Patent Application
20250116190
Embodiments described generally relate to analyzing reservoir formations. More particularly, such embodiments relate to processes for determining formation water salinity and/or saturation and directing downhole operations using same.
Thermal properties of rocks have been investigated for decades, with techniques for measuring thermal conductivity described in a wide variety of literature. The complexity of reservoir rocks, however, prevents this branch of rock physics from being applied extensively within the reservoir evaluation workflow. Reservoir rocks are typically composed of different minerals, such as quartz, calcite, and the like, each with different thermal properties. Reservoir rocks are porous and frequently saturated with one or more fluids, such as formation water and/or hydrocarbon fluids. Despite these difficult parameters, thermal conductivity remains an important physical parameter to many areas, particularly thermal recovery methods.
One approach, if the mineral composition of a rock is known, is to use one of several theoretical models to calculate the thermal parameters of the rock. Experiments conducted on porous rocks filled with air or water have shown a clear correlation between thermal conductivity and quartz abundancy in the matrix. However, none of the existing literature provides reservoir rock parameters.
Another approach involves modelling thermal conduction around pores and through pore fluids to create parallel and serial systems that return appreciable results in terms of predictability when samples are saturated with only a single fluid, such as water, gas, or oil. Regarding this, a relationship between thermal conductivity (K) and solidity (Y), a porosity complement parameter (γ=1−φ), was observed by several researchers. For example, modern experiments found a linear relationship between thermal conductivity and the square of the solidity: γ=f(γ2). More recently, other researchers have focused on analyzing properties of saturated rocks, but these lack a systematic approach.
There is a need, therefore, for improved processes for characterizing reservoir formation parameters, notably water salinity and water saturation, using thermal characteristics, and directing downhole operations based on the characterized reservoir formation.
Processes for characterizing reservoir formation parameters such as water salinity and water saturation are provided. In some embodiments, the process can include directing a heat impulse into a formation sample that can include a matrix component and a fluid component at an input location. The process can also include allowing the heat impulse to pass through the formation sample such that a matrix impulse forms through the matrix component and a fluid impulse forms through the fluid component. The matrix and fluid impulses can convolve at a measurement location to provide a convolved impulse. The process can also include performing derivative analysis of the convolved impulse to derive thermal transient measurements. The process can also include developing a fluid thermal model using the thermal transient measurements. The process can also include integrating the fluid thermal model with one or more downhole logs and/or input parameters to create an integrated model. The process can also include determining one or more reservoir parameters from the integrated model. The process can also include directing one or more field operations based on the determined one or more reservoir parameters.
In other embodiments, the process can include directing a heat impulse into a formation sample that can include a matrix component and a fluid component at an input location. The process can also include allowing the heat impulse to pass through the formation sample such that an oil impulse forms through an oil component and a water impulse forms through a water component. The oil and water impulses can convolve at a measurement location to provide a convolved impulse. The process can also include performing derivative analysis of the convolved impulse to derive thermal transient measurements. The process can also include developing a fluid thermal model using the thermal transient measurements. The process can include integrating the fluid thermal model with one or more downhole logs and/or input parameters to create an integrated model. The process can also include determining one or more reservoir parameters from the integrated model. The process can also include directing one or more field operations based on the determined one or more reservoir parameters.
In other embodiments, the process can include directing a heat impulse into a formation sample comprising a matrix component and a fluid component at an input location. The process can also include allowing the heat impulse to pass through the formation sample such that a saturated mineral matrix impulse forms through the saturated mineral matrix component and a water impulse forms through the water component. The saturated mineral matrix and water impulses can convolve at a measurement location to provide a convolved impulse. The process can also include performing derivative analysis of the convolved impulse to derive thermal transient measurements. The process can also include developing a fluid thermal model using the thermal transient measurements. The process can also include integrating the fluid thermal model with one or more downhole logs and/or input parameters to create an integrated model. The process can also include determining one or more reservoir parameters from the integrated model. The process can also include directing one or more field operations based on the determined one or more reservoir parameters.
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments. It is contemplated that elements disclosed in one embodiment can be utilized in other embodiments without specific recitation.
It is to be understood that the following disclosure describes several exemplary embodiments for implementing different features, structures, or functions of the invention. Exemplary embodiments of components, arrangements, and configurations are described below to simplify the present disclosure; however, these exemplary embodiments are provided merely as examples and are not intended to limit the scope of the invention. Additionally, the present disclosure can repeat reference numerals and/or letters in the various embodiments and across the figures provided herein. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations. Moreover, the exemplary embodiments presented below can be combined in any combination of ways, i.e., any element from one exemplary embodiment can be used in any other exemplary embodiment, without departing from the scope of the disclosure.
Additionally, certain terms are used throughout the following description and claims to refer to particular components. As one skilled in the art will appreciate, various entities can refer to the same component by different names, and as such, the naming convention for the elements described herein is not intended to limit the scope of the invention, unless otherwise specifically defined herein. Further, the naming convention used herein is not intended to distinguish between components that differ in name but not function.
Language of degree used herein, such as the terms “approximately,” “about,” “generally,” and “substantially” as used herein represent a value, amount, or characteristic close to the stated value, amount, or characteristic that still performs a desired function or achieves a desired result. For example, the terms “approximately,” “about,” “generally,” and “substantially” may refer to an amount that is within less than 10% of, within less than 5% of, within less than 1% of, within less than 0.1% of, and/or within less than 0.01% of the stated amount. Additionally, unless otherwise indicated herein, all numerical values are “about” or “approximately” the indicated value, meaning the values take into account experimental error, machine tolerances and other variations that would be expected by a person having ordinary skill in the art. It should also be understood that the precise numerical values used in the specification and claims constitute specific embodiments.
Furthermore, in the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to.”
The term “or” is intended to encompass both exclusive and inclusive cases, i.e., “A or B” is intended to be synonymous with “at least one of A and B,” unless otherwise expressly specified herein.
The indefinite articles “a” and “an” refer to both singular forms (i.e., “one”) and plural referents (i.e., one or more) unless the context clearly dictates otherwise. For example, embodiments using “an optical density difference” includes embodiments where one, two, or more optical density differences are used, unless specified to the contrary or the context clearly indicates that only one optical density difference is used.
A dual system can include any reservoir formation that includes at least one formation rock and water, gas, oil, or any mixture thereof that creates two distinct formation layers or components for analysis. In one or more embodiments, when a dual system, e.g., a porous saturated rock, is subjected to a heat impulse, each component in the system can act as a filter to the heat impulse. In some embodiments, the heat impulse can be in a form of a square signal or any other form for analyzing thermal properties. In one or more embodiments, the dual system can be reduced to a unidimensional geometry, with each component acting as a unique filter for the heat impulse. If the two components have different properties, the two heat waves can travel at different velocities through the dual system. The different velocities through the dual system can produce different temperature increases at a fixed point “x” at time “t”. A temperature change versus time relationship can be developed based on the different temperature increases. In one or more embodiments, the temperature change versus time relationship can depend on thermal conductivity, thermal capacity, and/or thermal diffusivity of the single medium and it can be regulated by one or more diffusivity equations. In some embodiments, the diffusivity equations can be given as follows:
After the heat impulse begins, a perturbance can be created as the impulse passes through the components that can be detected at the position or measurement plane “x”. At the measurement plane, the two impulses, i.e., the impulse through the matrix component or “matrix impulse” and the impulse through the fluid component or “fluid impulse”, can be convolved into one impulse, which can be referred to as a “convolved impulse”. In one or more embodiments, derivative analysis of the convolved impulse can be used to detect when the heat impulse passed through the fluid component. More particularly, the heat impulse through the fluid component can be more attenuated than the heat impulse through the rock component because the transfer function of the fluid component can be more attenuating than the transfer function of the matrix component. In one or more embodiments, derivative analysis of the convolved impulse can be validated using laboratory, experimental, historical data, and/or other testing data based on the reservoir formation rock.
In one or more embodiments, a fluid thermal model can be developed that can consider the porosity and the internal arrangement of the components in the system to be the same or nearly the same. The fluid thermal model can isolate water salinity variation and/or water saturation based on differences in detected thermal properties between the fluid impulse and the matrix impulse. In one or more embodiments, the fluid thermal model can estimate water salinity knowing mineralogical composition and porosity of a certain reservoir formation. The fluid thermal model can be distinct to individual reservoir formations. In one or more embodiments, the fluid thermal model can estimate water saturation knowing mineralogical composition and porosity of a certain formation and having assumed or measured 100% and 0% water saturation thermal transient as end members. In one or more embodiments, the fluid thermal model can estimate porosity knowing mineralogical composition and water salinity in 100% water saturated rocks.
In one or more embodiments, a user determined, experimentally known, or otherwise known set of filter parameters or properties applied to the heat impulse can be used to determine thermal transient measurements that can quantify the effects of water salinity and/or water saturation. The set of filter parameters or properties can include thermal resistivity, thermal conductivity, thermal capacity, boundary conditions, and the like, and/or any combination thereof. In one or more embodiments, the water saturation and/or water salinity can include any variety of naturally occurring salt-bearing water found in, near, or around formation reservoirs. In one or more embodiments, a matrix filter can include oil and formation rock that together can produce a filter that allows the heat impulse to travel through the component with higher speed as compared to a fluid filter. In one or more embodiments, knowing rock porosity and analyzing the cooling tail of the convolved impulse, a model for water saturation can be created. The model for water saturation can be created by laboratory measurements on formation core plugs or by petrophysical study in combination with the analysis of fluids samples of the zone of interest. In one or more embodiments, porosity and internal pore structure or texture can be responsible for the internal conduction between the two components in the system, where the fastest component, such as minerals and oil, can release heat to the slowest one, such as water.
The borehole logs 321 can include any applicable downhole logs or logging data that can include, but are not limited to, resistivity logs, nuclear logs, such as neutron, gamma ray, and/or cross-section capture, sonic logs, and the like, and/or any combination thereof. The core analysis 322 can include any laboratory data and/or sampling of reservoir formation rock that includes, but is not limited to, mineral composition, chemical composition, permeability, internal structure, and the like, and/or any combination thereof. The lithology data 323 can include data from either the borehole logs 321, the core analysis 322, or both, that can allow for the determination of lithological parameters regarding the reservoir formation. The porosity data 323 can include data from either the borehole logs 321, the core analysis 322, or both, that can allow for the determination of porosity parameters regarding the reservoir formation.
The machine learning model 330 can include receiving formulas, data, parameters, and the like, and/or any combination thereof, from the fluid thermal model 312, lithology data 322, and/or the porosity data 323. In one or more embodiments, the machine learning model 330 can include any semi-supervised model, neural network, and/or iterative solver appropriate to receive initial, continuous, and/or real-time data to produce accurate results and/or predictions regarding the reservoir formation. The machine learning model 330 can be configured to utilize and process the data from the fluid thermal model 312, lithology data 322, and/or the porosity data 323 to produce water saturation output 331 and water salinity output 332. The water saturation output 331 can include data and/or parameters that describe the amount of water within the reservoir formation. The water salinity output 332 can include data and/or parameters that describe the salt content of available water within the reservoir formation. In one or more embodiments, the machine learning model 330 can include estimation of the salinity or saturation or porosity with analytical methods such as inverse heat conduction problem (IHCP) or with mathematical inversion. Additionally, the estimation of the salinity and saturation can include labeling by the machine learning model 330 using laboratory measurements as a baseline.
The water saturation output 331 and the water salinity output 332 can be used to determine drilling, production, and/or field operations within the reservoir formation. Preferably, drilling, production, and/or field operations can minimize produced water and avoid high salinity water. In one or more embodiments, a user can utilize the water saturation output 331 and the water salinity output 332 to determine, direct, change, or otherwise control one or more drilling, production, and/or field operations in order to avoid or minimize the production of water and/or avoid or minimize the production of high salinity water. In one or more embodiments, the machine learning model 330 can produce reservoir parameters including, but not limited to, lithology, porosity, thermal conductivity, and/or thermal capacity, in addition to water salinity output 332, and/or water saturation output 331.
To achieve its desired functionality, the computing system 400 can include various hardware and software components. Among these components can be one or more processors 414 and a command actuator 440. These hardware components can be interconnected through the use of a number of electrical connections, buses, and/or network connections. In one embodiment, the processor 414, the chip 405, the chip 421, and the command actuator 440 can be communicatively coupled via a bus 422. The bus 422 can be or include any know computing system bus. The command actuator 440 can be internal to a data storage device 416.
The chip 405, the chip 421, and/or the command actuator 440 can include, either separately or in some combination, software and hardware, including tangible, non-transitory computer readable medium (not shown), for performing reservoir analysis. In some embodiments, the reservoir analysis can be interpreted via statistical formulas, such as average mean, median, standard deviation, and the like, or any combination thereof, and/or complex formulas, such as factor analysis, Fourier's transform, logarithmic methods, and the like, or any combination thereof. Other known algorithms and/or suitable algorithms developed in the future can also be used. In some embodiments, the command actuator 440 can be integrated into the chip 405, the chip 421, and/or the processor 414. In some embodiments, the chip 405 and/or the chip 421 can be integrated into the processor 414. Although the command actuator 440 is depicted as being internal to the data storage device 416, in other embodiments, the command actuator 440 can be a peripheral device (not shown) coupled to the computing system 412 or included within a peripheral device (not shown) coupled to the computing system 412.
The command actuator 440 can include instructions that when executed by the command actuator 440 can cause the command actuator 440 to implement at least the functionality of receiving information through a network adapter, processing the information from two or more downhole logs through the processor according to the instructions stored in the memory to create a command, and for performing reservoir analysis according to the command. In some embodiments, the instructions can, when executed by the command actuator 440, cause the command actuator 440 to use one or more inversion procedures or techniques to perform reservoir analysis using the information received. In some embodiments, the instructions can, when executed by the command actuator 440, cause the command actuator 440 to use one or more analyses to determine the reservoir analysis using the one or more models.
In one or more embodiments, the command actuator 440 can work in conjunction with the processor 414 to implement the functionality described above. In some embodiments, the command actuator 440 can execute firmware code stored on the computing system 400, such as on the chip 405, the chip 421, and/or the processor 414. The functionality of the computing system 400 and/or the command actuator 440 can be in accordance with the processes of the present specification described herein. In the course of executing code, the processor 414 and/or the command actuator 440 can receive input from and provide output to a number of the remaining hardware units.
The computing system 400 can be implemented in an electronic device. Examples of electronic devices include servers, desktop computers, laptop computers, cloud-based computers, personal digital assistants (“PDAs”), mobile devices, smartphones, gaming systems, and tablets, among other electronic devices. The computing system 400 can be utilized in any data processing scenario including, stand-alone hardware, mobile applications, through a computing network, or combinations thereof. Further, the computing system 400 can be used in a computing network, a public cloud network, a private cloud network, a hybrid cloud network, other forms of networks, or combinations thereof. In one example, the processes provided by the computing system 400 can be provided as a service by a third party.
To achieve its desired functionality, the computing system 400 can include various other hardware components. Among these other hardware components can be a number of data storage devices or tangible, non-transitory computer readable medium 416, a number of peripheral device adapters 418, and a number of network adapters 420. These hardware components can be interconnected through the use of a number of electrical connections, busses, and/or network connections.
The chip 405, the chip 421, and/or the processor 414 can include the hardware and/or firmware/software architecture to retrieve executable code from the data storage device 416 and execute the executable code. The executable code can, when executed by the chip 405, the chip 421, and/or the processor 414, cause the chip 405, the chip 421, and/or the processor 414 to implement at least the functionality of receiving information through a network adapter, processing the information from the two or more downhole logs and performing reservoir analysis according to the command.
The data storage device 416 can store data such as executable program code that is executed by the processor 414, the command actuator 440, or other processing devices. The processor 414 can be a central processing unit that is to execute an operating system in the computing system 400. As will be discussed, the data storage device 416 can specifically store computer code representing a number of applications that the processor 414 and/or the command actuator 440 can execute to implement at least the functionality described herein.
In one or more embodiments, the data storage device 416 can include various types of memory modules, including volatile and nonvolatile memory. In one or more embodiments, the data storage device 416 of the present example can include Random Access Memory (“RAM”) 424, Read Only Memory (“ROM”) 426, and Hard Disk Drive (“HDD”) storage 428. Many other types of memory can also be utilized, and the present specification contemplates the use of many varying type(s) of memory in the data storage device 416 as can suit a particular application of the principles described herein. In certain examples, different types of memory in the data storage device 416 can be used for different data storage requirements. In one or more embodiments, in certain examples the processor 414 can boot from Read Only Memory (“ROM”) 426, maintain nonvolatile storage in the Hard Disk Drive (“HDD”) memory 428, and execute program code stored in Random Access Memory (“RAM”) 424. In examples, the chip 405, and the chip 421 can boot from the Read Only Memory (“ROM”) 426.
The data storage device 416 can include a computer readable medium, a computer readable storage medium, or a non-transitory computer readable medium, among others. In one or more embodiments, the data storage device 416 can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, for example, the following: an electrical connection having a number of wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM, a Flash memory, a portable compact disc read only memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium can be any tangible medium that can contain, or store computer usable program code for use by or in connection with an instruction execution system, apparatus, or device. In another example, a computer readable storage medium can be any non-transitory medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The hardware adapters 418, 420 in the computing system 400 can enable the processor 414 to interface with various other hardware components, external and internal to the computing system 400. In one or more embodiments, the peripheral device adapters 418 can provide an interface to input/output devices, such as, for example, a display device 430, a mouse, and/or a keyboard. The peripheral device adapters 418 can also provide access to other external devices such as an external storage device, a number of network devices such as, for example, servers, switches, and routers, client devices, other types of computing devices, and combinations thereof.
The display device 430 can be provided to allow a user of the computing system 400 to interact with and implement the functionality of the computing system 400. Examples of display devices 430 can include a computer screen, a laptop screen, a mobile device screen, a personal digital assistant (“PDA”) screen, and/or a tablet screen, among other display devices 430.
The peripheral device adapters 418 can also create an interface between the processor 414 and the display device 430, a printer, or other media output devices. The network adapter 420 can provide an interface to other computing devices within, for example, a network, thereby enabling the transmission of data between the computing system 400 and other devices located within the network. The network adapter 420 can provide an interface to an external telecommunications network such as a cellular phone network or other radio frequency enabled network, thereby enabling the transmission of data between the computing system 400 and other external devices such as an external storage device, a number of network devices such as, for example, servers, switches, and routers, client servers, radio frequency enabled devices, other client devices, other types of computing devices, and combinations thereof.
The computing system 400 can further include a number of modules used in the implementation of the process and systems described herein. The various modules within the computing system 400 can include executable program code that can be executed separately. In this example, the various modules can be stored as separate computer program products. In another example, the various modules within the computing system 400 can be combined within a number of computer program products; each computer program product including a number of the modules.
The present disclosure further relates to any one or more of the following numbered embodiments:
A1. A process, comprising: directing a heat impulse into a formation sample comprising a matrix component and a fluid component at an input location; allowing the heat impulse to pass through the formation sample such that a matrix impulse forms through the matrix component and a fluid impulse forms through the fluid component, wherein the matrix and fluid impulses convolve at a measurement location to provide a convolved impulse; performing derivative analysis of the convolved impulse to derive thermal transient measurements; developing a fluid thermal model using the thermal transient measurements; integrating the fluid thermal model with one or more downhole logs and/or input parameters to create an integrated model; determining one or more reservoir parameters from the integrated model; and directing one or more field operations based on the determined one or more reservoir parameters.
A2. The process of paragraph A1, wherein the heat impulse is a square signal.
A3. The process of paragraph A1 or paragraph A2, wherein the convolved impulse includes velocity and temperature changes associated with the heat impulse.
A4. The process of any one of paragraphs A1 to A3, wherein the matrix component includes a mineral matrix.
A5. The process of any one of paragraphs A1 to A4, wherein the fluid component includes water, a gas, and/or a liquid hydrocarbon.
A6. The process of any one of paragraphs A1 to A5, wherein integrating the fluid thermal model includes a laboratory measurement of a formation sample obtained from downhole.
A7. The process of paragraph A6, wherein thermal transient measurements are obtained at 0% saturation and 100% saturation to provide end points within the laboratory measurement.
A8. The process of any one of paragraphs A1 to A7, wherein performing derivative analysis includes validating the convolved impulse using laboratory studies.
A9. The process of any one of paragraphs A1 to A8, wherein the integrated model includes a semi-supervised machine learning model, neural network, and/or an iterative solver.
A10. The process of any one of paragraphs A1 to A9, wherein the one or more downhole logs and/or input parameters include borehole logs, core analysis, lithology measurements, porosity measurements, mud parameters, resistivity measurements, nuclear measurements, and/or sonic measurements.
A11. The process of any one of paragraphs A1 to A10, wherein the one or more reservoir parameters include lithology, porosity, thermal conductivity, thermal capacity, salinity, and/or saturation values.
A12. The process of any one of paragraphs A1 to A11, wherein the one or more field operations include well location selection, well depth selection, and/or produced water management.
B1. A process, comprising: directing a heat impulse into a formation sample comprising a matrix component and a fluid component at an input location; allowing the heat impulse to pass through the formation sample such that an oil impulse forms through an oil component and a water impulse forms through a water component, wherein the oil and water impulses convolve at a measurement location to provide a convolved impulse; performing derivative analysis of the convolved impulse to derive thermal transient measurements; developing a fluid thermal model using the thermal transient measurements; integrating the fluid thermal model with one or more downhole logs and/or input parameters to create an integrated model; determining one or more reservoir parameters from the integrated model; and directing one or more field operations based on the determined one or more reservoir parameters.
B2. The process of paragraph B1, wherein performing derivative analysis includes validating the convolved impulse using laboratory studies.
B3. The process of paragraph B1 or paragraph B2, wherein developing the fluid thermal model is reservoir specific.
B4. The process of any one of paragraphs B1 to B3, wherein the integrated model includes a semi-supervised machine learning model, neural network, and/or an iterative solver.
B5. The process of any one of paragraphs B1 to B4, wherein the heat impulse is a square signal.
B6. The process of any one of paragraphs B1 to B5, wherein the convolved impulse includes velocity and temperature changes associated with the heat impulse.
B7. The process of any one of paragraphs B1 to B6, wherein integrating the fluid thermal model includes a laboratory measurement of a formation sample obtained from downhole.
B8. The process of paragraph B7, wherein thermal transient measurements are obtained at 0% saturation and 100% saturation to provide end points within the laboratory measurement.
B9. The process of any one of paragraphs B1 to B8, wherein the one or more downhole logs and/or input parameters include borehole logs, core analysis, lithology measurements, porosity measurements, mud parameters, resistivity measurements, nuclear measurements, and/or sonic measurements.
B10. The process of any one of paragraphs B1 to B9, wherein the one or more reservoir parameters include lithology, porosity, thermal conductivity, thermal capacity, salinity, and/or saturation values.
B11. The process of any one of paragraphs B1 to B10, wherein the one or more field operations include well location selection, well depth selection, and/or produced water management.
C1. A process, comprising: directing a heat impulse into a formation sample comprising a matrix component and a fluid component at an input location; allowing the heat impulse to pass through the formation sample such that a saturated mineral matrix impulse forms through the saturated mineral matrix component and a water impulse forms through the water component, wherein the saturated mineral matrix and water impulses convolve at a measurement location to provide a convolved impulse; performing derivative analysis of the convolved impulse to derive thermal transient measurements; developing a fluid thermal model using the thermal transient measurements; integrating the fluid thermal model with one or more downhole logs and/or input parameters to create an integrated model; determining one or more reservoir parameters from the integrated model; and directing one or more field operations based on the determined one or more reservoir parameters.
C2. The process of paragraph C1, wherein performing derivative analysis includes validating the convolved impulse using laboratory studies.
C3. The process of paragraph C1 or paragraph C2, wherein developing the fluid thermal model is reservoir specific.
C4. The process of any one of paragraphs C1 to C3, wherein the integrated model includes a semi-supervised machine learning model, neural network, and/or an iterative solver.
C5. The process of any one of paragraphs C1 to C4, wherein the heat impulse is a square signal.
C6. The process of any one of paragraphs C1 to C5, wherein the convolved impulse includes velocity and temperature changes associated with the heat impulse.
C7. The process of any one of paragraphs C1 to C6, wherein integrating the fluid thermal model includes a laboratory measurement of a formation sample obtained from downhole.
C8. The process of paragraph C7, wherein thermal transient measurements are obtained at 0% saturation and 100% saturation to provide end points within the laboratory measurement.
C9. The process of any one of paragraphs C1 to C8, wherein the one or more downhole logs and/or input parameters include borehole logs, core analysis, lithology measurements, porosity measurements, mud parameters, resistivity measurements, nuclear measurements, and/or sonic measurements.
C10. The process of any one of paragraphs C1 to C9, wherein the one or more reservoir parameters include lithology, porosity, thermal conductivity, thermal capacity, salinity, and/or saturation values.
C11. The process of any one of paragraphs C1 to C10, wherein the one or more field operations include well location selection, well depth selection, and/or produced water management.
While the present disclosure may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and have been described in detail herein. However, it should be understood that the present disclosure is not intended to be limited to the particular forms disclosed. Rather, the present disclosure is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the following appended claims.
Certain embodiments and features have been described using a set of numerical upper limits and a set of numerical lower limits. It should be appreciated that ranges including the combination of any two values, e.g., the combination of any lower value with any upper value, the combination of any two lower values, and/or the combination of any two upper values are contemplated unless otherwise indicated. Certain lower limits, upper limits and ranges appear in one or more claims below. All numerical values are “about” or “approximately” the indicated value, and take into account experimental error and variations that would be expected by a person having ordinary skill in the art.
Various terms have been defined above. To the extent a term used in a claim can be not defined above, it should be given the broadest definition persons in the pertinent art have given that term as reflected in at least one printed publication or issued patent. Furthermore, all patents, test procedures, and other documents cited in this application are fully incorporated by reference to the extent such disclosure can be not inconsistent with this application and for all jurisdictions in which such incorporation can be permitted.
While certain preferred embodiments of the present invention have been illustrated and described in detail above, it can be apparent that modifications and adaptations thereof will occur to those having ordinary skill in the art. It should be, therefore, expressly understood that such modifications and adaptations may be devised without departing from the basic scope thereof, and the scope thereof can be determined by the claims that follow.
