Thickened oil polymer flooding oil reservoir horizontal well productivity evaluation method and system

Abstract

The invention relates to the technical field of productivity evaluation, in particular to a thickened oil polymer flooding reservoir horizontal well productivity evaluation method and system.The method comprises the following steps that injection volume and daily fluid production data are collected to construct a sequence, rate transition and fluid volume inflection point judgment response starting are recognized, the fluid volume rising trend is analyzed to recognize a growth section, and the productivity of a horizontal well is evaluated. Dividing sub-intervals, evaluating fluctuation to determine an oil production stable section, and determining response association by combining concentration and a ratio to obtain a productivity section distribution interval. According to the method, the time sequence is constructed by collecting the daily injection volume, the rate transition is recognized, the production liquid increasing inflection point is extracted, the response starting moment is measured, the state of the horizontal well is recognized more accurately, based on the daily production liquid change comparison before and after starting, the daily oil production fluctuation in the lagging stage is evaluated, the stable oil production stage is screened, and the effective time of evaluation focusing is ensured. And a continuous descending starting point is delimited by comparing periodic oil production change, and the injection state is analyzed by combining the injection concentration and the liquid production ratio, so that productivity response staged identification is realized, and a basis is provided for adjustment.

Description

Technical Field

[0001] This invention relates to the field of productivity assessment technology, and in particular to a method and system for assessing the productivity of horizontal wells in heavy oil polymer flooding reservoirs. Background Technology

[0002] The field of heavy oil polymer flooding reservoir productivity assessment technology involves methods and techniques for analyzing the development efficiency of heavy oil reservoirs in reservoir engineering. It particularly focuses on the systematic assessment of the productivity change trends of horizontal wells in heavy oil reservoirs during polymer-driven development, using methods such as geological feature identification, well network layout analysis, and injection-production dynamic monitoring. Core aspects include identifying factors influencing heavy oil physical properties, classifying the characteristics of the polymer injection development stage, establishing and analyzing the injection-production relationship, and determining the relationship between injection pressure and productivity response. Quantitative methods are used to reflect the productivity performance of horizontal wells and provide a basis for dynamic adjustments. Methodologically, this field typically employs historical data review, layer segmentation modeling, production decline fitting, and dynamic response analysis to achieve comprehensive productivity analysis of single wells and regions. The traditional method for assessing the productivity of horizontal wells in heavy oil polymer flooding reservoirs refers to identifying and matching the productivity of horizontal wells that have already implemented polymer flooding in the development well network of heavy oil polymer flooding by performing stratified identification and injection-production matching based on parameters such as the geological unit's structure, sand bodies, interlayers, crude oil properties, polymer injection history, and well network control degree. This allows for the construction of a single-well productivity decline relationship, combined with qualitative analysis using dynamic parameters such as polymer injection volume and injection pressure. Traditional methods generally rely on segmented productivity summarization and statistics, manual fitting of injection-production curves, and trend analysis of polymer injection well pressure monitoring to complete this task.

[0003] Current technologies for assessing the production capacity of polymer flooding in horizontal wells for heavy oil generally rely on stratified statistical summarization of historical data and manual fitting of injection-production curves. This approach lacks timely response to injection-production dynamics, making it difficult to grasp dynamic trends in a timely manner and affecting the accuracy of the assessment. Because they employ holistic trend analysis and qualitative judgment, they often fail to distinguish subtle inflection points and lag effects between different stages, easily masking some short-term response characteristics and causing delays in adjustment decisions. Furthermore, the lack of clear definition of production stability and declining trends poses a risk of confusing periodic fluctuations with long-term declines, thus affecting the identification of effective production ranges. In addition, existing methods fail to effectively combine changes in injected concentration with the distribution of production ratios for comprehensive judgment, lacking a scientific assessment of the significance of the current injection state, which may lead to unreasonable injection adjustments. These shortcomings result in significant deviations in production capacity assessment, delayed dynamic adjustments, and an inability to fully exploit the potential of polymer flooding in horizontal wells, ultimately reducing development efficiency. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a method and system for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs, comprising the following steps:

[0006] S1: Collect daily injection volume data of polymer flooding development well section, construct time series and identify rate transitions in the injection change trend, extract the growth inflection point position in the daily production fluid change sequence, and determine the time point when the well section enters the response state based on the lag interval between the two types of time nodes, thus obtaining the well section response start time point.

[0007] S2: Based on the daily production data segment corresponding to the well section response start time point, compare the change range before and after to determine whether it has continuous improvement characteristics, identify the time interval of the growth trend, and obtain the production growth trend segment of the lag stage.

[0008] S3: Extract daily oil production data from the lagging stage liquid production growth trend segment, divide continuous sub-intervals and evaluate the change range and fluctuation degree between intervals, mark the oil production stage with stable characteristics, and obtain the stable daily oil production segment identification result.

[0009] S4: Based on the time period identified by the stable daily oil production segment, the time is extended backward, divided into multiple cycles, and the changing trend of oil production performance in each cycle is compared to identify the time limit of the continuous decline characteristic, thus obtaining the starting point of the continuous decline stage of daily oil production.

[0010] As a further aspect of the present invention, the well section response start time point includes the injection rate change transition position, the daily production fluid growth inflection point position, and the lag interval time point; the lag stage production fluid growth trend segment includes the production fluid growth amplitude range, the production fluid growth time range, and the continuous improvement characteristic range; the stable daily oil production segment identification result includes continuous sub-intervals, change amplitude evaluation value, and fluctuation degree mark; and the starting point of the continuous decline stage of daily oil production includes the cycle division boundary, the oil production change trend comparison boundary, and the continuous decline time boundary.

[0011] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0012] S101: Collect daily injection volume data of polymer flooding development well section, form a data sequence according to time order, call the injection volume of time point in the sequence and judge the change range with the time points before and after, filter the time points that reach the injection rate jump threshold, and obtain the injection rate jump time point sequence.

[0013] S102: Based on the injection rate transition time point sequence, call the daily liquid production change sequence of the same period, determine the daily liquid production change trend before and after the transition, extract the time point when the trend changes as the inflection point, and obtain the daily liquid production change inflection point position sequence.

[0014] S103: Based on the time interval between the daily production fluid change inflection point position sequence and the injection rate jump time point sequence, the time point with the shortest lag time is selected as the well section response start point to obtain the well section response start time point.

[0015] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0016] S201: Obtain the daily production fluid data segment corresponding to the well section response start time point, organize the daily production fluid data of the time points in chronological order, extract the daily production fluid changes between adjacent time points, and generate a daily production fluid change trend sequence.

[0017] S202: Based on the daily liquid production change trend sequence, judge the change trend between time points, identify the time segment with continuous upward movement and mark the start and end positions, and generate a continuous growth time interval;

[0018] S203: Based on the continuous growth time interval, combined with the daily production data segment after the well section response start time point, determine the growth trend segment within the lag stage and mark the corresponding time range, and generate the production growth trend segment of the lag stage.

[0019] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0020] S301: Obtain the daily oil production data of the liquid production growth trend segment in the lag stage, arrange them in time order to form a complete data sequence, detect the change direction of continuous segments in sequence, extract continuous segments that meet the growth characteristics, and obtain the growth trend interval sequence.

[0021] S302: Based on the growth trend interval sequence, statistically analyze the overall daily oil production level of the interval, compare the degree of difference between adjacent intervals, and at the same time detect the degree of deviation of the data within the interval from the overall level to obtain the interval change amplitude and fluctuation value.

[0022] S303: Based on the range of change and the degree of fluctuation of the interval, determine whether the interval meets the stability threshold, identify and classify the intervals that meet the conditions, and obtain the stable daily oil production segment marking results.

[0023] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0024] S401: Obtain the time period corresponding to the stable daily oil production segment identification result, mark the segment based on the daily oil production value of each time node in the time series data, merge the time nodes with the same daily oil production level and consecutive time nodes into a time period, and generate stable segment time interval values.

[0025] S402: Based on the stable time interval value, extend the time period backward, call the daily oil production value of the extended period in the extended time series data, calculate the difference in the changing trend of the daily oil production value within the period, compare the difference of the daily oil production values ​​of adjacent time periods within the period, and generate a series of changing trend differences during the period.

[0026] S403: Based on the weekly variation trend difference sequence, determine the continuous downward characteristics, and by comparing the relationship between the variation trend difference between adjacent weeks and the zero variation benchmark value, screen the starting position of continuous less than zero and locate the time period boundary to obtain the starting point of the continuous decline in daily oil production.

[0027] As a further aspect of the present invention, the method further includes:

[0028] S5: Call up the daily liquid production, daily oil production and injection concentration data within the period corresponding to the starting point of the continuous decline in daily oil production, identify the stage of stable concentration, and determine whether the injection state at the current concentration still has the significance of production capacity response by combining the distribution of the liquid production ratio, and obtain the distribution range of polymer flooding production capacity segment.

[0029] The polymer flooding capacity distribution range includes the daily liquid production stage, the daily oil production stage, the injection concentration stabilization stage, and the liquid production ratio distribution stage.

[0030] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0031] S501: Obtain the daily liquid production, daily oil production and injection concentration data within the starting period of the continuous decline phase of daily oil production, calculate the changing trend of the ratio of daily liquid production to daily oil production based on the time series, and compare the ratio with the absolute value trend of daily liquid production and daily oil production to obtain the trend data of the ratio of daily liquid production to oil production.

[0032] S502: Based on the daily production liquid oil ratio trend data and combined with the same period injection concentration sequence, classify and judge the changes in the fluctuation range of the ratio corresponding to the injection concentration, screen the concentration range with stable fluctuation range, and obtain the stable concentration range value.

[0033] S503: Based on the concentration stability interval value, combined with the distribution of daily liquid and oil production levels during the same period, the current concentration level value is compared with the distribution boundary value within the cycle to determine the capacity response segment and obtain the distribution interval of polymer flooding capacity segment.

[0034] As a further aspect of the present invention, the stable concentration range refers to the injection concentration range in which the liquid-to-oil ratio fluctuates less and changes more steadily over time by analyzing the relationship between the ratio and the injection concentration during the same period in the time series change trend of the daily liquid to daily oil ratio.

[0035] The production capacity response range refers to the comparison between the current injection concentration level and the upper and lower boundary values ​​within the cycle, based on the determination of the concentration stability range and the distribution of daily liquid and oil production levels during the same period, to determine the response of the well's liquid and oil production under different injection concentration levels.

[0036] A productivity assessment system for horizontal wells in heavy oil polymer flooding reservoirs includes:

[0037] The response start identification module acquires the daily injection volume and daily production fluid time series of the well section, monitors the rate transition interval in the injection volume sequence, extracts the growth inflection point position in the daily production fluid sequence, and determines the time when the well section enters the response state based on the lag interval between the rate transition node and the growth inflection point node, thus obtaining the response start time point of the well section.

[0038] The lagging growth judgment module judges the continuous improvement characteristics by comparing the changes before and after the daily production time series corresponding to the response start time of the well section, and identifies the growth trend time interval to obtain the production growth trend segment of the lagging stage.

[0039] The stable phase segmentation module calls the daily oil production time series within the lagging phase liquid production growth trend segment, divides continuous sub-intervals and evaluates the interval fluctuation amplitude, marks the stable characteristic time intervals, and obtains the stable daily oil production segment.

[0040] The continuous decline boundary identification module divides the oil production trend into multiple cycles based on the extended time period of the stable daily oil production segment, identifies the continuous decline time boundary, and obtains the starting point of the continuous decline stage of daily oil production.

[0041] The capacity segment determination module calls the daily liquid production, daily oil production, and injection concentration data within the cycle where the daily oil production continuously declines, identifies the concentration stabilization stage, and determines the capacity response status by combining the liquid production ratio distribution, thereby obtaining the polymer flooding capacity segment distribution range.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, by collecting daily injection volumes to construct a time series and identifying rate transitions, the inflection point of production growth is accurately extracted and the response start time is determined, making the identification of the horizontal well response state more timely and accurate. Based on the comparison of the daily production change amplitude before and after the start time, the continuous growth interval is identified and the lagged growth trend segment is extracted to reveal the stage characteristics. Furthermore, the daily oil production data of the lagged stage is subjected to fluctuation assessment to screen the stable oil production stage, ensuring that the evaluation focuses on the effective time period. By comparing the cyclical oil production change trend, the starting point of continuous decline is delineated to warn of decline. Combining the injection concentration and production ratio to analyze the significance of the current injection state response, the quantitative and staged identification of production capacity response is realized, providing a scientific basis for dynamic adjustment. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the steps of the present invention;

[0045] Figure 2 This is a flowchart of steps S1 of the present invention;

[0046] Figure 3 This is a flowchart of steps S2 of the present invention;

[0047] Figure 4 This is a flowchart of steps S3 of the present invention;

[0048] Figure 5 This is a flowchart of step S4 of the present invention;

[0049] Figure 6 This is a flowchart of steps S5 of the present invention;

[0050] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0052] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0053] Please see Figure 1 A method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs includes the following steps:

[0054] S1: Collect daily injection volume data of polymer flooding development well section, construct time series and identify rate transitions in the injection change trend, extract the growth inflection point position in the daily production fluid change sequence, and determine the time point when the well section enters the response state based on the lag interval between the two types of time nodes, thus obtaining the well section response start time point.

[0055] S2: Based on the daily production data segment corresponding to the well section response start time point, compare the change before and after to determine whether it has continuous improvement characteristics, identify the time interval of the growth trend, and obtain the production growth trend segment of the lag stage.

[0056] S3: Extract daily oil production data from the lagging stage liquid production growth trend segment, divide continuous sub-intervals and evaluate the change range and fluctuation degree between intervals, mark the oil production stage with stable characteristics, and obtain the stable daily oil production segment identification results.

[0057] S4: Based on the time period identified by the stable daily oil production segment, the time is extended backward, divided into multiple cycles, and the changing trend of oil production performance in the cycles is compared to identify the time limit of the continuous decline characteristic and obtain the starting point of the continuous decline stage of daily oil production.

[0058] S5: Call up the daily liquid production, daily oil production and injection concentration data within the period corresponding to the starting point of the continuous decline in daily oil production, identify the stage of stable concentration, and combine the distribution of the liquid production ratio to determine whether the injection state at the current concentration still has the significance of production capacity response, and obtain the distribution range of polymer flooding production capacity segment.

[0059] The well section response start time points include the injection rate change transition position, the daily fluid production growth inflection point position, and the lag interval time point. The fluid production growth trend segment in the lag stage includes the fluid production growth amplitude range, the fluid production growth time range, and the continuous improvement characteristic range. The stable daily oil production segment identification results include continuous sub-intervals, change amplitude assessment values, and fluctuation degree markers. The starting point of the continuous decline in daily oil production includes the cycle division boundary, the oil production change trend comparison boundary, and the continuous decline time boundary. The polymer flooding capacity segment distribution range includes the daily fluid production stage, the daily oil production stage, the injection concentration stable stage, and the fluid production ratio distribution stage.

[0060] Please see Figure 2 The specific steps of S1 are as follows:

[0061] S101: Collect daily injection volume data of polymer flooding development well section, form a data sequence according to time order, call the injection volume of time point in the sequence and judge the change range with the time points before and after, filter the time points that reach the injection rate jump threshold, and obtain the injection rate jump time point sequence.

[0062] When collecting daily injection volume data for polymer flooding development well sections, daily reports are extracted from the oilfield injection and production monitoring platform. The well section number, date, and injection volume are recorded and stored in the database. The data is arranged in ascending order of time to form a sequence. For example, the daily injection volumes for a certain well section from July 1st to July 10th are 50, 52, 51, 70, 72, 71, 90, 88, 87, and 105 m³. 3 / d, sequentially select the injection volume corresponding to each date, and compare the change with the injection volume of the previous day and the next day. The change is calculated by subtracting the previous day's injection volume from the current day's injection volume, then dividing by the previous day's injection volume, and converting it into a percentage. For example, the injection volume on July 4th increased by about 37% compared to July 3rd. If this percentage is higher than a preset threshold, the date is marked as a jump time point. The threshold is usually selected from a reasonable value in the range of 25% to 40% based on the historical statistics of the oilfield and the production-injection ratio design standard. Here, 30% is selected as the judgment criterion. All dates in the sequence are judged sequentially, and all time points that reach or exceed the threshold are selected to form a jump time point sequence, such as July 4th, July 7th, and July 10th. Data processing can be completed in batches by writing calculation formulas using spreadsheet software or by automating it through scripts, and the filtering results are output separately as a time point sequence.

[0063] S102: Based on the injection rate transition time point sequence, call the daily liquid production change sequence of the same period, determine the daily liquid production change trend before and after the transition, extract the time point when the trend changes as the inflection point, and obtain the daily liquid production change inflection point position sequence.

[0064] After obtaining the injection rate transition time sequence, the daily liquid production change sequence within the corresponding time period is retrieved. For example, the daily liquid production from July 1st to July 10th is 80, 79, 81, 95, 94, 92, 110, 112, 109, and 125 mg / L, respectively. 3 / d, for each transition time point, select the daily liquid production values ​​for several days before and after that time point for comparison. For example, select the average daily liquid production values ​​for two days before and after the transition time point for comparison. If the average daily liquid production value before the transition time point is 80m 3 / d, the average daily liquid production after the transition time point is 93m 3 / d, the change range is about 16%. The trend is judged according to the set standard. If the change is greater than 5%, it is recorded as an increase; if it is less than -5%, it is recorded as a decrease; if it is in between, it is recorded as flat. After the trend is judged to have an inflection point at the transition time point, which is either from an increase to a decrease or from a decrease to an increase, the date is recorded as the inflection point of daily liquid production change. For example, July 4 meets the conditions and is recorded as an inflection point. The trend judgment can be visually verified by drawing a line chart of daily liquid production, or the change trend can be detected by data analysis software. The inflection point judgment standard is determined to be a change threshold of about 5% based on the statistical change range of daily liquid production. Through the above judgment, an inflection point position sequence can be formed, such as July 4 and July 10.

[0065] S103: Based on the time interval between the inflection point sequence of daily fluid production change and the injection rate transition time sequence, the time point with the shortest lag time is selected as the well section response start point to obtain the well section response start time point.

[0066] The time interval between the inflection point sequence of daily fluid production changes and the injection rate transition time sequence is calculated. The lag time is obtained by subtracting the transition time from the inflection point time. For a given inflection point time, such as July 4th, the lag time is compared with each transition time point in the sequence. The transition time point with the smallest lag time is selected as the well section response start point. For example, the inflection point on July 4th corresponds to the same transition time point, with a lag time of 0 days. If an inflection point is July 10th, the nearest transition time point is July 7th, with a lag time of 3 days. The shortest lag time corresponding to all inflection point time points is calculated sequentially, and the corresponding transition time points are recorded to form the well section response start point sequence, such as July 4th and July 7th. The reasonableness of the lag time is judged based on historical experience values. Generally, less than 7 days is considered a normal response. By comparing the results, the well section response time points corresponding to each inflection point can be screened out, completing the extraction and recording of response time points.

[0067] Please see Figure 3 The specific steps of S2 are as follows:

[0068] S201: Obtain the daily production fluid data segment corresponding to the well section response start time point, organize the daily production fluid data of the time points in chronological order, extract the daily production fluid changes between adjacent time points, and generate a daily production fluid change trend sequence.

[0069] When acquiring the daily fluid production data segment corresponding to the well section response start time, first use the wellhead automated acquisition system to extract the historical daily fluid volume data of a certain oil well starting from the response start time. For example, after the start on July 1st, the daily fluid volumes are 120, 125, 130, 138, 142, 150, 158, 160, 165, and 168 m³. 3 This data is then organized into a time series by date; for example, July 1st corresponds to 120m. 3 July 2nd corresponds to 125m 3 Arranged sequentially up to July 10th, corresponding to 168m 3 After the data collection is completed, the daily change in liquid production between two adjacent time points is calculated. For example, the daily production on July 2nd is 5m more than that on July 1st. 3 July 3rd increased by 5m compared to July 2nd. 3 July 4th increased by 8m compared to July 3rd. 3 This process is repeated daily to obtain the trend of changes, for example, in the order of 5, 5, 8, 4, 8, 8, 2, 5, 3m. 3 This process generates a daily liquid production trend sequence. The daily trend can be generated directly in the data acquisition system or calculated in batches in common office software by setting the formula "liquid volume of the day minus the liquid volume of the previous day". A column of "daily liquid production trend" is generated for easy viewing. At the same time, a line graph can be drawn to visually display the daily changes. Through this process, a complete daily liquid production trend sequence can be obtained.

[0070] S202: Based on the daily liquid production change trend sequence, the change trend between time points is judged, the time segment with continuous upward movement is identified and the start and end positions are marked, and a continuous growth time interval is generated.

[0071] Based on the daily liquid production trend sequence, starting from July 1st, the daily change value is checked one by one. If the change on a certain day is greater than zero, it is marked as rising; if it is less than zero, it is marked as falling; if it is equal to zero, it is marked as flat. The judgment benchmark value is usually set to zero, that is, as long as it is greater than zero, it is considered rising. The status of each day is marked sequentially by date. At the same time, it is checked which days show a continuous upward trend. For example, if the daily change is greater than zero from July 1st to July 10th, the entire segment is determined to be a continuous growth period. If the change is negative or zero on a certain day, the continuous growth ends, the end date is recorded, and the judgment starts again from the next day. In the whole process, a column for recording status can be used and set to "mark one if the change is greater than zero, otherwise mark zero". The continuous growth period is obtained by statistically analyzing the start and end positions of the consecutive "one". For example, if there is continuous growth from July 1st to July 5th and a decrease occurs on July 6th, then July 1st to July 5th is a growth period. At the same time, a line graph can be drawn, and different colors can be marked at the start and end points of the segment to help identify and confirm these continuous growth periods.

[0072] S203: Based on the continuous growth time interval, combined with the daily production fluid data segment after the well section response start time point, determine the growth trend segment in the lag phase and mark the corresponding time range to generate the production fluid growth trend segment in the lag phase.

[0073] Based on the continuous growth time interval and the daily liquid production data interval after the response start time, the growth trend interval within the lag phase is further determined. The lag phase is typically defined as the time interval within five days after start-up. Whether it belongs to the lag phase is determined by checking if the difference in days between the start date and the start date is within five days. Then, the interval where both the start and end dates fall within the lag phase is selected from the continuous growth interval as the lag phase growth trend interval. For example, during the lag phase from July 1st to July 5th, the daily liquid production increased from 120m³... 3 Increased to 150m 3 The total growth is 30m 3 The growth rate is 25%. These values ​​can be directly calculated and recorded from the liquid volume on the first and last days. The start and end times and growth rate of the lagging growth phase can be listed separately and displayed in different colors on the trend chart to facilitate separation from the growth trend segments of subsequent phases.

[0074] Please see Figure 4 The specific steps of S3 are as follows:

[0075] S301: Obtain daily oil production data for the lagging stage liquid production growth trend segment, arrange them in chronological order to form a complete data sequence, detect the direction of change of continuous segments in turn, extract continuous segments that meet the growth characteristics, and obtain the growth trend interval sequence;

[0076] The specific calculation formula for detecting the direction of change of consecutive segments is as follows:

[0077]

[0078] Calculate the trend indicator Δ i Extract continuous segments that conform to growth characteristics to obtain a growth trend interval sequence;

[0079] Where, Δ i A dimensionless indicator representing the trend of daily oil production data in the lag phase of the i-th time period arranged in chronological order, Q. i Q represents the daily oil production data for the i-th day lag period, arranged in chronological order, in tons per day. i-1 This represents the daily oil production data for the (i-1)th day lag period, arranged in chronological order, in tons per day (T). i Let represent the sequential number of day i, a dimensionless positive integer; and let n represent the total number of data points for the lagging daily oil production data within the current continuous segment, also a dimensionless positive integer. This represents the arithmetic mean of daily oil production data during the lag phase within the current continuous segment, expressed in tons per day. It represents the sum of the absolute values ​​of the differences between the daily oil production data of the lagging phase and the average value of that phase within the current continuous segment, expressed in tons per day;

[0080] The parameters in the formula are obtained through on-site oilfield production data monitoring and historical data calculation; daily oil production data is collected daily by the oil well automatic monitoring system, in tons per day; the time sequence number is directly generated by recording continuous time series, without the need for quantification;

[0081] The average value and absolute deviation within the section were obtained through standard arithmetic operations. The parameter acquisition interval was determined based on the annual daily production data of Daqing Oilfield and publicly available literature search results; a reasonable range was 50–350 tons of oil produced per day. The example used a data sequence from July 1st to July 7th for a specific oil well: 80, 85, 90, 95, 100, 105, 110 (unit: tons per day). The length of the continuous section, n, was 7. An example calculation for the 7th day is shown below:

[0082] Q7 = 110 tons per day, Q6 = 105 tons per day, T7 = 7.

[0083]

[0084] First item:

[0085]

[0086] Second item:

[0087]

[0088] Third item:

[0089]

[0090] Substitute each term into the formula:

[0091] Δ7=0.341+0.090-0.077=0.354;

[0092] The results indicate that the trend index for the continuous segment on day 7 is 0.354, suggesting that the strength of the daily oil production trend in the continuous time series has been calculated. This value can be used as a reference for subsequently extracting continuous segments that meet the growth characteristics. By comparing the Δ values ​​of adjacent segments... i The value sequence selects continuous segments that conform to the growth trend to form a growth trend interval sequence. The numerical results directly determine the division boundary and direction of the growth trend interval sequence.

[0093] Additional parameter descriptions: Daily oil production data is collected daily through an automatic monitoring system. The data is archived and summarized into a time series by a data management platform. The time sequence number directly records the data collection order. The average value of a section is obtained by calculating the arithmetic mean. The absolute deviation is obtained by calculating the absolute value of the difference between the daily oil production data and the average value of the section and summing them up. All monitoring and calculations are based on a reasonable range within the field detection range of Daqing Oilfield to ensure the authenticity and validity of the data. The weight parameter n and the time number T change linearly with the length of the section or the progress of the time series and are not manually adjusted.

[0094] The formula's operational logic is based on the changing trend characteristics of daily oil production data in the time series during the lag phase. It comprehensively calculates the daily increment, overall segment stability, and time-lapse effect to measure trend strength. The first term normalizes the daily relative increase by calculating the difference between the current day's oil production and the previous day's oil production and dividing by the square root of the sum of the two days' production, while suppressing the impact of excessively large absolute values. The square root is used to reduce the extreme value fluctuation effect and maintain dimensional consistency. The second term sums the absolute values ​​of the differences between the daily oil production values ​​within the segment and the segment average, reflecting the degree of fluctuation within the segment. It is then divided by the segment average and the segment length to normalize into a dimensionless indicator, thereby quantifying segment consistency. The final term multiplies the current day's oil production by the time sequence number to represent the growth contribution under cumulative time progression. It is then compared with the product of the previous day's oil production and the segment average, and the difference is calculated to eliminate the interference of long-term stable levels in the lag effect. The three terms are added and subtracted to comprehensively measure trend strength and form a trend indicator that balances comparability, stability, and time sensitivity.

[0095] The trend indicator is a comprehensive value that measures the direction and intensity of changes in daily oil production data in a continuous time series during a lag phase. By combining the relative magnitude of daily increments, the stability of fluctuations within a segment, and the cumulative effect of time progression on production growth, it forms a dimensionless and comparable trend strength characterization. The larger the value of this indicator, the more obvious the upward trend and the stronger the stability of daily oil production at the current time point and within the segment. The smaller the value, the more unstable the daily oil production growth or even the possibility of decline. As a basis for dividing and judging growth trend intervals, the trend indicator helps to identify and extract continuous segments that meet the growth characteristics.

[0096] S302: Based on the growth trend interval sequence, the daily oil production level of the interval is statistically analyzed, the difference between adjacent intervals is compared, and the degree of deviation of the data within the interval from the overall level is detected to obtain the interval change amplitude and fluctuation value.

[0097] Based on the extracted growth trend interval sequence, the overall daily oil production level of each interval is statistically analyzed. The average value of daily data within each interval is calculated. For example, if the daily data for a certain interval are 50, 52, 55, 58, and 60 tons, then the average value is 55 tons. The average values ​​of two adjacent intervals are compared. If the difference is greater than 5 tons, it is considered a significant difference; otherwise, it is considered a general difference. Here, 5 tons is used as the threshold for significant difference based on the historical data fluctuation level. For example, when the long-term standard deviation is 2 tons, 5 tons is chosen as the judgment benchmark. At the same time, the degree of fluctuation of daily data within each interval relative to the interval average value is calculated. The fluctuation is measured by the average absolute value of the difference between each daily value and the interval mean. For example, if the fluctuation value of a certain interval is 3.8 tons, according to the classification rules, if the fluctuation value is less than or equal to 1 tons, it is considered low fluctuation; between 1 tons and 3 tons, it is considered moderate fluctuation; and greater than 3 tons, it is considered high fluctuation. The average value, adjacent differences, and degree of fluctuation of each interval are recorded and output. These calculation processes can be completed using the average, absolute difference, and subtraction operations in a spreadsheet. Trend charts can also be drawn to indicate the differences and fluctuations of each interval.

[0098] S303: Based on the range of change and the degree of fluctuation, determine whether the range meets the stability threshold, identify and classify the ranges that meet the conditions, and obtain the stable daily oil production segment marking results.

[0099] The stability requirements are determined based on the magnitude and volatility of each interval. The stability requirements are set as follows: the average level change is no greater than 2t and the volatility is no greater than 1t. Each interval is judged to meet both conditions simultaneously. If the change of an interval is 1.8t and the volatility is 0.9t, it is marked as a stable interval. If either of these conditions exceeds the set range, it is marked as an unstable interval. The stability thresholds of 2t and 1t are determined based on the standard deviation of long-term monitoring data. Generally, a reasonable stable range is the change within one standard deviation above and below the average value. After judging each interval individually, the stability status of each interval is marked in the statistical table. This can be achieved through logical judgment formulas or batch processing of output results. The final output result is the stability indicator of each growth trend interval, including the start and end time of the interval, daily oil production level, volatility, and stability indicator, forming a complete statistical result of stable intervals.

[0100] Please see Figure 5 The specific steps of S4 are as follows:

[0101] S401: Obtain the time period corresponding to the stable daily oil production segment identification result, mark the segment based on the daily oil production value of each time node in the time series data, merge the time nodes with the same daily oil production level and consecutive time nodes into a time period, and generate the stable segment time interval value.

[0102] When obtaining the time period corresponding to the stable daily oil production segment identification result, the time nodes and daily oil production values ​​are first extracted from each time series data, a data series index list is established, and a one-dimensional data table is drawn. Each time node is scanned step by step, and the daily oil production value of the current node is compared with the daily oil production value of the previous node. If the difference between the two is within a preset stable threshold range, for example, set at ±5% of the daily oil production level, with 2000 bbl / d as the benchmark, the threshold is approximately between 1900 bbl / d and 2100 bbl / d. Then, the two time nodes are considered as continuous segments at the same stable level, and the process continues. Scanning backwards along the time series, nodes that meet the conditions are merged and recorded into the current segment. The start and end times of the current segment are marked. For example, if the daily oil production value in the segment from January 1, 2014 to March 31, 2014 is between 1950 bbl / d and 2050 bbl / d, it can be identified as a stable segment and the interval value is recorded. When a certain time node exceeds the threshold range, the current segment ends and a new segment time period is recorded. In this way, the time series is divided into several stable segments, each segment is identified by the start and end times and the daily oil production level of the interval.

[0103] S402: Based on the stable time interval value, extend the time period backward, call the daily oil production value of the extended period in the extended time series data, calculate the difference in the changing trend of the daily oil production value within the period, compare the difference of the daily oil production values ​​of adjacent time periods within the period, and generate a series of changing trend differences during the period.

[0104] When extending the time period forward based on the stable interval value, the end time of each stable interval is extended by a fixed number of days, such as 30 days, to form a new time period boundary. Then, the daily oil production value sequence for this extended time period is extracted from the time series, and an extended data list is established. Within this extended period, the difference between two consecutive days of daily oil production value is calculated. That is, if the daily oil production value is 1980 bbl / d on a certain day and 1970 bbl / d on the next day, the difference is -10 bbl / d. The daily differences are recorded sequentially throughout the entire extended period to form a complete trend difference value sequence. The trend of each day is matched with the time index in the list according to the time sequence for easy subsequent positioning. This method is suitable for quickly grasping the trend of daily oil production level within the extended time period and is convenient for detecting the characteristics of later trend changes.

[0105] S403: Based on the weekly variation trend difference sequence, determine the continuous decline characteristics, and by comparing the relationship between the variation trend difference between adjacent weeks and the zero change benchmark value, screen the starting position of continuous less than zero and locate the time period boundary to obtain the starting point of the continuous decline in daily oil production.

[0106] The specific calculation formula for filtering consecutive starting positions less than zero and locating time period boundaries is as follows:

[0107]

[0108] Calculate the characteristic value of the continuous decline in daily oil production by determining the starting position of the difference in the trend over consecutive weeks being less than zero and defining the time period boundary. When S k >X th This was determined to be the starting point of the continuous decline in daily oil production, and S was... k The calculation results are input into the daily oil production monitoring system to trigger the alarm logic module;

[0109] Among them, S k ΔT represents the weighted absolute downward trend characteristic value for n consecutive weeks starting from week k. i R represents the difference in daily oil production trends between week i and the previous week (unit: barrels / day). i W represents the daily change rate of oil production on the i-th day (dimensionless). i X represents the weight coefficient (dimensionless) for week i. th The value represents the threshold for the continuous decline in daily oil production determined through experiments (unit: barrels / day), k represents the sequence number of the starting week (dimensionless), i represents the sequence number of the week in the calculation process (dimensionless), and n represents the number of weeks in the continuous time period (dimensionless).

[0110] ΔT i This represents the difference in daily oil production trends between week i and the previous week. This value is automatically calculated using daily oil production data collected by the daily oil production monitoring system, and is formed by the difference between the actual daily oil production value and the actual value of the previous week. Monitoring data shows that daily oil production in week 3 was 98,000 barrels per day, and daily oil production in week 2 was 100,000 barrels per day. Therefore, ΔT3 = 98,000 - 100,000 = -2,000 barrels per day.

[0111] R i This represents the daily oil production change rate on the i-th day. This value is calculated from daily oil production monitoring data, expressed as a percentage of the difference in daily oil production trends for the current day divided by the daily oil production of the previous day. Based on the monitoring data, daily oil production on the second day was 100,000 barrels per day, and daily oil production on the third day was 98,000 barrels per day. Therefore, ΔT3 = -2000.

[0112] W i This represents the weighting coefficient for week i. This value is adjusted based on the complexity of the oilfield operating environment, with complex terrain areas having a greater weighting than flat areas. This value is quantified based on the geomorphological survey report. For areas with a geomorphological barrier level of 3, the weighting coefficient is set to 1.2, increasing with the geomorphological level. The current monitoring point is located in a complex terrain area, therefore W3 = 1.2.

[0113] X thThis represents the threshold for a sustained decline in daily oil production. This value is determined through analysis of historical monitoring data. When the value exceeds the threshold, it is considered a sustained decline. Based on ten years of historical data from the oilfield, this threshold is set at 5000 barrels per day.

[0114] k represents the starting week number for calculation. The currently selected starting week is week 3, so k = 3, and the number of consecutive weeks to be calculated is n = 3.

[0115] Using data from week 3 for calculation:

[0116]

[0117] Given that in week 3, ΔT3 = -2000, R3 = -0.02, and W3 = 1.2;

[0118]

[0119] Multiply by the weighting factor:

[0120] -1980.3 × 1.2 ≈ -2376.36;

[0121] Take the absolute value:

[0122] |-2376.36|=2376.36;

[0123] Week 4 monitoring data: Daily oil production 95,000 barrels, compared to 98,000 barrels per day the previous week. ΔT4 = 95,000 - 98,000 = -3,000. The terrain is classified as level 3, and W4 = 1.2.

[0124]

[0125] Multiply by the weighting factor:

[0126] -2956.38×1.2≈-3547.66;

[0127] Take the absolute value:

[0128] |-3547.66|=3547.66;

[0129] Week 5 monitoring data: Daily oil production 91,000 barrels, compared to 95,000 barrels per day the previous week. ΔT5 = 91,000 - 95,000 = -4,000. The terrain is classified as level 3, and W5 = 1.2.

[0130]

[0131] Multiply by the weighting factor:

[0132] -3921.64 × 1.2 ≈ -4705.97;

[0133] Take the absolute value:

[0134] |-4705.97|=4705.97;

[0135] Add up the data from the three weeks:

[0136] S3=2376.36+3547.66+4705.97=10629.99;

[0137] The results indicate that the characteristic value of the daily oil production decline trend for three consecutive weeks starting from week 3 is 10,629.99 barrels per day. This value is greater than the threshold of 5,000 barrels per day, so it is determined that the current state is in a continuous decline, and the result is sent to the daily oil production monitoring system to trigger the alarm logic module.

[0138] The formula's operational logic is based on the need to quantify the continuous downward trend of daily oil production. The summation of parameters reflects the cumulative effect of the decline over consecutive weeks. The trend difference for each week is standardized and included in the summation in absolute form to reflect the overall intensity. The multiplication part is used to adjust the difference between periods according to the weight of topographic complexity to reflect the impact of different operating conditions on the trend. The square root operation is used to square root the value after adding one to the absolute value of the daily oil production change rate to reduce the nonlinear impact when the change rate is large and smooth the calculation results. Thus, while maintaining the original physical meaning of each parameter, it achieves the organic integration of data from different dimensions and forms a comprehensive feature value with comparability and identifiability.

[0139] The characteristic value of continuous decline in daily oil production represents a comprehensive value obtained by normalizing, smoothing, and weighting the daily oil production trend over a selected continuous period, taking into account multiple factors such as the magnitude and rate of change of the daily oil production trend and the complexity of the operating environment. It is used to measure the overall strength of the downward trend in daily oil production during that period. The larger the value, the more significant and persistent the decline. It is an important reference indicator for judging whether the oilfield's production capacity has entered a state of significant decline, and also serves as the basis for triggering alarm logic in the monitoring system and subsequent management decisions.

[0140] Please see Figure 6 The specific steps of S5 are as follows:

[0141] S501: Obtain data on daily liquid production, daily oil production, and injection concentration during the starting period of the continuous decline phase of daily oil production; calculate the trend of the ratio of daily liquid production to daily oil production based on time series; and compare the ratio with the absolute value trend of daily liquid production and daily oil production to obtain the trend data of the ratio of daily liquid production to oil production.

[0142] First, time-series data on daily fluid production, daily oil production, and injection concentration are extracted from daily production reports and water injection records. The time range is limited to the start of a significant and continuous decline in daily oil production to the current period. For example, for a certain well, the daily fluid production from January to June 2014 was 110 m³. 3 / d、105m 3 / d、98m 3 / d、95m 3 The daily production rates are 85t / d, 78t / d, 70t / d, 66t / d, and 62t / d, respectively, with injection concentrations of 1200mg / L, 1250mg / L, 1300mg / L, 1320mg / L, and 1350mg / L, respectively. A time-series data table is created, with dates, daily liquid production, daily oil production, and injection concentrations entered accordingly. After data processing, a spreadsheet software is used to create a series of daily liquid production to daily oil production ratios. The ratio is obtained by dividing the daily liquid production by the daily oil production. For example, the ratio on January 1st is 110 divided by 85, approximately 1.29; and on January 31st, the ratio is 105 divided by 78, approximately 1.35. The daily ratio data is plotted as a line graph to observe the trend over time. Simultaneously, time-series line graphs of daily liquid and oil production are plotted. By observing the trends of the three curves on the same time axis, the absolute value trends of daily liquid and oil production are compared with the ratio trends to analyze the patterns. The linear relationship between the ratio and time is calculated through trend line fitting analysis of the slope and intercept, and the trend data is output.

[0143] S502: Based on the daily production liquid-to-oil ratio trend data and combined with the same period injection concentration sequence, classify and judge the changes in the fluctuation range of the injection concentration corresponding to the ratio, screen the concentration range with stable fluctuation range, and obtain the stable concentration range value.

[0144] Based on the aforementioned trend data of the daily liquid-to-oil ratio, and combined with the time series of injected concentrations within the same time period, the two were matched to statistically analyze the fluctuation range of the daily liquid-to-oil ratio within each injected concentration range. For example, when the injected concentration was between 1200 and 1250 mg / L, the ratio fluctuated between 1.29 and 1.35, with a fluctuation range of approximately 0.06; when the injected concentration was between 1300 and 1320 mg / L, the ratio fluctuated between 1.36 and 1.40, with a fluctuation range of approximately 0.04; and when the injected concentration was 1350 mg / L, the ratio fluctuated by approximately 0.02. The fluctuation range for each concentration range was then calculated. After listing the ratios of all dates within a given range, the difference between the maximum and minimum values ​​is taken as the fluctuation range of that range. The range is then categorized based on the magnitude of the fluctuation: a fluctuation range not exceeding 0.05 is defined as a stable range; a range between 0.05 and 0.10 is a moderate range; and a range greater than 0.10 is a large range. This method is used to screen concentration ranges where the fluctuation range corresponding to the injected concentration level does not exceed 0.05. For example, if the fluctuation range within the 1300 to 1350 mg / L range meets the stable range standard, this range is determined as the stable concentration range value.

[0145] S503: Based on the stable concentration range, combined with the distribution of daily liquid and oil production levels during the same period, the current concentration level is compared with the distribution boundary value within the cycle to determine the capacity response segment and obtain the distribution range of polymer flooding capacity segment;

[0146] Based on the stable concentration range, and combined with the distribution range of daily liquid and oil production levels within the same time period, the current concentration level is compared with the distribution boundary value throughout the entire cycle to determine different production capacity response segments. First, the highest and lowest values ​​of daily liquid and oil production are statistically analyzed within the stable concentration range. For example, within a concentration range of 1300 to 1350 mg / L, the daily liquid production is between 92 and 98 mg / L. 3 The daily oil production is between 62 and 70 tons per day, and the daily liquid production at the current concentration of 1320 mg / L is 95 tons per day. 3 The daily liquid production and daily oil production of 66t / d are compared with the above ranges. When both are within the range, it is classified as a normal production capacity response segment. When either is below the minimum value, it is classified as a low production capacity response segment. When either is above the maximum value, it is classified as a high production capacity response segment. By comparing the current daily liquid production and daily oil production with the statistically determined upper and lower boundaries, it is determined that the current concentration of daily liquid production and daily oil production is within the normal range, and thus it is classified as a normal production capacity response segment.

[0147] Please see Figure 7 A productivity assessment system for horizontal wells in heavy oil polymer flooding reservoirs, comprising:

[0148] The response start identification module acquires the daily injection volume and daily production fluid time series of the well section, monitors the rate transition interval in the injection volume sequence, extracts the growth inflection point position in the daily production fluid sequence, and determines the time when the well section enters the response state based on the lag interval between the rate transition node and the growth inflection point node, thus obtaining the response start time point of the well section.

[0149] The lagging growth judgment module is based on the daily production time series corresponding to the well section response start time point. It compares the change before and after to judge the continuous improvement characteristics, identifies the growth trend time interval, and obtains the production growth trend segment in the lagging stage.

[0150] The stable phase segmentation module calls the daily oil production time series within the lagging phase liquid production growth trend segment, divides continuous sub-intervals and evaluates the interval fluctuation amplitude, marks the stable characteristic time intervals, and obtains the stable daily oil production segment.

[0151] The continuous decline boundary identification module divides the oil production trend into multiple cycles based on the extended time period of the stable daily oil production segment, identifies the continuous decline time boundary, and obtains the starting point of the continuous decline stage of daily oil production.

[0152] The capacity segment determination module calls the daily liquid production, daily oil production, and injection concentration data within the cycle of the starting point of the continuous decline in daily oil production, identifies the concentration stabilization stage, and determines the capacity response status by combining the liquid production ratio distribution, thus obtaining the polymer flooding capacity segment distribution range.

[0153] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs, characterized in that, Includes the following steps: S1: Collect daily injection volume data of polymer flooding development well section, construct time series and identify rate transitions in the injection change trend, extract the growth inflection point position in the daily production fluid change sequence, and determine the time point when the well section enters the response state based on the lag interval between the two types of time nodes, thus obtaining the well section response start time point. S2: Based on the daily production data segment corresponding to the well section response start time point, compare the change range before and after to determine whether it has continuous improvement characteristics, identify the time interval of the growth trend, and obtain the production growth trend segment of the lag stage. S3: Extract daily oil production data from the lagging stage liquid production growth trend segment, divide continuous sub-intervals and evaluate the change range and fluctuation degree between intervals, mark the oil production stage with stable characteristics, and obtain the stable daily oil production segment identification result. S4: Based on the time period identified by the stable daily oil production segment, the time is extended backward, divided into multiple cycles, and the changing trend of oil production performance in each cycle is compared to identify the time limit of the continuous decline characteristic, thus obtaining the starting point of the continuous decline stage of daily oil production.

2. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 1, characterized in that, The well section response start time point includes the injection rate change transition position, the daily fluid production growth inflection point position, and the lag interval time point. The fluid production growth trend segment in the lag stage includes the fluid production growth amplitude range, the fluid production growth time range, and the continuous improvement characteristic range. The stable daily oil production segment identification result includes continuous sub-intervals, change amplitude evaluation value, and fluctuation degree mark. The starting point of the continuous decline stage of daily oil production includes the cycle division boundary, the oil production change trend comparison boundary, and the continuous decline time boundary.

3. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collect daily injection volume data of polymer flooding development well section, form a data sequence according to time order, call the injection volume of time point in the sequence and judge the change range with the time points before and after, filter the time points that reach the injection rate jump threshold, and obtain the injection rate jump time point sequence. S102: Based on the injection rate transition time point sequence, call the daily liquid production change sequence of the same period, determine the daily liquid production change trend before and after the transition, extract the time point when the trend changes as the inflection point, and obtain the daily liquid production change inflection point position sequence. S103: Based on the time interval between the daily production fluid change inflection point position sequence and the injection rate jump time point sequence, the time point with the shortest lag time is selected as the well section response start point to obtain the well section response start time point.

4. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Obtain the daily production fluid data segment corresponding to the well section response start time point, organize the daily production fluid data of the time points in chronological order, extract the daily production fluid changes between adjacent time points, and generate a daily production fluid change trend sequence. S202: Based on the daily liquid production change trend sequence, judge the change trend between time points, identify the time segment with continuous upward movement and mark the start and end positions, and generate a continuous growth time interval; S203: Based on the continuous growth time interval, combined with the daily production data segment after the well section response start time point, determine the growth trend segment within the lag stage and mark the corresponding time range, and generate the production growth trend segment of the lag stage.

5. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Obtain the daily oil production data of the liquid production growth trend segment in the lag stage, arrange them in time order to form a complete data sequence, detect the change direction of continuous segments in sequence, extract continuous segments that meet the growth characteristics, and obtain the growth trend interval sequence. S302: Based on the growth trend interval sequence, statistically analyze the overall daily oil production level of the interval, compare the degree of difference between adjacent intervals, and at the same time detect the degree of deviation of the data within the interval from the overall level to obtain the interval change amplitude and fluctuation value. S303: Based on the range of change and the degree of fluctuation of the interval, determine whether the interval meets the stability threshold, identify and classify the intervals that meet the conditions, and obtain the stable daily oil production segment marking results.

6. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Obtain the time period corresponding to the stable daily oil production segment identification result, mark the segment based on the daily oil production value of each time node in the time series data, merge the time nodes with the same daily oil production level and consecutive time nodes into a time period, and generate stable segment time interval values. S402: Based on the stable time interval value, extend the time period backward, call the daily oil production value of the extended period in the extended time series data, calculate the difference in the changing trend of the daily oil production value within the period, compare the difference of the daily oil production values ​​of adjacent time periods within the period, and generate a series of changing trend differences during the period. S403: Based on the weekly variation trend difference sequence, determine the continuous downward characteristics, and by comparing the relationship between the variation trend difference between adjacent weeks and the zero variation benchmark value, screen the starting position of continuous less than zero and locate the time period boundary to obtain the starting point of the continuous decline in daily oil production.

7. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 1, characterized in that, The method further includes: S5: Call up the daily liquid production, daily oil production and injection concentration data within the period corresponding to the starting point of the continuous decline in daily oil production, identify the stage of stable concentration, and determine whether the injection state at the current concentration still has the significance of production capacity response by combining the distribution of the liquid production ratio, and obtain the distribution range of polymer flooding production capacity segment. The polymer flooding capacity distribution range includes the daily liquid production stage, the daily oil production stage, the injection concentration stabilization stage, and the liquid production ratio distribution stage.

8. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Obtain the daily liquid production, daily oil production and injection concentration data within the starting period of the continuous decline phase of daily oil production, calculate the changing trend of the ratio of daily liquid production to daily oil production based on the time series, and compare the ratio with the absolute value trend of daily liquid production and daily oil production to obtain the trend data of the ratio of daily liquid production to oil production. S502: Based on the daily production liquid oil ratio trend data and combined with the same period injection concentration sequence, classify and judge the changes in the fluctuation range of the ratio corresponding to the injection concentration, screen the concentration range with stable fluctuation range, and obtain the stable concentration range value. S503: Based on the concentration stability interval value, combined with the distribution of daily liquid and oil production levels during the same period, the current concentration level value is compared with the distribution boundary value within the cycle to determine the capacity response segment and obtain the distribution interval of polymer flooding capacity segment.

9. The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to claim 8, characterized in that, The stable concentration range refers to the injection concentration range in which the liquid-to-oil ratio fluctuates less and changes more steadily over time by analyzing the relationship between the ratio and the injection concentration during the same period in the time series change trend of the daily liquid to daily oil ratio. The production capacity response range refers to the comparison between the current injection concentration level and the upper and lower boundary values ​​within the cycle, based on the determination of the concentration stability range and the distribution of daily liquid and oil production levels during the same period, to determine the response of the well's liquid and oil production under different injection concentration levels.

10. A productivity assessment system for horizontal wells in heavy oil polymer flooding reservoirs, characterized in that, The method for evaluating the productivity of horizontal wells in heavy oil polymer flooding reservoirs according to any one of claims 1-9, wherein the system comprises: The response start identification module acquires the daily injection volume and daily production fluid time series of the well section, monitors the rate transition interval in the injection volume sequence, extracts the growth inflection point position in the daily production fluid sequence, and determines the time when the well section enters the response state based on the lag interval between the rate transition node and the growth inflection point node, thus obtaining the response start time point of the well section. The lagging growth judgment module judges the continuous improvement characteristics by comparing the changes before and after the daily production time series corresponding to the response start time of the well section, and identifies the growth trend time interval to obtain the production growth trend segment of the lagging stage. The stable phase segmentation module calls the daily oil production time series within the lagging phase liquid production growth trend segment, divides continuous sub-intervals and evaluates the interval fluctuation amplitude, marks the stable characteristic time intervals, and obtains the stable daily oil production segment. The continuous decline boundary identification module divides the oil production trend into multiple cycles based on the extended time period of the stable daily oil production segment, identifies the continuous decline time boundary, and obtains the starting point of the continuous decline stage of daily oil production. The capacity segment determination module calls the daily liquid production, daily oil production, and injection concentration data within the cycle where the daily oil production continuously declines, identifies the concentration stabilization stage, and determines the capacity response status by combining the liquid production ratio distribution, thereby obtaining the polymer flooding capacity segment distribution range.

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