A method and system for evaluating the value of an oil and gas resource block

Abstract

The present application relates to the field of oil and gas field development data analysis and production decision support, in particular to a kind of oil and gas resource block value evaluation method and system.The method synchronously collects and aligns daily oil production, oil production man-hours and auxiliary operation man-hours data of target block day by day;Gaussian weighting lag convolution processing is carried out on auxiliary operation man-hours to obtain effective contribution component, and it is combined with oil production man-hours weighted into total effective man-hours;According to oil production trend value and total effective man-hours, calculate man-hour normalized oil production index, and construct man-hour structure coefficient;Further calculate the short-term and medium-term change rate of man-hour normalized oil production index, and obtain comprehensive value momentum by fusion;Finally, based on man-hour normalized oil production index, man-hour structure coefficient and comprehensive value momentum, three-level judgment attribution analysis is carried out, and corresponding engineering decision suggestion is output.The present application realizes the deep fusion and linkage analysis of production and man-hour data, and can effectively support the scientific adjustment of oil production plant production system.

Description

Technical Field

[0001] This invention relates to the field of data analysis, specifically to a method and system for assessing the value of oil and gas resource blocks. Background Technology

[0002] In oil and gas field production management practice, block value assessment usually relies on basic data such as daily oil production, production man-hours, and auxiliary operation man-hours. Currently, the above data has been routinely collected in most oilfields, but there is a lack of effective integration and analysis mechanisms between them, and the assessment conclusions are mostly presented in the form of static indicators or qualitative descriptions.

[0003] Existing assessment methods have the following shortcomings: First, there is a lack of physical coupling mechanism between the temporal fluctuations in daily oil production and labor input, making it difficult to pinpoint the true cause of production changes to specific operational dimensions. Second, the production-increasing effects of auxiliary operations such as water injection and pump testing often take several days to materialize, but existing methods ignore this actual physical process, easily leading to timing mismatches and attribution biases. Third, the assessment process is difficult to effectively integrate with production management decisions. From sensor data to engineering efficiency and then to value changes, the assessment process remains at the static labeling level, and managers cannot drive the rolling revision of the oilfield's monthly production allocation plan. Summary of the Invention

[0004] This invention provides a method and system for evaluating the value of oil and gas resource blocks to solve existing problems: the lack of physical coupling between production and labor hours in existing oil and gas block value evaluations, the neglect of the lag effect of auxiliary operations, and the inability of the evaluation process to effectively support differentiated production decisions.

[0005] The present invention provides a method and system for assessing the value of oil and gas resource blocks, which adopts the following technical solution: In a first aspect, the present invention provides a method for evaluating the value of oil and gas resource blocks, the method comprising the following steps: Synchronously collect the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block. Align the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series day by day with the date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series. The effective auxiliary operation time sequence is processed by Gaussian weighted lag convolution to obtain the effective contribution component of each effective auxiliary operation time. The effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series are weighted and merged to obtain the total effective man-hours of each time series; the oil production trend value of each time series is obtained based on the average effective daily oil production of multiple time series before and after the effective daily oil production of each time series; and the man-hour normalized oil production index of each time series is obtained based on the total effective man-hours and the oil production trend value of each time series. Based on the effective contribution components of the effective oil production man-hours and effective auxiliary operation man-hours for each time series, the man-hour structure coefficient for each time series is obtained. Based on the normalized oil recovery index of working hours for each time series, the short-term and medium-term change rates of each time series are obtained. The short-term and medium-term change rates of each time series are weighted and fused to obtain the comprehensive value momentum of each time series. Based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series, a three-level attribution analysis is performed.

[0006] Furthermore, the method for synchronously collecting the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block, and aligning these time series daily by day using date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series, includes the following specific methods: With a collection cycle of no less than 90 consecutive calendar days, the daily oil production time series, oil production time series, and auxiliary operation time series of the target block are collected synchronously. These three types of data are aligned day by day with the date as the primary key. For any date, if all three types of data exist, they are retained. If there are no more than 3 consecutive missing days, linear imputation is used. If there are more than 3 consecutive missing days, all three types of data corresponding to the missing date are removed. The retained three types of data and the data after linear imputation are recorded as valid data, resulting in valid daily oil production time series, oil production time series, and auxiliary operation time series. The time series of each type of data includes the date sequence of each type of data and the data values ​​corresponding to different date sequences of each type of data.

[0007] Furthermore, the specific method for performing Gaussian weighted lag convolution processing on the effective auxiliary operation time sequence to obtain the effective contribution component of each effective auxiliary operation time includes: ; ; In the formula, Indicates the first Weighting coefficients with a time lag of one calendar day Indicates the first Effective auxiliary work hours per calendar day, in hours / day. Indicates the first The effective contribution of auxiliary work hours per calendar day, in hours / day. Represents the number of days of lag in the primary response, a dimensionless integer. For the discreteness parameter of response duration, take... , This represents the first integer used for counting. Indicates the first Number of date sequences in calendar days, Indicates the first The number of date sequences in a calendar day. .

[0008] Furthermore, the specific method for weighted merging of the effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series to obtain the total effective man-hours for each time series is as follows: ; In the formula, This represents the effective contribution component of the effective auxiliary work hours on the first natural day, in hours / day. Indicates the first Effective oil production hours per calendar day, in hours / day. Indicates the first The total effective working hours for a calendar day are expressed in hours per day. Indicates the first The number of date sequences in a calendar day. The auxiliary working-time output conversion factor is dimensionless. .

[0009] Furthermore, the specific method for obtaining the oil production trend value for each time series based on the average effective daily oil production of multiple time series before and after each time series includes: In the formula, Indicates the first Oil production trend value for a calendar day, in tons / day. Indicates the first Effective daily oil production per calendar day, in tons / day. Indicates the first The number of date sequences in a calendar day. Indicates the first The number of date sequences in a calendar day. This represents the second integer used for counting. This represents the sample data size used to calculate the average daily oil production over the preceding and following days of each natural day. =7, dimensionless.

[0010] Furthermore, the specific method for obtaining the normalized oil recovery index (NRI) for each time series based on the total effective working hours and oil production trend value for each time series includes: Construct a first sliding window with a length of 7; ; In the formula, Indicates the first Oil production trend value for a calendar day, in tons / day. Indicates the first The total effective working hours for a calendar day are expressed in hours per day. Indicates the index of the first sliding window, a dimensionless integer. Indicates the first The number of date sequences in a calendar day. Indicates the first The normalized oil recovery index for a given natural day, expressed in tons per hour, where 6 represents the maximum index number of the sliding window.

[0011] Furthermore, the specific method for obtaining the time structure coefficient for each time series based on the effective contribution components of the effective oil production man-hours and effective auxiliary operation man-hours for each time series includes: Construct a second sliding window with a length of 7; Calculate the first Percentage of oil production man-hours per natural day: ; Take the logarithmic ratio to obtain the first Work-hour structure coefficient for each natural day: ; In the formula, Indicates the first The percentage of oil production man-hours per natural day, dimensionless, range of values. ; Indicates the first Effective oil production man-hours per calendar day, in hours / day; Indicates the first The effective contribution of effective auxiliary work hours per natural day, in hours / day; Indicates the index of the second sliding window, a dimensionless integer. To prevent the exclusion of zero, it is dimensionless and a standard mathematical constant; 6 represents the maximum index number. Indicates the first The work-hour structure coefficient for each natural day.

[0012] Furthermore, the specific method for obtaining the short-term and medium-term change rates of each time series based on the normalized oil recovery index of each time series, and for weighted fusion of the short-term and medium-term change rates of each time series to obtain the comprehensive value momentum of each time series includes: Short-term rate of change : ; medium-term rate of change : ; In the formula, Indicates the first Short-term rate of change over a calendar day Indicates the first The medium-term rate of change over a calendar day To prevent the exclusion of zero, dimensionless, and standard mathematical constants, 7 represents the time interval for comparing short-term normalized oil recovery index, in days. This indicates the time interval for comparing the normalized enhanced oil recovery index (EOR) based on working hours over the medium term, in days. Indicates the first Normalized oil recovery index based on working hours per natural day Indicates the first Normalized oil recovery index based on working hours per natural day Indicates the first Normalized oil recovery index based on working hours per natural day ; The weighted fusion of short-term and medium-term change rates for each time series yields the comprehensive value momentum for that time series. The specific method is as follows: ; In the formula, Indicates the first The comprehensive value momentum of a natural day. Indicates the first weight. Indicates the second weight. Indicates the first Short-term rate of change over a calendar day Indicates the first The medium-term rate of change over a calendar day , .

[0013] Furthermore, the three-level attribution analysis based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series includes the following specific methods: Calculate the mean and standard deviation of the normalized oil recovery index (NER) for each time series of man-hours in the target block. Based on the Raida criterion for normality in statistical analysis, determine whether the NER for each time series of man-hours is abnormal. If the... If the normalized oil recovery index for a given number of natural days is abnormal, engineering personnel will be arranged to investigate on-site. If the first The normalized oil recovery index for the first natural day is normal, and the first... The normalized oil recovery index of the working hours of the first natural day is greater than or equal to the first natural day. The normalized oil recovery index for working hours per natural day will be maintained under the current system. If the first The normalized oil recovery index for the first natural day is normal, and the first... The normalized oil recovery index of the working hours of the first natural day is less than that of the first natural day. The normalized normalized oil recovery index of the working hours for the first natural day is used to determine the first... Is the comprehensive value momentum of the first natural day positive or negative? The overall value momentum over a single natural day is positive; continue observation for 3 to 5 days. If the first The comprehensive value momentum of the first natural day is negative, indicating that the first natural day... Is the work hour structure coefficient of the first natural day greater than or equal to that of the second natural day? The work-hour structure coefficient for the first natural day, if the first natural day... The work hour structure coefficient for the first natural day is greater than or equal to the first natural day. The work-hour structure coefficient for each natural day is used to arrange water injection and pump testing operations; if the first... The work hour structure coefficient for the first natural day is less than that for the second natural day. The working hour structure coefficient for each natural day is used to increase the number of oil production workers or extend their on-duty time.

[0014] A second aspect of the present invention provides an oil and gas resource block value assessment system, which includes a data acquisition module, an effective contribution component calculation module, a normalized oil recovery index calculation module, a work-hour structure coefficient calculation module, a comprehensive value momentum calculation module, and an attribution analysis module, wherein: The data acquisition module is used to synchronously collect the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block. The daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series are aligned day by day with the date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series. The effective contribution component calculation module is used to perform Gaussian weighted lag convolution processing on the time series of effective auxiliary operation hours to obtain the effective contribution component of each effective auxiliary operation hour. The time-normalized oil recovery index calculation module is used to weight and merge the effective contribution components of the effective oil recovery man-hours and the effective auxiliary operation man-hours of each time series to obtain the total effective man-hours of each time series; to obtain the oil production trend value of each time series based on the average effective daily oil production of multiple time series before and after the effective daily oil production of each time series; and to obtain the time-normalized oil recovery index of each time series based on the total effective man-hours and the oil production trend value of each time series. The time structure coefficient calculation module is used to obtain the time structure coefficient of each time series based on the effective contribution components of the effective oil production time and effective auxiliary operation time of each time series. The comprehensive value momentum calculation module is used to obtain the short-term and medium-term change rates of each time series based on the normalized oil recovery index of each time series, and to obtain the comprehensive value momentum of each time series by weighted fusion of the short-term and medium-term change rates of each time series. The attribution analysis module is used to perform three-level attribution analysis based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series.

[0015] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the above-described method for evaluating the value of an oil and gas resource block.

[0016] A fourth aspect of the present invention is a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the above-described method for evaluating the value of an oil and gas resource block.

[0017] The beneficial effects of the technical solution of the present invention are: This invention establishes a quantitative correlation mechanism between production fluctuations and the two types of time inputs by weighting and merging the effective oil production man-hours with the effective contribution components of auxiliary operations after Gaussian weighted hysteresis convolution processing into total effective man-hours, and constructing a time-normalized oil production index in conjunction with the oil production trend value. This enables the attribution of production changes to specific dimensions such as oil production operations or auxiliary operations, and the evaluation conclusions have engineering interpretability. Gaussian weighted lag convolution is used to process auxiliary operation time, projecting historical auxiliary operation time onto the time when the effect is manifested according to its actual physical response law. This ensures that the true contribution of auxiliary operation is correctly included in the total effective time on the date when it should take effect, avoiding attribution bias caused by time mismatch. By constructing a working hour structure coefficient to reveal the bias characteristics of working hour input, and by comprehensively representing the trend direction of efficiency changes through value momentum, a three-level judgment attribution logic is established on this basis. The evaluation results are automatically mapped into specific and executable engineering instructions such as maintaining the system, dispatching more oil production workers, arranging water injection and allocation, or pump inspection operations. This upgrades the evaluation system from a static label description to a decision-making tool that can drive the rolling revision of the production system. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a flowchart illustrating the steps of an oil and gas resource block valuation method according to the present invention. Figure 2 This is a structural block diagram of an oil and gas resource block value assessment system according to the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an oil and gas resource block value assessment method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] The following description, in conjunction with the accompanying drawings, details the specific scheme of the oil and gas resource block valuation method and system provided by this invention.

[0023] Please see Figure 1 It illustrates the first objective of the present invention, a flowchart of a method for assessing the value of oil and gas resource blocks, the method comprising the following steps: Step S001: Synchronously collect the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block. Align the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series day by day with the date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series.

[0024] It should be noted that production data is uploaded by the flow meter at 24:00 every day, while working hour data depends on the work order system entered by each work team after work. There may also be cases of supplementary entry or weekend backlog. If the misaligned daily data is used directly for calculation, the production change on the first day may be attributed to the working hours input on the second day in a time sequence error. Therefore, before the feature extraction operation, it is necessary to align and verify the three types of data day by day with the date as the primary key.

[0025] Specifically, the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block are collected synchronously. These time series are then aligned day by day using date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series. The specific method is as follows: With a collection cycle of no less than 90 consecutive calendar days, the daily oil production time series, oil production time series, and auxiliary operation time series of the target block are collected synchronously. These three types of data are aligned day by day with the date as the primary key. For any date, if all three types of data exist, they are retained. If there are no more than 3 consecutive missing days, linear imputation is used. If there are more than 3 consecutive missing days, all three types of data corresponding to the missing date are removed. The retained three types of data and the data after linear imputation are recorded as valid data, resulting in valid daily oil production time series, oil production time series, and auxiliary operation time series. The time series of each type of data includes the date sequence of each type of data and the data values ​​corresponding to different date sequences of each type of data.

[0026] If the first In the class of data, the first If data for a given natural day is missing, linear interpolation will be performed using the following formula: ; In the formula, Indicates the first Class Data Linear interpolation values ​​for a natural day, Indicates the first The number of date sequences in a calendar day. Indicates the first Class Data After one natural day, and with the first Class Data The most recent effective date is [date]. The number of date sequences corresponding to the data type. Indicates the first Class Data Before the [number] calendar day, and with the [number]th [day] Class Data The number of the most recent valid Class B data corresponding to the date sequence within a given natural day. Indicates the first In the class of data, the first One data value, Indicates the first In the class of data, the first Each data value.

[0027] It should be noted that the data collection period is no less than 90 consecutive calendar days, based on the monthly production plan cycle of the oilfield to which the target block belongs. Data must be collected continuously for no less than three complete production fluctuation cycles, with each complete cycle corresponding to one calendar month, accumulating to no less than three calendar months. The cycle length should cover the periodic fluctuations in production caused by factors such as water injection response and equipment maintenance, and should accumulate at least 30 valid samples for statistical regression, meeting the minimum sample requirement for statistical significance. Daily oil production can be collected at fixed times daily using a dual-rotor flowmeter at the wellhead, such as 24 hours a day, in tons per day. Working hours can be recorded through attendance terminals and oil production work order systems as direct oil production operations, such as "inspection," "start-up and shutdown," "parameter adjustment," and "sampling," in hours per day. Auxiliary operations can be recorded through attendance terminals and work order systems as auxiliary operations, such as "well repair," "water injection and mixing," and "depth testing," in hours per day. Personnel in this field can also use the working hours corresponding to oil and water well operations and station operations in oil production units as specified in the petroleum industry standard "Labor Quotas for Oil Production Engineering" (SY / T7381) as oil production working hours; and the working hours corresponding to downhole operation data items as specified in the "Specification for Downhole Operation Data Item Names" (SY / T 5705-2012) as auxiliary operation working hours.

[0028] It's worth noting that the physical reason for using linear interpolation instead of other complex interpolation methods is that variations in output and working hours over three consecutive days are typically caused by random factors, without high-frequency oscillations or abrupt changes. Linear interpolation can approximate the true value well within this short window, with extremely low computational cost, making it suitable for real-time processing scenarios in edge computing gateways. The three consecutive missing days correspond precisely to a week's rest period, from Friday to Sunday. Missing more than three days indicates a high probability of a malfunction in the data acquisition system itself; in such cases, any interpolation would introduce significant errors, hence the direct exclusion.

[0029] In one embodiment of the present invention, a data collection period of 90 consecutive calendar days was used. From June 1, 2025 to August 29, 2025, a dual rotor flowmeter (model: LWG-80, accuracy ±0.2%) was installed at the wellhead of each of the 12 production wells in Block A. The cumulative daily production was automatically read at 24:00 each day. Simultaneously, daily oil production hours and auxiliary operation hours were extracted from the oilfield attendance system and work order system. Statistics showed that 85 days out of the 90 calendar days had complete data from all three sources, while 5 days were missing. One window with no more than 3 consecutive missing days (totaling 1 day) was used, and linear interpolation was performed. One window with more than 3 consecutive missing days (totaling 4 days) was deleted, resulting in 86 days of valid data samples.

[0030] Step S002: Perform Gaussian weighted lag convolution on the time series of effective auxiliary operation hours to obtain the effective contribution component of each effective auxiliary operation hour.

[0031] It should be noted that both field engineering experience and historical statistics show that auxiliary operations such as well workover and pump inspection, and water injection and distribution differ from oil production operations such as inspections and start-ups / shutdowns. The production increase effect of well workover operations completed on the same day is not immediately apparent; it typically takes 3 to 7 days for the wellbore pressure to recover and the fluid to redistribute before it is reflected in the daily oil production. If auxiliary working hours are directly correlated with the daily production, the increase in daily production will be incorrectly attributed to the auxiliary working hours of that day, while the actual increase in daily production is attributed to auxiliary working hours performed five days prior. Therefore, before establishing a correlation between auxiliary working hours and production, it is necessary to project historical auxiliary working hours onto the future date where their effects will truly manifest through hysteresis convolution processing.

[0032] Specifically, the effective auxiliary operation time series is processed by Gaussian weighted lag convolution to obtain the effective contribution component of each effective auxiliary operation time. The specific method is as follows: ; ; In the formula, Indicates the first Weighting coefficients with a time lag of one natural day Indicates the first Effective auxiliary work hours per calendar day, in hours / day. Indicates the first The effective contribution of auxiliary work hours per calendar day, in hours / day. Represents the number of days of lag in the primary response, a dimensionless integer. For the discreteness parameter of response duration, take... , This represents the first integer used for counting. Indicates the first Number of date sequences in calendar days, Indicates the first The number of date sequences in a calendar day. .

[0033] It should be noted that when hour, Take the maximum value, which corresponds to the moment when the auxiliary operation effect reaches its peak. or hour, These correspond to the physical limits of having no effect on the given day and the effect completely fading away, respectively. There is no case where the value is 0. A value of 0 indicates that there were no auxiliary tasks performed that day. It may be 0, indicating no auxiliary working hours contribution.

[0034] It should be further noted that the number of days of main response lag... The specific method for obtaining this information is as follows: Extract the completion dates of 30 or more auxiliary operations and the production recovery dates of the working wells within the past 12 months for the target block, calculate the number of days between the two, and take the median of all intervals, rounding down to obtain the result. Rounding down takes the nearest integer less than or equal to the median. The requirement of 30 or more rounds is that in statistical estimation, when the sample size is greater than or equal to 30, the standard error of the median is usually no more than 0.3, which is sufficient for engineering accuracy requirements. If the sample size is less than 30, the statistical values ​​of adjacent blocks in the same region are used as a substitute. It should be noted that... Based on the full width at half maximum (FWHM) of the Gaussian function, the recovery of oil well production after water injection or well workover is not a sudden occurrence on a single day, but rather a gradual process from the start of recovery to full recovery. Wellbore pressure gradually recovers, fluid redistributes, and production increases daily. This diffusion-like physical process can be mathematically approximated by a Gaussian function, whose full width at half maximum (FWHM) is... Approximately equal to the number of days of the main response delay. According to the mathematical properties of the Gaussian function, Solving for According to engineering approximation This causes the Gaussian kernel to reach its peak at the main response day d, and in Covering approximately 68% of the effective contribution weights within the range, the Gaussian kernel shape corresponds to the first... The physical diffusion process with the strongest effect at the beginning and end, gradually weakening towards the end. hour, This indicates that there was no effect on that day; when hour, Take the maximum value, which also represents the peak effect. hour, The effect decays to 0.

[0035] In one embodiment of the present invention, records of 34 well repair and water injection operations in Block A over the past 12 months were extracted, and the number of days between the operation completion date and the corresponding well production recovery date was calculated, with the median being 4 days. It is approximately 1.33.

[0036] It should be further noted that in Gaussian convolution processing, the total length of the convolution kernel... That is, the maximum number of days from the date the auxiliary operation occurred until its effective contribution was deemed to have completely diminished is determined as follows: , is the total span of the lag convolution, in days, a dimensionless integer; d is the number of days of lag in the primary response, in days. The physical meaning is that the production-increasing effect of auxiliary operations, from its initial appearance to its complete decline, lasts for approximately twice the number of days the main response lags. Take the day as an example. After the auxiliary operations were completed, the effect peaked on the 4th day, with a period of increase and decrease before and after the peak. The entire effective contribution period was approximately 8 days. Engineering statistics show that auxiliary investments that did not show any effect after more than twice the main response time are likely to be ineffective investments, such as ineffective operations that failed to work over the well or water injection that did not reach the formation fracture pressure, and should not be included in the effective contribution.

[0037] In one embodiment of the present invention, records of 34 well repair and water injection operations over the past 12 months in Block A were extracted, and the number of days between the operation completion date and the corresponding well production recovery date was calculated. The median was 4 days. This determines the total span of the convolution kernel. Day, dispersion parameter The sky, half the peak, full width This explains the present invention. Under the given value rules, the full width at half maximum (FWHM) of the Gaussian kernel is approximately 78.5% of the lag days of the main response, meaning it covers the majority of the effective contribution within a range of approximately 1.5 to 1.6 days before and after the main response date. This aligns with the physical distribution where the effects of auxiliary operations are concentrated approximately 1.5 days before and after the main response date. Substitute the weighting coefficients In the calculation formula, the weight coefficients of each day of the 8-day lag convolution are calculated and presented in Gaussian distribution. The weights of the 1st to 8th days are: 0.02, 0.08, 0.22, 0.36, 0.22, 0.08, 0.02, 0.00.

[0038] It should be noted that, if The value continued to decline, but during the same period The original value remained unchanged, indicating that the recent auxiliary operations were insufficient, and the output may decline in the next 3 to 5 days due to the lack of water injection support.

[0039] Step S003: Weight and merge the effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series to obtain the total effective man-hours of each time series; obtain the oil production trend value of each time series based on the average effective daily oil production of multiple time series before and after the effective daily oil production of each time series; obtain the man-hour normalized oil production index of each time series based on the total effective man-hours and the oil production trend value of each time series.

[0040] It should be noted that the above process transforms the auxiliary operation time from the date of occurrence to the effective contribution date. However, the contribution of 1 hour of oil production time and 1 hour of auxiliary operation time to production is not equivalent. Auxiliary operations indirectly increase production by improving well conditions, and their efficiency is generally lower than that of direct maintenance operations. Therefore, the two types of time cannot be simply added together. It is necessary to first convert auxiliary time into oil production time and then merge it into a unified total effective time. Then, by dividing the production by the total effective time, we can obtain the efficiency index of how much oil is produced per effective time, thus eliminating the inconsistency and incomparability caused by the difference in the total amount of time input in different blocks.

[0041] Specifically, the effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series are weighted and combined to obtain the total effective man-hours for each time series. The specific method is as follows: ; In the formula, Indicates the first The effective contribution of auxiliary work hours per calendar day, in hours / day. Indicates the first Effective oil production hours per calendar day, in hours / day. Indicates the first Total effective working hours per calendar day, in hours per day. Indicates the first The number of date sequences in a calendar day. The auxiliary work-hour output conversion factor is dimensionless. .

[0042] It should be noted that, The acquisition method is as follows: extract 30 or more valid samples from the target block, in order to Using the independent variable, and the increase in daily output on the 5th day after the completion of auxiliary operations relative to the average daily output of the 3 days prior to the auxiliary operations as the dependent variable, a univariate linear regression was performed using the least squares method, and the regression slope was taken as the independent variable. .

[0043] In one embodiment of the present invention, 34 groups of valid samples are extracted from block A, in order to Using as the independent variable and the increase in output on the 5th day after the auxiliary operation as the dependent variable, the least squares regression method was used to obtain the slope. .

[0044] It should be noted that daily output fluctuates dramatically due to random factors such as tank rotation and oil hauling time. Directly using the original output value may lead to excessive fluctuations in efficiency indicators, failing to reflect the true production capacity of the block. Therefore, it is necessary to obtain the oil production trend value for each natural day based on the average daily oil production of the days before and after each natural day.

[0045] Specifically, the oil production trend value for each time series is obtained by averaging the effective daily oil production of multiple time series before and after each time series. The specific method is as follows: ; In the formula, Indicates the first Oil production trend value for a calendar day, in tons / day. Indicates the first Effective daily oil production per calendar day, in tons / day. Indicates the first The number of date sequences in a calendar day. Indicates the first The number of date sequences in a calendar day. This represents the second integer used for counting. This represents the sample data size used to calculate the average daily oil production over the preceding and following days of each natural day. =7, dimensionless.

[0046] It should be noted that the advantage of using center averaging in the above method is that it better preserves phase information and does not introduce lag; however, when the actual number of samples or actual available days at the boundary is less than [a certain value], Take the actual number of samples or the actual number of available days, and smooth them using this method. It reflects the basic output capacity of the block, rather than instantaneous fluctuations.

[0047] It should be further noted that in this invention... The value is set to 7 because oilfield work schedules operate on a 7-day cycle. Setting the sample size to less than 7 days might introduce weekly date bias due to different workdays. Monday's oil production hours are naturally higher than Sunday's, which isn't an efficiency change but rather a systemic fluctuation. A 7-day sample size perfectly covers a complete work and rest cycle, ensuring the weekly comparability of the hourly structure within the sample. As for daily oil production, daily output is affected by random factors such as tank rotation and the arrival time of oil tankers; its standard deviation divided by the mean is typically within a certain range. Between these points, according to the Central Limit Theorem, the volatility coefficient of the 7-day moving average is approximately equal to that of a single day. That is, to drop to This is sufficient to filter out high-frequency random noise while retaining the main signals of oil production trends. Furthermore, when using 7 samples to calculate the mean, the standard error is 0.38 times the standard deviation. For most oilfield data, 7 samples are sufficient to keep the confidence interval of the mean estimate within an acceptable range. Moreover, oilfield production scheduling meetings are usually held on a weekly basis, and 7 days is completely consistent with the existing weekly production allocation plan cycle of most oilfields. The final calculation results can be directly used as decision-making reference data for production meetings.

[0048] In one embodiment of the present invention, the production data for the seven days are 78.2, 76.5, 80.1, 75.8, 79.3, 77.6, and 76.9, respectively. Substituting these values ​​into the oil production trend value, the calculated daily production is approximately 77.77 tons / day. The daily fluctuation ranges are approximately +2.33 (about 3.00%) and -1.97 (about -2.53%), respectively, with the maximum deviation of the original daily production from the trend value being only +2.33. The production data within the seven-day window shows a mild fluctuation around the trend value.

[0049] In another embodiment of the present invention, the production data for the seven days were 78.2, 76.5, 80.1, 58.0, 79.3, 77.6, and 76.9, respectively. Due to the failure of the oil tanker to arrive in time, the production data could not be measured on that day, and the production data abnormally dropped from 75.8 to 58.0. If the original daily production was used directly for value assessment, 58.0 would be approximately 74% of the normal value, with an error of -25.4%. After substituting the oil production trend value into the calculation, the abnormal daily value of 58.0 was corrected to 75.32 by the normal values ​​before and after, deviating from the normal level of the 7-day window by only about 3.1%, and the error compared to the actual production capacity of 77.77 tons / day was only -3.3%.

[0050] It should be noted that after obtaining the total effective working hours and oil production trend value for each time series, these two parameters need to be correlated. The output is divided by the working hours input to obtain the output efficiency per unit working hour. A sliding window is used to ensure that the output and input correspond within the same time window to avoid random deviations in daily data.

[0051] Specifically, based on the total effective working hours and oil production trend value for each time series, the normalized oil recovery index (NRI) for each time series is obtained. The specific method is as follows: Construct a first sliding window with a length of 7; ; In the formula, Indicates the first Oil production trend value for a calendar day, in tons / day. Indicates the first Total effective working hours per calendar day, in hours per day. Indicates the index of the first sliding window, a dimensionless integer. Indicates the first The number of date sequences in a calendar day. Indicates the first The normalized oil recovery index for a given natural day, expressed in tons per hour, where 6 represents the maximum index number of the sliding window.

[0052] It should be noted that, This represents the number of tons of crude oil produced per equivalent effective working hour over the past 7 days, calculated using a 7-day sliding window rather than a single-day value. This is because daily output fluctuates wildly due to random factors such as tank rotation and oil delivery time. A one-week window smooths out these short-term disturbances, reflecting the stable production efficiency of the block. With the numerator and denominator within the same window, output and input are strictly correlated in time; that is, the total output within these 7 days is generated by the total working hours input within these 7 days, eliminating inter-period mismatch. The window length is set at 7 days because oilfield work schedules are based on a 7-day cycle. If the window were set to less than 7 days, it might introduce intra-weekly date deviations due to different workdays. Monday's oil production hours are naturally higher than Sunday's, which is not an efficiency change but a systemic fluctuation. A 7-day window covers a complete work and rest cycle, making the working hour structure within the window comparable on a weekly basis. Daily output is affected by random factors such as tank rotation and oil tanker arrival time; its standard deviation divided by the mean is usually within a certain range. Between these points, according to the Central Limit Theorem, the volatility coefficient of the 7-day moving average is approximately equal to that of a single day. That is, to drop to This is sufficient to filter out high-frequency random noise while retaining the main signals of oil production trends. Furthermore, when calculating the mean using 7 samples, the standard error is 0.38 times the standard deviation. For most oilfield data, 7 samples are sufficient to keep the confidence interval of the mean estimate within an acceptable range. Moreover, oilfield production scheduling meetings are typically held weekly, and the 7-day window is completely consistent with the existing weekly production allocation plan cycle of most oilfields. Therefore, the final calculation results can be directly used as decision-making reference data for production meetings. When the value is 0, it means that the total effective working hours in the past 7 days are 0, which corresponds to the extreme case of the entire block being on holiday. In this case, the denominator is 0, indicating that the block is in a state of complete shutdown and should not be valued. This method is not applicable and may trigger a data anomaly alarm.

[0053] In one embodiment of the present invention, the total effective working hours per day The total effective working hours over the past 7 days was 547.68, and the sum of the daily oil production trend values ​​over the past 7 days was 546. tons / hour.

[0054] It should be noted that when In this situation, the corresponding actual condition is that a large amount of effective working hours have been invested but the output is close to zero. Typical operating conditions include a severe decrease in downhole pump efficiency, insufficient fluid supply leading to dry pumping, or severe scaling in the pipeline causing flow resistance close to blockage. In this case, on-site engineering diagnostics should be triggered immediately because... This constitutes a serious abnormal signal independent of the work schedule structure, and engineering personnel should be immediately arranged to investigate on-site; when An abnormally high output corresponds to an unusually high output with extremely low working hours. This is highly likely to indicate that the flow meter has a positive drift error or the tank level reading is incorrect. For example, a stuck dual rotor flow meter may cause a falsely high reading or an incorrect tank level reading, while a radar level gauge may cause a false level reading due to wall adhesion.

[0055] It should be further explained that when Further analysis is needed to distinguish the root causes of the decline. To this end, this invention introduces a working hour structure coefficient and analyzes the relative changes of the working hour structure coefficient and the working hour normalized oil recovery index.

[0056] Step S004: Obtain the working time structure coefficient for each time series based on the effective contribution components of the effective oil production working hours and effective auxiliary operation working hours for each time series.

[0057] It should be noted that when When oil production declines, field managers need to distinguish whether the root cause is insufficient oil production hours or a break in auxiliary operations. Therefore, it is necessary to construct an indicator describing the working hours structure in addition to the normalized index of working hours. By tracking and comparing the relative changes in the working hours structure coefficient and the normalized index of working hours, the manager can determine the cause. The root cause of the change.

[0058] Specifically, based on the effective contribution components of the effective oil production man-hours and effective auxiliary operation man-hours for each time series, the man-hour structure coefficient for each time series is obtained. The specific method is as follows: Construct a second sliding window with a length of 7; Calculate the first Percentage of oil production man-hours per natural day: ; Take the logarithmic ratio to obtain the first Work-hour structure coefficient for each natural day: ; In the formula, Indicates the first The percentage of oil production man-hours per natural day, dimensionless, range of values. ; Indicates the first Effective oil production man-hours per calendar day, in hours / day; Indicates the first The effective contribution of effective auxiliary work hours per natural day, in hours / day; Indicates the index of the second sliding window, a dimensionless integer. To prevent the exclusion of zero, it is dimensionless and a standard mathematical constant; 6 represents the maximum index number. Indicates the first The work-hour structure coefficient for each natural day.

[0059] It should be noted that the logarithmic ratio is used instead of... The reason is that the logarithmic ratio maps a percentage of oil production hours above 50% to a positive number and a percentage below 50% to a negative number, and it exhibits symmetry. hour , hour ,make The symbols directly correspond to the deflection direction of the engineering structure, making it easy for engineers to interpret quickly. At the same time, they minimize the impact of extreme values ​​and avoid the negative effects of fluctuations in daily work hours. Violent tremors.

[0060] It should be further explained that, At that time, the proportion of oil production man-hours was higher than that of auxiliary man-hours, and the block was in an operation mode that emphasized current maintenance and neglected formation energy replenishment. At that time, auxiliary working hours account for a higher proportion, and the block is in a work mode of heavy stratum energy replenishment and light current maintenance; when hour,, Ancillary work hours account for a very high percentage; when hour, Oil extraction man-hours account for a very high percentage of total working hours; in actual engineering projects, It cannot be strictly equal to 0 or 1; both working hours coexist. The value of is a finite real number. Ensure when When the denominator is very close to 1, it is not 0. As is standard practice for those skilled in the art, the construction logic for the second sliding window is the same as that for the first sliding window.

[0061] It should be further explained that when During descent: Decline and The decrease indicates a reduction in the proportion of oil production hours within the window. The main reason for the decline was determined to be a problem with manpower allocation, and more oil workers should be deployed or their on-duty time should be extended. Decline and An increase indicates that the proportion of effective contribution from auxiliary working hours within the window has increased, but... If the production continues to decline, it is determined that the delayed production-increasing effect of auxiliary operations has expired, and new auxiliary operations have not been added in a timely manner. Priority should be given to water injection and pump testing. Based on the above rules, [the following can be done / said]. The regular decline can be precisely located to specific engineering management dimensions, realizing a closed loop from data anomaly discovery to attribution diagnosis.

[0062] In one embodiment of the present invention, the cumulative oil production hours over the past seven days are... Hours / day, cumulative effective contribution of auxiliary work hours Hour / day, , , This indicates that the proportion of oil production man-hours is higher than that of auxiliary man-hours, and the block is currently under heavy maintenance.

[0063] Step S005: Based on the normalized oil recovery index of working hours for each time series, obtain the short-term and medium-term change rates for each time series, and weight and fuse the short-term and medium-term change rates for each time series to obtain the comprehensive value momentum for each time series.

[0064] It should be noted that, Reflects the current value level. This explains the structural reasons behind this level, but for oilfield production planning, it's still necessary to clarify whether the value is improving or deteriorating, and at what rate. This step involves calculations... The short-term and medium-term rates of change are used to obtain value momentum, which is used to determine the future evolution direction of block value.

[0065] Specifically, based on the normalized oil recovery index (ERI) of each time series, the short-term and medium-term change rates for each time series are obtained. The specific method is as follows: Short-term rate of change : medium-term rate of change : ; In the formula, Indicates the first Short-term rate of change over a calendar day Indicates the first The medium-term rate of change over a calendar day To prevent the exclusion of zero, dimensionless, and standard mathematical constants, 7 represents the time interval for comparing short-term normalized oil recovery index, in days. This indicates the time interval for comparing the normalized enhanced oil recovery index (EOR) based on working hours over the medium term, in days. Indicates the first Normalized oil recovery index based on working hours per natural day Indicates the first Normalized oil recovery index based on working hours per natural day Indicates the first Normalized oil recovery index based on working hours per natural day .

[0066] It should be noted that the 7-day short-term change rate reflects recent sudden disturbances, such as a single equipment failure lasting 2 days, while the 30-day medium-term change rate reflects trend changes, such as a continuous decline in formation fluid supply capacity. The 30-day time length corresponds to a calendar month, which is completely consistent with the compilation cycle of the oilfield's monthly production allocation plan. At the same time, 30 days cover three complete 7-day weekly cycles, which is sufficient to statistically distinguish between short-term random disturbances and medium-term trend changes. The 7-day length corresponds to the sliding window length, and the value selection logic is the same as that of the first sliding window length.

[0067] It should be further noted that, in this invention, To prevent the denominator from being zero, a value of 0.0001 is used. This value ensures in practical engineering that the denominator correction does not exceed one percent of the true value, thus not affecting the effective accuracy of the calculation result. It also ensures that the denominator will not be zero under extreme conditions, preventing calculation overflow. If the target block... value or The values ​​are generally small, and those skilled in the art can... The value is reduced to ensure that the correction amount does not exceed 1% of the true value. This adjustment does not change the core logic of the invention and is a routine engineering operation for parameter scale adaptation.

[0068] In one embodiment of the present invention, A block of one day The value was 0.997 tons / hour, seven days ago in this block. The value was 1.050 tons / hour, thirty days ago. The value is 0.931 tons / hour, with a short-term change rate of approximately -0.050 and a medium-term change rate of approximately 0.071. The negative short-term change rate indicates that efficiency has been declining in the past week, while the positive medium-term change rate indicates that the overall trend has been upward over the past month. The two are not aligned, representing a short-term correction and a medium-term positive trend. If only the short-term change rate is considered, managers may misjudge it as a deteriorating trend and over-intervene; if only the medium-term change rate is considered, managers may overlook short-term risks. Therefore, it is necessary to weight and integrate the two, and also introduce the working hour structure coefficient for a comprehensive judgment.

[0069] The weighted fusion of short-term and medium-term change rates for each time series yields the comprehensive value momentum for that time series. The specific method is as follows: ; In the formula, Indicates the first The comprehensive value momentum of a natural day. Indicates the first weight. Indicates the second weight. Indicates the first Short-term rate of change over a calendar day Indicates the first The medium-term rate of change over a calendar day , .

[0070] It should be noted that, The reason is the recency effect; the production situation in the most recent 7 days should have a higher weighting on the current value assessment than the historical situation over the past 8-30 days. In this embodiment, the first weight is taken as... The second weight is taken The weighting ratio is calculated using historical production and working hours data from at least 30 blocks (including the target block and adjacent blocks in the same region) over the past 12 months as a sample set. For each sample, the first weight is used as the independent variable, and a grid search is performed within the interval [0.55, 1] ​​with a step size of 0.05 to calculate the comprehensive value momentum under each weight combination. The actual value on the 7th day in the future is then used as the basis for the calculation. Value relative to the day The increase or decrease in value is used as the true label, based on the actual value on the next 7 days. Value relative to the day The increase in value is considered positive, based on the actual value on the 7th day in the future. Value relative to the day The value decreases in the negative direction; with The direction of the sign is used as the prediction direction, with a positive sign indicating a positive direction and a negative sign indicating a negative direction. The accuracy rate of the predicted direction matching the actual change direction under each weight combination is calculated, and the weight combination with the highest accuracy rate is taken as the optimal weight.

[0071] It should be further explained that when When traversing to 1, That is, completely disregarding the medium-term rate of change; when When the minimum value of the first weight is taken, which is the minimum acceptable value of the recency effect boundary, the upper limit of the first weight is 1 and the lower limit is 0.55. This can ensure the search accuracy while controlling the amount of computation. The reason for requiring a sample size of more than or equal to 30 blocks is based on the central limit theorem. When the sample size is greater than or equal to 30, the sampling distribution of the sample mean is approximately a normal distribution. The standard error of the accuracy estimate is about 0.13, which is sufficient to distinguish the superiority or inferiority of adjacent weight combinations with a step size of 0.05, thus ensuring the statistical reliability of the optimization results.

[0072] Step S006: Based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum of each time series, a three-level attribution analysis is performed.

[0073] The above steps construct three evaluation indicators with different dimensions. The normalized production index reflects the actual output efficiency per unit of working hours in the block and is the core indicator for measuring production value. The working hour structure coefficient reveals the proportion of production working hours to auxiliary working hours, reflecting the focus of the current operation mode in the block. The comprehensive value momentum characterizes the direction and speed of change of efficiency indicators over time. However, a single indicator can only describe one aspect of the production status and cannot answer the core issue that is more important in actual engineering management: when efficiency changes, should the production operation schedule be adjusted or should auxiliary operation input be supplemented? The engineering solutions to these two questions are completely different. Therefore, this invention combines the three evaluation indicators with different dimensions and performs a three-level judgment according to a progressive logic. The first level is based on... As an entry point, it determines whether the current efficiency level is abnormal or in a downward trend; the second level... To provide further evidence, it is necessary to distinguish whether the decline in efficiency is a short-term random fluctuation or a trend of deterioration; Level 3 and above As an attribution tool, it is necessary to determine whether the root cause of the deterioration in efficiency is insufficient oil production hours or a lack of auxiliary operations.

[0074] Specifically, based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series, a three-level attribution analysis is conducted, using the following method: Calculate the mean and standard deviation of the normalized oil recovery index (NER) for each time series of man-hours in the target block. Based on the Raida criterion for normality in statistical analysis, determine whether the NER for each time series of man-hours is abnormal. If the... If the normalized oil recovery index for a given number of natural days is abnormal, engineering personnel will be arranged to investigate on-site. If the first The normalized oil recovery index for the first natural day is normal, and the first... The normalized oil recovery index of the working hours of the first natural day is greater than or equal to the first natural day. The normalized oil recovery index for working hours per natural day will be maintained under the current system. If the first The normalized oil recovery index for the first natural day is normal, and the first... The normalized oil recovery index of the working hours of the first natural day is less than that of the first natural day. The normalized normalized oil recovery index of the working hours for the first natural day is used to determine the first... Is the comprehensive value momentum of the first natural day positive or negative? The overall value momentum over a single natural day is positive; continue observation for 3 to 5 days. If the first The comprehensive value momentum of the first natural day is negative, indicating that the first natural day... Is the work hour structure coefficient of the first natural day greater than or equal to that of the first natural day? The work-hour structure coefficient for the first natural day, if the first natural day... The work hour structure coefficient for the first natural day is greater than or equal to the first natural day. The work-hour structure coefficient for each natural day is used to arrange water injection and pump testing operations; if the first... The work hour structure coefficient for the first natural day is less than that for the second natural day. The working hour structure coefficient for each natural day is used to increase the number of oil production workers or extend their on-duty time.

[0075] It should be noted that an abnormal Normalized Enhanced Oil Recovery Index (EOR) may indicate a problem with the production system itself, such as a metering malfunction, a sudden change in downhole operating conditions, or a data entry error. Regardless of the current timeline structure, the physical system must be checked. A normal EOR, and the [missing information - likely a specific indicator or timeline]... The normalized oil recovery index of the working hours of the first natural day is greater than or equal to the first natural day. The normalized oil recovery index for the working hours of the first calendar day will be maintained under the current system, as the current production organization and operational arrangements are effective and do not require adjustment. If the first... The normalized oil recovery index of the working hours of the first natural day is less than that of the first natural day. The normalized oil recovery index of working hours for a given natural day, and the first The overall value momentum over the past few days is positive, indicating that while efficiency remains within the normal range, a downward trend has emerged. However, considering the changes over the past 7 and 30 days, the overall trend remains upward, suggesting that this decline is likely a short-term disturbance, such as a temporary equipment failure or tank rotation. The medium-term fundamentals have not deteriorated, so no intervention is needed for now; observation for a few days is sufficient. If the... The overall value momentum of the first natural day is negative, indicating deterioration in both the short and medium term. This is not a random fluctuation, but a trend of declining efficiency, requiring attribution based on the work hour structure coefficient; if the first natural day... The work hour structure coefficient for the first natural day is greater than or equal to the first natural day. The work-hour structure coefficient for each calendar day indicates an increase compared to a week ago, meaning that the proportion of auxiliary work hours in the total work hours is increasing, while the proportion of oil production work hours is relatively decreasing. However, the increase in auxiliary work hours... The continued decline indicates that the delayed production-increasing effect of auxiliary operations has expired, and new auxiliary operations have not been added in a timely manner. The fundamental problem is a gap in auxiliary operations, which should be addressed by supplementing them. If the... The work hour structure coefficient for the first natural day is less than that for the second natural day. The daily work-hour structure coefficient decreased compared to a week ago, indicating that the proportion of oil extraction hours in the total work hours is decreasing. This suggests that insufficient oil extraction worker input is the main reason for the decline in efficiency, and the fundamental problem is insufficient manpower allocation. More manpower should be deployed or working hours extended.

[0076] It should be further noted that the Laida criterion for normal distribution in statistics is a commonly used algorithm in industrial quality inspection. This invention will not elaborate on it further, as it represents a method and means for those skilled in the art and does not depart from the core logic of this invention; a 7-day period is used as a comparison. and The duration of this timeframe is consistent with the preset window length, covering the entire work cycle and rest cycle; if the oil well runs dry, it may also lead to... If the value is too small, this process is outside the scope of the present invention and requires measurement and judgment by a person skilled in the art.

[0077] Please see Figure 2 It illustrates a structural block diagram of an oil and gas resource block valuation system, which is the second objective of the present invention and includes the following modules: The data acquisition module is used to synchronously collect the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block. It aligns the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series day by day with the date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series. The effective contribution component calculation module is used to perform Gaussian weighted lag convolution processing on the time series of effective auxiliary operation hours to obtain the effective contribution component of each effective auxiliary operation hour. The time-normalized oil recovery index calculation module is used to weight and merge the effective contribution components of the effective oil recovery man-hours and the effective auxiliary operation man-hours of each time series to obtain the total effective man-hours of each time series; to obtain the oil production trend value of each time series based on the average effective daily oil production of multiple time series before and after the effective daily oil production of each time series; and to obtain the time-normalized oil recovery index of each time series based on the total effective man-hours and the oil production trend value of each time series. The time structure coefficient calculation module is used to obtain the time structure coefficient of each time series based on the effective contribution components of the effective oil production time and effective auxiliary operation time of each time series. The comprehensive value momentum calculation module is used to obtain the short-term and medium-term change rates of each time series based on the normalized oil recovery index of each time series, and to obtain the comprehensive value momentum of each time series by weighted fusion of the short-term and medium-term change rates of each time series. The attribution analysis module is used to perform three-level attribution analysis based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series.

[0078] A third objective of this invention is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aforementioned method for evaluating the value of oil and gas resource blocks.

[0079] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for evaluating the value of oil and gas resource blocks.

[0080] This embodiment establishes a quantitative correlation mechanism between production fluctuations and the two types of time input by weighting and merging the effective oil production man-hours with the effective contribution component of auxiliary operations after Gaussian weighted hysteresis convolution processing into the total effective man-hours, and constructing a time-normalized oil production index in combination with the oil production trend value. This enables the attribution of production changes to specific dimensions such as oil production operations or auxiliary operations, and the evaluation conclusions have engineering interpretability. Gaussian weighted lag convolution is used to process auxiliary operation time, projecting historical auxiliary operation time onto the time when the effect is manifested according to its actual physical response law. This ensures that the true contribution of auxiliary operation is correctly included in the total effective time on the date when it should take effect, avoiding attribution bias caused by time mismatch. By constructing a working hour structure coefficient to reveal the bias characteristics of working hour input, and by comprehensively representing the trend direction of efficiency changes through value momentum, a three-level judgment attribution logic is established on this basis. The evaluation results are automatically mapped into specific and executable engineering instructions such as maintaining the system, dispatching more oil production workers, arranging water injection and allocation, or pump inspection operations. This upgrades the evaluation system from a static label description to a decision-making tool that can drive the rolling revision of the production system.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the value of oil and gas resource blocks, characterized in that, The method includes the following steps: Synchronously collect the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block. Align the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series day by day with the date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series. The effective auxiliary operation time sequence is processed by Gaussian weighted lag convolution to obtain the effective contribution component of each effective auxiliary operation time. The effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series are weighted and merged to obtain the total effective man-hours of each time series; the oil production trend value of each time series is obtained based on the average effective daily oil production of multiple time series before and after the effective daily oil production of each time series; and the man-hour normalized oil production index of each time series is obtained based on the total effective man-hours and the oil production trend value of each time series. Based on the effective contribution components of the effective oil production man-hours and effective auxiliary operation man-hours for each time series, the man-hour structure coefficient for each time series is obtained. Based on the normalized oil recovery index of working hours for each time series, the short-term and medium-term change rates of each time series are obtained. The short-term and medium-term change rates of each time series are weighted and fused to obtain the comprehensive value momentum of each time series. Based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series, a three-level attribution analysis is performed.

2. The method for evaluating the value of oil and gas resource blocks according to claim 1, characterized in that, The method for synchronously collecting the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block, and aligning these time series day by day using date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series, includes the following specific methods: With a collection cycle of no less than 90 consecutive calendar days, the daily oil production time series, oil production time series, and auxiliary operation time series of the target block are collected synchronously. These three types of data are aligned day by day with the date as the primary key. For any date, if all three types of data exist, they are retained. If there are no more than 3 consecutive missing days, linear imputation is used. If there are more than 3 consecutive missing days, all three types of data corresponding to the missing date are removed. The retained three types of data and the data after linear imputation are recorded as valid data, resulting in valid daily oil production time series, oil production time series, and auxiliary operation time series. The time series of each type of data includes the date sequence of each type of data and the data values ​​corresponding to different date sequences of each type of data.

3. The method for evaluating the value of oil and gas resource blocks according to claim 1, characterized in that, The specific method for performing Gaussian weighted lag convolution processing on the effective auxiliary operation time sequence to obtain the effective contribution component of each effective auxiliary operation time includes: ； ； In the formula, Indicates the first Weighting coefficients with a time lag of one calendar day Indicates the first Effective auxiliary work hours per calendar day, in hours / day. Indicates the first The effective contribution of auxiliary work hours per calendar day, in hours / day. Represents the number of days of lag in the primary response, a dimensionless integer. For the discreteness parameter of response duration, take... , This represents the first integer used for counting. Indicates the first Number of date sequences in calendar days, Indicates the first The number of date sequences in a calendar day. .

4. The method for evaluating the value of oil and gas resource blocks according to claim 1, characterized in that, The method for weighted merging of the effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series to obtain the total effective man-hours for each time series includes the following specific methods: ； In the formula, Indicates the first The effective contribution of auxiliary work hours per calendar day, in hours / day. Indicates the first Effective oil production hours per calendar day, in hours / day. Indicates the first The total effective working hours for a calendar day are expressed in hours per day. Indicates the first The number of date sequences in a calendar day. The auxiliary working-time output conversion factor is dimensionless. .

5. The method for evaluating the value of oil and gas resource blocks according to claim 1, characterized in that, The method for obtaining the oil production trend value for each time series based on the average of the effective daily oil production of multiple time series before and after each time series includes the following specific methods: ； In the formula, Indicates the first Oil production trend value for a calendar day, in tons / day. Indicates the first Effective daily oil production per calendar day, in tons / day. Indicates the first The number of date sequences in a calendar day. Indicates the first The number of date sequences in a calendar day. This represents the second integer used for counting. This represents the sample data size used to calculate the average daily oil production over the preceding and following days of each natural day. =7, dimensionless.

6. The method for evaluating the value of oil and gas resource blocks according to claim 1, characterized in that, The method for obtaining the normalized oil recovery index (NRI) for each time series based on the total effective working hours and oil production trend value is as follows: Construct a first sliding window with a length of 7; ； In the formula, Indicates the first Oil production trend value for a calendar day, in tons / day. Indicates the first The total effective working hours for a calendar day are expressed in hours per day. Indicates the index of the first sliding window, a dimensionless integer. Indicates the first The number of date sequences in a calendar day. Indicates the first The normalized oil recovery index for a given natural day, expressed in tons per hour, where 6 represents the maximum index number of the sliding window.

7. The method for evaluating the value of oil and gas resource blocks according to claim 1, characterized in that, The method for obtaining the time structure coefficient for each time series based on the effective contribution components of the effective oil production man-hours and effective auxiliary operation man-hours is as follows: Construct a second sliding window with a length of 7; Calculate the first Percentage of oil production man-hours per natural day: ； Take the logarithmic ratio to obtain the first Work-hour structure coefficient for each natural day: ； In the formula, Indicates the first The percentage of oil production man-hours per natural day, dimensionless, range of values. ; Indicates the first Effective oil production man-hours per calendar day, in hours / day; Indicates the first The effective contribution of effective auxiliary work hours per natural day, in hours / day; Indicates the index of the second sliding window, a dimensionless integer. To prevent the exclusion of zero (which is dimensionless), 6 represents the maximum index number. Indicates the first The work-hour structure coefficient for each natural day.

8. The method for evaluating the value of an oil and gas resource block according to claim 1, characterized in that, The method for obtaining the short-term and medium-term change rates of the oil recovery index based on the normalized oil recovery index of each time series, and then weighted and fused the short-term and medium-term change rates of each time series to obtain the comprehensive value momentum of each time series, includes the following specific method: short-term change rate : ； medium-term rate of change : ； In the formula, Indicates the first Short-term rate of change over a calendar day Indicates the first The medium-term rate of change over a calendar day To prevent the exclusion of small quantities, dimensionless values ​​are used. 7 represents the time interval for comparing short-term normalized oil recovery index (NER) working hours, in days. This indicates the time interval for comparing the normalized enhanced oil recovery index (EOR) based on working hours over the medium term, in days. Indicates the first Normalized oil recovery index based on working hours per natural day Indicates the first Normalized oil recovery index based on working hours per natural day Indicates the first Normalized oil recovery index based on working hours per natural day ; The weighted fusion of short-term and medium-term change rates for each time series yields the comprehensive value momentum for that time series. The specific method is as follows: ； In the formula, Indicates the first The comprehensive value momentum of a natural day Indicates the first weight. Indicates the second weight. Indicates the first Short-term rate of change over a calendar day Indicates the first The medium-term rate of change over a calendar day , .

9. The method for evaluating the value of an oil and gas resource block according to claim 1, characterized in that, The three-level attribution analysis based on the normalized oil recovery index, time structure coefficient, and comprehensive value momentum for each time series includes the following specific methods: Calculate the mean and standard deviation of the normalized oil recovery index (NER) for each time series of man-hours in the target block. Based on the Raida criterion for normality in statistical analysis, determine whether the NER for each time series of man-hours is abnormal. If the... If the normalized oil recovery index for a given number of natural days is abnormal, engineering personnel will be arranged to investigate on-site. If the first The normalized oil recovery index for the first natural day is normal, and the first... The normalized oil recovery index of the working hours of the first natural day is greater than or equal to the first natural day. The normalized oil recovery index for working hours per natural day will be maintained under the current system. If the first The normalized oil recovery index for the first natural day is normal, and the first... The normalized oil recovery index of the working hours of the first natural day is less than that of the first natural day. The normalized normalized oil recovery index of the working hours for the first natural day is used to determine the first... Is the comprehensive value momentum of the first natural day positive or negative? The overall value momentum over a single natural day is positive; continue observation for 3 to 5 days. If the first The comprehensive value momentum of the first natural day is negative, indicating that the first natural day... Is the work hour structure coefficient of the first natural day greater than or equal to that of the second natural day? The work-hour structure coefficient for the first natural day, if the first natural day... The work hour structure coefficient for the first natural day is greater than or equal to the first natural day. The work-hour structure coefficient for each natural day is used to arrange water injection and pump testing operations; if the first... The work hour structure coefficient for the first natural day is less than that for the second natural day. The working hour structure coefficient for each natural day is used to increase the number of oil production workers or extend their on-duty time.

10. A system for assessing the value of oil and gas resource blocks, characterized in that, The system includes the following modules: The data acquisition module is used to synchronously collect the daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series of the target block. The daily oil production time series, oil production man-hour time series, and auxiliary operation man-hour time series are aligned day by day with the date as the primary key to obtain the aligned effective daily oil production time series, effective oil production man-hour time series, and effective auxiliary operation man-hour time series. The effective contribution component calculation module is used to perform Gaussian weighted lag convolution processing on the time series of effective auxiliary operation hours to obtain the effective contribution component of each effective auxiliary operation hour. The time-normalized oil production index calculation module is used to weight and merge the effective contribution components of the effective oil production man-hours and the effective auxiliary operation man-hours of each time series to obtain the total effective man-hours of each time series. Based on the average effective daily oil production of each time series before and after each time series, the oil production trend value of each time series is obtained. Based on the total effective working hours and oil production trend value for each time series, the working hour normalized oil recovery index for each time series is obtained. The time structure coefficient calculation module is used to obtain the time structure coefficient of each time series based on the effective contribution components of the effective oil production time and effective auxiliary operation time of each time series. The comprehensive value momentum calculation module is used to obtain the short-term and medium-term change rates of each time series based on the normalized oil recovery index of each time series, and to obtain the comprehensive value momentum of each time series by weighted fusion of the short-term and medium-term change rates of each time series. The attribution analysis module is used to perform three-level attribution analysis based on the normalized oil recovery index, working time structure coefficient, and comprehensive value momentum for each time series.

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