WO2017058738A1 - Déroulement des opérations de simulation basée sur un réseau - Google Patents

Déroulement des opérations de simulation basée sur un réseau Download PDF

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Publication number
WO2017058738A1
WO2017058738A1 PCT/US2016/053864 US2016053864W WO2017058738A1 WO 2017058738 A1 WO2017058738 A1 WO 2017058738A1 US 2016053864 W US2016053864 W US 2016053864W WO 2017058738 A1 WO2017058738 A1 WO 2017058738A1
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WIPO (PCT)
Prior art keywords
model
data
simulation
data elements
network device
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PCT/US2016/053864
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English (en)
Inventor
Mark Wakefield
Kevin James SHAW
Ian AMBLER
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Schlumberger Technology Corporation
Schlumberger Canada Limited
Services Petroliers Schlumberger
Geoquest Systems B.V.
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Application filed by Schlumberger Technology Corporation, Schlumberger Canada Limited, Services Petroliers Schlumberger, Geoquest Systems B.V. filed Critical Schlumberger Technology Corporation
Publication of WO2017058738A1 publication Critical patent/WO2017058738A1/fr

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]

Definitions

  • Extracting fluid hydrocarbons from the subsurface involves ability to predict a multitude of technical, environmental and economic outcomes of various processes related to oilfield exploration and production.
  • numeric simulations of geological processes and fluid flow through subsurface rocks are performed on geologic models.
  • the geologic models describe properties of the subsurface rocks and hydrocarbon fluids.
  • one or more embodiments relate to a method for network based simulation.
  • Network based simulation includes receiving a request to execute multiple simulations from a client device. For each simulation, a model specification identifying a subset of data elements is received. The subset of data elements is assembled into a set of model realizations based on each model specification. The network based simulation further includes independently performing for each model realization a simulation to obtain simulation results, and transmitting the simulation results to a client device.
  • FIG. 1 is a schematic view, partially in cross-section, of a field in which one or more embodiments of cloud based simulation workflow may be implemented.
  • FIG. 2 shows a diagram of a system in accordance with one or more embodiments.
  • FIGs. 3, 4, and 5 show flowcharts in accordance with one or more embodiments.
  • FIG. 6 shows an example in accordance with one or more embodiments.
  • FIG. 7.1 shows computing system in accordance with one or more embodiments.
  • FIG. 7.2 shows a network system in accordance with one or more embodiments.
  • ordinal numbers e.g., first, second, third, etc.
  • an element i.e., any noun in the application.
  • the use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being a single element unless expressly disclosed, such as by the use of the terms "before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements.
  • a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.
  • One or more embodiments may be applied to numeric simulation of geologic models.
  • a network (e.g., cloud) based platform is provided that connects clients with simulators and data while maintaining the security protection of the data.
  • the subsurface oil and gas reservoirs are hard to measure and characterize. Further, the production of oil and gas from the oilfield reservoirs are difficult to predict without a good characterization of the reservoirs and other subsurface conditions.
  • engineers rely on computer simulations performed on computing platforms.
  • One or more embodiments offer clients a flexible resource that can provide simulation capabilities to match a variable and concurrent demand.
  • one or more embodiments provide for the network based simulations using data elements, whereby a data element is a partition of the data used for the simulations.
  • various data elements may be combined in different manners to form various model realizations that are used in different simulations. Thus, the same data element may be used across multiple simulations.
  • a specification of the data elements to combine may be sent.
  • Local data elements on the client may be transmitted via the network, where one or more of the local data elements may be used for multiple simulations.
  • one or more embodiments address the computer technology problems of network constraints of bandwidth, simulation request transmission delay to send via the network, and constraints on the size of storage device.
  • some of the data elements may be pre-simulated, thereby, saving on the processing cycles by reusing such data elements.
  • One or more embodiments may offer a platform to share the data blocks among multiple users, each user defining a custom simulation combining different data blocks, the combination may be unique to the user but the data blocks may be selected from a common pool of data blocks. For example, various clients may submit requests for performing multiple simulations using a resource pool offered on the network platform. The results of the simulations are transferred from the network platform to the client platform and visualized and analyzed on the client platform.
  • FIG. 1 depicts a schematic view, partially in cross section, of a field (100) in which one or more embodiments may be implemented.
  • one or more of the modules and elements shown in FIG. 1 may be omitted, repeated, and/or substituted. Accordingly, embodiments should not be considered limited to the specific arrangements of modules shown in FIG. 1.
  • a geologic sedimentary basin includes subterranean formations (104).
  • the subterranean formation (104) may include several geological structures (106-1 through 106-4).
  • the formation may include a shale layer (106-1), a limestone layer (106-2), a sandstone layer (106-3), and another shale layer (106-4).
  • a fault plane (107) may extend through the formation.
  • the geologic sedimentary basin includes rock formations and at least one reservoir including fluids.
  • the rock formations include at least one seal rock, for example, the shale layer (106-1), which may act as a top seal.
  • the rock formations may include at least one seal rock, for example, the shale layer (106-4), which may act as a bottom seal.
  • various survey tools and/or data acquisition tools are adapted to measure the formation and detect the characteristics of the geological structures of the formation.
  • survey operations and wellbore operations are referred to as field operations of the field (100). These field operations may be performed as directed by the surface unit (112).
  • the surface unit (112) is communicatively coupled to the exploration and production (E&P) computer system (118).
  • the E&P computer system may be the computer system described in FIGs. 9.1 and 9.2.
  • the data received by the surface unit (112) may be sent to the E&P computer system (118) for further analysis.
  • the E&P computer system (118) is configured to analyze, model, control, optimize, or perform management tasks of the aforementioned field operations based on the data provided from the surface unit (112).
  • the E&P computer system (118) is provided with functionality for manipulating and analyzing the data, such as performing simulation, planning, and optimization of production operations of the wellsite system A (114-1), wellsite system B (114-2), and/or wellsite system C (114-3).
  • the result generated by the E&P computer system (118) may be displayed for an analyst user to view the result in a 2D display, 3D display, or other suitable displays.
  • the surface unit (112) is shown as separate from the E&P computer system (118) in FIG. 1, in other examples, the surface unit (112) and the E&P computer system (118) may also be combined.
  • FIG. 1 shows a field (100) on the land, the field (100) may be an offshore field. In such a scenario, the subterranean formation may be in the sea floor. Further, field data may be gathered from the field (100) that is an offshore field using a variety of offshore techniques for gathering field data.
  • the data received by the surface unit (112) represents physical properties of the subterranean formation (104) and may include seismic data and/or information related to location of the horizon and fault surfaces or characteristics of the formation rocks like porosity, saturation, permeability, natural fractures, stress magnitude and orientations, elastic properties, etc., during a drilling, fracturing, logging, or production operation of the wellbore (103) at the wellsite system (110).
  • FIG. 2 shows more details of the E&P computer system (118) in which one or more embodiments of the technology may be implemented.
  • one or more of the modules and elements shown in FIG. 2 may be omitted, repeated, and/or substituted. Accordingly, embodiments of evaluation of fluid transport properties in heterogeneous geological formation should not be considered limited to the specific arrangements of modules shown in FIG. 2.
  • the E&P computer system (118) includes a client interface (201), a network (230), a network device (235), remote data repositories (220) and a field equipment module (270) for performing various tasks of the field operation.
  • the client interface (201) corresponds to a graphical user interface (GUI) that includes functionality to receive input from a user, transmit the input to the network device (235) over the network (230), receive data results from the network device (235) over the network (230), present or display numerical and graphical data to the user, and transmit control signal to the field equipment module (270).
  • GUI graphical user interface
  • the client interface (201) may include a 3D subsurface data viewer, a 2D subsurface profile viewer, and input fields for entering values for variable parameters (247) in accordance with one or more embodiments.
  • the input fields include functionality to receive input parameters from a user as fixed values or as a range of values.
  • the input parameters may include a set of parameters for a reservoir simulation, a range of reservoir porosity to test the hydrocarbon flow rates based on the specified range, an observation point angle defining a rendering of the 3D subsurface reservoir, a location to display a 2D section of subsurface reservoir or a color palette to map different reservoir rock properties.
  • the input fields may include selection boxes, text fields, drop-down menus, or any other type of field for a user to input data.
  • the client interface (201) may include additional user interface components for oilfield analysis.
  • the client interface (201) may include components for basin modeling of the oilfield, components for economic evaluation of the oilfield, components for interacting with the oilfield, including sending commands to the oilfield, and other components that are not shown or expressly described above.
  • the network (230) is defined as a communications network which allows computers to exchange data.
  • networked computers exchange data with each other using a data link.
  • the connections between computers are established using wired and/or wireless media.
  • the network is the Internet.
  • the remote data repositories (220) are a set of interfaces that links to a collection of data hosted on platforms external of the network device (235). In one or more embodiments, the remote data repositories (220) are accessible to the network device (235) through the network (230). In one or more embodiments, the data from the remote data repositories (220) may be incorporated in a model simulated in the network device (235). Although not shown in FIG. 2, data elements (244) may be stored in a remote data repository.
  • the network device (235) includes a data repository (240) for storing input data, intermediate data, and resultant outputs of the simulation and analysis, and an analysis toolset (250) for performing simulation and analysis.
  • the data repository (240) may include one or more disk drive storage devices, one or more semiconductor storage devices, other suitable computer data storage devices, or combinations thereof.
  • content stored in the data repository (240) may be stored as a data file, a linked list, a data sequence, a database, a graphical representation, any other suitable data structure, or combinations thereof.
  • the content stored in the data repository (240) includes field measurements (241), empirical functions (242), data elements (244), model specifications (246), variable parameters (247), model realizations (248) and simulation results (249). Each of these components is discussed below.
  • the field measurements (241) are values of the physical properties of the subsurface elements measured directly in the field or laboratory. In one or more embodiments, at least a portion of the values are based on measurements obtained from the data acquisition tools depicted in FIG. 1 above.
  • field measurements (241) may include measurements obtained from seismic response of the subsurface formations (108-1) in the oilfield (100) (e.g. seismic travel time), from gravity and magnetic surveys (e.g. gravity field intensity, magnetic field intensity, etc.), from core logs (108-2) and laboratory tests on core samples (e.g. permeability, natural gamma ray, etc.), from well logs (108-3) in the wellbores of the oilfield (e.g.
  • the empirical functions (242) are defined as functions that provide an estimate of an unknown attribute based on values of at least one known attribute or a field measurement.
  • the attributes represent properties of a field element (e.g. geologic layer burial depth, reservoir permeability, in place hydrocarbon accumulation volume, etc.), subsurface formation (e.g. shale porosity, sandstone thermal conductivity, stratigraphic layer thickness, etc.), or economic indicator (e.g.
  • one or more of the attributes may be physical properties, while other attributes may be intangible properties, such as economic properties.
  • the attribute may be measured directly in the field or laboratory.
  • the attributes may be derived from field measurements (241) or from another attribute by using an empirical function (242).
  • the empirical functions (242) are derived from laboratory experiments on samples of materials having both the unknown and known attribute. For example, a subsurface formation including shale layer (106-1) may have a porosity value estimated based on the depth of burial of the shale layer as resulted from a shale porosity curve.
  • the shale porosity curve is generated by plotting a multitude of observed porosity at various depth values of shale rock samples collected at different burial depths around the world, or from a specific sedimentary basin.
  • the empirical functions may be derived from application of mathematical or physical principles that address the relationship between the unknown and known attributes.
  • the data elements (244) are partial sections of independent and self- contained datasets used for simulation.
  • the data elements (244) include values for attributes used as input to the simulations of the models.
  • the data elements may be generated by the appropriate modeling software, e.g., fluid modeling and characterization software, geological modeling software, or economics software.
  • a data element may correspond to a separate model or portion of a model (e.g., velocity model, basin model, reservoir model, fluids model, production model) that is used to perform the simulation.
  • a model e.g., velocity model, basin model, reservoir model, fluids model, production model
  • Rock models may be generated in several workflows, the workflows being either automated or manual. For example, one is a mathematical function another is direct from measured data or another from averaged curves (perhaps a mix of the previous or even an analogue from a different field or literature). Further, a simulation could reference any or all of the rock models, i.e. the simulation may have any number of rock models with differing sources.
  • the data elements (244) are a shared data entity, a type of input, used but not altered by the simulator. In other words, the various data elements (244) are shared by multiple simulations. In one or more embodiments, one or more of the data elements (244) may be parameterized. A parameterized data element includes at least one variable parameter included in the data element definition. A value for the variable parameter is supplied for the reproduction of the data element in its entirety. In one or more embodiments, the data elements, which at the very least could be a flat file, may be a versioned resource in a data repository.
  • the model specifications (246) represent a collection of instructions and variable values that define a model simulation initialization and running.
  • the model specification lists, for each model realization, which data element(s) are used for the model realization and the device location from which the data element(s) may be recovered.
  • the model specification defined in a file indicates which data elements are available locally and which data elements are available from a remote data repository.
  • the model specification file might specify a location for the data elements, a unique identifier (ID) for each data element, and version number so that the data can be recovered from a central versioned repository.
  • ID unique identifier
  • variable parameters (247) are free value attributes included in data elements definition or model specification. In other words, the variable parameters (247) have no value defined within the data elements or model specification. Rather, the value is supplied by the user in order to obtain the data element or the model realization. In one or more embodiments, the variable parameters (247) may be defined for parameterization of data elements. In one or more embodiments, the variable parameters values are included in the model specification when the model specification refers to a parameterized data element. In other words, a modeling specification may have a single data element that is used for multiple model realizations (e.g., modified variable parameter values for different simulations).
  • the model realizations (248) are numeric representations of the subsurface domains that include spatial description of subsurface components and components physical properties.
  • a model realization may be composed of a multitude of data elements.
  • a model realization is built according to the information presented in the model specification.
  • the model realizations (248) are a collection of data elements, values for variable parameters and instructions ready to be read by simulator to perform a simulation.
  • a basin model realization of a sedimentary basin may include a set of stratigraphic surfaces data elements combined with a set of fades maps data elements, a set of thermal boundary data elements applied at the base of the model and 2% Total Organic Carbon (TOC) defined as a value for a parameterized kinetic hydrocarbon generation dataset, and an instruction to use Redlich-Kwong equation of state for estimate of the generated hydrocarbon volume.
  • TOC Total Organic Carbon
  • the model realization may be loaded in a basin modeling simulator and simulated resulting in calculated volumes of hydrocarbons generated in the sedimentary basin.
  • the simulation results (249) are defined as data resulted from model simulations.
  • the simulator loads a model realization and executes a simulation on the model realization.
  • the results of the simulation are written to the data repository as simulation results (249).
  • the simulation results may consist in results of further post-simulation processing of data. For example, the average temperature of a reservoir obtained by averaging temperature data of the reservoir volume resulted from the basin model simulation.
  • the network device (235) additionally includes an analysis toolset (250) in accordance with one or more embodiments.
  • the analysis toolset (250) includes a modeling package (251), a model assembler (253), a model simulator (255), and a results server (257). Each of these components is described below.
  • the modeling package (251) is a software component that is configured to generate data elements (244).
  • different packages generate different data elements.
  • the seismic interpretation package generates stratigraphic surfaces data elements
  • the sedimentary basin modeling package generates sediment distribution data elements
  • the fluid package generates fluid phase data elements
  • the well logging interpretation package generates reservoir properties data elements.
  • the model assembler (253) is a software component that is configured to build a model realization based on the instructions from model specifications.
  • the model assembler (253) is configured to read the specification file and, from the specification file construct the specified model realization on demand.
  • the model assembler (253) may be able to build a model realization using a set of references to data elements.
  • the model assembler (253) is configured to produce multiple model realizations using a single parameterized data element by applying multiple values to the variable parameter.
  • multiple top of reservoir surfaces may be interpolated by parameterizing the kriging method in terms of the search radius and using different values of the search radius passed to the reservoir structure data element for the parameterization.
  • the model assembler (253) is configured to produce multiple model realizations using various combination of interchangeable data elements or different versions of the same data elements.
  • a reservoir layer top surface that is interpolated by kriging method may be substituted for the same surface interpolated by natural neighbor method or by inverse distance weighted method.
  • a reservoir layer top surface interpolated by any method from a set of well picks may be substituted for the same surface interpolated for a new version of the well picks.
  • the model simulator (255) is a software component that is configured to perform numeric simulations of the physical or economic processes.
  • the model simulator (255) performs simulation on a model realization (248) according to the instructions set by model specifications (246) and may consider one or more variable parameters (247).
  • the model simulator represents the physical space of the model realization domain by an array of discrete grid blocks, delineated by a grid which may be regular or irregular. Each block in the grid represents a subsurface volume.
  • the array of grid blocks may be two-dimensional (2D), three-dimensional (3D), etc. Values for physical attributes, that are part of one or more data elements (244), may be associated with each grid block.
  • each attribute may vary across the simulation domain, but the value is applied uniformly throughout the grid block.
  • the model simulator (255) e.g., executing on a simulation server
  • the model simulator (255) is able to use data in the format of the data element (generated by the modeling package).
  • the model simulator (255) is configured to recover the data elements from the appropriate source, whether that is a file, a database, or a remote location, etc.
  • the model simulator (255) is configured to run the specified model realization, potentially in parallel, and potentially providing results about the whole collection of the models (e.g., statistical data including means, averages, etc.).
  • the results server (257) is a data server that is configured to retrieve simulation results and deliver the simulation results to the client interface.
  • the result server (257) is configured to perform additional processing of model results in order to deliver analysis reports and selective information regarding one or more simulation results.
  • the result server (257) is configured to deliver analysis reports for a combined set of simulations.
  • the results server can analyze the results of multiple scenario of production simulation of a well and deliver only a set of statistical parameters values like for example the average production, the minimum and maximum production, or a production distribution curve for all scenarios simulation.
  • the field equipment module (270) is configured to generate a field operation control signal based at least on a result generated by the computer system, such as based on the modeling analysis results.
  • Field equipment depicted in FIG. 1 above may be controlled by the field operation control signal.
  • the field operation control signal may be used to control drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices disposed about the field (100) depicted in FIG. 1 above.
  • FIG. 3 depicts a flowchart of an example method in accordance with one or more embodiments.
  • the method depicted in FIG. 3 may be practiced using the E&P computer system (118) described in reference to FIGS. 1 and 2 above.
  • one or more of the elements shown in FIG. 3 may be omitted, repeated, and/or performed in a different order. Accordingly, one or more embodiments should not be considered limited to the specific arrangements of elements shown in FIG. 3.
  • a request is received to execute multiple simulations, the request including for each simulation, a model specification identifying a subset of data elements.
  • the model specification may be considered a model of models, from which instances can be realized by substituting the parameters for the appropriate values and data elements.
  • the multiple simulations correspond to different scenarios. For example, different reservoir permeability scenarios exist for well production, different thermal histories of a field for hydrocarbon generation.
  • the selection of multiple simulations is based on different values passed to a parameterized model specification.
  • the model specification lists the data elements that are to be included in each model realization.
  • the parameterization of the model specification is enabled by combining different versions of data elements. For example, consider the effect of the new data over the old data. For example, reservoir layer is defined from the results of a geologic interpretation of a seismic volume acquired in an oil field. The seismic interpretation is converted from the time domain into a depth domain using an initial velocity model. Later, a new set of measurements from the field lead to an update of the velocity model, and when the new velocity model is applied to the seismic volume, a new reservoir layer definition is obtained.
  • a simulation is performed for each reservoir definition and the difference in the results is analyzed.
  • the parameterization of the model specification is enabled by combining different interchangeable data elements. For example, running multiple simulations for different basin velocity models, for different fluid models of a reservoir, or for different development strategies for an oilfield.
  • the data elements may be parameterized.
  • the parameterization of the data element is specified by the model specification.
  • the model assembler replicates any data element included in the model specification from the data element source origin and may further process the data element. For example, for a data element generated by averaging various curves, a re-average of the curves may be performed at any time since the location of the original data and the full details on how the average was executed or on which sets of curves was selected is known from the data elements themselves.
  • the task of the model assembler is not to build the data elements to be included in a simulation model because the various specialized modeling packages from each domain build the data elements.
  • the task of the model assembler is restricted to constructing the various data mappings and collating the required data already residing on the network device.
  • the sort of mappings that may be generated in-situ may include: the distribution of the fluid models and rock properties to regions of the model, and the relationship between well identifiers in the trajectory files and the well identifiers in the field development logic.
  • the data elements being assembled may be on the same physical location as the network device.
  • the model assembler is assembling the model by loading the local data elements to assemble a model realization on the same network device where the data elements are located. Because of the locality of the data elements and the specification only identifying storage locations, the bandwidth utilization for transferring data from the client to the network device is minimized.
  • the data elements being assembled may be on a different physical location than the network device.
  • the data elements may be on a remote data repository.
  • one or more embodiments may minimize the bandwidth utilization for transferring data from multiple physical locations when assembling a model realization from data elements located in different physical locations by reusing the data elements across multiple model realizations. Further bandwidth minimization may be performed through compression algorithms or other algorithms that mitigate data transfer redundancy.
  • a simulation is performed independently for each model realization of the multiple simulations, to obtain multiple simulation results.
  • reservoir simulations may solve a complex set of non-linear partial differential equations that model the fluid flow in porous media over a time interval.
  • Grid resolution may impact the accuracy of simulation results.
  • the grid resolution may be a variable parameter (247) specified in model specification (246).
  • simulations may use upscaling techniques in order to capture fine scale phenomena while using relatively coarse grids.
  • the simulation results are transmitted.
  • the simulation results are delivered by the results server (257) at a request from the client interface (201).
  • the results server allows query of results, and returns repeated queries quickly, because it can simply return stored results instead of regenerating them.
  • Web, mobile devices, and any other technology that may access the cloud may query the results server.
  • the present methodology removes the limitation that simulation clients have a fixed amount of computational power and licenses that are shared amongst various users and workflows.
  • the technology does not rely on a number of assumptions to be made on the baseline and peak usage of a local server cluster in order to right-size it.
  • One or more embodiments of the technology may provide simulation workflows which benefit from exercising elastic compute resourcing whilst minimizing the latency effects associated with the remote hardware.
  • FIGs. 4 and 5 show flowcharts in accordance with one or more embodiments. While the various blocks in these flowcharts are presented and described sequentially, one of ordinary skill will appreciate that at least some of the blocks may be executed in different orders, may be combined or omitted, and at least some of the blocks may be executed in parallel. Furthermore, the actions in the blocks may be performed actively or passively. For example, some actions may be performed using polling or be interrupt driven in accordance with one or more embodiments.
  • determination blocks may not involve a processor to process an instruction unless an interrupt is received to signify that condition exists in accordance with one or more embodiments. As another example, determination blocks may be performed by performing a test, such as checking a data value to test whether the value is consistent with the tested condition in accordance with one or more embodiments.
  • FIG. 4 shows a general flowchart to run pre-parameterized forecasting models on the cloud.
  • a parameterized model specification is transferred to the cloud but instead of selected set of parameters being specified up-front the user will run range of values for a specific parameter and inspect the results using the results server and viewer on the client interface.
  • a model specification is received from a client interface, the model specification including a range of values for a variable parameter.
  • the range of values is within an interval to be tested for scenarios of potential outcome.
  • the model specification includes information regarding the range of values of a variable parameter to be tested by multiple simulations that cover the entire range.
  • the model specification may further include the selection of data elements that will be integrated into model realizations. For example, a scenario of production of a well for a range of reservoir porosity between 40% and 20% may be accomplished by defining a set 10 simulation, each simulation testing the 20-40% interval in 2% porosity increments.
  • Block 420 based on model specification, the set of data elements are selected, the set including at least one parameterized data element.
  • the parameterized data element includes the variable parameter to be tested by the range of values presented in Block 410.
  • the rage testing may be accomplished by sampling the range interval of the values at the predefined sampling rate.
  • the data elements are assembled into a model, and a set of model realizations are generated that cover the range of values for the variable parameter.
  • the model specification is transported to the network device and the model assembler is building the model realizations according to a compact set of parameter values defined in the model specification.
  • the model specification is a minimal description of the generated model realization, removing any form of duplication especially in costly binary data such as the grid geometry or model properties. Because the network device provides a flexible computing time capability, the number of realization that can be run is also no longer limited by the fixed capacity of the client owned hardware or software licenses, but by the simulation budget which is potentially much more flexible.
  • the model assembler is sending the model realization to the simulator, transferring the required data elements (if their primary location is on a remote data repository (244), or if the primary location is the data repository (240) on the network device (235) providing the details to access them).
  • the model realizations are simulated to obtain a set of simulation results corresponding to each model realization.
  • the simulator processes the specification file and runs the simulations of multiple model realizations concurrently, outputting the simulation results to the data repository. Whilst many realizations may be run in order to properly sample the uncertainty space and evaluate the risk of various development strategies, the results of many of the individual realizations serve to construct the average response and on their own is not that time consuming.
  • the simulation results are sent to the client interface.
  • the simulation results are transferred by the result server, back to the user on the client interface.
  • simulation results are either at an individual model level, or statistically at the multiple model realizations level.
  • One or more embodiments allow users to be able to efficiently view the results without transferring all the data back from the cloud.
  • the visualization is accomplished by a result server that in response to queries could serve up the appropriate results for consumption by a thin client interface.
  • a small subset of model results could then be identified for being transferred back to the client interface environment for further analysis and development.
  • the subset of model results is likely to include the results and the corresponding parameter values.
  • FIG. 5 shows a general flowchart to edit parameters of history matching models on the cloud.
  • the observed historical evolution of a first attribute measurement is matched with the result of a simulation in order to determine values for a second attribute.
  • the second attribute is assigned to a variable parameter and simulations are run on parameterized model specifications. Multiple values for the variable parameter are tested until the values of the first attribute resulted from simulation are within a certain range of the observed measurements. Further, the user decides on the next set of parameters that should be trailed based on personal knowledge of the field and expertise. For example, the production evolution of the hydrocarbons in a well may be used to estimate the value for the permeability of a subsurface reservoir rock.
  • a set of field measurements to model is selected.
  • selection is made by a user on the client interface and the selection is sent through the network to the network device.
  • the selection of measurements may be a time sequence of values of the same attribute measurement.
  • the selection may include the rate of hydrocarbon flow into a well over time.
  • a set of values for a variable parameter is selected.
  • the variable parameter is associated with an attribute to be estimated.
  • the selected values may be manually selected by a user based on personal knowledge of the field and expertise.
  • the selected values may be provided in a batch file to be tested in a predefined sequence.
  • the selected set of values of the variable parameter is included in a model specification. For example, a set of values for the permeability of a sandstone reservoir are selected to be tested.
  • the model specification is sent to a network device, the model specification including the selection of values for the variable parameter. Further, the model specification may include information regarding the collection of data elements required to build a set of model realization, instructions to simulate the model realizations and may include additional variable parameters to be used to perform the simulation. In one or more embodiments, the model specification is built by a user on the client interface and sent to the network device through the network.
  • a set of data elements is selected by the network device, the set including at least one parameterized data element.
  • the data elements are selected by a model assembler based on information presented in the specification file.
  • the data elements may include a subsurface volume for the reservoir, a specific sandstone fades to fill the reservoir volume, and a type of fluid hydrocarbon to flow through the reservoir.
  • a parameterized flow model based on the Darcy equation may be selected, the model using the permeability as a variable parameter to calculate the flow of the fluid hydrocarbon through the volume of the reservoir filled with the selected facies of sandstone.
  • Block 550 the data elements are assembled into a model in the network device and a set of model realizations are generated for each of the values of the variable parameter in the selection.
  • the values of the variable parameter are fed to the parameterized data element to produce a corresponding set of model realizations. For example, starting with the reservoir filled with the selected facies of sandstone as presented above, and for each permeability value from the selected permeability values, a separate model realization is produced.
  • the model realizations are simulated in the network device, to obtain a set of simulation results corresponding to each model realization.
  • the model simulator reads each model realization and performs the model simulation according to the instructions defined in the model specification.
  • the simulator performs additional simulations for each value of the variable parameter. For example, the simulator may be instructed to perform simulations of each model realization at various grid resolutions. In one or more embodiments, the simulations may be performed sequential or concurrent.
  • a results server reads the simulation results and transmits the results to the client interface through the network.
  • the results server may perform additional analysis and results filtering for more than one simulation results and provide the user only a subset of the results or a summary of the results.
  • Block 580 the simulation results are compared with the field measurements. If no simulation results are found to match the production evolution as obtained in the field measurement, a new set of values are provided in block 520 for further simulations.
  • the results may be compared and analyzed visually by a user on the client interface.
  • a correlation coefficient may be defined and sent to the network device, and a statistic comparison may be automatically performed by the results server. Further, only the results that satisfy a criterion defined by the correlation coefficient may be sent to the client interface.
  • FIG. 6 shows an example of model realization assembly workflow in accordance with one or more embodiments.
  • the workflow may include the process of constructing and validating a model specification on the network device.
  • the model specification includes a map to data elements and may not actually include much volume of data, the operation is amenable to being easy transferred between the client interface and network device when an assumption is made that the data is already up on the network device in a central depot (data repository).
  • a scenario is presented where a reservoir model simulation is prepared to be run in the network device to assess the effect of reservoir compartmentalization on the production of fluid hydrocarbons to multiple wells of a field.
  • user Bob who is a reservoir engineer for an oil company, select a set of field measurements represented by the seismic volume (601) that will be used to delineate the reservoir. Before the seismic volume is used to delineate the reservoir, the volume needs to be migrated to convert the seismic information from the time domain into a depth domain. Bob send a request to the geophysics department to process the seismic volume and a migration package is used to create a migrated volume.
  • the geophysics department has to use a velocity model as a parameter to derive a migrated volume data element.
  • Two velocity models are available, first model (610) and second model (611), therefore two interchangeable migrated volume data elements may be created.
  • the geophysics department selects the first model (610) and performs the seismic migration.
  • the migrated volume is sent to the business development department in order to perform the geologic interpretation on the migrated seismic volume.
  • Joe who is a geophysics interpreter, performs the interpretation based on the stratigraphic surfaces picks from a preliminary analysis of the well logs in the field and a first version of the geologic interpretation (621) is produced.
  • the business development department is interested to have a second opinion on the seismic interpretation and, accordingly, assign Ashley, who is another seismic interpreter to perform her own interpretation of the same seismic volume.
  • Ashley's geologic interpretation (631) is slightly different than that of Joe and is further in contradiction with most analog fields interpretation, therefore the business development department retains Joe to perform the final geologic interpretation.
  • Joe performs the interpretation based on the stratigraphic surfaces picks from a new analysis of the well logs in the field, and a second version of the geologic interpretation (623) is produced.
  • the three geologic interpretations are assigned to three interchangeable data elements, the data elements based Joe interpretation having two versions of the single ID data element.
  • the interpreted surfaces have to be assigned a rock facies with different levels of compartmentalization of the sandstone reservoir deposits.
  • the compartmentalization is explored by a set of three sedimentary models (641, 642, and 643) that assume a progressive level of compartmentalization.
  • the models are derived by Nadine, a sedimentary basin modeler based on a parameterized data element with three values of the compartmentalization factor.
  • phase envelope based on analysis of fluid composition of the field samples.
  • a range of probability is used to determine the parameterized phase envelope data element.
  • Any of the derived phase envelope data elements is interchangeable for the simulation purpose. For example, the phase envelope based on 10% probability (651), 50% probability (651), and 90% probability (651) are derived.
  • Joe is able to create the model specification for the simulation. He may designate any combination of interchangeable data elements and design a multitude of model realizations (660) to be simulated in the network device.
  • the prepared grid includes subsurface information from a seismic migration package, surface picks from a geological interpretation software package, geological properties from a sedimentary basin modeling package, a compositional fluid model from a fluid modeling package.
  • the data elements have enough information and context to be imported and used for richer workflows as they are self-contained and carry enough information to work standalone.
  • Embodiments may be implemented on a computing system. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be used.
  • the E&P computing system (700) may include one or more computer processors (702), non-persistent storage (704) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (706) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.), a communication interface (712) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities.
  • non-persistent storage e.g., volatile memory, such as random access memory (RAM), cache memory
  • persistent storage e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.
  • the computer processor(s) (702) may be an integrated circuit for processing instructions.
  • the computer processor(s) may be one or more cores or micro-cores of a processor.
  • the E&P computing system (700) may also include one or more input devices (710), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.
  • the communication interface (712) may include an integrated circuit for connecting the E&P computing system (700) to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) and/or to another device, such as another computing device.
  • a network not shown
  • LAN local area network
  • WAN wide area network
  • the Internet such as the Internet
  • mobile network such as another computing device.
  • the E&P computing system (700) may include one or more output devices (708), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device.
  • a screen e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device
  • One or more of the output devices may be the same or different from the input device(s).
  • the input and output device(s) may be locally or remotely connected to the computer processor(s) (702), non-persistent storage (704), and persistent storage (706).
  • the computer processor(s) may be locally or remotely connected to the computer processor(s) (702), non-persistent storage (704), and persistent storage (706).
  • the aforementioned input and output device(s) may take other forms.
  • Software instructions in the form of computer readable program code to perform embodiments may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium.
  • the software instructions may correspond to computer readable program code that, when executed by a processor(s), is configured to perform one or more embodiments.
  • the E&P computing system (700) in FIG. 7.1 may be connected to or be a part of a network.
  • the network (720) may include multiple nodes (e.g., node X (722), node Y (724)).
  • Each node may correspond to a computing system, such as the computing system shown in FIG. 7.1, or a group of nodes combined may correspond to the computing system shown in FIG. 7.1.
  • embodiments may be implemented on a node of a distributed system that is connected to other nodes.
  • embodiments may be implemented on a distributed computing system having multiple nodes, where each portion may be located on a different node within the distributed computing system.
  • one or more elements of the aforementioned E&P computing system (700) may be located at a remote location and connected to the other elements over a network.
  • the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane.
  • the node may correspond to a server in a data center.
  • the node may correspond to a computer processor or micro- core of a computer processor with shared memory and/or resources.
  • the nodes (e.g., node X (722), node Y (724)) in the network (720) may be configured to provide services for a client device (726).
  • the nodes may be part of a cloud computing system.
  • the nodes may include functionality to receive requests from the client device (726) and transmit responses to the client device (726).
  • the client device (726) may be a computing system, such as the computing system shown in FIG. 7.1. Further, the client device (726) may include and/or perform at least a portion of one or more embodiments.
  • 7.1 and 7.2 may include functionality to perform a variety of operations disclosed herein.
  • the computing system(s) may perform communication between processes on the same or different system.
  • a variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file. Further details pertaining to a couple of these non-limiting examples are provided below.
  • sockets may serve as interfaces or communication channel end-points enabling bidirectional data transfer between processes on the same device.
  • a server process may create a first socket object.
  • the server process binds the first socket object, thereby associating the first socket object with a unique name and/or address.
  • the server process then waits and listens for incoming connection requests from one or more client processes (e.g., processes that seek data).
  • client processes e.g., processes that seek data.
  • the client process starts by creating a second socket object.
  • the client process then proceeds to generate a connection request that includes at least the second socket object and the unique name and/or address associated with the first socket object.
  • the client process transmits the connection request to the server process.
  • the server process may accept the connection request, establishing a communication channel with the client process, or the server process, busy in handling other operations, may queue the connection request in a buffer until server process is ready.
  • An established connection informs the client process that communications may commence.
  • the client process may generate a data request specifying the data that the client process wishes to obtain.
  • the data request is subsequently transmitted to the server process.
  • the server process analyzes the request and gathers the requested data.
  • the server process then generates a reply including at least the requested data and transmits the reply to the client process.
  • the data may be transferred, more commonly, as datagrams or a stream of characters (e.g., bytes).
  • Shared memory refers to the allocation of virtual memory space in order to substantiate a mechanism for which data may be communicated and/or accessed by multiple processes.
  • an initializing process first creates a shareable segment in persistent or non-persistent storage. Post creation, the initializing process then mounts the shareable segment, subsequently mapping the shareable segment into the address space associated with the initializing process. Following the mounting, the initializing process proceeds to identify and grant access permission to one or more authorized processes that may also write and read data to and from the shareable segment. Changes made to the data in the shareable segment by one process may immediately affect other processes, which are also linked to the shareable segment. Further, when one of the authorized processes accesses the shareable segment, the shareable segment maps to the address space of that authorized process. Often, one authorized process may mount the shareable segment, other than the initializing process, at any given time.
  • the computing system performing one or more embodiments may include functionality to receive data from a user.
  • a user may submit data via a GUI on the user device.
  • Data may be submitted via the GUI by a user selecting one or more GUI widgets or inserting text and other data into GUI widgets using a touchpad, a keyboard, a mouse, or any other input device.
  • information regarding the particular item may be obtained from persistent or non-persistent storage by the computer processor.
  • the contents of the obtained data regarding the particular item may be displayed on the user device in response to the user's selection.
  • a request to obtain data regarding the particular item may be sent to a server operatively connected to the user device through a network.
  • the user may select a uniform resource locator (URL) link within a web client of the user device, thereby initiating a Hypertext Transfer Protocol (HTTP) or other protocol request being sent to the network host associated with the URL.
  • HTTP Hypertext Transfer Protocol
  • the server may extract the data regarding the particular selected item and send the data to the device that initiated the request.
  • the contents of the received data regarding the particular item may be displayed on the user device in response to the user's selection.
  • the data received from the server after selecting the URL link may provide a web page in Hyper Text Markup Language (HTML) that may be rendered by the web client and displayed on the user device.
  • HTML Hyper Text Markup Language
  • the computing system may extract one or more data items from the obtained data.
  • the extraction may be performed as follows by the computing system in FIG. 7.1.
  • the organizing pattern e.g., grammar, schema, layout
  • the organizing pattern is determined, which may be based on one or more of the following: position (e.g., bit or column position, Nth token in a data stream, etc.), attribute (where the attribute is associated with one or more values), or a hierarchical/tree structure (consisting of layers of nodes at different levels of detail-such as in nested packet headers or nested document sections).
  • the raw, unprocessed stream of data symbols is parsed, in the context of the organizing pattern, into a stream (or layered structure) of tokens (where each token may have an associated token "type").
  • extraction criteria are used to extract one or more data items from the token stream or structure, where the extraction criteria are processed according to the organizing pattern to extract one or more tokens (or nodes from a layered structure).
  • the token(s) at the position(s) identified by the extraction criteria are extracted.
  • the token(s) and/or node(s) associated with the attribute(s) satisfying the extraction criteria are extracted.
  • the token(s) associated with the node(s) matching the extraction criteria are extracted.
  • the extraction criteria may be as simple as an identifier string or may be a query presented to a structured data repository (where the data repository may be organized according to a database schema or data format, such as XML).
  • the extracted data may be used for further processing by the computing system.
  • the computing system of FIG. 7.1 while performing one or more embodiments, may perform data comparison.
  • the comparison may be performed by submitting A, B, and an opcode specifying an operation related to the comparison into an arithmetic logic unit (ALU) (i.e., circuitry that performs arithmetic and/or bitwise logical operations on the two data values).
  • ALU arithmetic logic unit
  • the ALU outputs the numerical result of the operation and/or one or more status flags related to the numerical result.
  • the status flags may indicate whether the numerical result is a positive number, a negative number, zero, etc.
  • the comparison may be executed. For example, in order to determine if A > B, B may be subtracted from A (i.e., A - B), and the status flags may be read to determine if the result is positive (i.e., if A > B, then A - B > 0).
  • a and B may be vectors, and comparing A with B includes comparing the first element of vector A with the first element of vector B, the second element of vector A with the second element of vector B, etc. In one or more embodiments, if A and B are strings, the binary values of the strings may be compared.
  • the computing system in FIG. 7.1 may implement and/or be connected to a data repository.
  • a data repository is a database.
  • a database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion.
  • Database Management System is a software application that provides an interface for users to define, create, query, update, or administer databases.
  • the user, or software application may submit a statement or query into the
  • the DBMS interprets the statement.
  • the statement may be a select statement to request information, update statement, create statement, delete statement, etc.
  • the statement may include parameters that specify data, or data container (database, table, record, column, view, etc.), identifier(s), conditions (comparison operators), functions (e.g. join, full join, count, average, etc.), sort (e.g. ascending, descending), or others.
  • the DBMS may execute the statement.
  • the DBMS may access a memory buffer, a reference or index a file for read, write, deletion, or any combination thereof, for responding to the statement.
  • the DBMS may load the data from persistent or non-persistent storage and perform computations to respond to the query.
  • the DBMS may return the result(s) to the user or software application.
  • the computing system of FIG. 7.1 may include functionality to present raw and/or processed data, such as results of comparisons and other processing.
  • presenting data may be accomplished through various presenting methods.
  • data may be presented through a user interface provided by a computing device.
  • the user interface may include a GUI that displays information on a display device, such as a computer monitor or a touchscreen on a handheld computer device.
  • the GUI may include various GUI widgets that organize what data is shown as well as how data is presented to a user.
  • the GUI may present data directly to the user, e.g., data presented as actual data values through text, or rendered by the computing device into a visual representation of the data, such as through visualizing a data model.
  • a GUI may first obtain a notification from a software application requesting that a particular data object be presented within the GUI.
  • the GUI may determine a data object type associated with the particular data object, e.g., by obtaining data from a data attribute within the data object that identifies the data object type.
  • the GUI may determine any rules designated for displaying that data object type, e.g., rules specified by a software framework for a data object class or according to any local parameters defined by the GUI for presenting that data object type.
  • the GUI may obtain data values from the particular data object and render a visual representation of the data values within a display device according to the designated rules for that data object type.
  • Data may also be presented through various audio methods.
  • data may be rendered into an audio format and presented as sound through one or more speakers operably connected to a computing device.
  • Data may also be presented to a user through haptic methods.
  • haptic methods may include vibrations or other physical signals generated by the computing system.
  • data may be presented to a user using a vibration generated by a handheld computer device with a predefined duration and intensity of the vibration to communicate the data.

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Abstract

L'invention concerne un procédé de simulation basée sur un réseau, comprenant la réception, par un dispositif en réseau provenant d'un dispositif client, d'une demande d'exécution de simulations. La demande comprend, pour chaque simulation, une spécification de modèle qui identifie un sous-ensemble d'éléments de données. Pour chaque simulation, le sous-ensemble d'éléments de données est assemblé par le dispositif en réseau en une réalisation de modèle basée sur les spécifications de modèle. Le dispositif en réseau effectue, pour chaque réalisation de modèle, une simulation à l'aide des réalisations de modèle pour obtenir des résultats de simulation. Le dispositif en réseau peut transmettre les résultats de simulation à un dispositif client.
PCT/US2016/053864 2015-09-28 2016-09-27 Déroulement des opérations de simulation basée sur un réseau WO2017058738A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109933812A (zh) * 2017-12-15 2019-06-25 上汽通用汽车有限公司 空调除霜仿真方法、系统和计算机介质
CN109933811A (zh) * 2017-12-15 2019-06-25 上汽通用汽车有限公司 空调压降仿真方法、系统和计算机介质
CN110175118A (zh) * 2019-05-08 2019-08-27 重庆八戒电子商务有限公司 软件性能测试方法和装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020016703A1 (en) * 2000-07-10 2002-02-07 Claire Barroux Modelling method allowing to predict as a function of time the detailed composition of fluids produced by an underground reservoir under production
US20080234939A1 (en) * 2007-02-26 2008-09-25 John Foot Determining Fluid Rate and Phase Information for a Hydrocarbon Well Using Predictive Models
US20100084191A1 (en) * 2008-10-08 2010-04-08 Chapman Clinton D Combining belief networks to generate expected outcomes
US20120215511A1 (en) * 2011-02-17 2012-08-23 Chevron U.S.A. Inc. System and method for modeling a subterranean reservoir
US20140214392A1 (en) * 2013-01-28 2014-07-31 Ge Intelligent Platforms, Inc. Cloud based simulation and analysis for control appliances

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020016703A1 (en) * 2000-07-10 2002-02-07 Claire Barroux Modelling method allowing to predict as a function of time the detailed composition of fluids produced by an underground reservoir under production
US20080234939A1 (en) * 2007-02-26 2008-09-25 John Foot Determining Fluid Rate and Phase Information for a Hydrocarbon Well Using Predictive Models
US20100084191A1 (en) * 2008-10-08 2010-04-08 Chapman Clinton D Combining belief networks to generate expected outcomes
US20120215511A1 (en) * 2011-02-17 2012-08-23 Chevron U.S.A. Inc. System and method for modeling a subterranean reservoir
US20140214392A1 (en) * 2013-01-28 2014-07-31 Ge Intelligent Platforms, Inc. Cloud based simulation and analysis for control appliances

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109933812A (zh) * 2017-12-15 2019-06-25 上汽通用汽车有限公司 空调除霜仿真方法、系统和计算机介质
CN109933811A (zh) * 2017-12-15 2019-06-25 上汽通用汽车有限公司 空调压降仿真方法、系统和计算机介质
CN109933812B (zh) * 2017-12-15 2023-07-25 上汽通用汽车有限公司 空调除霜仿真方法、系统和计算机介质
CN110175118A (zh) * 2019-05-08 2019-08-27 重庆八戒电子商务有限公司 软件性能测试方法和装置

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