A method for predicting the productivity damage of strong velocity-sensitive reservoir particle migration

Abstract

The present application belongs to the oil and gas development industry oil and gas production engineering technical field, especially relates to a kind of strong speed sensitive reservoir particle migration productivity damage prediction method.The present application is by to the pre-processing of oil well production dynamic data, establishes a kind of pre-processing production dynamic data method to remove the influence of engineering intervention on productivity, can effectively reflect the productivity decline trend of reservoir under the condition of natural attenuation, improve the accuracy of productivity change analysis caused by particle migration factor;And based on production dynamic data calculation particle migration decline rate, provide the basis for the productivity decline damage caused by particle migration quantification;At the same time, the productivity ratio under the action of particle migration, productivity damage rate and damage rate calculation method are proposed, which can realize the quantitative characterization of the degree of reservoir productivity loss caused by particle migration, provide the basis and technical support for the prevention and control of particle migration measures, and have important engineering application value.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas extraction engineering technology in the oil and gas development industry, and particularly relates to a method for predicting the production capacity damage caused by highly rapid and sensitive reservoir particulate migration. Background Technology

[0002] In the development of loose sandstone oil and gas reservoirs, the migration of formation particles (such as clay minerals and fine-grained quartz) is common in highly velocity-sensitive loose sandstone reservoirs, affecting the near-wellbore area and even the entire reservoir. Formation fluid velocity disturbances can also trigger particle desorption, suspension, and migration, leading to pore blockage, reduced permeability, and ultimately irreversible decline in well productivity. Currently, the main technical means to control reservoir particle migration include reservoir-level production regime control and engineering-level near-wellbore and wellbore sand control measures. However, the prerequisite for such particle migration control is the accurate prediction of the reservoir damage caused by particle migration, i.e., the impact of particle migration on productivity. Accurate prediction of productivity damage caused by particle migration is a key basis for developing measures to prevent and control particle migration in advance. Therefore, how to predict and evaluate the impact of reservoir particle migration on well productivity is a crucial technical requirement in the development of highly velocity-sensitive loose sandstone reservoirs.

[0003] Chinese patent document CN119647759A discloses a method for evaluating reservoir productivity damage caused by a combination of reservoir particle migration damage and fracture-filled layer damage. The method includes the following steps: First, it uses a particle migration permeability damage assessment method under different production conditions in loose sandstone reservoirs to predict the degree of damage to reservoir permeability and productivity caused by particle migration. Second, it uses a conductivity damage assessment method under the combined effects of compaction, embedding, and plugging in fractured and filled wells to evaluate the degree of damage to fracture conductivity and productivity. Finally, it comprehensively considers both reservoir particle migration damage and fracture conductivity damage to achieve dynamic productivity prediction and evaluation. However, its core process relies heavily on experimental parameters, core mechanics data, and complex gridded numerical simulations. The calculation process is cumbersome, and the basic data requirements are high, making it difficult to promote and use in actual oilfields. Furthermore, this method is mainly aimed at fractured and filled wells, limiting its applicability and making it difficult to achieve universal evaluation for general production wells.

[0004] Furthermore, existing methods, such as those described in Chinese Patent CN118095576A and others, which predict the damage to production capacity caused by particulate migration, are based on theory and numerical simulation. While they can provide predictions of production capacity damage, their computational processes are complex, require a large amount of basic data, making practical application difficult, and their accuracy is hard to determine. In oilfield production, actual production dynamics data from oil wells most directly reflect production capacity fluctuations and declines, containing information on production capacity damage induced by reservoir particulate migration. However, because actual oil well production capacity is also directly affected by numerous engineering factors, there is currently no effective method to directly calculate the production capacity damage caused by particulate migration using on-site production dynamics data.

[0005] In summary, the following key technical issues currently exist:

[0006] (1) The actual production dynamic data of oil wells contains information on the decline in production capacity caused by the migration of reservoir particles. However, due to the simultaneous influence of various engineering measures, there is currently a lack of effective analytical methods to remove the impact of engineering intervention on production capacity from the production capacity dynamic data.

[0007] (2) There is no effective method to directly use oil well production dynamic data to calculate the decline in production capacity caused by reservoir particle migration.

[0008] (3) There is a lack of methods to quantitatively characterize reservoir productivity damage caused by particulate migration based on oil well production dynamics data.

[0009] Based on the above problems, this invention proposes a method for predicting the production capacity damage caused by particle migration in highly velocity-sensitive reservoirs. It is mainly used to analyze the degree of production capacity damage caused by formation sand particle migration in highly velocity-sensitive reservoirs based on oil well production dynamic data, providing a basis for formulating sand control and particle migration control measures, and has important engineering application value. Summary of the Invention

[0010] To address at least one of the aforementioned technical problems, this invention provides a method for predicting the production damage caused by particulate migration in highly rapid and sensitive reservoirs. The method aims to quantitatively characterize the damage to oil well production caused by particulate migration based on production dynamic data, and to provide a basis for formulating measures to prevent and control particulate migration.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] A method for predicting reservoir productivity damage caused by highly rapid and sensitive particle migration includes the following steps:

[0013] S1. Identify the abnormal liquid production volume segment in the original production dynamic data caused by engineering intervention, and preprocess the abnormal liquid production volume data of the segment to obtain the intrinsic liquid production volume, forming production dynamic data including the intrinsic liquid production volume.

[0014] S2. Based on the production dynamic data described in step S1, perform liquid production decrease fitting and particle migration decrease rate calculation.

[0015] S3. Based on the particle migration decline rate obtained in step S2, calculate the production damage rate and production damage rate caused by particle migration, so as to achieve quantitative prediction and evaluation of the impact of particle migration on oil well production.

[0016] Preferably, step S1, identifying abnormal liquid production segments in the original production dynamic data caused by engineering intervention, includes:

[0017] (1) Based on the daily production report, extract the production date and the corresponding raw liquid production data to obtain the daily liquid production sequence {Q}. N}, where N is the production record number; a candidate segment is formed by the daily liquid production data points in 30 consecutive daily liquid production sequences. That is, it consists of daily liquid production data from the Nth to the N+29th record; the candidate segment The candidate segment must exhibit a decreasing trend, which is determined by the daily liquid production at the beginning and end of the candidate segment meeting the following criteria: ;

[0018] (2) For candidate segments that meet the decreasing trend screening criteria Calculate the relative rate of change of daily liquid production between two adjacent lines within this section. ;

[0019] (3) Select the maximum relative rate of change in each candidate segment. As a stability index for the candidate section, it is used to characterize the worst-case scenario of daily liquid production fluctuation within the candidate section.

[0020] (4) Among all candidate segments that meet the decreasing trend criterion, select the candidate segment with the smallest R(Ω) as the stable liquid production segment.

[0021] The relative rate of change is calculated using the following formula:

[0022] (1)

[0023] In the formula, The relative change rate of daily liquid production between two adjacent values, % Let be the daily fluid production of a certain well in the Nth production record, in t / d; Let t / d be the daily fluid production of a well in the N-1th production record.

[0024] The maximum relative rate of change within the candidate segment is calculated using the following formula:

[0025] (2)

[0026] In the formula, The maximum relative rate of change within the candidate segment, %

[0027] (5) Calculate the average daily liquid production in the stable liquid production range. and according to Calculate the relative deviation coefficient Select within the stable liquid production range. maximum value Set anomaly detection threshold When satisfied > If there are at least 30 consecutive records of daily liquid production, the corresponding consecutive time period will be identified as an abnormal liquid production segment.

[0028] The relative deviation coefficient is calculated using the following formula:

[0029] (3)

[0030] In the formula, The relative deviation coefficient is % To stabilize the average daily liquid production in the liquid production range, t / d.

[0031] The anomaly detection threshold is calculated using the following formula:

[0032] (4)

[0033] In the formula, The anomaly detection threshold is % This is the anomaly detection threshold coefficient, used to adjust the sensitivity of anomaly detection. Its value can be determined based on the well area production fluctuation characteristics and historical data statistical experience. To stabilize the liquid production range The maximum value, %.

[0034] Preferably, the step S1 of preprocessing the abnormal liquid production data of the section to obtain the intrinsic liquid production specifically involves: calculating the engineering intervention compensation liquid production based on the abnormal liquid production data of the abnormal liquid production section, and correcting the daily liquid production data of each abnormal liquid production section based on the engineering intervention compensation liquid production to obtain the corrected daily liquid production data, which is the intrinsic liquid production.

[0035] More preferably, the engineering intervention compensation liquid production is the difference between the average daily liquid production of the abnormal liquid production segment and the average daily liquid production of the stable liquid production segment, and a time influence correction factor is introduced to correct the average daily liquid production of the stable liquid production segment, calculated according to the following formula:

[0036] (5)

[0037] in, To compensate for the amount of liquid produced by engineering intervention, t / d; The average daily liquid production in the abnormal liquid production range, t / d; To stabilize the average daily liquid production in the liquid production range, t / d; The time-related correction factor is used to correct the time representativeness deviation of the average daily production in a stable production range. Its value is determined based on the production variation characteristics of the well area and historical production experience.

[0038] By introducing a time-effect correction factor, the amount of liquid produced by engineering intervention can be effectively controlled, ensuring that the result after subtracting the amount of liquid produced by engineering intervention from the abnormal liquid production remains consistent with the actual production level in the period before the abnormality occurred, thereby improving the engineering rationality and applicability of the prediction results.

[0039] The intrinsic product volume is calculated according to the following formula:

[0040] (6)

[0041] In the formula, This represents the intrinsic liquid production rate, in t / d. This represents the daily liquid production within the abnormal liquid production range, expressed in t / d.

[0042] Preferably, step S2 includes:

[0043] S21. Fit the production dynamic data described in step S1 using the capacity decline model, determine the optimal decline model based on the goodness of fit, and calculate the theoretical comprehensive decline rate D0.

[0044] S22. Based on the optimal decline model and theoretical comprehensive decline rate D0 determined in step S21, calculate the comprehensive actual decline rate. ;

[0045] S23. Based on representative production data from the natural decline phase during the initial production stage of an oil well, calculate the average daily natural decline rate; compare the average daily natural decline rate with the comprehensive actual decline rate. The difference between them yields the particle transport decline rate.

[0046] A further preferred embodiment of the capacity decline model described in step S21 includes exponential decline, harmonic decline, and hyperbolic decline models, with the following formulas:

[0047] Exponentially decreasing model:

[0048] (7)

[0049] In the formula, Let T be the daily fluid production of a certain well at time T, expressed in t / d. The initial liquid production rate is expressed in t / d. The theoretical comprehensive decline rate is dimensionless; T is the production time, in days.

[0050] Hyperbolic decreasing model:

[0051] (8)

[0052] In the formula, b is the hyperbolic decreasing exponent, which is dimensionless;

[0053] Harmonic diminishing model:

[0054] (9)

[0055] The optimal decreasing model is determined based on the goodness of fit, and the goodness of fit is selected accordingly. The largest decreasing model is taken as the optimal decreasing model.

[0056] More preferably, step S22 includes:

[0057] S221. Calculate the fitted average liquid production rate based on the optimal decreasing model determined in step S21:

[0058] (10)

[0059] In the formula, To fit the average liquid production rate, t / d; For production time, d; For the first The daily liquid production rate fitted by the day, t / d,

[0060] S222. Calculate the average intrinsic product volume based on the intrinsic product volume obtained in step S1:

[0061] (11)

[0062] In the formula, The intrinsic product yield is the average, in t / d; For production time, d; For the first Intrinsic liquid production per day, t / d;

[0063] S223. Calculate the decline rate correction coefficient based on the fitted average production rate obtained in step S221 and the intrinsic production rate mean obtained in step S222:

[0064] (12)

[0065] S224. Based on the decline rate correction coefficient calculated in step S223 and the theoretical comprehensive decline rate D0 obtained in step S21, calculate the comprehensive actual decline rate D.

[0066] More preferably, in step S224, if the optimal decreasing model determined in step S21 is an exponential decreasing model, then the comprehensive actual decreasing rate is calculated according to formula (13); if the optimal decreasing model determined in step S21 is a hyperbolic decreasing model, then the comprehensive actual decreasing rate is calculated according to formula (14); if the optimal decreasing model determined in step S21 is a harmonic decreasing model, then the comprehensive actual decreasing rate is calculated according to formula (15), as follows:

[0067] (13)

[0068] (14)

[0069] (15)

[0070] More preferably, in step S23, the production data of oil wells in the target block during the initial four months of production are selected, and the average monthly natural decline rate is calculated. Then, based on the average monthly natural decline rate Calculate the average daily natural decline rate .

[0071] More preferably, the average monthly natural decline rate The formula is as follows:

[0072] (16)

[0073] In the formula, Let be the average monthly liquid production of the block in the i-th month, t / d, where i=2,3,4; To compare with the average monthly liquid production of block i in month i Corresponding production time nodes; The average monthly liquid production of the block in the first month, in t / d; The average monthly natural decline rate is dimensionless.

[0074] When the average monthly natural decline rate is less than 5%, the formula for calculating the average daily natural decline rate is as follows:

[0075] (17)

[0076] In the formula, The average daily natural decline rate is dimensionless. The average monthly natural decline rate is dimensionless.

[0077] When the average monthly natural decline rate is ≥5%, the formula for calculating the average daily natural decline rate is as follows:

[0078] (18)

[0079] Further preferred, step S23 involves comparing the average daily natural decline rate with the comprehensive actual decline rate. The difference between them yields the particle transport decline rate, which is calculated using the following formula:

[0080] (19)

[0081] In the formula, The particle transport decline rate is dimensionless.

[0082] Preferably, step S3 specifically includes:

[0083] S31. Based on the particle migration decline rate obtained in step S2, and combined with the initial production rate and production time data of the oil well, calculate the production rate under the action of particle migration. The formula is as follows;

[0084] (20)

[0085] (twenty one)

[0086] (twenty two)

[0087] in, At the current liquid production rate, The initial product volume; if the optimal decreasing model determined in step S21 is an exponential decreasing model, then calculate according to formula (20). If the optimal decreasing model determined in step S21 is a hyperbolic decreasing model, then calculate according to formula (21). If the optimal decreasing model determined in step S21 is a harmonic decreasing model, then calculate according to formula (22). ;

[0088] S32. Liquid production rate based on particle transport obtained in step S31 Calculate the production ratio caused by particulate transport factors. ;

[0089] (twenty three)

[0090] In the formula, This is a capacity ratio, dimensionless.

[0091] S33. Calculate the capacity damage rate based on the capacity ratio obtained in step S32. and production capacity damage rate :

[0092] (twenty four)

[0093] In the formula, The percentage represents the production capacity damage rate.

[0094] Production damage rate Equal to production capacity damage rate With production time The ratio:

[0095] (25)

[0096] In the formula, The rate of damage to production capacity is expressed as % / a. Production duration, in years.

[0097] Compared with the prior art, the present invention has the following beneficial effects:

[0098] (1) This invention provides a method for predicting the production capacity reduction and damage caused by the migration of particles in highly sensitive reservoirs based on the dynamic analysis of oil well production. Relying on the daily production data provided by the oil well site, the method achieves accurate identification of the production capacity damage caused by particle migration through data preprocessing, production capacity reduction fitting and quantitative calculation of particle migration damage.

[0099] (2) This invention can calculate the production capacity reduction caused by reservoir particle migration during the production process of oil wells based on daily production reports, quickly calculate the production capacity damage rate and rate caused by particle migration, and achieve quantitative characterization of the degree of production capacity damage, providing a basis for formulating measures to prevent and control particle migration in advance. Daily production reports, as the most direct source of data reflecting the actual production status of oil wells, make this method reliable. Compared with traditional methods that rely on experiments and simulations, this invention can obtain more reliable production capacity damage evaluation results based on real dynamic data.

[0100] (3) This invention addresses the problems of damage to production capacity and drastic fluctuations in production volume caused by the migration of microparticles in highly sensitive reservoirs. It clarifies the abnormal production capacity data caused by engineering intervention during dynamic production. By preprocessing the dynamic production data of oil wells, a method for preprocessing dynamic production data to eliminate the impact of engineering intervention on production capacity is established. This method can effectively reflect the decreasing trend of reservoir production capacity under natural decay conditions, improve the accuracy of production capacity change analysis caused by microparticle migration factors, and achieve effective repair of production capacity disturbance sections.

[0101] (4) Based on preprocessed production dynamic data, this invention establishes a method for calculating the comprehensive decline rate and the natural decline rate, and uses the difference between the two to calculate the particle migration decline rate, providing a basis for quantifying the production capacity decline damage caused by particle migration. This method does not require core experiments and numerical simulations; the particle migration decline rate can be obtained based on daily production reports. This lays the foundation for subsequent calculations of production capacity damage caused by particle migration.

[0102] (5) This invention proposes a method for calculating the production capacity ratio, production capacity damage rate and damage rate under the action of particulate migration, which can realize the quantitative characterization of the degree of reservoir production capacity loss caused by particulate migration, and provide a basis and technical support for the control of particulate migration measures. Attached Figure Description

[0103] Figure 1 This is a comparison chart of the original production dynamic data curve of an oilfield and the production data curve fitted by a hyperbolic decreasing model.

[0104] Figure 2 This is a comparison chart of the original production data curve of a high-rate-sensitive reservoir in an oilfield, the production data curve fitted by the decline model, and the production data curve corrected by the comprehensive actual decline rate D.

[0105] Figure 3 This is a schematic diagram illustrating the principle of calculating the extent of production capacity damage based on the particle transport decline rate curve of the present invention. Detailed Implementation

[0106] A method for predicting reservoir productivity damage caused by highly rapid and sensitive particle migration includes the following steps:

[0107] This invention first proposes a method for preprocessing production dynamic data to remove the impact of engineering measures on production capacity. This method is used to preprocess the actual production history data of oil wells, remove the influence of various engineering measures, and thus retain only the information on reservoir particle migration and production capacity damage.

[0108] S1. Identify the abnormal liquid production volume segment in the original production dynamic data caused by engineering intervention, and preprocess the abnormal liquid production volume data of the segment to obtain the intrinsic liquid production volume, forming production dynamic data including the intrinsic liquid production volume.

[0109] Specifically, based on daily oil well production reports, production dates and corresponding raw production volume data are extracted. The production volume variation curve over time is analyzed to obtain raw production dynamic data. For production volume data that shows a sudden increase or decrease in production volume due to engineering interventions (such as acidizing, fracturing, well workover, well start-up / shutdown, etc.) within a certain time period, and which does not conform to the normal decreasing trend, this data segment is identified as an abnormal production volume segment. An example of an abnormal segment is shown in the figure below. Figure 1 As shown, Figure 1 This is a comparison chart of the original production dynamic data curve of an oilfield and the production data curve fitted by a hyperbolic decreasing model.

[0110] Specifically, the process involves: firstly, determining the stable and abnormal production volume segments based on the decreasing relationship between production volume and time; and then further identifying the segments using a formula, as follows:

[0111] (1) Based on the daily production report, extract the production date and the corresponding raw liquid production data to obtain the daily liquid production sequence {Q}.N (where N is the production record number); a candidate segment is formed by the daily liquid production data points in 30 consecutive daily liquid production sequences. That is, it consists of daily liquid production data from the Nth to the N+29th record. The candidate segment... The candidate segment must exhibit a decreasing trend, which is determined by the daily liquid production at the beginning and end of the candidate segment meeting the following criteria: .

[0112] (2) For candidate segments that meet the decreasing trend screening criteria Calculate the relative rate of change of daily liquid production between two adjacent lines within this section. ;

[0113] (3) Select the maximum relative rate of change in each candidate segment. As a stability index for the candidate section, it is used to characterize the worst-case scenario of daily liquid production fluctuation within the candidate section.

[0114] (4) Among all candidate segments that meet the decreasing trend criterion, select the candidate segment with the smallest R(Ω) as the stable liquid production segment.

[0115] The relative rate of change is calculated using the following formula:

[0116] (1)

[0117] In the formula, The relative change rate of daily liquid production between two adjacent values, % Let be the daily fluid production of a certain well in the Nth production record, in t / d; Let t / d be the daily fluid production of a well in the N-1th production record.

[0118] The maximum relative rate of change within the candidate segment is calculated using the following formula:

[0119] (2)

[0120] In the formula, The maximum relative rate of change within the candidate segment, %

[0121] (5) Calculate the average daily liquid production in the stable liquid production range. Then, based on the average daily liquid production Calculate the relative deviation coefficient Select a stable liquid production range. maximum value Set anomaly detection threshold When satisfied > If there are at least 30 consecutive records of daily liquid production, the corresponding consecutive time period will be identified as an abnormal liquid production segment.

[0122] The relative deviation coefficient is calculated using the following formula:

[0123] (3)

[0124] In the formula, The relative deviation coefficient is % To stabilize the average daily liquid production in the liquid production range, t / d.

[0125] The anomaly detection threshold is calculated using the following formula:

[0126] (4)

[0127] In the formula, The anomaly detection threshold is % This is the anomaly detection threshold coefficient, used to adjust the sensitivity of anomaly detection. Its value can be determined based on the well area production fluctuation characteristics and historical data statistical experience. To stabilize the liquid production range The maximum value, %.

[0128] The intrinsic liquid production volume is obtained by preprocessing the abnormal liquid production volume data of the abnormal liquid production volume segment. Specifically, the engineering intervention compensation liquid production volume is calculated based on the abnormal liquid production volume data of the abnormal liquid production volume segment, and the daily liquid production volume data of each abnormal liquid production volume segment is corrected based on the engineering intervention compensation liquid production volume to obtain the corrected daily liquid production volume data, which is the intrinsic liquid production volume.

[0129] Specifically, for this abnormal liquid production range, the average daily liquid production during this period is calculated and denoted as . A time-effect correction factor is introduced to correct the time representativeness deviation of the average daily liquid production in the stable liquid production segment. The liquid production compensated by engineering intervention is defined as the difference between the average daily liquid production of the abnormal liquid production segment and the average daily liquid production of the stable liquid production segment after time-effect correction. This value not only reflects the overall increase in liquid production caused by engineering measures, but also serves as a unified benchmark for data correction.

[0130] The daily liquid production data is corrected based on engineering intervention to compensate for the liquid production, that is, the daily liquid production data Q for each abnormal liquid production range is corrected. ab Subtract the amount of liquid produced by engineering intervention compensation in sequence This allows us to obtain the corrected daily liquid production data. The corrected daily liquid production is defined as the intrinsic liquid production, denoted as Q. p The meaning is the daily liquid production level that can truly reflect the natural depletion law of reservoir energy after eliminating the influence of external engineering measures.

[0131] The amount of liquid produced by engineering intervention is calculated according to the following formula:

[0132] (5)

[0133] In the formula, To compensate for the amount of liquid produced by engineering intervention, t / d; The average daily liquid production in the abnormal liquid production range, t / d; To stabilize the average daily liquid production in the liquid production range, t / d; The time-related correction factor is used to correct the time representativeness deviation of the average daily production volume in a stable production range. Its value is determined based on the production variation characteristics of the well area and historical production experience.

[0134] The intrinsic product yield is calculated using the following formula:

[0135] (6)

[0136] In the formula, This represents the intrinsic liquid production rate, in t / d. This represents the daily liquid production within the abnormal liquid production range, expressed in t / d.

[0137] The above processing methods can eliminate abnormal interference caused by engineering measures, making the results of production capacity changes closer to the actual physical properties and energy change laws of the reservoir, and providing a reliable data basis for calculating production capacity damage caused by particle migration in highly sensitive reservoirs.

[0138] The present invention also proposes a method for fitting the production decline rate and calculating the particle migration decline rate based on production dynamic data. This method uses oil well production dynamic data to fit the production decline rate and obtain the production decline rate caused by reservoir particle migration, as follows:

[0139] S2. Based on the production dynamic data described in step S1, perform liquid production decrease fitting and particle migration decrease rate calculation.

[0140] First, this invention proposes a method for calculating the combined decrease rate of liquid production based on production dynamic data fitting and theoretical decrease rate, specifically as follows:

[0141] S21. Fit the production dynamic data described in step S1 using a capacity decline model (including exponential decline, harmonic decline and hyperbolic decline models), determine the optimal decline model based on the goodness of fit, and calculate the theoretical comprehensive decline rate D0.

[0142] Exponentially decreasing model:

[0143] (7)

[0144] In the formula, Let T be the daily fluid production of a certain well at time T, expressed in t / d. The initial liquid production rate is expressed in t / d. The theoretical comprehensive decline rate is dimensionless; T is the production time, in days.

[0145] Hyperbolic decreasing model:

[0146] (8)

[0147] In the formula, b is the hyperbolic decreasing exponent, which is dimensionless;

[0148] Harmonic diminishing model:

[0149] (9)

[0150] The optimal decreasing model is determined based on the goodness of fit, and the goodness of fit... The closer the value is to 1, the better the fit. Therefore, the goodness-of-fit value is selected. The largest decreasing model is the optimal decreasing model.

[0151] To improve the accuracy of the production rate decline fitting, this invention proposes a method for calculating the corrected decline rate based on the conventional decline model:

[0152] S22. Based on the optimal decline model and theoretical comprehensive decline rate D0 determined in step S21, calculate the comprehensive actual decline rate. ;

[0153] S221. Calculate the fitted average liquid production rate based on the optimal decreasing model determined in step S21:

[0154] (10)

[0155] In the formula, To fit the average liquid production rate, t / d; For production time, d; For the first The daily liquid production rate fitted by the day, t / d;

[0156] S222. Calculate the average intrinsic product volume based on the intrinsic product volume obtained in step S1:

[0157] (11)

[0158] In the formula, The intrinsic product yield is the average, in t / d; For production time, d; For the first Intrinsic liquid production per day, t / d;

[0159] S223. Calculate the decline rate correction coefficient based on the fitted average production rate obtained in step S221 and the intrinsic production rate mean obtained in step S222:

[0160] (12)

[0161] S224. Based on the decline rate correction coefficient calculated in step S223 and the theoretical overall decline rate D0 obtained in step S21, calculate the overall actual decline rate D:

[0162] If the optimal declining model determined in step S21 is an exponential declining model, then the comprehensive actual declining rate is calculated according to formula (13). Formula (13) is simplified to obtain the comprehensive declining rate calculation formula (16). If the optimal declining model determined in step S21 is a hyperbolic declining model, then the comprehensive actual declining rate is calculated according to formula (14). Formula (14) is simplified to obtain the comprehensive declining rate calculation formula (17). If the optimal declining model determined in step S21 is a harmonic declining model, then the comprehensive actual declining rate is calculated according to formula (15). Formula (15) is simplified to obtain the comprehensive declining rate calculation formula (18). The formulas are as follows:

[0163] (13)

[0164] (14)

[0165] (15)

[0166] In the formula, The overall actual decline rate is dimensionless; K is the decline rate correction coefficient, which is also dimensionless.

[0167] (16)

[0168] (17)

[0169] (18)

[0170] The actual descent rate can effectively eliminate the influence of fitting bias, and the principle of its corrected descent rate is as follows: Figure 2 As shown, Figure 2 The figure shows a comparison of the original production data curve of a high-velocity and sensitive reservoir in an oilfield, the production data curve fitted by the decline model, and the production data curve corrected by the comprehensive actual decline rate D. It can be seen from the figure that the production data curve corrected by the comprehensive actual decline rate D is closer to the original production data curve than the production data curve fitted by the decline model.

[0171] This invention also proposes a method for calculating the particle migration decline rate based on natural decline. Specifically, it proposes a method for calculating the natural decline rate based on representative data from the natural decline stage in the early stage of oil well production, and further proposes a method for calculating the particle migration decline rate based on the difference between the natural decline rate and the comprehensive decline rate.

[0172] S23. Based on representative production data during the natural decay phase in the early stage of oil well production, calculate the average daily natural decline rate; by comparing the difference between the average daily natural decline rate and the comprehensive actual decline rate, obtain the particle migration decline rate.

[0173] Specifically, production data from wells in the target block where this well is located were selected during the first four months of production to calculate the average monthly natural decline rate. Then, based on the average monthly natural decline rate Calculate the average daily natural decline rate .

[0174] The production data of oil wells in the target block during the initial four months of production were selected to calculate the average monthly natural decline rate. Specifically, to establish a baseline decline rate for the natural decay trend, production wells within the target block that maintained stable production practices and experienced no significant engineering disturbances for four consecutive months during the initial production phase were selected based on production records. Monthly production data for each corresponding month was extracted, and the production of each well was statistically averaged monthly to construct a sequence of monthly average production changes for the block. Using months as the time unit, an exponential decline model was used to fit the monthly average production of the block to obtain the average monthly natural decline rate. .

[0175] Whether a production well is in a stable production phase and whether an engineering disturbance has occurred can be determined based on production operation records, which include operation events such as well shut-in, well workover, fracturing, acidizing, and production enhancement measures, along with their timing information.

[0176] To ensure the representativeness of the extracted natural decline rate, production data from the first four consecutive months of production in the target block were selected for decline fitting. During this stage, the wells are typically in a state driven by the reservoir's original energy and have not yet been significantly affected by engineering disturbances or particulate blockage; the production decline is mainly due to the reservoir's own hydrodynamic mechanisms. Data from this period shows relatively small fluctuations and can effectively reflect the natural production evolution trend.

[0177] Specifically, the average monthly natural decline rate Calculate using the following formula:

[0178] (19)

[0179] In the formula, Let be the average monthly liquid production of the block in the i-th month, t / d, where i=2,3,4; To compare with the average monthly liquid production of block i in month i Corresponding production time nodes; The average monthly liquid production of the block in the first month, in t / d; The average monthly natural decline rate is dimensionless.

[0180] Calculate the average daily natural decline rate D d :

[0181] When the average monthly natural decline rate is less than 5%, the formula for calculating the average daily natural decline rate is as follows:

[0182] (20)

[0183] In the formula, The average daily natural decline rate is dimensionless. The average monthly natural decline rate is dimensionless.

[0184] When the average monthly natural decline rate is ≥5%, the formula for calculating the average daily natural decline rate is as follows:

[0185] (twenty one)

[0186] Based on the comprehensive actual decline rate D and the average daily natural decline rate D d The difference was used to obtain the non-natural decline rate corresponding to reservoir damage in a single well. After excluding the influence of engineering factors, the non-natural decline rate of oil wells is mainly related to the impairment of reservoir permeability, that is, particle migration causes pore throat blockage, resulting in decreased permeability and reduced production capacity, i.e., particle migration decline rate D. w To account for the difference between the actual decline rate D and the average daily natural decline rate, the formula is as follows:

[0187] (twenty two)

[0188] In the formula, The particle transport decline rate is dimensionless.

[0189] This invention also proposes a method for calculating the degree and rate of production capacity damage based on the particle migration decline rate, which is used to obtain the extent of production capacity damage caused by particle migration through comparative evaluation, and to achieve quantitative prediction and evaluation.

[0190] S3. Based on the particle migration decline rate obtained in step S2, calculate the production damage rate and production damage rate caused by particle migration, so as to achieve quantitative prediction and evaluation of the impact of particle migration on oil well production.

[0191] First, this section constructs an expression for the trend of production capacity change under the action of particulate transport, based on oil well production and production time data:

[0192] S31. Based on the particle migration decline rate obtained in step S2, and combined with the initial production rate and production time data of the oil well, calculate the production rate under the action of particle migration. ;

[0193] Based on the initial fluid production of the oil wells in the original production dynamic data Using the particle transport decline rate D w And, combined with the production time T of this stage, calculate the current liquid production volume. The trend of energy production change under particulate transport conditions was obtained, such as Figure 3 As shown. Figure 3 In the figure, the curve represents the dynamic process of oil well production gradually decreasing over time under the influence of particle transport. Based on... , Production time during this stage T It can be calculated This allows for the quantitative expression of the trend of energy production changes under the influence of particle transport, and provides a basis for subsequent calculations of indicators such as energy production damage rate and damage rate. The calculation formula is as follows:

[0194] (twenty three)

[0195] (twenty four)

[0196] (25)

[0197] If the optimal decreasing model determined in step S21 is an exponential decreasing model, then calculate according to formula (23). If the optimal decreasing model determined in step S21 is a hyperbolic decreasing model, then calculate according to formula (24). If the optimal decreasing model determined in step S21 is a harmonic decreasing model, then calculate according to formula (25). ;

[0198] Based on this, the present invention proposes calculation methods for particulate transport productivity ratio, productivity damage rate and damage rate, so as to realize the quantitative characterization of the impact of particulate transport on oil well productivity.

[0199] S32. Liquid production rate based on particle transport obtained in step S31 Calculate the production ratio caused by particulate transport factors. ;

[0200] Production capacity ratio P REquals the current liquid production calculated using the particle transport decline rate. Initial fluid production of oil well :

[0201] (26)

[0202] In the formula, This is a capacity ratio, dimensionless.

[0203] S33. Calculate the capacity damage rate based on the capacity ratio obtained in step S32. and production capacity damage rate .

[0204] Calculate the production capacity damage rate caused by particulate transport factors. :

[0205] (27)

[0206] In the formula, The percentage represents the production capacity damage rate.

[0207] Calculate the rate of productivity damage caused by particulate transport factors. :

[0208] Production damage rate Equal to production capacity damage rate With production time The ratio:

[0209] (28)

[0210] In the formula, The rate of damage to production capacity is expressed as % / a. Production time, in years.

[0211] Example 1

[0212] Using the method for predicting the productivity damage caused by particle migration in highly velocity-sensitive reservoirs proposed in this invention, an analysis of the productivity damage caused by particle migration in oil wells of oil field B was carried out, and productivity damage calculation was performed on an oil well.

[0213] (1) Calculation of pretreatment for declining capacity

[0214] S1. Identify the abnormal liquid production volume segment in the original production dynamic data caused by engineering intervention, and preprocess the abnormal liquid production volume data of the segment to obtain the intrinsic liquid production volume, forming production dynamic data including the intrinsic liquid production volume.

[0215] Based on the relationship curve between oil well production and time, stable and abnormal production ranges were identified using relevant calculation formulas. The stable production range in this embodiment is shown in Table 1, and specific examples of identified abnormal production are shown in Table 3. Using the proposed method for processing abnormal production data, relevant parameters for abnormal and stable ranges, average daily production, and engineering intervention compensation production were calculated, as shown in Table 2. The intrinsic production was further calculated, as shown in Table 3.

[0216] Table 1 Stable Fluid Production of Oil Wells in Oilfield B

[0217]

[0218] Table 2. Results of Data Processing for Abnormal Fluid Production in Oilfield x

[0219]

[0220] Table 3. Intrinsic Fluid Production of Well x in Oilfield B (Excerpt)

[0221]

[0222] (2) Fitting of the decrease in liquid production and calculation of the decrease rate of particle migration;

[0223] S2. Based on the production dynamic data described in step S1, perform liquid production decrease fitting and particle migration decrease rate calculation.

[0224] The production volume was fitted using the conventional decreasing formula, and the hyperbolic decreasing formula with the highest fitting degree was selected to calculate the particle migration decrease rate. The specific calculation results of each parameter are shown in Table 4.

[0225] Table 4. Calculation results of particle migration decline in oilfield x wells of oil field B.

[0226]

[0227] (3) Calculate the degree and rate of damage to production capacity.

[0228] S3. Based on the particle migration decline rate obtained in step S2, calculate the production damage rate and production damage rate caused by particle migration, so as to achieve quantitative prediction and evaluation of the impact of particle migration on oil well production.

[0229] The current production capacity can be calculated based on the expression of the production capacity change trend under the action of particle transport. The particle transport production capacity ratio, production capacity damage rate and damage rate are further calculated using this index. The specific results are shown in Table 5.

[0230] Table 5. Calculation Results of Productivity Damage Caused by Particulate Migration in Oilfield B x Wells

[0231]

[0232] Table 5 shows that under the influence of particulate migration, the current production capacity ratio of this well is 0.965, the production capacity damage rate is 3.5%, and the production capacity damage rate is 0.928% / year, indicating that particulate migration has caused significant and continuous damage to the well's production capacity. The production capacity damage rate and damage rate calculated based on daily production data can be used to quantitatively characterize the current degree of damage and its evolution trend, thereby providing early warning of particulate migration risks. This provides a quantitative basis for the subsequent formulation and optimization of particulate migration control and sand control measures, and can also be used for comparative evaluation of the effects before and after the implementation of relevant measures.

Claims

1. A method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration, characterized in that, Includes the following steps: S1. Identify the abnormal liquid production volume segment in the original production dynamic data caused by engineering intervention, and preprocess the abnormal liquid production volume data of the segment to obtain the intrinsic liquid production volume, forming production dynamic data including the intrinsic liquid production volume. S2. Based on the production dynamic data described in step S1, perform liquid production decrease fitting and particle migration decrease rate calculation. S3. Based on the particle migration decline rate obtained in step S2, calculate the production damage rate and production damage rate caused by particle migration, so as to achieve quantitative prediction and evaluation of the impact of particle migration on oil well production. Step S1, which involves identifying abnormal liquid production segments in the original production dynamic data caused by engineering intervention, includes: (1) Based on the daily production report, extract the production date and the corresponding raw liquid production data to obtain the daily liquid production sequence {Q}. N }, where N is the production record number; a candidate segment is formed by the daily liquid production data points in 30 consecutive daily liquid production sequences. That is, it consists of daily liquid production data from the Nth to the N+29th record; the candidate segment The candidate segment must exhibit a decreasing trend, categorized by the daily liquid production at the beginning and end of the test segment. ; (2) For candidate segments that meet the decreasing trend screening criteria Calculate the relative rate of change of daily liquid production between two adjacent lines within this section. ; (3) Select the maximum relative rate of change in each candidate segment. As a stability index for the candidate section, it is used to characterize the worst-case scenario of daily liquid production fluctuation within the candidate section. (4) Among all candidate segments that meet the decreasing trend criterion, select the candidate segment with the smallest R(Ω) as the stable liquid production segment; The relative rate of change is calculated using the following formula: （1） In the formula, The percentage change in daily liquid production between two adjacent values ​​is %. Let be the daily fluid production of a certain well in the Nth production record, in t / d; Let be the daily fluid production of a certain well in the (N-1)th production record, in t / d; The maximum relative rate of change within the candidate segment is calculated using the following formula: （2） In the formula, The maximum relative rate of change within the candidate segment, % (5) Calculate the average daily liquid production in the stable liquid production range. and according to Calculate the relative deviation coefficient Select within the stable liquid production range maximum value Set anomaly detection threshold When satisfied > If there are no fewer than 30 consecutive records of daily liquid production, the corresponding consecutive time period will be identified as an abnormal liquid production segment. The relative deviation coefficient is calculated using the following formula: （3） In the formula, The relative deviation coefficient is % To stabilize the average daily liquid production in the liquid production range, t / d; The anomaly detection threshold is calculated using the following formula: （4） In the formula, The anomaly detection threshold is % This is the anomaly detection threshold coefficient, used to adjust the sensitivity of anomaly detection. Its value can be determined based on the well area production fluctuation characteristics and historical data statistical experience. To stabilize the liquid production range The maximum value, % Step S1 describes preprocessing the abnormal liquid production data of the section to obtain the intrinsic liquid production, specifically: calculating the engineering intervention compensation liquid production based on the abnormal liquid production data of the abnormal liquid production section, and correcting the daily liquid production data of each abnormal liquid production section based on the engineering intervention compensation liquid production, to obtain the corrected daily liquid production data, which is the intrinsic liquid production. The engineering intervention compensation for liquid production is the difference between the average daily liquid production of the abnormal liquid production segment and the average daily liquid production of the stable liquid production segment. A time-effect correction factor is introduced to correct the average daily liquid production of the stable liquid production segment, calculated according to the following formula: (5) in, To compensate for the amount of liquid produced by engineering intervention, t / d; The average daily liquid production in the abnormal liquid production range, t / d; To stabilize the average daily liquid production in the liquid production range, t / d; The time-related effect correction factor; The intrinsic product volume is calculated according to the following formula: (6) In the formula, This represents the intrinsic liquid production rate, in t / d. This represents the daily liquid production within the abnormal liquid production range, expressed in t / d.

2. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 1, characterized in that, Step S2 includes: S21. Fit the production dynamic data described in step S1 using the capacity decline model, determine the optimal decline model based on the goodness of fit, and calculate the theoretical comprehensive decline rate D0. S22. Based on the optimal decline model and theoretical comprehensive decline rate D0 determined in step S21, calculate the comprehensive actual decline rate. ; S23. Based on representative production data from the natural decline phase during the initial production stage of an oil well, calculate the average daily natural decline rate; compare the average daily natural decline rate with the comprehensive actual decline rate. The difference between them yields the particle transport decline rate.

3. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 2, characterized in that, The capacity decline model mentioned in step S21 includes exponential decline, harmonic decline, and hyperbolic decline models, and the formulas are as follows: Exponentially decreasing model: （7） In the formula, Let T be the daily fluid production of a certain well at time T, expressed in t / d. The initial liquid production rate is expressed in t / d. The theoretical comprehensive decline rate is dimensionless; T is the production time, in days. Hyperbolic decreasing model: （8） In the formula, b is the hyperbolic decreasing exponent, which is dimensionless; Harmonic diminishing model: （9） The optimal decreasing model is determined based on the goodness of fit, and the goodness of fit is selected accordingly. The largest decreasing model is taken as the optimal decreasing model.

4. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 2, characterized in that, Step S22 includes: S221. Calculate the fitted average liquid production rate based on the optimal decreasing model determined in step S21: (10) In the formula, To fit the average liquid production rate, t / d; For production time, d; For the first The daily liquid production rate fitted by the day, t / d, S222. Calculate the average intrinsic product volume based on the intrinsic product volume obtained in step S1: (11) In the formula, The intrinsic product yield is the average, in t / d; For production time, d; For the first Intrinsic liquid production per day, t / d; S223. Calculate the decline rate correction coefficient based on the fitted average production rate obtained in step S221 and the intrinsic production rate mean obtained in step S222: (12) S224. Based on the decline rate correction coefficient calculated in step S223 and the theoretical comprehensive decline rate D0 obtained in step S21, calculate the comprehensive actual decline rate D.

5. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 4, characterized in that, In step S224, if the optimal decreasing model determined in step S21 is an exponential decreasing model, the comprehensive actual decreasing rate is calculated according to formula (13); if the optimal decreasing model determined in step S21 is a hyperbolic decreasing model, the comprehensive actual decreasing rate is calculated according to formula (14); if the optimal decreasing model determined in step S21 is a harmonic decreasing model, the comprehensive actual decreasing rate is calculated according to formula (15), as follows: （13） （14） （15）。 6. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 2, characterized in that, In step S23, the production data of oil wells in the target block during the initial four months of production are selected, and the average monthly natural decline rate is calculated. Then, based on the average monthly natural decline rate Calculate the average daily natural decline rate The comparison between the average daily natural decline rate and the comprehensive actual decline rate... The difference between them yields the particle transport decline rate, which is calculated using the following formula: (19) In the formula, The particle transport decline rate is dimensionless.

7. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 6, characterized in that, The average monthly natural decline rate The formula is as follows: （16） In the formula, Let be the average monthly liquid production of the block in the i-th month, t / d, where i = 2, 3, 4; To compare with the average monthly liquid production of block i in month i Corresponding production time nodes; The average monthly liquid production of the block in the first month, in t / d; The average monthly natural decline rate is dimensionless. When the average monthly natural decline rate is less than 5%, the formula for calculating the average daily natural decline rate is as follows: (17) In the formula, The average daily natural decline rate is dimensionless. The average monthly natural decline rate is dimensionless. When the average monthly natural decline rate is ≥5%, the formula for calculating the average daily natural decline rate is as follows: (18)。 8. The method for predicting energy production damage caused by highly rapid and sensitive reservoir particle migration according to claim 1, characterized in that, Step S3 is as follows: S31. Based on the particle migration decline rate obtained in step S2, and combined with the initial production rate and production time data of the oil well, calculate the production rate under the action of particle migration. The formula is as follows; (20) (21) (22) in, At the current liquid production rate, The initial product volume; if the optimal decreasing model determined in step S21 is an exponential decreasing model, then calculate according to formula (20). If the optimal decreasing model determined in step S21 is a hyperbolic decreasing model, then calculate according to formula (21). If the optimal decreasing model determined in step S21 is a harmonic decreasing model, then calculate according to formula (22). ; S32. Liquid production rate based on particle transport obtained in step S31 Calculate the production ratio caused by particulate transport factors. ; (23) In the formula, This is a capacity ratio, dimensionless. S33. Calculate the capacity damage rate based on the capacity ratio obtained in step S32. and production capacity damage rate : (24) In the formula, The capacity damage rate is % Production damage rate Equal to production capacity damage rate With production time The ratio: (25) In the formula, The rate of damage to production capacity is expressed as % / a. Production duration, in years.

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