Stratum porosity calculation method, system and device and storage medium

Abstract

The invention discloses a formation porosity calculation method, system and device and a storage medium. The formation porosity calculation method comprises the steps that resistivity logging data, acoustic logging data, rock measurement data and fluid measurement data of a formation are obtained; determining a resistance porosity estimation value according to the resistivity logging data, and determining a sound wave porosity estimation value according to the sound wave logging data; and substituting the resistance porosity estimation value, the acoustic wave porosity estimation value, the rock measurement data and the fluid measurement data into a preset formula for weighted calculation to obtain the porosity of the target stratum. According to the embodiment of the invention, the accuracy and reliability of formation porosity calculation can be improved, and the method can be widely applied to the technical field of geological survey.

Description

Technical Field

[0001] The present invention relates to the field of geological measurement technology, and in particular to a formation porosity calculation method, system, device and storage medium. Background Art

[0002] In the field of oil and gas exploration, accurate calculation of formation porosity is crucial for evaluating reservoir quality and predicting oil and gas production. Porosity is a parameter that characterizes the size of pore space in the formation, which directly affects the storage capacity and mobility of oil and gas.

[0003] At present, the calculation of porosity mainly relies on two methods: resistivity logging and sonic logging. Among the related technologies, resistivity logging is greatly affected by formation water salinity, temperature and rock skeleton resistivity, which may lead to deviations in porosity calculation results. Sonic logging may be affected by formation lithology, fluid type and formation stress state, which will also affect the calculation accuracy of porosity. Using resistivity or sonic logging data alone to calculate porosity may not accurately reflect the true porosity of the formation, especially under complex formation conditions, resulting in insufficient reliability and accuracy of formation porosity calculation results. Summary of the invention

[0004] In view of this, an object of the embodiments of the present invention is to provide a formation porosity calculation method, system, device and storage medium to improve the accuracy and reliability of formation porosity calculation.

[0005] In one aspect, an embodiment of the present invention provides a formation porosity calculation method, comprising: Obtaining resistivity logging data, sonic logging data, rock measurement data and fluid measurement data of the formation; determining a resistive porosity estimate based on the resistivity logging data, and determining a sonic porosity estimate based on the sonic logging data; The resistivity porosity estimate, the acoustic porosity estimate, the rock measurement data and the fluid measurement data are substituted into a preset formula for weighted calculation to obtain the target formation porosity.

[0006] Optionally, the resistivity porosity estimate is calculated according to the following formula:

[0007] in, represents the estimated value of resistivity porosity, a represents a constant, R t represents the resistivity of the formation, Indicates the formation porosity determined in addition to the resistivity calculation method, S w Represents the resistivity of formation water, mrepresents the porosity index of the rock, n Represents the saturation index.

[0008] Optionally, the acoustic porosity estimate is calculated according to the following formula:

[0009] in, represents the estimated value of sonic porosity, V formation Represents the acoustic velocity of the formation, V matrix Represents the acoustic wave velocity of the formation matrix, V fluid Represents the acoustic wave velocity of the formation fluid.

[0010] Optionally, the preset formula is determined by the following method: Determining a second weight coefficient of the resistive porosity estimate parameter based on the first weight coefficient of the acoustic porosity estimate parameter; Determine a density parameter according to the rock density and the fluid density, and determine a compression parameter according to the rock compression coefficient and the fluid compression coefficient; The preset formula is determined by weighting the acoustic porosity estimation parameter and the first weight coefficient, the resistivity porosity estimation parameter and the second weight coefficient, the density parameter and the third weight coefficient, and the compression parameter and the fourth weight coefficient.

[0011] Optionally, the expression of the preset formula satisfies the following relationship:

[0012] in, represents the target formation porosity, represents the acoustic porosity estimate, represents the estimated value of resistivity porosity, α represents the first weight coefficient, γ represents the third weight coefficient, δ represents the fourth weight coefficient, ρ rock represents the rock density, ρ fluid represents the fluid density, β rock represents the rock compressibility coefficient, β fluid Represents the compressibility of the fluid.

[0013] Optionally, the weight coefficient of the preset formula is determined by the following method: Acquire several groups of sample data, wherein the sample data include porosity measurement sample values, resistivity logging data sample values, sonic logging data sample values, rock data sample values, and fluid data sample values; Calculating a resistivity porosity sample value based on the resistivity logging data sample value, and calculating a sonic porosity sample value based on the sonic logging data sample value; Establishing a multivariate regression equation group according to the porosity measurement sample values, the resistivity porosity sample values, the acoustic porosity sample values, the rock data sample values, the fluid data sample values ​​and a number of weight coefficients to be determined; Several weight coefficients of the multivariate regression equation group are determined according to the least square method.

[0014] On the other hand, an embodiment of the present invention provides a formation porosity calculation system, including: The first module is used to obtain resistivity logging data, sonic logging data, rock measurement data and fluid measurement data of the formation; A second module is used to determine a resistivity porosity estimate based on the resistivity logging data and to determine a sonic porosity estimate based on the sonic logging data; The third module is used to substitute the resistivity porosity estimate value, the acoustic porosity estimate value, the rock measurement data and the fluid measurement data into a preset formula for weighted calculation to obtain the target formation porosity.

[0015] On the other hand, an embodiment of the present invention provides a formation porosity calculation device, comprising: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0016] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to perform the above method.

[0017] On the other hand, an embodiment of the present invention provides a formation porosity calculation system, comprising a well logging data acquisition device and a computer device connected to the well logging data acquisition device; wherein: The logging data acquisition equipment is used to acquire any one or more of resistivity logging data, sonic logging data, rock measurement data or fluid measurement data; The computer device comprises: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0018] The implementation of the embodiment of the present invention includes the following beneficial effects: In this embodiment, a resistivity porosity estimate is determined based on resistivity logging data, an acoustic porosity estimate is determined based on acoustic logging data, and the resistivity porosity estimate, acoustic porosity estimate, rock measurement data, and fluid measurement data are substituted into a preset formula for weighted calculation to obtain a target formation porosity. In the formation porosity calculation process, the resistivity logging data and the acoustic logging data are used in conjunction to fully utilize the complementary information between the resistivity logging data and the acoustic logging data, thereby improving the accuracy and reliability of the formation porosity calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the steps of a formation porosity calculation method provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of a step flow of determining a preset formula provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of a step flow of determining a weight coefficient in a preset formula provided by an embodiment of the present invention; Figure 4 is a structural block diagram of a formation porosity calculation system provided by an embodiment of the present invention; Figure 5 is a structural block diagram of a formation porosity calculation device provided by an embodiment of the present invention; Figure 6 is a structural block diagram of a computer device provided by an embodiment of the present invention; Figure 7 This is another structural block diagram of a formation porosity calculation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0021] It should be noted that, although the functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order from the module division in the device or the order in the flow chart. The terms "first", "second", etc. in the specification and claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0023] Some technical terms in this embodiment are explained below.

[0024] Resistivity logging infers porosity by measuring the resistance of the formation to electric current. Its principle is based on the relationship between the resistivity of the fluid in the formation pores and the resistivity of the formation. Acoustic logging estimates porosity by measuring the propagation speed of sound waves in the formation. Therefore, the speed of sound waves is related to the density and porosity of the formation.

[0025] like Figure 1 As shown, an embodiment of the present invention provides a formation porosity calculation method, including: S100, obtaining resistivity logging data, sonic logging data, rock measurement data and fluid measurement data of the formation.

[0026] Resistivity is a parameter that characterizes the conductivity of the formation and is closely related to the formation porosity, fluid properties (oil, gas / water), and rock composition. The principle of resistivity measurement is as follows: emit current (DC or AC) to the formation, and calculate the formation resistivity by measuring the voltage drop or electromagnetic field change on the current path. Different logging instruments (such as lateral logging and induction logging) use different physical methods.

[0027] Acoustic logging is an important logging method that evaluates rock physical parameters and formation structure by measuring the propagation characteristics of sound waves in the formation (such as speed, amplitude, frequency, etc.). The propagation speed of sound waves in the formation is closely related to the elastic modulus, density, porosity and fluid properties of the rock. The acoustic logging instrument records the time difference or speed of the sound waves by transmitting sound wave signals and receiving sound waves of different paths (direct waves, reflected waves).

[0028] It should be noted that the rock measurement data includes but is not limited to the measurement data such as the density and compressibility coefficient of the rock, and the fluid measurement data includes but is not limited to the measurement data such as the density and compressibility coefficient of the fluid.

[0029] S200, determining a resistivity porosity estimation value based on resistivity logging data, and determining a sonic porosity estimation value based on sonic logging data.

[0030] It should be noted that the method for calculating the estimated value of resistive porosity based on resistivity logging data is determined according to actual application, and this embodiment does not impose any specific restrictions. The method for calculating the estimated value of resistive porosity based on resistivity logging data can adopt an existing algorithm, a new algorithm, or an improved existing calculation method. Similarly, the method for calculating the estimated value of sonic porosity based on sonic logging data is determined according to actual application, and this embodiment does not impose any specific restrictions. The method for calculating the estimated value of sonic porosity based on sonic logging data can adopt an existing algorithm, a new algorithm, or an improved existing calculation method.

[0031] S300, substituting the resistivity porosity estimate value, the sonic porosity estimate value, the rock measurement data and the fluid measurement data into a preset formula for weighted calculation to obtain the target formation porosity.

[0032] It should be noted that the preset formula is determined according to actual application and is not specifically limited in this embodiment. The unknown parameters in the preset formula include resistivity porosity estimation value, acoustic porosity estimation value, rock measurement data and fluid measurement data, and the preset formula also includes several known weight coefficients.

[0033] Optionally, the resistivity porosity estimate is calculated according to the following formula:

[0034] in, represents the resistivity porosity estimate, which indicates the volume fraction of void space in the formation; a represents a constant used to convert resistivity to porosity; R t The resistivity of the formation indicates the resistance of the formation to the flow of electric current; Indicates the formation porosity determined by methods other than resistivity, such as density logging; S wIndicates the resistivity of formation water, which indicates the resistance of formation water to the flow of electric current; m Represents the porosity index of rock, used to describe the effect of porosity on resistivity; n It stands for saturation index and is used to describe the effect of fluid saturation on resistivity.

[0035] Optionally, an estimate of sonic porosity is calculated according to the following formula:

[0036] in, V represents the estimated value of sonic porosity, which represents the volume fraction of void space in the formation; formation V represents the velocity of sound waves in the formation, which means the speed at which sound waves propagate in the formation; matrix V represents the acoustic wave velocity of the formation matrix, which means the propagation speed of the acoustic wave in pure rock; fluid It represents the sound wave velocity of the formation fluid and the propagation speed of the sound wave in the formation pore fluid.

[0037] Optionally, see Figure 2 , the preset formula is determined by the following method: S301, determining a second weight coefficient of a resistivity porosity estimation parameter according to a first weight coefficient of a sonic porosity estimation parameter; S302, determining a density parameter according to the rock density and the fluid density, and determining a compression parameter according to the rock compression coefficient and the fluid compression coefficient; S303, weighting is performed according to the acoustic porosity estimation value parameter and the first weight coefficient, the resistivity porosity estimation value parameter and the second weight coefficient, the density parameter and the third weight coefficient, and the compression parameter and the fourth weight coefficient to determine a preset formula.

[0038] Specifically, the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are parameters to be determined, and the sum of the first weight coefficient and the second weight coefficient is a fixed value, such as 1. The density parameter is determined according to the relationship between the rock density and the fluid density and the influence on the formation porosity, and the compression parameter is determined according to the relationship between the rock compression coefficient and the fluid compression coefficient and the influence on the formation porosity. The product of the acoustic porosity estimation parameter and the first weight coefficient, the product of the resistivity porosity estimation parameter and the second weight coefficient, the product of the density parameter and the third weight coefficient, and the product of the compression parameter and the fourth weight coefficient are weighted to determine the preset formula.

[0039] Optionally, the expression of the preset formula satisfies the following relationship:

[0040] in, represents the target formation porosity, represents the acoustic porosity estimate, represents the estimated value of resistivity porosity, α represents the first weight coefficient, γ represents the third weight coefficient, δ represents the fourth weight coefficient, ρ rock represents the rock density, ρ fluid represents the fluid density, β rock represents the rock compressibility coefficient, β fluid Represents the fluid compressibility coefficient. Multiple weight coefficients are used to balance the influence of different parameters in the porosity calculation of the target formation.

[0041] Optionally, see Figure 3 , the weight coefficient of the preset formula is determined by the following method: S010, obtaining several groups of sample data, the sample data including porosity measurement sample values, resistivity logging data sample values, sonic logging data sample values, rock data sample values ​​and fluid data sample values; S020, calculating a resistivity porosity sample value according to the resistivity logging data sample value, and calculating a sonic porosity sample value according to the sonic logging data sample value; S030, establishing a multivariate regression equation group according to the porosity measurement sample values, the resistivity porosity sample values, the acoustic porosity sample values, the rock data sample values, the fluid data sample values ​​and a number of weight coefficients to be determined; S040. Determine several weight coefficients of the multivariate regression equation system according to the least squares method.

[0042] Collect training sample data: Take the core porosity measurement sample values ​​of the cored well section in the study block, as well as the resistivity sample values, acoustic velocity sample values, rock density sample values, fluid density sample values, rock compressibility sample values, and fluid compressibility sample values ​​at the corresponding depth. Assume that the sample capacity is n , the sample points are recorded as follows:

[0043] in, S i Indicates the sample i Sample points, y i Indicates i The porosity sample value of the core sampling point is Indicates i The resistivity porosity sample value of each sample point, Indicates i The acoustic porosity sample value of each sample point, Indicates i The rock density sample value of each sample point, Indicates i The sample value of fluid density at each sample point, Indicates i Sample values ​​of rock compression coefficient at sample points, Indicates i Sample values ​​of fluid compressibility at each sample point.

[0044] The resistivity porosity sample value is calculated based on the resistivity logging data sample value and formula (1): x 1 Recorded as The acoustic porosity sample value is calculated based on the acoustic logging data sample value and formula (2). x 2 Recorded as .

[0045] Establish a multivariate regression equation system and determine the model weight coefficients by the least squares method:

[0046] For the convenience of describing the method, , , , , i =1,2,..., n , then formula (5) is transformed into:

[0047] It can be expressed in matrix form as:

[0048] Use the least squares method to solve the regression equations and determine the weighting coefficients:

[0049] In formula (11), is the estimated value of the weight coefficient in formula (3).

[0050] The implementation of the embodiment of the present invention includes the following beneficial effects: In this embodiment, a resistivity porosity estimate is determined based on resistivity logging data, an acoustic porosity estimate is determined based on acoustic logging data, and the resistivity porosity estimate, acoustic porosity estimate, rock measurement data, and fluid measurement data are substituted into a preset formula for weighted calculation to obtain a target formation porosity. In the formation porosity calculation process, the resistivity logging data and the acoustic logging data are used in conjunction to fully utilize the complementary information between the resistivity logging data and the acoustic logging data, thereby improving the accuracy and reliability of the formation porosity calculation.

[0051] See also Figure 4 , an embodiment of the present invention provides a formation porosity calculation system, comprising: The first module is used to obtain resistivity logging data, sonic logging data, rock measurement data and fluid measurement data of the formation; The second module is used to determine the resistivity porosity estimate based on the resistivity logging data and determine the sonic porosity estimate based on the sonic logging data; The third module is used to substitute the resistivity porosity estimate, the acoustic porosity estimate, the rock measurement data and the fluid measurement data into a preset formula for weighted calculation to obtain the target formation porosity.

[0052] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0053] See also Figure 5 , an embodiment of the present invention provides a formation porosity calculation device, comprising: at least one processor; at least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above method.

[0054] Among them, the memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. The memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a remote memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0055] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0056] In addition, the embodiment of the present application also discloses a computer program product or a computer program, and the computer program product or the computer program is stored in a computer-readable storage medium. The processor of the computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the above method. Similarly, the contents in the above method embodiment are all applicable to the storage medium embodiment, and the functions specifically implemented by the storage medium embodiment are the same as those in the above method embodiment, and the beneficial effects achieved are also the same as those achieved by the above method embodiment.

[0057] An embodiment of the present invention further provides a computer-readable storage medium, which stores a program executable by a processor. The program executable by the processor is used to implement the above method when executed by the processor.

[0058] It is understood that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0059] Specifically, see Figure 6The computer device 600 may include an RF (Radio Frequency) circuit 610, a memory 620 including one or more computer-readable storage media, an input unit 630, a display unit 640, a sensor 650, an audio circuit 660, a short-range wireless transmission module 670, a processor 680 including one or more processing cores, and a power supply 690. Those skilled in the art will appreciate that Figure 6 The device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0060] The RF circuit 610 can be used for receiving and sending signals during information transmission or calls. In particular, after receiving the downlink information of the base station, it is handed over to one or more processors 680 for processing; in addition, the data related to the uplink is sent to the base station. Generally, the RF circuit 610 includes but is not limited to an antenna, at least one amplifier, a tuner, one or more oscillators, a user identity module (SIM) card, a transceiver, a coupler, an LNA (Low Noise Amplifier), a duplexer, etc. In addition, the RF circuit 610 can also communicate with the network and other devices through wireless communication. Wireless communication can use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication), GPRS (General Packet Radio Service), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), LTE (Long Term Evolution), email, SMS (Short Messaging Service), etc. The memory 620 can be used to store software programs and modules. The processor 680 executes various functional applications and data processing by running the software programs and modules stored in the memory 620. The memory 620 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the device 600 (such as audio data, a phone book, etc.), etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 620 may also include a memory controller to provide the processor 680 and the input unit 630 with access to the memory 620. Although Figure 6 The RF circuit 610 is shown, but it is understandable that it is not an essential component of the device 600 and can be omitted as required without changing the essence of the invention.

[0061] The input unit 630 can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, the input unit 630 may include a touch-sensitive surface 631 and other input devices 632. The touch-sensitive surface 631, also known as a touch display screen or touchpad, can collect user touch operations on or near it (such as operations performed by users using fingers, styluses, or any other suitable objects or accessories on or near the touch-sensitive surface 631), and drive corresponding connection devices according to a pre-set program. Optionally, the touch-sensitive surface 631 may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 680, and can receive and execute commands sent by the processor 680. In addition, the touch-sensitive surface 631 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch-sensitive surface 631, the input unit 630 may further include other input devices 632. Specifically, the other input devices 632 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. The display unit 640 can be used to display information input by the user or information provided to the user and various graphical user interfaces of the control 600, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit 640 may include a display panel 641. Optionally, the display panel 641 may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface 631 may be covered on the display panel 641. When the touch-sensitive surface 631 detects a touch operation on or near it, it is transmitted to the processor 680 to determine the type of touch event. Subsequently, the processor 680 provides a corresponding visual output on the display panel 641 according to the type of touch event. Although in Figure 6 In the embodiment, the touch-sensitive surface 631 and the display panel 641 are implemented as two independent components to implement input and output functions, but in some embodiments, the touch-sensitive surface 631 and the display panel 641 can be integrated to implement input and output functions.

[0062] The computer device 600 may also include at least one sensor 650, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 641 according to the brightness of the ambient light, and the proximity sensor may turn off the display panel 641 and / or the backlight when the device 600 is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that can also be configured in the device 600, they will not be repeated here.

[0063] The audio circuit 660, the speaker 661, and the microphone 662 can provide an audio interface between the user and the device 600. The audio circuit 660 can transmit the electrical signal converted from the received audio data to the speaker 661, which is converted into a sound signal for output; on the other hand, the microphone 662 converts the collected sound signal into an electrical signal, which is received by the audio circuit 660 and converted into audio data, and then the audio data is processed by the output processor 680 and sent to another control device through the RF circuit 610, or the audio data is output to the memory 620 for further processing. The audio circuit 660 may also include an earplug jack to provide communication between an external headset and the device 600.

[0064] The short-distance wireless transmission module 670 may be a WIFI (wireless fidelity) module, a Bluetooth module, an infrared module, etc. The device 600 may transmit information with a wireless transmission module provided on a competing device through the short-distance wireless transmission module 670 . The processor 680 is the control center of the device 600. It uses various interfaces and lines to connect various parts of the entire control device. By running or executing software programs and / or modules stored in the memory 620, and calling data stored in the memory 620, it executes various functions of the device 600 and processes data, thereby monitoring the control device as a whole. Optionally, the processor 680 may include one or more processing cores; optionally, the processor 680 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 650.

[0065] The device 600 also includes a power supply 690 (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the processor 680 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 690 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0066] Although not shown, the device 600 may also include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0067] See also Figure 7 The embodiment of the present invention provides a formation porosity calculation system, including a well logging data acquisition device and a computer device connected to the well logging data acquisition device; wherein, Well logging data acquisition equipment, used to collect any one or more of resistivity logging data, sonic logging data, rock measurement data or fluid measurement data; Computer equipment includes: at least one processor; at least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above method.

[0068] Specifically, the logging data acquisition equipment includes but is not limited to a combination of multiple devices, such as instruments for collecting resistivity logging data include but are not limited to micro-sphere focused logging or micro-resistivity scanning imaging, etc., such as instruments for collecting acoustic logging data include but are not limited to compensated acoustic logging instruments, array acoustic logging instruments, dipole shear wave logging instruments, etc.; and as for the computer equipment, it can be different types of electronic devices, including but not limited to terminals such as desktop computers, laptops, servers or wearable devices.

[0069] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0070] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0071] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. It is estimated that there may be other division methods during implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0072] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to estimated needs to achieve the purpose of the solution of this embodiment.

[0073] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A formation porosity calculation method, characterized in that: include: Obtaining resistivity logging data, sonic logging data, rock measurement data and fluid measurement data of the formation; determining a resistive porosity estimate based on the resistivity logging data, and determining a sonic porosity estimate based on the sonic logging data; Substituting the resistivity porosity estimate, the acoustic porosity estimate, the rock measurement data and the fluid measurement data into a preset formula for weighted calculation to obtain the target formation porosity; The resistivity porosity estimate is calculated according to the following formula: in, represents the estimated value of resistivity porosity, a represents a constant, R t represents the resistivity of the formation, Indicates the formation porosity determined in addition to the resistivity calculation method, S w Represents the resistivity of formation water, m represents the porosity index of the rock, n represents the saturation index; The estimated value of the acoustic porosity is calculated according to the following formula: in, represents the estimated value of sonic porosity, V formation Represents the acoustic velocity of the formation, V matrix Represents the acoustic wave velocity of the formation matrix, V fluid Represents the acoustic wave velocity of the formation fluid.

2. The method according to claim 1, characterized in that: The preset formula is determined by the following method: Determining a second weight coefficient of the resistive porosity estimate parameter based on the first weight coefficient of the acoustic porosity estimate parameter; Determine density parameters according to rock density and fluid density, and determine compression parameters according to rock compressibility and fluid compressibility; The preset formula is determined by weighting the acoustic porosity estimation parameter and the first weight coefficient, the resistivity porosity estimation parameter and the second weight coefficient, the density parameter and the third weight coefficient, and the compression parameter and the fourth weight coefficient.

3. The method according to claim 2, characterized in that The expression of the preset formula satisfies the following relationship: in, represents the target formation porosity, represents the sonic porosity estimate, represents the estimated value of resistivity porosity, α represents the first weight coefficient, γ represents the third weight coefficient, δ represents the fourth weight coefficient, ρ rock represents the rock density, ρ fluid represents the fluid density, β rock represents the rock compressibility coefficient, β fluid Represents the compressibility of the fluid.

4. The method according to claim 2, characterized in that: The weight coefficient of the preset formula is determined by the following method: Acquire several groups of sample data, wherein the sample data include porosity measurement sample values, resistivity logging data sample values, sonic logging data sample values, rock data sample values, and fluid data sample values; Calculating a resistivity porosity sample value based on the resistivity logging data sample value, and calculating a sonic porosity sample value based on the sonic logging data sample value; Establishing a multivariate regression equation group according to the porosity measurement sample values, the resistivity porosity sample values, the acoustic porosity sample values, the rock data sample values, the fluid data sample values ​​and a number of weight coefficients to be determined; Several weight coefficients of the multivariate regression equation group are determined according to the least square method.

5. A formation porosity calculation system, characterized in that: include: The first module is used to obtain resistivity logging data, sonic logging data, rock measurement data and fluid measurement data of the formation; A second module is used to determine a resistivity porosity estimate based on the resistivity logging data and to determine a sonic porosity estimate based on the sonic logging data; The third module is used to substitute the resistivity porosity estimation value, the acoustic porosity estimation value, the rock measurement data and the fluid measurement data into a preset formula for weighted calculation to obtain the target formation porosity; The resistivity porosity estimate is calculated according to the following formula: in, represents the estimated value of resistivity porosity, a represents a constant, R t represents the resistivity of the formation, Indicates the formation porosity determined in addition to the resistivity calculation method, S w Represents the resistivity of formation water, m represents the porosity index of the rock, n represents the saturation index; The estimated value of the acoustic porosity is calculated according to the following formula: in, represents the estimated value of sonic porosity, V formation Represents the acoustic velocity of the formation, V matrix Represents the acoustic wave velocity of the formation matrix, V fluid Represents the acoustic wave velocity of the formation fluid.

6. A formation porosity calculation device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 4 when executed by the processor.

8. A formation porosity calculation system, characterized in that: It includes a well logging data acquisition device and a computer device connected to the well logging data acquisition device; wherein, The logging data acquisition equipment is used to acquire any one or more of resistivity logging data, sonic logging data, rock measurement data or fluid measurement data; The computer device comprises: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 4.

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