Weak gel reservoir four-dimensional well testing interpretation method and device

Abstract

The application discloses a weak gel profile control reservoir four-dimensional well testing interpretation method and device, which is based on fluid percolation theory in porous media, and is a kind of weak gel profile control reservoir four-dimensional well testing forward and inversion algorithm established and solved according to mathematical physics method.According to the method, the fluid parameter distribution in the detection radius range around the well can be inverted by using the measured pressure data of the well testing after weak gel profile control, and then the weak gel viscosity distribution after profile control is obtained.Through the experimental data of the relationship between weak gel viscosity and concentration, the weak gel concentration distribution in the detection radius range around the well can be inversely solved by using the weak gel viscosity distribution.And through the weak gel concentration peak value in the distribution graph, the weak gel profile control plugging position can be determined.The weak gel profile control reservoir four-dimensional well testing forward and inversion algorithm established by the application solves the problem that the weak gel concentration distribution of the weak gel profile control reservoir plugging position is difficult to determine, and provides a theoretical basis for the next step of profile control scheme.

Description

Technical Field

[0001] This invention belongs to the field of well test interpretation for weak gel-driven reservoirs, and relates to a four-dimensional well test interpretation method and apparatus for weak gel-driven reservoirs. Background Technology

[0002] The Bohai Oilfield is characterized by complex reservoir types and severe heterogeneity, primarily relying on water injection to enhance oil recovery. However, the long-term scouring effect of injected water causes it to surge along high-permeability channels, exacerbating inter-layer dynamics. Conventional chemical injection methods have shown poor moderating effects. Weak gel deep-seated moderating technology, as an enhanced oil recovery technique to improve the development of severely heterogeneous oilfields, has demonstrated good moderating effects and is widely used in the Bohai Oilfield.

[0003] After cross-linking, the polymer solution forms a network structure. Water molecules in the solution are "locked" by the cross-linked polymer network, losing some of the fluidity of the liquid but gaining some solid-like fixation properties, ultimately forming a hydrogel, or weak gel, which is between a liquid and a solid. Weak gels can enter high-permeability rock formations along water flow channels. After gelation, they can block dominant pores to a certain extent, thereby changing the direction of waterflooding and utilizing crude oil contained in low-permeability rock formations, thus achieving profile control. After blocking for a period of time, due to increased displacement pressure, the weak gel may lose its blocking ability in the pore and instead migrate forward with the water flow, exerting an oil displacement effect until it migrates to a smaller pore, where it can block again.

[0004] After weak gel-based flood control operations, the effectiveness is typically judged based on whether the water cut of the affected well decreases, thus determining the next flood control plan. However, when formulating a flood control plan, due to the non-uniform influx of weak gel, its migration pattern is unclear, the sealing location is unknown, and the next weak gel injection volume is difficult to determine. Therefore, for weak gel-based flood control reservoirs with strong heterogeneity, developing a flood control effect identification method can provide theoretical guidance for flood control plan formulation. Currently, flood control effect identification methods are generally divided into two categories based on the evaluation object (injection wells and production wells). For evaluating the effectiveness of flood control in production wells, commonly used methods include water drive curve evaluation, decline curve evaluation, and net oil increase and water decrease methods; for evaluating the effectiveness of flood control in injection wells, it is usually evaluated through water absorption profiles, apparent water absorption index, Hall curves, and pressure changes.

[0005] Existing methods can efficiently evaluate the effect of modulated driving, but their application in the formulation of modulated driving schemes is limited because they cannot directly evaluate the distribution of weak gels and the location of blockage. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a four-dimensional well test interpretation method and apparatus for weak gel-driven reservoirs. This method incorporates the radially non-uniformly distributed fluid viscosity into the seepage mathematical model, establishing a four-dimensional well test interpretation method for weak gel-driven reservoirs. It solves the problem that existing methods cannot directly identify the distribution of weak gel within the water injection well's affected area, making it difficult to formulate subsequent drive control strategies.

[0007] The technical solution for achieving the objective of this invention is as follows:

[0008] The first aspect of this invention provides a four-dimensional well test interpretation method for weak gel-modified oil reservoirs. This method uses measured pressure data from well tests after weak gel-modified oil flow to invert the distribution of fluid parameters within the wellbore detection radius, thereby determining the viscosity distribution of the weak gel after modification. Using experimental data on the relationship between weak gel viscosity and concentration, the concentration distribution of the weak gel within the wellbore detection radius is inverted from the viscosity distribution. Finally, the location of the weak gel-modified oil flow blockage is determined by the peak concentration of the weak gel in the distribution map. The method includes the following steps:

[0009] Step 1: Based on the measured pressure data from the well test after weak gel displacement adjustment, calculate t using equation (61). n Detection radius r at any time n corresponding Thus, the fluid parameter distribution is obtained;

[0010] The expression for calculating the distribution of fluid parameters is:

[0011]

[0012] Where t n For the nth time step, d;r n denoted as , where is the detection radius (m); k is the reservoir permeability (mD); h is the reservoir thickness (m); μ is the fluid viscosity (mPa·s); n is the time step; and δ is the microvariable.

[0013] These are the fluid parameters at the nth time step;

[0014] Step Two, Through The value of t after adjustment can be obtained. n Detection radius r at any time n The corresponding weak gel viscosity μ n distributed;

[0015] Step 3: Based on the weak gel viscosity μ in Step 2 n Distribution curves were plotted to determine the viscosity μ of the weak gel in the injected well peri-well reservoir. n With the detection radius r n A plan view of the distribution;

[0016] Step 4: Test the viscosity of weak gels of different concentrations after stabilization through experiments, plot the relationship curve between the stable viscosity and concentration of the weak gel, and regress the relationship between viscosity and concentration.

[0017] Step 5: Combine the adjusted t obtained in Step 3 n Detection radius r at any time n The corresponding weak gel viscosity μ n The distribution and the relationship between viscosity and concentration regressed in step four are used to back-calculate the concentration of weak gel in the formation after the displacement as a function of the detection radius r. n The distribution curve;

[0018] Step 6: By analyzing the concentration distribution curve of the weak gel in the formation after the wellbore displacement, plot the concentration of the weak gel in the reservoir around the injection well as a function of the detection radius r. n The distribution planar map can be used to determine the concentration of weak gel in the reservoir and identify the area with the highest concentration of weak gel.

[0019] Furthermore, the detection radius r n The calculation formula is:

[0020]

[0021] The meanings of each parameter in the formula are as follows: Let mD be the probe front permeability at the nth time step; t n For the nth time step, d; φ is the porosity, decimal; μ is the fluid viscosity, mPa·s; C t This is the overall compression factor, a decimal.

[0022] A second aspect of the present invention provides an apparatus for implementing the above-described four-dimensional well test interpretation method for weak gel-driven reservoirs, comprising:

[0023] The fluid parameter calculation module calculates t based on the measured pressure data from well tests after weak gel displacement. n Detection radius r at any time n corresponding Thus, the fluid parameter distribution is obtained;

[0024] The viscosity distribution calculation module calculates t after the drive adjustment. n Detection radius r at any time n The corresponding weak gel viscosity μ n Distribution, plotting the weak gel viscosity μ in the injection well peri-well reservoir. n With the detection radius r n A plan view of the distribution;

[0025] The viscosity-concentration conversion module uses experiments to test the viscosity of weak gels at different concentrations after stabilization, plots the relationship between the stable viscosity and concentration of the weak gel, and regresses the relationship between viscosity and concentration.

[0026] The concentration distribution calculation module, combined with the obtained t after adjustment, n Detection radius r at any time n The corresponding weak gel viscosity μ n The distribution and the relationship between viscosity and concentration regressed in step four are used to back-calculate the concentration of weak gel in the formation after the displacement as a function of the detection radius r. n Distribution curves; by plotting the concentration distribution curves of weak gel in the formation after wellbore displacement, the concentration of weak gel in the injection well perimeter reservoir as a function of the detection radius r is determined. n A plan view of the distribution;

[0027] The plugging location calculation module is based on the weak gel concentration varying with the detection radius r. n The distribution planar map determines the concentration of weak gel in the reservoir and identifies the region with the highest concentration of weak gel.

[0028] A third aspect of the present invention is to provide an electronic device, including a memory and a processor; wherein:

[0029] Memory: Used to store instructions that can be executed by the processor;

[0030] Processor: The processor is configured to perform the following: using the measured pressure data from the well test after weak gel regulation to invert the distribution of fluid parameters within the well perimeter detection radius, and then to obtain the weak gel viscosity distribution after regulation; using experimental data on the relationship between weak gel viscosity and concentration, using the weak gel viscosity distribution to invert the weak gel concentration distribution within the well perimeter detection radius; and using the weak gel concentration peak value in the distribution chart to determine the location of weak gel regulation and plugging.

[0031] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program for causing the computer to execute the aforementioned four-dimensional well test interpretation method for weak gel-driven reservoirs.

[0032] Advantages and beneficial effects of the present invention:

[0033] Based on the four-dimensional well test interpretation method for weak gel-driven reservoirs of the present invention, combined with experimental data on the viscosity and concentration of weak gel and well test data, the distribution of weak gel within the injection well's coverage area can be explained. This method can provide theoretical guidance for the formulation of the next step of the drive scheme. Attached Figure Description

[0034] Figure 1 A graph showing the injection well pressure and pressure derivative.

[0035] Figure 2 This is an inversion curve showing the relationship between the viscosity and radius of the weak gel in the reservoir near the injection well;

[0036] Figure 3This is a field diagram showing the viscosity distribution of the weak gel in the reservoir near the injection well.

[0037] Figure 4 To experimentally test the viscosity change curves of weak gels of different concentrations over time;

[0038] Figure 5 To test the regression relationship between the stable viscosity and concentration of weak gels;

[0039] Figure 6 This is an inversion curve showing the relationship between the weak gel concentration and radius of the reservoir near the injection well;

[0040] Figure 7 This is a field diagram showing the viscosity distribution of the weak gel in the reservoir near the injection well. Detailed Implementation

[0041] The present invention will be further described in detail below through specific embodiments. The following embodiments are merely descriptive and not limiting, and should not be used to limit the scope of protection of the present invention.

[0042] This invention provides a four-dimensional well test interpretation method for weak gel-driven oil displacement reservoirs, the method comprising the following steps:

[0043] Step 1: Set up a well with a fixed production rate in an infinitely large radially heterogeneous oil reservoir. The upper and lower boundaries are closed. The reservoir fluid mobility is a function of the radial distance. The formation porosity, thickness, rock compressibility, and fluid compressibility are constants. The effects of gravity and capillary force are ignored.

[0044] Step 2: Establish a dimensionless seepage mathematical model. Based on the assumptions in Step 1, the dimensionless seepage mathematical model for a single well producing a constant output in an infinitely large radially heterogeneous reservoir is established as follows:

[0045]

[0046] The initial conditions are as follows:

[0047]

[0048] The inner boundary conditions are as follows:

[0049]

[0050] The outer boundary conditions are as follows:

[0051]

[0052] In the formula: r D k is the dimensionless radial distance. D (r D ) represents the dimensionless formation permeability, which is a function of the dimensionless radial distance; p Dt is the dimensionless formation pressure; D p is dimensionless time; D (r D ,t D ) represents the dimensionless formation pressure, which is a function of the dimensionless radial distance and the dimensionless time.

[0053] Step 3: Define the dimensionless variables in the dimensionless seepage mathematical model. The dimensionless variables in the dimensionless seepage mathematical model in Step 2 are defined as follows:

[0054] Dimensionless time:

[0055]

[0056] Dimensionless radius:

[0057]

[0058] Dimensionless pressure:

[0059]

[0060] Dimensionless bottom hole pressure:

[0061]

[0062] Dimensionless bottom hole pressure derivative:

[0063]

[0064] Dimensionless radial permeability

[0065]

[0066] in, This is for reference penetration rate.

[0067] In the formula: Reference permeability, mD; t, time, d; φ, porosity, decimal; C t The comprehensive compressibility coefficient is a decimal; μ is the viscosity of natural gas, in mPa·s; r w r is the wellbore radius (m); r is the radial distance (m); h is the reservoir thickness (m); p i p(r,t) is the initial formation pressure, MPa; p(r,t) is the formation pressure, a function of radial distance and time, MPa; q is the natural gas production, m. 3 / d; B is the natural gas formation volume factor, a decimal; p wD p is the dimensionless bottom hole flowing pressure; w The bottom hole flowing pressure is in MPa; p' wD is the dimensionless bottomhole flowing pressure derivative; k(r) is the formation permeability, a function of radial distance, mD.

[0068] Step 4: Solve the mathematical model of seepage. Use perturbation theory and Laplace theory to solve the radial heterogeneous seepage diffusion equations, and obtain analytical solutions for approximate pressure and pressure derivative under arbitrary permeability distribution.

[0069] First, define the following penetration rate:

[0070]

[0071] In the formula, ε is a small quantity that represents the formation permeability fluctuating slightly around the reference permeability.

[0072] The dimensionless diffusion equation is written in operator form:

[0073]

[0074] According to Laplace theory, the Laplace transform of equation (12) is:

[0075]

[0076] According to perturbation theory, the bottom-hole pressure solution in Lagrange space can be expressed as:

[0077]

[0078] This equation represents the diffusion equation for a homogeneous oil reservoir where permeability and angle remain unchanged. This represents the zeroth-order solution of the pressure in a dimensionless homogeneous reservoir in Laplace space. Using the work of Van Everdingen and Hurst, the zeroth-order solution can be expressed as:

[0079]

[0080] In the formula: s—Laplace variable, and the characters marked with "~" at the top represent Laplace space variables.

[0081] K0 is a zeroth-order modified Bessel function of the second kind;

[0082] K1 is a first-order modified Bessel function of the second kind.

[0083] Based on the analysis of the planar heterogeneous mathematical model, the first-order solution of the dimensionless first perturbation pressure in Laplace space can be conveniently derived.

[0084]

[0085] When r D When = 1, the bottom hole pressure is obtained:

[0086]

[0087] Simplify ψ using the following relationship D1 The expression:

[0088]

[0089]

[0090] Let t→∞ (s→0), that is, when the time is long enough, we can obtain approximate solutions for the dimensionless bottom hole pressure and the dimensionless bottom hole pressure derivative in real space through Laplace numerical inversion:

[0091]

[0092]

[0093] In the formula: s is the Laplace variable, and the character marked with "~" at the top indicates the Laplace space variable; K0 is the zeroth-order modified Bessel function of the second kind; K1 is the first-order modified Bessel function of the second kind.

[0094] Step 5: Establish an inversion algorithm for parameters such as reservoir viscosity before and after weak gel regulation.

[0095] Define k(r) as the harmonic mean of k(r, θ) at position r:

[0096]

[0097] Dimensionless variables are defined as:

[0098]

[0099]

[0100]

[0101]

[0102] Oliver's solution can be expressed as:

[0103]

[0104] The modified kernel function:

[0105]

[0106] Although Oliver assumed that the permeability was slightly perturbed around the reference permeability k when using perturbation theory for the derivation, the study shows that when the permeability varies greatly, such perturbation solutions may still give reasonable approximate solutions.

[0107] when At that time, under homogeneous reservoir conditions, we can obtain from equation (27):

[0108]

[0109] Another situation is For homogeneous reservoirs with a constant permeability k1, equation (29) becomes:

[0110] Based on the characteristics of the well test curves, the magnitude of the derivative value in the radial flow stage is positively correlated with permeability. Therefore, the pressure derivative curve characteristics satisfy the following relationship:

[0111]

[0112] From equations (27) and (30), we can derive:

[0113]

[0114] For the convenience of the following derivation, let K1 * (r D , t D ) = 2K * (r D , t D ),but:

[0115]

[0116] Substituting equation (32) into equation (27), we get:

[0117]

[0118] The dimensionless pressure derivatives from equations (25) and (26) can be expressed as:

[0119]

[0120] Combining equations (26), (33), and (34), we obtain

[0121]

[0122] As can be seen from equation (35), the reference permeability is only implicit in dimensionless time. Combining the characteristics of equation (35), the reference fluid parameter is redefined and denoted as...

[0123]

[0124] because It is related to the pressure derivative at a specific moment, and is called These are instantaneous fluid parameters.

[0125] From equation (35), we can derive...

[0126]

[0127] Equation (37) shows that the instantaneous fluid parameters It is a harmonic mean with a weighted function. Therefore, the correct definition of formation mean fluid parameters that vary with radial distance (in the above derivation, it is assumed that the fluid parameters are functions of radial distance r and the variation with angle θ is not considered) should be based on the harmonic mean.

[0128] because It contains more reservoir information; research shows that if... Replace dimensionless time t D In The result obtained from equation (37) will be more accurate.

[0129] Define dimensionless pseudotime:

[0130]

[0131] For dimensionless time estimation, To detect the frontal permeability, t is time, r w Where is the radius of the wellbore.

[0132] Then equation (37) becomes:

[0133]

[0134]

[0135] In order to The permeability k(r) is obtained from the given information. Assume that k(r) can be expressed as a discrete piecewise function, i.e., when r... i-1 <r≤r i (i=1, 2,...,n), k(r)=k i Thus, the essence of this inversion algorithm is to solve for the value at time t. n Detection radius r at any time n Corresponding Detection radius r n Defined as:

[0136]

[0137] Let t be the probe front permeability corresponding to the nth time step. n For the nth time step, φ is the porosity, μ is the fluid viscosity, and C... t This is the overall compression coefficient.

[0138] definition:

[0139]

[0140]

[0141] At t=t n At that moment, equation (39) is transformed into:

[0142]

[0143] From equation (32), we can obtain:

[0144]

[0145] Therefore, equation (44) can be rewritten as:

[0146]

[0147] From equation (42), we can obtain:

[0148]

[0149] Substituting equation (47) into equation (46), we get:

[0150]

[0151] We can first find

[0152]

[0153] In equation (49), t1 represents the first time point where a reliable pressure derivative value exists. Then, using equation (3-40), the result can be obtained. Equation (48) can be transformed into:

[0154]

[0155] because

[0156]

[0157] The integral in equation (50) can be expressed as:

[0158]

[0159] According to the definition of dimensionless Boltzmann variables, the corresponding instantaneous dimensionless Boltzmann variables are defined as follows:

[0160]

[0161] From equation (52), we get:

[0162]

[0163] Substituting equation (53) into equation (51), we get:

[0164]

[0165] Oliver's research indicates that in the region Kernel function K outside of the local area * (r D ,t D )≈0. Here, the inner radius of Oliver's dimensionless probe is defined as follows:

[0166]

[0167] The corresponding outer detection radius is:

[0168]

[0169] For any function F(r) D The following relationship exists:

[0170]

[0171] The accuracy of equation (57) depends on the expression of the function. Studies have confirmed that when the function F(r) D When ) is a constant, equation (57) has sufficiently high accuracy.

[0172] Based on the analysis of equations (52) to (57), equation (51) can be further expressed as:

[0173]

[0174] If we let:

[0175]

[0176] Then equation (58) can be simplified as:

[0177]

[0178] According to the definition in equation (42), the expression for calculating the distribution of fluid parameters is:

[0179]

[0180] t n For the nth time step, r n Where is the detection radius, k is the reservoir permeability, h is the reservoir thickness, μ is the fluid viscosity, n is the time step, and δ is the microvariable.

[0181] Step 6: Using the measured pressure data from the well test after weak gel displacement, calculate the instantaneous fluid parameters using equation (34).

[0182] Step 7: Calculate the detection radius r according to equation (41). n .

[0183] Step 8: Combining Figure 1 The time corresponding to the horizontal axis of the (injection well pressure and pressure derivative curve) is calculated by equation (38) for the dimensionless pseudo-time.

[0184] Step 9: Calculate the fluid parameters using equation (61). Then you can get t n Detection radius r at any time n corresponding This allows us to obtain the fluid parameter distribution.

[0185] Step 10, Pass The value of t after adjustment can be obtained. n Detection radius r at any time n The corresponding weak gel viscosity μ n distributed( Figure 2 ).

[0186] Step 11: Based on the weak gel viscosity μ in Step 10 n Distribution curves were plotted to determine the viscosity μ of the weak gel in the injected well peri-well reservoir. n With the detection radius r n Distribution plan ( Figure 3 ).

[0187] Step 12: The viscosity of the stabilized weak gel at different concentrations was tested experimentally. This experiment tested the viscosity of the stabilized weak gel at concentrations ranging from 1000 to 4000 mg / L. Figure 4 ).

[0188] Step 13: Based on the viscosity of the weak gel after stabilization at different concentrations tested in Step 12, a curve showing the relationship between the stable viscosity and concentration of the weak gel was plotted, and the relationship between viscosity and concentration was regressed. Figure 5 ).

[0189] Step Fourteen: Combine the adjusted t obtained in Step Ten n Detection radius r at any time n The corresponding weak gel viscosity μ n distributed( Figure 2 The relationship between viscosity and concentration regressed in step thirteen ( Figure 5 This allows for the calculation of the concentration of weak gel in the formation after the adjustment and displacement, as a function of the detection radius r. n Distribution curve ( Figure 6 ).

[0190] Step 15: Analyze the concentration distribution curve of the weak gel in the formation after the displacement process. Figure 6 This allows us to plot the concentration of weak gel in the injection well perimeter reservoir as a function of the detection radius r. n Distribution plan ( Figure 7 Based on the legend in the figure, the concentration of weak gel in the reservoir can be determined. That is, the red area is the area with the best sealing effect of weak gel, which is about 16 to 30 meters away from the well.

[0191] The present invention further provides an apparatus for implementing the above-mentioned four-dimensional well test interpretation method for weak gel-driven reservoirs, comprising:

[0192] The fluid parameter calculation module calculates t based on the measured pressure data from well tests after weak gel displacement. n Detection radius r at any time n corresponding Thus, the fluid parameter distribution is obtained;

[0193] The viscosity distribution calculation module calculates t after the drive adjustment. n Detection radius r at any time n The corresponding weak gel viscosity μ n Distribution, plotting the weak gel viscosity μ in the injection well peri-well reservoir. n With the detection radius r n A plan view of the distribution;

[0194] The viscosity-concentration conversion module uses experiments to test the viscosity of weak gels at different concentrations after stabilization, plots the relationship between the stable viscosity and concentration of the weak gel, and regresses the relationship between viscosity and concentration.

[0195] The concentration distribution calculation module, combined with the obtained t after adjustment, n Detection radius r at any time n The corresponding weak gel viscosity μ n The distribution and the relationship between viscosity and concentration regressed in step four are used to back-calculate the concentration of weak gel in the formation after the displacement as a function of the detection radius r. n Distribution curves; by plotting the concentration distribution curves of weak gel in the formation after wellbore displacement, the concentration of weak gel in the injection well perimeter reservoir as a function of the detection radius r is determined. n A plan view of the distribution;

[0196] The plugging location calculation module is based on the weak gel concentration varying with the detection radius r. n The distribution planar map determines the concentration of weak gel in the reservoir and identifies the region with the highest concentration of weak gel.

[0197] The present invention further provides an electronic device, including a memory and a processor; wherein:

[0198] Memory: Used to store instructions that can be executed by the processor;

[0199] Processor: The processor is configured to perform the following: using the measured pressure data from the well test after weak gel regulation to invert the distribution of fluid parameters within the well perimeter detection radius, and then to obtain the weak gel viscosity distribution after regulation; using experimental data on the relationship between weak gel viscosity and concentration, using the weak gel viscosity distribution to invert the weak gel concentration distribution within the well perimeter detection radius; and using the weak gel concentration peak value in the distribution chart to determine the location of weak gel regulation and plugging.

[0200] The present invention further provides a computer-readable storage medium storing a computer program for causing the computer to execute the above-described four-dimensional well test interpretation method for weak gel-driven reservoirs.

[0201] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the inventive concept, and these all fall within the protection scope of the present invention.

Claims

1. A weak gel profile control reservoir four-dimensional well test interpretation method, characterized in that, The well testing measured pressure data after using weak gel profile control is inversed to obtain the fluid parameter distribution in the well exploration radius range, and then the weak gel viscosity distribution after profile control is obtained; the weak gel viscosity distribution is used to inversely obtain the weak gel concentration distribution in the well exploration radius range through the experimental data of the relationship between weak gel viscosity and concentration; and the weak gel concentration peak in the distribution map is used to determine the weak gel profile control position; including the following steps: Step one, according to the weak gel profile control after the well testing measured pressure data, by (61) formula to calculate t n Instantaneous detection radius r n Corresponding So as to obtain the fluid parameter distribution; The expression for calculating the fluid parameter distribution is: where: t n is the nth time step, r n is the detection radius, k is the reservoir permeability, h is the reservoir thickness, μ is the fluid viscosity, n is the time step, and δ is the perturbation variable; fluid parameters for the nth time step; Step two, by The t n The radius r n The corresponding weak gel viscosity μ n Distribution; Step three, the weak gel viscosity μ n distribution curve, plotting the weak gel viscosity μ n with the detection radius r n plan view of the distribution; Step four, the viscosity of the weak gel after stabilization is tested through experiment, the relationship curve between the stabilized viscosity of the weak gel and the concentration is drawn, and the relationship between the viscosity and the concentration is regressed; Step five, combining the weak gel viscosity μ corresponding to the detected radius r at the time t after the profile control n Step five, combining the weak gel viscosity μ corresponding to the detected radius r at the time t after the profile control n Step five, combining the weak gel viscosity μ corresponding to the detected radius r at the time t after the profile control n Step five, combining the weak gel viscosity μ corresponding to the detected radius r at the time t after the profile control n Step five, combining the weak gel viscosity μ corresponding to the detected radius r at the time t after the profile control Step six, draw the weak gel concentration distribution curve of the reservoir around the injection well with the detection radius r n The plan view of the distribution can be used to determine the weak gel concentration in the reservoir and to identify the area with the maximum weak gel concentration.

2. The weak gel profile control reservoir four-dimensional well test interpretation method according to claim 1, characterized in that, The detection radius r n The calculation formula is: where: is the probe front permeability at the nth time step, t n is the nth time step, φ is the porosity, μ is the fluid viscosity, C t is the compressibility.

3. The device for reservoir four-dimensional well test interpretation of weak gel profile control oil according to any one of claims 1-2, characterized in that, Including: Fluid parameter calculation module, according to the well testing measured pressure data after weak gel profile control, calculate t n Instantaneous detection radius r n Corresponding So as to obtain the fluid parameter distribution; viscosity distribution calculation module, calculating the viscosity of weak gel μ n corresponding to the detection radius r n corresponding to the detection radius r n distribution, drawing the weak gel viscosity μ n with the detection radius r n distribution plan; The viscosity and concentration conversion module, the viscosity of the weak gel after stabilization is tested through experiment, the relationship curve between the stabilized viscosity of the weak gel and the concentration is drawn, and the relationship between the viscosity and the concentration is regressed; a concentration distribution calculation module, combining the detected concentration of the weak gel at time t n the detected radius r n the corresponding viscosity μ of the weak gel n the relationship between the viscosity and the concentration of the weak gel, and inversely calculating the concentration of the weak gel in the formation after the profile control and flooding, the detected radius r n the curve of the concentration distribution of the weak gel in the formation after the profile control and flooding; and n the planar graph of the concentration distribution of the weak gel in the reservoir around the injection well The blocking position calculation module determines the weak gel concentration in the reservoir according to the weak gel concentration distribution with the detection radius r n The plan view of the distribution determines the weak gel concentration in the reservoir, and the area with the maximum weak gel concentration is determined.

4. An electronic device, comprising: Including a memory and a processor; wherein: The memory is used to store instructions executable by the processor; The processor is configured to execute the weak gel profile control reservoir four-dimensional well testing interpretation method of any one of claims 1-2.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is used to make the computer execute the weak gel profile control reservoir four-dimensional well testing interpretation method of any one of claims 1-2. The computer readable storage medium stores a computer program, and the computer program is used to make the computer execute the weak gel profile control reservoir four-dimensional well testing interpretation method of any one of claims 1-2.

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