Three-parameter-based dry rock bulk modulus calculation method, device, equipment and medium

Abstract

The application discloses a three-parameter-based dry rock volume modulus calculation method, device, equipment and medium. The method comprises the following steps: determining a first physical characteristic parameter of a target dry rock; and determining a first dry rock volume modulus by using a relational model according to the first physical characteristic parameter of the target dry rock. The method can solve the problem that the adaptability of the existing dry rock volume modulus calculation method is poor, so that the seismic wave velocity calculation formed on the basis is not accurate enough, and the calculation precision of the dry rock volume modulus is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of exploration and development, and particularly relates to a three-parameter-based dry rock bulk modulus calculation method, device, equipment and medium. BACKGROUND

[0002] At present, with the deepening of oil and gas reservoir exploration and development, on the one hand, oil and gas reservoir exploration gradually changes from structural oil and gas reservoirs to subtle oil and gas reservoirs, resulting in more and more difficult discovery of oil and gas reservoirs, on the other hand, the understanding of the distribution of remaining oil in oil and gas reservoir development is more and more difficult, in order to better solve the difficulties faced by the two aspects, it is necessary to increase the research on the adaptability of rock physics theory, especially the research on the dry rock bulk modulus suitable for a region, and the reasonable calculation of the dry rock bulk modulus is often the key to whether the rock physics is suitable for a region.

[0003] However, at present, due to the poor adaptability of the dry rock bulk modulus calculation method, the calculation of the seismic wave velocity based on the existing dry rock bulk modulus calculation method is not accurate enough, which restricts the correct understanding of the oil and gas reservoir exploration and development, and affects the deployment of appropriate exploration and development plan. SUMMARY

[0004] The present application provides a three-parameter-based dry rock bulk modulus calculation method, device, equipment and medium to solve the problem of poor adaptability of the dry rock bulk modulus calculation method.

[0005] According to one aspect of the present application, a three-parameter-based dry rock bulk modulus calculation method is provided, comprising:

[0006] determining a first physical property parameter of a target dry rock, the first physical property parameter being data obtained by data collection on the target dry rock, and the first physical property parameter at least including the density of the target dry rock, the porosity of the target dry rock and the effective pressure, the effective pressure being the pressure applied to the target dry rock per unit area;

[0007] determining a first dry rock bulk modulus according to the first physical property parameter of the target dry rock by using a relationship model, the relationship model being a relationship formula constructed according to the second physical property parameter of a dry rock sample and the dry rock bulk modulus of the dry rock sample, the dry rock sample being a rock sample in the same region as the target dry rock and having the same physical property, and the second physical property parameter being data obtained by data collection on the dry rock sample.

[0008] According to another aspect of the present application, a three-parameter-based dry rock bulk modulus calculation device is provided, comprising:

[0009] The physical parameter determination module is configured to determine a first physical characteristic parameter of the target dry rock, the first physical characteristic parameter being data obtained by data collection on the target dry rock, and the first physical characteristic parameter at least including a density of the target dry rock, a porosity of the target dry rock, and an effective pressure, the effective pressure being a pressure applied to the target dry rock per unit area.

[0010] The dry rock bulk modulus determination module is configured to determine a first dry rock bulk modulus according to the first physical characteristic parameter of the target dry rock by using a relational model, the relational model being a relational expression constructed according to a second physical characteristic parameter of a dry rock sample and a dry rock bulk modulus of the dry rock sample, the dry rock sample being a rock sample in the same region as the target dry rock and having the same physical characteristics, and the second physical characteristic parameter being data obtained by data collection on the dry rock sample.

[0011] According to another aspect of the present application, an electronic device is provided, which comprises:

[0012] at least one processor; and a memory connected to the at least one processor in communication;

[0013] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the three-parameter-based dry rock bulk modulus calculation method according to any one of the embodiments of the present application.

[0014] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the three-parameter-based dry rock bulk modulus calculation method according to any one of the embodiments of the present application when executed by the processor.

[0015] The technical solution of the embodiments of the present application can determine a first dry rock bulk modulus according to a first physical characteristic parameter of a target dry rock by using a relational model according to the first physical characteristic parameter of the target dry rock. This method can solve the problem of poor adaptability of the existing dry rock bulk modulus calculation method, so that the seismic wave velocity calculation based thereon is not accurate enough, and the calculation accuracy of the dry rock bulk modulus is improved.

[0016] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0018] Figure 1 A flow chart of a three-parameter-based dry rock bulk modulus calculation method provided by the embodiment of the present application;

[0019] Figure 2 A cross plot of dry rock bulk modulus and porosity of different dry rock samples when the effective pressure is 42.76 MPa, provided by the embodiment of the present application;

[0020] Figure 3 A cross plot of dry rock bulk modulus and porosity of different dry rock samples when the effective pressure is 30.34 MPa, provided by the embodiment of the present application;

[0021] Figure 4 A cross plot of dry rock bulk modulus and density of different dry rock samples when the effective pressure is 42.76 MPa, provided by the embodiment of the present application;

[0022] Figure 5 A cross plot of dry rock bulk modulus and density of different dry rock samples when the effective pressure is 30.34 MPa, provided by the embodiment of the present application;

[0023] Figure 6 A cross plot of dry rock bulk modulus and effective pressure of a dry rock sample, provided by the embodiment of the present application;

[0024] Figure 7 A correlation diagram of a first dry rock bulk modulus and an actual dry rock bulk modulus, provided by the embodiment of the present application;

[0025] Figure 8 A structural schematic diagram of a three-parameter-based dry rock bulk modulus calculation device provided by the embodiment of the present application;

[0026] Figure 9 A structural schematic diagram of an electronic device for implementing the three-parameter-based dry rock bulk modulus calculation of the embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the protection scope of the present application.

[0028] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] Figure 1 A flowchart of a three-parameter-based dry rock bulk modulus calculation method is provided for the embodiments of the present application. The embodiments can be applicable to the obtained dry rock bulk modulus. The method can be performed by a three-parameter-based dry rock bulk modulus calculation device, which can be realized in the form of hardware and / or software, and can be configured in any electronic device with network communication function. As shown in the figure, the method comprises: Figure 1

[0030] S110, determining a first physical property parameter of a target dry rock.

[0031] The first physical property parameter is data obtained by data collection on the target dry rock, and at least includes the density of the target dry rock, the porosity of the target dry rock and the effective pressure, which is the pressure applied to the target dry rock per unit area.

[0032] For example, the target dry rock is collected from the S oilfield. After oil removal and drying treatment, the mass and volume of the target dry rock are measured, and the density of the target dry rock is obtained according to the mass and volume. The porosity of the dry rock is obtained by volume method. The effective pressure applied to the target dry rock per unit area is measured.

[0033] ​The process of measuring the porosity of the dry rock by volume method is: drying the sample, measuring its weight m1; then saturating the sample with water, measuring its weight m2 again; then measuring the porosity of the dry rock Por=(m2-m1) / (rock sample volume*water density).

[0034] S120, determining the first dry rock bulk modulus according to the first physical property parameter of the target dry rock by using the relational model.

[0035] The relational model is a relational expression constructed according to the second physical property parameter of the dry rock sample and the dry rock bulk modulus of the dry rock sample. The dry rock sample is a rock sample with the same physical properties as the target dry rock in the same region. The second physical property parameter is the data obtained by data collection on the dry rock sample.

[0036] The first dry rock bulk modulus is obtained by inputting the first physical property parameter of the target dry rock into the relational model.

[0037] Optionally, the process of constructing the relational model includes steps A1-A2:

[0038] Step A1, obtaining the second physical property parameter and the second dry rock bulk modulus data of the dry rock sample.

[0039] The second dry rock bulk modulus data is the dry rock bulk modulus data of the dry rock sample under different effective pressures.

[0040] For example, dry rock samples are collected from S oilfield, and the dry rock samples are dried and deoiled. The mass and volume of the dry rock sample are measured, and the density of the target dry rock is obtained according to the mass and volume. The porosity of the dry rock is obtained by using the volume method. The dry rock sample is tested under pressure to obtain the second dry rock bulk modulus data.

[0041] Optionally, obtaining the second physical property parameter and the second dry rock bulk modulus data of the dry rock sample includes steps B1-B4:

[0042] Step B1, after drying and deoiling the target rock sample, the dry rock sample is numbered.

[0043] For example, N pieces of target rock samples obtained from the S oilfield reservoir are dried and deoiled, and then numbered to obtain dry rock samples i, where i=1, 2, …, N.

[0044] Step B2, determining the density of the dry rock sample according to the mass and volume corresponding to the dry rock sample.

[0045] For example, the dry rock density is obtained by using the mass and volume of n dry rock samples, denoted as i where i = 1, 2, …, n.

[0046] For example, assuming that there are 9 target rock samples, the dry rock density is obtained by using the mass and volume of 9 dry rock samples after drying and oil removal, denoted as i where i = 1, 2, …, 9, as shown in Table 1.

[0047] Table 1 Density data of 9 dry rock samples

[0048]

[0049]

[0050] Step B3, measuring the porosity of the dry rock sample to obtain the porosity of the dry rock sample.

[0051] For example, the porosity of n dry rock samples is obtained by using the volume method, denoted as where i = 1, 2, …, n.

[0052] For example, the porosity of 9 dry rock samples is obtained by using the volume method, denoted as where i = 1, 2, …, 9, as shown in Table 2.

[0053] Table 2 Porosity data of 9 dry rock samples

[0054] Sample name Porosity S1 0.27 S2 0.22 S3 0.30 S4 0.22 S5 0.20 S6 0.10 S7 0.28 S8 0.30 S9 0.28

[0055] Step B4, performing a pressure test on the dry rock sample to obtain second dry rock bulk modulus data.

[0056] For example, n dry rock samples are respectively subjected to m times of pressure test, and dry rock bulk modulus data of n dry rock samples under m different effective pressures are obtained, and second dry rock bulk modulus data of n rock samples under m different effective pressures are obtained, denoted as where i = 1, 2, …, n; j = 1, 2, …, m.

[0057] For example, 9 dry rock samples are respectively subjected to 6 times of effective pressure to obtain dry rock bulk modulus, and second dry rock bulk modulus data of rock samples under 6 different effective pressures are obtained, denoted as where i = 1, 2, …, n; j = 1, 2, …, m.

[0058] Table 3 Second dry rock bulk modulus data measured at 6 different effective pressures for 9 rock samples

[0059]

[0060] Step A2, constructing a relationship model of the dry rock sample according to the second physical property parameters of the dry rock sample.

[0061] constructing a relationship model of the dry rock sample according to the relationship between the second physical property parameters of the dry rock sample.

[0062] Optionally, constructing a relationship model of the dry rock sample according to the second physical property parameters of the dry rock sample comprises steps C1-C4:

[0063] Step C1, constructing a cross plot according to the porosity and the second dry rock bulk modulus of the dry rock sample to obtain a first approximate relationship.

[0064] The first approximate relationship is an approximate relationship between the porosity and the second dry rock bulk modulus of the dry rock sample.

[0065] For example, the second dry rock bulk modulus and the dry rock porosity data of the dry rock sample under a certain pressure test corresponding effective pressure are generated into a cross plot, and the cross plot is simulated and analyzed to analyze the approximate linear relationship between the dry rock bulk modulus and the dry rock porosity, as shown in Figure 2 , Figure 3 . Figure 2 A cross plot of the second dry rock bulk modulus and the porosity of different dry rock samples at an effective pressure of 42.76 MPa is provided, and the circles in the figure represent the corresponding second dry rock bulk modulus of different samples at different porosities. The green line is the fitted relationship between the second dry rock bulk modulus and the porosity, which has a typical linear characteristic. For other pressures, the dry rock bulk modulus and the porosity also have this linear characteristic. As shown in Figure 2 A cross plot of the second dry rock bulk modulus and the porosity of different dry rock samples at an effective pressure of 30.34 MPa is shown.

[0066] Step C2, constructing a cross plot according to the density and the second dry rock bulk modulus of the dry rock sample to obtain a second approximate relationship.

[0067] The second approximate relationship is an approximate relationship between the density and the second dry rock bulk modulus of the dry rock sample.

[0068] For example, a cross-plot is generated by comparing the bulk modulus and density data of dried rock under a certain effective pressure. Analysis of the cross-plot reveals an approximately linear relationship between the bulk modulus and density of the dried rock. Figure 4 , Figure 5 As shown. Figure 4 A cross-plot of the second bulk modulus of dried rock versus density is provided for different dried rock samples at an effective pressure of 42.76 MPa. Circles in the plot represent the second bulk modulus of different samples at different densities, and the green line represents the fitted relationship between the second bulk modulus and density, which exhibits a typical linear characteristic. This linear characteristic is also observed for other pressures. Figure 5 The cross plot shows the bulk modulus and density of the second dry rock samples at an effective pressure of 30.34 MPa.

[0069] Step C3: Construct a cross-plot based on the bulk modulus and effective pressure of the second dry rock to obtain the third approximate relationship.

[0070] The third approximation is the approximate relationship between the bulk modulus and effective pressure of the second dry rock.

[0071] For example, a cross-plot is generated by comparing the bulk modulus and effective pressure data of dry rocks with a certain porosity and density. Analysis of the cross-plot reveals an approximate power-law relationship between the bulk modulus and effective pressure of the dry rocks. Figure 6 As shown.

[0072] Step C4: Construct a relational model for the dry rock sample based on the first approximation relationship, the second approximation relationship, and the third approximation relationship.

[0073] The first, second, and third approximate relationships were analyzed, and a relationship model for dry rock samples was constructed based on this analysis.

[0074] Optional relational models for dry rock samples include:

[0075] The relationship model for the dry rock samples is determined using the following formula:

[0076]

[0077] in, ρ represents the porosity of the dried rock sample; ρ is the density of the dried rock sample, in g / cm³. 3 P represents the effective pressure in MPa; a0, a1, a2, a3, a4, a5, and a6 are fitting coefficients.

[0078] Optional, the process for determining the relational model of dry rock samples includes:

[0079] The fitting parameters in the relational model of dry rock samples were determined using the nonlinear least squares method.

[0080] The Levenberg-Marquardt method, a nonlinear least squares algorithm provided in Matlab software, was used to determine the fitting parameters in the relational model of the dried rock samples. The fitting parameters in the relational model of the dried rock samples are shown in Table 4.

[0081] Table 4 Fitting parameters in the relational model of dry rock samples

[0082] [a0] [a1] [a2] [a3] [a4] [a5] [a6] -3.93 7.11 2.24 -3.55 16.67 0.12 -2.89

[0083] The Levenberg-Marquardt method is a least-squares estimation method for regression parameters in nonlinear regression. Optimization aims to find the parameter vector that minimizes the function value.

[0084] Optionally, after determining the bulk modulus of the first dried rock using a relational model based on the physical property parameters of the target dried rock, the process includes steps D1-D2:

[0085] Step D1: Compare the first dry rock bulk modulus with the actual dry rock bulk modulus to obtain the correlation between the first dry rock bulk modulus and the actual dry rock bulk modulus.

[0086] The bulk modulus of the first dry rock is compared with that of the actual dry rock, and the correlation between the first dry rock bulk modulus and the actual dry rock bulk modulus is determined based on the relationship curve obtained from the comparison.

[0087] Step D2: Optimize the relational model based on the correlation.

[0088] Based on the correlation between the first dry rock bulk modulus and the actual dry rock bulk modulus, the fitting parameters of the relationship model are optimized until the correlation meets the preset requirements.

[0089] For example, such as Figure 7 The figure shows a comparison between the first dry rock volume modulus and the actual dry rock volume modulus. It can be seen from the figure that the first dry rock volume modulus and the actual dry rock volume modulus have a good correlation, with a correlation of 0.9502.

[0090] The technical scheme of the embodiment determines the first dry rock volume modulus according to the first physical characteristic parameter of the target dry rock by using a relational model according to the first physical characteristic parameter of the target dry rock. The method can solve the problem that the existing dry rock volume modulus calculation method has poor adaptability, so that the seismic wave velocity calculation formed on this basis is not accurate enough, and the calculation precision of the dry rock volume modulus is improved.

[0091] Figure 8 A structural schematic diagram of a three-parameter-based dry rock volume modulus calculation device is provided for the embodiment of the application. The three-parameter-based dry rock volume modulus calculation device can be realized in the form of hardware and / or software and can be configured in any electronic device with network communication function. As shown in the figure, the device comprises a physical parameter determination module 210 and a dry rock volume modulus determination module 220, wherein: Figure 8

[0092] The physical parameter determination module 210 is configured to determine the first physical characteristic parameter of the target dry rock, wherein the first physical characteristic parameter is data obtained by data collection on the target dry rock, and the first physical characteristic parameter at least comprises the density of the target dry rock, the porosity of the target dry rock and the effective pressure, wherein the effective pressure is the pressure applied to the target dry rock per unit area.

[0093] The dry rock volume modulus determination module 220 is configured to determine the first dry rock volume modulus according to the first physical characteristic parameter of the target dry rock by using a relational model, wherein the relational model is a relational expression constructed according to the second physical characteristic parameter of the dry rock sample and the dry rock volume modulus of the dry rock sample, the dry rock sample is a rock sample in the same region as the target dry rock and having the same physical characteristics, and the second physical characteristic parameter is data obtained by data collection on the dry rock sample.

[0094] Optionally, the dry rock volume modulus determination module 220 comprises:

[0095] The data acquisition unit is configured to acquire the second physical characteristic parameter of the dry rock sample and the second dry rock volume modulus data, wherein the second dry rock volume modulus data is the dry rock volume modulus data of the dry rock sample under different effective pressures.

[0096] The relational model determination unit is configured to construct the relational model of the dry rock sample according to the second physical characteristic parameter of the dry rock sample.

[0097] Optionally, the data acquisition unit comprises: ​

[0098] The dry rock sample determination subunit is configured to number the target rock sample after drying and oil removal treatment to obtain a dry rock sample;

[0099] The density determination subunit is configured to determine the density of the dry rock sample according to the mass and volume of the dry rock sample;

[0100] The porosity determination subunit is configured to measure the porosity of the dry rock sample to obtain the porosity of the dry rock sample;

[0101] The second dry rock bulk modulus determination subunit is configured to perform a pressure test on the dry rock sample to obtain second dry rock bulk modulus data.

[0102] Optionally, the relationship model determination unit comprises:

[0103] The first approximate relationship determination subunit is configured to construct a crossplot according to the porosity of the dry rock sample and the second dry rock bulk modulus to obtain a first approximate relationship, the first approximate relationship being an approximate relationship between the porosity of the dry rock sample and the second dry rock bulk modulus;

[0104] The second approximate relationship determination subunit is configured to construct a crossplot according to the density of the dry rock sample and the second dry rock bulk modulus to obtain a second approximate relationship, the second approximate relationship being an approximate relationship between the density of the dry rock sample and the second dry rock bulk modulus;

[0105] The third approximate relationship determination subunit is configured to construct a crossplot according to the second dry rock bulk modulus and the effective pressure to obtain a third approximate relationship, the third approximate relationship being an approximate relationship between the second dry rock bulk modulus and the effective pressure;

[0106] The relationship model determination subunit is configured to construct a relationship model of the dry rock sample according to the first approximate relationship, the second approximate relationship, and the third approximate relationship.

[0107] Optionally, the relationship model determination unit is specifically configured to:

[0108] The relationship model of the dry rock sample is determined by the following formula:

[0109]

[0110] wherein, is the porosity of the dry rock sample; ρdry rock sampleis the density of the dry rock sample, in units of g / cm 3 ; P is the effective pressure, in units of MPa; a0, a1, a2, a3, a4, a5, a6 are fitting coefficients.

[0111] Optionally, the relationship model determining sub-unit is specifically used for:

[0112] The nonlinear least square method is adopted to determine the fitting parameter in the relationship model of the dry rock sample.

[0113] Optionally, the dry rock volume modulus determining module 220 comprises:

[0114] The correlation determining unit is used for comparing the first dry rock volume modulus with the actual dry rock volume modulus to obtain the correlation between the first dry rock volume modulus and the actual dry rock volume modulus.

[0115] The optimization unit is used for optimizing the relationship model according to the correlation.

[0116] The three-parameter-based dry rock volume modulus calculation device provided in the embodiments of the present application can execute the three-parameter-based dry rock volume modulus calculation method provided in any of the embodiments of the present application, has the corresponding functions and advantages of executing the three-parameter-based dry rock volume modulus calculation method, and the detailed process is referred to the related operations of the three-parameter-based dry rock volume modulus calculation method in the foregoing embodiments.

[0117] Figure 9 The structure of the electronic device for implementing the three-parameter-based dry rock volume modulus calculation of the embodiments of the present application is shown in the figure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown in this figure, their connections, and their functions are merely examples, and are not intended to limit the implementation of the present application described and / or claimed herein.

[0118] As Figure 9As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0119] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0120] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the three-parameter based dry rock bulk modulus calculation method.

[0121] In some embodiments, the three-parameter based dry rock bulk modulus calculation method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the three-parameter based dry rock bulk modulus calculation method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the three-parameter based dry rock bulk modulus calculation method by any other appropriate means, such as by means of firmware.

[0122] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0123] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented on the computer or other programmable apparatus. The computer programs can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0124] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0125] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0126] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0127] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0128] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0129] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A method for calculating the bulk modulus of dry rock based on three parameters, characterized in that, include: The first physical property parameters and effective pressure of the target dry rock are determined. The first physical property parameters are data obtained by data collection on the target dry rock. The first physical property parameters include at least the density and porosity of the target dry rock. The effective pressure is the pressure applied to the target dry rock per unit area. The bulk modulus of the first dry rock is determined by a relational model based on the first physical property parameters of the target dry rock. The relational model is a relationship constructed based on the second physical property parameters of the dry rock sample and the bulk modulus of the dry rock sample. The dry rock sample is a rock sample that is located in the same region as the target dry rock and has the same physical properties. The second physical property parameters are data obtained by data collection from the dry rock sample. The process of constructing the relational model includes: The second physical property parameters and the second dry rock bulk modulus data of the dry rock sample are obtained. The second dry rock bulk modulus data are the dry rock bulk modulus data of the dry rock sample under different effective pressures. A relational model of the dried rock samples is constructed based on the second physical property parameters of the dried rock samples; The process of constructing a relational model for the dried rock samples based on the second physical property parameters of the dried rock samples includes: A cross-plot is constructed based on the porosity of the dried rock sample and the bulk modulus of the second dried rock to obtain a first approximate relationship, which is an approximate linear relationship between the porosity of the dried rock sample and the bulk modulus of the second dried rock. A cross-plot is constructed based on the density of the dried rock sample and the bulk modulus of the second dried rock to obtain a second approximate relationship, which is an approximate linear relationship between the density of the dried rock sample and the bulk modulus of the second dried rock. Based on the cross-plot of the second dry rock bulk modulus and the effective pressure, a third approximate relationship is obtained, which is an approximate power-law relationship between the second dry rock bulk modulus and the effective pressure. A relational model for the dry rock samples is constructed based on the first approximation relationship, the second approximation relationship, and the third approximation relationship. The relational model of the dried rock samples includes: The relationship model for the dry rock samples is determined using the following formula: in, Porosity of the dried rock sample; Density of dry rock samples, in g / cm³ 3 ; Effective pressure, unit: MPa; , , , , , , The fitting coefficients are denoted as .

2. The method according to claim 1, characterized in that, Obtain the second physical property parameters and the second dry rock bulk modulus data of the dried rock samples, including: After drying and degreasing the target rock samples, they are numbered to obtain dried rock samples; The density of the dried rock sample is determined based on its mass and volume. The porosity of the dried rock sample was measured to obtain the porosity of the dried rock sample; The dried rock sample was subjected to a pressure test to obtain the second dry rock bulk modulus data.

3. The method according to claim 1, characterized in that, The process of determining the relational model for the dried rock samples includes: The fitting parameters in the relational model of the dry rock samples were determined using the nonlinear least squares method.

4. The method according to claim 1, characterized in that, After determining the bulk modulus of the first dried rock using a relational model based on the physical property parameters of the target dried rock, the following steps are included: The correlation between the first dry rock bulk modulus and the actual dry rock bulk modulus is obtained by comparing the first dry rock bulk modulus with the actual dry rock bulk modulus. The relationship model is optimized based on the correlation.

5. A device for calculating the bulk modulus of dry rock based on three parameters, characterized in that, include: The physical parameter determination module is used to determine the first physical characteristic parameters of the target dry rock. The first physical characteristic parameters are data obtained by data collection on the target dry rock. The first physical characteristic parameters include at least: the density of the target dry rock, the porosity of the target dry rock, and the effective pressure, wherein the effective pressure is the pressure applied to the target dry rock per unit area. The dry rock bulk modulus determination module is used to determine the first dry rock bulk modulus based on the first physical property parameters of the target dry rock using a relational model. The relational model is a relationship constructed based on the second physical property parameters of the dry rock sample and the dry rock bulk modulus of the dry rock sample. The dry rock sample is a rock sample located in the same region as the target dry rock and having the same physical properties. The second physical property parameters are data obtained by data collection from the dry rock sample. The dry rock bulk modulus determination module includes: Data acquisition unit: used to acquire the second physical property parameters and the second dry rock bulk modulus data of the dry rock sample, wherein the second dry rock bulk modulus data is the dry rock bulk modulus data of the dry rock sample under different effective pressures; Relationship Model Determination Unit: Used to construct a relationship model of the dry rock sample based on the second physical property parameters of the dry rock sample; The relation model determination unit includes: First approximation relationship determination subunit: used to construct a cross-plot based on the porosity of the dry rock sample and the bulk modulus of the second dry rock to obtain a first approximation relationship, wherein the first approximation relationship is an approximate linear relationship between the porosity of the dry rock sample and the bulk modulus of the second dry rock; The second approximation relationship determination subunit is used to construct a cross-plot based on the density of the dry rock sample and the bulk modulus of the second dry rock to obtain a second approximation relationship, which is an approximate linear relationship between the density of the dry rock sample and the bulk modulus of the second dry rock. The third approximation relationship determination subunit is used to construct a cross-plot based on the bulk modulus of the second dry rock and the effective pressure to obtain the third approximation relationship, which is an approximate power exponential relationship between the bulk modulus of the second dry rock and the effective pressure. Relationship model determines sub-units: a relationship model for dry rock samples is constructed based on the first approximate relationship, the second approximate relationship, and the third approximate relationship; The relational model determines the sub-units, specifically for: The relationship model for the dry rock samples is determined using the following formula: in, Porosity of the dried rock sample; Density of dry rock samples, in g / cm³ 3 ; Effective pressure, unit: MPa; , , , , , , The fitting coefficients are denoted as .

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for calculating the bulk modulus of dry rock based on any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for calculating the bulk modulus of dry rock based on any one of claims 1-4.

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