A functional bacteria oil recovery optimization injection and production system

Abstract

The present invention relates to the field of oil production technology, specifically a functional bacteria oil recovery optimization injection and production system, the system includes a strain environment adaptation module, a bacteria attachment behavior evaluation module, a bacteria liquid diffusion boundary identification module, a drive cycle limit determination module, and a spatial injection and production direction allocation module. In the present invention, by performing structured extraction and screening of well group temperature and pressure data, it is possible to quickly identify applicable geological units in combination with the physiological needs of the target bacteria, avoid metabolic delays or inactivation caused by environmental mismatch after bacteria injection, effectively identify the range of bacterial liquid propagation, determine the bacterial group efficiency attenuation cycle in combination with the change in emulsified product concentration, guide the setting of injection and production cycle boundaries, ensure the continuous output of bacterial strain activity, reversely infer the effective injection and production path through the temporal coincidence relationship between injection and production behavior, improve the bacteria liquid guidance accuracy and oil recovery coverage efficiency, and achieve the overall coordinated improvement of strain adaptability, injection and production accuracy, and diffusion effectiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of oil production, in particular to a functional bacteria oil recovery optimization injection and production system. Background Art

[0002] The field of oil extraction technology involves the exploration, development, and recovery of oil and gas resources, covering multiple core issues such as reservoir geological evaluation, drilling and completion, oil production engineering, enhanced oil recovery technology, and development dynamic analysis. In the mid- and late stages of oilfield development, improving the utilization of crude oil becomes a major task, especially in heavy oil reservoirs. Conventional thermal recovery methods are gradually restricted due to factors such as high energy consumption, heavy carbon emissions, and high costs, prompting green oil recovery methods represented by bioreplacement to attract attention. As one of the biological means of enhancing oil recovery, microbial flooding utilizes the growth and metabolism of specific functional bacteria in the formation to change the physical properties and seepage conditions of the reservoir through processes such as viscosity reduction, emulsification, and gas production to achieve crude oil utilization. It is an important part of the green transformation in the current heavy oil development.

[0003] Among them, the traditional functional bacteria oil recovery optimization injection and production system refers to the selection and breeding of bacteria with functions such as viscosity reduction, emulsification, and acid production, and the preparation of bacterial liquid at a certain concentration. It is injected into the wellbore together with a nutrient solution, transported through the formation into the target oil layer, and interacts with the crude oil in the reservoir through the production of metabolic products under suitable temperature and pressure conditions, thereby improving its fluidity and recovery conditions. The technical issues addressed by this system include injection and production imbalance caused by the high viscosity of heavy oil, low displacement efficiency, and difficulty in maintaining stable microbial activity. The traditional method uses a combination of quantitative bacterial liquid and carbon and nitrogen source nutrient system. Through periodic alternating injection and adjustment of injection and production intensity, the activity area and action intensity of the microorganisms are controlled to realize their oil recovery capacity. It also relies on field experience to adjust the bacterial concentration and injection frequency to adapt to different oil layer conditions.

[0004] Existing technologies rely on the alternating injection of quantitative bacterial solution and nutrient solution and the regulation of microbial activity areas by injection-production intensity. There is a lack of assessment of the matching between the temperature and pressure conditions of the well group formation and the physiological needs of the bacterial species. Large fluctuations in reservoir temperature and pressure can easily lead to the inability of the bacterial community to initiate metabolism or maintain activity. Furthermore, the microbial diffusion path and action boundary mainly rely on the on-site production fluid change trend and empirical judgment. There is a lag in boundary identification, making it difficult to accurately define the scope of bacterial solution influence. Cycle settings are mostly based on fixed intervals and empirical rules, making it difficult to respond to changes in bacterial community activity in a timely manner. This can easily lead to problems such as bacterial solution waste or insufficient displacement capacity. There is also a lack of data-driven analysis in the injection-production path, making it difficult to achieve directional guidance and coverage optimization, thereby reducing oil recovery efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a functional bacteria oil recovery optimization injection and production system.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a functional bacteria oil recovery optimization injection and production system comprising:

[0007] The strain environment adaptation module obtains the bottom hole temperature and bottom hole pressure data corresponding to the injection and production well groups, extracts the bottom hole temperature difference and pressure difference in each pair of well groups, sets the upper and lower temperature limits and the pressure stability range, determines whether the combination falls into the adaptation range, and obtains a regional strain-oriented adaptation list;

[0008] The bacterial attachment behavior assessment module applies the bacterial species to the heavy oil sample under a temperature-controlled environment according to the bacterial species number in the targeted adaptation list of the regional bacterial species, obtains the target viscosity value of each section in the heavy oil sample, records the expansion of the bacterial species' attachment area per unit time and the duration of attachment at each viscosity value, and generates a bacterial species heavy oil adhesion performance distribution map;

[0009] The bacterial liquid diffusion boundary recognition module collects pressure change data of the injection well and the adjacent production well based on the distribution map of the bacterial strain's heavy oil adhesion performance, identifies the time and spatial location of the inflection point of the production well pressure change, and constructs the spatial contour line of the diffusion boundary of the injection and production area;

[0010] The driving cycle boundary determination module calls the diffusion boundary spatial contour line of the injection and production area, extracts the concentration of the emulsified product in the production liquid, records the starting time and duration of the continuous downward trend, determines whether it exceeds the reference cycle of the bacterial physiological stage, outputs the boundary identification point group of the bacterial community action stage, and defines the diffusion range of the bacterial liquid.

[0011] As a further solution of the present invention, the regional bacterial strain targeted adaptation list includes a bacterial strain number label, an applicable unit number, a metabolic starting sequence, and a survival maintenance time limit block; the bacterial strain heavy oil adhesion performance distribution map includes an adhesion viscosity interval distribution, an interface contact area annotation, and a continuous adhesion time period index; the injection and production area diffusion boundary spatial contour line includes a bacterial liquid expansion boundary coordinate point set, a boundary connection line sequence structure, and a regional boundary coverage distribution; the bacterial community action stage boundary identification point group includes a boundary identification time point, an emulsification product concentration threshold, a continuous decline period length, and a bacterial body physiological cycle over-limit mark.

[0012] As a further solution of the present invention, the bacterial strain environment adaptation module includes:

[0013] The well group construction submodule obtains the bottom hole temperature and bottom hole pressure data corresponding to the injection and production well groups, constructs a well group set according to the injection wells and production wells arranged in pairs, calculates the difference between the bottom hole temperature values ​​of the injection wells and production wells in each pair of well groups, and combines them according to the sequence position of the well groups to generate temperature and pressure difference pairs;

[0014] The parameter combination screening submodule calls the temperature-pressure combination difference pair, and determines whether the temperature difference and pressure difference fall within the dual adaptation interval based on the temperature upper and lower limits and pressure stability interval set by the functional strain, and screens the well groups that meet the dual adaptation conditions to obtain the strain adaptation well group identification set;

[0015] The applicable unit sorting submodule calls the bacterial species metabolic start time and survival duration corresponding to the adaptation well group according to the bacterial species adaptation well group identification set, calculates the adaptation priority value of the bacterial species in the adaptation well group, arranges the priority values ​​in descending order, and obtains the regional bacterial species targeted adaptation list.

[0016] As a further embodiment of the present invention, the bacterial attachment behavior evaluation module includes:

[0017] The bacterial species action submodule selects heavy oil samples based on the bacterial species numbers in the regional bacterial species targeted adaptation list under a temperature-controlled environment and acts on the sample surfaces one by one according to the numbers, calling the heavy oil sample numbers and temperature control values ​​to obtain a viscosity range sample set;

[0018] The attachment behavior recording submodule calls the viscosity segment sample set, records the attachment area expansion value and attachment duration per unit time of the bacterial species under the differentiated viscosity values, and obtains the viscosity segment attachment amount sequence;

[0019] The performance distribution submodule calls the viscosity segment adhesion amount under the corresponding strain number according to the viscosity segment adhesion amount sequence, calculates the adhesion performance index of the strain number in the heavy oil sample, and visualizes the adhesion performance index distribution according to the viscosity segment to generate a strain heavy oil adhesion performance distribution map.

[0020] As a further solution of the present invention, the bacterial liquid diffusion boundary recognition module includes:

[0021] The bacterial strain location identification submodule identifies the pairing relationship between each injection well and the adjacent production wells based on the bacterial strain heavy oil adhesion performance distribution map and compares the spatial correspondence between the injection start well and the surrounding production wells, thereby generating a target well group pairing list;

[0022] The pressure inflection point screening submodule collects the pressure change sequence data of the injection well and the adjacent wells during the injection cycle according to the target well group pairing list, determines the inflection point position of the pressure change rate in each sequence, records the time and space position of the inflection point, and obtains the early response point distribution map;

[0023] The diffusion boundary contour construction submodule calls the early response point distribution map, performs spatial position aggregation analysis and boundary drawing, calculates the bacterial liquid action intensity value, selects the boundary point set according to the intensity value variation range, and establishes the diffusion boundary spatial contour line of the injection and production area.

[0024] As a further solution of the present invention, the driving cycle limit determination module includes:

[0025] The well location extraction submodule extracts a sequence of spatial coordinate points of the well locations according to the diffusion boundary spatial contour line of the injection and production area, numbers the well locations, classifies the well locations according to the time series, and obtains well location sequence data;

[0026] The emulsion product monitoring submodule extracts the concentration of the emulsion product in the production fluid based on the well location sequence data, analyzes the starting time and duration of the continuous downward trend, and associates the time series of the well location with the corresponding emulsion product concentration to obtain the emulsion product concentration change identification result;

[0027] The bacterial colony stage determination submodule calls the identification result of the emulsified product concentration change, compares the relationship between the duration of the downward trend of the sampling well location and the reference cycle value of the bacterial physiological stage, selects the point whose duration exceeds the reference cycle value of the bacterial physiological stage as the cycle termination reference node, and generates a bacterial colony action stage boundary identification point group.

[0028] As a further solution of the present invention, the system further includes a spatial injection and production direction allocation module:

[0029] The spatial injection and production direction allocation module extracts the time series of liquid collection volume and the time interval series of liquid injection in the injection direction according to the direction of the line connecting the collection well position and the injection well in the boundary identification point group of the bacterial colony action stage, determines the overlapping order of the start time of the liquid collection change and the injection time interval, and selects the direction with the priority of the overlapping start time as the injection and production direction, thereby generating a parameter set of the bacterial liquid directional control path;

[0030] The bacterial liquid directional control path parameter set includes the injection deflection direction, the connection priority channel, and the delivery rhythm sequence.

[0031] As a further solution of the present invention, the space injection and production direction allocation module includes:

[0032] The liquid collection analysis submodule uses the location of the collection well and the direction of the connection line of the injection well in the boundary identification point group of the bacterial community action stage, collects the time series data of the liquid collection volume, detects the change of the liquid collection volume at different time points, and obtains the time interval of the liquid collection change;

[0033] The injection time judgment submodule records the start and end times of the differentiated injection cycles according to the liquid sampling change time interval and the injection time interval sequence data, selects the injection cycles with overlapping time, and generates an injection time overlapping sequence;

[0034] The injection and collection direction screening submodule determines the overlapping order of the collection change start time and the injection time according to the order relationship of the injection time overlapping sequence, screens the priority injection and collection direction, and generates a bacterial liquid directional control path parameter set.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are:

[0036] In the present invention, by performing structured extraction and screening of well group temperature and pressure data, it is possible to quickly identify applicable geological units in combination with the physiological needs of the target bacterial species, avoid metabolic delay or inactivation caused by environmental mismatch after bacterial species injection, and at the same time determine the injection priority in combination with metabolic initiation and duration, improve the effective action ratio of the bacterial community, evaluate the bacterial attachment and diffusion behavior under the rheological properties of heavy oil, obtain the dynamic diffusion trend and action boundary, effectively identify the propagation range of the bacterial solution, determine the bacterial community efficiency attenuation period in combination with the change in emulsified product concentration, guide the setting of injection and production cycle boundaries, ensure the continuous output of bacterial species activity, reversely infer the effective injection and production path through the temporal overlap relationship between injection and production behavior, improve the bacterial solution guidance accuracy and oil displacement coverage efficiency, and achieve an overall synergistic improvement in bacterial species adaptability, injection and production accuracy, and diffusion effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a system flow chart of the present invention;

[0038] Figure 2 This is a flow chart of the bacterial strain environment adaptation module in the present invention;

[0039] Figure 3 This is a flow chart of the bacterial attachment behavior evaluation module in the present invention;

[0040] Figure 4 This is a flow chart of the bacterial liquid diffusion boundary identification module in the present invention;

[0041] Figure 5 This is a flow chart of the drive cycle limit determination module in the present invention;

[0042] Figure 6 This is a flow chart of the space injection and production direction adjustment module in the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0045] See also Figure 1 A functional bacteria oil recovery optimization injection and production system includes:

[0046] The strain environment adaptation module obtains the bottomhole temperature and bottomhole pressure data corresponding to the injection and production well groups, constructs a well group set according to the injection well and production well pair arrangement, extracts the bottomhole temperature difference and pressure difference in each well group, and combines the two one-to-one according to the sequence position. Based on the upper and lower temperature limits and pressure stability range set by the functional strain, it determines whether the combination falls into the adaptation range. The well groups determined to be adapted are marked as strain-suitable units. The strains are prioritized according to the metabolic start-up time and survival duration of the strains in the applicable units to obtain a regional strain-specific adaptation list.

[0047] The bacterial attachment behavior assessment module applies the bacterial strains to the heavy oil sample under a temperature-controlled environment according to the strain number in the regional targeted adaptation list. It obtains the target viscosity value for each section of the heavy oil sample, records the expansion of the attachment area per unit time and the duration of attachment at each viscosity value, and generates a distribution map of the bacterial strain's heavy oil adhesion performance.

[0048] The bacterial solution diffusion boundary identification module collects pressure change data from the injection well and adjacent production wells during injection, based on the bacterial strain location indicated by the bacterial strain heavy oil adhesion performance distribution map. It identifies the time and spatial location of the inflection point of the production well pressure change, selects points where the inflection point time is lower than the standard period after the injection start, and delineates them as the bacterial solution outer edge boundary, thereby constructing the spatial contour line of the diffusion limit of the injection and production area.

[0049] The driving cycle boundary determination module calls the production well location in the diffusion boundary spatial contour line of the injection and production area, extracts the emulsified product concentration in the production fluid over time, records the starting time and duration of the continuous downward trend, determines whether it exceeds the reference cycle of the bacterial physiological stage, uses the point that meets the conditions as the cycle end reference node, and outputs the bacterial community action stage boundary identification point group;

[0050] The spatial injection and production direction allocation module extracts the time series of liquid collection volume and the time interval of liquid injection in the injection direction based on the location of the collection wells and the direction of the injection well connection in the boundary identification point group of the bacterial colony action stage. It determines the overlapping order of the start time of the liquid collection change and the injection time interval, and selects the direction with the priority of the overlapping start time as the injection and production direction to generate the parameter set of the bacterial liquid directional control path.

[0051] The regional bacterial strain targeted adaptation list includes the bacterial strain number label, applicable unit number, metabolic starting sequence, and survival maintenance time limit block. The bacterial strain heavy oil adhesion performance distribution map includes the adhesion viscosity range distribution, interface contact area annotation, and continuous adhesion time period index. The diffusion boundary spatial contour line of the injection and production area includes the bacterial liquid expansion boundary coordinate point set, boundary connection sequence structure, and regional boundary coverage distribution. The boundary identification point group of the bacterial community action stage includes the boundary identification time point, emulsification product concentration threshold, continuous decline period length, and bacterial physiological cycle over-limit mark. The bacterial liquid targeted control path parameter set includes the injection deflection direction, connection priority channel, and release rhythm sequence.

[0052] See also Figure 2 , the strain environment adaptation module includes:

[0053] The well group construction submodule obtains the bottom hole temperature and bottom hole pressure data corresponding to the injection and production well groups, constructs a well group set according to the injection wells and production wells arranged in pairs, calculates the difference between the bottom hole temperature values ​​of the injection wells and production wells in each pair of well groups, and combines them according to the sequence position of the well groups to generate temperature and pressure difference pairs;

[0054] Select the coordinate information and logging numbers of the injection wells and oil production wells in the same operating block, call the bottom hole temperature measurement records and pressure test records, set the injection well number JZ01 and the production well number CY01 to complete the temperature and pressure data collection on October 10, 2024 and October 12, 2024, respectively, and obtain the bottom hole temperature of JZ01 to be 98.5℃, the bottom hole temperature of CY01 to be 91.2℃, the bottom hole pressure of JZ01 to be 18.2MPa, and the bottom hole pressure of CY01 to be 14.7MPa. Construct well group sets in pairs, according to the injection well number JZ01 and the production well number CY01. Wells are grouped into pairs in the order of injection and production wells. The difference in bottomhole temperature of each pair of injection and production wells is then calculated. The difference calculation formula is temperature difference = injection well temperature - production well temperature. In the above example, the temperature difference is 7.3°C, and the pressure difference = injection well pressure - production well pressure, resulting in a value of 3.5 MPa. After the calculation of the well groups is completed, a two-dimensional array is formed in the order of numbering and a sequence index is established. The temperature difference and pressure difference values ​​of the same well group are combined in a one-to-one correspondence. To verify the correctness of the calculation process, multiple data groups are set for actual calculation, and the following difference groups are calculated, as shown in Table 1:

[0055] Table 1: Temperature and pressure difference value table

[0056] ;

[0057] As shown in Table 1, the temperature-pressure combination difference pair is the basic data structure for constructing the well group environment adaptation judgment, and the temperature-pressure combination difference pair is obtained.

[0058] The parameter combination screening submodule calls the temperature-pressure combination difference pair. Based on the upper and lower temperature limits and pressure stability range set by the functional strain, it determines whether the temperature difference and pressure difference fall within the dual adaptation range, screens the well groups that meet the dual adaptation conditions, and obtains the strain-adapted well group identification set.

[0059] A judgment operation is performed on each pair of difference values. The judgment standard is determined according to the adaptation range of the specific functional strain to the environmental parameters. It is known that the temperature difference range for strain A is set to 5℃8℃, and the pressure difference stability range is 3.2MPa4.0MPa. This range is set as the strain environment adaptation threshold. The screening process requires judging the temperature difference and pressure difference one by one. The judgment criterion is that if the temperature difference value ∈ [5, 8] and the pressure difference value ∈ [3.2, 4.0], it is an adapted well group. Taking the JZ01-CY01 well group as an example, its temperature difference of 7.3℃ meets the temperature difference range condition, and the pressure difference of 3.5MPa meets the pressure stability range, so it is determined to be an adapted well group. If either the temperature difference or the pressure difference of a well group is not in the adaptation range, it is marked as unsuitable. During the screening, the Boolean judgment method is used to generate a logical identification matrix. A successful match is marked as "1" and an unmatched match is marked as "0". An adaptation list is established, and the following adaptation results are obtained, as shown in Table 2:

[0060] Table 2: Well group suitability determination table

[0061] ;

[0062] As shown in Table 2 , the judgment process is based on clear interval parameters to screen out the strain-matching well group identification set.

[0063] The applicable unit sorting submodule calls the bacterial strain metabolic start time and survival duration data corresponding to the adaptation well group according to the bacterial strain adaptation well group identification set, using the formula:

[0064] ;

[0065] Calculate the adaptation priority values ​​of the strains in the adaptation well group, sort the priority values ​​in descending order, and obtain the regional strain-specific adaptation list;

[0066] in, Representative The adaptation priority value of the strain in the adaptation unit, Indicates the The metabolic start-up time of the bacteria in each adaptation unit, Indicates the The survival duration of the bacteria in each adaptation unit, Indicates the The bottom hole temperature difference in each adapter unit, Indicates the The bottom hole pressure difference in each adapter unit, Indicates the The original temperature value of the injection well in each adapter unit, Indicates the The original pressure value of the production well in each adaptation unit;

[0067] The calculation logic of the formula is: the metabolic start time of the bacteria in the adaptation well group is and survival duration Add up to form the total time dimension of bacterial activity, multiplied by the bottom well temperature difference and pressure difference The absolute value between them expresses the intensity of temperature and pressure difference faced by the strain, and the square root is used to calculate the effect of injection well temperature. Well pressure The square root of the sum of the values ​​was taken to establish the temperature and pressure background load intensity factor. The formula structure reflects the adaptation efficiency of the strains through the model of "activity time × difference intensity / background intensity". In this structure, the summation operation reflects the impact of the strain duration on the priority adaptation ranking, the absolute value operation avoids the offset of the intensity by negative differences, and the square root operation is used to balance the suppression of the ranking value by excessive temperature and pressure background, ensuring data stability. This formula can be used to uniformly quantify the adaptation capabilities of different strains in multiple pairs of well groups, realizing a hierarchical and graded expression of regional adaptability.

[0068] The adaptation priority value refers to the comprehensive adaptability score of a certain bacterial species under a specific well group combination, which is used to reflect the degree of match between its biological activity and viability under a specific temperature and pressure environment. The higher the value, the more suitable the bacterial species is for targeted use under the conditions of the well group.

[0069] The metabolic start-up time and survival duration of strain A in JZ01-CY01 were set as 4 hours and 36 hours respectively. The bottom hole temperature difference was set to 7.3℃, the pressure difference was set to 3.5MPa, the injection well temperature was set to 98.5℃, and the production well pressure was set to 14.7MPa. 、 、 、 Substitute into the formula to calculate the priority value;

[0070] ;

[0071] Repeat the above steps to calculate the priority values ​​for JZ02-CY02 and JZ03-CY03 respectively. Assuming that the corresponding metabolic start time and survival duration are as follows: JZ02-CY02 is 5 hours and 30 hours, and JZ03-CY03 is 6 hours and 28 hours, the calculation is as follows:

[0072] ;

[0073] ;

[0074] Sort the calculation results in descending order to obtain the priority sorted well group sequence, as shown in the following table:

[0075] Table 3: Priority ranking of bacterial species

[0076] ;

[0077] As shown in Table 3, a list of regional strain-directed adaptations was obtained.

[0078] See also Figure 3 , the bacterial attachment behavior assessment module includes:

[0079] The strain action submodule selects heavy oil samples based on the strain number in the regional strain targeted adaptation list under a temperature-controlled environment and acts on the sample surface one by one according to the number. It calls the heavy oil sample number and temperature control value to obtain a viscosity range sample set.

[0080] The calling of the strain number in the strain-directed adaptation list is completed based on the guidance of the matching relationship matrix between the specific strain and the target viscosity. In this matrix, the horizontal axis is the strain number and the vertical axis is the target viscosity segment value. For a certain strain A1 and the target viscosity μ1, the table lookup method is used to determine whether A1 can be used for μ1. If there is a corresponding mark, the strain A1 is selected. If not, the similar target viscosity μ2, μ3 and other neighboring viscosity values ​​are sequentially retrieved, and the corresponding numbers are B1, C1 respectively, until a matching strain is found. The temperature control environment value T is adjusted to the target set value in real time through the PID temperature control. Set T = 37 ° C, the constant error is within ± 0.5 ° C, and the bran oil sample number is assigned according to the batch number. For example, the samples are numbered S001 to S050, and they are taken out one by one for surface treatment. The treatment process uses an automatic dripping device, and the single drop volume is set to 10 μL. Three drops of bacterial liquid are added to the surface of each sample. The treatment time is uniformly controlled to 15 minutes. During this time period, the real-time temperature T of the sample and its surface state image are collected as the original input for subsequent analysis. Finally, the bacterial treatment results of each sample from S001 to S050 under this temperature control are obtained to form a viscosity segment sample set.

[0081] The attachment behavior recording submodule calls the viscosity segment sample set, records the attachment area expansion value and attachment duration per unit time under different viscosity values, and obtains the viscosity segment attachment amount sequence;

[0082] Image recognition is used to automatically identify the edge of the attachment area. The image sequence is collected once per second, and the total collection time is T = 900 seconds. Each image is binarized and the area is calculated. , convert the number of pixels to area, assuming the image resolution is 640×480 pixels, the unit conversion rate is 1 pixel = 1.2μm², then if the attachment area in the image at a certain time t is 1600 pixels, then its area is 1920μm², and it is recorded every second , calculated at each viscosity value Attachment extension value under , and judge its duration. If the continuous expansion exceeds the set reference value β=500μm² and lasts for more than 300 seconds, it is determined to be a valid attachment event and recorded in each The adhesion area sequence A1, A2, ..., An and its corresponding duration T1, T2, ..., Tn under the condition of the strain are used to obtain the adhesion amount sequence of the viscosity segment corresponding to the strain;

[0083] Table 4: Sample data table

[0084] ;

[0085] As shown in Table 4, the main attachment behavior parameters of strain A1 in different viscosity ranges are listed to facilitate further calculation of its performance index according to the formula.

[0086] The performance distribution submodule calls the viscosity segment adhesion amount under the corresponding strain number according to the viscosity segment adhesion amount sequence, using the formula:

[0087] ;

[0088] Calculate the adhesion performance index of the bacterial species number in the heavy oil sample, visualize the adhesion performance index distribution according to the viscosity segment, and generate the bacterial species heavy oil adhesion performance distribution map;

[0089] in, Representative strain number The adhesion performance index, Representative strains In the viscosity range The expansion value of the attachment area per unit time under Representative strains In the viscosity range The duration of attachment, Viscosity range The viscosity value, is the mean viscosity value of the viscosity range, For strains The adhesion distribution stability coefficient, For strains The viscosity response change, For strains In the viscosity range The attachment delay time under is the number of viscosity segments;

[0090] The calculation logic of the formula is as follows: By summing the product of the bacterial species' attachment area per unit time and the square root of its attachment time within the viscosity range, and subtracting the absolute difference between the viscosity of the range and the average viscosity, the adhesion ability of the bacterial species under different viscosity conditions is reflected. This weighted sum is then used as the numerator, and the sum of the standard deviation (stability) of the bacterial species' attachment distribution, the response change rate (the degree of response to different viscosities), and the maximum delayed attachment time (start-up speed) is used as the denominator to achieve a normalized expression of the adhesion ability. This structure ensures that the bacterial species must have both high adhesion ability and distribution stability and a short response delay time to achieve a high adhesion performance index value.

[0091] The actual calculation example is as follows. Three samples of strain A1 are selected with viscosity values ​​of V1=30mPa·s, V2=60mPa·s, and V3=90mPa·s:

[0092] The adhesion areas are: A1=1200μm² / s, A2=1350μm² / s, A3=1000μm² / s;

[0093] The attachment durations were: T1 = 300 s, T2 = 450 s, and T3 = 270 s;

[0094] The delay times are: L1=40s, L2=35s, L3=50s;

[0095] mPa·s;

[0096] The viscosity difference parts are: |V1-60|=30, |V2-60|=0, |V3-60|=30;

[0097] They are 17.32, 21.21, and 16.43 respectively;

[0098] Calculate the molecular part:

[0099] ;

[0100] Denominator: :The area standard deviation is calculated as: σ=sqrt(((1200-1183.3)²+(1350-1183.3)²+(1000-1183.3)²) / 3)≈145.6

[0101] : Segment change rate: (|150| / 30+|-350| / 30)=5+11.67=16.67;

[0102] ;

[0103] The denominator is:

[0104] ;

[0105] Substitute into the formula for calculation:

[0106] ;

[0107] The results showed that the adhesion performance index of strain A1 in the set viscosity range was 310.1. The adhesion performance index is used to quantify the product of the adhesion area per unit time and the duration of a certain strain in different viscosity ranges, and is normalized by combining its stability, response change rate and maximum delayed attachment time between each viscosity range to reflect the overall adhesion ability of the strain under different environmental viscosities, and is used for strain performance ranking and screening.

[0108] See also Figure 4 , the bacterial liquid diffusion boundary recognition module includes:

[0109] The bacterial strain location identification submodule, based on the bacterial strain heavy oil adhesion performance distribution map, compares the spatial correspondence between the injection start well and the surrounding production wells, identifies the pairing relationship between each injection well and the adjacent production wells, and generates a target well group pairing list;

[0110] Taking the well location map of the injection and production area as the spatial reference, the hot zone with the bacterial species attachment intensity greater than 0.7 was selected as the priority identification area, and the injection wells in the intersection area with the hot zone were numbered as ZJ-01 to ZJ-03. For the injection wells, a spatial buffer zone with a radius of 200 meters was constructed, and the production well numbers in the buffer zone were extracted. The spatial straight-line distance between the injection well and its adjacent production well was recorded. Taking ZJ-01 as an example, the distance to its adjacent CJ-01 was 120.5 meters, the distance to ZJ-02 was 135.0 meters, and the distance to ZJ-03 was 135.0 meters. -03, the distance is 148.3 meters, and the start time of each injection well is marked as injection 0 hours. The relative well position relationship between each group of injection and production wells is obtained, and their spatial arrangement direction and well network distribution density are recorded. A linear topological diagram of the well connection structure is constructed, and the number, horizontal distance, start time and spatial position number of each pair of injection and production wells are stored in the form of an array. The structure is {ZJ-01, CJ-01, 120.5, 0}, etc. Each pairing relationship is used as a unit to establish the corresponding effective well group, and a unified target number is assigned to generate a target well group pairing list.

[0111] The pressure inflection point screening submodule collects pressure change sequence data of the injection well and adjacent wells during the injection cycle based on the target well group pairing list, determines the inflection point position of the pressure change rate in each sequence, records the time and space location of the inflection point, and obtains the early response point distribution map;

[0112] Read the numbers of each pair of injection and production wells in turn, obtain the pressure change data from 0 to 24 hours after the start of injection, set the sampling interval to 0.5 hours, and construct the corresponding pressure time series. Taking the ZJ-01 / CJ-01 pair as an example, a total of 48 groups of pressure data points are collected, and the first-order difference operation is performed on the sequence to calculate the pressure change rate of each time period, that is, the rate of the nth time period is the pressure at the n+1th moment minus the pressure at the nth moment. For the point in the sequence where the rate changes from positive to negative, it is determined to be a declining inflection point, and the time point is recorded as the inflection point time. If the inflection point time of a point is 3.5 hours and the standard period after the injection is started is 6.0 hours, then the point meets the inflection time earlier than the standard period. Quasi-periodic conditions determine them as early response points. In the same way, CJ-02 has an inflection point at 4.0 hours, and CJ-03 has an inflection point at 5.2 hours, both earlier than 6.0 hours. Therefore, all three are early response points. The injection-production straight-line distance corresponding to each point is recorded, which are 120.5 meters, 135.0 meters, and 148.3 meters, respectively. At the same time, the injection-production ratio is calculated, which is 1.8 for CJ-01, 1.6 for CJ-02, and 1.5 for CJ-03. The injection volumes are 120.0, 130.0, and 145.0 cubic meters, respectively. The inflection point time, injection-production ratio, distance, and injection volume of the points are organized as structured data to obtain the early response point distribution map.

[0113] The diffusion boundary contour construction submodule calls the early response point distribution map to perform spatial position aggregation analysis and boundary drawing, using the formula:

[0114] ;

[0115] Calculate the bacterial liquid action intensity value, select the boundary point set according to the intensity value variation range, and establish the diffusion boundary spatial contour line of the injection and production area;

[0116] in, Indicates the bacterial solution intensity value, Indicates the The time corresponding to the inflection point of the well pressure, Indicates the The standard cycle time point after the injection is started, Indicates the Corresponding to the spatial distance between the injection well and the production well, Indicates the The injection-production ratio of each injection-production section, Indicates the The injection volume of each point injection, Indicates the total number of response points, Represents the response point number;

[0117] The calculation logic of the formula is as follows: For each point, the time difference between the inflection point and the standard period is obtained and the absolute value is taken to reflect the degree of advance in the time when the bacterial solution triggers a response at that point. The horizontal distance between the injection well and the production well is extracted and the square root is taken to reflect the physical length of the diffusion path. This is then multiplied by the time difference to represent the coupling effect between space and response speed. This product is used as the numerator and divided by the denominator composed of the sum of the injection-production ratio and the injection volume to form the intensity ratio of the unit diffusion efficiency. This value reflects the comprehensive expression of the bacterial solution diffusion process at that point, combining flow rate and receiving capacity.

[0118] The bacterial liquid action intensity value indicates the efficiency level of the bacterial liquid in causing a response at the target point within a unit diffusion time and diffusion distance. A larger value indicates that the bacterial liquid arrives earlier and the diffusion rate is significant.

[0119] This value comprehensively reflects the response timeliness, spatial propagation and injection efficiency, and has a direct quantitative support role in constructing the diffusion boundary contour line;

[0120] In the formula, Indicates the The turning point time of the production well at each point is in hours. Assume P1=3.5, P2=4.0, P3=5.2; The standard cycle time for liquid injection startup is set to 6.0 hours; is the horizontal distance between injection and production wells, in meters, which are 120.5, 135.0, and 148.3 respectively; is the injection-production ratio, which are 1.8, 1.6, and 1.5 respectively; is the injection volume of the injection liquid, in cubic meters, which are 120.0, 130.0, and 145.0 respectively; , is the total number of early response points;

[0121] The first point:

[0122] ;

[0123] The second point:

[0124] ;

[0125] The third point:

[0126] ;

[0127] The average value is:

[0128] ;

[0129] The bacterial liquid action intensity value is 0.1561, which is greater than the set minimum recognition threshold of 0.05, indicating that the identified points can be used to construct the contour boundary. The three points are calibrated in the well network map, and the polygon edge is constructed through spatial connection. The shortest boundary connection algorithm is used to connect the points to form a spatial closed line. The polygon outer edge is extracted to establish the spatial contour line of the diffusion boundary of the injection and production area.

[0130] See also Figure 5 , the driving cycle limit determination module includes:

[0131] The well location extraction submodule extracts the spatial coordinate point sequence of the well location based on the spatial contour line of the diffusion boundary of the injection and production area, numbers the well location, classifies the well location according to the time series, and obtains the well location sequence data;

[0132] Initial data on well locations is obtained through the spatial contour lines of the diffusion boundaries of the injection and production area. The data comes from geological exploration reports or field measurements. Geographic Information System (GIS) software is used to extract the spatial coordinate point sequence of each well location. The contour lines are converted into point data using the "Feature to Point" tool in ArcGIS to obtain the latitude and longitude coordinates of each well location. The extracted coordinate points are numbered. Automatic numbering can be used to ensure that each well location has a unique identifier. The numbering rule is "W well area number - well number", such as "W01-001". The well locations are classified according to the time series and their production time or time attributes. The production locations put into production in the same year are grouped together to form time series data and obtain well location sequence data.

[0133] The emulsion product monitoring submodule extracts the concentration of emulsion products in the production fluid based on the well location sequence data, analyzes the starting time and duration of the continuous downward trend, and correlates the time series of the well location with the corresponding emulsion product concentration to obtain the emulsion product concentration change identification result;

[0134] Based on the well location sequence data, determine the location and time information of each well. Use online monitoring equipment or laboratory analysis to regularly measure the concentration of emulsion products in the production fluid. Collect samples once a week and use a spectrophotometer to determine the concentration of emulsion products in mg / L. Analyze the time series data of emulsion product concentration to identify the starting time and duration of the continuous downward trend. The moving average method can be used to smooth the data, calculate the average concentration at each time point, and determine the time period of continuous decline. If the emulsion product concentration of a well starts to decline continuously from the 10th week and continues to the 15th week, the starting time is the 10th week and the duration is 6 weeks. Correlate the time series of the well location with the corresponding emulsion product concentration, including the well number, time, emulsion product concentration, the starting time and duration of the downward trend, to obtain the emulsion product concentration change identification result.

[0135] The bacterial colony stage determination submodule uses the emulsified product concentration change identification results to compare the duration of the downward trend at the sampling well location with the reference cycle value of the bacterial physiological stage. Points whose duration exceeds the reference cycle value of the bacterial physiological stage are selected as the cycle end reference nodes to generate a bacterial colony action stage boundary identification point group;

[0136] The emulsified product concentration change data is called to obtain the duration of the downward trend at each sampling well position, and the reference cycle value of the bacterial physiological stage is determined. This value is determined based on experimental data or literature. The reference cycle of the physiological stage of a certain bacterial body is set to 4 weeks. The relationship between the duration of the downward trend at each sampling well position and the reference cycle value is compared, and the sampling well positions with a duration exceeding the reference cycle value are screened. If the downward trend of a certain well lasts for 6 weeks, which exceeds the reference cycle value of 4 weeks, the sampling well position is screened out. The screened out sampling well position is used as the reference node for the end of the cycle, and its spatial coordinates and time information are recorded to generate a group of boundary identification points for the bacterial community action stage.

[0137] See also Figure 6 , the spatial injection and production direction allocation module includes:

[0138] The liquid collection analysis submodule uses the location of the collection well and the direction of the injection well connection in the boundary identification point group of the bacterial community action stage, collects the time series data of the liquid collection volume, detects the change of the liquid collection volume at different time points, and obtains the time interval of the liquid collection change;

[0139] The geological data of the designated production well location and injection well connection direction are retrieved and extracted, including well location coordinates, injection-production well connection direction, and time series production volume. Through database table association operations, the time series production volume and injection-production direction data are matched and associated to generate a preliminary data set. The generated data set is sorted according to the time axis to ensure temporal consistency. Differentiation detection is performed, and the time series data of production volume are dynamically monitored. During the data analysis process, the mean and standard deviation of the time series production volume are first calculated. Then, based on the mean and standard deviation, it is determined whether some observed values ​​of production volume deviate significantly from the data. When the degree of deviation exceeds a certain range, it is marked as a differentiated time point. The detected differentiated time points are aggregated to form a preliminary production change time interval. At the same time, 5-day data segments are taken before and after each differentiated time point, and data smoothing is performed using the local averaging method. After smoothing, the differentiated time points are reconfirmed and misjudged points are eliminated. Misjudged isolated points are eliminated to determine the production change time interval.

[0140] The injection time judgment submodule records the start and end times of differentiated injection cycles based on the time interval of liquid collection changes and the injection time interval sequence data, filters out injection cycles with overlapping times, and generates an injection time overlapping sequence;

[0141] Retrieve the injection cycle of the corresponding injection well, extract the injection start time and end time information, use the liquid production change time interval as the screening condition, perform time series comparative analysis, compare the time period of the injection cycle with the liquid production change time interval one by one, sort the injection cycles in chronological order, compare the injection cycle and the liquid production time interval, calculate the overlapping part of the two, and if there is a time intersection, it is determined that there is time overlap. The injection cycles with time overlap are recorded in the injection time overlap sequence. The starting point and end point of the time overlap period are extended forward and backward by 1 day respectively. The extended time period is compared with the liquid production time interval again. The injection cycles with time overlap are iteratively analyzed to form an injection time overlap sequence.

[0142] The injection and collection direction screening submodule determines the overlapping order of the collection change start time and the injection time based on the order of the injection time overlap sequence, screens the priority injection and collection direction, and generates a set of bacterial liquid directional control path parameters;

[0143] The start and end time of each injection cycle are extracted in chronological order, compared with the start time of the liquid production change time interval, sorted by time sequence, and the order relationship between the start time of the liquid production change and the injection cycle is determined. The order of priority screening is performed to extract the start time of the liquid production change and the start time of the injection cycle. If the start time of the liquid production change is later than the start time of the injection cycle, it is determined that the liquid production occurs after the injection. Otherwise, it is determined that the liquid production occurs before the injection. By prioritizing the order of each group of injection cycles, the injection and production directions corresponding to the injection cycles with high priorities are added to the bacterial liquid directional control path parameter set. At the same time, the relative weights of the injection pressure and the liquid production volume are set according to the priority of the injection and production directions. The weights can be adjusted according to the actual formation pressure difference and the liquid production volume difference to form the bacterial liquid directional control path parameter set.

[0144] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A functional bacteria oil recovery optimization injection and production system, characterized in that: The system comprises: The strain environment adaptation module obtains the bottom hole temperature and bottom hole pressure data corresponding to the injection and production well groups, extracts the bottom hole temperature difference and pressure difference in each pair of well groups, sets the upper and lower temperature limits and the pressure stability range, determines whether the combination falls into the adaptation range, and obtains a regional strain-oriented adaptation list; The bacterial attachment behavior assessment module applies the bacterial species to the heavy oil sample under a temperature-controlled environment according to the bacterial species number in the targeted adaptation list of the regional bacterial species, obtains the target viscosity value of each section in the heavy oil sample, records the expansion of the bacterial species' attachment area per unit time and the duration of attachment at each viscosity value, and generates a bacterial species heavy oil adhesion performance distribution map; The bacterial liquid diffusion boundary recognition module collects pressure change data of the injection well and the adjacent production well based on the distribution map of the bacterial strain's heavy oil adhesion performance, identifies the time and spatial location of the inflection point of the production well pressure change, and constructs the spatial contour line of the diffusion boundary of the injection and production area; The driving cycle boundary determination module calls the diffusion boundary spatial contour line of the injection and production area, extracts the concentration of the emulsified product in the produced liquid, records the starting time and duration of the continuous downward trend, determines whether it exceeds the reference cycle of the bacterial physiological stage, outputs the bacterial community action stage boundary identification point group, and defines the bacterial liquid diffusion range; The regional bacterial strain targeted adaptation list includes a bacterial strain number label, an applicable unit number, a metabolic starting sequence, and a survival maintenance time limit block. The bacterial strain heavy oil adhesion performance distribution map includes an adhesion viscosity interval distribution, an interface contact area annotation, and a continuous adhesion time period index. The injection and production area diffusion boundary spatial contour line includes a bacterial liquid expansion boundary coordinate point set, a boundary connection line sequence structure, and a regional boundary coverage distribution. The bacterial community action stage boundary identification point group includes a boundary identification time point, an emulsification product concentration threshold, a continuous decline period length, and a bacterial body physiological cycle overlimit mark.

2. The functional bacteria oil displacement optimization injection and production system according to claim 1, characterized in that: The bacterial strain environment adaptation module includes: The well group construction submodule obtains the bottom hole temperature and bottom hole pressure data corresponding to the injection and production well groups, constructs a well group set according to the injection wells and production wells arranged in pairs, calculates the difference between the bottom hole temperature values ​​of the injection wells and production wells in each pair of well groups, and combines them according to the sequence position of the well groups to generate temperature and pressure difference pairs; The parameter combination screening submodule calls the temperature-pressure combination difference pair, and determines whether the temperature difference and pressure difference fall within the dual adaptation interval based on the temperature upper and lower limits and pressure stability interval set by the functional strain, and screens the well groups that meet the dual adaptation conditions to obtain the strain adaptation well group identification set; The applicable unit sorting submodule calls the bacterial species metabolic start time and survival duration corresponding to the adaptation well group according to the bacterial species adaptation well group identification set, calculates the adaptation priority value of the bacterial species in the adaptation well group, arranges the priority values ​​in descending order, and obtains the regional bacterial species targeted adaptation list.

3. The functional bacteria oil displacement optimization injection and production system according to claim 2, characterized in that: The bacterial attachment behavior evaluation module includes: The bacterial species action submodule selects heavy oil samples based on the bacterial species numbers in the regional bacterial species targeted adaptation list under a temperature-controlled environment and acts on the sample surfaces one by one according to the numbers, calling the heavy oil sample numbers and temperature control values ​​to obtain a viscosity range sample set; The attachment behavior recording submodule calls the viscosity segment sample set, records the attachment area expansion value and attachment duration per unit time of the bacterial species under the differentiated viscosity values, and obtains the viscosity segment attachment amount sequence; The performance distribution submodule calls the viscosity segment adhesion amount under the corresponding strain number according to the viscosity segment adhesion amount sequence, calculates the adhesion performance index of the strain number in the heavy oil sample, and visualizes the adhesion performance index distribution according to the viscosity segment to generate a strain heavy oil adhesion performance distribution map.

4. The functional bacteria oil displacement optimization injection and production system according to claim 3, characterized in that: The bacterial liquid diffusion boundary recognition module includes: The bacterial strain location identification submodule identifies the pairing relationship between each injection well and the adjacent production wells based on the bacterial strain heavy oil adhesion performance distribution map and compares the spatial correspondence between the injection start well and the surrounding production wells, thereby generating a target well group pairing list; The pressure inflection point screening submodule collects the pressure change sequence data of the injection well and the adjacent wells during the injection cycle according to the target well group pairing list, determines the inflection point position of the pressure change rate in each sequence, records the time and space position of the inflection point, and obtains the early response point distribution map; The diffusion boundary contour construction submodule calls the early response point distribution map, performs spatial position aggregation analysis and boundary drawing, calculates the bacterial liquid action intensity value, selects the boundary point set according to the intensity value variation range, and establishes the diffusion boundary spatial contour line of the injection and production area.

5. The functional bacteria oil displacement optimization injection and production system according to claim 4 is characterized in that: The driving cycle limit determination module includes: The well location extraction submodule extracts a sequence of spatial coordinate points of the well locations according to the diffusion boundary spatial contour line of the injection and production area, numbers the well locations, classifies the well locations according to the time series, and obtains well location sequence data; The emulsion product monitoring submodule extracts the concentration of the emulsion product in the production fluid based on the well location sequence data, analyzes the starting time and duration of the continuous downward trend, and associates the time series of the well location with the corresponding emulsion product concentration to obtain the emulsion product concentration change identification result; The bacterial colony stage determination submodule calls the identification result of the emulsified product concentration change, compares the relationship between the duration of the downward trend of the sampling well location and the reference cycle value of the bacterial physiological stage, selects the point whose duration exceeds the reference cycle value of the bacterial physiological stage as the cycle termination reference node, and generates a bacterial colony action stage boundary identification point group.

6. The functional bacteria oil displacement optimization injection and production system according to claim 5, characterized in that: The system also includes a spatial injection and production direction adjustment module: The spatial injection and production direction allocation module extracts the time series of liquid collection volume and the time interval series of liquid injection in the injection direction according to the direction of the line connecting the collection well position and the injection well in the boundary identification point group of the bacterial colony action stage, determines the overlapping order of the start time of the liquid collection change and the injection time interval, and selects the direction with the priority of the overlapping start time as the injection and production direction, thereby generating a parameter set of the bacterial liquid directional control path; The bacterial liquid directional control path parameter set includes the injection deflection direction, the connection priority channel, and the delivery rhythm sequence.

7. The functional bacteria oil displacement optimization injection and production system according to claim 6, characterized in that: The space injection and production direction allocation module includes: The liquid collection analysis submodule uses the location of the collection well and the direction of the connection line of the injection well in the boundary identification point group of the bacterial community action stage, collects the time series data of the liquid collection volume, detects the change of the liquid collection volume at different time points, and obtains the time interval of the liquid collection change; The injection time judgment submodule records the start and end times of the differentiated injection cycles according to the liquid sampling change time interval and the injection time interval sequence data, selects the injection cycles with overlapping time, and generates an injection time overlapping sequence; The injection and collection direction screening submodule determines the overlapping order of the collection change start time and the injection time according to the order relationship of the injection time overlapping sequence, screens the priority injection and collection direction, and generates a bacterial liquid directional control path parameter set.

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