A method for improving the productivity of volume fracturing in tight reservoirs based on the imbibition mechanism

Abstract

The present invention relates to the technical field of oil and gas exploration, and discloses a method for improving the productivity of volume fracturing in tight reservoirs based on the imbibition mechanism, comprising the following steps: predicting the fracture propagation under different injection strategies by using dynamic simulation technology for fracturing; combining machine learning for parameter optimization and designing the injection parameters of the imbibition medium and the fracturing parameters; adding nanoparticle materials to the imbibition medium to enhance the contact area between the imbibition medium and the crude oil; introducing a real-time monitoring system and monitoring the formation pressure change through sensors to dynamically adjust the soaking time; and regulating the flowback rate of the fracturing fluid by using automatic control. By monitoring the formation pressure change in real time and using machine learning algorithms or expert systems to evaluate the current soaking state, the present invention can more accurately judge whether the imbibition process is completed, thereby avoiding unnecessary waiting time or poor effects caused by ending the soaking prematurely.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and particularly to a method for improving the productivity of volume fracturing in tight reservoirs based on the imbibition mechanism. Background Art

[0002] The exploration and development of conventional medium-high permeability reservoirs have entered the late stage with increasing difficulty. Moreover, as the country's dependence on imported energy is growing day by day and its own energy supply faces serious challenges, the exploration and development of unconventional oil and gas such as tight oil have become the direction and breakthrough point for improving its own energy supply capacity.

[0003] A prior patent discloses a method for improving the productivity of volume fracturing in tight reservoirs based on the imbibition mechanism (publication number CN113417617A). It includes the following steps: fracturing and parameter design; injecting an imbibition medium; conducting large-scale fracturing; shut-in; flowback and production. In the technology disclosed in this patent, due to different geological characteristics of different reservoirs, the traditional fixed shut-in time scheme is difficult to meet the needs of all situations. Therefore, the shut-in strategy cannot be flexibly adjusted according to the actual situation, which will greatly increase the exploration cost. Summary of the Invention

[0004] The main technical problem to be solved by the present invention is to provide a method for improving the productivity of volume fracturing in tight reservoirs based on the imbibition mechanism, which solves the problems in the above background art.

[0005] To solve the above technical problem, according to one aspect of the present invention, more specifically, a method for improving the productivity of volume fracturing in tight reservoirs based on the imbibition mechanism includes the following steps:

[0006] S1. Using dynamic simulation technology to predict the fracture propagation under different injection strategies for fracturing;

[0007] S2. Combining machine learning for parameter optimization and designing the injection parameters of the imbibition medium and the fracturing parameters;

[0008] S3. Adding nanoparticle materials to the imbibition medium to enhance the contact area between the imbibition medium and the crude oil;

[0009] S4. Introducing a real-time monitoring system and dynamically adjusting the shut-in time by monitoring the formation pressure change through sensors;

[0010] S5. Using automatic control to regulate the flowback rate of the fracturing fluid.

[0011] Furthermore, in step S1, the in-situ stress field information of the target reservoir is obtained through logging data. The in-situ stress field information specifically includes elastic modulus, Poisson's ratio, and fracture toughness. And based on this in-situ stress field information, it is judged whether to increase the fracturing range, as follows: Wherein, represents the condition coefficient for increasing the fracturing range, represents the actual pressure generated by the fracturing equipment, represents the minimum pressure required for crack propagation.

[0012] Furthermore, the minimum pressure required for crack propagation can be estimated by the following formula, then there is: Wherein, represents the minimum pressure required for crack propagation, represents the fracture toughness coefficient of the ground in this area, represents the half-length of the crack of the ground fracture in this area, represents the minimum horizontal principal stress in the initial stress field.

[0013] Furthermore, when it means that the prediction by the dynamic simulation technology will require an increase in the fracturing range;

[0014] When it means that the prediction by the dynamic simulation technology will not require an increase in the fracturing range.

[0015] Furthermore, the nanoparticle material added in step S3 is composed of silica nanoparticles, alumina nanoparticles and carbon nanotubes.

[0016] Furthermore, the mass ratio between the silica nanoparticles, alumina nanoparticles and carbon nanotubes is 6:2:0.1.

[0017] Furthermore, the specific steps for dynamically adjusting the soaking time in step 4 are as follows:

[0018] 1), Based on real-time data, use machine learning algorithms or expert systems to evaluate the current soaking state;

[0019] 2), Update the numerical simulation model according to the latest data, and re-predict the crack propagation situation and production increase potential;

[0020] 3), If the data shows that the current soaking time is too long or too short, then dynamically adjust the soaking time:

[0021] a. If the formation pressure has not reached the expected equilibrium point, it indicates that the imbibition process has not been completed, and the soaking time can be appropriately extended;

[0022] b. If the formation pressure has stabilized and there is no further upward trend, it indicates that the imbibition process has been completed, and the soaking stage can be ended in advance.

[0023] A method for improving the volume fracturing productivity of tight oil reservoirs based on the imbibition mechanism provided by the present invention, compared with the prior art, the effects obtained by this method are:

[0024] 1. In the present invention, silicon dioxide nanoparticles, aluminum oxide nanoparticles and carbon nanotubes are mixed in a mass ratio of 6:2:0.1. This not only can introduce additional functional characteristics while ensuring the basic performance, but also can effectively balance the cost and efficiency, and is particularly suitable for oil extraction applications under complex geological conditions.

[0025] 2. By real-time monitoring the change of formation pressure and using machine learning algorithms or expert systems to evaluate the current soaking well state, the present invention can more accurately judge whether the imbibition process is completed, thus avoiding unnecessary waiting time or poor effects caused by ending the soaking well prematurely.

[0026] 3. By obtaining the in-situ stress field information of the target reservoir through well logging data, the in-situ stress field information specifically includes elastic modulus, Poisson's ratio, and fracture toughness. Based on this in-situ stress field information, it is possible to quickly and accurately judge whether to increase the fracturing range, which can greatly improve the exploration efficiency of oil and gas. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of the present invention;

[0028] Figure 2 is a schematic diagram showing the influence of fracture half-length on fracture pressure in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0029] To make the technical solution of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and specific embodiments. Embodiment

[0030] As Figure 1 、 2 shown, the in-situ stress field information of the target reservoir is obtained through well logging data. The in-situ stress field information specifically includes elastic modulus, Poisson's ratio, and fracture toughness. Based on this in-situ stress field information, it is judged whether to increase the fracturing range, and there is: In the formula, represents the condition coefficient for needing to increase the fracturing range. When , it means that it is predicted by dynamic simulation technology that the fracturing range needs to be increased; when , it means that it is predicted by dynamic simulation technology that the fracturing range does not need to be increased. represents the actual pressure generated by the fracturing equipment. represents the minimum pressure required for crack propagation.

[0031] The minimum pressure required for crack propagation can be estimated by the following formula, then there is: In the formula, represents the minimum pressure required for crack propagation, Indicates the fracture toughness coefficient of the ground in this area. Indicates the half crack length of the ground fracture in this area. Indicates the minimum horizontal principal stress in the initial stress field.

[0032] Among them, the fracture toughness coefficient of the ground in this area is taken as ( ), the minimum horizontal principal stress in the initial stress field is taken as ( ), the half crack length of the ground fracture in this area is taken as (m), then there is: From the above calculations, it can be known that the minimum pressure required for crack propagation is ( ). And when the actual pressure generated by the fracturing equipment is taken as ( ), then there is: From the above calculations, it can be known that through the prediction of dynamic simulation technology, it will not be necessary to increase the fracturing range. Embodiment

[0033] As Figure 1 shown, the nano-particle materials added in step three are composed of silica nano-particles, alumina nano-particles and carbon nanotubes. The mass ratio between silica nano-particles, alumina nano-particles and carbon nanotubes is 6:2:0.1. Silica nano-particles can effectively adjust the viscosity of the solution, which helps to control the flow characteristics of the liquid in the formation and ensure more effective penetration and distribution. Alumina nano-particles are known for their high hardness, while silica nano-particles provide good dispersibility and chemical stability. By combining these two materials, the mechanical strength of the overall structure can be improved without significantly increasing the viscosity of the system. Therefore, the mixture of silica nano-particles, alumina nano-particles and carbon nanotubes in a mass ratio of 6:2:0.1 can not only introduce additional functional characteristics while ensuring the basic performance, but also effectively balance the cost and efficiency, and is particularly suitable for oil extraction applications under complex geological conditions. Embodiment

[0034] As Figure 1 shown, the specific steps for dynamically adjusting the soaking time in step four are as follows:

[0035] Step 1: Based on real-time data, use machine learning algorithms or expert systems to evaluate the current soaking state. By monitoring the formation pressure change in real time and using machine learning algorithms or expert systems to evaluate the current soaking state, it is possible to more accurately judge whether the imbibition process is completed, thus avoiding unnecessary waiting time or poor effects caused by ending the soaking prematurely.

[0036] Step 2: Update the numerical simulation model according to the latest data and re-predict the fracture propagation and stimulation potential.

[0037] Step 3: If the data shows that the current soaking time is too long or too short, dynamically adjust the soaking time:

[0038] If the formation pressure has not reached the expected equilibrium point, it indicates that the imbibition process is not yet complete, and the soaking time can be appropriately extended.

[0039] If the formation pressure has stabilized and there is no further upward trend, it indicates that the imbibition process is complete, and the soaking stage can be ended prematurely.

[0040] Moreover, due to the different geological characteristics of different reservoirs, the traditional fixed soaking time scheme is difficult to meet the requirements of all situations. The dynamic adjustment method can flexibly adjust the soaking strategy according to the actual situation and better adapt to various complex geological conditions.

[0041] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A method for improving the volume fracturing capacity of tight oil reservoirs based on the imbibition mechanism, characterized in that: The following steps are involved: S1. Use dynamic simulation technology to predict the crack expansion under different injection strategies to perform fracturing; S2. Combine machine learning to optimize parameters and design the injection parameters of the imbibition medium and the fracturing parameters; S3, adding nanoparticle material to the imbibition medium to increase the contact area between the imbibition medium and the crude oil; S4. Introduce a real-time monitoring system and use sensors to monitor changes in formation pressure to dynamically adjust the soaking time; S5. Regulating the flowback rate of the fracturing fluid by automated control; In step S1, the geostress field information of the target reservoir is obtained through logging data, and the geostress field information specifically includes elastic modulus, Poisson's ratio, and fracture toughness. It is determined whether to increase the fracturing range based on the geostress field information, as follows: In the formula, The condition coefficient indicating the need to increase the fracturing range; Indicates the actual pressure generated by the fracturing equipment, in units ; Indicates the minimum pressure required for crack expansion, in units ; The minimum pressure required for the crack to expand can be estimated by the following formula: In the formula, Indicates the minimum pressure required for crack expansion, in units ; It represents the fracture toughness coefficient of the ground in the area, in units of ; It represents the half length of the crack of the ground fracture in the area, unit, m; It represents the minimum horizontal principal stress in the initial stress field, in units of .

2. The method for improving the volume fracturing productivity of tight oil reservoirs based on the imbibition mechanism according to claim 1, characterized in that: when When , it means that the prediction of dynamic simulation technology will require the expansion of the fracturing range; when When , it means that the prediction of dynamic simulation technology will not require increasing the fracturing range.

3. The method for improving the volume fracturing productivity of tight oil reservoirs based on the imbibition mechanism according to claim 1, characterized in that: The nanoparticle material added in step S3 is composed of silicon dioxide nanoparticles, aluminum oxide nanoparticles and carbon nanotubes.

4. The method for improving the volume fracturing productivity of tight oil reservoirs based on the imbibition mechanism according to claim 3, characterized in that: The mass ratio of the silicon dioxide nanoparticles, the aluminum oxide nanoparticles and the carbon nanotubes is 6:2:0.

1.

5. The method for improving the volume fracturing productivity of tight oil reservoirs based on the imbibition mechanism according to claim 1, characterized in that: The specific steps of dynamically adjusting the soaking time in step 4 are as follows: 1) Based on real-time data, use machine learning algorithms or expert systems to evaluate the current well status; 2) Update the numerical simulation model based on the latest data and re-predict the fracture expansion and production potential; 3) If the data shows that the current well-insulating time is too long or too short, dynamically adjust the well-insulating time: a. If the formation pressure does not reach the expected equilibrium point, it indicates that the imbibition process has not been completed and the soaking time can be appropriately extended; b. If the formation pressure has stabilized and there is no further upward trend, it indicates that the imbibition process has been completed and the soaking stage can be ended early.

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