Rock-based fluid prediction method, system and equipment and readable medium

Abstract

The invention provides a rock-based fluid prediction method, system and equipment and a readable medium, and relates to the technical field of oil exploration and development, and the method comprises the steps: carrying out the statistics of a fitting equation to obtain a corresponding relation between the parameter characteristics of reservoir fluid, and building a rock physical model based on the corresponding relation; a proper logging curve is selected to reconstruct the acoustic curve, and a pseudo-acoustic curve is constructed; establishing an internal structure of the frame model by utilizing the stratum boundary line, and constructing a fine framework model based on the internal structure of the frame model to ensure that the structure of the fine framework model conforms to the geological model; and obtaining an identification and prediction result, and performing fluid verification on the identification and prediction result by using the reservoir encountered by the drilled well. The method has the advantages that prediction precision is improved, fluid inversion precision is improved, a more accurate result can be obtained, the method is suitable for conventional lithologic oil and gas reservoirs, and the method is wide in application prospect and has great significance in oil reservoir development.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration and development, and in particular to a rock-based fluid prediction method, system, device and readable medium. Background Art

[0002] Seismic fluid inversion identification, that is, using seismic data to identify and describe the characteristics of fluid in the reservoir, is the key to the successful selection of favorable areas for oil and gas field exploration and development and well site deployment. Usually, pre-stack and post-stack data are combined with seismic wave impedance data or pseudo-wave impedance data for inversion, and the oil and gas enrichment area is identified through the distribution law of fluid, thereby guiding the development of oil and gas reservoirs.

[0003] The pre-stack data is too large and the operation is cumbersome, which affects the efficiency of fluid prediction. At present, in the post-stack inversion technology, some scholars have proposed resistivity parameters and relative resistivity parameters to distinguish reservoir fluids. However, in many oil and gas reservoirs, sometimes the resistivity of the oil layer and the gas layer is the same, sometimes the resistivity of the oil layer is greater than the resistivity of the gas layer, and sometimes the resistivity of the oil layer is less than the resistivity of the gas layer. It is difficult to distinguish the reservoir fluid with only one parameter, which affects the accuracy of oil and gas reservoir prediction using electrical characteristics. Resistivity curves and relative resistivity curves are difficult to use as effective identification basis. The current post-stack inversion method cannot effectively indicate the fluid type and reservoir status. Summary of the invention

[0004] The purpose of the present invention is to provide a rock-based fluid prediction method, system, device and readable medium to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a rock-based fluid prediction method, comprising:

[0006] Obtaining the corresponding relationship between parameter characteristics of reservoir fluid by using fitting equation statistics, and establishing a rock physics model based on the corresponding relationship;

[0007] Select appropriate logging curves to reconstruct the acoustic wave curve and construct a pseudo-acoustic wave curve;

[0008] Establishing the internal structure of the framework model using the stratigraphic boundaries, and constructing a fine grid model based on the internal structure of the framework model to ensure that the structure of the fine grid model conforms to the geological model;

[0009] The fluid is identified and its distribution is predicted based on the rock physics model, the pseudo-acoustic curve and the fine grid model to obtain an identification prediction result, and the identification prediction result is fluid-verified using the reservoir encountered by the drilling.

[0010] Preferably, the corresponding relationship between the parameter characteristics of the reservoir fluid is obtained by using the fitting equation statistics, and the rock physics model is established based on the corresponding relationship, which includes:

[0011] The formula of the fitting equation is: Y = -1.12E + 7 + 1818968X - 92080X 2 -26.85X 3 +153.12X 4 -4.949X 5 +0.05X 6

[0012] In the formula, X is the well diameter value, and Y is the resistance value. After the above formula is decomposed, the first corresponding relationship between different well diameter values ​​and resistivity value characteristics is obtained;

[0013] Selecting rock physics model parameters, wherein the rock physics model parameters are parameters used to describe changes in underground reservoir lithology, pore development, and changes in fluid properties;

[0014] The rock physics model parameters are used to draw a cross-plot, and the logging curve parameters in the cross-plot are extracted to distinguish the fluid properties of the area, and it is verified and analyzed whether there is a consistent relationship between the first corresponding relationship and the fluid properties.

[0015] Preferably, the selecting of a suitable logging curve to reconstruct the acoustic wave curve to construct a pseudo-acoustic wave curve includes:

[0016] Selecting a sample well in the target area to be tested;

[0017] Extracting a first curve and a second curve, wherein the first curve includes the acoustic wave curve of the sample well as a low-frequency component, and the second curve includes the well diameter value and resistivity curve of the sample well extracted as high-frequency components for constraint;

[0018] Reconstructing the acoustic wave curve according to the low-frequency component and the high-frequency component, calculating the wave impedance data, and outputting the pseudo-acoustic wave curve;

[0019] The common structural information contained in the pseudo-acoustic curve and the logging curve is mined to perform seismic inversion.

[0020] Preferably, the internal structure of the framework model is established by using the stratum boundaries, and the fine grid model is constructed based on the internal structure of the framework model, which includes:

[0021] Selecting first data, the first data including the top and bottom of the oil group of the target layer and a larger layer boundary inside the target layer, and using the first data to establish a boundary framework model;

[0022] selecting second data, wherein the second data includes a stratum of a target layer capable of controlling a seismic reflection structure;

[0023] Based on the boundary framework model, the first data and the second data, the internal structure of the framework model is controlled, and a fine grid model is constructed based on the internal structure of the framework model.

[0024] In a second aspect, the present application also provides a rock-based fluid prediction system, comprising a calculation module, a reconstruction module, a construction module and a prediction module, wherein:

[0025] Calculation module: used to obtain the corresponding relationship between parameter characteristics of reservoir fluid by using fitting equation statistics, and establish a rock physics model based on the corresponding relationship;

[0026] Reconstruction module: used to select appropriate logging curves to reconstruct the acoustic wave curve and construct a pseudo-acoustic wave curve;

[0027] Construction module: used to establish the internal structure of the framework model using the stratum boundary, and to construct a fine grid model based on the internal structure of the framework model, so as to ensure that the structure of the fine grid model conforms to the geological model;

[0028] Prediction module: used to identify and predict the distribution of fluids according to the rock physics model, the pseudo-acoustic curve and the fine grid model, obtain identification and prediction results, and use the reservoirs encountered by drilling to verify the fluids of the identification and prediction results.

[0029] In a third aspect, the present application also provides a rock-based fluid prediction device, comprising:

[0030] Memory for storing computer programs;

[0031] A processor is used to implement the steps of the rock-based fluid prediction method when executing the computer program.

[0032] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned rock-based fluid prediction method are implemented.

[0033] The beneficial effects of the present invention are:

[0034] 1. The purpose of the present invention is to provide a fluid prediction method based on post-stack inversion of rock physical analysis to solve the problem that the inversion error of resistivity or relative resistivity parameters is large and the planar distribution cannot effectively reflect the actual reservoir fluid distribution.

[0035] 2. Starting from the rock physics model, the present invention extracts the data connection between the logging characteristic curve and the reservoir fluid, as well as the intersection diagram of resistivity and wellbore diameter. This intersection diagram can better distinguish gas, oil and water (single or more than three conventional curves: wellbore diameter, natural gamma, natural potential, acoustic wave, density, neutron, resistivity, and radioactivity cannot distinguish), and the interpretation accuracy of the intersection diagram is verified by the logging interpretation of the sample well; the transformation or reconstruction of the sound wave in the present invention is not arbitrary, and it consists of two parts, namely, retaining the low-frequency part of the original sound wave and only changing the high-frequency part, ensuring the consistency of the reconstructed sound wave synthetic record with the original sound wave, and does not affect the inversion result of the mid-frequency band.

[0036] 3. The present invention obtains new curves that conform to geological laws and can distinguish fluids well, improves logging-constrained seismic inversion in areas where fluid differences are not obvious, improves the ability of inversion results to distinguish fluids, recalculates wave impedance (pseudo-wave impedance), and mines common structural information contained in similar waveforms and logging curves, thereby performing seismic inversion, improving the accuracy of fluid inversion, and obtaining more accurate results.

[0037] 4. The present invention is a high-precision fluid inversion method, which is applicable to conventional lithologic oil and gas reservoirs. The method has broad application prospects and is of great significance to reservoir development.

[0038] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic flow chart of the rock-based fluid prediction method described in an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of the structure of the rock-based fluid prediction system described in an embodiment of the present invention;

[0042] Figure 3 It is a schematic diagram of the structure of the rock-based fluid prediction device described in an embodiment of the present invention.

[0043] In the figure: 701, computing module; 7011, computing unit; 7012, first selection unit; 7013, first extraction unit; 702, reconstruction module; 7021, second selection unit; 7022, second extraction unit; 7023, reconstruction unit; 7024, mining unit; 703, building module; 7031, third selection unit; 7032, fourth selection unit; 7033, building unit; 704, prediction module; 800, rock-based fluid prediction device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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 part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0046] Embodiment 1:

[0047] This embodiment provides a rock-based fluid prediction method.

[0048] The present invention starts from the rock physics model, extracts the data connection between the logging characteristic curve and the reservoir fluid, and the cross-plot composed of resistivity and well diameter, and this cross-plot can better distinguish gas, oil, and water (single or more than three conventional curves: well diameter, natural gamma, natural potential, sound wave, density, neutron, resistivity, radioactivity cannot be distinguished), and the interpretation accuracy of the cross-plot is verified by the logging interpretation of the sample well; on this basis, the two logging curves are used to synthesize the pseudo-sound wave curve (about the pseudo-sound wave problem, the reason why some people do not trust it is to worry that the sound wave curve will no longer have a corresponding relationship with the seismic waveform after being transformed). In fact, this is not the case. In fact, since the seismic record has an effective frequency band, after exceeding the frequency band, the earthquake cannot be reflected. Therefore, the transformation or reconstruction of the sound wave in the present invention is not arbitrary, and it has two parts, that is, the low-frequency part of the original sound wave is retained, and only the high-frequency part is changed. The so-called boundary between low frequency and high frequency is the effective frequency band of the earthquake, which ensures that the reconstructed sound wave synthesis record is consistent with the original sound wave, so it does not affect the inversion result of the medium frequency band. For the part above the seismic frequency band, it is the result of well interpolation. Whether to use the original acoustic wave curve or the reconstructed acoustic wave curve has nothing to do with the seismic record, so it is completely possible to choose a curve that can better express the fluid characteristics. Distrust of reconstructed curve inversion is still a legacy of the past sparse pulse inversion period, which is completely different from the current high-resolution inversion. At present, similar patents or documents have been retrieved in the market or other public channels: a seismic fluid inversion identification method and device (patent publication number: CN201510581489), a method for predicting oil and gas distribution based on relative resistivity random simulation inversion (patent publication number: CN201610148059), a post-stack seismic fluid prediction method based on CEEMDSPWVD time-frequency spectrum analysis (patent publication number: CN201710399155) and a seismic low-frequency information fluid prediction method based on EEMDICA (patent publication number: CN201710842580). Among them, the original key steps in the listed patents are ① establishing a lithology inversion model ② establishing a reservoir inversion model ③ establishing a fluid relative resistivity curve ④ establishing a relative resistivity inversion model ⑤ predicting the fluid distribution area. However, the original method has problems such as complicated operation process and low fluid inversion accuracy. Therefore, the present invention solves such problems and is suitable for conventional lithology oil and gas reservoirs. The method has broad application prospects and is of great significance to oil reservoir development. The following is the content of the present invention:

[0049] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300 and step S400.

[0050] S100, using fitting equations to statistically obtain corresponding relationships between parameter characteristics of reservoir fluids, and establishing a rock physics model based on the corresponding relationships.

[0051] It can be understood that step S100 includes S101, S102 and S103, wherein:

[0052] S101. The formula of the fitting equation is:

[0053] Y=-1.12E+7+1818968X-92080X 2 -26.85X 3 +153.12X 4 -4.949X 5 +0.05X 6 (Formula 1)

[0054]

[0055]

[0056]

[0057] Where X is the wellbore value, and Y is the resistance value. After the above formula 1 is decomposed, the parameters of formulas 2-4 can be statistically expressed to represent the characteristics of the reservoir fluid, that is, fluid prediction is achieved, and the first corresponding relationship between different wellbore values ​​and resistivity value characteristics is obtained;

[0058] S102, selecting rock physics model parameters, wherein the rock physics model parameters are parameters used to describe changes in underground reservoir lithology, pore development, and changes in fluid properties;

[0059] It should be noted that rock physics model parameters are the most important part of reservoir and fluid prediction. They can be used to describe changes in underground reservoir lithology, pore development, and changes in fluid properties. Selecting appropriate parameters can reduce the uncertainty and ambiguity of fluid detection.

[0060] S103, using the rock physics model parameters to draw a cross plot, extracting logging curve parameters in the cross plot to distinguish fluid properties in the area, and verifying and analyzing whether there is a consistent relationship between the first corresponding relationship and the fluid properties.

[0061] It should be noted that the logging curve parameters in the intersection diagram are used to distinguish the fluid properties in the area, which is difficult to distinguish with the previous conventional parameters. In order to verify the accuracy of the predicted fluid, it is necessary to verify the logging interpretation defined by resistivity and well diameter parameters with the original interpretation. The results show good consistency, which also paves the way for subsequent fluid inversion work.

[0062] S200, selecting a suitable logging curve to reconstruct the acoustic wave curve and constructing a pseudo-acoustic wave curve.

[0063] It can be understood that step S200 includes S201, S202, S203 and S204, wherein:

[0064] S201, selecting a sample well in the target area to be tested;

[0065] S202, extracting a first curve and a second curve, wherein the first curve includes the acoustic wave curve of the sample well as a low-frequency component, and the second curve includes the well diameter value and resistivity curve of the sample well extracted as high-frequency components for constraint;

[0066] S203, reconstructing the acoustic wave curve according to the low-frequency component and the high-frequency component, calculating wave impedance data, and outputting a pseudo-acoustic wave curve;

[0067] S204: mining common structural information contained in the pseudo-acoustic curve and the logging curve to perform seismic inversion.

[0068] It should be noted that in the target area, the logging curve of a sample well is selected as the low-frequency component, and another curve of a well is constrained as the high-frequency component. The acoustic wave curve is reconstructed, the wave impedance (pseudo-wave impedance) is recalculated, and the common structural information contained in similar waveforms and logging curves is excavated. The overall trend of the pseudo-acoustic wave curve is consistent with that of the acoustic wave curve. What changes is only the high-frequency part of the vertical resolution improved by compressing the seismic wavelet, which will not affect the inversion results of the target area, and then seismic inversion is performed to improve the accuracy of fluid inversion.

[0069] S300, establishing the internal structure of the framework model using the stratum boundary, and constructing a fine grid model based on the internal structure of the framework model to ensure that the structure of the fine grid model conforms to the geological model.

[0070] It can be understood that step S300 includes S301, S302 and S303, wherein:

[0071] S301, selecting first data, wherein the first data includes the top and bottom of the oil group of the target layer and a larger layer boundary inside the target layer, and using the first data to establish a boundary framework model;

[0072] S302, selecting second data, wherein the second data includes a stratum of a target layer capable of controlling a seismic reflection structure;

[0073] S303: Control the internal structure of the framework model based on the boundary framework model, the first data and the second data, and construct a fine grid model based on the internal structure of the framework model.

[0074] S400, identifying and predicting the distribution of fluids according to the rock physics model, the pseudo-acoustic curve and the fine grid model to obtain identification and prediction results, and verifying the identification and prediction results by using the reservoirs encountered by the drilling.

[0075] It should be noted that this method, driven by seismic waveforms, mines the common structural information contained in similar waveforms and well logging curves to perform seismic inversion. Starting from the rock physics model, the data connection between the well logging characteristic curve and the reservoir fluid is found, that is, the intersection diagram composed of resistivity and well diameter can better distinguish gas, oil and water. The interpretation accuracy of the intersection diagram is directly verified by the well logging interpretation of the sample well. On this basis, the two well logging curves are used to synthesize the pseudo-acoustic wave curve, and the wave impedance (pseudo-wave impedance) is recalculated to improve the accuracy of fluid inversion.

[0076] Specifically, in this embodiment, there are 10 wells in the work area. In order to verify the accuracy of the waveform indication simulation inversion fluid, sample wells were carried out to participate in this test, and all 10 wells participated in the verification. The identification prediction results were verified by using the reservoirs encountered in the wells. From the test results, the pseudo-wave impedance inversion fluid reconstructed by the well diameter and resistivity is highly similar to the fluid encountered in the actual drilling, with a compliance rate of 90%, as shown in Table 1 below, which is in line with geological laws and lays a foundation for the development of oil reservoirs in the study area.

[0077] Verification Well Destination Layer Top Explanation Target layer top prediction explanation Compliance rate (%) X19-5-1 Oil layer Oil layer 100 F7 Oil layer Oil layer 100 X20-5-1 Oil layer Oil layer 100 X20-5-5 Water layer Oil layer 0 X20-5 Oil layer Oil layer 100 X18-5 Oil layer Oil layer 100 X17-5-7 Air layer Air layer 100 X17-5-5 Air layer Air layer 100 F26-33 Air layer Air layer 100 total 90

[0078] Table 1 Fluid verification results

[0079] It can be understood that the purpose of the present invention is to provide a fluid prediction method based on post-stack inversion of rock physical analysis to solve the problem that the inversion error of resistivity or relative resistivity parameters is large and the planar distribution cannot effectively reflect the actual reservoir fluid distribution.

[0080] Embodiment 2:

[0081] like Figure 2 As shown, this embodiment provides a rock-based fluid prediction system, see Figure 2 The system includes a calculation module 701, a reconstruction module 702, a construction module 703 and a prediction module 704, wherein:

[0082] Calculation module 701: used to obtain the corresponding relationship between parameter characteristics of reservoir fluid by using fitting equation statistics, and establish a rock physics model based on the corresponding relationship;

[0083] Reconstruction module 702: used to select appropriate logging curves to reconstruct the acoustic wave curves and construct pseudo-acoustic wave curves;

[0084] Construction module 703: used to establish the internal structure of the framework model using the stratum boundary, and to construct a fine grid model based on the internal structure of the framework model, so as to ensure that the structure of the fine grid model conforms to the geological model;

[0085] Prediction module 704: used to identify and predict the distribution of fluids according to the rock physics model, the pseudo-acoustic curve and the fine grid model, obtain identification and prediction results, and perform fluid verification on the identification and prediction results using the reservoirs encountered by the drilling.

[0086] Specifically, the calculation module 701 includes a calculation unit 7011, a first selection unit 7012 and a first extraction unit 7013, wherein:

[0087] Calculation unit 7011: The formula for fitting the equation is

[0088] Y=-1.12E+7+1818968X-92080X 2 -26.85X 3 +153.12X 4 -4.949X 5 +0.05X 6

[0089] In the formula, X is the well diameter value, and Y is the resistance value. After the above formula is decomposed, the first corresponding relationship between different well diameter values ​​and resistivity value characteristics is obtained;

[0090] The first selection unit 7012 is used to select rock physics model parameters, where the rock physics model parameters are parameters used to describe changes in underground reservoir lithology, pore development, and changes in fluid properties;

[0091] The first extraction unit 7013 is used to draw a cross-plot using the rock physics model parameters, extract the logging curve parameters in the cross-plot to distinguish the fluid properties of the area, and verify and analyze whether there is a consistent relationship between the first corresponding relationship and the fluid properties.

[0092] Specifically, the reconstruction module 702 includes a second selection unit 7021, a second extraction unit 7022, a reconstruction unit 7023 and a mining unit 7024, wherein:

[0093] The second selection unit 7021 is used to select sample wells in the target area to be tested;

[0094] The second extraction unit 7022 is used to extract a first curve and a second curve, wherein the first curve includes the acoustic wave curve of the sample well as a low-frequency component, and the second curve includes the well diameter value and resistivity curve of the sample well extracted as high-frequency components for constraint;

[0095] The reconstruction unit 7023 is used to reconstruct the acoustic wave curve according to the low-frequency component and the high-frequency component, calculate the wave impedance data, and output the pseudo-acoustic wave curve;

[0096] Mining unit 7024: used to mine the common structural information contained in the pseudo-acoustic curve and the logging curve, so as to perform seismic inversion.

[0097] Specifically, the construction module 703 includes a third selection unit 7031, a fourth selection unit 7032 and a construction unit 7033, wherein:

[0098] The third selection unit 7031 is used to select first data, wherein the first data includes the top and bottom of the oil group of the target layer and a larger layer boundary inside the target layer, and use the first data to establish a boundary framework model;

[0099] The fourth selection unit 7032 is used to select second data, wherein the second data includes a stratum of a target layer capable of controlling a seismic reflection structure;

[0100] Construction unit 7033: used to control the internal structure of the framework model based on the boundary framework model, the first data and the second data, and to construct a fine grid model based on the internal structure of the framework model.

[0101] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0102] Embodiment 3:

[0103] Corresponding to the above method embodiment, a rock-based fluid prediction device is also provided in this embodiment. The rock-based fluid prediction device described below and the rock-based fluid prediction method described above can refer to each other.

[0104] Figure 3 8 is a block diagram of a rock-based fluid prediction device 800 according to an exemplary embodiment. Figure 3 As shown, the rock-based fluid prediction device 800 includes: a processor 801 and a memory 802. The rock-based fluid prediction device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0105] The processor 801 is used to control the overall operation of the rock-based fluid prediction device 800 to complete all or part of the steps in the rock-based fluid prediction method. The memory 802 is used to store various types of data to support the operation of the rock-based fluid prediction device 800, which may include, for example, instructions for any application or method operating on the rock-based fluid prediction device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse or a button, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the rock-based fluid prediction device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: a Wi-Fi module, a Bluetooth module or an NFC module.

[0106] In an exemplary embodiment, the rock-based fluid prediction device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned rock-based fluid prediction method.

[0107] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the rock-based fluid prediction method described above are implemented. For example, the computer-readable storage medium may be the memory 802 including the program instructions, and the program instructions may be executed by the processor 801 of the rock-based fluid prediction device 800 to complete the rock-based fluid prediction method described above.

[0108] Embodiment 4:

[0109] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the rock-based fluid prediction method described above can refer to each other.

[0110] A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, the steps of the rock-based fluid prediction method of the above method embodiment are implemented.

[0111] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A rock-based fluid prediction method, characterized in that: include: Obtaining the corresponding relationship between parameter characteristics of reservoir fluid by using fitting equation statistics, and establishing a rock physics model based on the corresponding relationship; Select appropriate logging curves to reconstruct the acoustic wave curve and construct a pseudo-acoustic wave curve; Establishing the internal structure of the framework model using the stratigraphic boundaries, and constructing a fine grid model based on the internal structure of the framework model to ensure that the structure of the fine grid model conforms to the geological model; The fluid is identified and its distribution is predicted based on the rock physics model, the pseudo-acoustic curve and the fine grid model to obtain an identification prediction result, and the identification prediction result is fluid-verified using the reservoir encountered by the drilling.

2. The rock-based fluid prediction method according to claim 1, characterized in that: The corresponding relationship between the parameter characteristics of the reservoir fluid is obtained by using the fitting equation statistics, and a rock physics model is established based on the corresponding relationship, which includes: The formula of the fitting equation is: Y = -1.12E + 7 + 1818968X - 92080X 2 -26.85X 3 +153.12X 4 -4.949X 5 +0.05X 6 In the formula, X is the well diameter value, and Y is the resistance value. After the above formula is decomposed, the first corresponding relationship between different well diameter values ​​and resistivity value characteristics is obtained; Selecting rock physics model parameters, wherein the rock physics model parameters are parameters used to describe changes in underground reservoir lithology, pore development, and changes in fluid properties; The rock physics model parameters are used to draw a cross-plot, and the logging curve parameters in the cross-plot are extracted to distinguish the fluid properties of the area, and it is verified and analyzed whether there is a consistent relationship between the first corresponding relationship and the fluid properties.

3. The rock-based fluid prediction method according to claim 1, characterized in that: The process of selecting a suitable logging curve to reconstruct the acoustic wave curve and constructing a pseudo-acoustic wave curve includes: Selecting a sample well in the target area to be tested; Extracting a first curve and a second curve, wherein the first curve includes the acoustic wave curve of the sample well as a low-frequency component, and the second curve includes the well diameter value and resistivity curve of the sample well extracted as high-frequency components for constraint; Reconstructing the acoustic wave curve according to the low-frequency component and the high-frequency component, calculating the wave impedance data, and outputting the pseudo-acoustic wave curve; The common structural information contained in the pseudo-acoustic curve and the logging curve is mined to perform seismic inversion.

4. The rock-based fluid prediction method according to claim 1, characterized in that: The internal structure of the framework model is established by using the stratum boundary, and a fine grid model is constructed based on the internal structure of the framework model, which includes: Selecting first data, the first data including the top and bottom of the oil group of the target layer and a larger layer boundary inside the target layer, and using the first data to establish a boundary framework model; selecting second data, wherein the second data includes a stratum of a target layer capable of controlling a seismic reflection structure; Based on the boundary framework model, the first data and the second data, the internal structure of the framework model is controlled, and a fine grid model is constructed based on the internal structure of the framework model.

5. A rock-based fluid prediction system, characterized in that: include: Calculation module: used to obtain the corresponding relationship between parameter characteristics of reservoir fluid by using fitting equation statistics, and establish a rock physics model based on the corresponding relationship; Reconstruction module: used to select appropriate logging curves to reconstruct the acoustic wave curve and construct a pseudo-acoustic wave curve; Construction module: used to establish the internal structure of the framework model using the stratum boundary, and to construct a fine grid model based on the internal structure of the framework model, so as to ensure that the structure of the fine grid model conforms to the geological model; Prediction module: used to identify and predict the distribution of fluids according to the rock physics model, the pseudo-acoustic curve and the fine grid model, obtain identification and prediction results, and verify the identification and prediction results by using the reservoirs encountered by the drilling.

6. The rock-based fluid prediction system according to claim 5, characterized in that: The computing module includes: Calculation unit: The formula used to fit the equation is Y=-1.12E+7+1818968X-92080X 2 -26.85X 3 +153.12X 4 -4.949X 5 +0.05X 6 In the formula, X is the well diameter value, and Y is the resistance value. After the above formula is decomposed, the first corresponding relationship between different well diameter values ​​and resistivity value characteristics is obtained; The first selection unit is used to select rock physics model parameters, where the rock physics model parameters are used to describe the changes in underground reservoir lithology, pore development, and fluid properties; The first extraction unit is used to draw a cross-plot using the rock physics model parameters, extract the logging curve parameters in the cross-plot to distinguish the fluid properties of the area, and verify and analyze whether there is a consistent relationship between the first corresponding relationship and the fluid properties.

7. The rock-based fluid prediction system according to claim 5, characterized in that: The reconstruction module includes: The second selection unit is used to select sample wells in the target area to be tested; A second extraction unit is used to extract a first curve and a second curve, wherein the first curve includes the acoustic wave curve of the sample well as a low-frequency component, and the second curve includes the well diameter value and resistivity curve of the sample well extracted as high-frequency components for constraint; Reconstruction unit: used for reconstructing the sound wave curve according to the low-frequency component and the high-frequency component, calculating the wave impedance data, and outputting the pseudo-sound wave curve; Mining unit: used for mining the common structural information contained in the pseudo-acoustic curve and the logging curve, so as to perform seismic inversion.

8. The rock-based fluid prediction system according to claim 5, characterized in that: The building blocks include: The third selection unit is used to select first data, wherein the first data includes the top and bottom of the oil group of the target layer and a larger layer boundary inside the target layer, and the boundary framework model is established by using the first data; A fourth selection unit is used to select second data, wherein the second data includes a stratum of a target layer capable of controlling a seismic reflection structure; Construction unit: used to control the internal structure of the framework model based on the boundary framework model, the first data and the second data, and to construct a fine grid model based on the internal structure of the framework model.

9. A rock-based fluid prediction device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the rock-based fluid prediction method as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the rock-based fluid prediction method according to any one of claims 1 to 4 is implemented.

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