Pressure prediction method, device, equipment and medium based on improved mudstone impedance difference

Abstract

The present invention provides a pressure prediction method, device, equipment and medium based on improved mudstone impedance difference, and the pressure prediction method comprises the following steps: S1, using logging data to intersect the formation pressure and mudstone longitudinal wave differential impedance to determine empirical parameters; S2, based on seismic inversion, obtaining mudstone longitudinal wave velocity and mudstone longitudinal wave impedance to calculate the formation pressure. The pressure prediction method provided by the present invention more accurately reflects the formation pressure changes and improves the prediction accuracy, thereby providing reliable technical support for the prediction of pre-drilling pressure in the process of oil exploration, which is conducive to large-scale promotion and application.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum exploration, and relates to a pressure prediction method, in particular to a pressure prediction method, device, equipment and medium based on improved mudstone impedance difference. Background Art

[0002] The research on abnormal formation pressure prediction methods has developed from the early balanced depth method, Eaton method and rock strength method based on the experience of exploration and development personnel to the current comprehensive prediction method based on rock physical testing, logging and seismic. Among them, the method dominated by logging data and the method dominated by seismic data are currently more efficient methods for abnormal formation pressure prediction research. The more mature and common methods used at home and abroad for predicting formation pore pressure using logging data are the balanced depth method, Brower method and Eaton index method. The compaction balance equation method, equivalent depth method, Eaton method, Stone method, Fillippone method and improved Fillippone method are used to predict formation pore pressure using seismic layer velocity. The above methods are limited by the calculation accuracy of seismic layer velocity. For this reason, technicians have proposed an abnormal formation pressure prediction method based on velocity and acoustic impedance inversion, and developed a method based on prestack AVA inversion to improve the calculation accuracy of seismic layer velocity, thereby effectively improving the accuracy of abnormal formation pressure prediction.

[0003] However, in the actual pre-drilling pressure prediction, the above methods still have some shortcomings, mainly manifested in the insufficient accuracy of seismic velocity bodies (seismic velocity is the biggest challenge facing geophysics, the accuracy of conventional seismic velocity is very low, and the calculation error is large in areas with drastic changes in velocity in the horizontal and vertical directions), so finding more accurate elastic sensitivity parameters is a current research hotspot. For example, there is a certain relationship between P- and S-wave velocities and effective stress, and the formation pore pressure can be predicted by inverting P- and S-wave velocities; similar to P- and S-waves, Poisson's ratio is more sensitive to pressure changes, and the combination of Poisson's ratio and P-wave impedance to form a normal formation pressure identification factor can better predict abnormal formation pressure. The advantage of using elastic sensitivity factors to predict pore pressure is that elastic parameters can be obtained by pre-stack inversion, but pore pressure is insensitive to the high-frequency components of elastic parameters, and the accuracy of low-frequency components directly determines the accuracy of pore pressure.

[0004] It can be seen that how to provide a method for predicting abnormal formation pressure to more accurately reflect the changes in formation pressure and improve the prediction accuracy, thereby providing reliable technical support for the prediction of pre-drilling pressure during oil exploration, has become an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0005] The purpose of the present invention is to provide a pressure prediction method, device, equipment and medium based on improved mudstone impedance difference. The pressure prediction method more accurately reflects the changes in formation pressure and improves the prediction accuracy, thereby providing reliable technical support for the prediction of pre-drilling pressure during oil exploration and facilitating large-scale promotion and application.

[0006] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a pressure prediction method based on improved mudstone impedance difference, the pressure prediction method comprising the following steps:

[0008] S1. Use logging data to intersect the formation pressure and the mudstone longitudinal wave differential impedance to determine the empirical parameters;

[0009] S2. Based on seismic inversion, the longitudinal wave velocity and longitudinal wave impedance of mudstone are obtained to calculate the formation pressure.

[0010] The pressure prediction method provided by the present invention predicts the pore pressure of deep formations by means of seismic physics. Starting from the logging data, the error caused by the well condition affecting the logging curve is taken into consideration, and the influence of the density factor on the prediction result is reduced by the density factor. The method is more stable than conventional methods, thereby providing high-precision pore pressure prediction results for the wellbore structure design of drilling projects, which is conducive to large-scale promotion and application.

[0011] Preferably, the intersection relationship between the formation pressure and the mudstone longitudinal wave differential impedance in step S1 is obtained according to the following formula (1):

[0012] (dt-dt 压实趋势 ) / α-rhob(1)

[0013] Where: dt is the acoustic time difference of the logging longitudinal wave; dt 压实趋势 is the time difference of normal compaction trend of P-wave; rhob is the logging density; α is the regional empirical parameter.

[0014] Preferably, step S1 includes: adjusting the α parameter so that the intersection relationship between the formation pressure and the mudstone longitudinal wave differential impedance reaches a linear optimum, counting at least two characteristic wells, and establishing a regional empirical formula.

[0015] Preferably, step S2 comprises:

[0016] S2.1, respectively invert the seismic P-wave velocity and P-wave impedance of the well point to be calculated using the pre-stack inversion technique and the post-stack inversion technique;

[0017] S2.2, perform wavelet transformation on the seismic P-wave velocity and P-wave impedance obtained by inversion, and determine the minimum P-wave velocity and P-wave impedance difference according to the corresponding relationship between the local extreme value of the wavelet spectrum and the mudstone;

[0018] S2.3, performing Hilbert transformation on the minimum seismic P-wave velocity and the minimum P-wave impedance difference detected to obtain continuous and smoothed mudstone P-wave velocity and mudstone P-wave impedance;

[0019] S2.4. Calculate the formation pressure using the mudstone longitudinal wave velocity and mudstone longitudinal wave impedance.

[0020] Preferably, the wavelet transform in step S2.2 is performed according to the following formula (2):

[0021]

[0022] Where: C a,b is the coefficient of continuous wavelet transform; st is the signal to be transformed; φ a, is the fundamental wave; a is the transformation scale; b is the offset.

[0023] Preferably, the Hilbert transform in step S2.3 is performed according to the following formula (3):

[0024]

[0025] Where: Hω) is the Hilbert transform frequency domain factor.

[0026] Preferably, the formation pressure in step S2.4 is calculated according to the following formula (4):

[0027]

[0028] Where: P is the formation pressure; V is the inverted mudstone velocity; V 压实趋势 is the normal compaction speed of mudstone; I 纵波阻抗 is the longitudinal wave impedance of mudstone; α is the regional empirical parameter.

[0029] In a second aspect, the present invention provides a pressure prediction device based on improved mudstone impedance difference, the pressure prediction device comprising:

[0030] An empirical parameter determination module is used to determine empirical parameters by intersecting formation pressure and mudstone longitudinal wave differential impedance using well logging data;

[0031] The formation pressure calculation module is used to obtain the mudstone longitudinal wave velocity and mudstone longitudinal wave impedance based on seismic inversion and calculate the formation pressure.

[0032] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0033] at least one processor; and

[0034] a memory communicatively connected to the at least one processor; wherein,

[0035] 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 so that the at least one processor can execute the pressure prediction method based on improved mudstone impedance difference described in the first aspect.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a processor to implement the pressure prediction method based on improved mudstone impedance difference described in the first aspect when executed.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The pressure prediction method provided by the present invention predicts the pore pressure of deep formations by means of seismic physics. Starting from the logging data, the error caused by the well condition affecting the logging curve is taken into consideration, and the influence of the density factor on the prediction result is reduced by the density factor. The method is more stable than conventional methods, thereby providing high-precision pore pressure prediction results for the wellbore structure design of drilling projects, which is conducive to large-scale promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 is an intersection relationship diagram of the formation pressure at the reference point and the mudstone longitudinal wave differential impedance in the pressure prediction method provided in Example 1;

[0041] Figure 2 is an inversion diagram of the mudstone longitudinal wave velocity and mudstone longitudinal wave impedance of the well point to be calculated in the pressure prediction method provided in Example 1;

[0042] Figure 3 is a schematic diagram of the structure of the pressure prediction device provided in Example 2;

[0043] Figure 4 It is a schematic diagram of the structure of an electronic device provided in Example 3 for implementing the pressure prediction method based on improved mudstone impedance difference. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solutions provided by the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0045] Example 1

[0046] The present embodiment provides a pressure prediction method based on improved mudstone impedance difference, which can be applied to the accurate prediction of pore pressure in deep marine formations, thereby providing reliable technical support for the prediction of pre-drilling pressure during oil exploration. The method can be executed by a pressure prediction device based on improved mudstone impedance difference, which can be implemented in the form of hardware and / or software and can be configured in an electronic device with a pressure prediction function based on improved mudstone impedance difference.

[0047] Taking the test well A in the East China Sea as an example, the pressure prediction method provided in this embodiment includes the following steps:

[0048] S1. Use logging data to intersect the formation pressure and the mudstone longitudinal wave differential impedance to determine the empirical parameters, which are specifically divided into:

[0049] S1.1. Obtain the intersection relationship between formation pressure and mudstone P-wave differential impedance according to the following formula (1): Figure 1 );

[0050] (dt-dt 压实趋势 ) / α-rhob(1)

[0051] Where: dt is the acoustic time difference of the logging longitudinal wave; dt 压实趋势 is the time difference of normal compaction trend of P-wave; rhob is the logging density; α is the regional empirical parameter.

[0052] S1.2. Adjust the α parameter to make the intersection relationship between formation pressure and mudstone P-wave differential impedance reach the linear optimum. Count three characteristic wells and establish a regional empirical formula.

[0053] S2. Based on seismic inversion, the longitudinal wave velocity and longitudinal wave impedance of mudstone are obtained to calculate the formation pressure, which is specifically divided into:

[0054] S2.1. Invert the seismic P-wave velocity and P-wave impedance of the well point to be calculated using the pre-stack inversion technique and the post-stack inversion technique (see Figure 2 ).

[0055] S2.2, according to the following formula (2), the seismic P-wave velocity and P-wave impedance obtained by inversion are subjected to wavelet transformation, and the seismic P-wave minimum velocity and P-wave impedance difference are determined according to the corresponding relationship between the local extreme value of the wavelet spectrum and the mudstone;

[0056]

[0057] Where: C a,b is the coefficient of continuous wavelet transform; st is the signal to be transformed; φ a, is the fundamental wave; a is the transformation scale; b is the offset.

[0058] S2.3. Perform Hilbert transformation on the detected minimum seismic P-wave velocity and minimum P-wave impedance difference according to the following formula (3) to obtain continuous and smoothed mudstone P-wave velocity and mudstone P-wave impedance.

[0059]

[0060] Where: Hω) is the Hilbert transform frequency domain factor.

[0061] S2.4. Calculate the formation pressure according to the following formula (4).

[0062]

[0063] Where: P is the formation pressure; V is the inverted mudstone velocity; V 压实趋势 is the normal compaction speed of mudstone; I 纵波阻抗 is the longitudinal wave impedance of mudstone; α is a regional empirical parameter, which is consistent with formula (1).

[0064] The pressure prediction method provided in this embodiment predicts the pore pressure of deep formations through seismic physics means. Starting from the logging data, taking into account the errors caused by the well conditions affecting the logging curve, the density factor is combined to reduce the influence of data illusions on the prediction results. It is more stable than conventional methods, thereby providing high-precision pore pressure prediction results for the wellbore structure design of drilling projects, which is conducive to large-scale promotion and application.

[0065] Example 2

[0066] This embodiment provides a pressure prediction device based on improved mudstone impedance difference, such as Figure 3 As shown, the device includes: an empirical parameter determination module 110 and a formation pressure calculation module 120. Among them:

[0067] An empirical parameter determination module 110 is used to determine empirical parameters by intersecting formation pressure and mudstone longitudinal wave differential impedance using well logging data;

[0068] The formation pressure calculation module 120 is used to obtain the mudstone longitudinal wave velocity and mudstone longitudinal wave impedance based on seismic inversion and calculate the formation pressure.

[0069] In the empirical parameter determination module 110, the intersection relationship between the formation pressure and the mudstone longitudinal wave differential impedance is obtained according to the following formula (1):

[0070] (dt-dt 压实趋势 ) / α-rhob(1)

[0071] Where: dt is the acoustic time difference of the logging longitudinal wave; dt 压实趋势 is the time difference of normal compaction trend of P-wave; rhob is the logging density; α is the regional empirical parameter.

[0072] In the empirical parameter determination module 110, the α parameter is adjusted so that the intersection relationship between the formation pressure and the mudstone P-wave differential impedance reaches a linear optimum, and at least two characteristic wells are counted to establish a regional empirical formula.

[0073] In the formation pressure calculation module 120, the following calculation process is performed:

[0074] (1) The seismic P-wave velocity and P-wave impedance of the well point to be calculated are inverted using pre-stack inversion technology and post-stack inversion technology respectively.

[0075] (2) performing wavelet transformation on the seismic P-wave velocity and P-wave impedance obtained by inversion according to the following formula (2), and determining the minimum P-wave velocity and P-wave impedance difference of the seismic P-wave according to the corresponding relationship between the local extreme value of the wavelet spectrum and the mudstone;

[0076]

[0077] Where: C a,b is the coefficient of continuous wavelet transform; st is the signal to be transformed; φ a, is the fundamental wave; a is the transformation scale; b is the offset.

[0078] (3) According to the following formula (3), the detected seismic minimum P-wave velocity and minimum P-wave impedance difference are subjected to Hilbert transformation to obtain continuous and smoothed mudstone P-wave velocity and mudstone P-wave impedance.

[0079]

[0080] Where: Hω) is the Hilbert transform frequency domain factor.

[0081] (4) Calculate the formation pressure according to the following formula (4).

[0082]

[0083] Where: P is the formation pressure; V is the inverted mudstone velocity; V 压实趋势 is the normal compaction speed of mudstone; I 纵波阻抗 is the longitudinal wave impedance of mudstone; α is a regional empirical parameter, which is consistent with formula (1).

[0084] The device provided in this embodiment can execute the pressure prediction method based on improved mudstone impedance difference provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0085] Example 3

[0086] This embodiment provides an electronic device for implementing a pressure prediction method based on improved mudstone impedance difference, such as Figure 4 As shown, 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 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0087] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. 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.

[0088] A number of 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, speakers, etc.; a storage unit 18, such as a second storage area, 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.

[0089] The processor 11 may be a variety of general and / or special 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 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 executes the various methods and processes described above, such as the pressure prediction method based on the improved mudstone impedance difference.

[0090] In some embodiments, the pressure prediction method based on improved mudstone impedance difference can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on 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 pressure prediction method based on improved mudstone impedance difference described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the pressure prediction method based on improved mudstone impedance difference in any other appropriate manner (for example, by means of firmware).

[0091] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable target determination device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0093] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0095] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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.

[0096] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0097] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the information expected by the technical solution of this application can be achieved, and this document is not limited here.

[0098] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A pressure prediction method based on improved mudstone impedance difference, characterized in that: The pressure prediction method comprises the following steps: S1. Use logging data to intersect the formation pressure and the mudstone longitudinal wave differential impedance to determine the empirical parameters; S2. Obtain the longitudinal wave velocity and longitudinal wave impedance of mudstone based on seismic inversion and calculate the formation pressure; The intersection relationship between the formation pressure and the mudstone P-wave differential impedance in step S1 is obtained according to the following formula (1): （1） Where: is the acoustic time difference of logging longitudinal wave; is the time difference of normal compaction trend of P-wave; is the logging density; is the regional experience parameter.

2. The pressure prediction method according to claim 1, characterized in that: Step S1 includes: adjusting Parameters are set to make the intersection relationship between formation pressure and mudstone P-wave differential impedance reach the linear optimum, and at least two characteristic wells are counted to establish a regional empirical formula.

3. The pressure prediction method according to claim 2, characterized in that: Step S2 includes: S2.1, respectively invert the seismic P-wave velocity and P-wave impedance of the well point to be calculated using the pre-stack inversion technique and the post-stack inversion technique; S2.2, perform wavelet transformation on the seismic P-wave velocity and P-wave impedance obtained by inversion, and determine the minimum P-wave velocity and P-wave impedance difference according to the corresponding relationship between the local extreme value of the wavelet spectrum and the mudstone; S2.3, performing Hilbert transformation on the minimum seismic P-wave velocity and the minimum P-wave impedance difference detected to obtain continuous and smoothed mudstone P-wave velocity and mudstone P-wave impedance; S2.

4. Calculate the formation pressure using the mudstone longitudinal wave velocity and mudstone longitudinal wave impedance.

4. The pressure prediction method according to claim 3, characterized in that: The wavelet transform in step S2.2 is performed according to the following formula (2): （2） Where: is the coefficient of continuous wavelet transform; is the signal to be transformed; is the fundamental wave; To transform the scale; is the offset.

5. The pressure prediction method according to claim 4, characterized in that: The Hilbert transform in step S2.3 is performed according to the following formula (3): （3） Where: is the Hilbert transform frequency domain factor.

6. The pressure prediction method according to claim 5, characterized in that: The formation pressure in step S2.4 is calculated according to the following formula (4): （4） Where: is the formation pressure; To invert the mudstone velocity; is the normal compaction speed of mudstone; is the longitudinal wave impedance of mudstone; is the regional experience parameter.

7. A pressure prediction device based on improved mudstone impedance difference, characterized in that: The pressure prediction device comprises: An empirical parameter determination module is used to determine empirical parameters by intersecting formation pressure and mudstone longitudinal wave differential impedance using well logging data; The formation pressure calculation module is used to obtain the mudstone longitudinal wave velocity and mudstone longitudinal wave impedance based on seismic inversion and calculate the formation pressure.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, 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 so that the at least one processor can execute the pressure prediction method based on improved mudstone impedance difference as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the pressure prediction method based on improved mudstone impedance difference as described in any one of claims 1 to 6 when executed.

Images