CN119622978A - Dual-factor fluid property identification method based on array acoustic logging and related equipment - Google Patents
Dual-factor fluid property identification method based on array acoustic logging and related equipment Download PDFInfo
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Abstract
本发明公开了一种基于阵列声波测井的双因子流体性质识别方法及相关设备，利用阵列声波测井直接测量得到的储层岩石的纵波速度和横波速度，计算得到不同的弹性参数，根据岩石弹性参数建立不同弹性参数之间的交会图，按流体类型划分需要提取并计算敏感弹性参数，提取敏感弹性参数构建双评价因子，计算双评价因子进行流体性质判别，从而实现复杂油水层流体性质的有效识别。本发明方法简单，普适性强，识别准确度高，满足油田生产需求，一般解释人员均可操作，有利于生产上的快速规模应用，克服了现有技术中常规测井方法受测量模式的限制，很难摆脱岩石骨架对测井响应的影响，核磁共振测井技术成本高、处理解释过程复杂的问题。
The invention discloses a dual-factor fluid property identification method based on array acoustic logging and related equipment, which uses the longitudinal wave velocity and the shear 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 the sensitive elastic parameters to construct dual evaluation factors, calculate the dual evaluation factors to distinguish the fluid properties, thereby realizing the effective identification of the fluid properties of complex oil and water layers. The method of the invention is simple, has strong universality, high identification accuracy, meets the production needs of oil fields, can be operated by general interpreters, is conducive to rapid large-scale application in production, overcomes the conventional logging method in the prior art is limited by the measurement mode, is difficult to get rid of the influence of the rock skeleton on the logging response, and the nuclear magnetic resonance logging technology has high cost and complex processing interpretation process.
Description
Technical Field
The invention relates to the technical field of reservoir logging evaluation, in particular to a dual-factor fluid property identification method based on array acoustic logging and related equipment.
Background
Fluid property identification is an important content of reservoir logging evaluation, directly influences geological awareness and reserve scale, and is an important link of reservoir evaluation in exploration and development. The current fluid property identification method for complex oil-water layers mainly comprises a derivative parameter method based on conventional logging, a fluid identification method based on nuclear magnetic logging and a plurality of classical optimization algorithms, such as a neural network method, a support vector machine, a fuzzy clustering method and the like. The conventional well logging derivative parameter method is used for carrying out complex oil-water fluid identification by analyzing a low-resistance oil layer formation mechanism and an oil-containing main control factor to construct a fluid sensitive factor, so that a certain effect is obtained.
However, the conventional logging method is limited by a measurement mode, so that the influence of a rock framework on logging response is difficult to get rid of, and particularly for a low-contrast low-saturation oil layer with relatively complex formation, the response characteristics of resistivity on an oil and gas layer are difficult to distinguish due to other factors, so that the conventional logging series identification of fluid properties of a complex reservoir is difficult.
The nuclear magnetic resonance logging technology is slightly influenced by the rock skeleton, can reflect pore fluid information better, has a better recognition effect on complex oil-water layer recognition, but has high cost and complex processing and interpretation process, and the nuclear magnetic resonance logging data of old wells or oil fields are very deficient, so that the application of nuclear magnetic resonance logging is limited to a certain extent. The key of the application of the optimization algorithm is that a large number of training samples capable of reflecting the differences of different fluid properties are needed to be used as input to ensure the accuracy of the prediction model result, and the method lacks theoretical basis.
Disclosure of Invention
The invention aims to provide a dual-factor fluid property identification method based on array acoustic logging and related equipment, which are used for solving the problems that the conventional logging method in the prior art is limited by a measurement mode, is difficult to get rid of the influence of a rock framework on logging response, and has high nuclear magnetic resonance logging technology cost and complex processing and interpretation process.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the dual factor fluid property identification method based on array acoustic logging comprises the following steps:
calculating rock elasticity parameters of the tested oil well target interval;
Establishing an intersection diagram among different elastic parameters according to rock elastic parameters, and extracting and calculating sensitive elastic parameters according to fluid type division;
Calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well, and establishing a reservoir double evaluation factor intersection chart;
and calculating double evaluation factors of the reservoir fluid to be identified in the reservoir fluid property oil well to be identified according to the reservoir double evaluation factor intersection diagram, and determining the property of the reservoir fluid to be identified in the oil well.
Preferably, the rock elasticity parameters of the tested well interval of interest include shear modulus, bulk modulus, poisson's ratio and pull Mei Jishu.
Preferably, the shear modulus is calculated by the following formula:
Wherein ρ is the formation density, μs is the shear modulus, ΔTs is the shear wave time difference;
the calculation formula of the bulk modulus is:
ks is bulk modulus, ΔTs is transverse wave moveout, and ΔTc is longitudinal wave moveout.
Preferably, the poisson ratio has a calculation formula:
wherein sigma is Poisson's ratio, deltaTs is transverse wave time difference, deltaTc is longitudinal wave time difference
Specifically, the calculation formula of the prune coefficient is:
Lambda is the pull Mei Jishu, ρ is the formation density, vp is the formation longitudinal and Vs is the transverse wave velocity.
Preferably, establishing the intersection between different elastic parameters according to the rock elastic parameters includes a shear modulus and bulk modulus intersection, a poisson ratio and praise coefficient intersection, and a poisson ratio and density and praise coefficient intersection.
Preferably, the sensitive parameters include:
The first sensitive parameters are:
I1＝Ks-μs
Where K s is bulk modulus and μ s is shear modulus.
The second sensitive parameter is poisson's ratio;
the third sensitive parameter is:
I3=ρλ
Where λ is the pull Mei Jishu and ρ is the formation density.
Preferably, the method for calculating the reservoir fluid identification double-evaluation factor according to the sensitive elastic parameter comprises the following steps:
F1＝I′1+I′2+I′3
wherein, I' 1、I′2、I′3 is the normalized data of different sensitive elastic parameters;
F2=ρλ;
Wherein lambda is La Mei Jishu, which is dimensionless, and rho is stratum density.
A dual factor fluid property identification system based on array acoustic logging, comprising:
The calculation module is used for calculating rock elasticity parameters of the tested oil well target interval;
The extraction module is used for establishing an intersection diagram among different elastic parameters according to the rock elastic parameters, and extracting and calculating sensitive elastic parameters according to the fluid type division;
the second calculation module is used for calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
The map building module is used for counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well and building a reservoir double evaluation factor intersection map;
And the property determining module is used for calculating double evaluation factors of reservoir fluid to be identified in the reservoir fluid property oil well to be identified according to the reservoir double evaluation factor intersection diagram and determining the property of the reservoir fluid to be identified in the oil well.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the array acoustic logging based two-factor fluid property identification method when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the array acoustic logging based two-factor fluid property identification method.
Compared with the prior art, the dual-factor fluid property identification method based on the array acoustic logging has the advantages that the array acoustic logging is utilized to directly measure the longitudinal wave speed and the transverse wave speed of reservoir rock, different elastic parameters are obtained through calculation, an intersection chart among the different elastic parameters is built according to the rock elastic parameters, sensitive elastic parameters are extracted and calculated according to fluid type division requirements, dual-evaluation factors are built by extracting the sensitive elastic parameters, fluid property identification is carried out by calculating the dual-evaluation factors, and therefore effective identification of complex oil-water fluid properties is achieved. The method is simple, has strong universality and high identification accuracy, meets the production requirements of oil fields, can be operated by common interpreters, and is beneficial to the rapid large-scale application in production.
Drawings
FIG. 1 is a flow chart of a dual factor fluid property identification method based on array acoustic logging in accordance with the present invention;
FIG. 2 is a block diagram of a dual factor fluid property identification system based on array acoustic logging in accordance with the present invention;
FIG. 3 is a flow chart of a method for identifying the properties of a dual factor fluid based on array acoustic logging in accordance with an embodiment of the present invention;
FIG. 4 is a plot of shear modulus versus bulk modulus intersection for an embodiment of the present invention.
Fig. 5 is a poisson's ratio-lablab coefficient intersection diagram according to an embodiment of the present invention.
Fig. 6 is a poisson's ratio-density x praise coefficient intersection diagram according to an embodiment of the present invention.
FIG. 7 is a graph of the results of two factor fluid identification for validating acoustic well logging of a well array in accordance with an embodiment of the present invention;
FIG. 8 is a graph of validation well array sonic logging two-factor fluid identification results.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly stated or limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected through an intermediate medium, or communicating between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the present invention provides a dual factor fluid property identification method based on array acoustic logging, comprising:
S101, calculating rock elasticity parameters of a tested oil well target interval;
s102, establishing an intersection diagram among different elastic parameters according to rock elastic parameters, and extracting and calculating sensitive elastic parameters according to fluid type division;
s103, calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
s104, counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well, and establishing a reservoir double evaluation factor intersection chart;
S105, calculating double evaluation factors of reservoir fluid to be identified in a reservoir fluid property oil well to be identified according to the reservoir double evaluation factor intersection diagram, and determining the property of the reservoir fluid to be identified in the oil well.
The rock elastic parameters of the tested well interval of interest include shear modulus, bulk modulus, poisson's ratio and pull Mei Jishu.
The shear modulus was calculated as:
Wherein ρ is the formation density, μs is the shear modulus, ΔTs is the shear wave time difference;
the calculation formula of the bulk modulus is:
ks is bulk modulus, ΔTs is transverse wave moveout, and ΔTc is longitudinal wave moveout.
The poisson ratio has a calculation formula as follows:
wherein sigma is Poisson's ratio, deltaTs is transverse wave time difference, deltaTc is longitudinal wave time difference
Specifically, the calculation formula of the prune coefficient is:
Lambda is the pull Mei Jishu, ρ is the formation density, vp is the formation longitudinal and Vs is the transverse wave velocity.
Establishing intersection graphs among different elastic parameters according to the rock elastic parameters, wherein the intersection graphs comprise a shear modulus and bulk modulus intersection graph, a Poisson ratio and Raschig coefficient intersection graph and a Poisson ratio and density and Raschig coefficient intersection graph.
The sensitive parameters include:
The first sensitive parameters are:
I1＝Ks-μs
Where K s is bulk modulus and μ s is shear modulus.
The second sensitive parameter is poisson's ratio;
the third sensitive parameter is:
I3=ρλ
Where λ is the pull Mei Jishu and ρ is the formation density.
The method for calculating the reservoir fluid identification double-evaluation factor according to the sensitive elastic parameter comprises the following steps:
F1＝I′1+I′2+I′3
wherein, I' 1、I′2、I′3 is the normalized data of different sensitive elastic parameters;
F2=ρλ;
Wherein lambda is La Mei Jishu, which is dimensionless, and rho is stratum density.
As shown in fig. 2, the present invention provides a dual factor fluid property identification system based on array acoustic logging, comprising:
The calculation module is used for calculating rock elasticity parameters of the tested oil well target interval;
The extraction module is used for establishing an intersection diagram among different elastic parameters according to the rock elastic parameters, and extracting and calculating sensitive elastic parameters according to the fluid type division;
the second calculation module is used for calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
The map building module is used for counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well and building a reservoir double evaluation factor intersection map;
And the property determining module is used for calculating double evaluation factors of reservoir fluid to be identified in the reservoir fluid property oil well to be identified according to the reservoir double evaluation factor intersection diagram and determining the property of the reservoir fluid to be identified in the oil well.
Examples:
as shown in fig. 3, the invention provides a dual factor fluid property identification method based on array acoustic logging, comprising the following steps:
Step (1) calculating rock elasticity parameters of a tested oil well target interval in a research area;
Step (2) establishing an intersection diagram among different elastic parameters, and extracting and calculating sensitive elastic parameters according to fluid type division;
step (3) calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
Step (4) counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well, and establishing a reservoir double evaluation factor intersection diagram in the research area;
Step (5) for the well whose reservoir fluid properties are to be identified in the investigation region, calculating a double evaluation factor of the reservoir fluid to be identified in the well to determine the properties of the reservoir fluid to be identified in the well;
Further, in the step (1), the rock elasticity parameter of the tested oil well target interval is calculated by using array acoustic logging data.
Specifically, the shear modulus is calculated as:
specifically, the calculation formula of the bulk modulus is:
specifically, the poisson ratio has a calculation formula:
specifically, the calculation formula of the prune coefficient is:
Wherein K s is bulk modulus, mpa, mu s is shear modulus, mpa, sigma is Poisson's ratio, lambda is Law Mei Jishu, lambda is Law, rho is stratum density, g/cm 3, delta Ts is transverse wave time difference, mu s/m, delta Tc is longitudinal wave time difference, mu s/m, and Vp and Vs are stratum longitudinal and transverse wave speeds, m/s.
Further, in the step (2), the intersection graph between different elastic parameters is created, which is a shear modulus and bulk modulus intersection graph, a poisson ratio and praise coefficient intersection graph, and a poisson ratio and density praise coefficient intersection graph, and sensitive parameters are extracted according to the intersection graph analysis.
Specifically, the expression of the first sensitive parameter I 1 is:
I1＝Ks-μs
Wherein K s is bulk modulus, mpa, mu s is shear modulus, mpa.
Specifically, the second sensitivity parameter I 2 is poisson's ratio σ;
specifically, the expression of the third sensitive parameter I 3 is:
I3=ρλ
wherein lambda is La Mei Jishu, which is dimensionless, rho is stratum density, g/cm 3.
Further, in the step (3), the calculation of the reservoir fluid identification dual-evaluation factor is performed according to the sensitivity parameter I 1、I2、I3.
Specifically, the sensitive parameter I 1、I2、I3 is normalized, and the normalization expressions are respectively:
Wherein, I' 1 is normalized data, I 1min is minimum value and dimensionless of the calculated sensitive parameter 1, and I 1max is maximum value and dimensionless of the calculated sensitive parameter 1.
Wherein, I' 2 is normalized data, I 2min is minimum value and dimensionless of the calculated sensitive parameter 2, and I 2max is maximum value and dimensionless of the calculated sensitive parameter 2.
Wherein, I' 3 is normalized data, I 3min is minimum value and dimensionless of the calculated sensitive parameter 3, and I 3max is maximum value and dimensionless of the calculated sensitive parameter 3.
Specifically, the expression of the double evaluation factor is:
F1＝I′1+I′2+I′3
wherein, I' 1、I′2、I′3 is normalized data, I 3min is minimum value and dimensionless of the calculated sensitive parameter 3, and I 3max is maximum value and dimensionless of the calculated sensitive parameter 3.
F2=ρλ
Wherein lambda is La Mei Jishu, which is dimensionless, rho is stratum density, g/cm 3.
Further, in the step (4), the double evaluation factor intersection map of the reservoir in the research area is established by establishing an F1 and F2 intersection map corresponding to the double evaluation factors of the reservoir fluids with different properties of the target interval of the test oil well through statistical calculation, so as to determine the double evaluation factor limit value of each reservoir fluid.
Further, the calculation of the double evaluation factors of the reservoir fluids in the oil well to be identified in the step (5) is performed according to the first, second and third steps, and the calculated double evaluation factors of the reservoir fluids to be identified in the oil well are compared with the reservoir fluid double evaluation factor intersection map in the research area established in the step four to determine the properties of the reservoir fluids to be identified in the oil well.
The specific application implementation comprises the following steps:
Referring to fig. 3, an embodiment of the present invention provides a dual factor fluid identification method based on array acoustic logging, including the following steps:
And step one, calculating the rock elasticity parameters of the tested oil well target interval by using the array acoustic logging data.
The shear modulus was calculated as:
the calculation formula of the bulk modulus is:
The poisson ratio is calculated as:
the calculation formula of the plum pulling coefficient is as follows:
Wherein K s is bulk modulus, mpa, mu s is shear modulus, mpa, sigma is Poisson's ratio, lambda is Law Mei Jishu, lambda is Law, rho is stratum density, g/cm 3, delta Ts is transverse wave time difference, mu s/m, delta Tc is longitudinal wave time difference, mu s/m, and Vp and Vs are stratum longitudinal and transverse wave speeds, m/s.
And step two, establishing a shear modulus and bulk modulus intersection chart (figure 4), a poisson ratio and praise coefficient intersection chart (figure 5) and a poisson ratio and density x praise coefficient intersection chart (figure 6). By the shear modulus vs bulk modulus plot (fig. 4), we can better distinguish between dry and oil layers, but not between the oil and water layers. Therefore, the oil-water same layer can be better distinguished from the water layer by the Poisson ratio and the Lawsonia coefficient intersection chart (figure 5). Based on this, we found that using poisson's ratio versus density x-raume coefficient intersection graph (fig. 6) can more clearly identify different types of fluid properties. To further quantitatively distinguish between different fluid properties, a sensitive parameter I 1、I2、I3 was constructed based on the above three plates, respectively.
The expression for the first sensitive parameter I 1 is:
I1＝Ks-μs
Wherein K s is bulk modulus, mpa, mu s is shear modulus, and Mpa.
The second sensitivity parameter I 2 is poisson's ratio σ;
the expression of the third sensitive parameter I 3 is:
I3=ρλ
Lambda is La Mei Jishu, dimensionless, rho is stratum density, g/cm 3.
And thirdly, calculating a reservoir fluid identification double-evaluation factor according to the sensitive parameter I 1、I2、I3.
Specifically, the sensitive parameter I 1、I2、I3 is normalized, and the normalization expressions are respectively:
Wherein, I' 1 is normalized data, I 1min is minimum value and dimensionless of the calculated sensitive parameter 1, and I 1max is maximum value and dimensionless of the calculated sensitive parameter 1.
Wherein, I' 2 is normalized data, I 2min is minimum value and dimensionless of the calculated sensitive parameter 2, and I 2max is maximum value and dimensionless of the calculated sensitive parameter 2.
Wherein, I' 3 is normalized data, I 3min is minimum value and dimensionless of the calculated sensitive parameter 3, and I 3max is maximum value and dimensionless of the calculated sensitive parameter 3.
According to the sensitive parameter I 1、I2、I3, calculating a reservoir fluid identification double-evaluation factor, wherein the expression of the double-evaluation factor is as follows:
F1＝I′1+I′2+I′3
wherein, I' 1、I′2、I′3 is normalized data, I 3min is minimum value and dimensionless of the calculated sensitive parameter 3, and I 3max is maximum value and dimensionless of the calculated sensitive parameter 3.
F2=ρλ
Wherein lambda is La Mei Jishu, which is dimensionless, rho is stratum density, g/cm 3.
And step four, calculating and counting double evaluation factors corresponding to reservoir fluids with different properties in the target interval of the test oil well in the research area, and establishing an array acoustic logging double-factor fluid identification chart (figure 7), thereby determining the double evaluation factor limit value of each reservoir fluid in the research area. The oil layer discrimination standard is F 1<0.6,F2 <2.8, the oil-water same layer discrimination standard is F 1<1.3,F2 <3.8, and the oil layer discrimination standard is F 1>1.3,F2 >3.8.
Step five, selecting a first oil well to be identified in the research area, and calculating double evaluation factors F 1、F2 of reservoir fluid according to the step one, the step two and the step three, wherein the double evaluation factors are an eighth path and a ninth path in FIG. 6 respectively. The calculation results show that the layers F 1 and F 2 are respectively 0.42 and 3.27, the layers 42 and F 1 and F 2 are respectively 0.36 and 2.67, and the layers 44 and 45 are respectively oil and water layers compared with the double evaluation factor intersection diagram of the reservoir fluid in the study area established in the step four. And (3) carrying out oil testing on the 42 layers to find out 13.35t of daily oil production, 9.5m 3 of daily water production, confirming that the 42 layers are oil-water identical layers, carrying out oil testing on the 44 layers and the 45 layers to find out 26.10t of daily oil production, and 0m 3 of daily water production, and confirming that the 44 layers and the 45 layers are oil layers, thereby confirming the reliability of the dual-factor fluid identification method based on array acoustic logging and being suitable for complex reservoir fluid property identification.
In the actual data processing process, a double-factor fluid identification method based on array acoustic logging is realized by programming. FIG. 8 is a graph of the results of identifying well array acoustic double-factor fluid, according to the procedure from step one to step three, the longitudinal wave velocity and the transverse wave velocity of reservoir rock are first obtained based on array acoustic logging data, different elastic parameters are calculated, then sensitive elastic parameters are calculated, and finally the purpose of accurately identifying the fluid property is achieved by calculating double evaluation factors. From the actual data processing result, the fluid identification effect of the method on the complex oil-water layer is obvious, and the identification result and the oil test conclusion have good consistency, thus indicating the feasibility of the method.
The embodiment of the invention provides terminal equipment. The terminal device of this embodiment comprises a processor, a memory and a computer program stored in the memory and executable on the processor. The steps of the various method embodiments described above are implemented when the processor executes the computer program. Or the processor, when executing the computer program, performs the functions of the modules/units in the above-described device embodiments.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described specific embodiments and application fields, which are merely illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may make many forms without departing from the scope of the invention as claimed.
Claims (10)
1. The dual-factor fluid property identification method based on array acoustic logging is characterized by comprising the following steps of:
calculating rock elasticity parameters of the tested oil well target interval;
Establishing an intersection diagram among different elastic parameters according to rock elastic parameters, and extracting and calculating sensitive elastic parameters according to fluid type division;
Calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well, and establishing a reservoir double evaluation factor intersection chart;
and calculating double evaluation factors of the reservoir fluid to be identified in the reservoir fluid property oil well to be identified according to the reservoir double evaluation factor intersection diagram, and determining the property of the reservoir fluid to be identified in the oil well.
2. The method of two-factor fluid property identification based on array acoustic logging of claim 1, wherein the rock elastic parameters of the tested well interval of interest include shear modulus, bulk modulus, poisson's ratio and pull Mei Jishu.
3. The method for identifying the two-factor fluid properties based on the array acoustic logging of claim 2, wherein the shear modulus is calculated according to the formula:
Wherein ρ is the formation density, μs is the shear modulus, ΔTs is the shear wave time difference;
the calculation formula of the bulk modulus is:
ks is bulk modulus, ΔTs is transverse wave moveout, and ΔTc is longitudinal wave moveout.
4. The method for identifying the two-factor fluid properties based on the array acoustic logging of claim 2, wherein the poisson ratio is calculated according to the formula:
wherein sigma is Poisson's ratio, deltaTs is transverse wave time difference, deltaTc is longitudinal wave time difference
Specifically, the calculation formula of the prune coefficient is:
Lambda is the pull Mei Jishu, ρ is the formation density, vp is the formation longitudinal and Vs is the transverse wave velocity.
5. The method of two-factor fluid property identification based on array acoustic logging of claim 2, wherein establishing intersection plots between different elastic parameters based on rock elastic parameters includes a shear modulus and bulk modulus intersection plot, a poisson's ratio and a praise coefficient intersection plot, and a poisson's ratio and density and praise coefficient intersection plot.
6. The method of two-factor fluid property identification based on array acoustic logging of claim 1, wherein the sensitive parameters comprise:
The first sensitive parameters are:
I1＝Ks-μs
Wherein K s is bulk modulus, and μ s is shear modulus;
The second sensitive parameter is poisson's ratio;
the third sensitive parameter is:
I3=ρλ
Where λ is the pull Mei Jishu and ρ is the formation density.
7. The dual factor fluid property identification method based on array acoustic logging of claim 1, wherein the calculation of reservoir fluid identification dual evaluation factors according to sensitive elastic parameters is specifically implemented by the following method:
F1＝I′1+I′2+I′3
wherein, I' 1、I′2、I′3 is the normalized data of different sensitive elastic parameters;
F2=ρλ;
Wherein lambda is La Mei Jishu, which is dimensionless, and rho is stratum density.
8. A dual factor fluid property identification system based on array acoustic logging, comprising:
The calculation module is used for calculating rock elasticity parameters of the tested oil well target interval;
The extraction module is used for establishing an intersection diagram among different elastic parameters according to the rock elastic parameters, and extracting and calculating sensitive elastic parameters according to the fluid type division;
the second calculation module is used for calculating reservoir fluid identification double-evaluation factors according to the sensitive elastic parameters;
The map building module is used for counting double evaluation factors corresponding to reservoir fluids with different properties of the test oil well and building a reservoir double evaluation factor intersection map;
And the property determining module is used for calculating double evaluation factors of reservoir fluid to be identified in the reservoir fluid property oil well to be identified according to the reservoir double evaluation factor intersection diagram and determining the property 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 the processor, when executing the computer program, implements the steps of the array acoustic logging based two-factor fluid property identification method according to any of claims 1 to 7.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the array acoustic logging based two-factor fluid property identification method of any of claims 1 to 7.
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