An interwell connectivity evaluation method based on dynamic pressure difference and time lag scanning

Abstract

This invention discloses a method for evaluating inter-well connectivity based on dynamic differential pressure and time-delay scanning, comprising the following steps: S1: acquiring dynamic monitoring data of the target well group; S2: performing time alignment and unified time-scale resampling on the dynamic monitoring data to obtain an analysis time-series data table; S3: constructing a dynamic response characteristic sequence of production wells; S4: smoothing to obtain a smoothed injection volume sequence of injection wells and a smoothed dynamic response characteristic sequence of production wells; S5: progressively time-shifting the smoothed injection volume sequence of injection wells to obtain a time-delay correlation coefficient sequence under different lag times τ; S6: evaluating the inter-well connectivity strength based on the maximum correlation coefficient and evaluating the inter-well conduction speed based on the optimal lag time. This invention can reduce the impact of fluctuations in the operating conditions of production wells on the inter-well connectivity identification results, improve the stability, accuracy, and engineering applicability of inter-well connectivity evaluation, and provide technical support for the dynamic monitoring and connectivity evaluation of injection-production well groups.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development and reservoir dynamic monitoring and analysis technology, and in particular to a method for evaluating inter-well connectivity based on dynamic pressure difference and time lag scanning. Background Technology

[0002] In complex fault-block reservoirs, sandstone reservoirs, and water-injected development reservoirs, the dynamic connectivity assessment between injection wells and production wells is a crucial basis for guiding adjustments to the injection-production structure, tapping remaining oil potential, and identifying water channeling. Accurate well connectivity assessment can reveal the transmission patterns of injected energy within the formation and the phased changes in the injection-production response relationship. Existing methods for assessing reservoir dynamic connectivity mainly fall into two categories: analytical or semi-analytical methods based on physical mechanisms, and dynamic data-driven methods based on statistics. However, existing methods all have limitations when dealing with high-frequency, multi-well, and multivariate dynamic data generated by modern automated oilfield monitoring systems.

[0003] On the one hand, physical mechanism-based methods, such as capacitive resistance models and injection-production response models, are applicable to processing low-frequency, long-cycle, and relatively stable production data. However, in actual oilfield development, injection-production regimes are frequently adjusted, and the inter-well response relationship changes significantly over time. Field monitoring data typically exhibits characteristics such as high-frequency fluctuations, short-cycle pulses, and multi-factor coupling. Under these conditions, traditional physical models often require joint fitting of multiple parameters, which can easily lead to problems such as severe parameter coupling, unstable solutions, and sensitivity to initial values. This results in decreased stability and interpretability of the inter-well connectivity feature identification results, making it difficult to meet the needs of daily dynamic monitoring and analysis.

[0004] On the other hand, pure data-driven methods, such as correlation analysis, cross-correlation analysis, and other statistical signal matching methods, have advantages such as simple modeling, high computational efficiency, and convenient engineering implementation, and are therefore widely used in field dynamic analysis. However, these methods typically directly match changes in water injection volume in injection wells with changes in the original pressure or production fluid in production wells, without effectively handling response disturbances caused by changes in the production well's own operating conditions. In actual production, the bottom hole pressure and production fluid of a production well are affected not only by interference from water injection in adjacent wells, but also by changes in its own operating conditions such as parameter adjustments, start-up and shutdown, regime changes, and short-term fluctuations. If the original pressure sequence or original production fluid sequence is directly used for correlation analysis, it is easy for the production well's own fluctuation signal to be superimposed with the real interference signal between wells, thereby reducing the accuracy of the inter-well connectivity identification results, and even producing spurious correlations or misjudgments.

[0005] With the advancement of digital oilfield construction, the dynamic monitoring data available on-site has significantly improved in both temporal resolution and data scale. Traditional connectivity evaluation methods based on direct correlation of a single variable struggle to simultaneously consider physical significance, computational stability, and engineering applicability. Especially in short-cycle analysis scenarios, how to extract response characteristics that better reflect inter-well transmission effects from the combined changes in production well pressure and fluid production, and how to achieve a quantitative evaluation of the strength and speed of inter-well connectivity while considering time lag, has become a pressing issue that needs to be addressed by existing technologies. Summary of the Invention

[0006] To address the aforementioned issues, this invention aims to provide a method for evaluating well connectivity based on dynamic differential pressure and time-delay scanning. By utilizing conventional dynamic monitoring data such as injection volume from injection wells, bottom-hole pressure from production wells, and production volume, dynamic response characteristics of production wells with clear engineering significance are constructed on a unified time scale. Furthermore, time-delay correlation scanning is combined to extract well connectivity strength and conduction hysteresis parameters, thereby improving the stability, accuracy, and engineering applicability of well connectivity evaluation.

[0007] The technical solution of the present invention is as follows: A method for evaluating inter-well connectivity based on dynamic differential pressure and time-delay scanning includes the following steps: S1: Acquire dynamic monitoring data of the target well group, the dynamic monitoring data including the time-series signal of water injection volume from the injection wells. Production well bottom hole pressure timing signal and production well fluid production time series signal ; S2: Perform time alignment and uniform time-scale resampling on the dynamic monitoring data to obtain an analysis time-series data table; S3: Based on the production well bottom pressure timing signal Constructing dynamic response characteristic sequences of production wells ; S4: Time sequence signal of water injection volume for the injection well and the dynamic response characteristic sequence of the production well Smoothing was performed separately to obtain the smoothed water injection volume sequence of the injection wells. and smoothed production well dynamic response feature sequence ; S5: Within the preset analysis time window, analyze the smoothed water injection volume sequence of the injection wells. Perform stepwise time shifting, and at each candidate lag time When the number of corresponding effective overlapping sample points is greater than a preset lower limit, the dynamic response feature sequence of the production well is calculated and compared with the smoothed sequence. The correlation coefficient was used to obtain different lag times. The time-delay correlation coefficient sequence ; S6: From the time-delay correlation coefficient sequence Extracting the maximum correlation coefficient and its corresponding optimal lag time According to the maximum correlation coefficient To evaluate the inter-well connectivity strength, based on the optimal hysteresis time... Evaluate the speed of inter-well conduction.

[0008] Preferably, in step S2, the specific method of time alignment and unified time scale resampling is as follows: the original monitoring time of different wells is uniformly converted into a standard time index, and resampling is performed according to a preset time scale.

[0009] Preferably, the preset time scale is a daily scale.

[0010] Preferably, step S3 specifically includes the following sub-steps: S31: Obtain the production well bottom pressure timing signal within a preset sliding time window. Construct a reference pressure sequence with the maximum value ; S32: According to the reference pressure sequence With the bottom hole pressure timing signal of the production well The dynamic pressure difference sequence is constructed using the following formula. : (1) In the formula: This is the preset minimum dynamic differential pressure threshold; S33: Through the production well fluid production time-series signal With the dynamic differential pressure sequence The ratio of the production well dynamic response feature sequence is used to construct the dynamic response feature sequence of the production well. .

[0011] Preferably, in step S4, the smoothing process is implemented using a moving average method.

[0012] Preferably, in step S5, the time-delay correlation coefficient sequence The following formula is used to calculate: (2) In the formula: Lag time The number of sample points that satisfy the condition that the number of valid overlapping sample points is greater than the preset lower limit; In order to delay time Below, the smoothed water injection volume sequence of the injection wells at time... The value of ; To be within the current effective overlapping interval, the first The time points corresponding to each sample point; The smoothed dynamic response characteristic sequence of the production well at time 1 The value of ; Lag time Mean value of injection volume sequence of injection wells after smoothing; This represents the mean of the smoothed dynamic response characteristic sequence of the production well.

[0013] Preferably, in step S6, the maximum correlation coefficient The larger the inter-well connectivity, the stronger the optimal hysteresis time. The smaller the diameter, the faster the conduction between wells.

[0014] Preferably, the following steps are also included: S7: Repeat steps S2 to S6 for multiple historical analysis time windows to achieve phased comparison of the dynamic changes in well connectivity.

[0015] Preferably, in step S7, the multiple historical analysis time windows are several time windows divided according to changes in development system, adjustment stage of injection-production relationship, water injection pulse stage, or manually designated analysis stage.

[0016] The beneficial effects of this invention are: 1. This invention does not directly use the original bottom hole pressure sequence of the production well for inter-well correlation analysis. Instead, it constructs a normalized response characteristic based on the production fluid production and dynamic pressure difference of the production well. This reduces the interference of the production well's own operating condition fluctuations on the inter-well connectivity identification results to a certain extent, and improves the stability and reliability of the inter-well connectivity evaluation results.

[0017] 2. By constructing a time-delay correlation analysis framework of "injection driving sequence - response characteristic sequence", this invention can simultaneously extract relevant parameters characterizing the strength of inter-well connectivity and hysteresis parameters characterizing the speed of conduction, so that the evaluation results have both strength and time significance.

[0018] 3. This invention, by conducting inter-well connectivity analysis at different historical analysis stages, can reflect the dynamic characteristics of inter-well connectivity changes during the development process, thereby providing a basis for optimizing injection-production structure, formulating allocation measures, and identifying water channeling risks.

[0019] 4. The data relied upon in this invention are all dynamic monitoring data that can be routinely obtained during oilfield development. The method is simple to implement and is applicable to well group connectivity analysis and dynamic monitoring evaluation in water-injected oil reservoirs. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and technical features described in this application can be combined with each other. It should also be pointed out that, unless otherwise indicated, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "comprising" or "including" and similar words used in this invention refer to elements or objects preceding the word that encompass the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0023] like Figure 1 As shown, this invention provides a method for evaluating inter-well connectivity based on dynamic differential pressure and time-delay scanning, comprising the following steps: S1: Acquire dynamic monitoring data of the target well group, the dynamic monitoring data including the time-series signal of water injection volume from the injection wells. Production well bottom hole pressure timing signal and production well fluid production time series signal .

[0024] S2: Perform time alignment and uniform time scale resampling on the dynamic monitoring data to obtain an analysis time series data table.

[0025] In one specific embodiment, the time alignment and unified time-scale resampling are performed by converting the original monitoring times of different wells into a standard time index and resampling them according to a preset time scale. Optionally, the preset time scale is a daily scale.

[0026] In the above embodiments, using a daily scale as a unified time scale can suppress high-frequency local noise to a certain extent, avoid the amplification of random fluctuations caused by excessively fine time granularity, and at the same time retain the main dynamic trends in the injection-production relationship, making it more suitable for subsequent time-delay correlation analysis.

[0027] S3: Based on the production well bottom pressure timing signal Constructing dynamic response characteristic sequences of production wells .

[0028] In a specific embodiment, step S3 specifically includes the following sub-steps: S31: Obtain the production well bottom pressure timing signal within a preset sliding time window. Construct a reference pressure sequence with the maximum value The expression is: (3) In the formula: ; S32: According to the reference pressure sequence With the bottom hole pressure timing signal of the production well The dynamic pressure difference sequence is constructed using the following formula. : (1) In the formula: This is the preset minimum dynamic differential pressure threshold; S33: Through the production well fluid production time-series signal With the dynamic differential pressure sequence The ratio of the production well dynamic response feature sequence is used to construct the dynamic response feature sequence of the production well. The expression is: (4) In the above embodiments, constructing a reference pressure sequence using the maximum bottom hole pressure within a sliding window is beneficial for characterizing the relative high-pressure reference state of the production well within a local time range, and thereby forming a dynamic pressure difference with clear engineering significance. When the difference between the reference pressure and the current bottom hole pressure is small, by setting the minimum dynamic pressure difference threshold, the abnormal amplification of the response characteristics caused by an excessively small denominator in step S33 can be avoided, thereby improving the stability of the dynamic response characteristic expression.

[0029] S4: Time sequence signal of water injection volume for the injection well and the dynamic response characteristic sequence of the production well Smoothing was performed separately to obtain the smoothed water injection volume sequence of the injection wells. and smoothed production well dynamic response feature sequence .

[0030] In this invention, the original bottomhole pressure sequence of the production well is not directly used as the response signal. This is because the change in the original bottomhole pressure of the production well is not entirely determined by the injection from adjacent wells; it is also affected by fluctuations in the production fluid itself, regime adjustments, and short-term operating conditions. If only the original pressure sequence is used for subsequent time-delay correlation analysis, it is easy to misidentify the production well's own disturbances as inter-well response signals. This invention introduces reference pressure, dynamic pressure difference, and production rate normalization to construct response characteristics that better reflect the inter-well driving effect. Using these as the response signal enables the invention to obtain more accurate evaluation results.

[0031] In one specific embodiment, the smoothing process is implemented using a moving average method, and the smoothed water injection volume sequence of the injection wells... and the smoothed production well dynamic response feature sequence They are represented as follows: (5) (6) In the formula: Set the default smooth window length.

[0032] It should be noted that smoothing can reduce the impact of short-period random fluctuations, local outliers, and metrological noise on the results of time-delay correlation analysis. The smoothing method in the above embodiments is only a preferred method of the present invention. Other smoothing methods in the prior art that can achieve this purpose can also be applied to the present invention without changing the technical concept of the present invention.

[0033] S5: Within the preset analysis time window, analyze the smoothed water injection volume sequence of the injection wells. Perform stepwise time shifting, and at each candidate lag time When the number of corresponding effective overlapping sample points is greater than a preset lower limit, the dynamic response feature sequence of the production well is calculated and compared with the smoothed sequence. The correlation coefficient was used to obtain different lag times. The time-delay correlation coefficient sequence .

[0034] It should be noted that the effective overlapping samples refer to samples within a given candidate lag time. Below is a smoothed water injection volume sequence of injection wells after time shift. With smoothed production well dynamic response feature sequence There are sample points that correspond one-to-one in time and both have valid values.

[0035] In one specific embodiment, the time-delay correlation coefficient sequence The following formula is used to calculate: (2) In the formula: Lag time The number of sample points that satisfy the condition that the number of valid overlapping sample points is greater than the preset lower limit; In order to delay time Below, the smoothed water injection volume sequence of the injection wells at time... The value of ; To be within the current effective overlapping interval, the first The time points corresponding to each sample point; The smoothed dynamic response characteristic sequence of the production well at time 1 The value of ; Lag time Mean value of injection volume sequence of injection wells after smoothing; This represents the mean of the smoothed dynamic response characteristic sequence of the production well.

[0036] S6: From the time-delay correlation coefficient sequence Extracting the maximum correlation coefficient and its corresponding optimal lag time According to the maximum correlation coefficient To evaluate the inter-well connectivity strength, based on the optimal hysteresis time... Evaluate the speed of inter-well conduction.

[0037] In one specific embodiment, the maximum correlation coefficient The larger the inter-well connectivity, the stronger the optimal hysteresis time. The smaller the diameter, the faster the conduction between wells.

[0038] In a specific embodiment, the well connectivity evaluation method based on dynamic differential pressure and time lag scanning of the present invention further includes the following steps: S7: Repeat steps S2 to S6 for multiple historical analysis time windows to achieve phased comparison of the dynamic changes in well connectivity.

[0039] In one specific embodiment, the multiple historical analysis time windows are several time windows divided according to changes in development system, injection-production relationship adjustment stage, water injection pulse stage, or manually designated analysis stage.

[0040] In this invention, instead of simply matching changes in water injection volume in injection wells with changes in the original bottomhole pressure of production wells, the invention first couples production well pressure and production volume on a unified time scale to construct a normalized response characteristic that reflects the dynamic response capability of the production well. This response characteristic is then used as the response signal and subjected to time-delay correlation scanning with the water injection well drive signal to extract inter-well connectivity strength parameters and conduction hysteresis parameters. Compared to existing evaluation methods that rely solely on original pressure, original production volume, or single-point-time statistics, this invention can reduce the interference of changes in the operating conditions of the production well itself, short-period fluctuations, and local anomalies on the inter-well connectivity identification results to a certain extent. It is more suitable for evaluating inter-well connectivity under high-frequency dynamic data conditions in water-injected reservoirs.

[0041] Example 1 Taking a water injection development block as an example, the well connectivity evaluation method based on dynamic pressure difference and time delay scanning described in this invention is used to evaluate its well connectivity. In this embodiment, to protect the on-site block information, well network deployment information, and real well number information, all wells involved are represented anonymously, including one water injection well and two production wells, denoted as water injection well A, production well B, and production well C, respectively. The specific steps include: (1) Obtain dynamic monitoring data of the target well group In this embodiment, injection well A is selected as the driving well, and production wells B and C are selected as response wells to obtain dynamic monitoring data for the corresponding historical period. The dynamic monitoring data includes at least the time-series data of the injection volume of injection well A. Time series data of bottom hole pressure of production wells B and C And time-series production data of production wells B and C. .

[0042] All the above data were directly acquired by the conventional oilfield production dynamic monitoring system, without the need for additional special testing equipment. In actual oilfield production, dynamic monitoring data from different wells typically originate from different acquisition systems or stages. Therefore, the original records often exhibit inconsistencies in timestamps, different sampling frequencies, missing data for certain periods, and localized abnormal fluctuations. If a simple correlation analysis is performed directly on the injection volume of injection wells and the original pressure or production volume of production wells at the raw data level, it is easily affected by changes in the operating conditions of the production wells themselves and local noise disturbances, making it difficult to accurately identify the true drive-response relationship between wells. Therefore, this embodiment first acquires multidimensional dynamic monitoring data and then performs unified standardization and feature construction in subsequent steps.

[0043] In this embodiment, two representative historical analysis periods are further selected as demonstration windows, denoted as Phase 1 and Phase 2. Phase 1 covers the period from June 20, 2021 to September 28, 2021, and Phase 2 covers the period from January 16, 2022 to April 26, 2022. Both of these time windows represent dynamic periods with significant changes in injection and production, good data integrity, and engineering representativeness, making them suitable for illustrating the effectiveness of the method of this invention in identifying well connectivity relationships across different historical periods.

[0044] (2) Perform time alignment and uniform time scale resampling on the dynamic monitoring data. In this embodiment, since the original monitoring times of different wells may have inconsistent acquisition frequencies, misaligned timestamps, or partial missing measurements, the original monitoring times of each well are first converted into a standard time index. Then, resampling is performed according to a preset time scale, preferably a daily scale, to form an analysis time series data table for the target well group. After time alignment and unified time scale processing, it can be ensured that the injection well drive signal and the production well response signal are analyzed under the same time reference.

[0045] In this embodiment, the original dynamic data is resampled daily to obtain: the daily average water injection volume sequence of injection well A; the daily average bottom hole pressure sequence and daily average fluid production sequence of production well B; and the daily average bottom hole pressure sequence and daily average fluid production sequence of production well C.

[0046] In Phase 1, injection well A exhibited a significant enhancement in injection volume, with the average daily injection rate rising rapidly from a low level and then fluctuating and maintaining near a high level. Production well B maintained a high overall production level, with its bottomhole pressure showing a continuous and slow change. Production well C, however, exhibited a different pattern in terms of production volume and pressure fluctuations compared to production well B. In Phase 2, injection well A also underwent an injection volume adjustment process, but the corresponding production well response characteristics differed significantly from those in Phase 1, indicating that the inter-well connectivity may exhibit phased changes.

[0047] (3) Constructing a dynamic response characteristic sequence of production wells First, the bottom hole pressure timing signal of the production well is acquired within the preset sliding time window. Construct a reference pressure sequence with the maximum value Then, from the reference pressure sequence Timing signal of bottom hole pressure in production well Constructing a dynamic pressure difference sequence using equation (1) Furthermore, based on the time-series signal of production well fluid production... With dynamic pressure difference sequence The ratio constructs a dynamic response characteristic sequence of production wells. This processing allows the response signal to simultaneously reflect the combined effects of changes in production well pressure and flow rate, thereby mitigating the impact of fluctuations in the production well's own operating conditions on the inter-well drive-response identification results to some extent. Table 1 shows some of the corresponding daily-scale raw data and intermediate calculation results for a representative analysis phase: Table 1. Partial daily-scale data and response characteristics of injection wells A to production wells B in Phase 1.

[0048] As shown in Table 1, within the representative analysis interval, the injection volume of the injection wells changed significantly, while the bottom hole pressure and production volume of the production wells only showed slow changes or local fluctuations. If only the original bottom hole pressure or original production volume is observed directly, it is not easy to accurately identify the inter-well drive-response relationship; however, after the dynamic pressure difference normalization process in step (3), the constructed dynamic response characteristics can more effectively characterize the dynamic response trend of the production wells to the water injection drive.

[0049] Furthermore, in stage two, the same processing was applied to some daily-scale data from production well C, yielding the results shown in Table 2: Table 2. Daily-scale data and response characteristics of injection wells A to production wells C in Phase II.

[0050] As shown in Table 2, under low differential pressure conditions, the dynamic differential pressure is repeatedly limited by the minimum dynamic differential pressure threshold. In this case, using the dynamic differential pressure normalized response feature can maintain a stable expression of the response feature under low differential pressure conditions, thus avoiding local distortions that occur when directly using the original bottom hole pressure for analysis. Therefore, the dynamic response feature constructed in step (3) can not only reflect the response changes of the production well to water injection, but also maintain good feature stability under low differential pressure conditions.

[0051] (4) Smoothing the driving signal and response characteristic signal. In this embodiment, a moving average method is used for smoothing, and the preferred smoothing window length is 5 time steps; when the unified time scale is daily, it corresponds to a 5-day moving average. After smoothing, the smoothed water injection volume sequence of the injection wells can be obtained. and the smoothed dynamic response characteristic sequence of production wells After smoothing, the main trend of the injection well drive signal is clearer, and the local spikes and short-period disturbances in the production well response characteristic sequence are suppressed, which is more conducive to subsequent lag-by-lag correlation scanning and identification of the true drive-response time series relationship.

[0052] (5) Perform time-delay correlation scan In this embodiment, within a preset analysis time window, the smoothed water injection volume sequence of the injection wells is analyzed. Perform stepwise time shifts; at each candidate lag time Below, only when the number of corresponding effective overlapping sample points is greater than a preset lower limit will the dynamic response feature sequence of the production well be compared with the effective overlapping sample interval. The correlation coefficient is calculated (according to equation (2)) to obtain different lag times. The time-delay correlation coefficient sequence .

[0053] (6) Extracting inter-well connectivity evaluation parameters In obtaining the time-delay correlation coefficient sequence Then, extract the maximum correlation coefficient. and its corresponding optimal lag time Among them, the maximum correlation coefficient Optimal hysteresis time is used to characterize the inter-well connectivity strength between injection wells and production wells. This parameter is used to characterize the speed at which the driving signal is transmitted from the injection well to the production well. By simultaneously introducing these two evaluation parameters, the well connectivity evaluation results can have both intensity and temporal significance.

[0054] (7) Conduct dynamic comparison of inter-well connectivity in multiple stages In this embodiment, steps (2)-(6) are repeated for multiple historical analysis time windows to obtain the maximum correlation coefficient corresponding to each historical analysis time window. and optimal lag time Based on this, the dynamic changes in well connectivity were compared in stages. The multiple historical analysis time windows can be divided according to changes in development regime, injection-production relationship adjustment stages, water injection pulse stages, or manually designated analysis stages. Through multi-stage comparisons, the dynamic characteristics of well connectivity evolution during the development process can be reflected, providing a basis for subsequent injection-production structure optimization and dynamic control. The results are shown in Table 3. Table 3. Evaluation results of inter-well connectivity of anonymized well groups at different stages

[0055] As shown in Table 3, in Stage 1, the maximum correlation coefficient between injection well A and production well B is 0.883, and the optimal lag time is 10 days, indicating a strong inter-well drive-response relationship and rapid transmission speed. In contrast, the maximum correlation coefficient between injection well A and production well C is only 0.321, and the optimal lag time reaches 30 days, indicating that the driving effect of injection well A on production well C is weak, or the inter-well transmission process is slow in this stage.

[0056] In Phase Two, the maximum correlation coefficient between injection well A and production well B remained at a high level of 0.824, but the optimal lag time extended to 28 days, indicating that although the well pair still had significant connectivity, the transmission lag was significantly increased compared to Phase One. Meanwhile, the maximum correlation coefficient between injection well A and production well C further decreased to 0.137, indicating that the inter-well connectivity was less pronounced in this phase.

[0057] This demonstrates that, under the same injection drive conditions, the method of this invention can clearly distinguish the differences in the effectiveness of different production wells relative to the same injection well. Production well B exhibits a higher correlation peak and a shorter hysteresis time, indicating a better drive-response matching relationship between it and injection well A; while production well C exhibits a lower correlation peak and a longer hysteresis time, indicating a relatively weaker connectivity between it and injection well A.

[0058] Furthermore, to illustrate the invention's ability to identify dynamic changes in well connectivity, Table 4 summarizes the comprehensive comparison results of the two stages: Table 4. Comprehensive Comparison of Well Connectivity Evaluation Results Between Phase 1 and Phase 2

[0059] Table 4 further shows that: (1) The water injection well A-production well B pair showed a high maximum correlation coefficient in both stages, indicating that there is a relatively stable inter-well connectivity relationship between the pair; however, the optimal lag time in stage two is significantly greater than that in stage one, indicating that although the connectivity relationship still exists, the transmission efficiency of the driving signal to production well B decreases and the response is delayed.

[0060] (2) The maximum correlation coefficient of the water injection well A-production well C pair in both stages was significantly lower than that of production well B, indicating that the overall connection between them and water injection well A was weaker; especially in stage two, the maximum correlation coefficient was further reduced, indicating that the dynamic response effect was weaker in this stage.

[0061] (3) By comparing the maximum correlation coefficient and the optimal lag time at the same time, the present invention can comprehensively evaluate the relationship between wells from the two dimensions of “connection strength” and “transmission speed”, which is superior to the traditional method that relies on a single pressure sequence or a single production sequence for judgment.

[0062] Comparative Example 1 Unlike Example 1, this comparative example directly uses the original bottomhole pressure sequence for well connectivity calculation. It employs the same target well group, historical analysis time window, time alignment and unified time-scale resampling method, smoothing method, and time-delay correlation scan range as Example 1. The difference lies in that, instead of constructing a dynamic differential pressure normalized response feature sequence, it directly uses the smoothed original bottomhole pressure sequence of the production wells as the response signal, performs a time-delay correlation scan with the smoothed injection volume sequence of the injection wells, and extracts the maximum correlation coefficient and its corresponding optimal lag time as the well connectivity evaluation parameters.

[0063] In Phase 1, the maximum correlation coefficient between injection well A and production well B is... The optimal lag time is 0.568. The value is 11 days. This shows that although directly using raw data to evaluate inter-well connectivity can identify certain drive-response relationships, the correlation peak is low, and the characterization of changes in water injection drive response is relatively weak.

[0064] Comparing the results of Comparative Example 1 and Example 1, it can be seen that, under the same analysis stage and the same well pair conditions, the maximum correlation coefficient obtained by directly using the original bottomhole pressure sequence for well connectivity calculation is 0.568, while the maximum correlation coefficient corresponding to the method of the present invention is 0.883; the optimal lag times for the two are 11 days and 10 days, respectively. Combined with the injection-production response of this well pair in actual production, it can be seen that, during this analysis stage, injection well A and production well B exhibit good connectivity characteristics, and the results obtained by the method of the present invention have better consistency with the on-site production understanding. The results indicate that while directly using the original bottomhole pressure sequence can reflect the inter-well drive-response relationship to some extent, the obtained correlation peak value is relatively low under the same well pair and analysis stage conditions. This suggests that the method has relatively weak characterization clarity of the response changes caused by water injection drive, and the identification results are more susceptible to fluctuations in the operating conditions of the production well itself and local anomalies. In contrast, this invention, by constructing a dynamic differential pressure normalized response feature, can improve the correlation peak value and identification clarity of the drive-response relationship while maintaining the basic consistency of the hysteresis identification results. This, in turn, improves the stability and engineering interpretability of the inter-well connectivity evaluation results to some extent.

[0065] In summary, this invention achieves quantitative evaluation of inter-well connectivity and conduction hysteresis in water-injected reservoirs through a processing flow of "dynamic monitoring data acquisition—time alignment and resampling—construction of dynamic differential pressure normalized response features—smoothing processing—time-delay correlation scanning—evaluation parameter extraction—multi-stage comparison." Compared with the method of directly using the original bottomhole pressure sequence for correlation analysis, the dynamic response features constructed in this invention can improve the correlation peak value and identification clarity of the drive-response relationship while maintaining the basic consistency of hysteresis identification results. This, to a certain extent, reduces the impact of fluctuations in the operating conditions of the production wells themselves on the identification results, and improves the stability and engineering applicability of inter-well connectivity evaluation.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations 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 scope of the present invention.

Claims

1. A method for evaluating interwell connectivity based on dynamic pressure differential and time-lag sweep, characterized in that, Includes the following steps: S1: acquiring dynamic monitoring data of a target well group, the dynamic monitoring data comprising a water injection rate time series signal of a water injection well , a bottom hole pressure time series signal of a production well , and a liquid production rate time series signal of the production well ; S2: Perform time alignment and uniform time-scale resampling on the dynamic monitoring data to obtain an analysis time-series data table; S3: Based on the production well bottom pressure timing signal Constructing dynamic response characteristic sequences of production wells ; S4: Time sequence signal of water injection volume for the injection well and the dynamic response characteristic sequence of the production well Smoothing was performed separately to obtain the smoothed water injection volume sequence of the injection wells. and smoothed production well dynamic response feature sequence ; S5: Within the preset analysis time window, analyze the smoothed water injection volume sequence of the injection wells. Perform stepwise time shifting, and at each candidate lag time When the number of corresponding effective overlapping sample points is greater than a preset lower limit, the dynamic response feature sequence of the production well is calculated and compared with the smoothed sequence. The correlation coefficient was used to obtain different lag times. The time-delay correlation coefficient sequence ; S6: From the time-delay correlation coefficient sequence Extracting the maximum correlation coefficient and its corresponding optimal lag time According to the maximum correlation coefficient To evaluate the inter-well connectivity strength, based on the optimal hysteresis time... Evaluate the speed of inter-well conduction.

2. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 1, characterized in that, In step S2, the specific method of time alignment and unified time scale resampling is as follows: the original monitoring time of different wells is uniformly converted into a standard time index, and resampling is performed according to a preset time scale.

3. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 2, characterized in that, The preset time scale is a daily scale.

4. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 1, characterized in that, Step S3 specifically includes the following sub-steps: S31: Obtain the production well bottom pressure timing signal within a preset sliding time window. Construct a reference pressure sequence with the maximum value ; S32: According to the reference pressure sequence With the bottom hole pressure timing signal of the production well The dynamic pressure difference sequence is constructed using the following formula. : （1） In the formula: This is the preset minimum dynamic differential pressure threshold; S33: Through the production well fluid production time-series signal With the dynamic differential pressure sequence The ratio of the production well dynamic response feature sequence is used to construct the dynamic response feature sequence of the production well. .

5. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 1, characterized in that, In step S4, the smoothing process is implemented using a moving average method.

6. The well connectivity evaluation method based on dynamic differential pressure and time lag scanning according to any one of claims 1-5, characterized in that, In step S5, the time-delay correlation coefficient sequence The following formula is used to calculate: （2） In the formula: Lag time The number of sample points that satisfy the condition that the number of valid overlapping sample points is greater than the preset lower limit; In order to delay time Below, the smoothed water injection volume sequence of the injection wells at time... The value of ; To be within the current effective overlapping interval, the first The time points corresponding to each sample point; The smoothed dynamic response characteristic sequence of the production well at time 1 The value of ; Lag time Mean value of injection volume sequence of injection wells after smoothing; This represents the mean of the smoothed dynamic response characteristic sequence of the production well.

7. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 1, characterized in that, In step S6, the maximum correlation coefficient The larger the inter-well connectivity, the stronger the optimal hysteresis time. The smaller the diameter, the faster the conduction between wells.

8. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 1, characterized in that, It also includes the following steps: S7: Repeat steps S2 to S6 for multiple historical analysis time windows to achieve phased comparison of the dynamic changes in well connectivity.

9. The well connectivity evaluation method based on dynamic differential pressure and time delay scanning according to claim 8, characterized in that, In step S7, the multiple historical analysis time windows are several time windows divided according to changes in development system, adjustment stage of injection-production relationship, water injection pulse stage, or manually designated analysis stage.

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