Two-factor fluid property identification method based on array acoustic logging and related equipment

Abstract

The invention discloses a two-factor fluid property identification method based on array acoustic logging and related equipment, different elastic parameters are calculated by utilizing the longitudinal wave velocity and the transverse wave velocity of reservoir rock directly measured by the array acoustic logging, a cross plot among the different elastic parameters is established according to the elastic parameters of the rock, and the two-factor fluid property identification method based on the array acoustic logging is obtained. According to fluid type division requirements, sensitive elastic parameters are extracted and calculated, the sensitive elastic parameters are extracted to construct double evaluation factors, the double evaluation factors are calculated to carry out fluid property discrimination, and therefore effective identification of complex oil-water layer fluid properties is achieved. The method is simple, high in universality and high in recognition accuracy, meets the production requirements of an oil field, can be operated by general interpretation personnel, is beneficial to rapid and large-scale application in production, overcomes the defect that a conventional logging method in the prior art is limited by a measurement mode and is difficult to get rid of the influence of a rock skeleton on logging response, and has a good application prospect. The nuclear magnetic resonance logging technology is high in cost, and the processing and interpretation process is complex.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir logging evaluation, and in particular to a dual-factor fluid property identification method based on array acoustic logging and related equipment. Background Art

[0002] Fluid property identification is an important part of reservoir logging evaluation. It directly affects geological understanding and reserve scale, and is an important part of reservoir evaluation in exploration and development. At present, the fluid property identification methods for complex oil and water layers mainly include the derivative parameter method based on conventional logging, the fluid identification method based on nuclear magnetic resonance logging, and some classic optimization algorithms, such as neural network method, support vector machine and fuzzy clustering method. Among them, the conventional logging derivative parameter method constructs fluid sensitivity factors by analyzing the formation mechanism of low-resistivity oil layers and the main controlling factors of oil content to identify fluids in complex oil and water layers, and has achieved certain results.

[0003] However, conventional logging methods are limited by the measurement mode and it is difficult to get rid of the influence of the rock skeleton on the logging response. Especially for low-contrast and low-saturation oil layers with relatively complex genesis, the response characteristics of resistivity to oil and gas layers are difficult to distinguish due to the influence of other factors. Therefore, it is very difficult to identify fluid properties in conventional logging series for complex reservoirs.

[0004] Nuclear magnetic resonance logging technology is less affected by the rock skeleton and can better reflect the information of pore fluids. It has a good recognition effect on complex oil and water layers, but its cost is high, the processing and interpretation process is complicated, and the nuclear magnetic resonance logging data of some old well areas or oil fields are very scarce, which limits the application of nuclear magnetic resonance logging to some extent. The key to the application of the optimization algorithm is to have a large number of training samples that can reflect the differences in the properties of different fluids as input to ensure the accuracy of the prediction model results, and this method lacks a theoretical basis. Summary of the invention

[0005] The purpose of the present invention is to provide a dual-factor fluid property identification method based on array acoustic logging and related equipment to overcome the problems that conventional logging methods in the prior art are limited by measurement modes, it is difficult to get rid of the influence of rock skeleton on logging response, and nuclear magnetic resonance logging technology has high cost and complex processing and interpretation process.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The dual-factor fluid property identification method based on array acoustic logging includes:

[0008] Calculate the rock elastic parameters of the target layer of the tested well;

[0009] Establish the cross-plot between different elastic parameters according to the rock elastic parameters, and extract and calculate the sensitive elastic parameters according to the fluid type;

[0010] Calculate the dual evaluation factors for reservoir fluid identification based on sensitive elastic parameters;

[0011] Statistically calculate the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells, and establish a cross plot of the reservoir dual evaluation factors;

[0012] According to the reservoir dual evaluation factor cross plot, the dual evaluation factors of the reservoir fluid to be identified in the oil well with the reservoir fluid property to be identified are calculated to determine the property of the reservoir fluid to be identified in the oil well.

[0013] Preferably, the rock elastic parameters of the target layer section of the tested oil well include shear modulus, bulk modulus, Poisson's ratio and Lame coefficient.

[0014] Preferably, the calculation formula of the shear modulus is:

[0015]

[0016] Among them, ρ is the formation density, μs is the shear modulus, and ΔTs is the shear wave time difference;

[0017] The calculation formula for bulk modulus is:

[0018]

[0019] Ks is the bulk modulus, ΔTs is the transverse wave time difference, and ΔTc is the longitudinal wave time difference.

[0020] Preferably, the calculation formula of the Poisson's ratio is:

[0021]

[0022] Among them, σ is Poisson's ratio, ΔTs is the transverse wave time difference, and ΔTc is the longitudinal wave time difference

[0023] Specifically, the calculation formula of the Lamé coefficient is:

[0024]

[0025] λ is the Lame coefficient, ρ is the formation density, Vp is the formation longitudinal velocity, and Vs is the shear wave velocity.

[0026] Preferably, the cross plots between different elastic parameters established according to the rock elastic parameters include a shear modulus and bulk modulus cross plot, a Poisson's ratio and Lame coefficient cross plot, and a Poisson's ratio and density and Lame coefficient cross plot.

[0027] Preferably, the sensitive parameters include:

[0028] The first sensitive parameter is:

[0029] I1=Ks -μ s

[0030] In the formula, K s is the bulk modulus, μ s is the shear modulus.

[0031] The second sensitive parameter is Poisson’s ratio;

[0032] The third sensitive parameter is:

[0033] I3=ρλ

[0034] Where λ is the Lame coefficient and ρ is the formation density.

[0035] Preferably, the method for calculating the reservoir fluid identification dual evaluation factor according to the sensitive elastic parameters is as follows:

[0036] F1=I′1+I′2+I′3

[0037] Among them, I′1, I′2, and I′3 are normalized data of different sensitive elastic parameters;

[0038] F2=ρλ;

[0039] Where λ is the Lame coefficient, dimensionless; ρ is the formation density.

[0040] A dual-factor fluid property identification system based on array acoustic logging, comprising:

[0041] Calculation module: used to calculate the rock elastic parameters of the target layer of the tested oil well;

[0042] Extraction module: Establish the intersection diagram between different elastic parameters according to rock elastic parameters, and extract and calculate sensitive elastic parameters according to fluid type;

[0043] The second calculation module: calculates the reservoir fluid identification dual evaluation factor according to the sensitive elastic parameters;

[0044] Mapping module: Count the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells and establish the reservoir dual evaluation factor intersection diagram;

[0045] Property determination module: According to the reservoir dual evaluation factor intersection diagram, the dual evaluation factors of the reservoir fluid to be identified in the oil well are calculated to determine the properties of the reservoir fluid to be identified in the oil well.

[0046] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the dual-factor fluid property identification method based on array acoustic logging are implemented.

[0047] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the dual-factor fluid property identification method based on array acoustic logging are implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a dual-factor fluid property identification method based on array acoustic logging, using the longitudinal wave velocity and transverse wave velocity of the reservoir rock directly measured by array acoustic logging to calculate different elastic parameters, establish a cross-plot between different elastic parameters according to the rock elastic parameters, extract and calculate sensitive elastic parameters according to the fluid type classification, extract sensitive elastic parameters to construct dual evaluation factors, calculate the dual evaluation factors to distinguish fluid properties, thereby achieving effective identification of complex oil and water layer fluid properties. The method of the present invention is simple, has strong universality, high identification accuracy, meets the production needs of oil fields, can be operated by general interpreters, and is conducive to rapid large-scale application in production. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a dual-factor fluid property identification method based on array acoustic logging of the present invention;

[0050] Figure 2 This is a block diagram of a dual-factor fluid property identification system based on array acoustic logging of the present invention;

[0051] Figure 3 This is a flow chart of a dual-factor fluid property identification method based on array acoustic logging according to an embodiment of the present invention;

[0052] Figure 4 This is a shear modulus-bulk modulus cross-plot of an embodiment of the present invention.

[0053] Figure 5 It is a cross-plot of Poisson's ratio and Lame coefficient according to an embodiment of the present invention.

[0054] Figure 6 This is a cross-plot of Poisson's ratio-density×Lame coefficient according to an embodiment of the present invention.

[0055] Figure 7 This is a diagram of the double-factor fluid identification results of the well array acoustic logging verification embodiment of the present invention;

[0056] Figure 8 To verify the results of double-factor fluid identification of well array acoustic logging. DETAILED DESCRIPTION

[0057] 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, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0058] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0060] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0061] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0062] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0064] like Figure 1As shown, the present invention provides a dual-factor fluid property identification method based on array acoustic logging, comprising:

[0065] S101 calculates the rock elastic parameters of the target layer of the tested oil well;

[0066] S102 establishes a cross-plot between different elastic parameters based on rock elastic parameters, and extracts and calculates sensitive elastic parameters according to the fluid type;

[0067] S103 calculates the reservoir fluid identification dual evaluation factor based on the sensitive elastic parameters;

[0068] S104 counts the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells, and establishes a reservoir dual evaluation factor cross plot;

[0069] S105 calculates the dual evaluation factors of the reservoir fluid to be identified in the oil well according to the reservoir dual evaluation factor cross-plot, so as to determine the properties of the reservoir fluid to be identified in the oil well.

[0070] The rock elastic parameters of the target layer of the tested oil wells include shear modulus, bulk modulus, Poisson's ratio and Lame coefficient.

[0071] The calculation formula for shear modulus is:

[0072]

[0073] Among them, ρ is the formation density, μs is the shear modulus, and ΔTs is the shear wave time difference;

[0074] The calculation formula for bulk modulus is:

[0075]

[0076] Ks is the bulk modulus, ΔTs is the transverse wave time difference, and ΔTc is the longitudinal wave time difference.

[0077] The calculation formula of the Poisson's ratio is:

[0078]

[0079] Among them, σ is Poisson's ratio, ΔTs is the transverse wave time difference, and ΔTc is the longitudinal wave time difference

[0080] Specifically, the calculation formula of the Lamé coefficient is:

[0081]

[0082] λ is the Lame coefficient, ρ is the formation density, Vp is the formation longitudinal velocity, and Vs is the shear wave velocity.

[0083] The cross plots between different elastic parameters established according to the rock elastic parameters include the cross plot of shear modulus and bulk modulus, the cross plot of Poisson's ratio and Lame coefficient, and the cross plot of Poisson's ratio, density and Lame coefficient.

[0084] Sensitive parameters include:

[0085] The first sensitive parameter is:

[0086] I1=K s -μ s

[0087] In the formula, K s is the bulk modulus, μ s is the shear modulus.

[0088] The second sensitive parameter is Poisson’s ratio;

[0089] The third sensitive parameter is:

[0090] I3=ρλ

[0091] Where λ is the Lame coefficient and ρ is the formation density.

[0092] The specific method for calculating the dual evaluation factors for reservoir fluid identification based on sensitive elastic parameters is as follows:

[0093] F1=I′1+I′2+I′3

[0094] Among them, I′1, I′2, and I′3 are normalized data of different sensitive elastic parameters;

[0095] F2=ρλ;

[0096] Where λ is the Lame coefficient, dimensionless; ρ is the formation density.

[0097] like Figure 2 As shown, the present invention provides a dual-factor fluid property identification system based on array acoustic logging, comprising:

[0098] Calculation module: used to calculate the rock elastic parameters of the target layer of the tested oil well;

[0099] Extraction module: Establish the intersection diagram between different elastic parameters according to rock elastic parameters, and extract and calculate sensitive elastic parameters according to fluid type;

[0100] The second calculation module: calculates the reservoir fluid identification dual evaluation factor according to the sensitive elastic parameters;

[0101] Mapping module: Count the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells and establish the reservoir dual evaluation factor intersection diagram;

[0102] Property determination module: According to the reservoir dual evaluation factor intersection diagram, the dual evaluation factors of the reservoir fluid to be identified in the oil well are calculated to determine the properties of the reservoir fluid to be identified in the oil well.

[0103] Example:

[0104] like Figure 3 As shown, the present invention provides a dual-factor fluid property identification method based on array acoustic logging, comprising the following steps:

[0105] Step (1) calculating the rock elastic parameters of the target layer of the tested oil wells in the study area;

[0106] Step (2) establishing a cross-plot between different elastic parameters, extracting and calculating sensitive elastic parameters according to the fluid type;

[0107] Step (3) calculating a reservoir fluid identification dual evaluation factor according to the sensitive elastic parameters;

[0108] Step (4) counting the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells, and establishing a cross-plot of the dual evaluation factors of the reservoirs in the study area;

[0109] Step (5) for the oil wells in the study area whose reservoir fluid properties are to be identified, calculating the dual evaluation factors of the reservoir fluid to be identified in the oil wells to determine the properties of the reservoir fluid to be identified in the oil wells;

[0110] Furthermore, the calculation of the rock elastic parameters of the target layer of the tested oil well in step (1) is performed using array acoustic logging data.

[0111] Specifically, the calculation formula of shear modulus is:

[0112]

[0113] Specifically, the calculation formula of bulk modulus is:

[0114]

[0115] Specifically, the calculation formula of Poisson's ratio is:

[0116]

[0117] Specifically, the calculation formula of the Lamé coefficient is:

[0118]

[0119] In the formula, K s is the bulk modulus, MPa; μ sis the shear modulus, MPa; σ is the Poisson's ratio, dimensionless; λ is the Lame coefficient, dimensionless; ρ is the formation density, g / cm 3 ; ΔTs is the shear wave time difference, μs / m; ΔTc is the longitudinal wave time difference, μs / m; Vp and Vs are the longitudinal and shear wave velocities of the formation, m / s.

[0120] Furthermore, the step (2) of establishing the cross-plots between different elastic parameters is to establish the cross-plots between shear modulus and bulk modulus, the cross-plots between Poisson's ratio and Lame coefficient, and the cross-plots between Poisson's ratio and density*Lame coefficient, and to extract sensitive parameters based on the cross-plot analysis.

[0121] Specifically, the expression of the first sensitive parameter I1 is:

[0122] I1=K s -μ s

[0123] In the formula, K s is the bulk modulus, MPa; μ s is the shear modulus, MPa.

[0124] Specifically, the second sensitive parameter I2 is the Poisson’s ratio σ;

[0125] Specifically, the expression of the third sensitive parameter I3 is:

[0126] I3=ρλ

[0127] Where λ is the Lame coefficient, dimensionless; ρ is the formation density, g / cm 3 .

[0128] Furthermore, the calculation of the reservoir fluid identification dual evaluation factor in step (3) is calculated based on the sensitive parameters I1, I2, and I3.

[0129] Specifically, the sensitive parameters I1, I2, and I3 are normalized, and the normalized expressions are:

[0130]

[0131] Where, I′1 is normalized data, dimensionless; I 1min To calculate the minimum value of sensitive parameter 1, dimensionless; I 1max To calculate the maximum value of sensitive parameter 1, dimensionless.

[0132]

[0133] Where, I′2 is normalized data, dimensionless; I 2min To calculate the minimum value of sensitive parameter 2, dimensionless; I 2max To calculate the maximum value of sensitive parameter 2, dimensionless.

[0134]

[0135] Where, I′3 is normalized data, dimensionless; I 3min To calculate the minimum value of sensitive parameter 3, dimensionless; I 3max To calculate the maximum value of sensitive parameter 3, dimensionless.

[0136] Specifically, the expression of the dual evaluation factor is:

[0137] F1=I′1+I′2+I′3

[0138] Where I′1, I′2, and I′3 are normalized data and dimensionless; I 3min To calculate the minimum value of sensitive parameter 3, dimensionless; I 3max To calculate the maximum value of sensitive parameter 3, dimensionless.

[0139] F2=ρλ

[0140] Where λ is the Lame coefficient, dimensionless; ρ is the formation density, g / cm 3 .

[0141] Furthermore, the step (4) of establishing a double evaluation factor cross plot of the reservoir in the study area is to establish F1 and F2 cross plots by using the double evaluation factors corresponding to the reservoir fluids of different properties in the target layer of the test well through statistical calculation, thereby determining the limit values ​​of the double evaluation factors of each reservoir fluid.

[0142] Furthermore, the dual evaluation factors of the reservoir fluid in the oil well to be identified calculated in step (5) are calculated based on steps one, two and three, and the calculated dual evaluation factors of the reservoir fluid to be identified in the oil well are compared with the cross-plot of the dual evaluation factors of the reservoir fluid in the study area established in step four to determine the properties of the reservoir fluid to be identified in the oil well.

[0143] Specific application implementation:

[0144] See also Figure 3 The embodiment of the present invention provides a dual-factor fluid identification method based on array acoustic logging, comprising the following steps:

[0145] Step 1: Calculate the rock elastic parameters of the target layer of the tested well using array acoustic logging data.

[0146] The calculation formula for shear modulus is:

[0147]

[0148] The calculation formula for bulk modulus is:

[0149]

[0150] The calculation formula of Poisson's ratio is:

[0151]

[0152] The calculation formula of the Lame coefficient is:

[0153]

[0154] Where K s is the bulk modulus, MPa; μ s is the shear modulus, MPa; σ is the Poisson's ratio, dimensionless; λ is the Lame coefficient, dimensionless; ρ is the formation density, g / cm 3 ; ΔTs is the shear wave time difference, μs / m; ΔTc is the longitudinal wave time difference, μs / m; Vp and Vs are the longitudinal and shear wave velocities of the formation, m / s.

[0155] Step 2: Create a cross plot of shear modulus and bulk modulus ( Figure 4 ), Poisson's ratio and Lame coefficient cross plot ( Figure 5 ), Poisson's ratio and density × Lame coefficient cross plot ( Figure 6 ). Through the cross plot of shear modulus and bulk modulus ( Figure 4 ), we can distinguish the dry layer and the oil layer well, but we cannot distinguish the oil-water layer and the water layer. Therefore, we can further use the cross plot of Poisson's ratio and Lame coefficient ( Figure 5 ) can better distinguish the oil-water layer from the water layer. On this basis, we found that the cross plot of Poisson's ratio and density*Lame coefficient ( Figure 6 ), the properties of different types of fluids can be more clearly identified. In order to further quantitatively distinguish the properties of different fluids, sensitive parameters I1, I2, and I3 are constructed based on the above three plates.

[0156] The expression of the first sensitive parameter I1 is:

[0157] I1=K s -μ s

[0158] Where K s is the bulk modulus, MPa; μ s is the shear modulus, MPa.

[0159] The second sensitive parameter I2 is Poisson’s ratio σ;

[0160] The expression of the third sensitive parameter I3 is:

[0161] I3=ρλ

[0162] λ is the Lame coefficient, dimensionless; ρ is the formation density, g / cm 3 .

[0163] Step three, the reservoir fluid identification double evaluation factor is calculated according to the sensitive parameters I1, I2, and I3.

[0164] Specifically, the sensitive parameters I1, I2, and I3 are normalized, and the normalized expressions are:

[0165]

[0166] Where, I′1 is normalized data, dimensionless; I 1min To calculate the minimum value of sensitive parameter 1, dimensionless; I 1max To calculate the maximum value of sensitive parameter 1, dimensionless.

[0167]

[0168] Where, I′2 is normalized data, dimensionless; I 2min To calculate the minimum value of sensitive parameter 2, dimensionless; I 2max To calculate the maximum value of sensitive parameter 2, dimensionless.

[0169]

[0170] Where, I′3 is normalized data, dimensionless; I 3min To calculate the minimum value of sensitive parameter 3, dimensionless; I 3max To calculate the maximum value of sensitive parameter 3, dimensionless.

[0171] The double evaluation factor for reservoir fluid identification is calculated based on the sensitive parameters I1, I2, and I3. The expression of the double evaluation factor is:

[0172] F1=I′1+I′2+I′3

[0173] Where I′1, I′2, and I′3 are normalized data and dimensionless; I 3min To calculate the minimum value of sensitive parameter 3, dimensionless; I 3max To calculate the maximum value of sensitive parameter 3, dimensionless.

[0174] F2=ρλ

[0175] Where λ is the Lame coefficient, dimensionless; ρ is the formation density, g / cm 3 .

[0176] Step 4: By calculating and statistically analyzing the dual evaluation factors corresponding to the reservoir fluids of different properties in the target intervals of the test wells in the study area, a dual factor fluid identification chart for array acoustic logging is established ( Figure 7), thereby determining the limit values ​​of the dual evaluation factors of each reservoir fluid in the study area. The criteria for oil layer identification are F1<0.6, F2<2.8; the criteria for oil and water in the same layer are F1<1.3, F2<3.8; and the criteria for oil layer identification are F1>1.3, F2>3.8.

[0177] Step 5: Select an oil well to be identified in the study area, and calculate the dual evaluation factors F1 and F2 of the reservoir fluid according to steps 1, 2 and 3, which are: Figure 6 The calculation results show that the F1 and F2 of layer 42 are 0.42 and 3.27 respectively. Compared with the reservoir fluid double evaluation factor cross plot established in step 4, layer 42 is interpreted as an oil-water layer. The F1 and F2 of layers 44 and 45 are 0.36 and 2.67 respectively. Compared with the reservoir fluid double evaluation factor cross plot established in step 4, layers 44 and 45 are interpreted as oil layers. Oil testing of layer 42 revealed that the daily oil production was 13.35t and the daily water production was 9.5m 3 It was confirmed that the 42nd layer was an oil-water layer. The 44th and 45th layers were tested for oil and found to produce 26.10 tons of oil and 0m of water per day. 3 , confirming that layers 44 and 45 are oil layers, thus confirming the reliability of the dual-factor fluid identification method based on array acoustic logging, which is suitable for the identification of fluid properties in complex reservoirs.

[0178] In the actual data processing process, a dual-factor fluid identification method based on array acoustic logging is implemented by writing a program. Figure 8 In order to verify the well array acoustic wave dual-factor fluid identification results, according to the process of steps one to three, firstly, the longitudinal wave velocity and transverse wave velocity of the reservoir rock are obtained based on the array acoustic wave logging data, and different elastic parameters are calculated, and then the sensitive elastic parameters are calculated, and finally the purpose of accurately identifying the fluid properties is achieved by calculating the dual evaluation factors. From the actual data processing results, this patent has a significant effect on the fluid identification of complex oil and water layers, and its identification results are very consistent with the oil test conclusions, which shows the feasibility of this method.

[0179] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0180] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0181] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0182] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0183] The memory may be used to store the computer program and / or module, and the processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0184] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0185] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields, and the above-mentioned specific embodiments are only illustrative and instructive, rather than restrictive. Under the guidance of the specification, a person skilled in the art can also make many forms without departing from the scope of protection of the claims of the present invention, all of which belong to the scope of protection of the present invention.

Claims

1. A dual-factor fluid property identification method based on array acoustic logging, characterized in that: include: Calculate the rock elastic parameters of the target layer of the tested well; Establish the cross-plot between different elastic parameters according to rock elastic parameters, and extract and calculate sensitive elastic parameters according to the fluid type; Calculate the dual evaluation factors for reservoir fluid identification based on sensitive elastic parameters; Statistically calculate the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells, and establish a cross plot of the reservoir dual evaluation factors; According to the reservoir dual evaluation factor cross plot, the dual evaluation factors of the reservoir fluid to be identified in the oil well with the reservoir fluid property to be identified are calculated to determine the property of the reservoir fluid to be identified in the oil well.

2. The dual-factor fluid property identification method based on array acoustic logging according to claim 1, characterized in that: The rock elastic parameters of the target layer section of the tested oil well include shear modulus, bulk modulus, Poisson's ratio and Lame coefficient.

3. The dual-factor fluid property identification method based on array acoustic logging according to claim 2, characterized in that: The calculation formula of the shear modulus is: Among them, ρ is the formation density, μs is the shear modulus, and ΔTs is the shear wave time difference; The calculation formula for bulk modulus is: Ks is the bulk modulus, ΔTs is the transverse wave time difference, and ΔTc is the longitudinal wave time difference.

4. The dual-factor fluid property identification method based on array acoustic logging according to claim 2, characterized in that: The calculation formula of the Poisson's ratio is: Among them, σ is Poisson's ratio, ΔTs is the transverse wave time difference, and ΔTc is the longitudinal wave time difference Specifically, the calculation formula of the Lamé coefficient is: λ is the Lame coefficient, ρ is the formation density, Vp is the formation longitudinal velocity, and Vs is the shear wave velocity.

5. The dual-factor fluid property identification method based on array acoustic logging according to claim 2, characterized in that: The cross plots between different elastic parameters established according to the rock elastic parameters include the cross plot of shear modulus and bulk modulus, the cross plot of Poisson's ratio and Lame coefficient, and the cross plot of Poisson's ratio, density and Lame coefficient.

6. The dual-factor fluid property identification method based on array acoustic logging according to claim 1, characterized in that: The sensitive parameters include: The first sensitive parameter is: I1=K s -μ s In the formula, K s is the bulk modulus, μ s is the shear modulus; The second sensitive parameter is Poisson’s ratio; The third sensitive parameter is: I3=ρλ Where λ is the Lame coefficient and ρ is the formation density.

7. The dual-factor fluid property identification method based on array acoustic logging according to claim 1, characterized in that: The specific method for calculating the dual evaluation factors for reservoir fluid identification based on sensitive elastic parameters is as follows: F1=I′1+I′2+I′3 Among them, I′1, I′2, and I′3 are normalized data of different sensitive elastic parameters; F2=ρλ; Where λ is the Lame coefficient, dimensionless; ρ is the formation density.

8. A dual-factor fluid property identification system based on array acoustic logging, characterized in that: include: Calculation module: used to calculate the rock elastic parameters of the target layer of the tested oil well; Extraction module: Establish the intersection diagram between different elastic parameters according to rock elastic parameters, and extract and calculate sensitive elastic parameters according to fluid type; The second calculation module: calculates the reservoir fluid identification dual evaluation factor according to the sensitive elastic parameters; Mapping module: Count the dual evaluation factors corresponding to reservoir fluids of different properties in the test wells and establish the reservoir dual evaluation factor intersection diagram; Property determination module: According to the reservoir dual evaluation factor intersection diagram, the dual evaluation factors of the reservoir fluid to be identified in the oil well are calculated to determine the properties of the reservoir fluid to be identified in the oil well.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the dual-factor fluid property identification method based on array acoustic logging as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the dual-factor fluid property identification method based on array acoustic logging as claimed in any one of claims 1 to 7 are implemented.

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