Gel particle and reservoir matching optimization method

Abstract

The invention relates to the field of oil and gas reservoir development, and discloses a gel particle and reservoir matching optimization method which specifically comprises the following steps: (1) determining pore throat radius distribution of a target reservoir; (2) customizing a three-channel parallel micro-fluidic chip; (3) carrying out a PIV experiment to obtain flow fields of three channels in different time periods; (4) calculating the shunting rates of the three channels under different injection volumes according to the flow velocity, and drawing a curve of the shunting rates; (5) substituting the shunting rate data into a three-channel section optimization value formula, and calculating a section optimization value; and (6) drawing a particle size-profile optimization value curve, and preferably selecting the gel particle size with the highest matching property with the reservoir. The method has the multi-target detection capability, can visually observe the displacement phenomenon, has high-reliability judgment criteria and judgment parameters, is suitable for a chemical flooding method containing gel particles, and is used for optimizing the matching property of the gel particles and a reservoir.

Description

Technical Field

[0001] This invention falls under the technical field of oil and gas field development engineering, specifically relating to a method for optimizing the matching of gel particles with reservoirs. Background Technology

[0002] Many old oilfields in my country have entered the late stage of development, and these oilfields generally face the problem of high water cut. Due to severe reservoir heterogeneity, a large amount of residual oil still exists in low-permeability areas. To improve oil recovery, polymer flooding is widely used in conventional oilfield development. However, the capacity of ordinary polymer flooding is limited; after polymer flooding, about half of the crude oil remains in the formation, and the profile optimization capability of polymer flooding is weak, with limited effect on improving the oil flow profile. In contrast, gel particles have good transport capabilities and can preferentially block high-permeability areas, improving the oil displacement profile and thus significantly improving crude oil recovery. However, the reservoir properties of different oilfields vary greatly, and the matching relationship between pore radius and gel particle size seriously affects the effect of gel flooding. If the gel particle size is too large, it will completely block the channels, affecting the normal production of crude oil; while if the particle size is too small, the blocking ability is insufficient, and the effect of improving the oil displacement profile is not obvious. There are roughly four existing methods for particle selection: ① Core displacement experiments, which use multiple cores with different permeabilities to conduct displacement experiments, and evaluate particle matching by combining the ratio of particle size to pore throat diameter with the pressure curve during injection. This method uses multiple cores characterized by permeability, is cumbersome and complex, and cannot observe the flow of gel particles in the core, resulting in a single matching criterion; ② Microscopic visualization experiments, which inject gel into a microscopic visualization glass core model and observe the morphology of gel particles as they pass through pore throats of different sizes, classifying them into smooth passage, deformed passage, and throat blockage, and using this to evaluate particle size matching. This method only observes particle morphology, uses a small number of gel particles in the experiment, is highly random, and the evaluation results are unreliable; ③ Numerical simulation technology, which performs extensive calculations on a numerical model to observe gel migration and blockage. This method is highly dependent on the model, computationally difficult, and the simulation results need to be verified by experiments to ensure accuracy; ④ Relying on historical experience, this method is highly subjective, varies greatly between different reservoirs, and the evaluation results are inaccurate and have strong limitations.

[0003] Therefore, a method is proposed to optimize the gel particle size to match the reservoir properties, enabling it to form an effective plug in high-permeability zones while maintaining unobstructed low-permeability zones. This method allows for direct observation of the improved displacement effect of the gel particles and provides highly reliable evaluation criteria and parameters, which is crucial for improving the oil displacement profile, achieving better oil displacement results, and increasing oil recovery.

[0004] Based on the above analysis, gel flooding faces two major challenges: first, selecting gel particles with a high degree of matching with the target reservoir size; and second, current methods for optimizing gel particles are complex, not intuitive, and lack effective evaluation methods due to the limited range of factors considered in the matching assessment. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the compatibility of gel particles with reservoirs, finding the optimal particle size for the gel particles best suited to the reservoir, and providing a specific optimization method. The method is implemented according to the following steps:

[0006] (1) Determine the pore throat radius distribution of the target reservoir. Take a standard core of the target reservoir and obtain mercury intrusion data through indoor mercury intrusion experiments. Calculate the pore throat radius data of the reservoir and draw a frequency variation diagram of the pore throat radius distribution based on this.

[0007] (2) Custom-made microfluidic chip: Based on the frequency variation diagram of reservoir pore throat radius distribution, select the radius values ​​of the three pore throats with the highest frequency and the frequency of occurrence being 0.15 times the highest frequency, and based on these three radii, customize a microfluidic chip with three parallel channels of high permeability, medium permeability and low permeability.

[0008] (3) Conduct PIV experiments to obtain flow field data of the three channels at different time periods. Prepare various injection solutions with different gel particle sizes. Conduct water injection, gel solution injection, and subsequent water injection experiments under the PIV velocity measurement system. For each 0.25PV of solution displaced, the flow velocity of the three channels is measured once using PIV. Finally, the flow field velocity data of the three microchannels at different times throughout the process are obtained.

[0009] (4) Obtain the flow rate change curve of the three microchannels. Based on the flow rate data of the three microchannels at different times throughout the process, calculate the flow rate of the three channels under different injection volumes, and then obtain the flow rate data of the three channels throughout the process. Based on this, draw the change curve of injection pore volume multiple - flow rate.

[0010] (5) Determine the profile optimization value of gels with different particle sizes in a three-channel parallel microfluidic chip. Obtain the flow rate data of the initial and subsequent water injection in the high-permeability, medium-permeability, and low-permeability channels from the displacement volume-flow rate curve. Calculate the profile optimization value corresponding to gels with different particle sizes using the three-channel profile optimization value calculation formula.

[0011] (6) Optimize the gel particle size with the highest compatibility with the reservoir. Plot a particle size-optimized profile value curve with gel particle size as the abscissa and profile optimization value as the ordinate. The abscissa value of the highest point of the curve is the particle size of the gel particle with the highest compatibility with the reservoir. Details are as follows:

[0012] A reservoir core with a diameter of 25 mm and a length of 100 mm was taken for an indoor mercury intrusion porosimetry (MIP) test. After the core was evacuated, mercury was gradually pumped into it under pressure until the maximum pressure was reached, and then the pressure was reduced to obtain the capillary force data of the mercury intrusion. The pore throat radius corresponding to the pressure data was calculated by the capillary force formula (Equation (1)).

[0013]

[0014] In the formula, p c σ is the capillary force, MPa; θ is the wetting angle between mercury and the solid surface of the core, N / m; σ is the surface tension between mercury and air.

[0015] The frequency distribution of the pore throat radius is obtained as a normal curve. The pore throat radius with the highest frequency and the frequency 0.15 times the highest frequency is found from the curve. Based on these three pore throat radii, a three-channel parallel microfluidic chip is customized. The three channels are high-permeability, medium-permeability, and low-permeability channels according to their size.

[0016] Twenty gel solutions with different particle sizes and a concentration of 2000 mg / L were prepared. The smallest gel particle size was about 10 times the pore size of the hypotonic channel, and the largest gel particle size was about 10 times the pore size of the hypertonic channel. Liquid was injected under the PIV velocity measurement system with a constant displacement rate of 2 μL / min. First, 1 PV of deionized water was injected into the microfluidic chip, then 2 PV of gel solution with a certain particle size was injected, and finally 2 PV of deionized water was injected. After each displacement of 0.25 PV, the flow rates v1, v2, and v3 of the hypertonic, mesotonic, and hypotonic channels were recorded. The flow rates Q1, Q2, and Q3 of the three microchannels were calculated according to formula (2).

[0017] Q i =v i A i , i = 1, 2, 3 (2)

[0018] In the formula, i = 1, 2, 3 represent high-permeability, intermediate-permeability, and low-permeability channels, respectively; Q i V represents the flow rate of channel i after each displacement of 0.25 PV, in μL / min; v i The flow rate of channel i is 0.25 PV per displacement, in mm / min; A i The cross-sectional area of ​​channel i is mm. 2 ;

[0019] After displacing a 5PV gel solution of a certain particle size, the displacement volume of the gel of that particle size and the change curve of the split rate of the three microchannels were calculated and plotted. The split rate expression is shown in equation (3):

[0020]

[0021] In the formula, η i The shunt rate for channel i;

[0022] The diversion rates of high-permeability, medium-permeability, and low-permeability channels in the early and later stages of water flooding are obtained from the displacement volume-diversion rate change curve. Substituting these values ​​into the calculation formula for the profile optimization value (Equation (4)), the profile optimization value of the gel displacement using this particle size is obtained.

[0023]

[0024] In the formula, f is the optimized profile value; Q hb and Q ha Q represents the flow rates of the initial and subsequent water drives in the high-permeability channel, respectively, in μL / min; mb and Q ma Q represents the flow rates of the initial and subsequent water flooding in the intermediate infiltration channel, respectively, in μL / min; lb and Q la The values ​​represent the flow rates of the initial and subsequent water drives in the low-permeability channel, respectively, in μL / min.

[0025] Plot the gel particle size on the x-axis and the profile optimization value on the y-axis. The particle size corresponding to the highest point of the profile optimization value in the graph is the gel particle size that best matches the reservoir.

[0026] The beneficial effects of this invention are as follows:

[0027] 1. This invention utilizes microfluidic technology to conduct microscopic seepage experiments. At the microscale, the phase field and flow field characteristics of the fluid are measured non-contactly using a PIV velocimetry system, providing accurate flow field distribution. Compared with core-scale particle size matching experiments, the experimental materials are simpler, and the changes in the flow field between channels after the gel particles are injected can be directly observed.

[0028] 2. This invention is based on a microfluidic chip constructed from the pore-throat distribution characteristics of real rock cores. It uses a slug injection gel and quantitatively characterizes the flow field of three channel profiles to screen the particle size of gel particles. Compared with microscopic visualization particle size matching experiments and numerical simulation techniques, particle size screening has lower randomness and more accurate results, and does not require repeated verification. Attached Figure Description

[0029] Figure 1 Flowchart for optimizing the compatibility between gel particles and reservoir;

[0030] Figure 2 A diagram showing the distribution of reservoir pore throat radii;

[0031] Figure 3 This is a schematic diagram of a microfluidic chip;

[0032] Figure 4The graph shows the change in the shunt rate of a three-channel parallel microfluidic chip as a multiple of the injection orifice volume.

[0033] Figure 5 This is a graph showing the relationship between gel particle size and optimized profile values. Detailed Implementation

[0034] 1. Determine the pore throat radius distribution of the target reservoir and customize a three-channel parallel microfluidic chip with different channel diameters: Take a reservoir core with a diameter of 25mm and a length of 100mm and conduct an indoor mercury intrusion test. After the core is evacuated, gradually increase the pressure and pump mercury in, until the maximum pressure is reached and then decrease the pressure to obtain the capillary force data of mercury intrusion. Calculate the pore throat radius corresponding to the pressure data using the capillary force formula (Equation (1)).

[0035]

[0036] In the formula, p c σ is the capillary force, MPa; θ is the wetting angle between mercury and the solid surface of the core, °; σ is the surface tension between mercury and air, N / m;

[0037] The frequency distribution curve of the reservoir pore throat radius is thus obtained as follows: Figure 1 As shown, the pore throat radius with the highest frequency of occurrence in the curve is approximately 15 μm, with a frequency of 16.21%. The frequency of occurrence is 0.15 times the highest frequency, which is approximately 2.3%. There are two pore throat radii corresponding to this 2.3% frequency distribution, namely 30 μm and 5 μm. Based on these three pore throat radius data, a three-channel parallel microfluidic chip suitable for this reservoir is customized, such as... Figure 2 As shown in Table 1, the parameters of the microfluidic chip are as follows:

[0038] Table 1 Microfluidic chip parameters

[0039]

[0040] 2. Conduct PIV experiments to obtain flow field data for the three channels at different time periods and determine the optimal profile values ​​for gels of different particle sizes in a three-channel parallel microfluidic chip: Prepare 20 gel solutions with different particle sizes and a concentration of 2000 mg / L. The minimum particle size of the gel particles is approximately 10 times the pore size of the hypotonic channel, and the maximum particle size is approximately 10 times the pore size of the hypertonic channel. Therefore, the 20 selected gel particle sizes are: 100 μm, 120 μm, 150 μm, 180 μm, 200 μm, 220 μm, 250 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 350 μm, 380 μm, and 400 μm. 420μm, 450μm, 480μm, 500μm, 600μm; Liquid was injected under the PIV velocity measurement system, and the displacement rate was kept constant at 2μL / min. First, 1PV of deionized water was injected into the microfluidic chip, and then 2PV of a gel solution with a certain particle size was injected (250μm gel was used as an example). Finally, 2PV of deionized water was injected. After each 0.25PV displacement, the flow rates v1, v2, and v3 of the hypertonic, mesotonic, and hypotonic channels were processed and recorded. The flow rates Q1, Q2, and Q3 of the three microchannels corresponding to the 250μm gel were calculated according to formula (2). The following is the process of calculating the flow rate of the three channels of the microfluidic chip after injecting 1PV of deionized water and then injecting 0.25PV of 320μm gel solution.

[0041] Q1 = v1A1 = 1 × π × 30 2 =2827.43 μL / min

[0042] Q² = v²A² = 0.5 × π × 15 2 = 353.43 μL / min (6)

[0043] Q3 = v3A3 = 0.2 × π × 5 2 =15.71 μL / min

[0044] In the formula, i = 1, 2, 3 represent high-permeability, intermediate-permeability, and low-permeability channels, respectively; Q i V represents the flow rate of channel i after each displacement of 0.25 PV, in μL / min; v i The flow rate of channel i is 0.25 PV per displacement, in mm / min; A i The cross-sectional area of ​​channel i is mm. 2 ;

[0045] The current diversion rate is:

[0046]

[0047] In the formula, η i The shunt rate for channel i;

[0048] After injecting a 5PV solution with a particle size of 250 μm, the displacement volume of the gel with this particle size was calculated and plotted as a function of the split rate of the three microchannels, as shown in the figure. Figure 3 As shown; from Figure 3 The diversion rates of high-permeability, medium-permeability, and low-permeability channels in the early and later stages of water flooding are obtained. Substituting these values ​​into the calculation formula for the profile optimization value (Equation (4)), the profile optimization value of gel displacement using this particle size can be calculated.

[0049]

[0050] 3. Optimize the gel particle size with the highest compatibility with the reservoir: Calculate the optimized profile values ​​for each of the 20 gel particle sizes. Plot a curve showing the relationship between gel particle size and optimized profile values, with gel particle size on the x-axis and optimized profile values ​​on the y-axis. (Example:) Figure 4 As shown in the figure, the particle size corresponding to the highest point of the profile optimization value is the particle size of the gel particles with the highest compatibility with the reservoir, that is, the gel particles with a particle size of about 293 μm have the highest matching degree with the reservoir.

Claims

1. A method for optimizing the compatibility of gel particles with reservoirs, characterized in that, The specific steps are as follows: (1) Determine the pore throat radius distribution of the target reservoir. Take a standard core of the target reservoir and obtain mercury intrusion data through indoor mercury intrusion experiments. Calculate the pore throat radius data of the reservoir and draw a frequency variation diagram of the pore throat radius distribution based on this. (2) Custom-made microfluidic chip: Based on the frequency variation diagram of reservoir pore throat radius distribution, select the radius values ​​of the three pore throats with the highest frequency and the frequency of occurrence being 0.15 times the highest frequency, and use these three radii as the basis to customize a microfluidic chip with three parallel channels of high permeability, medium permeability and low permeability. (3) Conduct PIV experiments to obtain flow field data of the three channels at different time periods. Prepare various injection solutions with different gel particle sizes. Conduct water injection, gel solution injection, and subsequent water injection experiments under the PIV velocity measurement system. For each 0.25PV of solution displaced, the flow velocity of the three channels is measured once using PIV. Finally, the flow field velocity data of the three microchannels at different times throughout the process are obtained. (4) Obtain the flow rate change curve of the three microchannels. Based on the flow rate data of the three microchannels at different times throughout the process, calculate the flow rate of the three channels under different injection volumes, and then obtain the flow rate data of the three channels throughout the process. Based on this, draw the change curve of injection pore volume multiple - flow rate. (5) Determine the profile optimization value of gels with different particle sizes in a three-channel parallel microfluidic chip. Obtain the flow rate data of the initial and subsequent water injection in the high-permeability, medium-permeability, and low-permeability channels from the displacement volume-flow rate curve. Calculate the profile optimization value corresponding to gels with different particle sizes using the three-channel profile optimization value calculation formula. (6) Select the gel particle size with the highest compatibility with the reservoir. Plot a particle size-profile optimization value curve with gel particle size as the abscissa and profile optimization value as the ordinate. The abscissa value of the highest point of the curve is the particle size of the gel particle with the highest compatibility with the reservoir.

2. The method for optimizing the compatibility of gel particles with reservoirs according to claim 1, characterized in that, The specific steps for determining the target reservoir pore throat radius distribution in step (1) are as follows: A standard core sample with a diameter of 25 mm and a length of 100 mm from the target reservoir was taken for an indoor mercury intrusion porosimetry (MIP) test. First, the core was evacuated and saturated with mercury. Mercury was then gradually pumped into the core under increasing pressure until the pressure gauge read the maximum pressure. The pressure was then reduced to obtain the capillary force data of the mercury intrusion. The pore throat radius corresponding to the pressure data was calculated using the capillary force formula (Equation (1)). In the formula, p c σ is the capillary force, MPa; θ is the wetting angle between mercury and the solid surface of the core, N / m; σ is the surface tension between mercury and air. Plotting the pore throat radius on the x-axis and the pore diameter distribution frequency on the y-axis yields a curve showing the frequency distribution of the pore throat radius. This curve visually illustrates the pore throat radius distribution of the target reservoir.

3. The method for optimizing the compatibility of gel particles with reservoirs according to claim 1, characterized in that, The specific steps for customizing the microfluidic chip in step (2) are as follows: Based on the reservoir pore throat radius distribution map obtained in step 2, find the values ​​of the three pore throat radii that appear most frequently and whose frequency is 0.15 times that of the highest frequency. Based on these three pore throat radius data, customize a three-channel parallel microfluidic chip. The three channels correspond to the three pore throat radii and are classified as high-permeability, medium-permeability, and low-permeability channels according to their size.

4. The method for optimizing the compatibility of gel particles with reservoirs according to claim 1, characterized in that, In step (3), a PIV experiment was conducted to obtain flow field data for the three channels at different time periods. The specific details are as follows: Twenty gel solutions with different particle sizes and a concentration of 2000 mg / L were prepared. The smallest gel particle size was approximately 10 times the pore size of the hypotonic channel, and the largest gel particle size was approximately 10 times the pore size of the hypertonic channel. Liquid was injected using a PIV velocimetry system at a constant displacement rate of 2 μL / min. The injection process was as follows: First, 1 PV of deionized water was injected into the microfluidic chip. Then, 2 PV of a gel solution with a specific particle size was injected. Finally, 2 PV of deionized water was injected again, for a total of 5 PV of reagent injected. During this process, a PIV velocimetry measurement was performed every 0.25 PV of displacement. The flow rates v1, v2, and v3 of the hypertonic, mesotonic, and hypotonic channels were processed and recorded after each 0.25 PV displacement. This process was repeated 20 times to obtain the changes in the microscopic flow field data of the three channels of the microfluidic chip throughout the entire process.

5. The method for optimizing the compatibility of gel particles with reservoirs according to claim 1, characterized in that, The specific details of the shunt rate variation curves of the three microchannels obtained in step (4) are as follows: Based on the flow rate data of the three channels of the microfluidic chip during the entire injection process in step (4), namely v1, v2, and v3 under different injection volumes, the flow rates Q1, Q2, and Q3 of the three microchannels are calculated according to formula (2). Q i =v i A i ,i=1,2,3 (2) In the formula, i = 1, 2, 3 represent high-permeability, intermediate-permeability, and low-permeability channels, respectively; Q i V represents the flow rate of channel i after each displacement of 0.25 PV, in μL / min; v i The flow rate of channel i is 0.25 PV per displacement, in mm / min; A i The cross-sectional area of ​​channel i is mm. 2 ; The shunting rate of the three channels was calculated after each 0.25 PV of reagent was replaced. The expression for the shunting rate is shown in Equation (3): In the formula, η i The shunt rate for channel i; Therefore, by plotting the injection pore volume multiple (PV) as the x-axis and the shunting rate as the y-axis, a graph showing the shunting rate of the three-channel parallel microfluidic chip as a function of the injection pore volume multiple is obtained.

6. The method for optimizing the compatibility of gel particles with reservoirs according to claim 1, characterized in that, The process of determining the optimal profile values ​​of gels with different particle sizes in the three-channel parallel microfluidic chip in step (5) is as follows: The diversion rates of high-permeability, medium-permeability, and low-permeability channels in the early and later stages of water flooding are obtained from the displacement volume-diversion rate change curve. Substituting these values ​​into the calculation formula (Equation (4)) for the optimized profile value of the three channels, the optimized profile value of the gel displacement using this particle size can be calculated. In the formula, f is the optimized profile value; Q hb and Q ha Q represents the flow rates of the initial and subsequent water drives in the high-permeability channel, respectively, in μL / min; mb and Q ma Q represents the flow rates of the initial and subsequent water flooding in the intermediate infiltration channel, respectively, in μL / min; lb and Q la The values ​​represent the flow rates of the initial and subsequent water drives in the low-permeability channel, respectively, in μL / min.

7. The method for optimizing the compatibility of gel particles with reservoirs according to claim 1, characterized in that, The preferred gel particle size for step (6) that best matches the reservoir is specifically: Based on the profile optimization value calculated in step (2) for a certain particle size, plot the gel particle size as the abscissa and the profile optimization value as the ordinate. The particle size corresponding to the highest point of the profile optimization value in the figure is the gel particle size with the highest matching with the reservoir.

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