WO2007022289A2 - Methodologie de modelisation pour developpement d'applications dans l'industrie du petrole - Google Patents

Methodologie de modelisation pour developpement d'applications dans l'industrie du petrole Download PDF

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Publication number
WO2007022289A2
WO2007022289A2 PCT/US2006/032014 US2006032014W WO2007022289A2 WO 2007022289 A2 WO2007022289 A2 WO 2007022289A2 US 2006032014 W US2006032014 W US 2006032014W WO 2007022289 A2 WO2007022289 A2 WO 2007022289A2
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Prior art keywords
oil field
asset components
modeling
model
graphical
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PCT/US2006/032014
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English (en)
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WO2007022289A3 (fr
Inventor
Cong Zhang
Viktor K. Prasanna
Abdollah Orangi
William J. Da Sie
Amol Bakshi
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University Of Southern California
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Priority to AU2006279464A priority Critical patent/AU2006279464B2/en
Priority to GB0804785A priority patent/GB2444874A/en
Priority to EA200800599A priority patent/EA200800599A1/ru
Publication of WO2007022289A2 publication Critical patent/WO2007022289A2/fr
Publication of WO2007022289A3 publication Critical patent/WO2007022289A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/10—Requirements analysis; Specification techniques

Definitions

  • the present invention relates to a modeling methodology for application development in the petroleum industry.
  • This invention provides formal metamodels to describe the individual formalisms in the desired unified environment.
  • These metamodels created preferably with a modeling development environment, e.g., the Generic Modeling Environment (GME), define the domain-specific modeling language for application development in the petroleum industry.
  • GME Generic Modeling Environment
  • Integrated Asset Modeling is the technique used to model different assets (physical: wells, blocks, etc.; non physical: control strategies, optimizers, etc.) to provide efficient management between the assets.
  • the petroleum industry has recognized the opportunity for Integrated Asset Modeling to address multiple challenges.
  • Integrated Asset Modeling is an "enabling technology" for Integrated Asset Management (IAM).
  • Integrated Asset Management presents an intensive operational environment involving continuous series of decisions based on multiple criteria including safety, environmental policy, component reliability, efficient capital, operating expenditures, and revenue. Asset management decisions require interactions among multiple domain experts, each capable of running detailed technical analysis on highly specialized and often compute intensive applications.
  • the asset management toolkit needs to be configurable and custom fit for purpose to handle a great diversity of needs over a large portfolio of assets that range from big to small, low cost - low volume to major capital-high volume, onshore-offshore, brownfield (mature) to greenfield (new developments). It is also desired to implicitly couple technical compute applications to run as one distributed system while maintaining component ownership within the respective domains.
  • a need for optimization of the asset that encompasses all system components and potentially involves multiple nested optimization loops and sequences of actions within a control strategy also forms an important challenge addressed by IAM.
  • This invention overcomes the above-described shortcomings of known methods and systems.
  • the invention in one embodiment includes a modeling framework configured to facilitate integration of complex software applications in the petroleum industry including: (a) a graphical modeling language including: (1) classes including a plurality of oil field asset components and connectors and a grammar defining the allowed and necessary connections between the asset components configured and adapted for making a plurality of graphical models compliant with the graphical modeling language where the graphical models represent a plurality of oil field asset components and the connections between them and each model having a plurality of levels of detail; (2) the graphical modeling language configured and adapted for modeling the asset components of different oil fields having different numbers, types, and configurations of asset components; and (b) a model interpreter for each of a plurality of software applications specific to the oil field domain for storing, analyzing, displaying, or manipulating oil field data associated with at least one of the oil field asset components, each model interpreter configured and adapted for passing information between the plurality of oil field asset components.
  • a graphical modeling language including: (1) classes including a plurality of oil field asset components and connectors and a
  • a method of modeling and integration of complex software applications in the petroleum industry including: Making at least one model of an oil field using a graphical modeling language including: classes including a plurality of oil field asset components and connectors and a grammar defining the allowed and necessary connections between the asset components configured and adapted for making a plurality of graphical models compliant with the graphical modeling language where the graphical models represent a plurality of oil field asset components and the connections between them and each model having a plurality of levels of detail; the graphical modeling language configured and adapted for modeling the asset components of different oil fields having different numbers, types, and configurations of asset components; and making at least one model interpreter for each of a plurality of software applications domains specific to the oil field for storing, analyzing, displaying, or manipulating oil field data associated with at least one of the oil field asset components, each model interpreter configured and adapted for passing information between the plurality of oil field asset components.
  • a graphical modeling language including: classes including a plurality of oil field asset components and connectors and a grammar defining the
  • FIG. 1 is a schematic model of the system architecture for one embodiment of the MIC-based framework for integrated asset management aspect of the invention.
  • FIG. 2 is an exemplary top-level schematic of the hierarchical approach for designing modeling paradigms depicting a metamodel of the physical and non-physical top-level components of a producing oil field.
  • FIG. 3 is a schematic entity-relationship diagram of the physical and non- physical subcomponents of a producing oil field.
  • FIG. 4 is a schematic diagram of an exemplary metamodel for data types for inputs and outputs of each processing component of a producing oil field using class diagram notation similar to UML.
  • FIG. 5 is a schematic diagram of an exemplary metamodel of application models and data type models composed together.
  • the method and system of the invention utilizes a unified generic environment will help provide the required interaction between the existing software applications.
  • These applications are based on existing simulation software.
  • Development of the application consists of three major steps: (1) Capturing a simulation scenario. (2) Constructing a software-based structural model to map the component tasks in the scenario to a software structure. This mapping is necessary because multiple simulators may exist for the same component task. (3) Determining intersoftware coordination mechanisms, such as interface wrapping and low-level communication within the application.
  • the invention is based on Model-Integrated Computing (MIC).
  • MIC employs domain-specific models to represent applications being designed.
  • the models specify the desired application functionality and available simulation tools.
  • the modeling language capturing the application functionality is based on finite state machine.
  • Modeling has been widely used for software development. Many analysis and design techniques use models to describe the necessary class and inheritance relationships in the software. However, models created using these techniques are loosely coupled to the actual system development cycle.
  • the concept of MIC can be used to form a tightly coupled environment.
  • the software is modeled along with the environment and integration constraints.
  • the generic modeling environment (GME) is one known modeling environment used in MIC. It is a configurable graphical tool suite supporting MIC. GME allows the designer to create domain- specific models.
  • a metamodel (modeling paradigm) is a formal description of model construction semantics. Once the metamodel is specified by the user, it can be used to configure GME itself to present a modeling environment specific to the problem domain. All other known or later developed modeling environments are contemplated for use with this invention.
  • FIG. 1 is a schematic model of the system architecture for one embodiment of the MIC-based framework for integrated asset management aspect of the invention.
  • the framework which is based on MIC, will be used to facilitate activities in the integrated asset management, such as data integration, simulation application development, etc.
  • Multiple Simulators 140 and multiple Optimizers 142 are, optionally, existing petroleum industry domain-specific software applications. These can include, e.g., applications for optimizing oil production from an oilfield, evaluating the heat distribution in an oilfield, or optimizing steaming of an oilfield.
  • Control Strategies 165 may include, e.g., software-implemented control strategies such as available through known Supervisory Control and Data Acquisition (“SCADA") systems or other process control systems.
  • SCADA Supervisory Control and Data Acquisition
  • the MIC-based framework for Integrated Asset Management 100 has three components - Execution Environment 130, Modeling Environment 100, and Configurable Graphic User Interface (GUI) 110.
  • GUI Configurable Graphic User Interface
  • Modeling environment 120 is in operably connected to a Configurable GUI 110. Both the Configurable GUI 110 and the Modeling Environment 120 are configured to receive User Input 112 via any known or future-developed input devices or mechanisms.
  • Sensor Network 160 and Historical Data 155 are operably connected to Databases 162 (only one shown).
  • Models provide a more effective way to develop large, reliable, real-time software systems because they help manage complexity through abstractions and formal descriptions of the physical systems.
  • Model-based software synthesis is a part of the larger discipline of knowledge-based software engineering. It integrates artificial intelligence and software engineering by supporting specification methods, software synthesis, and analysis with application-specific knowledge formalized into models.
  • MIC is a system software development approach that promotes the use of domain-specific models to represent relevant aspects of a system.
  • the use of domain-specific models have many benefits. For example, they help users specify systems using domain concepts. Domain specific modeling also allows users to specify systems at a higher level of abstraction.
  • One of the most important characteristics of MIC is that it allows us to dynamically resynthesize a running system without changing anything but the relevant components. MIC works well when software requirements and specifications are constantly changing.
  • Modeling has been used to capture the system design, synthesize executable systems and perform analysis or drive simulation.
  • Our models have been designed to capture the various simulation scenarios possible in the petroleum industry.
  • a simulation scenario can be defined as the interaction in terms of event flows and relative ordering of such flows. To model such a scenario, we need to capture both the building components and the interaction between them.
  • the building components include simulators, optimization tools, databases, etc. Since our target applications are based on existing software, we are not concerned with modeling the internal structures or implementation of the building software components. Instead we only capture their interfaces each of which can be characterized with a set of input signals and a set of output signals.
  • GME can been used to create a prototype framework of an integrated petroleum asset management system, with intent to develop processing for integrated forecasting, reliability estimation, and real time data flow mapping.
  • the hierarchical model provides the user with different levels of abstraction, levels of integration and levels of visualization. The topmost level provides a high level of abstraction and the models get more detailed as one goes to the lower levels.
  • Figure 2 shows the top level sub- components in this prototype application. It is an exemplary top-level schematic 200 of the hierarchical approach for designing modeling paradigms depicting a metamodel of the physical and non-physical top-level components of a producing oil field.
  • the components can be classified into physical (block, well, pipe-network, separator, process, etc.) and non-physical (control strategies, drilling schedule, reliability models, assumptions, etc.) entities.
  • Reservoir Model 210 consists of Block Container Model 235, Fluid Region Container Model 250, Well Container Model 220, and Recovery Curve Container Model 212.
  • Each component model in turn consists of other models. That is, Well Container Model 220 consists of a Well Model that is represented by Well Model Proxy 215. The ModelProxy is employed because the definition of the Well Model is specified elsewhere.
  • Fluid Region Container Model 250 consists of Fluid Region Properties Model 245.
  • Block Container Model 235 consists of Block Models that are represented by Block Model Proxy 230 and defined elsewhere in the metamodel specification.
  • Recovery Curve Container Model 212 consists of Recovery Curve models that are represented by Recovery Curve Model Proxy 240.
  • Block With information in the geological model and reservoir simulator, a reservoir can be divided into several reservoir volume elements called the blocks in this framework. In our simple metamodel, each block is defined with the following parameters: Original Oil In Place (OOIP), primary decline curve, secondary decline curve, voidage target, among others.
  • OOIP Original Oil In Place
  • primary decline curve primary decline curve
  • secondary decline curve secondary decline curve
  • voidage target among others.
  • Wells provide the primary production-injection conduits to the reservoir.
  • Our well model consists of various parameters and models to describe the physical structure and relationship with other operational components.
  • the well model provides the information about where the well is physically located, which type of well (e.g., producer, water injection well, or gas injection well) it is, etc.
  • Pipe network A pipe network is a gathering system that collects all fluids from all the wells.
  • Separator A separator is used to separate oil from gas and water. Our separator model is used to represent the operations and components of a separator.
  • Process After separation, the following processing actions are performed: product treatment, compressing gas to high pressure and sending it to the market.
  • FIG. 3 is a schematic entity-relationship diagram 300 of the physical and non- physical subcomponents and of a producing oil field and associated connectors. Four connections are shown. First, in figure portion 310, depicting the connection of a Well Model represented by the Well Model Proxy 325 to Gas Compressor Stage represented by the Gas Compressor Stage Model Proxy 315 via Gas Compressor Stage to Well Connection 320.
  • FIG. 320 depicting the connection of a Well Model represented by the Well Model Proxy 325 to Separator System Model represented by Separator System Model Proxy 345 via Well to Separator System Connection 355, and Separator System Model represented by Separator System Model Proxy 345 to Gas Compressor System Model represented by Gas Compressor System Model Proxy 340 via Separator System to Gas Compressor System Connection 320.
  • FIG portion 370 depicting the connection of Well Model represented by Well Model Proxy 325 to Water Injection Train Model represented by the Water Injection Train Model Proxy 375 via Injection Train to Well Connection 380.
  • Non-physical components provide the required control for the proper management of the physical components in the IAM system. These help us provide certain assumptions, schedules, strategies, etc for efficient asset management. Some of the non-physical sub-components, which have been modeled are listed as below.
  • Assumption model maintains consistency of parallel modeling efforts and diverge assumptions regarding boundary conditions between domains.
  • Drilling schedule model is a complement of the on stream date parameter in describing when a well will be active.
  • Real-time data model consists of three aspects: realtime production data, real-time access by an asset, and real-time action.
  • Control strategy The control strategy model provides both the heuristic operating rules and the optimization control for the system.
  • Reliability Reliability models are used capture the reliability and availability of each component and its building components. Once the components have been modeled, the next step is to model the interactions between different components.
  • This invention is focused on modeling the data flowing among various software components as part of the overall modeling methodology for application development in the petroleum industry.
  • the data type models described in this section represents a lower level of abstraction than the application models. They are attached to the application models, more accurately describing the data to/from each component in an application.
  • Data type models are used to capture the inputs and outputs of each processing component in an application model.
  • Each signal of a component is associated with a data type.
  • This can be a simple built-in type such as a double, float, integer or character or an array of any one of these, or a user- defined aggregate type.
  • Aggregate types are explicitly modeled by combining single (and arrays of) built-in types. This signal typing is easily specified within each component instance.
  • the aggregate signal types include elements to allow for breaking signal into constituent parts or combining simple types into complex types. Merging several simple streams into a larger structure helps reduce higher level interconnection visual complexity if a logical grouping can be developed.
  • the data type metamodel is shown by Figure 4.
  • FIG. 4 is a schematic diagram 600 of an exemplary metamodel for data types for inputs and outputs of each processing component of a producing oil field using UML class diagram notation.
  • TypeBase Model class 610 has three subclasses, i.e., PrimitiveType Model class 615, Logical Model class 620, and CompoundType Model class 625.
  • PrimitiveType Model class 615 has two subclass, i.e., Float Model class 630 and lnt Model class 635.
  • CompoundType Model class 625 has four subclasses, i.e., Union Model class 675, Struct Model class 680, LibraryType Model class 685, and TypeReference FCO class 645.
  • TypeReference FCO class 645 has three subclasses, i.e., PITypeRef Reference 650, LTypeRef Reference class 655, and CTypeRef Reference class 670.
  • Float Model class 630 has subclass PFTypeRef Reference class 640.
  • lnt Model class 635 has subclass PITypeRef Reference class 650.
  • Logical Model class 620 has subclass LTypeRef Reference class 655.
  • the relations of the data types are also modeled. For example, if a given type needs to be converted to another type with a conversion function, a model capturing the conversion function has to be used between the two types.
  • FIG. 5 is a schematic diagram 700 of an exemplary metamodel of application models and data type models composed together.
  • Component Model Proxy 710 relates to TypeRefBase FCO Proxy 740 and TypeConnection Connection 720 which connects
  • TypeRefBase FCO Proxy 740 and Signal Atom Proxy 730 are representative of corresponding parts that are modeled in detail elsewhere in the metamodel description.
  • TypeConnection connection between component signals and the TypeRefBase class is introduced.
  • TypeRefBase FCO Proxy 740 represents a reference to data type models.
  • TypeConnection Connection 720 assigns the referred type to the given port. H. Exemplary Benefits
  • the invention is a unified environment is created for developing a class of applications in petroleum engineering, which consist of various software components.
  • the modeling paradigms reflect our current understanding of petroleum engineering domain.
  • the hierarchical approach provides various levels of abstraction, integration and visualization. By adding data type models to application models, two-level abstraction has been achieved. To improve the modeling paradigms with more levels and more capabilities, we will have more interactions with the domain experts.

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Abstract

La présente invention concerne un schéma de modélisation conçu pour faciliter l'intégration d'applications logicielles complexes dans l'industrie du pétrole. Le schéma de modélisation décrit dans cette invention comprend: (a) un langage de modélisation graphique qui contient: (1) des classes regroupant plusieurs éléments d'actif de champs de pétrole et plusieurs connecteurs et une grammaire définissant les connexions nécessaires et autorisées entre les éléments d'actif conçus et adaptés pour la réalisation de plusieurs modèles graphiques adaptables au langage de modélisation graphique; les modèles graphiques représentant plusieurs éléments d'actif de champs de pétrole et les connexions entre eux, et chaque modèle présentant des plusieurs niveaux de détail; (2) le langage de modélisation graphique conçu et adapté pour la modélisation des éléments d'actif de différents champs de pétrole ayant différents nombres, types et configurations d'éléments d'actif; et (b) un interpréteur de modèle pour chacun des différents domaines d'applications logicielles propre au champ de pétrole pour stocker, analyser, diffuser ou manipuler des données de champ de pétrole associées à au moins l'un des éléments d'actif de champ de pétrole, chaque interpréteur de modèle étant conçu et adapté pour acheminer les informations entre les multiples éléments d'actif de champ de pétrole.
PCT/US2006/032014 2005-08-15 2006-08-15 Methodologie de modelisation pour developpement d'applications dans l'industrie du petrole WO2007022289A2 (fr)

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GB0804785A GB2444874A (en) 2005-08-15 2006-08-15 Modelling application development in the petroleum industry
EA200800599A EA200800599A1 (ru) 2005-08-15 2006-08-15 Методика моделирования для разработки приложений в нефтяной промышленности

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Cited By (23)

* Cited by examiner, † Cited by third party
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US7895241B2 (en) * 2006-10-16 2011-02-22 Schlumberger Technology Corp. Method and apparatus for oilfield data repository
WO2011149553A1 (fr) * 2010-05-27 2011-12-01 The Mathworks, Inc. Partitionnement de schémas fonctionnels en modèles contextuels exécutables
US20130159202A1 (en) * 2011-12-14 2013-06-20 Ii Thomas Francis Darden Systems & methods for automated assessment for remediation and/or redevelopment of brownfield real estate
US8776895B2 (en) 2006-03-02 2014-07-15 Exxonmobil Upstream Research Company Method for quantifying reservoir connectivity using fluid travel times
US9058445B2 (en) 2010-07-29 2015-06-16 Exxonmobil Upstream Research Company Method and system for reservoir modeling
US9058446B2 (en) 2010-09-20 2015-06-16 Exxonmobil Upstream Research Company Flexible and adaptive formulations for complex reservoir simulations
US9134454B2 (en) 2010-04-30 2015-09-15 Exxonmobil Upstream Research Company Method and system for finite volume simulation of flow
US9187984B2 (en) 2010-07-29 2015-11-17 Exxonmobil Upstream Research Company Methods and systems for machine-learning based simulation of flow
US9260947B2 (en) 2009-11-30 2016-02-16 Exxonmobil Upstream Research Company Adaptive Newton's method for reservoir simulation
US9489176B2 (en) 2011-09-15 2016-11-08 Exxonmobil Upstream Research Company Optimized matrix and vector operations in instruction limited algorithms that perform EOS calculations
US9594186B2 (en) 2010-02-12 2017-03-14 Exxonmobil Upstream Research Company Method and system for partitioning parallel simulation models
US10036829B2 (en) 2012-09-28 2018-07-31 Exxonmobil Upstream Research Company Fault removal in geological models
US10087721B2 (en) 2010-07-29 2018-10-02 Exxonmobil Upstream Research Company Methods and systems for machine—learning based simulation of flow
US10319143B2 (en) 2014-07-30 2019-06-11 Exxonmobil Upstream Research Company Volumetric grid generation in a domain with heterogeneous material properties
US10318653B1 (en) 2015-02-26 2019-06-11 The Mathworks, Inc. Systems and methods for creating harness models for model verification
US10521197B1 (en) 2016-12-02 2019-12-31 The Mathworks, Inc. Variant modeling elements in graphical programs
US10657208B2 (en) 2010-05-27 2020-05-19 The Mathworks, Inc. Analyzing model based on design interest
US10719645B1 (en) 2010-05-27 2020-07-21 The Mathworks, Inc. Model structure analysis with integration of transformed slice
US10803534B2 (en) 2014-10-31 2020-10-13 Exxonmobil Upstream Research Company Handling domain discontinuity with the help of grid optimization techniques
US10839114B2 (en) 2016-12-23 2020-11-17 Exxonmobil Upstream Research Company Method and system for stable and efficient reservoir simulation using stability proxies
WO2022087081A1 (fr) * 2020-10-22 2022-04-28 Aveva Software, Llc Système et serveur permettant d'effectuer un traçage de produit et un verrouillage complexe dans un système de commande de processus
US11409023B2 (en) 2014-10-31 2022-08-09 Exxonmobil Upstream Research Company Methods to handle discontinuity in constructing design space using moving least squares
US11829689B1 (en) 2020-06-09 2023-11-28 The Mathworks, Inc. Systems and methods for creating variant regions in acausal simulation models

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7184991B1 (en) * 2002-07-12 2007-02-27 Chroma Energy, Inc. Pattern recognition applied to oil exploration and production

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7184991B1 (en) * 2002-07-12 2007-02-27 Chroma Energy, Inc. Pattern recognition applied to oil exploration and production

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8776895B2 (en) 2006-03-02 2014-07-15 Exxonmobil Upstream Research Company Method for quantifying reservoir connectivity using fluid travel times
US8326888B2 (en) * 2006-10-16 2012-12-04 Schlumberger Technology Corporation Method and apparatus for oilfield data repository
US7895241B2 (en) * 2006-10-16 2011-02-22 Schlumberger Technology Corp. Method and apparatus for oilfield data repository
US9260947B2 (en) 2009-11-30 2016-02-16 Exxonmobil Upstream Research Company Adaptive Newton's method for reservoir simulation
US9594186B2 (en) 2010-02-12 2017-03-14 Exxonmobil Upstream Research Company Method and system for partitioning parallel simulation models
US9134454B2 (en) 2010-04-30 2015-09-15 Exxonmobil Upstream Research Company Method and system for finite volume simulation of flow
WO2011149553A1 (fr) * 2010-05-27 2011-12-01 The Mathworks, Inc. Partitionnement de schémas fonctionnels en modèles contextuels exécutables
US10719645B1 (en) 2010-05-27 2020-07-21 The Mathworks, Inc. Model structure analysis with integration of transformed slice
US8812276B2 (en) 2010-05-27 2014-08-19 The Mathworks, Inc. Determining model components suitable for verification analysis
US10691578B2 (en) 2010-05-27 2020-06-23 The Mathworks, Inc. Deriving contextual information for an execution constrained model
US10657029B2 (en) 2010-05-27 2020-05-19 The Mathworks, Inc. Partitioning block diagrams into executable contextual models
US10657208B2 (en) 2010-05-27 2020-05-19 The Mathworks, Inc. Analyzing model based on design interest
US10087721B2 (en) 2010-07-29 2018-10-02 Exxonmobil Upstream Research Company Methods and systems for machine—learning based simulation of flow
US9187984B2 (en) 2010-07-29 2015-11-17 Exxonmobil Upstream Research Company Methods and systems for machine-learning based simulation of flow
US9058445B2 (en) 2010-07-29 2015-06-16 Exxonmobil Upstream Research Company Method and system for reservoir modeling
US9058446B2 (en) 2010-09-20 2015-06-16 Exxonmobil Upstream Research Company Flexible and adaptive formulations for complex reservoir simulations
US9489176B2 (en) 2011-09-15 2016-11-08 Exxonmobil Upstream Research Company Optimized matrix and vector operations in instruction limited algorithms that perform EOS calculations
US20130159202A1 (en) * 2011-12-14 2013-06-20 Ii Thomas Francis Darden Systems & methods for automated assessment for remediation and/or redevelopment of brownfield real estate
US10036829B2 (en) 2012-09-28 2018-07-31 Exxonmobil Upstream Research Company Fault removal in geological models
US10319143B2 (en) 2014-07-30 2019-06-11 Exxonmobil Upstream Research Company Volumetric grid generation in a domain with heterogeneous material properties
US11409023B2 (en) 2014-10-31 2022-08-09 Exxonmobil Upstream Research Company Methods to handle discontinuity in constructing design space using moving least squares
US10803534B2 (en) 2014-10-31 2020-10-13 Exxonmobil Upstream Research Company Handling domain discontinuity with the help of grid optimization techniques
US10318653B1 (en) 2015-02-26 2019-06-11 The Mathworks, Inc. Systems and methods for creating harness models for model verification
US10521197B1 (en) 2016-12-02 2019-12-31 The Mathworks, Inc. Variant modeling elements in graphical programs
US10866789B1 (en) 2016-12-02 2020-12-15 The Mathworks, Inc. Variant modeling elements in graphical programs
US11126407B1 (en) 2016-12-02 2021-09-21 The Mathworks, Inc. Variant modeling elements in graphical programs
US11409504B1 (en) 2016-12-02 2022-08-09 The Mathworks, Inc. Variant modeling elements in graphical programs
US10839114B2 (en) 2016-12-23 2020-11-17 Exxonmobil Upstream Research Company Method and system for stable and efficient reservoir simulation using stability proxies
US11829689B1 (en) 2020-06-09 2023-11-28 The Mathworks, Inc. Systems and methods for creating variant regions in acausal simulation models
WO2022087081A1 (fr) * 2020-10-22 2022-04-28 Aveva Software, Llc Système et serveur permettant d'effectuer un traçage de produit et un verrouillage complexe dans un système de commande de processus

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AU2006279464A1 (en) 2007-02-22
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AU2006279464B2 (en) 2011-11-10
GB2444874A (en) 2008-06-18
EA200800599A1 (ru) 2008-08-29

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