Extrapolating viscosity estimation of undefined petroleum fractions

Abstract

A method for determining a viscosity of undefined petroleum fractions includes obtaining an experimental data set representing a viscosity of a fluid in a well. The method also includes determining a correlation of the viscosity. The method also includes determining a discontinuity in the correlation. The method also includes generating synthetic extrapolation data based upon the discontinuity. The method also includes determining a resulting correlation based upon the synthetic extrapolation data and a training portion of the experimental data set. The method also includes determining the viscosity of undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation.

Description

Attorney Docket No.: IS24.1592-WOEXTRAPOLATING VISCOSITY ESTIMATION OE UNDEFINED PETROLEUM FRACTIONSCross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 722,149, filed on November 19, 2024, which is incorporated by reference.Background

[0002] Petroleum fractions are the various components obtained from crude oil through the process of fractional distillation. The main fractions include gasoline, kerosene, diesel, fuel, oil, and bitumen. Petroleum fractions may be considered “ill-defined” if their properties are known at some points (e.g. standard liquid density and boiling point), but not at other points. The estimation of viscosity for these types of fractions may prove to be difficult because standard equations are accurate for specific ranges of boiling points and densities that are not valid for heavy fractions. Further, these equations may suffer from discontinuities. Therefore, what is needed is an improved system and method for extrapolating a viscosity estimation of undefined petroleum fractions.Summary

[0003] A method for determining a viscosity of undefined petroleum fractions is disclosed. The method includes obtaining an experimental data set representing a viscosity of a fluid in a well. The method also includes determining a correlation of the viscosity. The method also includes determining a discontinuity in the correlation. The method also includes generating synthetic extrapolation data based upon the discontinuity. The method also includes determining a resulting correlation based upon the synthetic extrapolation data and a training portion of the experimental data set. The method also includes determining the viscosity of undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation.

[0004] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include obtaining an experimental data set representing a viscosity of a fluid in a well . The operations alsoAttorney Docket No.: IS24.1592-WO include determining a correlation of the viscosity using a first correlation equation. The operations also include determining a discontinuity in the correlation. The operations also include generating synthetic extrapolation data based upon the discontinuity. The operations also include selecting a portion of the experimental data set as a training data set. The operations also include determining a resulting correlation equation based upon the synthetic extrapolation data and the training data set. The operations also include determining the viscosity of undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation equation.

[0005] A non-transitory computer-readable medium is also disclosed. The medium includes instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include obtaining an experimental data set representing a viscosity of a fluid in a well. The operations also include determining a correlation of the viscosity using a first correlation equation. The operations also include determining a discontinuity in the correlation. The operations also include generating synthetic extrapolation data based upon the discontinuity. The operations also include selecting a portion of the experimental data set as a training data set. The operations also include calculating a regression of parameters to define a resulting correlation equation based upon the synthetic extrapolation data and the selected portion of the experimental data. The operations also include determining the viscosity of undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation equation. The operations also include executing one or more physical operations based at least in part on the viscosity of the undefined petroleum fractions.

[0006] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and / or claimed below. Accordingly, this summary is not intended to be limiting.Brief Description of the Drawings

[0007] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0008] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0009] Figures 2A and 2B illustrate a flowchart of a method.Attorney Docket No.: IS24.1592-WO

[0010] Figure 3 illustrates a plot of deficiencies in extrapolation of viscosities at 100°F using current methods, evidencing discontinuities shown as peaks in the plot.

[0011] Figure 4 illustrates a plot of deficiencies in extrapolation of viscosities at 210°F using current standard correlations, evidencing discontinuities shown as peaks in the plot.

[0012] Figure 5 illustrates a plot of expansion of applicability of viscosity correlation using an embodiment of the present disclosure.

[0013] Figure 6 illustrates a plot of continuous extrapolation of viscosities at 100°F calculated using an embodiment of the present disclosure.

[0014] Figure 7 illustrates a plot of continuous extrapolation of viscosities at 210°F calculated using an embodiment of the present disclosure.

[0015] Figure 8 illustrates a schematic view of a computing system, according to an embodiment.Detailed Description

[0016] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0017] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0018] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well,Attorney Docket No.: IS24.1592-WO unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0019] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.

[0020] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0021] In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well / logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

[0022] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 may include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and otherAttorney Docket No.: IS24.1592-WO information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0023] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes may be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0024] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0025] As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (Schlumberger Limited, Houston Texas), the INTERSECT™ reservoir simulator (Schlumberger Limited, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).Attorney Docket No.: IS24.1592-WO

[0026] As an example, the simulation component 120 may include one or more features of a simulator such as SYMMETRY™ software (SLB, Houston, Texas). More particularly, SYMMETRY™ may process workflows in a single integrated environment with accurate thermodynamic fluid representation and consistent modeling across multiple disciplines including process, production, and HSE. The simulator integrates steady-state and transient (e.g., dynamic) analyses that may be tailored for each domain. This approach enables users to optimize processes in upstream, midstream, and downstream sectors while maximizing profits and minimizing capital expenditures. It may also help reduce emissions, energy consumption, and waste.

[0027] As an example, the simulation component 120 may include one or more features of a simulator such as PIPESIM™ (SLB, Houston, Texas). More particularly, PIPESIM™ is steadystate multiphase flow simulator that incorporates the three areas of flow modeling: multiphase flow, heat transfer and fluid behavior.

[0028] As an example, the simulation component 120 may include one or more features of a simulator such as OLGA™ (SLB, Houston, Texas). More particularly, OLGA™ is a dynamic multiphase flow simulator that models transient flow (e.g., time-dependent behaviors) to maximize production potential. Transient modeling is a component for feasibility studies and field development design. Dynamic simulation is useful in deep water and is used in both offshore and onshore developments to investigate transient behavior in pipelines and wellbores. Transient simulation with the OLGA™ simulator provides an added dimension to steady-state analysis by predicting system dynamics, such as time-varying changes in flow rates, fluid compositions, temperature, solids deposition, and operational changes.

[0029] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that may output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) may develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may beAttorney Docket No.: IS24.1592-WO considered a data-driven application (e g., where data is input for purposes of modeling, simulating, etc.).

[0030] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (Schlumberger Limited, Houston, Texas) allows for integration of addons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user- friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0031] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software may include a framework for model building and visualization.

[0032] As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

[0033] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications may display their data while the user interfaces 188 may provide a common look and feel for application user interface components.Attorney Docket No.: IS24.1592-WO

[0034] As an example, the domain objects 182 may include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0035] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project may be accessed and restored using the model simulation layer 180, which may recreate instances of the relevant domain objects.

[0036] In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0037] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural andAttorney Docket No.: IS24.1592-WO artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0038] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).Extrapolating Viscosity Estimation of Undefined Petroleum Fractions

[0039] Embodiments of the disclosure include a methodology that combines synthetic and experimental data to regress parameters for viscosity correlations to make their estimations suitable for petroleum fractions out of the range of the equation applicability like fractions with large boiling points and densities (e.g., heavy oils) without compromising the estimation of viscosity in the validity ranges. These parameters may not show discontinuities across a wide range of conditions.

[0040] The data used for the regression of the equation parameters may include synthetic and experimental data. Synthetic data may be considered as any data that does not come from an experiment.

[0041] Embodiments of the present disclosure may generate synthetic data that is mixed with experimental data to regress parameters for a standard equation for predicting viscosity. These predictions may show a high degree of accuracy across many types of oils including light, mediumAttorney Docket No.: IS24.1592-WO and heavy petroleum fractions, providing a technological enhancement over current methods that implement equations and parameters that are not accurate at least for heavy fractions.

[0042] The estimation of viscosity for petroleum fractions is difficult because users often lack enough information about the fluid. The most readily available information from petroleum fractions are boiling point and standard liquid density, which may be captured in the physical properties called American Petroleum Institute (API) gravity and Watson characterization factor (KW).

[0043] Embodiments of the present disclosure implement equations that may estimate viscosity based on these two properties, API Gravity and KW. The standard equation for estimation of viscosity of ill-defined petroleum fractions is published in the American Petroleum Institute (API) Technical Data Book as a set of parameters for estimation of viscosity at 100 degrees Fahrenheit (°F) and 210°F. These parameters are not accurate for heavy petroleum fractions and the API Technical Data Book-based predictions become discontinuous or unstable (tend to infinity) when extrapolating beyond their range of applicability.

[0044] Figures 2A and 2B illustrate a flowchart of a method 200, according to an example of the present disclosure. The method 200 may include obtaining (e.g., including defining) two sets of experimental data that represents API gravity and Watson Factor (KW) values: a training data set to be used for the parameter regression and a second one for the validation of the obtained parameters, as at 202. The validation data set may be a portion of the experimental data set not selected as the training data set. The method may also include defining a range for extrapolation on both API gravity values and Watson Factor (KW) values, as at 204.

[0045] The method 200 may further include determining, as at 206, a correlation of a first (e.g., baseline) correlation equation, e.g., as far as these ranges with the current equation parameters. The method 200 may include determining one or more discontinuities in the experimental (e.g., training) data, as at 208, and generating, as at 210, synthetic extrapolation data based upon the one or more discontinuities. The synthetic extrapolation data may be generated by extending an increase in the viscosity from the one or more discontinuities, and wherein the increase includes an exponential increase. A series of extrapolated data at different API and KW may be selected to be used as part of the regression process. The selection of this data may include less than 10% of the total points being used for the regression.Attorney Docket No.: IS24.1592-WO

[0046] In some examples, the method 200 may include selecting a portion of the experimental data, as at 212. The selected experimental data may include a portion of the relevant experimental data available across a random, representative sampling, or otherwise selected portion of one or more sections of the available regions. The representative sampling may include maxima and minima of the API gravity values and the KW values. These experimental points may be reconciled with the synthetic data.

[0047] The method 200 may also include determining that one or more outliers are present in the experimental data, as at 214, and removing any outliers from the experimental data, as at 216. The outliers are defined as any data that does not follow the accepted shape for the equation by more than a predetermined amount, for example, about 5%.

[0048] The method 200 may then include calculating the regression of parameters, as at 218, and validating, as at 220, the parameters of the resulting correlation equation against the validation data set. An example of this is shown below:At 100° F, log v100= -17.94566 - 2.47986 / f - 0.53384K2- 0.00930AP72+ 0.279017f(4P7), 43.39620A2+ 81.219414P7 - 0.023854P72- 19.01122 (AP7)+0.25722AP7 + 32.49718 + 1.14684KAt 210 °F, l°9y210= -10.35005 - 1.705297C - 0.09657K2- 0.001494P / 2+ 0.075727f(4P7), 15.81869K2+ 54.183444P7 - 0.369564P72- 6.40194K(4P7)+0.690374P7 + 27.29327 + 1.46355KWhere: vlOO = kinematic viscosity at 100 °F in cSt v210 = kinematic viscosity at 210 °F in cStAPI = API gravityK = Watson K factorTb = mean average boiling point in °RAttorney Docket No.: IS24.1592-WO

[0049] The method 200 may also include displaying one or more outputs. The outputs may include the regression of parameters, the resulting correlation equation, the viscosity of the fluid, or a combination thereof. Illustrative outputs are shown in Figures 3-7.

[0050] Figure 3 illustrates a plot of deficiencies in extrapolation of viscosities at 100°F using current methods, evidencing discontinuities shown as peaks in the plot.

[0051] Figure 4 illustrates a plot of deficiencies in extrapolation of viscosities at 210°F using current standard correlations, evidencing discontinuities shown as peaks in the plot. The vertical axis represents the kinematic viscosity in logarithmic scale.

[0052] Figure 5 illustrates a plot of expansion of applicability of viscosity correlation using an embodiment of the present disclosure. The applicability of the current viscosity equations is represented by the box near the center of the figure and bounded by dashed lines. The present disclosure applies to a wider range of WK and API gravity, as indicated by the outermost dashed box.

[0053] Figure 6 illustrates a plot of continuous extrapolation of viscosities at 100°F calculated using an embodiment of the present disclosure.

[0054] Figure 7 illustrates a plot of continuous extrapolation of viscosities at 210°F calculated using an embodiment of the present disclosure.

[0055] In at least some embodiments, the method 200 may be implemented in one or more oilfield operations, e.g., drilling, completion, production, etc. For example, a viscosity of a fluid in a well may be calculated based at least in part on the resulting correlation equation, as at 222. Further, one or more physical operations may be executed (e.g., implemented or adjusted) based at least in part on the viscosity determined using the resulting correlation, as at 224. The physical operations may include one or more of designing and operating a distillation tower (bottom has heaviest viscosities), designing and operating a pipeline, designing and operating equipment that is sensitive to viscosity, or a combination thereof. The equipment may include the components of a distillation tower such as vertical shell, reboiler, condenser, trays and reflux drums and the components of a pipeline such as the pipe, fittings, pumps, valves, joints and storage facilities connected to the pipeline.

[0056] In one or more embodiments, a method disclosed herein may be implemented on a computing system. A non-transitory computer-readable medium may be used to store instructions that may allow a method to be executed by one or more processors of a computing system.Attorney Docket No.: IS24.1592-WO

[0057] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 8 illustrates an example of such a computing system 800, in accordance with some embodiments. The computing system 800 may include a computer or computer system 801A, which may be an individual computer system 801A or an arrangement of distributed computer systems. The computer system 801A includes one or more analysis modules 802 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 802 executes independently, or in coordination with, one or more processors 804, which is (or are) connected to one or more storage media 806. The processor(s) 804 is (or are) also connected to a network interface 807 to allow the computer system 801 A to communicate over a data network 809 with one or more additional computer systems and / or computing systems, such as 80 IB, 801C, and / or 801D (note that computer systems 801B, 801C and / or 801D may or may not share the same architecture as computer system 801 A, and may be located in different physical locations, e.g., computer systems 801A and 801B may be located in a processing facility, while in communication with one or more computer systems such as 801 C and / or 80 ID that are located in one or more data centers, and / or located in varying countries on different continents).

[0058] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0059] The storage media 806 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 8 storage media 806 is depicted as within computer system 801A, in some embodiments, storage media 806 may be distributed within and / or across multiple internal and / or external enclosures of computing system 801A and / or additional computing systems. Storage media 806 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readableAttorney Docket No.: IS24.1592-WO storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[0060] In some embodiments, computing system 800 contains one or more viscosity extrapolation module(s) 808. In the example of computing system 800, computer system 801A includes the viscosity extrapolation module 808. In some embodiments, a single viscosity extrapolation module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of viscosity extrapolation modules may be used to perform some aspects of methods herein.

[0061] It should be appreciated that computing system 800 is merely one example of a computing system, and that computing system 800 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 8, and / or computing system 800 may have a different configuration or arrangement of the components depicted in Figure 8. The various components shown in Figure 8 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0062] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0063] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 800, Figure 8), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves hasAttorney Docket No.: IS24.1592-WO become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

[0064] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

Attorney Docket No.: IS24.1592-WOCLAIMSWhat is claimed is:

1. A method for determining a viscosity of undefined petroleum fractions, the method comprising: obtaining an experimental data set representing the viscosity of a fluid in a well; determining a correlation of the viscosity; determining a discontinuity in the correlation; generating synthetic extrapolation data based upon the discontinuity; determining a resulting correlation based upon the synthetic extrapolation data and a training portion of the experimental data set; and determining the viscosity of the undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation.

2. The method of claim 1, further comprising defining a range for extrapolation of the viscosity, wherein: the viscosity is represented at different American Petroleum Institute (API) gravity values and Watson Factor (KW) values, the range for extrapolation is defined at the API gravity values and the KW values, the correlation is determined over the range for extrapolation, and the correlation is determined between the viscosity, the API gravity values, and the KW values.

3. The method of claim 2, wherein a series of extrapolated data, at the different API gravity values and the different KW values, is used to determine the resulting correlation.

4. The method of claim 3, wherein the series of extrapolated data comprises less than 10% of total points used for a regression of parameters that is used to define the resulting correlation.Attorney Docket No.: IS24.1592-WO5. The method of claim 1, wherein the correlation of the viscosity is determined using a first correlation equation, and wherein one or more outliers are identified that do not follow an accepted shape for the first correlation equation by more than a predetermined amount.

6. The method of claim 5, wherein the predetermined amount is 5%.

7. The method of claim 5, further comprising removing the one or more outliers, wherein the resulting correlation is determined after the one or more outliers are removed.

8. The method of claim 1, further comprising validating parameters of the resulting correlation against a validation portion of the experimental data set.

9. A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining an experimental data set representing a viscosity of a fluid in a well; determining a correlation of the viscosity using a first correlation equation; determining a discontinuity in the correlation; generating synthetic extrapolation data based upon the discontinuity; selecting a portion of the experimental data set as a training data set; determining a resulting correlation equation based upon the synthetic extrapolation data and the training data set; and determining the viscosity of undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation equation.

10. The computing system of claim 9, wherein determining the correlation further comprises generating a model of the correlation, and wherein the model comprises a computational model or a plot.Attorney Docket No.: IS24.1592-WO11 . The computing system of claim 10, wherein the discontinuity is determined based at least in part on the model.

12. The computing system of claim 9, wherein the operations further comprise validating parameters of the resulting correlation equation against a validation data set of the experimental data set.

13. The computing system of claim 9, wherein the operations further comprise executing one or more operations based at least in part on the viscosity of the undefined petroleum fractions, wherein the one or more operations comprise generating or transmitting a signal that recommends, instructs, or causes a physical action to occur.

14. The computing system of claim 13, wherein the physical action comprises adjusting operation a distillation tower, adjusting operation a pipeline, adjusting operation equipment that is sensitive to the viscosity, or a combination thereof.

15. The computing system of claim 14, wherein the operating equipment comprises: components of the distillation tower including a vertical shell, a reboiler, a condenser, trays and / or reflux drums; and / or components of the pipeline including a pipe, fittings, pumps, valves, joints, and / or storage facilities connected to the pipeline.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: obtaining an experimental data set representing a viscosity of a fluid in a well; determining a correlation of the viscosity using a first correlation equation; determining a discontinuity in the correlation; generating synthetic extrapolation data based upon the discontinuity; selecting a portion of the experimental data set as a training data set;Attorney Docket No.: IS24.1592-WO calculating a regression of parameters to define a resulting correlation equation based upon the synthetic extrapolation data and the selected portion of the experimental data; determining the viscosity of undefined petroleum fractions of the fluid in the well based at least in part on the resulting correlation equation; and executing one or more physical operations based at least in part on the viscosity of the undefined petroleum fractions.

17. The non-transitory computer-readable medium of claim 16, wherein the synthetic extrapolation data is generated by extending an increase in the viscosity from the discontinuity, and wherein the increase comprises an exponential increase.

18. The non-transitory computer-readable medium of claim 16, wherein the selected portion of the experimental data set comprises a portion of the experimental data set available across a random, representative sampling.

19. The non-transitory computer-readable medium of claim 18, wherein: the viscosity is represented at different American Petroleum Institute (API) gravity values and Watson Factor (KW) values, a range for extrapolation is defined at the API gravity values and the KW values, the correlation is determined over the range for extrapolation, the correlation is determined between the viscosity, the API gravity values, and the KW values, and the random, representative sampling includes maxima and minima of the API gravity values and the KW values.

20. The non-transitory computer-readable medium of claim 16, the operations further comprising validating the parameters of the resulting correlation equation against a validation data set, wherein the validation data set is a portion of the experimental data set not selected as the training data set.

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