A method for constructing a carbon dioxide flooding flow field of a mud streak type shale oil reservoir
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- DAQING OILFIELD CO LTD
- Filing Date
- 2024-12-30
- Publication Date
- 2026-05-29
Smart Images
Figure CN120649857B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of low-carbon oil extraction technology, specifically to a method for constructing a carbon dioxide flooding field in mud-textured shale oil reservoirs. Background Technology
[0002] Shale reservoirs of the mudstone type differ from conventional reservoirs, primarily consisting of micro- and nano-sized pores with low permeability and porosity. Both domestically and internationally, large-scale horizontal well fracturing and depletion development is commonly employed, with primary development recovery rates mostly between 2% and 8%, rarely exceeding 10%. The abundant reserves and low recovery rates of shale oil provide significant room for the development of enhanced oil recovery (EOR) technologies, and related research is currently very active.
[0003] Using CO2 (carbon dioxide) to enhance the recovery of shale oil reservoirs is a major research direction at present. However, some problems still exist. First, although CO2 injection as an oil displacement medium after large-scale fracturing of horizontal wells is not a problem, setting the CO2 injection rate is difficult. Furthermore, due to the low permeability of the matrix, the range of the injection and discharge is limited and the oil displacement effect is poor, making it difficult to significantly improve the recovery rate of shale oil. Second, after large-scale fracturing of horizontal wells, the displacement of oil through different wells is difficult because the fractures are interconnected. The CO2 displacement medium can easily flow through the fractures, making it difficult to mobilize the matrix and limiting the affected volume, thus making it difficult to significantly improve the recovery rate of shale oil. Third, due to the extremely low permeability of shale reservoirs in mudstone-type shale oil reservoirs, it is difficult to establish an effective displacement system between traditional vertical well injection and production well networks.
[0004] Therefore, optimizing the CO2 injection rate and effectively controlling the well flow pressure to form a stable and reliable CO2 drive field and an effective injection-production well displacement system are key to solving the problem of low shale oil recovery efficiency in conventional fracturing and CO2 injection of mud-textured shale reservoirs. Summary of the Invention
[0005] To address the aforementioned technical problems, this application provides a method for constructing a carbon dioxide flooding field in mud-textured shale reservoirs, thereby resolving existing issues.
[0006] The present application discloses a method for constructing a carbon dioxide flooding field in mudstone shale reservoirs, employing the following technical solution:
[0007] One embodiment of this application provides a method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir, comprising the following steps:
[0008] Numerical simulation was performed on CO2 flooding after conventional fracturing of shale oil vertical wells to obtain CO2 injection rate, production and oil exchange rate during the CO2 flooding process, and the CO2 flooding process was divided into multiple optimization stages;
[0009] Based on the change in production well flowing pressure at each sampling time relative to other sampling times in each optimization stage, the flowing pressure fluctuation value at each sampling time in each optimization stage is constructed. Based on the difference between the change in flowing pressure fluctuation value and the change in CO2 injection rate in each optimization stage, the influence degree of flowing pressure fluctuation at each sampling time in each optimization stage is obtained.
[0010] Based on the average level of the influence of flow pressure fluctuations in each optimization stage and the difference in the influence of flow pressure fluctuations at different sampling times, and combined with the degree of change of oil change rate at all sampling times in each optimization stage, the optimization control strength of each optimization stage is constructed.
[0011] By utilizing the optimization control intensity of each optimization stage and combining the difference between the CO2 injection rate at each sampling time within each optimization stage and the preset expected value of the CO2 injection rate, the optimized value of the injection rate at each sampling time in each optimization stage is obtained. This value is then used as the injection rate in the injection well during the CO2 oil displacement process to inject the CO2 gas source into the injection well, forming a CO2 flow field.
[0012] Preferably, the optimization phase is divided into multiple time periods, with each time period corresponding to an optimization phase.
[0013] Preferably, the method for calculating the flow pressure fluctuation value at each sampling time in each optimization stage is as follows:
[0014] In the formula, DP t,j Let P be the flow pressure fluctuation value at the j-th sampling time in the t-th optimization stage, H be the number of sampling times in the t-th optimization stage, and P be the flow pressure fluctuation value at the j-th sampling time. t,j and P t,i These are the production well flow pressures at the j-th and i-th sampling times, respectively, during the t-th optimization stage.
[0015] Preferably, the method for calculating the impact of the flow pressure fluctuation includes:
[0016] Based on the rate of change of the flow pressure fluctuation value and the rate of change of the CO2 injection rate in each optimization stage, the gradient change vector of the injection rate and the gradient change vector of the flow pressure fluctuation in each optimization stage are constructed.
[0017] Calculate the difference vector between the gas injection rate and the gradient change vector of the flow pressure fluctuation in each optimization stage, and obtain the reciprocal of the absolute value of each element in the difference vector as the influence degree of the flow pressure fluctuation at each sampling time in each optimization stage.
[0018] Preferably, the construction of the gradient change vector of the gas injection rate and the gradient change vector of the flow pressure fluctuation in each optimization stage includes:
[0019] Calculate the slope of CO2 injection rate change and the slope of flow pressure fluctuation at each sampling time in each optimization stage. Arrange the slopes of CO2 injection rate change at all sampling time positions in each optimization stage in chronological order to form the gradient change vector of injection rate in each optimization stage. Arrange the slopes of flow pressure fluctuation at all sampling time positions in each optimization stage in chronological order to form the gradient change vector of flow pressure fluctuation in each optimization stage.
[0020] Preferably, the process for obtaining the slope of the CO2 injection rate change and the slope of the flow pressure fluctuation value is as follows:
[0021] Curve fitting was performed on the CO2 injection rate and flow pressure fluctuation values at all sampling times within each optimization stage. The slopes of the fitted curves at each sampling time were statistically analyzed and used as the slopes of the CO2 injection rate and flow pressure fluctuation values at each sampling time.
[0022] Preferably, the calculation method for the optimization control intensity of each optimization stage is as follows:
[0023] In the formula, Vg t Let Gs be the optimization control strength for the t-th optimization stage. t Let Xc be the probability of gas channeling aggravation in the t-th optimization stage, H be the number of sampling times in the t-th optimization stage, and Xc be the probability of gas channeling aggravation. t fc is the average value of the influence of all flow pressure fluctuations in the t-th optimization stage. t,k and FC t,k-1 , respectively, represent the influence of flow pressure fluctuations at the k-th and (k-1)-th sampling times in the t-th optimization stage, where ∈ is a constant to avoid the denominator being zero.
[0024] Preferably, the method for calculating the probability of gas channeling aggravation further includes:
[0025] Gs t =1-exp(-Xu t In the formula, exp() is an exponential function with the natural constant as the base, Xu t The significant value of the change in oil change rate in the t-th optimization stage is obtained by analyzing the change in oil change rate at all sampling times within the t-th optimization stage.
[0026] Preferably, the oil change rates at all sampling times in the t-th optimization stage are used to form an oil change rate vector. The sum of all negative values in the first-order difference vector of the oil change rate vector is calculated, and the absolute value of the sum is taken as the significant value of the change in oil change rate in the t-th optimization stage.
[0027] Preferably, the method for calculating the optimized gas injection rate value at each sampling time in each optimization stage is as follows:
[0028] V t,j =Vr t,j -(Vr t,j -Vp)×norm(Vg t In the formula, V t,j Vr t,j These represent the optimized injection rate and CO2 injection rate at the j-th sampling time in the t-th optimization stage, respectively, Vg t Let Vp be the optimization control intensity for the t-th optimization stage, norm() be the normalization function, and Vp be the preset expected CO2 injection rate.
[0029] This application has at least the following beneficial effects:
[0030] This application constructs a flow pressure fluctuation value based on the analysis of the fluctuation characteristics of the flowing pressure in production wells. The flow pressure fluctuation value reflects the fluctuation characteristics of the flowing pressure in production wells caused by excessively high CO2 injection rates during CO2 oil displacement. Based on the differences between the gradient changes of injection rates and flowing pressure fluctuations in each optimization stage, a flow pressure fluctuation influence degree is constructed. The flow pressure fluctuation influence degree reflects the degree of influence of injection rates on the flowing pressure fluctuations at the bottom of the production well. This is used to more accurately analyze the influence relationship between CO2 injection rates and the changes in the fluctuation characteristics of flowing pressure in production wells during CO2 oil displacement, thereby more accurately determining the CO2 injection rate during CO2 oil displacement.
[0031] Meanwhile, based on the analysis of the significance probability of the aggravated gas channeling characteristics of CO2 fluid in the fracture between production wells and injection wells, a gas channeling aggravation probability is constructed. Combined with the influence of flowing pressure fluctuations, an optimization control strength is constructed. The optimization control strength reflects the magnitude of the adjustment of CO2 injection rate during CO2 oil displacement. The greater the optimization control strength, the higher the weight of optimizing the CO2 injection rate during the optimization stage. Ultimately, the injection rate during CO2 oil displacement is optimized, so that CO2 is evenly distributed in the shale reservoir of mudstone-type shale oil reservoirs and a stable and reliable CO2 flow field is formed. This makes the optimized injection rate more suitable for the recovery of shale oil in mudstone-type shale oil reservoirs.
[0032] Therefore, this application proposes to inject CO2 into injection-production wells after conventional fracturing in mud-textured shale oil reservoirs. By optimizing the CO2 injection rate, CO2 is uniformly distributed and efficiently displaced in the shale reservoir, forming a stable and reliable CO2 flow field and an effective displacement system for injection-production wells. This solves the problem of low shale oil recovery efficiency in existing technologies, thereby significantly improving shale oil recovery efficiency. Attached Figure Description
[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0034] Figure 1 A flowchart illustrating the steps of a method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir, as provided in this application.
[0035] Figure 2 This is a schematic diagram of the main fracture displacement provided in this application for vertical well fracturing. Detailed Implementation
[0036] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0037] Unless otherwise defined, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0038] The following description, in conjunction with the accompanying drawings, details a specific scheme for constructing a carbon dioxide flooding field in a mud-textured shale reservoir, as provided in this application.
[0039] This application provides an embodiment of a method for constructing a carbon dioxide flooding field in a mudstone shale reservoir. For details, please refer to [link to specific documentation]. Figure 1 This includes the following steps:
[0040] Step 1: Numerical simulation of CO2 flooding after conventional fracturing of shale oil vertical wells is performed to obtain the CO2 injection rate, production well flowing pressure and oil exchange rate during the CO2 flooding process, and the CO2 flooding process is divided into multiple optimization stages.
[0041] The purpose of this invention is to inject CO2 into injection-production wells in mud-textured shale oil reservoirs after conventional fracturing. By accurately optimizing and controlling the CO2 injection rate, CO2 is uniformly distributed in the shale reservoir and efficiently displaced, forming a stable and reliable CO2 flow field and an effective displacement system for injection-production wells. This solves the problem of low shale oil recovery efficiency in existing technologies, thereby significantly improving shale oil recovery efficiency.
[0042] Mud-type shale reservoirs include injection wells and production wells, all developed using conventional fracturing technology. The entire reservoir section of both injection and production wells is perforated and fractured to form a fracture network. This fracture network allows CO2 injection fluid to flow in the reservoir and come into contact with crude oil, improving crude oil fluidity and recovery rate.
[0043] In this embodiment, the schematic diagram of vertical well fracturing to create the main fracture displacement is as follows: Figure 2 As shown, Figure 2 It includes injection wells and two production wells on both sides. In the fracture network, blue arrows indicate the displacement direction, red arrows indicate the fracture propagation direction, and cubes represent underground oil reservoirs storing crude oil. The function of injection wells is to use gas injection pumps to inject CO2 fluid into the oil reservoir, increase the reservoir pressure, and drive the crude oil towards the production wells through the CO2 displacement effect. Oil is then extracted from the production wells using production pumps.
[0044] Meanwhile, mud-patterned shale oil reservoirs employ a vertical well pattern for CO2 injection. The injection well row is aligned with the direction of the maximum horizontal principal stress in the reservoir. The injection well spacing is determined based on the length of the fracturing fracture, and the injection well spacing is equal to the fracture length. The row spacing is determined based on the horizontal permeability of the reservoir.
[0045] However, due to the difficulty in setting the CO2 injection rate, it is impossible to effectively control the flowing pressure of the production well, which in turn makes it impossible to form a stable and reliable CO2 flow field and an effective injection-production well displacement system, resulting in the problem of low shale oil recovery rate in mudstone shale reservoirs.
[0046] To optimize the CO2 injection rate and establish a stable and reliable CO2 displacement field and an effective injection-production well displacement system, a formation fluid model was established using geological data and well logging data from mudstone shale reservoirs. This geological data and well logging data included reservoir porosity, permeability, fracture half-length, reservoir temperature, and formation oil viscosity. The CO2 displacement was numerically simulated using CMG (Computer Modelling Group) software, yielding simulated results of CO2 injection rate, production well flowing pressure, and oil exchange rate during the CO2 displacement process. The construction of the formation fluid model and the CO2 displacement numerical simulation are well-known techniques, and the specific process will not be elaborated further.
[0047] In this embodiment, the CO2 oil displacement process is divided into multiple optimization stages, that is, the duration of the CO2 oil displacement process is divided into multiple optimization stages. In this embodiment, it is evenly divided into K optimization stages, where K is 18. It should be noted that in actual application scenarios, implementers can divide the process according to their actual situation.
[0048] Step 2: Based on the changes in production well flow pressure at each sampling time relative to other sampling times in each optimization stage, construct the flow pressure fluctuation value at each sampling time in each optimization stage. Based on the difference between the changes in flow pressure fluctuation value and the changes in CO2 injection rate in each optimization stage, obtain the influence degree of flow pressure fluctuation at each sampling time in each optimization stage.
[0049] In CO2 flooding technology for mudstone shale reservoirs, optimizing the CO2 injection rate is crucial. A well-controlled CO2 injection rate creates a stable and reliable CO2 flow field, enabling control of wellbore flowing pressure and preventing pressure-sensitive damage under unreasonable injection-production pressure differentials. This significantly contributes to formation energy replenishment and oil production rates. Furthermore, dividing the CO2 flooding process in mudstone shale reservoirs into different optimization stages allows for more accurate control of the CO2 injection rate, thereby substantially improving shale oil recovery.
[0050] Meanwhile, if the CO2 injection rate is too low during the CO2 fluid injection process, it will affect the oil production rate, while if the CO2 injection rate is too high, it will easily lead to gas channeling, causing unstable fluctuations in the flowing pressure of the production well, which will reduce the oil exchange rate and pose a risk of fracturing the formation.
[0051] The appropriate flow pressure characteristics during the CO2 fluid injection process are measured to more accurately optimize and control the CO2 injection rate. Based on the changes in production well flow pressure at each sampling time relative to other sampling times in each optimization stage, the flow pressure fluctuation value at each sampling time in each optimization stage is constructed. In this embodiment, the specific calculation formula is as follows:
[0052] In the formula, DP t,j Let P be the flow pressure fluctuation value at the j-th sampling time in the t-th optimization stage, H be the number of sampling times in the t-th optimization stage, and P be the flow pressure fluctuation value at the j-th sampling time. t,j and P t,i These are the production well flow pressures at the j-th and i-th sampling times, respectively, during the t-th optimization stage.
[0053] Among them, the flowing pressure fluctuation value reflects the characteristics of the fluctuation of the flowing pressure of the production well caused by the excessively high CO2 injection rate during the CO2 oil displacement process. The greater the difference in the flowing pressure of the production well between each sampling time, the more significant the characteristics of the fluctuation of the flowing pressure of the production well caused by the excessively high CO2 injection rate, and the more it reflects the characteristics of poor CO2 injection suitability. Therefore, the flowing pressure fluctuation value is larger.
[0054] Therefore, the flow pressure fluctuation values at all sampling times in each optimization stage can be obtained. The flow pressure fluctuation values at all sampling times in each optimization stage reflect the changes in the flow pressure fluctuation characteristics of the production well during the CO2 oil displacement process. The larger the fluctuation characteristics, the more likely it is to be affected by the excessively high CO2 gas injection rate, which can easily lead to gas channeling and affect the efficiency of CO2 oil displacement.
[0055] Furthermore, the influence relationship between the CO2 injection rate and the fluctuation characteristics of the production well flow pressure during the CO2 enhanced oil recovery process is analyzed. In this embodiment, the slope of the CO2 injection rate change and the slope of the flow pressure fluctuation value change at each sampling time in the t-th optimization stage are calculated respectively. The vector composed of the CO2 injection rate change slopes at all sampling time positions in the t-th optimization stage in chronological order is denoted as the gradient change vector of the injection rate in the t-th optimization stage.
[0056] Similarly, the vector formed by the slopes of the flow pressure fluctuation values at all sampling times in the t-th optimization stage, arranged in chronological order, is denoted as the gradient vector of the flow pressure fluctuation in the t-th optimization stage. It should be noted that the slopes of the CO2 injection rate and flow pressure fluctuation values at each sampling time in each optimization stage are obtained using existing techniques. By performing curve fitting on the CO2 injection rate and flow pressure fluctuation values at all sampling times within the optimization stage, the slopes of the CO2 injection rate and flow pressure fluctuation values corresponding to each sampling time can be obtained.
[0057] Among them, the gradient change vectors of gas injection rate and pressure fluctuation in the t-th optimization stage reflect the gradient change of gas injection rate and the gradient change of pressure fluctuation in the bottom of the production well during CO2 oil displacement, respectively. The smaller the difference between the two, the greater the influence of gas injection rate on pressure fluctuation in the bottom of the production well in this optimization stage, which shows that the degree of unsuitability of CO2 gas injection rate is higher, and it is impossible to effectively form a stable and reliable CO2 displacement field. The calculation of the slope is a well-known technique, and the specific process will not be described in detail.
[0058] Furthermore, in this embodiment, the difference vector between the gradient change vectors of the gas injection rate and the flow pressure fluctuation in the t-th optimization stage is calculated, and the reciprocal of the absolute value of each element in the difference vector is obtained and recorded as the flow pressure fluctuation influence degree at each sampling time in the t-th optimization stage.
[0059] It should be noted that, to avoid the denominator being zero during the reciprocal calculation, a constant is added to the denominator to prevent it from being zero. The constant ranges from 0 to 0.1; in this embodiment, it is set to 0.001. In actual applications, the implementer can set the value as needed. The influence of flow pressure fluctuation reflects the degree to which the injection rate affects the flow pressure fluctuation at the bottom of the production well. The greater the influence of flow pressure fluctuation, the more unsuitable the CO2 injection rate is, and the less effectively a stable and reliable CO2 drive field can be formed.
[0060] Step 3: Based on the average level of the influence of flow pressure fluctuations in each optimization stage and the difference in the influence of flow pressure fluctuations at different sampling times, and combined with the degree of change of oil change rate at all sampling times in each optimization stage, construct the optimization control strength for each optimization stage.
[0061] Generally, the stronger the continuity between the influence degrees of flowing pressure fluctuations within the optimization stage, and the higher the average level of all flowing pressure fluctuation influence degrees within the optimization stage, the more it indicates the long-term influence of the gas injection rate on the flowing pressure fluctuations of the production well during the CO2 oil displacement stage. In this case, the risk of formation fracturing is higher, and the gas injection rate needs to be optimized and controlled more effectively during the optimization stage. Simultaneously, the more significant the downward trend in the oil exchange rate during CO2 oil displacement, the greater the impact of formation heterogeneity during CO2 fluid injection. A higher CO2 injection rate exacerbates the gas channeling of the CO2 fluid, causing the CO2 oil exchange rate to exhibit a downward trend.
[0062] Based on the above analysis, the oil change rates at all sampling times in the t-th optimization stage are used to form an oil change rate vector. The sum of all negative values in the first-order difference vector of the oil change rate vector is calculated, and the absolute value of the sum is taken as the significant value of the oil change rate change in the t-th optimization stage. Based on the significant value of the oil change rate change in each optimization stage, the probability of gas channeling aggravation in each optimization stage is calculated. In this embodiment, the specific calculation formula is as follows:
[0063] Gs t =1-exp(-Xu t In the formula, Gs t Let be the probability of increased airflow during the t-th optimization stage, and exp() be an exponential function with the natural constant as the base. t The value representing the significant change in oil change rate during the t-th optimization stage.
[0064] Among them, the probability of gas channeling aggravation reflects the magnitude of the significant probability of the gas channeling aggravation characteristic of CO2 fluid in the fracture between the production well and the injection well. The larger the significant value of the change in oil exchange rate, the more significant the downward trend of the oil exchange rate during the CO2 oil displacement process, and the more likely it is to aggravate the gas channeling characteristic of CO2 fluid. That is, the greater the significant probability of the gas channeling aggravation characteristic, the greater the probability of gas channeling aggravation, and the more necessary it is to optimize and control the gas injection rate during this optimization stage.
[0065] Based on the average level of the influence of all flow and pressure fluctuations in each optimization stage and the differences in the influence of different flow and pressure fluctuations, and combined with the probability of gas channeling aggravation in each optimization stage, the optimization control strength for each optimization stage is constructed. In this embodiment, the specific calculation formula is as follows:
[0066] In the formula, Vg t Let Xc be the optimization control strength in the t-th optimization stage, H be the number of sampling times in the t-th optimization stage, and Xc be the control strength. t fc is the average value of the influence of all flow pressure fluctuations in the t-th optimization stage. t,k and FC t,k-1 , respectively, are the influence degree of flow pressure fluctuation at the k-th and (k-1)-th sampling times in the t-th optimization stage, and ∈ is a constant to avoid the denominator being zero, with a value range of 0 to 0.1, and in this embodiment, the value is 0.001.
[0067] The smaller the difference between the different pressure fluctuation influences during the optimization stage, the stronger the continuity between the pressure fluctuation influences. Furthermore, the higher the average level of pressure fluctuation influence and the greater the probability of gas channeling aggravation, the more the gas injection rate in the CO2 oil displacement stage will affect the fluctuation characteristics of the production well's pressure. At this time, the risk of fracturing the formation is higher, and the more necessary it is to optimize and control the gas injection rate during the optimization stage, the greater the optimization control effort.
[0068] Step 4: Utilize the optimization control strength of each optimization stage, and combine the difference between the CO2 injection rate at each sampling time in each optimization stage and the preset expected value of the CO2 injection rate, to obtain the optimized value of the injection rate at each sampling time in each optimization stage, and use it as the injection rate in the injection well during the CO2 oil displacement process to inject the CO2 gas source into the injection well, forming a CO2 flow field.
[0069] Furthermore, in this embodiment, based on the optimization control intensity of the CO2 injection rate in each optimization stage, and combined with the difference between the CO2 injection rate at each sampling time within each optimization stage and the preset expected CO2 injection rate, the optimized injection rate value at each sampling time in each optimization stage is obtained. The specific calculation formula is as follows:
[0070] V t,j =Vr t,j -(Vr t,j -Vp)×norm(Vg t In the formula, Vt ,j Let Vr be the optimized value of the gas injection rate at the j-th sampling time in the t-th optimization stage. t,j Vp represents the CO2 injection rate at the j-th sampling time in the t-th optimization stage, Vp is the preset expected value of the CO2 injection rate, which is 15000 cubic meters per day in this embodiment, and norm() is the normalization function.
[0071] Furthermore, the optimized injection rate values at all sampling times in each optimization stage constitute the optimized adjustment vector of the CO2 injection rate for each optimization stage, specifically: V(t) = (V t,1 V t,2 V t,3 ,…,V t,n In the formula, V(t) is the optimized adjustment vector of the CO2 injection rate in the t-th optimization stage. t,1 V t,2 V t,3 and V t,n Let V be the optimized injection rate value at the 1st, 2nd, 3rd, and nth sampling times in the CO2 injection rate sub-vector of the t-th optimization stage. t,n Let be the optimized injection rate value at the last sampling moment in the CO2 injection rate sub-vector of the t-th optimization stage.
[0072] It is understandable that in this embodiment, V t,j During the acquisition process, the normalized result of the optimized control strength is used as the adjustment weight to optimize the CO2 injection rate during the optimization stage, where (Vr t,j -Vp) has positive and negative directions. If the CO2 injection rate is higher than the expected value, it will be optimized downward; if the CO2 injection rate is lower than the expected value, it will be optimized upward. This allows the optimized injection rate to significantly reduce gas channeling between fractures in the formation, making it easier to recover shale oil in mud-type shale reservoirs, improving the oil exchange rate of shale oil, and preventing the risk of formation fracture pressure caused by excessively high injection rates.
[0073] In this embodiment, in order to form a stable and reliable CO2 flow field and an effective injection-production well displacement system during the CO2 oil displacement process, the optimized adjustment vectors of the CO2 injection rate in each optimization stage are arranged in chronological order and denoted as the optimized CO2 injection rate vector in the optimized control process.
[0074] Perforation fracturing was performed in a reservoir with an effective thickness of 94.5 meters, forming an east-west trending main fracture with a half-fracture length of 150 meters and a fracture height equal to the effective thickness of 94.5 meters. In addition, the wellhead injection pressure of the injection well did not exceed 32 MPa, while the well flow pressure of the production well was 20 MPa. The injection rate within the optimized vector of the CO2 injection rate in the optimized CO2 displacement process was used as the actual injection rate in the CO2 displacement process. CO2 gas was injected into the injection well through the injection pump in the CO2 displacement system, ensuring uniform distribution of CO2 in the shale reservoir of the mudstone-type shale oil reservoir and forming a stable and reliable CO2 displacement field. The shale oil in the production well was then extracted by the production pump in the CO2 displacement system. The CO2 displacement system is equipped with an oil-gas separator, which recirculates the separated CO2 fluid back into the injection well, improving reservoir control and oil washing efficiency, and significantly increasing the recovery rate of the shale oil reservoir.
[0075] It is understood that references to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the specific features, structures, or characteristics described in connection with that embodiment. Therefore, the appearance of phrases such as "in one embodiment," "in some embodiments," "in other embodiments," or "in still other embodiments" in different parts of this specification does not necessarily refer to the same embodiment, but rather means "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.
[0076] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous. Moreover, the sequence numbers of the steps in the embodiments do not imply a specific order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.
[0077] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for constructing a carbon dioxide flooding field in mud-textured shale reservoirs, characterized in that, Includes the following steps: Numerical simulation was performed on CO2 flooding after conventional fracturing of shale oil vertical wells to obtain CO2 injection rate, production well flowing pressure and oil exchange rate during the CO2 flooding process, and the CO2 flooding process was divided into multiple optimization stages; Based on the change in production well flowing pressure at each sampling time relative to other sampling times in each optimization stage, the flowing pressure fluctuation value at each sampling time in each optimization stage is constructed. Based on the difference between the change in flowing pressure fluctuation value and the change in CO2 injection rate in each optimization stage, the influence degree of flowing pressure fluctuation at each sampling time in each optimization stage is obtained. Based on the average level of the influence of flow pressure fluctuations in each optimization stage and the difference in the influence of flow pressure fluctuations at different sampling times, and combined with the degree of change of oil change rate at all sampling times in each optimization stage, the optimization control strength of each optimization stage is constructed. By utilizing the optimization control intensity of each optimization stage, and combining the difference between the CO2 injection rate at each sampling time in each optimization stage and the preset expected value of CO2 injection rate, the optimized value of injection rate at each sampling time in each optimization stage is obtained, and used as the injection rate in the injection well during the CO2 oil displacement process to inject CO2 gas source into the injection well, forming a CO2 displacement flow field. The method for calculating the flow pressure fluctuation value at each sampling time in each optimization stage is as follows: In the formula, Let J be the flow pressure fluctuation value at the j-th sampling time in the t-th optimization stage. Let be the number of sampling times in the t-th optimization stage. and These are the production well flow pressures at the j-th and i-th sampling times, respectively, during the t-th optimization stage; The calculation method for the optimization control intensity of each optimization stage is as follows: In the formula, Let be the optimization control strength for the t-th optimization stage. Let be the probability of increased airflow during the t-th optimization stage. Let be the number of sampling times in the t-th optimization stage. Let be the average value of the influence of all flow and pressure fluctuations in the t-th optimization stage. and These represent the influence of flow pressure fluctuations at the k-th and (k-1)-th sampling times in the t-th optimization stage, respectively. To avoid constants with a denominator of zero; The method for calculating the optimized gas injection rate value at each sampling time in each optimization stage is as follows: In the formula, These represent the optimized injection rate and CO2 injection rate at the j-th sampling time in the t-th optimization stage, respectively. Let be the optimization control strength for the t-th optimization stage. For normalization function, The preset CO2 injection rate is the expected value.
2. The method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir as described in claim 1, characterized in that, The optimization phase division process is as follows: the CO2 oil displacement process is divided into multiple time periods, and each time period corresponds to an optimization phase.
3. The method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir as described in claim 1, characterized in that, The calculation method for the influence of the flow pressure fluctuation includes: Based on the rate of change of the flow pressure fluctuation value and the rate of change of the CO2 injection rate in each optimization stage, the gradient change vector of the injection rate and the gradient change vector of the flow pressure fluctuation in each optimization stage are constructed. Calculate the difference vector between the gas injection rate and the gradient change vector of the flow pressure fluctuation in each optimization stage, and obtain the reciprocal of the absolute value of each element in the difference vector as the influence degree of the flow pressure fluctuation at each sampling time in each optimization stage.
4. The method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir as described in claim 3, characterized in that, The construction of the gradient change vectors of the gas injection rate and the gradient change vectors of the flow pressure fluctuation in each optimization stage includes: Calculate the slope of CO2 injection rate change and the slope of flow pressure fluctuation at each sampling time in each optimization stage. Arrange the slopes of CO2 injection rate change at all sampling time positions in each optimization stage in chronological order to form the gradient change vector of injection rate in each optimization stage. Arrange the slopes of flow pressure fluctuation at all sampling time positions in each optimization stage in chronological order to form the gradient change vector of flow pressure fluctuation in each optimization stage.
5. The method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir as described in claim 4, characterized in that, The process for obtaining the slope of the CO2 injection rate change and the slope of the flow pressure fluctuation value is as follows: Curve fitting was performed on the CO2 injection rate and flow pressure fluctuation values at all sampling times within each optimization stage. The slopes of the fitted curves at each sampling time were statistically analyzed and used as the slopes of the CO2 injection rate and flow pressure fluctuation values at each sampling time.
6. The method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir as described in claim 1, characterized in that, The method for calculating the probability of gas channeling aggravation further includes: In the formula, It is an exponential function with the natural constant as its base. The significant value of the change in oil change rate in the t-th optimization stage is obtained by analyzing the change in oil change rate at all sampling times within the t-th optimization stage.
7. The method for constructing a carbon dioxide flooding field in a mud-textured shale reservoir as described in claim 6, characterized in that, The oil change rate at all sampling times in the t-th optimization stage is used to form an oil change rate vector. The sum of all negative values in the first-order difference vector of the oil change rate vector is calculated, and the absolute value of the sum is taken as the significant value of the change in oil change rate in the t-th optimization stage.