A method, device and medium for evaluating CO2 flooding effect in low permeability reservoirs

Abstract

The present application discloses a method, device and medium for evaluating the effect of CO2 flooding in low permeability reservoirs, and relates to the technical field of CO2 flooding in low permeability reservoirs. The method comprises: obtaining real-time parameter data of carbon dioxide, and obtaining the volume of carbon dioxide produced in the processing cycle according to the real-time parameter data of carbon dioxide; obtaining real-time formation pressure data through a formation pressure monitoring model during the process of carbon dioxide flooding; collecting periodic flow rate of the produced reservoir to determine the volume of the reservoir produced in the processing cycle to determine the periodic gas-oil ratio; evaluating the correlation degree of flooding effect of the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine the correlation degree sequence; determining the effect of carbon dioxide flooding in low permeability reservoirs according to the correlation degree sequence. The present application solves the problems of long evaluation cycle, poor accuracy and inability to realize real-time evaluation in analyzing the evaluation of carbon dioxide flooding effect in low permeability reservoirs through the above method.

Description

Technical Field

[0001] The present application relates to the technical field of CO2 flooding in low permeability reservoirs, and in particular to a method, device and medium for evaluating the effect of CO2 flooding in low permeability reservoirs. Background Art

[0002] With the continuous growth of global energy demand and the increasing awareness of environmental protection, the exploitation and utilization of oil resources are facing huge challenges. Especially in the exploitation process of low permeability reservoirs, due to their low permeability and complex pore structure, traditional exploitation methods often fail to achieve the ideal recovery rate. Therefore, exploring new exploitation technologies and improving the recovery rate of low permeability reservoirs have become one of the hot topics in the current petroleum industry research.

[0003] At present, the evaluation of the effect of carbon dioxide flooding in low permeability reservoirs is mainly carried out through two methods: experimental research and numerical simulation. Experimental research can directly observe and analyze the interaction process between carbon dioxide and crude oil, but its cost is high and the cycle is long. Numerical simulation methods can simulate the flooding process under different conditions and predict the trend of recovery rate, but the accuracy of the results is affected by model parameters and boundary conditions, and the analysis of the above two methods cannot achieve real-time analysis. Summary of the invention

[0004] The embodiments of the present application provide a method, device and medium for evaluating the effect of CO2 flooding in low permeability oil reservoirs, which solves the technical problems of long evaluation cycle, poor accuracy and inability to achieve real-time evaluation in the prior art analysis of the effect of carbon dioxide flooding in low permeability oil reservoirs.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the effect of CO2 flooding in a low permeability oil reservoir, the method comprising: monitoring carbon dioxide flooding through a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and obtaining the volume of carbon dioxide produced in a processing cycle through a preset periodic accumulation method based on the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide, real-time production data of carbon dioxide; the periodic accumulation method comprises: based on the real-time parameter data of carbon dioxide, through real-time production pressure data of carbon dioxide analysis, and dynamically accumulating the gas volume according to a preset period at the pressure nodes obtained by the pressure data analysis; in the process of carbon dioxide flooding, obtaining real-time formation pressure data through a formation pressure monitoring model; wherein the formation pressure monitoring model includes : Acoustic wave monitoring module, electromagnetic wave monitoring module, through the acoustic wave monitoring module and the electromagnetic wave monitoring module, respectively, to monitor the formation acoustic wave and formation electromagnetic wave in real time, and perform multi-scale processing on the real-time monitored data; collect the periodic flow of the produced oil reservoir to determine the volume of the oil reservoir produced in the processing cycle, so as to determine the periodic gas-oil ratio according to the volume of carbon dioxide produced in the processing cycle and the volume of the oil reservoir; evaluate the correlation degree of oil recovery effect of the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine the correlation degree sequence; according to the correlation degree sequence, determine the carbon dioxide oil recovery effect of low permeability oil reservoirs through the preset oil recovery effect evaluation model; wherein, the oil recovery effect evaluation model analyzes the correlation degree sequence through the grey correlation method, and judges the oil recovery effect of the data obtained by the grey correlation method through ideal mining comparison.

[0006] In one implementation of the present application, based on the real-time parameter data of carbon dioxide, the volume of carbon dioxide produced in the processing cycle is obtained through a preset periodic accumulation method, specifically including: smoothing and continuously processing the real-time carbon dioxide production data to obtain a real-time pressure data graph of carbon dioxide; marking the pressure nodes of the real-time carbon dioxide data graph to determine the carbon dioxide volume accumulation node; based on the carbon dioxide volume accumulation node, dynamically accumulating the gas volume through a preset period to obtain the volume of carbon dioxide produced in the processing cycle.

[0007] In one implementation of the present application, during the carbon dioxide recovery process, real-time formation pressure data is obtained through a formation pressure monitoring model, specifically including: determining the formation acoustic wave velocity through the acoustic wave monitoring model to determine the first real-time formation pressure data; determining the formation electromagnetic wave velocity through the electromagnetic wave monitoring module to determine the second real-time formation pressure data; dividing the first real-time formation pressure data and the second real-time formation pressure data into formation regions to obtain real-time formation regional pressure data; performing formation pressure multi-scale processing on the real-time formation regional pressure data to obtain real-time formation pressure data.

[0008] In one implementation of the present application, periodic flow acquisition is performed on the produced oil reservoir to determine the volume of the oil reservoir produced within the processing cycle, specifically including: based on the produced oil reservoir, determining the real-time produced oil reservoir flow through a preset produced oil reservoir flowmeter; removing impurities from the produced oil reservoir to obtain a pure oil reservoir, and determining the volume shrinkage ratio based on the reservoir change data after the oil reservoir is removed; based on the real-time produced oil reservoir flow and the volume shrinkage ratio, obtaining the periodic acquisition flow of the pure oil reservoir to determine the volume of the oil reservoir produced within the processing cycle.

[0009] In one implementation of the present application, the correlation between the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio is evaluated for oil recovery effect to determine a correlation sequence, specifically including: serializing the oil recovery effect of the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine an oil recovery effect comparison sequence; performing dimensionless processing on the oil recovery effect comparison sequence to obtain a sequence to be associated; and determining a correlation sequence by matching a reference sequence based on the sequence to be associated.

[0010] In one implementation of the present application, the carbon dioxide oil recovery effect in low permeability oil reservoirs is determined according to a correlation sequence through a preset oil recovery effect evaluation model, specifically including: performing grey correlation processing on the correlation sequence to determine the correlation between the correlation sequences; performing real-time correlation of carbon dioxide oil recovery in low permeability oil reservoirs according to the correlation to obtain real-time evaluation parameters; and comparing the real-time evaluation parameters with preset ideal production parameters to determine the carbon dioxide oil recovery effect in low permeability oil reservoirs.

[0011] In one implementation of the present application, after determining the carbon dioxide oil recovery effect of a low permeability oil reservoir through a preset oil recovery effect evaluation model according to a correlation sequence, the method further includes: quantizing the data of the carbon dioxide oil recovery effect of the low permeability oil reservoir to obtain vector data of the carbon dioxide oil recovery effect of the low permeability oil reservoir; inputting the carbon dioxide oil recovery effect of the low permeability oil reservoir into a preset carbon dioxide oil recovery time series model of the low permeability oil reservoir to obtain the development degree of the low permeability oil reservoir; and determining the low permeability oil reservoir development strategy according to the development degree of the low permeability oil reservoir.

[0012] In one implementation of the present application, before monitoring carbon dioxide oil recovery through a carbon dioxide monitoring system to obtain real-time carbon dioxide parameter data, the method further includes: performing a carbon dioxide injection assessment on formation parameters of a low permeability oil reservoir to determine carbon dioxide injection parameters.

[0013] In a second aspect, an embodiment of the present application further provides a device for evaluating the effect of CO2 flooding in a low permeability oil reservoir, characterized in that the device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: monitor the carbon dioxide flooding through a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and obtain the volume of carbon dioxide produced in the processing cycle through a preset periodic accumulation method based on the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide includes: real-time injection parameters of carbon dioxide, real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, through the real-time production pressure data of carbon dioxide analysis, and the pressure nodes obtained by the pressure data analysis are dynamically accumulated according to the preset period of gas volume; when performing carbon dioxide During the oil recovery process, real-time formation pressure data is obtained through a formation pressure monitoring model; wherein the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave monitoring module and the electromagnetic wave monitoring module are used to respectively monitor the formation acoustic wave and the formation electromagnetic wave in real time, and multi-scale processing is performed on the real-time monitored data; periodic flow acquisition is performed on the produced reservoir to determine the volume of the reservoir produced in the processing cycle, so as to determine the periodic gas-oil ratio according to the volume of carbon dioxide produced in the processing cycle and the reservoir volume; the correlation degree of oil recovery effect is evaluated on the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine the correlation degree sequence; according to the correlation degree sequence, the carbon dioxide oil recovery effect of low permeability oil reservoirs is determined through a preset oil recovery effect evaluation model; wherein the oil recovery effect evaluation model analyzes the correlation degree sequence through a grey correlation method, and judges the oil recovery effect of the data obtained by the grey correlation method through ideal production comparison.

[0014] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for evaluating the effect of CO2 flooding in a low permeability oil reservoir, the medium storing computer executable instructions, characterized in that the computer executable instructions are configured to: monitor carbon dioxide flooding through a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and obtain the volume of carbon dioxide produced within the processing cycle through a preset periodic accumulation method based on the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide, real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, through the real-time production pressure data of carbon dioxide analysis, and dynamically accumulate the gas volume according to the preset period at the pressure nodes obtained by the pressure data analysis; in the process of carbon dioxide flooding, obtain real-time formation pressure through a formation pressure monitoring model. formation pressure data; wherein, the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave monitoring module and the electromagnetic wave monitoring module are used to monitor the formation acoustic wave and the formation electromagnetic wave in real time respectively, and the real-time monitored data are processed in multiple scales; the produced oil reservoir is subjected to periodic flow acquisition to determine the volume of the oil reservoir produced in the processing cycle, so as to determine the periodic gas-oil ratio according to the volume of carbon dioxide produced in the processing cycle and the volume of the oil reservoir; the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio are evaluated for the correlation degree of oil recovery effect to determine the correlation degree sequence; according to the correlation degree sequence, the carbon dioxide oil recovery effect of the low permeability oil reservoir is determined through the preset oil recovery effect evaluation model; wherein, the oil recovery effect evaluation model analyzes the correlation degree sequence through the grey correlation method, and judges the oil recovery effect of the data obtained by the grey correlation method through the ideal production comparison.

[0015] The embodiments of the present application provide a method, device and medium for evaluating the effect of CO2 flooding in low permeability oil reservoirs. By real-time monitoring of multiple real-time parameters in the CO2 oil flooding process in low permeability oil reservoirs, the effect of carbon dioxide oil flooding in low permeability oil reservoirs is determined through multiple real-time parameters. The technical problems of long evaluation cycle, poor accuracy and inability to achieve real-time evaluation in the prior art analysis of the evaluation of carbon dioxide oil flooding effect in low permeability oil reservoirs are solved. The method has the technical effects of real-time, high accuracy and short evaluation cycle of carbon dioxide oil flooding evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A flow chart of a method for evaluating the effect of CO2 flooding in a low permeability reservoir provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the internal structure of a device for evaluating the effect of CO2 flooding in a low permeability reservoir provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0020] The embodiments of the present application provide a method, device and medium for evaluating the effect of CO2 flooding in low permeability oil reservoirs. By real-time monitoring of multiple real-time parameters in the CO2 oil flooding process in low permeability oil reservoirs, the effect of carbon dioxide oil flooding in low permeability oil reservoirs is determined through multiple real-time parameters. The technical problems of long evaluation cycle, poor accuracy and inability to achieve real-time evaluation in the prior art analysis of the evaluation of carbon dioxide oil flooding effect in low permeability oil reservoirs are solved. The method has the technical effects of real-time, high accuracy and short evaluation cycle of carbon dioxide oil flooding evaluation.

[0021] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0022] Figure 1 A flow chart of a method for evaluating the effect of CO2 flooding in a low permeability reservoir provided in an embodiment of the present application. Figure 1 As shown, a method for evaluating the effect of CO2 flooding in a low permeability reservoir provided in an embodiment of the present application specifically comprises the following steps:

[0023] Step 101, monitor the carbon dioxide flooding through a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and obtain the volume of carbon dioxide produced in the processing cycle through a preset cycle accumulation method based on the real-time parameter data of carbon dioxide.

[0024] Among them, the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide and real-time production data of carbon dioxide.

[0025] Specifically, it includes: smoothing and continuously processing the real-time carbon dioxide production data to obtain a real-time carbon dioxide pressure data graph; marking the pressure nodes of the real-time carbon dioxide data graph to determine the carbon dioxide volume accumulation node; based on the carbon dioxide volume accumulation node, dynamically accumulating the gas volume through a preset period to obtain the volume of carbon dioxide produced in the processing period.

[0026] The present application determines the volume of carbon dioxide produced in the processing cycle by performing real-time node determination and periodic accumulation processing on real-time carbon dioxide production data, thereby achieving node-based acquisition of the volume of carbon dioxide for carbon dioxide flooding in low permeability reservoirs.

[0027] Before monitoring the carbon dioxide flooding through the carbon dioxide monitoring system to obtain real-time carbon dioxide parameter data, the method also includes: evaluating the formation parameters of the low permeability oil reservoir by carbon dioxide injection to determine the carbon dioxide injection parameters.

[0028] This application determines the parameter data basis for evaluating low permeability reservoirs by evaluating the carbon dioxide injection volume before carbon dioxide flooding in low permeability reservoirs, thereby improving the accuracy of evaluating the CO2 flooding effect in low permeability reservoirs.

[0029] In the examples of the present application, detailed explanation is given by the following Example 1.

[0030] Example 1: Due to the inherent properties of low permeability reservoirs, CO2 flooding is a highly efficient oil recovery method, especially in oil wells that are about to be abandoned in the conventional sense or oil wells that basically do not produce oil using conventional oil recovery methods.

[0031] Before conducting carbon dioxide flooding in low permeability reservoirs, it is necessary to analyze the formation parameters of the oil well.

[0032] It is necessary to conduct a detailed analysis of the oil layer through methods such as rock physical property analysis, permeability measurement, porosity analysis, formation pressure and temperature measurement, and sedimentary environment analysis to determine whether the oil well is suitable for carbon dioxide recovery and parameters such as the injection amount and concentration of carbon dioxide.

[0033] After determining the CO2 injection parameters, CO2 flooding is carried out and CO2 is injected into the formation. Since it is necessary to pay attention to the pore throat size in the formation (CO2 can handle pore throats of 0.01 μm in size), the injection rate and concentration of CO2 also need to be preliminarily adjusted.

[0034] During the injection process, the optical fiber sensor installed at the designated position monitors the concentration of the injected carbon dioxide in real time, and generates a real-time change curve of the carbon dioxide concentration through the optical fiber sensing method matched with the optical fiber sensor.

[0035] Then, a pipeline pressure meter and a pipeline flow meter set at a designated position of the carbon dioxide injection pipeline collect data of real-time carbon dioxide pressure and real-time carbon dioxide flow, and convert the collected data into a continuous and smooth curve.

[0036] It should be noted that the continuity of the existing data collection method for gas parameters is not truly continuous. When the collection accuracy is high enough, it is essentially a discrete data set with extremely low time intervals. Therefore, it is necessary to smooth and continuously process the collected data to obtain a continuous real-time carbon dioxide pressure data graph. Similarly, a real-time carbon dioxide flow data graph can also be obtained according to the above method.

[0037] For the injected carbon dioxide, since it has not yet combined with crude oil in the formation, it is only necessary to determine the real-time parameters of the injected carbon dioxide, namely the pressure, flow rate and concentration of the carbon dioxide in the injection pipeline.

[0038] Regarding the produced carbon dioxide, it should be noted that the gas-oil ratio is a key indicator to characterize the production effect. Among them, carbon dioxide is used as oil-displacing gas, and its volume is the volume after oil and gas separation; similarly, the volume of the produced oil reservoir is also the pure crude oil after oil and gas separation.

[0039] First, it is necessary to determine the real-time carbon dioxide flow data graph through the above method, and determine the carbon dioxide volume accumulation node according to the actual required time period. It should be noted that the determination of the gas volume must be obtained within the time period, that is, the carbon dioxide flow parameters and flow time determine the volume of carbon dioxide.

[0040] According to the carbon dioxide flow parameters and flow time, the volume of carbon dioxide produced during the treatment period is determined by performing corresponding mathematical analysis on the real-time carbon dioxide flow data graph.

[0041] Step 102: During the carbon dioxide flooding process, real-time formation pressure data is obtained through a formation pressure monitoring model.

[0042] Among them, the formation pressure monitoring model includes: acoustic wave monitoring module and electromagnetic wave monitoring module.

[0043] Specifically, it includes: determining the formation acoustic wave velocity through an acoustic wave monitoring model to determine the first real-time formation pressure data; determining the formation electromagnetic wave velocity through an electromagnetic wave monitoring module to determine the second real-time formation pressure data; dividing the first real-time formation pressure data and the second real-time formation pressure data into formation areas to obtain real-time formation area pressure data; performing formation pressure multi-scale processing on the real-time formation area pressure data to obtain real-time formation pressure data.

[0044] This application performs multi-scale processing on the formation acoustic wave velocity and the formation electromagnetic wave velocity through a formation pressure monitoring model to eliminate the influence of formation parameters on the formation pressure data, thereby achieving accurate measurement of the formation pressure data.

[0045] In the examples of the present application, detailed explanation is given by the following Example 2.

[0046] Example 2: The acoustic wave monitoring module uses the propagation speed of acoustic waves in underground rocks to infer the formation pressure. The acoustic wave speed is related to parameters such as formation density and elastic modulus, and the formation pressure data is affected by the physical properties of the rock.

[0047] The electromagnetic wave monitoring module measures the propagation speed of electromagnetic waves in underground rocks to estimate the formation pressure. The electromagnetic wave speed is related to parameters such as the formation dielectric constant and magnetic permeability. The formation pressure data is affected by factors such as groundwater that interfere with the transmission of electromagnetic waves.

[0048] The present application determines the velocity of formation acoustic waves through an acoustic wave monitoring model to determine first real-time formation pressure data; and determines the velocity of formation electromagnetic waves through an electromagnetic wave monitoring module to determine second real-time formation pressure data.

[0049] According to the stratigraphic parameters of the low permeability reservoir obtained in advance, the stratigraphic area of ​​the low permeability reservoir is divided to determine the specific coordinates of the affected area.

[0050] According to the specific coordinates of the affected area, the first real-time formation pressure data and the second real-time formation pressure data are divided into corresponding formation areas. In the divided first real-time formation pressure data and the second real-time formation pressure data, the area where the wave velocity is directly measured and the area where multi-scale analysis is required, i.e., the affected area, can be determined.

[0051] The first real-time formation pressure data and the second real-time formation pressure data are subjected to multi-scale analysis, the measurement results of the data determined by the acoustic wave monitoring module and the electromagnetic wave monitoring module at different scales are fused and compared, and the influence of the impact area on the formation pressure data is eliminated to obtain real-time formation area pressure data.

[0052] When the acoustic wave propagation velocity in the formation is measured to be 3456.3 m / s and the electromagnetic wave propagation velocity is measured to be 2.03×10^8 m / s, the first real-time formation pressure is determined to be 45 MPa and the second real-time formation pressure is determined to be 43 MPa.

[0053] It should be noted that the formation pressure is the formation pressure value of one area after the formation area is divided.

[0054] The time and space scales are processed through multi-scale analysis. The bandpass filtering method is used for the time scale, which is convenient for directly referring to the data obtained by the formation pressure monitoring model to determine the filtering range; the spatial scale adopts spatial fusion, and Bayesian fusion can be used for data processing.

[0055] The real-time formation pressure of the corresponding multi-scale analysis area is 44.2 Mpa. The above data are examples for easy understanding of the technical solution.

[0056] Step 103, collect periodic flow rate of the produced oil reservoir to determine the volume of the produced oil reservoir in the processing cycle, and determine the periodic gas-oil ratio according to the volume of carbon dioxide produced in the processing cycle and the volume of the oil reservoir.

[0057] Specifically, it includes: based on the produced oil reservoir, determining the real-time produced oil reservoir flow rate through a preset produced oil reservoir flow meter; removing impurities from the produced oil reservoir to obtain a pure oil reservoir, and determining the volume shrinkage ratio based on the reservoir change data after the oil reservoir is removed; based on the real-time produced oil reservoir flow rate and the volume shrinkage ratio, obtaining the periodic acquisition flow rate of the pure oil reservoir to determine the volume of the produced oil reservoir within the processing period.

[0058] The present application collects periodic flow rate of produced oil reservoirs, performs purification processing and sample selection according to the collection time and the collected oil reservoir volume, and analyzes the collected volume of the oil reservoir produced during the processing period according to the shrinkage ratio of the purified oil reservoir to determine the volume of the oil reservoir produced during the processing period, thereby achieving real-time monitoring of the volume of low-permeability oil reservoirs during the processing period, and solving the technical problem in the prior art that key characterization parameters of the oil reservoir can only be analyzed through experiments or modeling after the oil reservoir is produced.

[0059] In the examples of the present application, this is explained in detail through the following Example 3.

[0060] Example 3: It should be noted that the continuity of the existing data collection method for produced oil reservoirs is not truly continuous. When the collection accuracy is high enough, it is essentially a discrete data set with extremely low time intervals. Therefore, the periodic flow collection method is still used to perform flow and volume analysis of produced oil reservoirs.

[0061] For reservoir decontamination, the low permeability oil produced and the carbon dioxide mixture are initially separated. For oil and gas mixtures containing carbon dioxide, further treatment is required to remove the carbon dioxide.

[0062] The real-time produced oil reservoir flow rate is determined by setting a produced oil reservoir flow meter at a designated location. The volume of the produced oil reservoir is similar to the method for determining the volume of produced carbon dioxide, which will not be described in detail here.

[0063] The reservoir decontamination process first selects sample sets corresponding to different time periods and different collection volumes, and performs reservoir decontamination on the sample sets.

[0064] Then, the volume shrinkage ratio is determined based on the reservoir change data after the reservoir is decontaminated.

[0065] After obtaining the data set of volume shrinkage ratio, a standard volume shrinkage ratio is determined by variance analysis. According to the standard volume shrinkage ratio and combined with the volume of the produced oil reservoir, the volumes of the produced oil reservoir and the produced carbon dioxide can be determined, and then the periodic gas-oil ratio can be determined.

[0066] Step 104: evaluate the correlation of oil recovery effect with the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine a correlation sequence.

[0067] Specifically, the method includes: serializing the oil recovery effect of the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine the oil recovery effect comparison sequence; dimensionlessly processing the oil recovery effect comparison sequence to obtain the sequence to be associated; and determining the correlation degree sequence by matching the reference sequence according to the sequence to be associated.

[0068] The present application performs correlation evaluation on the acquired relevant low permeability reservoir recovery effect parameters and determines their correlation sequence, thereby realizing the processing of background data for low permeability reservoir recovery effect evaluation under the condition of real-time parameter collection, and solves the technical problem in the prior art that key characterization parameters of the reservoir can only be analyzed through experiments or modeling after the oil reservoir is exploited.

[0069] In the examples of the present application, a detailed explanation is given by the following Example 4.

[0070] Example 4: The real-time parameter data of carbon dioxide, real-time formation pressure data and periodic gas-oil ratio are serialized to determine the oil recovery effect through grey correlation analysis.

[0071] First, it is necessary to determine the reference sequence, that is, the CO2 flooding effect (oil recovery effect) in low permeability reservoirs. In the case of using it as a reference sequence, the standard pattern of the reference sequence needs to be determined in advance.

[0072] Then, a comparison sequence needs to be determined, wherein the comparison sequence includes: real-time parameter data of carbon dioxide, real-time formation pressure data and periodic gas-oil ratio.

[0073] After the corresponding reference sequence and comparison sequence are determined, they can be used as the correlation degree sequence of the grey correlation analysis method.

[0074] Step 105: According to the correlation degree sequence, the carbon dioxide flooding effect of the low permeability reservoir is determined by using a preset flooding effect evaluation model.

[0075] Specifically, the method includes: performing grey correlation processing on the correlation degree sequence to determine the correlation degree between the correlation degree sequences; performing real-time correlation of carbon dioxide flooding in low permeability reservoirs according to the correlation degree to obtain real-time evaluation parameters; and comparing the real-time evaluation parameters with the preset ideal production parameters to determine the effect of carbon dioxide flooding in low permeability reservoirs.

[0076] After determining the carbon dioxide oil recovery effect of the low permeability oil reservoir through a preset oil recovery effect evaluation model according to the correlation sequence, the method also includes: quantizing the data of the carbon dioxide oil recovery effect of the low permeability oil reservoir to obtain the vector data of the carbon dioxide oil recovery effect of the low permeability oil reservoir; inputting the carbon dioxide oil recovery effect of the low permeability oil reservoir into a preset carbon dioxide oil recovery time series model of the low permeability oil reservoir to obtain the development degree of the low permeability oil reservoir; and determining the low permeability oil reservoir development strategy according to the development degree of the low permeability oil reservoir.

[0077] The present application uses the grey correlation analysis method and the oil recovery effect evaluation model to comprehensively evaluate and determine the carbon dioxide oil recovery effect and low permeability oil reservoir development strategy in low permeability oil reservoirs, and realizes the evaluation of the carbon dioxide oil recovery effect in low permeability oil reservoirs under the condition of real-time parameter collection, which solves the technical problem that the key characterization parameters of the oil reservoir can only be analyzed through experiments or modeling after the oil reservoir is exploited in the prior art.

[0078] In the examples of the present application, this is explained in detail through the following Example 5.

[0079] Example 5: First, the correlation degree sequence is analyzed by grey correlation analysis. The grey correlation formula is expressed by the following formula (1):

[0080] (1)

[0081] in, It represents the correlation between the i-th factor and the j-th factor;

[0082] and Respectively represent the correlation data series of the i-th factor and the j-th factor,

[0083] Δ is the correlation analysis constant.

[0084] After determining the correlation between the i-th factor and the j-th factor, it is necessary to determine the correlation between the reference sequence and the comparison sequence, which is expressed by the following formula (2):

[0085] (2)

[0086] in, The grey correlation coefficient is serialized for low permeability reservoirs;

[0087] is a sequence curve group;

[0088] is the resolution coefficient;

[0089] is the minimum difference between the two levels;

[0090] is the minimum difference between the two levels;

[0091] is the absolute difference, that is, the absolute difference between each point on the comparison series curve and each point on the reference series curve.

[0092] Finally, the coefficient of correlation is determined by the following formula (3):

[0093] (3)

[0094] Where n is the length of the data sequence;

[0095] is the degree of relevance;

[0096] These are the grey correlation coefficients for serialization of low permeability reservoirs.

[0097] By determining the degree of association , the effect of carbon dioxide flooding in low permeability reservoirs can be determined, and then the development degree of low permeability reservoirs obtained according to the carbon dioxide flooding time series model of low permeability reservoirs can be determined to determine the development strategy of low permeability reservoirs.

[0098] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this embodiment of the application also provides a device for evaluating the CO2 flooding effect in a low permeability reservoir, and its structure is as follows: Figure 2 shown.

[0099] Figure 2 This is a schematic diagram of the internal structure of a device for evaluating the CO2 flooding effect in a low permeability reservoir provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0100] at least one processor 201;

[0101] and, a memory 202 communicatively connected to the at least one processor;

[0102] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 to enable at least one processor 201 to:

[0103] The carbon dioxide flooding is monitored by a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and the volume of carbon dioxide produced in the processing cycle is obtained by a preset periodic accumulation method based on the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide, real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, through the real-time production pressure data of carbon dioxide analysis, and the pressure nodes obtained by the pressure data analysis are dynamically accumulated according to the preset period of gas volume; in the process of carbon dioxide flooding, real-time formation pressure data is obtained through a formation pressure monitoring model; wherein the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave The monitoring module and the electromagnetic wave monitoring module respectively monitor the formation acoustic waves and formation electromagnetic waves in real time, and perform multi-scale processing on the real-time monitored data; collect the periodic flow rate of the produced oil reservoir to determine the volume of the oil reservoir produced in the processing period, so as to determine the periodic gas-oil ratio according to the volume of carbon dioxide produced in the processing period and the volume of the oil reservoir; evaluate the correlation degree of oil recovery effect of the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine the correlation degree sequence; determine the carbon dioxide oil recovery effect of low permeability oil reservoirs through a preset oil recovery effect evaluation model based on the correlation degree sequence; wherein, the oil recovery effect evaluation model analyzes the correlation degree sequence through the grey correlation method, and judges the oil recovery effect of the data obtained by the grey correlation method through ideal production comparison.

[0104] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for a method of evaluating CO2 flooding effect in a low permeability reservoir, storing computer executable instructions, wherein the computer executable instructions are set as:

[0105] The carbon dioxide flooding is monitored by a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and the volume of carbon dioxide produced in the processing cycle is obtained by a preset periodic accumulation method based on the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide, real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, through the real-time production pressure data of carbon dioxide analysis, and the pressure nodes obtained by the pressure data analysis are dynamically accumulated according to the preset period of gas volume; in the process of carbon dioxide flooding, real-time formation pressure data is obtained through a formation pressure monitoring model; wherein the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave The monitoring module and the electromagnetic wave monitoring module respectively monitor the formation acoustic waves and formation electromagnetic waves in real time, and perform multi-scale processing on the real-time monitored data; collect the periodic flow rate of the produced oil reservoir to determine the volume of the oil reservoir produced in the processing period, so as to determine the periodic gas-oil ratio according to the volume of carbon dioxide produced in the processing period and the volume of the oil reservoir; evaluate the correlation degree of oil recovery effect of the real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio to determine the correlation degree sequence; determine the carbon dioxide oil recovery effect of low permeability oil reservoirs through a preset oil recovery effect evaluation model based on the correlation degree sequence; wherein, the oil recovery effect evaluation model analyzes the correlation degree sequence through the grey correlation method, and judges the oil recovery effect of the data obtained by the grey correlation method through ideal production comparison.

[0106] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0107] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0113] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0114] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0115] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0116] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for evaluating the effect of CO2 flooding in a low permeability reservoir, characterized in that: The method comprises: The carbon dioxide flooding is monitored by a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and the volume of carbon dioxide produced in the processing cycle is obtained by a preset periodic accumulation method according to the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide and real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, analyzing the real-time production pressure data of carbon dioxide, and dynamically accumulating the gas volume according to a preset period at the pressure nodes obtained by the pressure data analysis; In the process of carbon dioxide flooding, real-time formation pressure data is obtained through a formation pressure monitoring model; wherein the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave monitoring module and the electromagnetic wave monitoring module are used to respectively monitor the formation acoustic wave and the formation electromagnetic wave in real time, and multi-scale processing is performed on the real-time monitored data; Collecting periodic flow rate of produced oil reservoir to determine the volume of produced oil reservoir in the period to be processed, and determining the periodic gas-oil ratio according to the volume of carbon dioxide produced in the period to be processed and the volume of oil reservoir; Performing oil recovery effect correlation evaluation on the real-time carbon dioxide parameter data, the real-time formation pressure data and the periodic gas-oil ratio to determine a correlation sequence; According to the correlation sequence, the carbon dioxide oil recovery effect in low permeability oil reservoirs is determined through a preset oil recovery effect evaluation model; wherein the oil recovery effect evaluation model analyzes the correlation sequence through a grey correlation method, and evaluates the oil recovery effect of the data obtained by the grey correlation method through ideal mining comparison.

2. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: According to the real-time parameter data of carbon dioxide, the volume of carbon dioxide extracted in the processing cycle is obtained by a preset cycle accumulation method, specifically including: The real-time carbon dioxide extraction data is processed smoothly and continuously to obtain a real-time carbon dioxide pressure data graph; Marking the pressure nodes of the carbon dioxide real-time data graph to determine the carbon dioxide volume accumulation node; Based on the carbon dioxide volume accumulation node, the gas volume is dynamically accumulated through a preset period to obtain the volume of carbon dioxide produced in the period to be processed.

3. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: During the carbon dioxide flooding process, real-time formation pressure data is obtained through the formation pressure monitoring model, including: Determine the formation acoustic wave velocity through the acoustic wave monitoring model to determine the first real-time formation pressure data; Determine the electromagnetic wave velocity of the formation through the electromagnetic wave monitoring module to determine the second real-time formation pressure data; Divide the first real-time formation pressure data and the second real-time formation pressure data into formation regions to obtain real-time formation regional pressure data; The real-time formation regional pressure data is subjected to formation pressure multi-scale processing to obtain the real-time formation pressure data.

4. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: The produced oil reservoir is subjected to periodic flow acquisition to determine the volume of the produced oil reservoir within the processing period, including: Based on the produced oil reservoir, determining a real-time produced oil reservoir flow rate through a preset produced oil reservoir flow meter; Decontaminate the produced oil reservoir to obtain a pure oil reservoir, and determine the volume shrinkage ratio according to the reservoir change data after decontaminate the oil reservoir; According to the real-time produced oil reservoir flow rate and the volume shrinkage ratio, the periodic collection flow rate of the pure oil reservoir is obtained to determine the volume of the oil reservoir produced in the processing period.

5. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: The real-time parameter data of carbon dioxide, the real-time formation pressure data and the periodic gas-oil ratio are evaluated for oil recovery effect correlation to determine a correlation sequence, specifically including: Sequencing the oil recovery effect of the carbon dioxide real-time parameter data, the real-time formation pressure data and the periodic gas-oil ratio to determine an oil recovery effect comparison sequence; Performing dimensionless processing on the oil displacement effect comparison sequence to obtain a sequence to be associated; The correlation degree sequence is determined by matching the sequence to be correlated with a reference sequence.

6. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: According to the correlation degree sequence, the carbon dioxide flooding effect of low permeability reservoirs is determined by a preset flooding effect evaluation model, specifically including: Performing grey correlation processing on the correlation degree sequences to determine the correlation degree between the correlation degree sequences; According to the correlation degree, real-time correlation of carbon dioxide flooding in low permeability reservoirs is performed to obtain real-time evaluation parameters; The real-time evaluation parameters are compared with preset ideal production parameters to determine the carbon dioxide recovery effect of the low permeability oil reservoir.

7. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: After determining the carbon dioxide flooding effect of low permeability reservoirs according to the correlation sequence through a preset flooding effect evaluation model, the method further includes: Vectorizing the data of the carbon dioxide flooding effect of the low permeability oil reservoir to obtain vector data of the carbon dioxide flooding effect of the low permeability oil reservoir; Inputting the carbon dioxide recovery effect of the low permeability oil reservoir into a preset carbon dioxide recovery time series model of the low permeability oil reservoir to obtain the development degree of the low permeability oil reservoir; According to the development degree of the low permeability oil reservoir, the development strategy of the low permeability oil reservoir is determined.

8. A method for evaluating the effect of CO2 flooding in a low permeability reservoir according to claim 1, characterized in that: Before monitoring the carbon dioxide flooding through the carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, the method further includes: The formation parameters of low permeability reservoirs are evaluated for CO2 injection to determine the CO2 injection parameters.

9. A device for evaluating the effect of CO2 flooding in low permeability reservoirs, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: The carbon dioxide flooding is monitored by a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and the volume of carbon dioxide produced in the processing cycle is obtained by a preset periodic accumulation method according to the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide and real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, analyzing the real-time production pressure data of carbon dioxide, and dynamically accumulating the gas volume according to a preset period at the pressure nodes obtained by the pressure data analysis; In the process of carbon dioxide flooding, real-time formation pressure data is obtained through a formation pressure monitoring model; wherein the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave monitoring module and the electromagnetic wave monitoring module are used to respectively monitor the formation acoustic wave and the formation electromagnetic wave in real time, and multi-scale processing is performed on the real-time monitored data; Collecting periodic flow rate of produced oil reservoir to determine the volume of produced oil reservoir in the period to be processed, and determining the periodic gas-oil ratio according to the volume of carbon dioxide produced in the period to be processed and the volume of oil reservoir; Performing oil recovery effect correlation evaluation on the real-time carbon dioxide parameter data, the real-time formation pressure data and the periodic gas-oil ratio to determine a correlation sequence; According to the correlation sequence, the carbon dioxide oil recovery effect in low permeability oil reservoirs is determined through a preset oil recovery effect evaluation model; wherein the oil recovery effect evaluation model analyzes the correlation sequence through a grey correlation method, and evaluates the oil recovery effect of the data obtained by the grey correlation method through ideal mining comparison.

10. A non-volatile computer storage medium for evaluating the effect of CO2 flooding in a low permeability reservoir, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: The carbon dioxide flooding is monitored by a carbon dioxide monitoring system to obtain real-time parameter data of carbon dioxide, and the volume of carbon dioxide produced in the processing cycle is obtained by a preset periodic accumulation method according to the real-time parameter data of carbon dioxide; wherein the real-time parameter data of carbon dioxide include: real-time injection parameters of carbon dioxide and real-time production data of carbon dioxide; the periodic accumulation method includes: based on the real-time parameter data of carbon dioxide, analyzing the real-time production pressure data of carbon dioxide, and dynamically accumulating the gas volume according to a preset period at the pressure nodes obtained by the pressure data analysis; In the process of carbon dioxide flooding, real-time formation pressure data is obtained through a formation pressure monitoring model; wherein the formation pressure monitoring model includes: an acoustic wave monitoring module and an electromagnetic wave monitoring module, through which the acoustic wave monitoring module and the electromagnetic wave monitoring module are used to respectively monitor the formation acoustic wave and the formation electromagnetic wave in real time, and multi-scale processing is performed on the real-time monitored data; Collecting periodic flow rate of produced oil reservoir to determine the volume of produced oil reservoir in the period to be processed, and determining the periodic gas-oil ratio according to the volume of carbon dioxide produced in the period to be processed and the volume of oil reservoir; Performing oil recovery effect correlation evaluation on the real-time carbon dioxide parameter data, the real-time formation pressure data and the periodic gas-oil ratio to determine a correlation sequence; According to the correlation sequence, the carbon dioxide oil recovery effect in low permeability oil reservoirs is determined through a preset oil recovery effect evaluation model; wherein the oil recovery effect evaluation model analyzes the correlation sequence through a grey correlation method, and evaluates the oil recovery effect of the data obtained by the grey correlation method through ideal mining comparison.

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