A numerical simulation method for tight sandstone gas reservoirs considering time-varying reservoir parameters

Abstract

The present invention belongs to the technical field of numerical simulation of tight gas reservoirs, and specifically relates to a numerical simulation method for tight sandstone gas reservoirs that takes into account the time-varying reservoir parameters, comprising the following steps: based on commercial numerical simulation software, combined with stress sensitivity and gas-water two-phase permeability experimental results, through model derivation and coupling, a numerical simulation method for tight sandstone gas reservoirs that takes into account complex seepage mechanisms such as stress sensitivity and time-varying permeability is established. Since the stress sensitivity and gas-water permeability changes under real reservoir conditions are taken into account during the simulation process, it can truly reflect the changes in production, formation pressure, and water saturation during the actual production process. The patent of the present invention has the characteristics of simple application, wide application, and accurate results, and provides a theoretical basis for analyzing the changes in gas well production and formulating development plans.

Description

Technical Field

[0001] The present invention belongs to the technical field of tight gas reservoir numerical simulation, and in particular relates to a tight sandstone gas reservoir numerical simulation method taking into account time-varying reservoir parameters. Background Art

[0002] Tight gas's contribution to oil and gas reserves and production has been increasing annually, making it one of the largest unconventional natural gas resources currently under development. Tight sandstone gas reservoirs discovered in China are mostly lithologic, characterized by close-packed accumulation and complex pore structures. This results in diverse gas-water distribution patterns, with gas-water differentiation occurring only in certain structural locations or areas with developed fractures. This complex water production process complicates development efforts, significantly impacting development effectiveness. This complex gas-water distribution leads to constant changes in gas-water relative permeability and stress sensitivity during development, making simulation and characterization challenging.

[0003] In existing research, 202210300381.X provides a numerical simulation method for shale gas reservoirs, establishes an integrated coupling model of ground model and digital model, and improves the recovery rate of shale gas wells. 202411336386.3 discloses a fluid-solid coupling numerical simulation method, device and medium for shale gas reservoirs considering the distribution of proppants, and establishes a numerical simulation method for shale gas reservoirs that considers the actual distribution characteristics of proppants in fractures and the stress sensitivity of the conductivity of the propped fracture area. 202410792120.3 discloses a numerical simulation method for shale condensate gas reservoir development based on a general pEDFM, providing a numerical simulation tool with the best theoretical comprehensive computing performance for the development of fractured shale condensate gas reservoirs. 202010548844.5 discloses a numerical simulation method for coalbed methane and tight gas combined production, which reduces the overall number of model grids, optimizes the model size, and speeds up the calculation rate. 202310361157.6 discloses a numerical simulation characterization method for high-multiple water flooding in bottom water reservoirs considering the time-varying phase permeability. It proposes an improvement to the time-varying starting endpoint of the current phase permeability time-varying technology in numerical simulation, and makes the prediction of water flooding efficiency and ultimate recovery rate in actual oil field production more accurate.

[0004] However, there is a problem with these publicly available research results: tight gas reservoirs generally have low porosity, low permeability, and high water saturation. This leads to unique seepage mechanisms during reservoir development, such as time-varying relative permeability and stress sensitivity. However, conventional commercial software currently fails to account for these unique seepage mechanisms of tight gas, resulting in significant errors in simulations. Therefore, accounting for these unique seepage mechanisms in numerical simulations is crucial for the efficient development of tight gas reservoirs. Summary of the Invention

[0005] In order to solve the above-mentioned drawbacks of the prior art, the present invention discloses a method which adopts the following technical means:

[0006] The patent of this invention provides a numerical simulation method for tight sandstone gas reservoirs that takes into account the time-varying reservoir parameters. It is based on the INTERSECT numerical simulator of the commercial software Petrel, combined with the stress sensitivity and gas-water two-phase permeability experimental results. Through Python code, the above experimental results are imported into the INTERSECT numerical simulator to establish a numerical simulation method for tight sandstone gas reservoirs that takes into account complex seepage mechanisms such as stress sensitivity and time-varying phase permeability.

[0007] To achieve the above objectives, the present invention provides a numerical simulation method for tight sandstone gas reservoirs that takes into account the time-varying reservoir parameters, using the following technical means:

[0008] A numerical simulation method for tight sandstone gas reservoirs considering time-varying reservoir parameters is disclosed. The reservoir parameters of the present invention refer to reservoir permeability and relative permeability. The time-varying reservoir parameters are characterized by stress sensitivity and relative permeability, respectively. The simulation method of the present invention comprises the following steps:

[0009] S1. Based on the core samples taken from the simulated gas field, a stress sensitivity experiment is constructed to test the change of the core permeability with time (i.e., with formation pressure) under the initial permeability and initial water saturation.

[0010] The initial permeability and initial water saturation refer to the initial permeability and initial water saturation of the reservoir of the gas field to be simulated.

[0011] The specific method of the stress sensitivity experiment can be referred to the National Standard of the People's Republic of China "GB / T 29172-2012 Core Analysis Method", which will not be described in detail in the present invention.

[0012] S2. Based on the stress sensitivity experimental results of step S1, fitting the relationship between the core permeability and the formation pressure, wherein the relationship between the core permeability and the formation pressure is represented by a mathematical model of the influence of the core permeability on the formation pressure, or a standardized curve of the core stress sensitivity;

[0013] S3. Based on the relationship between the core permeability and the formation pressure described in step S2, construct a relationship between the core permeability and the conductivity multiplier;

[0014] S4. Based on the relationship between the core permeability and the formation pressure described in step S2 and the relationship between the core permeability and the conductivity multiplier described in step S3, modify the conductivity multiplier in the rock compressibility table of the Petrel software to achieve stress sensitivity characterization;

[0015] By using the method disclosed in the present invention to correct the conductivity multiplier in the Petrel software, subsequent numerical simulations of tight sandstone gas reservoirs will be more accurate. For example, when simulating and predicting the reservoir pressure field, the corrected conductivity multiplier takes into account the impact of pressure changes on permeability, and the prediction results will be closer to the pressure field changes during actual production.

[0016] S5: Use the non-steady-state method to carry out gas-water two-phase relative permeability experiments under different permeabilities and water saturations, determine the change law of the relative permeability endpoint over time (i.e., formation pressure and permeability) (for specific experimental methods, please refer to: Wang Hao, Sun Jianmeng, Cui Ruikang, et al. Data processing method of non-steady-state gas-water relative permeability experiment [J]. Well Logging Technology, 2023, 47(02): 161-166. This invention will not repeat it here), and establish the correlation formula between the relative permeability endpoint and the formation pressure and permeability;

[0017] S6: Based on the relationship between the conductivity multiplier and the core permeability, a correlation equation is established between the phase permeability endpoint and the formation pressure and the conductivity multiplier;

[0018] S7: Write Python language code in the INTERSECT numerical simulator of Petrel software to define the output of the required properties of phase permeability time variation and output the pressure and conductivity multipliers of each grid at all times after numerical simulation;

[0019] S8: Write numerical simulation code based on Python language, convert the relationship between the phase permeability endpoint and the formation pressure and conductivity multiplier into code language, and put it into the INTERSECT numerical simulator to realize the representation of the time-varying phase permeability in the numerical model.

[0020] The present invention corrects the phase permeability endpoint by considering the relationship between the phase permeability endpoint and the formation pressure and conductivity multiplier, and inputs it into the INTERSECT numerical simulator to realize the correction of the phase permeability time variation. The subsequent numerical simulation of tight sandstone gas reservoirs will be more accurate. For example, when simulating and predicting the saturation field of the reservoir, the corrected phase permeability time variation is used for prediction, and the prediction result will be closer to the saturation field in the actual production process.

[0021] Furthermore, in step S2, the mathematical model of the influence of formation pressure on the core permeability is:

[0022] (I)

[0023] In formula (I), K is the core permeability, mD; K 0 is the initial permeability of the reservoir, mD; exp is the exponential function with the natural number e as the base; P is the formation pressure; 、 cis a constant;

[0024] The model of formula (I) proposed in this invention is applicable to all tight sandstone gas reservoirs. 、 c The values ​​are different, the gas field to be simulated 、 c The value is obtained by substituting the stress sensitivity test results of step S1 (i.e., multiple sets of formation pressures and their corresponding permeability data) into the model of formula (I).

[0025] Furthermore, in formula (I), -1< <0, 0 <c<1。

[0026] Furthermore, in step S3, the relationship between the core permeability and the conductivity multiplier is:

[0027] (II)

[0028] In formula (II), K is the core permeability, mD; K e The core permeability corresponding to the relationship between the core permeability and the formation pressure fitted in step S2 is mD; T m is the conductivity multiplier, which is the core permeability and K e ratio.

[0029] The initial formation pressure is the initial formation pressure of the reservoir of the gas field to be simulated.

[0030] Furthermore, the relative permeability endpoint is characterized by irreducible water saturation. In step S5, the relationship between the relative permeability endpoint and the formation pressure and permeability is:

[0031] (III)

[0032] In formula (III), S wc is the bound water saturation, %; K is the core permeability, mD; P is the formation pressure, MPa; a, b, c, d are constants;

[0033] The values ​​of a, b, c, and d of the gas field to be simulated are obtained by substituting the experimental results of the gas-water two-phase permeability experiment under different permeabilities and water saturations in step S5 into the correlation formula of formula (III).

[0034] The model of formula (III) proposed in the present invention is applicable to all tight sandstone gas reservoirs. The difference is that the values ​​of a, b, c, and d are different for different tight sandstone gas reservoirs. The values ​​of a, b, c, and d of the gas field to be simulated are obtained by substituting the experimental results of the gas-water two-phase permeability experiment at different permeabilities and water saturations in step S5 (i.e., multiple sets of formation pressure, permeability, and corresponding irreducible water saturation data) into the model of formula (I).

[0035] Furthermore, in formula (III), -1 <a<0,0<b<1,-1<c<0,0<d<1。

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] Based on commercial reservoir numerical simulation software, this study, based on the characteristics of tight sandstone gas reservoirs with high water content, combines core stress sensitivity experiments and core gas-water two-phase permeability experiments to establish a time-varying permeability model with improved permeability endpoints. This numerical simulation, which considers stress sensitivity and the time-varying permeability endpoints, achieves higher accuracy in predicting indicators such as decline rate and recovery factor, providing a theoretical basis for the study and extraction of residual gas in gas fields.

[0038] Based on commercial software, the present invention establishes a numerical simulation method for tight sandstone gas reservoirs taking into account the time-varying reservoir parameters, clarifies the influence of stress sensitivity and phase permeability time-varying effects on the reservoir pressure field and saturation field, and provides a theoretical basis for analyzing the reservoir pressure drop law, saturation change law and production decline change law, so that the recovery rate prediction and residual gas distribution are more consistent with the actual oil field production. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the core stress sensitivity normalized curve of Example 1;

[0040] Figure 2 is the curve of irreducible water saturation changing with formation pressure;

[0041] Figure 3 Comparison diagram of formation pressure field before and after considering time-varying reservoir parameters;

[0042] Figure 4 This is a comparison diagram of the water saturation field before and after considering the time-varying reservoir parameters;

[0043] Figure 5 The irreducible water saturation change curves at different development times;

[0044] Figure 6 This is a flow chart of the numerical simulation method for tight sandstone gas reservoirs in Example 1. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings.

[0046] Example 1

[0047] Taking a well area in the D gas field in the Ordos Basin as an example, the initial formation pressure in the well area is 28.5 MPa, the initial formation permeability is 0.13 mD, the porosity is 28%, and the initial formation water saturation is 60%.

[0048] like Figure 6 As shown, this embodiment discloses a numerical simulation method for tight sandstone gas reservoirs considering time-varying reservoir parameters, comprising the following steps:

[0049] S1. Based on core samples from the well area of ​​the D gas field, a stress-sensitive experimental device was built to measure the initial formation permeability and the change of core permeability with time (i.e., with formation pressure) at the initial water saturation.

[0050] S2. Based on the core stress sensitivity test results of step S1, a mathematical model of the influence of formation pressure on the core permeability of the well area is established by fitting the relationship:

[0051]

[0052] And the core stress sensitivity normalized curve of the well area, such as Figure 1 shown.

[0053] S3, according to the mathematical model of step S2 or Figure 1 , defines the conductivity multiplier at the original reservoir pressure (28.5MPa) ( T m ) is 1, calculate the ratio of the permeability at different formation pressures to the permeability at the original reservoir pressure when the water saturation is 60% (i.e., the conductivity multiplier), and regress the relationship between the conductivity multiplier and the permeability:

[0054]

[0055] S4. Based on the results of steps S2 and S3, set the conductivity multiplier under different formation pressures in the rock compressibility coefficient table (Table 1) of the Petrel software to achieve stress sensitivity characterization. The Petrel software can fit the relationship between formation pressure and conductivity multiplier based on the rock compressibility coefficient table.

[0056] Table 1 Rock compression coefficient

[0057]

[0058] S5. Use the non-steady-state method to carry out gas-water two-phase permeability experiments under different permeabilities and water saturations, and study the variation of irreducible water saturation with formation pressure under different permeabilities (e.g. Figure 2 As shown, Figure 2 A curve is given as an example), and the correlation between irreducible water saturation, formation pressure and permeability is further established:

[0059]

[0060] S6. The relationship between the conductivity multiplier and the permeability (obtained in step S3) is coupled to the correlation equation between the irreducible water saturation, the formation pressure, and the permeability obtained in step S5 to obtain the correlation equation between the irreducible water saturation, the formation pressure, and the conductivity:

[0061]

[0062] S7. Write Python language code in the INTERSECT numerical simulator of Petrel software to obtain the pressure and conductivity multipliers of the model at each time step during the numerical simulation process.

[0063] S8. Write Python code in the INTERSECT numerical simulator to convert the relationship between irreducible water saturation and formation pressure and conductivity into code. Use the pressure and conductivity multipliers obtained in step 7 to calculate the irreducible water saturation for the next time step. Write the calculated irreducible water saturation into the numerical simulation model to replace the original irreducible water saturation. Update the irreducible water saturation once every time step to achieve the representation of the time-varying phase permeability in the numerical simulation model of the well area.

[0064] S9. Use the above numerical simulation model (i.e., the simulation model after correcting the conductivity multiplier and irreducible water saturation in the simulation model provided by the software) to calculate the results of the well area after 5 years of development, and compare them with the results without considering the time-varying reservoir parameters, and analyze the pressure field after 5 years of development (such as Figure 3 As shown in ), water saturation field (as shown in Figure 4 ) and the differences in bound water saturation during development (e.g. Figure 5 The changing pattern of ).

[0065] in, Figure 3 The left side in the middle shows the pressure field without considering the time-varying reservoir parameters, and the back side shows the pressure field with considering the time-varying reservoir parameters. Figure 4 The left side in the middle is the water saturation field without considering the time-varying reservoir parameters, and the back side is the water saturation field with considering the time-varying reservoir parameters. Figure 5 It can be seen that when the time-varying reservoir parameters are not considered, the irreducible water saturation remains constant. After considering the time-varying reservoir parameters, the irreducible water saturation tends to increase with the increase of development years, which is closer to the actual state of the tight sandstone gas reservoir during production.

Claims

1. A numerical simulation method for tight sandstone gas reservoirs considering time-varying reservoir parameters, characterized in that: The steps include: S1. Based on the core samples taken from the field of the simulated gas field, a stress sensitivity experiment was constructed to test the change of the core permeability with time under the initial permeability and initial water saturation; S2. Based on the stress sensitivity experimental results of step S1, fitting the relationship between the core permeability and the formation pressure, wherein the relationship between the core permeability and the formation pressure is represented by a mathematical model of the influence of the core permeability on the formation pressure, or a standardized curve of the core stress sensitivity; S3. Based on the relationship between the core permeability and the formation pressure described in step S2, construct a relationship between the core permeability and the conductivity multiplier; S4. Based on the relationship between the core permeability and the formation pressure described in step S2 and the relationship between the core permeability and the conductivity multiplier described in step S3, modify the conductivity multiplier in the rock compressibility table of the Petrel software to achieve stress sensitivity characterization; S5: Use the non-steady-state method to conduct gas-water two-phase permeability experiments at different permeabilities and water saturations, determine the temporal variation of the permeability endpoint, and establish a correlation between the permeability endpoint and formation pressure and permeability; S6: Based on the relationship between the conductivity multiplier and the core permeability, a correlation equation is established between the phase permeability endpoint and the formation pressure and the conductivity multiplier; S7: Write Python language code in the INTERSECT numerical simulator of Petrel software to define the output of the required properties of phase permeability time variation and output the pressure and conductivity multipliers of each grid at all times after numerical simulation; S8: Write numerical simulation code based on Python, convert the correlation between the phase permeability endpoint and the formation pressure and conductivity multiplier into code language, and put it into the INTERSECT numerical simulator to realize the representation of the time-varying phase permeability in the numerical model; The phase permeability endpoint is characterized by irreducible water saturation. In step S5, the correlation formula between the phase permeability endpoint and the formation pressure and permeability is: （Ⅲ） In formula (III), S wc is the bound water saturation, %; K is the core permeability, mD; P is the formation pressure, MPa; a, b, c, d are constants; The values ​​of a, b, c, and d of the gas field to be simulated are obtained by substituting the experimental results of the gas-water two-phase permeability experiment under different permeabilities and water saturations in step S5 into the correlation formula of formula (III).

2. The method for numerical simulation of tight sandstone gas reservoirs considering time-varying reservoir parameters according to claim 1, characterized in that: In step S2, the mathematical model of the influence of formation pressure on the core permeability is: （Ⅰ） In formula (I), K is the core permeability, mD; K 0 is the initial permeability of the reservoir, mD; exp is the exponential function with the natural number e as the base; P is the formation pressure; 、 c is a constant; The gas field to be simulated 、 c The value is obtained by substituting the stress sensitivity experimental results of S1 into the model of formula (I).

3. The method for numerical simulation of tight sandstone gas reservoirs considering time-varying reservoir parameters according to claim 2, characterized in that: In formula (I), -1< <0, 0 <c<1。 4. The method for numerical simulation of tight sandstone gas reservoirs considering time-varying reservoir parameters according to claim 1, characterized in that: In step S3, the relationship between the core permeability and the conductivity multiplier is: （Ⅱ） In formula (II), K is the core permeability, mD; K e The core permeability corresponding to the relationship between core permeability and formation pressure fitted in step S2 is mD; T m is the conductivity multiplier.

5. The method for numerical simulation of tight sandstone gas reservoirs considering time-varying reservoir parameters according to claim 1, characterized in that: In formula (III), -1 <a<0,0<b<1,-1<c<0,0<d<1。 6. A numerical simulation method for tight sandstone gas reservoirs taking into account time-varying reservoir parameters according to claim 1, characterized in that: The time-varying reservoir parameters include stress sensitivity and phase permeability time-varying.

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