A rock-based fluid prediction method, system, device, and readable medium

Abstract

The application provides a rock-based fluid prediction method, system, device and readable medium, relates to the technical field of oil exploration and development, and comprises the following steps: a corresponding relationship between parameter characteristics of reservoir fluid is obtained by using a fitting equation, and a rock physics model is established based on the corresponding relationship; a suitable logging curve is selected to reconstruct an acoustic curve, and a pseudo-acoustic curve is constructed; a framework model internal structure is established by using a stratigraphic boundary, a fine framework model is constructed based on the framework model internal structure, and it is ensured that the structure of the fine framework model is consistent with a geological model; an identification prediction result is obtained, and the identification prediction result is verified by fluid of a drilled reservoir of a drilled well. The application has the beneficial effects that the prediction accuracy is improved, the fluid inversion accuracy is improved, more accurate results can be obtained, is suitable for conventional lithologic oil and gas reservoirs, and has a wide application prospect and is very important for oil reservoir development.

Description

TECHNICAL FIELD

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

[0002] Seismic fluid inversion identification, that is, identifying and describing the fluid characteristics of the reservoir by using seismic data, for the oil and gas reservoirs with complex fluid distribution in the reservoir, how to accurately predict the oil and gas enrichment area and effectively identify the oil and gas water interface is the key to the success of the favorable area selection and well deployment of oil and gas field exploration and development. Usually, pre-stack and post-stack data are combined with seismic wave impedance data or pseudo-wave impedance data for inversion, the oil and gas enrichment area is determined through the distribution rule of the fluid, and then the oil and gas reservoir development is guided.

[0003] Due to the large amount of pre-stack data and complicated operation, the efficiency of fluid prediction is affected. At present, the post-stack inversion technology has scholars to distinguish the reservoir fluid by using resistivity parameter and relative resistivity parameter, but there are many oil and gas reservoirs, sometimes the resistivity of oil layer and gas layer is the same, sometimes the resistivity of oil layer is greater than that of gas layer, and sometimes the resistivity of oil layer is less than that of gas layer. It is difficult to distinguish the reservoir fluid only by one parameter, which affects the accuracy of oil and gas reservoir prediction by using electrical characteristics. The resistivity curve and the relative resistivity curve cannot be used as an effective identification basis, and the current post-stack inversion method cannot effectively indicate the fluid type and reservoir state. SUMMARY

[0004] The purpose of the present application 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 scheme adopted by the present application is as follows:

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

[0006] The corresponding relationship between the parameter characteristics of the reservoir fluid is obtained by using the fitting equation, and a rock physics model is established based on the corresponding relationship;

[0007] Suitable logging curves are selected to reconstruct the acoustic curve, and a pseudo-acoustic curve is constructed;

[0008] The internal structure of the framework model is established by using the stratigraphic boundary, a fine framework model is constructed based on the internal structure of the framework model, and the structure of the fine framework model is ensured to be consistent with the geological model;

[0009] The fluid is identified and distributed according to the rock physics model, the pseudo-acoustic curve and the fine framework model, the identification and prediction results are obtained, and the identification and prediction results are verified by the reservoir drilled by the drilled well.

[0010] Preferably, the statistical corresponding relationship between the parameter characteristics of the reservoir fluid is obtained by using the fitting equation, and a petrophysical model is established based on the corresponding relationship, which comprises:

[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 caliper value, and Y is the resistivity value; after the above formula is decomposed, a first corresponding relationship between different caliper values and resistivity value characteristics is obtained;

[0013] The petrophysical model parameters are selected, and the petrophysical model parameters are parameters used to describe the changes of the lithology, the pore development, and the fluid properties of the underground reservoir;

[0014] The crossplot is drawn by using the petrophysical model parameters, the logging curve parameters in the crossplot are extracted to distinguish the fluid properties of the area, and whether the first corresponding relationship and the fluid properties have a consistent relationship is verified and analyzed.

[0015] Preferably, the suitable logging curve is selected to reconstruct the acoustic curve, and a pseudo-acoustic curve is constructed, which comprises:

[0016] The sample well in the target area to be measured is selected;

[0017] The first curve and the second curve are extracted, wherein the first curve comprises the acoustic curve of the sample well as a low-frequency component, and the second curve comprises the caliper value and the resistivity curve of the sample well as a high-frequency component for constraint;

[0018] The acoustic curve is reconstructed according to the low-frequency component and the high-frequency component, the wave impedance data is calculated, and thus the pseudo-acoustic curve is output;

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

[0020] Preferably, the framework model internal structure is established by using the stratigraphic boundary, and a fine framework model is constructed based on the framework model internal structure, which comprises:

[0021] The first data is selected, the first data comprises the top and bottom of the oil group of the target layer and the larger stratigraphic boundary inside the target layer, and the boundary framework model is established by using the first data;

[0022] selecting second data, the second data including a formation of a target layer capable of controlling a seismic reflection structure;

[0023] controlling a framework model internal structure based on the boundary framework model, the first data and the second data, and constructing a fine framework model based on the framework model internal structure.

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

[0025] The calculation module is configured to statistically obtain a corresponding relationship between parameter characteristics of reservoir fluids by using a fitting equation, and establish a rock physics model based on the corresponding relationship;

[0026] The reconstruction module is configured to select appropriate logging curves to reconstruct acoustic curves, and construct pseudo-acoustic curves;

[0027] The construction module is configured to establish a framework model internal structure by using a formation boundary, and construct a fine framework model based on the framework model internal structure, so as to ensure that the structure of the fine framework model conforms to a geological model;

[0028] The prediction module is configured to identify and distribute fluids according to the rock physics model, the pseudo-acoustic curves and the fine framework model, obtain an identification prediction result, and verify the identification prediction result by using a drilled reservoir of a drilled well.

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

[0030] A memory is configured to store a computer program;

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

[0032] In a fourth aspect, the application further provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to implement steps of the rock-based fluid prediction method.

[0033] The application has the following beneficial effects:

[0034] 1. The application aims to provide a rock physics analysis post-stack inversion-based fluid prediction method, so as to solve the problems of large parameter inversion error of resistivity or relative resistivity and that a plane distribution cannot effectively reflect actual reservoir fluid distribution.

[0035] 2、The present application extracts the data connection between the well logging characteristic curve and the reservoir fluid, and the crossplot composed of resistivity and well diameter, which can distinguish gas, oil and water (single or more than three conventional curves: well diameter, natural gamma, natural potential, sonic, density, neutron, resistivity, radioactivity) well, and the interpretation accuracy of the crossplot is verified by the well logging interpretation of the sample well; the reconstruction of the sonic wave is not random, and it has two parts, that is, the low frequency part of the original sonic wave is reserved, and only the high frequency part is changed, so that the consistency of the reconstructed sonic wave synthetic record and the original sonic wave is ensured, and the inversion result of the medium frequency band is not affected.

[0036] 3、The present application obtains a new curve which conforms to the geological law and can well distinguish the fluid, improves the well logging constrained seismic inversion in the area where the fluid difference is not obvious, improves the distinguishing ability of the inversion result on the fluid, recalculates the wave impedance (pseudo wave impedance), mines the common structure information contained in the similar waveforms and well logging curves, so as to perform seismic inversion, improves the precision of the fluid inversion, and can obtain more accurate results.

[0037] 4、The present application is a high-precision fluid inversion method, which is suitable for conventional lithologic oil and gas reservoirs, and has wide application prospect and great significance for oil reservoir development.

[0038] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application as described in the written description and claims hereof. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some embodiments of the present application, and should not be regarded as a limitation to the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0040] Figure 1 The flow chart of the rock-based fluid prediction method described in the embodiments of the present application is shown in the figure.

[0041] Figure 2 The structure schematic diagram of the rock-based fluid prediction system described in the embodiments of the present application is shown in the figure.

[0042] Figure 3 The structure schematic diagram of the rock-based fluid prediction device described in the embodiments of the present application is shown in the figure.

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

[0044] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work under the premise that the present application falls within the scope of protection of the present application.

[0045] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. Meanwhile, in the description of the present application, 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] The embodiment provides a rock-based fluid predicting method.

[0048] The present application starts from the rock physics model, extracts the data relationship between the logging characteristic curve and the reservoir fluid, and the crossplot composed of resistivity and caliper, which can better distinguish gas, oil and water (single or more than three conventional curves: caliper, natural gamma, natural potential, acoustic wave, density, neutron, resistivity, radioactivity cannot distinguish), and the interpretation accuracy of the crossplot is verified by the logging interpretation of the sample well; on this basis, the two logging curves are used to synthesize the pseudo-acoustic wave curve (about the pseudo-acoustic wave problem, some people do not trust it because they are worried that after the acoustic wave curve is modified, it is no longer corresponding to the seismic waveform), in fact, this is not the case, in fact, since the seismic record has an effective frequency band, beyond the frequency band, the seismic cannot reflect. Therefore, the modification or reconstruction of the acoustic wave in the present application is not arbitrary, which has two parts, that is, the low frequency part of the original acoustic wave is retained, only the high frequency part is changed, and the division of low frequency and high frequency is the effective frequency band of the seismic, which ensures that the synthesized record of the reconstructed acoustic wave is consistent with the original acoustic wave, so it does not affect the inversion result of the medium frequency band. For the part higher than the seismic frequency band, it is originally the result of well interpolation, and there is no relationship between the original acoustic wave curve and the reconstructed acoustic wave curve and the seismic record, so the curve which can better express the fluid characteristics can be selected. The distrust of the reconstruction curve inversion is the understanding left over from the past sparse pulse inversion period, which is completely different from the present high resolution inversion. At present, similar patents or documents are searched in the market or other public channels: a seismic fluid inversion identification method and device (patent publication number: CN201510581489), an oil and gas distribution prediction method 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 relative resistivity curve of fluid, ④ establishing a relative resistivity inversion model, and 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 application solves these problems, is suitable for conventional lithologic oil and gas reservoirs, has a wide application prospect, and has a very important significance for oil reservoir development, and the contents of the present application are as follows:

[0049] Referring to Figure 1 , the method includes steps S100, S200, S300 and S400.

[0050] S100, the corresponding relationship between the parameter characteristics of the reservoir fluid is obtained by using the fitting equation, and the rock physics model is established based on the corresponding relationship.

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

[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] In the formula, X is the well diameter value, and Y is the resistance value; when the above formula 1 is decomposed, the parameters of formulas 2-4 representing the characteristics of the reservoir fluid can be counted, that is, the fluid prediction is realized, and the first corresponding relationship between different well diameter values and the characteristics of the resistivity value is obtained.

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

[0059] It should be noted that the rock physical model parameters are the most important link in the reservoir and fluid prediction link, which can be used to describe the changes of the lithology, the pore development and the fluid properties of the underground reservoir, and selecting appropriate parameters can reduce the uncertainty and multi-solution of fluid detection.

[0060] S103, using the rock physical model parameters to describe the crossplot, extracting the logging curve parameters in the crossplot to distinguish the fluid properties of the area, verifying and analyzing whether the first corresponding relationship and the fluid properties have a consistent relationship.

[0061] It should be noted that the logging curve parameters in the crossplot are used to distinguish the fluid properties of the area, and the conventional parameters in the past are difficult to distinguish. In order to verify the accuracy of the predicted fluid, the logging interpretation limited by the resistivity and well diameter parameters needs to be verified with the original interpretation, and the result shows that the coincidence is good, which also lays a foundation for the subsequent fluid inversion work.

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

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

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

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

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

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

[0068] It should be noted that the logging curve of a sample well in the target area is selected as a low-frequency component, another curve of the well is selected as a high-frequency component for constraint, the acoustic curve is reconstructed, the wave impedance (pseudo-wave impedance) is recalculated, the common structural information contained in the similar waveform and the logging curve is mined, the overall trend of the pseudo-acoustic curve and the acoustic curve is consistent, only the high-frequency part compressed by the seismic wavelet is changed to improve the longitudinal resolution, and the inversion result of the target area is not affected, and then the seismic inversion is performed to improve the accuracy of fluid inversion.

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

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

[0071] S301, selecting first data, the first data including an oil group top and bottom of a target layer and a larger layer boundary inside the target layer, and establishing a boundary framework model using the first data;

[0072] S302, selecting second data, the second data including a formation of the target layer capable of controlling seismic reflection structure;

[0073] S303, controlling the internal structure of the framework model based on the boundary framework model, the first data and the second data, and constructing a fine framework model based on the framework model internal structure.

[0074] S400, identifying and predicting fluid according to the rock physical model, the pseudo-acoustic curve and the fine framework model, obtaining an identification prediction result, and verifying the identification prediction result by using a reservoir drilled by a drilled well.

[0075] It should be noted that the method excavates the common structure information contained in the similar waveforms and the logging curves under the driving of the seismic waveforms, thereby performing seismic inversion. Starting from a rock physical model, data association between a logging characteristic curve and a reservoir fluid is found out, that is, a crossplot of resistivity and caliper can better distinguish gas, oil and water, and the crossplot interpretation accuracy is verified directly by using logging interpretation of a sample well, on the basis of which, a pseudo-acoustic curve is synthesized by using the two logging curves, wave impedance (pseudo-wave impedance) is recalculated, and the fluid inversion accuracy is improved.

[0076] Specifically, in the embodiment, there are totally 10 drilled wells in the work area, to verify the accuracy of the waveform indicating simulation inversion fluid, a sample well participates in the test, and 10 wells all participate in verification. The identification prediction result is verified by using a reservoir drilled by a drilled well, and from the test result, the pseudo-wave impedance restructured by the caliper and the resistivity is used to invert fluid, the result is highly similar to the fluid drilled by the drilled well, the coincidence rate is 90%, as shown in Table 1, and the result conforms to the geological law, thereby laying a foundation for oil reservoir development in the research area.

[0077] Verification well Top of objective layer interpretation Top of objective layer predicted interpretation Conformance (%) X19-5-1 Oil Oil 100 F7 Oil Oil 100 X20-5-1 Oil Oil 100 X20-5-5 Oil Oil 0 X20-5 Oil Oil 100 X18-5 Oil Oil 100 X17-5-7 Gas Gas 100 X17-5-5 Gas Gas 100 F26-33 Gas Gas 100 Gas 90

[0078] Table 1 fluid verification result table

[0079] It can be understood that the purpose of the present application is to provide a fluid prediction method based on rock physical analysis poststack inversion, to solve the problems of large inversion error of resistivity or relative resistivity parameters and that the plane distribution cannot effectively reflect the actual reservoir fluid distribution.

[0080] Embodiment 2:

[0081] As shown in Total The embodiment provides a rock-based fluid prediction system, and the system is shown in Figure 2 The system comprises a calculation module 701, a reconstruction module 702, a construction module 703 and a prediction module 704, and specifically comprises:

[0082] The calculation module 701 is used for statistically obtaining a corresponding relationship between parameter characteristics of a reservoir fluid by using a fitting equation, and establishing a rock physical model based on the corresponding relationship.

[0083] The reconstruction module 702 is used for selecting appropriate logging curves to reconstruct acoustic curves and construct pseudo-acoustic curves.

[0084] The constructing module 703 is configured to establish a framework model internal structure by using the stratigraphic boundary, construct a fine framework model based on the framework model internal structure, and ensure that the structure of the fine framework model conforms to the geological model.

[0085] The predicting module 704 is configured to identify and predict the distribution of fluid according to the rock physical model, the pseudo-acoustic wave curve and the fine framework model, obtain an identification prediction result, and verify the identification prediction result by using the reservoir drilled by the drilled well.

[0086] Specifically, the calculating module 701 includes a calculating unit 7011, a first selecting unit 7012 and a first extracting unit 7013.

[0087] The calculating unit 7011 is configured to fit the equation, and the formula of the equation is Y = -1.12E+7 + 1818968X - 92080X

[0088] - 26.85X + 153.12X - 4.949X + 0.05X. 2 3 4 5 6

[0089] In the formula, X is a caliper value, and Y is a resistivity value; after the above formula is decomposed, a first corresponding relationship between different caliper values and resistivity value characteristics is obtained.

[0090] The first selecting unit 7012 is configured to select a rock physical model parameter, and the rock physical model parameter is a parameter used to describe the change of underground reservoir lithology, the change of pore development and the change of fluid properties.

[0091] The first extracting unit 7013 is configured to depict a crossplot by using the rock physical model parameter, extract a logging curve parameter in the crossplot to distinguish the fluid properties of the area, verify and analyze whether there is a consistent relationship between the first corresponding relationship and the fluid properties.

[0092] Specifically, the reconstructing module 702 includes a second selecting unit 7021, a second extracting unit 7022, a reconstructing unit 7023 and a mining unit 7024.

[0093] The second selecting unit 7021 is configured to select a sample well in a target area to be measured.

[0094] The second extracting unit 7022 is configured to extract a first curve and a second curve, wherein the first curve includes an acoustic curve of the sample well as a low-frequency component, and the second curve includes a caliper value and a resistivity curve of the sample well as a high-frequency component for constraint. ​​​​

[0095] Reconstruction unit 7023: configured to reconstruct the acoustic curve according to the low frequency component and the high frequency component, calculate wave impedance data, and output an acoustic-like curve;

[0096] Mining unit 7024: configured to mine common structural information contained in the acoustic-like curve and the logging curve, and 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.

[0098] The third selection unit 7031 is configured to select first data including the top and bottom of the oil group of the target layer and the larger layer boundary inside the target layer, and establish a boundary framework model using the first data.

[0099] The fourth selection unit 7032 is configured to select second data including the formation of the target layer that can control the seismic reflection structure.

[0100] The construction unit 7033 is configured to 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 framework model based on the internal structure of the framework model.

[0101] It should be noted that, as for 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 described in detail here.

[0102] Embodiment 3:

[0103] Corresponding to the above method embodiment, the present embodiment also provides a rock-based fluid prediction device. The rock-based fluid prediction device described below can be mutually corresponding to the rock-based fluid prediction method described above.

[0104] Figure 2 is a block diagram of a rock-based fluid prediction device 800 according to an exemplary embodiment. 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. Figure 3 Figure 3

[0105] ​The processor 801 is configured to control overall operations of the rock-based fluid prediction device 800 to complete all or part of the steps in the above-described rock-based fluid prediction method. The memory 802 is configured to store various types of data to support the operations of the rock-based fluid prediction device 800, which can 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, and the like. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk. The multimedia component 803 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which can be a keyboard, a mouse, or a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the rock-based fluid prediction device 800 and other devices. The 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 example embodiment, the rock-based fluid prediction device 800 can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic devices to execute the above-described rock-based fluid prediction method.

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

[0108] Embodiment 4:

[0109] Corresponding to the above method embodiments, a readable storage medium is also provided in this embodiment, and the readable storage medium described below can be referred to in correspondence with the above-described rock-based fluid prediction method.

[0110] The computer program stored on the readable storage medium implements the steps of the above-described rock-based fluid prediction method of the method embodiments when executed by a processor.

[0111] The readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0112] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0113] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A rock-based fluid prediction method, characterized by, The method comprises the following steps: corresponding relationships between parameter characteristics of reservoir fluids are obtained by using fitting equations, and a rock physical model is established based on the corresponding relationships; a suitable logging curve is selected to reconstruct an acoustic curve, and a pseudo-acoustic curve is constructed; a framework model internal structure is established by using stratigraphic boundaries, and a fine framework model is constructed based on the framework model internal structure, so as to ensure that the structure of the fine framework model conforms to a geological model; fluids are identified and distributed by using the rock physical model, the pseudo-acoustic curve and the fine framework model, so as to obtain an identification and prediction result, and the identification and prediction result is verified by using reservoirs drilled by drilled wells; the corresponding relationships between parameter characteristics of reservoir fluids are obtained by using fitting equations, and a rock physical model is established based on the corresponding relationships, which comprises the following steps: 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; when the above formula is decomposed, the first corresponding relationship between different well diameter values and the resistance value characteristics is obtained; rock physical model parameters are selected, the rock physical model parameters being parameters used to describe changes in underground reservoir lithology, changes in pore development and changes in fluid properties; a crossplot is drawn by using the rock physical model parameters, logging curve parameters in the crossplot are extracted to distinguish fluid properties in the area, and consistency between the first corresponding relationship and the fluid properties is verified and analyzed; the suitable logging curve is selected to reconstruct the acoustic curve, and the pseudo-acoustic curve is constructed, which comprises the following steps: a sample well in a target area to be measured is selected; a first curve and a second curve are extracted, the first curve comprising an acoustic curve of the sample well as a low-frequency component, and the second curve comprising a caliper value and a resistivity curve of the sample well as a high-frequency component for constraint; the acoustic curve is reconstructed according to the low-frequency component and the high-frequency component, wave impedance data is calculated, and a pseudo-acoustic curve is output; common structural information contained in the pseudo-acoustic curve and logging curves is mined, so as to perform seismic inversion.

2. The rock-based fluid prediction method of claim 1, wherein, the framework model internal structure is established by using stratigraphic boundaries, and the fine framework model is constructed based on the framework model internal structure, which comprises the following steps: first data is selected, the first data comprising oil group top and bottom of a target layer and larger layer boundaries in the target layer, and a boundary framework model is established by using the first data; second data is selected, the second data comprising strata of the target layer capable of controlling seismic reflection structure; the framework model internal structure is controlled based on the boundary framework model, the first data and the second data, and a fine framework model is constructed based on the framework model internal structure.

3. A rock-based fluid prediction system, characterized by, The method comprises the following steps: a calculation module is used to obtain corresponding relationships between parameter characteristics of reservoir fluids by using fitting equations, and a rock physical model is established based on the corresponding relationships; a reconstruction module is used to select a suitable logging curve to reconstruct an acoustic curve, and a pseudo-acoustic curve is constructed; a construction module is used to establish a framework model internal structure by using stratigraphic boundaries, and a fine framework model is constructed based on the framework model internal structure, so as to ensure that the structure of the fine framework model conforms to a geological model; The prediction module is configured to identify and predict distribution of the fluid according to the rock physical model, the pseudo-acoustic curve and the fine framework model, to obtain an identification prediction result, and to verify the identification prediction result by using a reservoir drilled by a drilled well. The calculation module comprises: The calculation unit is configured to fit the equation Y = -1.12E+7 + 1818968X - 92080X 2 -26.85X 3 +153.12X 4 -4.949X 5 +0.05X 6 wherein, X is a caliper value, and Y is a resistivity value; and after the above equation is decomposed, a first corresponding relationship between different caliper values and resistivity value characteristics is obtained. The first selecting unit is configured to select rock physical model parameters, which are parameters used to describe changes in lithology, pore development and fluid properties of a subsurface reservoir. The first extracting unit is configured to use the rock physical model parameters to draw crossplots, extract logging curve parameters in the crossplots to distinguish fluid properties in the area, and verify and analyze whether the first corresponding relationship and the fluid properties have a consistent relationship. The reconstruction module comprises: The second selecting unit is configured to select a sample well in a target area to be measured. The second extracting unit is configured to extract a first curve and a second curve, wherein the first curve comprises an acoustic curve of the sample well as a low-frequency component, and the second curve comprises a caliper value and a resistivity curve of the sample well as a high-frequency component for constraint. The reconstruction unit is configured to reconstruct the acoustic curve according to the low-frequency component and the high-frequency component, to calculate wave impedance data, and to output a pseudo-acoustic curve. The mining unit is configured to mine common structural information contained in the pseudo-acoustic curve and the logging curve, and to perform seismic inversion.

4. The rock-based fluid prediction system of claim 3, wherein, The construction module comprises: The third selecting unit is configured to select first data, which comprises a top and bottom of an oil group of a target layer and a larger layer boundary inside the target layer, and to use the first data to establish a boundary framework model. The fourth selecting unit is configured to select second data, which comprises a target layer that can control a seismic reflection structure. The construction unit is configured to control an internal structure of a framework model based on the boundary framework model, the first data and the second data, and to construct a fine framework model based on the internal structure of the framework model.

5. A rock-based fluid prediction device, characterized by, The computer program is stored in the storage and executed by the processor to implement the rock-based fluid prediction method according to any one of claims 1 to 2. The computer program is stored in the storage and executed by the processor to implement the rock-based fluid prediction method according to any one of claims 1 to 2. ​ 6. A readable storage medium characterized by: ​

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