Landmark Graphics Corporation

Saudi Arabian Oil Company

Halliburton Energy Services, Inc.

Marko Maucec

Jeffrey Marc Yarus

Gustavo Carvajal

Steven Patton Knabe

Ajay Pratap Singh

Seyed M Mirzadeh

Lu Ming Liang

Babatunde Moriwawon

Alvin Stanley Cullick

Richard L Chambers

Rae Mohan Srivastava

Otto E Meza Camargo

Mohammad Methgal Al Shammari

Ronald Glen Dusterhoft

Richard Dale Rickman

Genbao Shi

Dwight David Fulton

Vitaly Khoriakov

Luigi Saputelli

Otto Meza Camargo

Hasnain A Khan

Michael Nikolaou

Ronald A Gibson

Karla Olvera Carranza

Maiquel Manuel Querales

Ridwan S Jalali

Andrey Flippov

Arturo Diaz

Ali A Taiban

Cesar Pardo

Mubarak N Dossary

Olugbenga Olukoko

Srimoyee Bhattacharya

Luigi A Saputelli

Richard Chambers

Sayed M Mirzadeh

Gustavo Adolfo Carvajal

Andrey Filppov

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.

System and method to identify high-impact discrete fracture model realizations for accelerated calibration of reservoir simulation models

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.

Aggregation functions for nodes in ontological frameworks in representation learning for massive petroleum network systems

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.

Constrained natural fracture parameter hydrocarbon reservoir development

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. A recommendation and advisory systems layer is provided for providing recommendations and advisories associated with the facility.

Representation learning in massive petroleum network systems

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.

Dynamic calibration of reservoir simulation models using flux conditioning

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.

Dynamic calibration of reservoir simulation models using pattern recognition

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.

Methods and systems for generating graph neural networks for reservoir grid models

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.

Path optimization in production network systems

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.

Method for dynamic calibration and simultaneous closed-loop inversion of simulation models of fractured reservoirs

Systems and methods for a hybrid approach to assisted history matching in large reservoirs based on a reservoir model built using connectivity between each production well and each corresponding injection well, aquifer or gas cap.

Hybrid approach to assisted history matching in large reservoirs

Systems and methods for updating posterior geological models by integrating various reservoir data to support dynamic-quantitative data-inversion, stochastic-uncertainty-management and smart reservoir-management.

Systems and methods for the quantitative estimate of production-forecast uncertainty

A system and method for performing classification and regression tree flow simulations for reservoir. A partial flow simulation is performed. Dynamic properties associated with the partial flow simulation are stored for each time step. CART is utilized to predict changes in the dynamic properties utilizing stored dynamic properties determined by the partial flow simulation.

Cart-based proxy flow simulation procedure for ranking geostatistical realizations of rock properties

Systems and methods for creating virtual production logging tool profiles for improved history matching and proactive control of smart wells.

Creating virtual production logging tool profiles for improved history matching

The disclosed embodiments include a method, apparatus, and computer program product for performing gridless simulation of a fluvio-deltaic environment. For example, one disclosed embodiment includes a system that includes at least one processor, and at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations that include generating a set of channel centerlines corresponding to a set of channels that are indicative of flow units in a fluvio-deltaic environment; and generating channel widths for each of the channel centerlines. In one embodiment, the operations for generating the set of channel centerlines of the reservoir include selecting a seed point for each channel, assigning each seed point a direction of propagation, and iteratively generating each channel.

Gridless simulation of a fluvio-deltaic environment

Systems and methods for generating updates of large scale 3D geological models with multi-model facies, permeability or porosity distribution.

Growing community of inventors

Englewood, CO, United States of America

Marko Maucec