scenario of a simulation model based on the discrete fracture network model and the geo-model; performing a forward simulation based on the scenario of the simulation model; determining that a misfit of the forward simulation is below a threshold by evaluating an objective function; and producing a model based on the forward simulation.

Abstract: Systems and methods include a method for providing recommendations and advisories associated with a facility. Source data is received in real-time from disparate sources and in disparate formats. The source data provides information about a facility and external systems with which the facility interacts. The source data is aggregated to form ontological frameworks. Each ontological framework models a category of components selected from components of a Things category, components of an Events category, and components of a Methods category. An abstraction layer is created based on the ontological frameworks. The abstraction layer includes abstractions that support queries, ontologies, metadata, and data mapping. A knowledge discovery layer for discovering knowledge from the abstraction layers is provided. Discovering the knowledge includes graph/network computation, graph/network training and validation, and graph representation learning.