Method for diagnosing deep fractured reservoir parameters based on distributed optical fiber temperature measurement data

Abstract

The invention discloses a method for diagnosing parameters of a deep fractured reservoir based on distributed optical fiber temperature measurement data. The method comprises the following steps: acquiring actually measured bottom hole temperature and pressure data of the deep fractured reservoir by using a distributed optical fiber sensor; obtaining approximate analytic solutions of the bottom hole pressure at the early stage and the later stage of reservoir production; constructing a reservoir production early stage heat transfer model, and obtaining an approximate analytic solution of the production early stage bottom hole temperature; constructing a reservoir production later-stage heat transfer model, and obtaining an approximate analytical solution of the bottom hole temperature in the production later-stage; establishing a deep fractured reservoir parameter diagnosis method by using the bottom hole temperature approximate analytic solutions at the early stage and the later stage of production and the bottom hole pressure approximate analytic solutions at the later stage; and according to the actually measured bottom hole temperature and pressure data of the reservoir and a reservoir parameter diagnosis method, determining solved values of the reservoir parameters. The method provided by the invention is simple and easy to understand, has strong operability, and can improve the diagnosis efficiency of the deep fractured reservoir parameters.

Description

Technical Field

[0001] The present invention belongs to the field of reservoir characterization in geothermal development engineering, and specifically relates to a method for diagnosing deep fracture reservoir parameters based on distributed optical fiber temperature measurement data. Background Art

[0002] my country has abundant geothermal resources, with a total amount of geothermal resources equivalent to 853.2 billion tons of standard coal, mainly distributed in the eastern and southwestern regions. Vigorously developing geothermal energy is of great significance to the implementation of carbon peak and carbon neutrality goals, and it is urgent to carry out relevant technical research to accelerate the development and utilization of geothermal energy.

[0003] Geothermal resources can be divided into three categories according to the storage space in the thermal reservoir rock: porous geothermal resources, fractured geothermal resources and karst fractured geothermal resources. Among them, deep fractured geothermal resources are widely distributed and rich in geothermal resources, accounting for a large proportion in actual development. Accurately obtaining the parameters of deep fractured geothermal reservoirs (such as adiabatic expansion coefficient, Joule-Thomson coefficient and formation flow coefficient) is the basis for evaluating the geothermal development potential of reservoirs and designing efficient development plans. However, due to the complexity of fractured reservoirs and the diversity of reservoir media, the existing reservoir parameter diagnosis methods need to discretely solve the reservoir and fractures, and then fit the measured temperature or pressure data of the production process to obtain reservoir parameters. These methods are difficult to solve and calculate, and are not convenient for field application in oil fields, which restricts the development benefits of deep fractured geothermal resources; in addition, deep fractured geothermal reservoirs are in a high temperature and high pressure environment, which brings great difficulties to the downhole and testing of conventional instruments, and it is difficult to obtain measured temperature and pressure data. Therefore, it is necessary to propose a simple and easy technology and method for diagnosing deep fractured reservoir parameters. Summary of the invention

[0004] The purpose of the present invention is to provide a method for diagnosing deep fractured reservoir parameters based on distributed optical fiber temperature measurement data, using distributed optical fiber to monitor the bottom hole temperature and pressure data of the deep fractured geothermal reservoir production process, to solve the problem of downhole testing with conventional testing instruments, and at the same time propose a simple and easy method for diagnosing deep fractured reservoir parameters based on the actual measured temperature and pressure data of the reservoir. The specific technical scheme is as follows.

[0005] Step S1: using distributed optical fiber sensors to obtain measured bottom hole temperature and pressure data of deep fractured reservoirs.

[0006] Step S2: Obtain an approximate analytical solution for the early and late bottom hole pressures of deep fractured reservoirs. According to Kazemi's work (Kazemi, H. (1969). Pressure transient analysis of naturally fractured reservoirs with uniform fracture distribution. Society of petroleum engineers Journal, 9 (04), 451-462.), the approximate analytical solution for the early bottom hole pressure of deep fractured reservoirs is:

[0007]

[0008] According to the work of Abdassah and Ershaghi (Abdassah, D., Ershaghi, I. (1986). Triple-porosity systems for representing naturally fractured reservoirs. SPE Formation Evaluation, 1 (02), 113-127.), the approximate analytical solution for the bottom hole pressure in the late production period of deep fractured reservoirs is:

[0009]

[0010] Where: p wf1 ——bottom hole pressure in the early stage of production, Pa; p wf2 ——bottom hole pressure in the late production period, Pa; p i ——Original formation pressure, Pa.

[0011] Step S3: Construct a heat transfer model for the early stage of production of deep fractured reservoirs to obtain an approximate analytical solution for the bottom hole temperature in the early stage of production. Based on the law of conservation of energy, the heat transfer model for the early stage of production of deep fractured reservoirs is:

[0012]

[0013] Where: r——radial distance, m; ——Crack effective density-heat capacity product, J / (m 3 ·K).

[0014] Based on the approximate analytical solution of the bottom hole pressure in the early stage of production of deep fractured reservoirs and the Boltzmann transform method to solve formula (8), the approximate analytical solution of the bottom hole temperature in the early stage of production of deep fractured reservoirs is obtained, which is:

[0015]

[0016] Step S4: Construct a heat transfer model for the late production of deep fractured reservoirs to obtain an approximate analytical solution for the bottom hole temperature in the late production period. Based on the law of conservation of energy, the heat transfer model for the late production period of deep fractured reservoirs is:

[0017]

[0018] Based on the approximate analytical solution of the bottom hole pressure in the late production period of deep fractured reservoirs and the characteristic line method to solve formula (10), the approximate analytical solution of the bottom hole temperature in the late production period of deep fractured reservoirs is obtained, which is:

[0019]

[0020] Step S5: establishing a deep fracture reservoir parameter diagnosis method using the approximate analytical solutions of the early and late production temperatures and the approximate analytical solutions of the late production pressure.

[0021] First, according to formula (7), it can be found that the bottom hole pressure of deep fractured reservoirs in the late production period has a linear relationship with the logarithmic time, and its slope expression is:

[0022]

[0023] Therefore, the slope of the measured bottom hole pressure and logarithmic time in the late production period of the deep fractured reservoir is regressed, and the formation flow coefficient of the reservoir is calculated by formula (12).

[0024] Then, according to formula (9), it can be found that the bottom hole temperature in the early stage of production of deep fractured reservoirs has a linear relationship with the logarithmic time, and its slope expression is:

[0025]

[0026] It can be understood that the ratio of formula (13) to formula (12) is the adiabatic expansion coefficient of the reservoir. Therefore, the adiabatic expansion coefficient of the reservoir can be obtained by regressing the ratio of the slope of the measured bottom hole temperature and logarithmic time in the early production period of the deep fractured reservoir to the slope of the measured bottom hole pressure and logarithmic time in the late production period.

[0027] Finally, according to formula (11), it can be found that the bottom hole temperature in the late production period of deep fractured reservoirs has a linear relationship with the logarithmic time, and its slope expression is:

[0028]

[0029] It can be understood that the ratio of formula (14) to formula (12) is the Joule-Thomson coefficient of the reservoir. Therefore, the Joule-Thomson coefficient of the reservoir can be obtained by regressing the ratio of the slope of the measured bottom hole temperature and logarithmic time in the late production period of the deep fractured reservoir to the slope of the measured bottom hole pressure and logarithmic time in the late production period.

[0030] Step S6: Determine the calculated value of the reservoir parameter according to the measured bottom hole temperature data and pressure data of the reservoir and the deep fracture reservoir parameter diagnosis method.

[0031] First, a relationship curve between the measured bottom hole pressure data of the reservoir and the logarithmic time is drawn, and the measured bottom hole pressure data of the reservoir in the late production period is selected to regress its linear relationship with the logarithmic time and obtain the slope value m of the regression line. p , substitute into formula (15) to calculate the formation flow coefficient of the reservoir:

[0032]

[0033] Then, a relationship curve between the measured bottom hole temperature data of the reservoir and the logarithmic time is drawn, and the measured bottom hole temperature data of the reservoir in the early production period is selected to regress the linear relationship between the bottom hole temperature data and the logarithmic time and obtain the slope value m of the regression line. Te , substitute into formula (16) to calculate the effective adiabatic expansion coefficient of the reservoir:

[0034]

[0035] Finally, the measured bottom hole temperature data of the reservoir in the late production period is selected, and its linear relationship with logarithmic time is regressed to obtain the slope value m of the regression line. Tl , substitute into formula (17) to calculate the Joule-Thomson coefficient of the reservoir:

[0036]

[0037] The same symbols involved in all formulas in the present invention have the same meaning before and after the symbols, and after being marked once, they are universally used.

[0038] The advantage of the present invention is that the traditional method needs to establish a deep fractured reservoir seepage and heat transfer numerical model, fit the measured bottom hole temperature data and pressure data, and then obtain the reservoir parameters. The calculation process is difficult and the parameter acquisition procedure is complicated. The deep fractured reservoir parameter diagnosis method proposed by the present invention only needs to select part of the measured bottom hole temperature data and pressure data, regress its linear relationship with the logarithmic time, and obtain the slope value of the corresponding straight line to obtain the reservoir parameters. It does not involve complex modeling and calculation processes. The parameter acquisition process is simpler and more operable. It is more suitable for geothermal development on-site applications and provides parameter support for the design of efficient development plans for geothermal development. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0040] Figure 1 is a flow chart of a method for diagnosing deep fracture reservoir parameters based on distributed optical fiber temperature measurement data in an embodiment of the present invention;

[0041] Figure 2 is a physical model of a deep fractured reservoir in an embodiment of the present invention;

[0042] Figure 3 Schematic diagram of bottom hole temperature data and pressure data measured by a distributed optical fiber sensor during production of a deep fractured reservoir in an embodiment of the present invention;

[0043] Figure 4 1 is a schematic diagram of a semi-logarithmic curve of measured bottom hole pressure data and time in an embodiment of the present invention;

[0044] Figure 5 Schematic diagram of a semi-logarithmic curve of measured bottom hole temperature data and time in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0046] Example: Take a vertical well in X fracture reservoir as an example. The cumulative testing time of the well is about 2500 hours, and the production of each repeating unit is 100m 3 / d( Figure 2 ), the fluid volume coefficient is 1.

[0047] According to step S1 of the invention, the measured bottom hole temperature data and pressure data of the X-fracture reservoir are obtained by using a distributed optical fiber sensor ( Figure 3 ).

[0048] According to step S6 of the invention, the reservoir parameters (adiabatic expansion coefficient, Joule-Thomson coefficient and formation flow coefficient) are determined based on the measured bottom hole temperature and pressure data of the reservoir and the deep fracture reservoir parameter diagnosis method. The specific calculation process is as follows:

[0049] First, draw the relationship curve between the measured bottom hole pressure data of the reservoir and the logarithmic time ( Figure 4), select the measured bottom hole pressure data of the reservoir in the later stage of production, regress its linear relationship with logarithmic time and obtain the slope value m of the regression line p =-0.0621 MPa / ln(s), and the formation flow coefficient of the reservoir is 3.415×10 -9 m 3 / Pa·s;

[0050]

[0051] Then, a relationship curve between the measured bottom hole temperature data of the reservoir and the logarithmic time is drawn ( Figure 5 ), select the measured bottom hole temperature data of the reservoir in the early stage of production, regress its linear relationship with logarithmic time and obtain the slope value m of the regression line Te =-0.002K / ln(s), and substituting it into formula (16) yields the effective adiabatic expansion coefficient of the reservoir as 3.221×10 -8 K / Pa;

[0052]

[0053] Finally, the measured bottom hole temperature data of the reservoir in the late production period is selected, and its linear relationship with logarithmic time is regressed to obtain the slope value m of the regression line. Tl is 0.0111K / ln(s), and the Joule-Thomson coefficient of the reservoir is calculated to be -1.787×10 -7 K / Pa.

[0054]

[0055] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for diagnosing deep fracture reservoir parameters based on distributed optical fiber temperature measurement data, characterized in that: The method comprises: S1: Use distributed optical fiber sensors to obtain the measured bottom hole temperature and pressure data of the reservoir; S2: Obtain the approximate analytical solution of the bottom hole pressure in the early and late production stages of deep fractured reservoirs; S3: Construct a heat transfer model for the early production of deep fractured reservoirs and obtain an approximate analytical solution for the bottom hole temperature in the early production period; S4: Construct a heat transfer model for deep fractured reservoirs in the late production period to obtain an approximate analytical solution for the bottom hole temperature in the late production period; S5: establishing a deep fracture reservoir parameter diagnosis method using the approximate analytical solutions of the bottom hole temperature in the early and late production stages and the approximate analytical solutions of the bottom hole pressure in the late production stages; S6: Determine the calculated value of the reservoir parameter according to the measured bottom hole temperature and pressure data of the reservoir and the reservoir parameter diagnosis method.

2. The method for diagnosing deep fracture reservoir parameters according to claim 1, characterized in that: The reservoir parameters include at least adiabatic expansion coefficient, Joule-Thomson coefficient and formation flow coefficient.

3. The method for diagnosing deep fracture reservoir parameters according to claim 1, characterized in that: In step S3, a heat transfer model for the early production of deep fractured reservoirs is constructed to obtain an approximate analytical solution for the bottom hole temperature in the early production period: Where: T wf1 ——Bottom hole temperature in the early stage of production, K; T i ——original formation temperature, K; q——well production, m 3 / s; B——fluid volume coefficient; N——number of repeating units; μ F ——Fluid viscosity, Pa·s; h F ——thickness of fracture medium, m; k F ——Permeability of fracture medium, m 2 ; r w ——wellbore radius, m; t——time, s; ζ F * ——Effective adiabatic expansion coefficient of fracture medium, K / Pa; η F ——pressure conductivity of fracture medium, m 2 / s; γ——Euler constant.

4. The method for diagnosing deep fracture reservoir parameters according to claim 1, characterized in that: In step S4, a heat transfer model for the late production period of deep fractured reservoirs is constructed to obtain an approximate analytical solution for the bottom hole temperature in the late production period: Where: T wf2 ——Bottom hole temperature in the early stage of production, K; μ JT ——Joule-Thomson coefficient, K / Pa; χ F =h F / (h F +h M / 2+h V / 2)——fracture medium volume ratio; h M ——Thickness of matrix medium, m; h V ——Thickness of porous medium, m; ρ f ——Fluid density, kg / m 3 ;c pf ——heat capacity of fluid, J / (kg·K); ζ R * ——Effective adiabatic expansion coefficient of reservoir, K / Pa; η R ——Reservoir pressure conductivity, m 2 / s; ——Reservoir effective density-heat capacity product, J / (m 3 ·K).

5. The method for diagnosing deep fracture reservoir parameters according to claim 1, characterized in that: In step S5, a method for diagnosing parameters of deep fractured reservoirs is established using the approximate analytical solutions of bottom hole temperature in the early and late production stages and the approximate analytical solutions of bottom hole pressure in the late production stage, specifically: Using the approximate analytical solution of the bottom hole pressure in the late production period, constructing a slope expression of the linear relationship between the bottom hole pressure in the late production period of the fractured reservoir and the logarithmic time, and obtaining the formation flow coefficient; Using the approximate analytical solution of the bottom hole temperature in the late production period, constructing a slope expression of the linear relationship between the bottom hole temperature in the late production period of the fractured reservoir and the logarithmic time, and obtaining the Joule-Thomson coefficient of the reservoir; The approximate analytical solution of the bottom hole temperature in the early stage of production is used to construct a slope expression of the linear relationship between the bottom hole temperature in the early stage of production of a fractured reservoir and the logarithmic time, and the effective adiabatic expansion coefficient of the reservoir is obtained.

6. The method for diagnosing deep fracture reservoir parameters according to claim 1, characterized in that: In step S6, the reservoir parameter values ​​are determined according to the measured bottom hole temperature and pressure data of the reservoir and the reservoir parameter diagnosis method, specifically: Draw a curve graph of the measured bottom hole pressure data and logarithmic time, regress the linear relationship between the measured bottom hole pressure data and logarithmic time in the late production period, obtain the semi-logarithmic straight line slope of the measured bottom hole pressure data in the late production period, and calculate the formation flow coefficient of the reservoir: Draw a curve graph of the measured bottom hole temperature data and logarithmic time, regress the linear relationship between the measured bottom hole temperature data and logarithmic time in the late production period, obtain the semi-logarithmic straight line slope of the measured bottom hole temperature data in the late production period, and calculate the Joule-Thomson coefficient of the reservoir: The linear relationship between the measured bottom hole temperature data in the early stage of production and the logarithmic time is regressed to obtain the semi-logarithmic straight line slope of the measured bottom hole temperature data in the early stage of production, and the effective adiabatic expansion coefficient of the reservoir is calculated: Where: k F h F / μ——formation flow coefficient, m 3 / Pa·s;m p ——The slope of the semi-logarithmic straight line of the measured bottom hole pressure in the late production period; m Tl ——The slope of the semi-logarithmic straight line of the measured bottom hole temperature in the late production period; m Te ——The slope of the semi-logarithmic straight line of the measured bottom hole temperature in the early stage of production.

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