A method for evaluating well connectivity based on time-lag correlation analysis

Abstract

The present invention discloses a well-to-well connectivity evaluation method based on time-lag correlation analysis, comprising the following steps: S1: collecting production dynamic data of two target oil wells whose well-to-well connectivity is to be determined, and determining whether they are applicable to the well-to-well connectivity evaluation method; the production dynamic data include production date, production layer, switch well dynamics, and daily oil pressure data; if applicable, continue; S2: extracting the daily oil pressure data of the two target oil wells, and pre-processing them; S3: using the Pearson correlation coefficient analysis method based on time lag to analyze the correlation between the daily oil pressure data of the two target oil wells after pre-processing, and drawing a pressure correlation curve; S4: analyzing the pressure correlation curve to determine the well-to-well connectivity of the two target oil wells. The present invention can quickly analyze the pressure of the target production well and determine the connectivity of the two wells under the condition of using only the conventional production dynamic data of the oil well, and has the advantages of low cost and high efficiency.

Description

Technical Field

[0001] The invention relates to the field of oil and gas reservoir development geological technology, and in particular to an inter-well connectivity evaluation method based on time lag correlation analysis. Background Art

[0002] Well connectivity is an issue that needs to be studied in the process of oil and gas field development. Accurate analysis of well connectivity can not only provide a reliable basis for designing sidetracking of old wells and deployment of new wells, but also play a guiding role in designing development plans during the water injection development stage of oil fields. The current methods for analyzing well connectivity are mainly divided into two categories: static analysis and dynamic analysis.

[0003] The static analysis method mainly analyzes the possibility of well connectivity through static data such as logging data and seismic data. For example, the logging curves of the same formation usually have similar morphological characteristics. If the logging curves of two production wells have similar morphologies, the two wells may be connected; similarly, seismic data can reflect the lateral changes of reservoirs in a larger area. If the seismic response of the reservoirs between the two wells is similar, the possibility of the two wells being connected is high. The advantage of the static analysis method is that it is relatively simple to obtain data, and the data used are all common data in oil fields, but the disadvantage is that the accuracy is not high, and it usually needs to be used in conjunction with the dynamic analysis method.

[0004] The dynamic analysis method mainly analyzes connectivity by obtaining data generated by oil wells in production dynamics. Specific methods include interference well test analysis, tracer analysis, pressure system analysis, etc. The interference well test analysis method requires that one of the production wells be shut down and remain silent, and disturbances are generated by changing the production system of the surrounding wells to observe whether the silent well can detect the disturbances generated by the surrounding wells to determine the connectivity between the two wells; the tracer analysis method injects a chemically stable tracer into one well and observes whether the other well produces tracers. If tracers are produced, it means that the two wells are connected, and the time from tracer injection to production and the concentration of the output can be used to determine the quality of the connectivity between the two wells; the pressure system analysis method analyzes connectivity by comparing the trend of the formation pressure of the oil wells. The principle is that the bottom pressure of each oil well under the same hydrodynamic system should have the same trend of change. After one well is put into production, the formation pressure of the remaining production wells will drop synchronously. The advantage of the dynamic analysis method is that its analysis results are extremely accurate, but its disadvantage is that it is expensive to use, requires a lot of manpower and material resources, and is time-consuming. For example, in the interference well test analysis method, two or more target production wells analyzed cannot maintain normal production, resulting in reduced oil field efficiency; the chemical tracers used in the tracer analysis method are usually expensive, and it is necessary to detect the output of the tracer over a long period of time; the disadvantage of the pressure system analysis method is that the pressure change trend of each oil well formation in the short term cannot accurately reflect the connectivity between wells, so it is necessary to track the formation pressure changes of each well for a long time, which takes a high time cost.

[0005] In addition, there is also a method to evaluate well connectivity by studying the correlation of dynamic data between oil wells, but the defect of this method is that it only uses the data correlation of a single time node to describe connectivity, while ignoring that the transmission of interference between wells is a continuous process. Therefore, how to obtain accurate well connectivity evaluation results at a low cost has become an important issue in the field of oil and gas development. Summary of the invention

[0006] In view of the above problems, the present invention aims to provide a method for evaluating well connectivity based on time-lag correlation analysis.

[0007] The technical solution of the present invention is as follows:

[0008] A method for evaluating well connectivity based on time lag correlation analysis comprises the following steps:

[0009] S1: Collecting production dynamic data of two target oil wells whose inter-well connectivity is to be determined, and determining whether the two target oil wells are suitable for the inter-well connectivity evaluation method;

[0010] The production dynamic data include production date, production layer, well opening dynamics, well closing dynamics, and daily oil pressure data;

[0011] If applicable, proceed to step S2; if not applicable, use a static analysis method or a dynamic analysis method to evaluate the inter-well connectivity of the two target oil wells;

[0012] S2: extracting daily oil pressure data of two target oil producing wells, and preprocessing the daily oil pressure data;

[0013] S3: Use the Pearson correlation coefficient analysis method based on time lag to analyze the correlation between the daily oil pressure data of the two target oil wells after preprocessing, and draw a pressure correlation curve;

[0014] S4: Analyze the pressure correlation curve to determine the inter-well connectivity between the two target oil wells.

[0015] Preferably, in step S1, when judging whether the two target oil wells are suitable for the well-to-well connectivity evaluation method, if the production performance data of the two target oil wells meet the following basic conditions, then the two target oil wells are suitable for the well-to-well connectivity evaluation method:

[0016] C1: The two target oil wells are produced in the same time period;

[0017] C2: The two target oil wells are not in a shutdown state exceeding threshold 1 during the same period, and no other production wells are shut down or put into production within threshold 2 around the two target oil wells, and no other water injection wells are newly injected or stopped within threshold 3 around the two target oil wells;

[0018] C3: The formation of two target oil wells when they are producing at the same time is the same formation.

[0019] Preferably, in basic condition C1, the production time of the two target oil wells in the same time period is more than two years.

[0020] Preferably, in basic condition C2, the time of the same period is more than six months; the threshold one is one month, and the threshold two and the threshold three are both 1000 meters.

[0021] Preferably, in step S2, preprocessing the daily oil pressure data includes deleting data segments with continuous missing data, eliminating abnormal data, and filling in data missing points.

[0022] Preferably, when deleting a data segment with continuous missing data, the missing time of the continuous missing data is more than ten days; when filling in missing data points, the missing data points where abnormal data are located and the missing data points where the continuous missing daily oil pressure data is less than or equal to ten days are filled in.

[0023] Preferably, when eliminating abnormal data, the quartile method is used to determine the abnormal data in the daily oil pressure data.

[0024] Preferably, step S3 specifically includes the following sub-steps:

[0025] S31: Calculate the Pearson correlation coefficient between the pre-processed daily oil pressure data of two target oil wells, where the pre-processed daily oil pressure data of one target oil well is S1 = [x1, x2, ..., x t ], the daily oil pressure data of another target oil well after preprocessing is S2=[y1,y2,…,y t ], where x t and t They represent the daily oil pressure data of the two target oil wells on day t respectively;

[0026] In this step, the Pearson correlation coefficient is calculated by the following formula:

[0027]

[0028] Where: Pearson lag＝0 represents the initial Pearson correlation coefficient before time lag; t is time; x i is the daily oil pressure data of one of the target oil wells after preprocessing on the i-th day; i The daily oil pressure data of another target oil well after preprocessing on the i-th day; and They are the average daily oil pressure data of the two target oil wells respectively;

[0029] S32: Use S1 as the calibration data, perform a time lag operation on S2, move each data point in S2 to the next time point by a time interval, and then calculate the Pearson correlation coefficient with S1 until the time lag reaches the preset maximum time lag value lag max ;

[0030] S33: Use S1 as the calibration data, perform a time lag operation on S2, move each data point in S2 to the previous time point by one time interval, and then calculate the Pearson correlation coefficient with S1 until the time lag reaches -lag max ;

[0031] In step S32 and step S33, the Pearson correlation coefficient is calculated by the following formula:

[0032]

[0033] S34: According to the Pearson correlation coefficient obtained in steps S31-S33, a pressure correlation curve is drawn with the time lag lag as the horizontal coordinate and the Pearson correlation coefficient as the vertical coordinate.

[0034] Preferably, in step S4, when determining the inter-well connectivity of the two target oil wells, the two target oil wells are connected if the pressure correlation curve has the following characteristics:

[0035] Taking point A corresponding to the initial Pearson correlation coefficient before time lag as the standard, if there is a BCD segment on the curve on the side of point A, it is judged that the two target oil wells are connected;

[0036] In the BCD segment, point B is defined as: the Pearson correlation coefficient of the time lag point before and after point B is greater than the Pearson correlation coefficient corresponding to this point, and from point B to point D, as the absolute value of the time lag increases, the Pearson correlation coefficient of each point increases monotonically;

[0037] Point D is defined as: the Pearson correlation coefficient of any point in a time lag before and after point D is smaller than the Pearson correlation coefficient corresponding to point D;

[0038] Point C is defined as: there is a point on curve BD whose Euclidean distance to line segment BD is greater than the Euclidean distance from other points on curve BD to line segment BD. This point is point C.

[0039] As a preferred embodiment, the time lag number of days corresponding to point B is b. The farther point b is from the zero point, the longer it takes for the interference emitted by one target oil well to reach the other target oil well.

[0040] Point D corresponds to a time lag of d days, which means that the interference from one target oil well completely disappears on the other target oil well after d days;

[0041] The time lag corresponding to point C is c, which means that from day b to day c after the interference from one of the target oil wells occurs, the impact on the other target oil well continues to increase and reaches a peak on day c. From day c to day d, the impact on the other target oil well continues to weaken and completely disappears on day d.

[0042] The beneficial effects of the present invention are:

[0043] The present invention can quickly analyze the pressure of the target production well and determine the connectivity of two wells using only conventional production dynamic data of the oil well, and has the advantages of low cost and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 A schematic diagram of a flow chart of a time-lag operation of the present invention;

[0046] Figure 2 A schematic diagram of a pressure correlation curve of two wells connected in the present invention;

[0047] Figure 3 It is a schematic diagram of the original daily oil pressure data results of a specific embodiment;

[0048] Figure 4 It is a schematic diagram of pressure correlation curve results of two wells in a specific embodiment;

[0049] Figure 5 The figure is a schematic diagram of the tracer production curve results of Well 1 when the tracer analysis method is used in two wells in a specific embodiment. DETAILED DESCRIPTION

[0050] The present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the absence of conflict, the embodiments in this application and the technical features in the embodiments can be combined with each other. It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as those generally understood by those of ordinary skill in the art to which this application belongs. The words "including" or "comprising" and the like used in the disclosure of the present invention mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0051] The present invention provides a method for evaluating well connectivity based on time lag correlation analysis, comprising the following steps:

[0052] S1: Collect the production dynamic data of the two target oil wells whose inter-well connectivity is to be determined, and determine whether the two target oil wells are suitable for the inter-well connectivity evaluation method; the production dynamic data include production date, production layer, well opening dynamics, well closing dynamics, and daily oil pressure data; if applicable, proceed to step S2; if not applicable, use static analysis method or dynamic analysis method to evaluate the inter-well connectivity of the two target oil wells.

[0053] In a specific embodiment, when judging whether two target oil wells are suitable for the well-to-well connectivity evaluation method, if the production performance data of the two target oil wells meet the following basic conditions, they are suitable for the well-to-well connectivity evaluation method:

[0054] C1: The two target oil wells are produced in the same time period; optionally, the two target oil wells are produced in the same time period for more than two years;

[0055] C2: The two target oil wells do not have a shutdown state exceeding threshold 1 in the same period, and no other production wells are shut down or put into production within threshold 2 around the two target oil wells, and no other water injection wells are newly injected or stopped within threshold 3 around them; optionally, the time of the same period is more than six months; threshold 1 is one month, and thresholds 2 and 3 are both 1000 meters; it should be noted that in the above embodiment, one month refers to one consecutive month, not one cumulative month;

[0056] C3: The formation of two target oil wells when they are producing at the same time is the same formation.

[0057] It should be noted that in the above embodiment, the time and threshold value preferred by the basic conditions C1 and C2 are only preferred parameters for improving the accuracy of the evaluation method of the present invention; when using the present invention, other times and other threshold values ​​that are suitable for actual working conditions can also be selected as needed, for example, threshold two and threshold three can be different, and the specific values ​​can be selected according to the maximum well spacing between well interference, etc.

[0058] It should be noted that when the two target oil wells are not suitable for evaluating their inter-well connectivity using the inter-well connectivity evaluation method of the present invention, the static analysis method and the dynamic analysis method used are both prior arts, as mentioned in the background technology of the present invention, and the specific methods will not be repeated here.

[0059] S2: extracting daily measured oil pressure data of two target oil producing wells, and preprocessing the daily measured oil pressure data.

[0060] In a specific embodiment, the preprocessing of the daily oil pressure data includes deleting data segments with continuous missing data, eliminating abnormal data, and filling in data missing points.

[0061] Optionally, when deleting a data segment with continuous missing data, the missing time of the continuous missing data is more than ten days; when filling in missing data points, the missing data points where the abnormal data are located and the missing data points where the continuous missing daily oil pressure data is less than or equal to ten days are filled in.

[0062] Optionally, when filling in missing data points, the average value of 20 data before and after the missing point is used for replacement and filling. It should be noted that the average value of other data amounts can also be used for replacement and filling, such as 15, 25, 30, etc. In addition, in addition to the mean filling method that directly uses the average value, other filling methods in the prior art such as interpolation method, regression filling method, multiple filling method, etc. can be used for filling.

[0063] Optionally, when eliminating abnormal data, the quartile method is used to determine the abnormal data in the daily oil pressure data. The quartile method is to arrange the data from small to large according to the numerical value, select the data arranged at the 25%, 50% and 75% as quartiles Q1, Q2, Q3, and divide the data into four equal parts. Calculate the upper bound L based on the obtained quartiles up and the lower bound L low , the calculation formula is as follows:

[0064] L up =Q3+1.5(Q3-Q1) (3)

[0065] L low =Q1-1.5(Q3-Q1) (4)

[0066] The number in the original data that is greater than the upper limit or less than the lower limit is identified as an outlier. It should be noted that, in addition to the quartile method, other methods for identifying outliers in the prior art can also be applied to the present invention, such as directly setting upper and lower thresholds, or calculating the average value and then calculating the error between each value and the average value, setting an error threshold, and judging outliers according to the error threshold, etc.

[0067] It should be noted that the missing time selected in the above embodiment is the preferred time for improving the accuracy of the evaluation method. When using the present invention, other times that are suitable for the actual working conditions can also be selected according to needs. In addition, the daily oil pressure data is preprocessed to obtain the daily oil pressure data of the target oil producing wells of the same time and length while retaining as much original data as possible. In addition to the preprocessing method used in the above embodiment, other preprocessing methods in the prior art that can achieve this goal can also be applied to the present invention.

[0068] S3: The Pearson correlation coefficient analysis method based on time lag is used to analyze the correlation between the daily oil pressure data of the two target oil wells after preprocessing, and a pressure correlation curve is drawn.

[0069] In a specific embodiment, step S3 specifically includes the following sub-steps:

[0070] S31: Calculate the Pearson correlation coefficient between the pre-processed daily oil pressure data of two target oil wells, where the pre-processed daily oil pressure data of one target oil well is S1 = [x1, x2, ..., x t ], the daily oil pressure data of another target oil well after preprocessing is S2=[y1,y2,…,y t ], where x t and t They represent the daily oil pressure data of the two target oil wells on day t respectively;

[0071] In this step, the Pearson correlation coefficient is calculated by the following formula:

[0072]

[0073] Where: Pearson lag＝0 represents the initial Pearson correlation coefficient before time lag; t is time; x i is the daily oil pressure data of one of the target oil wells after preprocessing on the i-th day; i The daily oil pressure data of another target oil well after preprocessing on the i-th day; and They are the average daily oil pressure data of the two target oil wells respectively;

[0074] S32: Figure 1 As shown in the figure, S1 is used as the calibration data, and a time lag operation is performed on S2. Each data point in S2 is moved to the next time point by a time interval, and then the Pearson correlation coefficient is calculated with S1 until the time lag reaches the preset maximum time lag lag. max ;

[0075] S33: Use S1 as the calibration data, perform a time lag operation on S2, move each data point in S2 to the previous time point by one time interval, and then calculate the Pearson correlation coefficient with S1 until the time lag reaches -lag max ;

[0076] In step S32 and step S33, the Pearson correlation coefficient is calculated by the following formula:

[0077]

[0078] S34: According to the Pearson correlation coefficient obtained in steps S31-S33, a pressure correlation curve is drawn with the time lag lag as the horizontal coordinate and the Pearson correlation coefficient as the vertical coordinate.

[0079] S4: Analyze the pressure correlation curve to determine the inter-well connectivity between the two target oil wells.

[0080] In a specific embodiment, when determining the inter-well connectivity of two target oil wells, if the pressure correlation curve has the following characteristics, the two target oil wells are connected:

[0081] like Figure 2 As shown, taking point A corresponding to the initial Pearson correlation coefficient before time lag as the standard, if there is a BCD segment on the curve on the side of point A, it is judged that the two target oil wells are connected;

[0082] In the BCD segment, point B is defined as: the Pearson correlation coefficient of the time lag point before and after point B is greater than the Pearson correlation coefficient corresponding to this point, and from point B to point D, as the absolute value of the time lag increases, the Pearson correlation coefficient of each point increases monotonically;

[0083] Point D is defined as: the Pearson correlation coefficient of any point in a time lag before and after point D is smaller than the Pearson correlation coefficient corresponding to point D;

[0084] Point C is defined as: there is a point on curve BD whose Euclidean distance to line segment BD is greater than the Euclidean distance from other points on curve BD to line segment BD. This point is point C.

[0085] It should be noted that whether the BCD segment is on the left or right side of point A does not affect the judgment of connectivity. If the two wells are connected, in the judgment process, if S1 is used as the calibration data and S2 is time-lag operated, the BCD segment is on the left side of point A. Then, correspondingly, if S2 is used as the calibration data and S1 is time-lag operated, the BCD segment will appear on the right side of point A.

[0086] In a specific embodiment, the time lag number of days corresponding to point B is b, and the farther point b is from the zero point, the longer it takes for the interference emitted by one target oil well to reach another target oil well;

[0087] Point D corresponds to a time lag of d days, which means that the interference from one target oil well completely disappears on the other target oil well after d days;

[0088] The time lag corresponding to point C is c, which means that from day b to day c after the interference from one of the target oil wells occurs, the impact on the other target oil well continues to increase and reaches a peak on day c. From day c to day d, the impact on the other target oil well continues to weaken and completely disappears on day d.

[0089] In a specific embodiment, taking two target oil wells in the Tarim region of China that have been verified to be connected by tracer dynamic monitoring interpretation results as an example, the well connectivity evaluation method based on time lag correlation analysis of the present invention is used to evaluate the well connectivity, which specifically includes the following steps:

[0090] (1) Collect the production date, production layer, well opening dynamics and well closing dynamics of the two wells. In this embodiment, Well 1 and Well 2 were put into production at the same time, the production layer was the same during this period, and there were no new wells put into production, old wells shut down, or water injection well system changes around the two wells. Therefore, Well 1 and Well 2 can be applied to the well inter-connectivity evaluation method of the present invention to analyze their well inter-connectivity;

[0091] (2) Collect daily oil pressure data from two wells. The results are as follows: Figure 3 As shown; in this embodiment, since the oil pressure data of well 2 is missing for a long period of time after 327 days, the first 327 data of the oil pressure of the two wells are selected for analysis;

[0092] (3) The Pearson correlation coefficient analysis method based on time lag is used to analyze the correlation between the daily oil pressure data of the two target oil wells after preprocessing, and a pressure correlation curve is drawn. The results are shown in Figure 4 As shown;

[0093] (4) Yes Figure 4 The pressure correlation curve shown in the figure is analyzed to determine the well connectivity between the two target oil wells; specifically: when lag = -54, that is, point B', the correlation of the oil pressure data of the two wells begins to increase significantly, corresponding to Figure 2 Point B in the figure indicates that the inter-well pressure interference of one well reaches another well at this time; when lag = -80, that is, point C', the increasing trend of the correlation between the oil pressure data of the two wells slows down, corresponding to Figure 2 Point C in the figure indicates that the pressure interference between the wells reaches its maximum at this time; when lag = -98, that is, point D', the correlation between the oil pressure data of the two wells begins to decrease, corresponding to Figure 2 Point D in the figure indicates that the pressure interference between wells disappears completely at this time.

[0094] In this embodiment, the tracer production curve of Well 1 is as follows: Figure 5 As shown, 61 days after the tracer was injected into Well 2, the tracer was produced in Well 1, and the production continued until the 91st day. The dynamic monitoring and interpretation results of the tracer showed that there was good connectivity between the two wells.

[0095] Combination Figure 4 and Figure 5 It can be seen that the tracer production time and disappearance time are basically consistent with the time when the correlation between the pressure data of the two wells increases (point B') and decreases (point D'), and Figure 4The D' point has a high correlation, indicating that the connectivity judgment result of the well connectivity evaluation method based on time lag correlation analysis described in the present invention is reliable and has practical application value. In addition, the reason why the number of days between the present invention and the tracer analysis method is not completely consistent is because the pressure interference and the transfer mode of the tracer in the formation are different, there is a certain difference between the two, and the different tracer concentrations will also result in different end times obtained by the tracer analysis method.

[0096] In summary, the present invention can quickly and accurately determine the connectivity of two wells using only conventional production dynamic data of oil wells. Compared with the prior art, the present invention is a significant improvement.

[0097] The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. 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 content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating well connectivity based on time-lag correlation analysis, characterized in that: The following steps are involved: S1: Collecting production dynamic data of two target oil wells whose inter-well connectivity is to be determined, and determining whether the two target oil wells are suitable for the inter-well connectivity evaluation method; The production dynamic data include production date, production layer, well opening dynamics, well closing dynamics, and daily oil pressure data; If applicable, proceed to step S2; if not applicable, use a static analysis method or a dynamic analysis method to evaluate the inter-well connectivity of the two target oil wells; S2: extracting daily oil pressure data of two target oil producing wells, and preprocessing the daily oil pressure data; S3: Analyze the correlation between the pre-processed daily oil pressure data of the two target oil wells using the Pearson correlation coefficient analysis method based on time lag, and draw a pressure correlation curve; specifically, it includes the following sub-steps: S31: Calculate the Pearson correlation coefficient between the pre-processed daily oil pressure data of two target oil wells, where the pre-processed daily oil pressure data of one target oil well is S1 = [x1, x2, ..., x t ], the daily oil pressure data of another target oil well after preprocessing is S2=[t1,y2,…,y t ], where x t and t They represent the daily oil pressure data of the two target oil wells on day t respectively; In this step, the Pearson correlation coefficient is calculated by the following formula: Where: Pearson lag＝0 represents the initial Pearson correlation coefficient before time lag; t is time; x i is the daily oil pressure data of one of the target oil wells after preprocessing on the i-th day; i The daily oil pressure data of another target oil well after preprocessing on the i-th day; and They are the average daily oil pressure data of the two target oil wells respectively; S32: Use S1 as the calibration data, perform a time lag operation on S2, move each data point in S2 to the next time point by a time interval, and then calculate the Pearson correlation coefficient with S1 until the time lag reaches the preset maximum time lag value lag max ; S33: Use S1 as the calibration data, perform a time lag operation on S2, move each data point in S2 to the previous time point by one time interval, and then calculate the Pearson correlation coefficient with S1 until the time lag reaches -lag max ; In step S32 and step S33, the Pearson correlation coefficient is calculated by the following formula: S34: according to the Pearson correlation coefficient obtained in steps S31-S33, a pressure correlation curve is drawn with the time lag lag as the abscissa and the Pearson correlation coefficient as the ordinate; S4: Analyze the pressure correlation curve to determine the inter-well connectivity between the two target oil wells.

2. The well connectivity evaluation method based on time lag correlation analysis according to claim 1, characterized in that: In step S1, when judging whether the two target oil wells are suitable for the well-to-well connectivity evaluation method, if the production performance data of the two target oil wells meet the following basic conditions, they are suitable for the well-to-well connectivity evaluation method: C1: The two target oil wells are produced in the same time period; C2: The two target oil wells are not in a shutdown state exceeding threshold 1 during the same period, and no other production wells are shut down or put into production within threshold 2 around the two target oil wells, and no other water injection wells are newly injected or stopped within threshold 3 around the two target oil wells; C3: The formation of two target oil wells when they are producing at the same time is the same formation.

3. The well connectivity evaluation method based on time lag correlation analysis according to claim 2, characterized in that: In basic condition C1, the two target oil wells are produced for more than two years in the same period.

4. The well connectivity evaluation method based on time lag correlation analysis according to claim 2, characterized in that: In basic condition C2, the time of the same period is more than six months; the threshold 1 is one month, and the threshold 2 and the threshold 3 are both 1000 meters.

5. The well connectivity evaluation method based on time lag correlation analysis according to claim 1, characterized in that: In step S2, the daily oil pressure data is preprocessed, including deleting data segments with continuous missing data, eliminating abnormal data, and filling in data missing points.

6. The well connectivity evaluation method based on time lag correlation analysis according to claim 5, characterized in that: When deleting a data segment with continuous missing data, the missing time of the continuous missing data is more than ten days; when filling in missing data points, fill in the missing data points where the abnormal data is located and the missing data points where the continuous missing daily oil pressure data is less than or equal to ten days.

7. The well connectivity evaluation method based on time lag correlation analysis according to claim 5, characterized in that: When eliminating abnormal data, the quartile method is used to determine the abnormal data in the daily oil pressure data.

8. The method for evaluating well connectivity based on time-lag correlation analysis according to any one of claims 1 to 7, characterized in that: In step S4, when determining the inter-well connectivity of the two target oil wells, if the pressure correlation curve has the following characteristics, the two target oil wells are connected: Taking point A corresponding to the initial Pearson correlation coefficient before time lag as the standard, if there is a BCD segment on the curve on the side of point A, it is judged that the two target oil wells are connected; In the BCD segment, point B is defined as: the Pearson correlation coefficient of the time lag point before and after point B is greater than the Pearson correlation coefficient corresponding to this point, and from point B to point D, as the absolute value of the time lag increases, the Pearson correlation coefficient of each point increases monotonically; Point D is defined as: the Pearson correlation coefficient of any point in a time lag before and after point D is smaller than the Pearson correlation coefficient corresponding to point D; Point C is defined as: there is a point on curve BD whose Euclidean distance to line segment BD is greater than the Euclidean distance from other points on curve BD to line segment BD. This point is point C.

9. The well connectivity evaluation method based on time lag correlation analysis according to claim 8, characterized in that: The time lag number of days corresponding to point B is b. The farther point b is from zero, the longer it takes for the interference from one target oil well to reach the other target oil well. Point D corresponds to a time lag of d days, which means that the interference from one target oil well completely disappears on the other target oil well after d days; The time lag corresponding to point C is c, which means that from day b to day c after the interference from one of the target oil wells occurs, the impact on the other target oil well continues to increase and reaches a peak on day c. From day c to day d, the impact on the other target oil well continues to weaken and completely disappears on day d.

Images