Method for selecting sand control mode of oil and gas well in unconsolidated sandstone reservoir

Abstract

The present application belongs to the technical field of oil and gas development and exploitation engineering, and particularly relates to a method for selecting a sand control mode of an oil and gas well in a loose sandstone reservoir. The method is based on stablely obtainable geological and production parameters in an engineering field, and a parameter obtainability index PAI is introduced i The sand control evaluation indexes are screened, and based on historical operation data and the screened sand control indexes, main sand control indexes of different sand control modes are screened, and the main sand control indexes of the screened sand control modes are divided into positive indexes and negative indexes. On this basis, an applicability index (UI), a failure risk index (FRI) and a comprehensive adaptation index (CSI) are respectively calculated, so as to realize quantitative evaluation and optimization of the sand control mode, and overcome problems of the existing sand control mode selection method, such as small coverage of the selected sand control mode, single factor considered, strong subjectivity, large calculation amount, and difficult parameter acquisition.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas development and exploitation engineering, and particularly relates to a method for selecting a sand control mode of an oil and gas well in a loose sandstone reservoir. BACKGROUND

[0002] The reserves and production of loose sandstone oil and gas reservoirs play an important role in oil and gas development and are one of the main battlefields for increasing reserves and production of oil and gas energy in China. However, due to the loose cementation and low strength of loose sandstone oil and gas reservoirs, sand production is prone to occur during production. Sand production refers to the peeling of part of the reservoir rock particles to form sand particles during the production of an oil and gas well. These sand particles are brought into the wellbore and produced together with oil and gas. When sand production is serious, it can cause equipment damage and oil and gas well shutdown, which seriously affects the normal production of oil and gas wells. Sand control is the main process means to solve this problem. At present, the main sand control modes include chemical sand consolidation, chemical cementation artificial well wall, independent mechanical screen pipe sand control, expanded screen pipe sand control, conventional squeeze packing sand control, high-speed water packing sand control, high saturation squeeze packing, double-grain-size multi-slug squeeze packing, early screen pipe circulation packing, late screen pipe circulation packing, conventional fracturing packing sand control, micro-fracturing packing sand control, fiber composite screenless fracturing packing sand control, composite fracturing packing, circulation packing sand control agent composite sand control, squeeze packing sand control agent composite sand control, mechanical screen pipe chemical composite sand control, mechanical screen pipe artificial well wall composite sand control, multi-stage composite packing sand control, and water control and sand control integration. Due to the diversity of oil and gas reservoir types, different geological conditions and production situations, and the different technical principles and types of existing sand control methods, the applicable conditions are different. Therefore, according to the reservoir characteristics and oil and gas well conditions, scientific selection of a reasonable sand control mode is the key to ensuring sand control effect and production benefit.

[0003] CN105003233A proposes a method for determining the boundary control points of four sand control modes through full-size sand production simulation experiments based on five formation parameters, including reservoir sand sample particle size median d 50 , fine particle content, Uc value, Sc value, and absolute content of montmorillonite, and drawing a radar chart to quickly determine the most suitable sand control mode. However, the sand control mode types are not comprehensive, and it is difficult to cope with complex and variable reservoirs and production situations.

[0004] CN110514571A conducts indoor full-size sand production experiments by simulating formation sand particle size and composition, systematically evaluates the influence of different screen mesh accuracy, gravel packing thickness, and particle size combinations on gas production capacity and sand control effect, establishes a dual-index evaluation system of production capacity and sand production concentration, and finally constructs a sand control mode and sand control accuracy optimization method for high-yield gas wells, providing experimental support and screening basis for sand control scheme design under complex reservoir conditions. However, its applicable scenarios are limited, and it is only applicable to high-yield gas wells, and the index system is relatively simple.

[0005] A series of academic papers represented by He H F. Stratified sand control and stratified oil production technology in offshore unconsolidated sandstone reservoirs[J]. Oil Drilling Technology, 2021, 49(6): 99-104; Li J, et al. New method for comprehensive prediction of sand production risk in unconsolidated sandstone reservoirs and its application[J]. Daqing Petroleum Geology and Development, 2021; Wang L H, et al. Research on reservoir grain size neural network prediction model[J]. Journal of Southwest Petroleum University (Natural Science Edition), 2016, 38(1): 53-59; Zhang L, et al. Research on optimization and decision-making method of offshore unconsolidated sandstone sand control mode[J]. Journal of Yangtze University (Natural Science Edition), 2019, 16(2): 44-50 have studied and applied different sand control mode optimization methods, covering direct experience comparison method, comprehensive fuzzy evaluation method, artificial neural network method and simple chart method. Among them, the direct experience comparison method relies on field experience, has strong subjectivity, the result accuracy is easily affected by human judgment, and it is difficult to quantify complex factors; the comprehensive fuzzy evaluation method can handle multi-index problems, but the model construction and weight distribution are complex, the data quality requirement is high, and the calculation amount is relatively large; the artificial neural network method has strong nonlinear modeling capability, but it is strongly dependent on data quantity and quality, and the model training and parameter adjustment process is complex; the simple chart method is convenient to operate, but the precision is limited, the boundary is fuzzy, and the influencing factors are less considered. SUMMARY

[0006] To solve the above problems, the present application provides a loose sandstone reservoir oil and gas well sand control method, which is based on the stable geological and production parameters obtained in the engineering field, and introduces the parameter availability index PAI i The sand control evaluation index is selected, and based on the historical operation data and the selected sand control index, the main sand control index of different sand control modes is selected, and the main sand control index of each sand control mode after selection is divided into positive index and negative index, and on this basis, the applicability index (UI), the failure risk index (FRI) and the comprehensive adaptation index (CSI) are calculated respectively, realizing the quantitative evaluation and optimization of sand control mode, and overcoming the problems of small coverage of selected sand control mode, single consideration factor, strong subjectivity, large calculation amount and difficult parameter acquisition of existing sand control mode selection method.

[0007] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0008] A loose sandstone reservoir oil and gas well sand control method, comprising:

[0009] S1, based on the availability index PAI i The sand control index in the sand control process is selected, and the basic data of each sand control index after selection is normalized;

[0010] S2, based on the historical operation data and the sand control indexes screened in step S1, screening the main sand control indexes of different sand control modes, and dividing the screened main sand control indexes of each sand control mode into positive indexes and negative indexes;

[0011] S3, calculating the applicability index UI and the failure risk index FRI of each sand control mode according to the normalized results of the normalized sand control indexes obtained in step S1 and the positive indexes and negative indexes of each sand control mode obtained in step S2, and calculating the comprehensive adaptation index CSI according to the applicability index UI and the failure risk index FRI, and selecting the sand control mode with the highest comprehensive adaptation index CSI value as the optimal sand control mode.

[0012] Preferably, the availability index PAI in step S1 is calculated according to the following formula: i In order to quantitatively reflect the overall availability of the parameters in engineering application by weighting and synthesizing the acquisition path, acquisition cost and acquisition stability factors, the calculation method is as follows:

[0013] (1)

[0014] Wherein, i is the i th sand control index, PAI i is the availability index, A i is the acquisition path factor, B i is the acquisition cost factor, C i is the acquisition stability factor.

[0015] Further preferably, the acquisition path factor A i is used to represent whether the parameter can be directly obtained through the conventional data flow process, and reflects the complexity of the parameter acquisition path, and the specific assignment is shown in Table 1.

[0016] Table 1 Acquisition path factor assignment table

[0017]

[0018] The acquisition cost factor B i is used to represent whether the parameter needs additional engineering operation or increases the economic and time cost, and reflects the engineering cost level of parameter acquisition, and the calculation method is as follows:

[0019] (2)

[0020] In the formula, is the additional engineering cost required for acquiring the sand control index; is the maximum acceptable additional cost in the same type of parameter;

[0021] The acquisition stability factor C iThis method is used to characterize the data integrity and stability of parameters during multi-well, multi-time-period applications, reflecting the reliability of continuous parameter acquisition. The calculation method is as follows:

[0022] (3)

[0023] In the formula, The number of wells or time periods for which sand control indicators are missing in historical well data; This represents the number of wells or time periods missing from historical well data.

[0024] Preferably, in step S1, the sand control index in the sand control process is selected based on the PAI of the sand control index. i A value of at least 0.75 is used as a screening criterion.

[0025] Preferably, the sand control methods described in step S2 include: chemical sand fixation, chemically cemented artificial wellbore, independent mechanical screen sand control, expansion screen sand control, conventional extrusion filling sand control, high-speed water filling sand control, high-saturation extrusion filling, micro-saturation extrusion filling, dual-stage multi-stage plug extrusion filling, early screen circulation filling, late screen circulation filling, conventional fracturing filling sand control, micro-fracturing filling for increased production sand control, fiber composite screenless fracturing filling sand control, composite fracturing filling, circulating filling sand suppressant composite sand control, extrusion filling sand suppressant composite sand control, mechanical screen chemical composite sand control, mechanical screen artificial wellbore composite sand control, multi-stage composite filling sand control, and integrated water control and sand control.

[0026] Preferably, in step S2, the data sequence corresponding to each sand control index after screening in step S1 and the effective period t of each sand control method included in the historical operation data are calculated. f The correlation coefficients between data sequences are used to screen out the main sand control indicators of different sand control methods, and the main sand control indicators of different sand control methods are classified into positive indicators or negative indicators. For each sand control method, the weight coefficients of positive and negative indicators are assigned according to the magnitude of the absolute value of the correlation coefficient.

[0027] In a further preferred embodiment, step S2 specifically includes:

[0028] S21. Obtain historical operation data corresponding to each sand control method, and use the Shapiro-Wilk normality test method to test the sand control index data sequence and sand control validity period data sequence contained therein. When the significance level α=0.05, if the p value of the corresponding data sequence is not less than 0.05, it is considered to follow a normal distribution; otherwise, it is considered not to follow a normal distribution.

[0029] S22. For data sets where both the sand control index and the sand control validity period follow a normal distribution, calculate the sand control index and the sand control validity period t after screening in step S1.f The Pearson correlation coefficient between them; for data sets where either the sand control index or the sand control effectiveness period does not satisfy a normal distribution, calculate the correlation coefficient between each sand control index and the sand control effectiveness period t. f Spearman's rank correlation coefficient between them;

[0030] S23. Determine whether the absolute value of the Pearson correlation coefficient or Spearman rank correlation coefficient calculated in step S22 is ≥0.2. Select the indicators that meet the above conditions as the main sand control indicators of the sand control method, and determine whether they are positive or negative indicators based on the value of the correlation coefficient.

[0031] A positive correlation coefficient indicates a positive indicator; a negative correlation coefficient indicates a negative indicator.

[0032] S24. For each sand control method, the positive or negative indicators are allocated proportionally according to the absolute value of the correlation coefficient, and then normalized so that the sum of the weight coefficients of all positive indicators and the sum of the weight coefficients of all negative indicators in each sand control method are 1.

[0033] More preferably, step S22 is calculated according to the following formula:

[0034] The Pearson correlation coefficient r p , is used to measure the degree of linear correlation between two continuous variables, is dimensionless, and takes values ​​in the range [-1, 1]. Where, when r p A value greater than 0 indicates a positive correlation, when r p A value less than 0 indicates a negative correlation, when r < 0 p =0 indicates no linear correlation, when |r p |≈1 indicates a strong correlation, and when |r ≈1, it indicates a strong correlation. p |≈0 indicates a weak correlation, calculated as follows:

[0035] (4)

[0036] In the formula, X represents the value of the sand control index in the historical operation data, and Y represents the value of the sand control validity period in the historical operation data. This is the average value of all sand control indicators in this set of historical operation data. This is the average effective period of all sand control measures in this group of historical operational data;

[0037] The Spearman rank correlation coefficient r s r is used to measure the degree of monotonic correlation between two variables. It is a rank-based nonparametric correlation index with dimensionless values. s The range is [-1, 1], and the level refers to the sequence number assigned to each data point after sorting a set of data from smallest to largest or largest to smallest. When r sA value greater than 0 indicates a positive correlation between the ranks of the two variables; when r... s A value less than 0 indicates a negative correlation between the levels; when r < 0, the correlation is negative. s =0 indicates that there is no monotonic correlation, when |r s | When | is close to 1, it indicates a strong monotonic relationship between the variables, while |r s | A value close to 0 indicates a weak monotonic relationship. The calculation method is as follows:

[0038] (5)

[0039] In the formula, R x R represents the level of sand control index in historical operation data. y This refers to the level of sand control effectiveness in historical operational data. This is the average value of all sand control index levels in this group of historical operation data. This is the average value of the effective sand control period levels for all data in this group from historical operational data.

[0040] Preferably, the applicability index UI mentioned in step S3 is used to quantify the fit between well conditions and sand control methods. The more comprehensive the positive indicators, the higher the index value. It reflects the "feasibility advantage" of the sand control method under geological and engineering parameters and is an important basis for determining whether it is worth giving priority to. The calculation method is as follows:

[0041] (6)

[0042] In the formula, w i f is the weight coefficient of the i-th positive indicator, which is dimensionless; i Let be the normalized value of the i-th positive indicator, which is dimensionless.

[0043] The Failure Risk Index (FRI) measures the degree of risk of failure or diminished effectiveness of the sand control method under well conditions. The more prominent the negative indicators, the higher the index value. It reflects the potential adverse conditions of the method in practical applications and serves as an important reference for risk control and solution elimination. The calculation method is as follows:

[0044] (7)

[0045] In the formula, v j g is the weight coefficient of the j-th negative indicator, which is dimensionless; j Let be the normalized value of the j-th negative indicator, which is dimensionless.

[0046] The Comprehensive Suitability Index (CSI) is directly used to guide on-site process selection and technology decisions. The calculation method is as follows:

[0047] (8)

[0048] In the formula, UI is the suitability index, FRI is the failure risk index, and α and β are the weighting coefficients of the suitability index UI and the failure risk index FRI, respectively.

[0049] Further preferred, 0 < α ≤ 1, 0 < β ≤ 1.

[0050] More preferably, α=β=1. To be more conservative, the value of β can be increased and the value of α can be decreased to emphasize risk.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The method of this invention is based on geological and production parameters that can be stably obtained at the engineering site, and introduces the parameter availability index (PAI). i The evaluation indicators for sand control were screened, and a system of positive and negative key factor indicators covering mainstream sand control technologies was constructed. Based on this, the Applicability Index (UI), Failure Risk Index (FRI), and Comprehensive Suitability Index (CSI) were calculated to achieve quantitative evaluation and optimization of sand control methods. This overcomes the problems of existing sand control method selection methods, such as small coverage of selected sand control methods, single consideration of factors, strong subjectivity, large amount of calculation, difficulty in obtaining parameters, reliance on a large number of experiments or experience, and insufficient engineering applicability.

[0053] 2. This invention introduces the Parameter Availability Index (PAI) i Quantitative screening of sand control evaluation indicators is carried out, prioritizing parameters that can be obtained through conventional data, avoiding reliance on data that is difficult to obtain or requires special experiments, thereby improving the operability and stability of sand control method selection in actual engineering from the source.

[0054] 3. This invention constructs a positive and negative index system covering almost all mainstream sand control processes. By using a unified evaluation framework to conduct a horizontal comparison of different sand control methods, it solves the problem that the applicable scope of sand control methods is limited and it is difficult to systematically select the best method in the prior art.

[0055] 4. The method for selecting sand control methods for sand-producing oil and gas wells provided by this invention is scientifically sound and reasonable, with a simple calculation process, convenient parameter acquisition, and a small amount of required data. This makes the selection of sand control technology more objective and scientific, with strong field operability, improving the pertinence and adaptability of sand control design, significantly reducing the risk of sand control failure, extending the wellbore service life, and improving the production efficiency and economic value of oil and gas wells. It has good prospects for promotion and application. Detailed Implementation

[0056] Example 1

[0057] A method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs includes,

[0058] S1, Based on the Accessibility Index (PAI) i Screening sand control indicators during the sand control process and normalizing the basic data of each screened sand control indicator;

[0059] Currently, the sand control evaluation system includes the following sand control indicators: reservoir homogeneity (J), critical sand production index (B), reservoir temperature (T), reservoir permeability (K), reservoir thickness (H), and water cut (f). w Reservoir clay content G SR Production intensity Y, sand deficit index C, reservoir particle migration initiation radius R, reservoir fluid salinity M, and median formation sand grain size d 50 Reservoir microparticle migration capacity S, formation sand particle size uniformity coefficient U, reservoir cementation strength S0, subsequent operation requirements PD, and wellbore irregularity I. r Distance W between the bottom and edge of the water layer;

[0060] In the practical application of sand control method selection and evaluation, the difficulty of obtaining different parameters on-site varies significantly. If the evaluation method relies on a large amount of data that is difficult to obtain directly or requires specialized experiments, the engineering feasibility of the method will be significantly reduced. Therefore, it is necessary to quantitatively determine the availability of parameters in the application process and select evaluation parameters and indicators accordingly.

[0061] The method of this invention quantitatively evaluates the availability of parameters in the application process through three dimensions: parameter acquisition path, acquisition cost, and acquisition stability, thereby determining whether they are suitable as input parameters or evaluation indicators for selecting sand control methods.

[0062] Specifically, the availability index PAI mentioned in step S1 i To quantitatively reflect the overall availability of parameters in engineering applications by weighting and synthesizing acquisition path, acquisition cost, and acquisition stability factor, the calculation method is as follows:

[0063] (1)

[0064] In the formula, i is the i-th sand control index, and the path factor A is obtained. i The weight of factor B is higher than the other two factors because whether additional work is required directly determines feasibility. Therefore, assigning a value of 0.4 is sufficient to reflect its "priority" without becoming a "sole veto factor." i With obtaining the stability factor C i Equally important, high cost means practical use is unlikely, poor stability means the algorithm cannot be applied in batches. This weight setting reflects the principle in engineering applications that "whether it can be obtained" takes precedence over "how well it can be obtained".

[0065] The path factor A is obtained iThis is used to characterize whether parameters can be directly obtained through conventional data processes such as well logging interpretation and production measurement, reflecting the complexity of the parameter acquisition path. The specific values ​​are shown in Table 1.

[0066] Table 1. Path Factor Assignment Table

[0067]

[0068] The acquisition cost factor B i This is used to characterize whether acquiring parameters requires additional engineering work or increases economic and time costs, reflecting the engineering cost level of parameter acquisition. The calculation method is as follows:

[0069] (2)

[0070] In the formula, Additional engineering costs required to obtain sand control indicators; This represents the maximum acceptable additional cost among similar parameters.

[0071] The acquisition of stability factor C i This method is used to characterize the data integrity and stability of parameters during multi-well, multi-time-period applications, reflecting the reliability of continuous parameter acquisition. The calculation method is as follows:

[0072] (3)

[0073] In the formula, The number of wells or time periods for which sand control indicators are missing in historical well data; This represents the number of wells or time periods missing from historical well data.

[0074] Based on the parameter availability index calculation results, the preferred PAI is selected. i Parameters with a value not lower than 0.75 are considered easily obtainable parameters, and easily obtainable indicators are selected based on the obtained parameters. Through calculation, the easily obtainable indicators are finally determined to be reservoir permeability K, effective reservoir thickness H, formation temperature T, and production water cut f. w Reservoir edge / bottom water distance W, clay content G SR and wellbore irregularity I r The indicators include: fluid production intensity index Y, particle size uniformity coefficient U, sand production critical index B, reservoir particle migration capacity S, and median formation sand grain size d. 50 ;

[0075] The basic data for the screened sand control indicators are shown in Table 2, covering the reservoir's geological characteristics, production conditions, and key parameters related to sand control. The basic data used in this embodiment comes from Well H in a block of the Penglai Oilfield.

[0076] Table 2 Basic Data for Each Sand Control Index

[0077]

[0078] In step S1, the normalization process is as follows:

[0079] Based on the normalization formula shown in Table 3, the sand control indicators are normalized to allow indicators of different dimensions and orders of magnitude to be compared under the same evaluation system. By mapping each sand control indicator to the 0-1 interval, the influence of dimensional differences on the calculation results can be effectively eliminated, ensuring the comparability and consistency of the results of different sand control methods in applicability and risk assessment. This provides a unified standardized input for the subsequent calculation of the Applicability Index (UI), Failure Risk Index (FRI), and Comprehensive Suitability Index (CSI).

[0080] Table 3 Normalization formulas and reference ranges for various sand control indices

[0081]

[0082] In step S1, the critical sand production index B is a unit for judging the ease of sand production from a reservoir, expressed in MPa; the larger the value, the less likely the reservoir is to produce sand. The calculation method is as follows:

[0083] (4)

[0084] In the formula, E d The elastic modulus, measured in MPa and μ, is obtained from rock acoustic logging and density logging. d Poisson's ratio, obtained from rock acoustic logging and density logging, is dimensionless.

[0085] The distance W between the water layer and the edge of the interlayer is a unit for judging the extent of reservoir fracturing operations, expressed in meters (m). A larger value indicates a greater distance between the reservoir and the water layer, allowing for larger-scale fracturing operations. The calculation method is as follows:

[0086] (5)

[0087] In the formula, H 油 H represents the bottom depth of the reservoir, in meters (m). 水 The depth of the top of the water layer is in meters (m).

[0088] The reservoir particle migration capacity S is a dimensionless quantity used to determine the strength of reservoir particle migration; a larger value indicates that the reservoir is more prone to particle migration. The reservoir particle migration capacity S is related to the formation sand grain size, clay content, and sphericity, and is calculated as follows:

[0089] (6)

[0090] In the formula, d maxThe maximum grain size of the formation sand is in mm; d 50 G represents the particle size (mm) corresponding to a cumulative distribution ratio of 50% in the system. SR Y represents the reservoir clay content, dimensionless. d The sphericity of the formation sand is dimensionless.

[0091] The fluid production intensity Y is a dimensionless measure used to determine the production intensity of a reservoir during the oil and gas well production process, in meters. 3 / (d˙m); The larger the value, the greater the degree of particle migration that may occur in the reservoir due to high-intensity fluid production, and the more severe the sand production during the production process. The calculation method is as follows:

[0092] (7)

[0093] In the formula, Q is the volumetric fluid production at the bottom of the production well, in meters. 3 / s;h e The effective thickness of the reservoir is in meters (m).

[0094] The moisture content f w This refers to the proportion of water in formation fluids, reflecting the water content and water content in oil well produced fluids. It is dimensionless and calculated as follows:

[0095] (8)

[0096] In the formula, K ro K represents the relative permeability of the oil phase, which is dimensionless. rw μ represents the relative permeability of the aqueous phase, which is dimensionless. o Oil viscosity, mPa·s; μ w ρ is the viscosity of water, mPa·s.

[0097] The core function of the formation sand particle size uniformity coefficient U is to quantify the uniformity of formation sand particle size distribution. The higher the value, the better the compatibility between proppant and formation sand, reducing the risk of particle migration and pore blockage, and improving filling density. The calculation method is as follows:

[0098] (9)

[0099] In the formula, d 10 The particle size, in mm, corresponds to the cumulative distribution percentage of 10% in the system; d 60 The particle size, in mm, corresponds to the cumulative distribution ratio in the system reaching 60%.

[0100] The wellbore irregularity I r This parameter measures the degree to which the wellbore shape deviates from the designed wellbore diameter. It is used to evaluate the smoothness, roundness, and stability of the wellbore. It is dimensionless, and the calculation method is as follows:

[0101] (10)

[0102] In the formula, D m The measured average well diameter is in mm; D b The value is the drill bit diameter, in mm.

[0103] The basic data of sand control indicators were normalized using a variable normalization formula, and the results are shown in the table below. Normalization maps all parameters to the range of 0 to 1, eliminating dimensional differences and ensuring the comparability and consistency of various indicators in the comprehensive evaluation.

[0104] Table 4. Results of each sand control index after normalization.

[0105]

[0106] S2. Based on historical operation data and the sand control indicators screened in step S1, the main sand control indicators of different sand control methods are screened out, and the main sand control indicators of each screened sand control method are divided into positive indicators and negative indicators.

[0107] The sand control methods include: chemical sand fixation, chemical cementation of artificial well walls, independent mechanical screen sand control, expansion screen sand control, conventional extrusion filling sand control, high-speed water filling sand control, high-saturation extrusion filling, micro-saturation extrusion filling, dual-stage multi-stage plug extrusion filling, early screen circulation filling, late screen circulation filling, conventional fracturing filling sand control (not applicable to W<30m), micro-fracturing filling for increased production sand control (not applicable to W<3m), fiber composite screenless fracturing filling sand control (not applicable to W<30m), composite fracturing filling (not applicable to W<30m), circulating filling sand suppressant composite sand control, extrusion filling sand suppressant composite sand control, mechanical screen chemical composite sand control, mechanical screen artificial well wall composite sand control, multi-stage composite filling sand control, and integrated water control and sand control.

[0108] In step S2, the main sand control indicators for different sand control methods are screened out. During the screening process, W is a rigid constraint parameter in the application of the four sand control methods: conventional fracturing and filling sand control, micro-fracturing and filling production enhancement sand control, fiber composite screenless fracturing and filling sand control, and composite fracturing and filling. Therefore, the sand control indicator W is retained as the sand control indicator required for screening these four fracturing and filling sand control methods.

[0109] Specifically, in step S2, the data sequence corresponding to each sand control index selected in step S1 and the effective period t of each sand control method included in the historical operation data are calculated. fThe correlation coefficients between data sequences are used to screen out the main sand control indicators of different sand control methods, and the main sand control indicators of different sand control methods are classified into positive indicators or negative indicators. For each sand control method, the weight coefficients of positive and negative indicators are assigned according to the magnitude of the absolute value of the correlation coefficient.

[0110] Specifically, the effective period of the sand control is t f It refers to the cumulative production time from the completion of sand control measures and the resumption of production of oil and gas wells to the failure of the sand control system (or the sand concentration exceeds the acceptable economic / technical limit).

[0111] Step S2 is as follows:

[0112] S21. Obtain historical operation data corresponding to each sand control method, and use the Shapiro-Wilk normality test method to test the sand control index data sequence and sand control validity period data sequence contained therein. When the significance level α=0.05, if the p value of the corresponding data sequence is not less than 0.05, it is considered to follow a normal distribution; otherwise, it is considered not to follow a normal distribution.

[0113] S22. For data sets where both sand control indicators and sand control effectiveness period follow a normal distribution, calculate the relationship between each sand control indicator and sand control effectiveness period t after screening in step S1. f The Pearson correlation coefficient between them; for data sets where either the sand control index or the sand control effectiveness period does not satisfy a normal distribution, calculate the correlation coefficient between each sand control index and the sand control effectiveness period t. f Spearman's rank correlation coefficient between them;

[0114] The Pearson correlation coefficient r p , is used to measure the degree of linear correlation between two continuous variables, is dimensionless, and takes values ​​in the range [-1, 1]. Where, when r p A value greater than 0 indicates a positive correlation, when r p A value less than 0 indicates a negative correlation, when r < 0 p =0 indicates no linear correlation, when |r p |≈1 indicates a strong correlation, and when |r ≈1, it indicates a strong correlation. p |≈0 indicates a weak correlation, calculated as follows:

[0115] (11)

[0116] In the formula, X represents the value of the sand control index in the historical operation data, and Y represents the value of the sand control validity period in the historical operation data. This is the average value of all sand control indicators in this set of historical operation data. This is the average effective period of all sand control measures in this group of historical operational data.

[0117] The Spearman rank correlation coefficient r s , used to measure the degree of monotonic correlation between two variables, is a rank-based nonparametric correlation index (rank is a sequence number assigned to each data point after sorting a set of data from smallest to largest or largest to smallest; in this example, it is a sequence number assigned to each data point after sorting from smallest to largest). Its value is dimensionless and ranges from [-1, 1]. When r s A value greater than 0 indicates a positive correlation between the ranks of the two variables; when r... s A value less than 0 indicates a negative correlation between the levels; when r < 0, the correlation is negative. s =0 indicates that there is no monotonic correlation, when |r s | When | is close to 1, it indicates a strong monotonic relationship between the variables, while |r s | A value close to 0 indicates a weak monotonic relationship. The calculation method is as follows:

[0118] (12)

[0119] In the formula, R x R represents the level of sand control index in historical operation data. y This refers to the level of sand control effectiveness in historical operational data. This is the average value of all sand control index levels in this group of historical operation data. This is the average value of the effective sand control period levels for all data in this group from historical operational data.

[0120] For each sand control method, operational data from multiple wells in historical records were selected. This historical operational data includes the sand control evaluation index values ​​and the effective sand control period (t) for each well. f Using the sand control evaluation index of different wells under the same sand control method as one set of sample data, and the sand control effectiveness period of the corresponding well as another set of sample data, under the condition that the sample size meets the requirements of statistical analysis, correlation analysis is performed on the two sets of data to calculate the correlation coefficient between the sand control index and the sand control effectiveness period.

[0121] The process is entirely driven by historical data, and all correlation coefficients have passed the statistical significance test (t>t). 0.05 This ensures that the final weights can quantify the true impact of different indicators on sand control effectiveness.

[0122] The t-test statistic is used to determine whether the discovered association is statistically significant and not accidental. The calculation method is as follows:

[0123] (13)

[0124] In the formula, r is the value of the correlation coefficient, and n is the sample size.

[0125] S23. Determine whether the absolute value of the Pearson correlation coefficient or Spearman rank correlation coefficient calculated in step S22 meets the requirement of ≥0.2. Select the indicators that meet the above conditions as the main sand control indicators of the sand control method, and determine whether they are positive or negative indicators based on the value of the correlation coefficient.

[0126] A positive correlation coefficient indicates a positive indicator; a negative correlation coefficient indicates a negative indicator.

[0127] S24. For each sand control method, the positive or negative indicators are allocated proportionally according to the absolute value of the correlation coefficient, and then normalized so that the sum of the weight coefficients of all positive indicators and the sum of the weight coefficients of all negative indicators in each sand control method are 1.

[0128] The calculation results are shown in Table 5.

[0129] Table 5. Positive and negative indicators and their weighting coefficients for each sand control method

[0130]

[0131] S3. Based on the normalized results of the sand control index after normalization processing obtained in step S1, and based on the positive and negative indicators of each sand control method obtained in step S2, calculate the applicability index UI and failure risk index FRI of each sand control method. Based on the applicability index UI and failure risk index FRI, calculate the comprehensive fit index CSI, and select the sand control method with the highest comprehensive fit index CSI value as the optimal sand control method.

[0132] The applicability index UI is used to quantify the fit between well conditions and sand control methods. The more comprehensive the positive indicators, the higher the index value. It reflects the "feasibility advantage" of the sand control method under geological and engineering parameters and is an important basis for determining whether it is worth prioritizing. The calculation method is as follows:

[0133] (14)

[0134] In the formula, w i f is the weight coefficient of the i-th positive indicator, which is dimensionless; i Let be the normalized value of the i-th positive indicator, which is dimensionless.

[0135] The Failure Risk Index (FRI) measures the degree of risk of failure or diminished effectiveness of the sand control method under well conditions. The more prominent the negative indicators, the higher the index value. It reflects the potential adverse conditions of the method in practical applications and serves as an important reference for risk control and solution elimination. The calculation method is as follows:

[0136] (15)

[0137] In the formula, v j g is the weight coefficient of the j-th negative indicator, which is dimensionless; j Let be the normalized value of the j-th negative indicator, which is dimensionless.

[0138] The Comprehensive Suitability Index (CSI) is a comprehensive criterion formed on the basis of balancing applicability and risk. It considers both the matching degree between the method and the well condition, as well as potential risks. It is the core indicator for the selection and ranking of sand control methods and is directly used to guide on-site process selection and technical decisions. The calculation method is as follows:

[0139] (16)

[0140] In the formula, α and β are the weighting coefficients of the applicability index UI and the failure risk index FRI, respectively, which can be adjusted according to the actual sand control method selection requirements.

[0141] According to equations (14) to (16), the applicability index (UI), failure risk index (FRI), and comprehensive suitability index (CSI) of each sand control method are calculated respectively. The applicability index (UI) reflects the positive matching degree between the sand control method and the reservoir and production conditions; the failure risk index (FRI) characterizes the potential failure risk of the sand control method under current conditions; and the comprehensive suitability index (CSI) comprehensively considers the relationship between the two and is used to quantitatively evaluate the overall suitability of the sand control process. By ranking the comprehensive suitability index (CSI) values ​​of each sand control method, sand control methods with higher comprehensive suitability index (CSI) are given priority recommendation, thus achieving scientific screening and optimization of sand control processes.

[0142] When calculating formulas (14) to (16), the corresponding weight coefficients should be selected according to the characteristics of different sand control methods. All weight coefficients are normalized so that the sum of positive and negative weights is 1, thereby ensuring that the calculation results of each sand control method in applicability and risk assessment are comparable and scientific.

[0143] Specifically, based on normalized data and the weighting factors of positive and negative indicators for each sand control method, the Comprehensive Suitability Index (CSI) for each sand control method was calculated. The calculation results are shown in the table below, where α=β=1. By weighted summing of the positive and negative indicators for each sand control method, its overall adaptability and failure risk level under current reservoir conditions were obtained. The CSI integrates the results of the Applicability Index (UI) and the Failure Risk Index (FRI), providing a more comprehensive reflection of the overall suitability of sand control methods. By comparing these indices, the most suitable sand control technology can be selected more scientifically, providing strong protection for the production safety and long-term stability of oil wells.

[0144] Table 6 Calculation results for each sand control method

[0145]

[0146] According to the Comprehensive Suitability Index (CSI), the three sand control methods with the strongest adaptability are post-screen tube circulation filling, expanded screen tube sand control, and pre-screen tube circulation filling. Their CSI indices are 0.474, 0.361, and 0.240, respectively, indicating that these three sand control methods perform well in the comprehensive evaluation considering applicability and risk. Among them, post-screen tube circulation filling has the highest CSI. Therefore, based on these evaluation results, it is ultimately recommended to prioritize post-screen tube circulation filling for sand control. This scheme can provide a longer sand control effectiveness period and shows good stability and economy in practical applications.

Claims

1. A method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs, characterized in that, include: S1, Based on the Accessibility Index (PAI) i Screening sand control indicators during the sand control process and normalizing the basic data of each screened sand control indicator; S2. Based on historical operation data and the sand control indicators screened in step S1, the main sand control indicators of different sand control methods are screened out, and the main sand control indicators of each screened sand control method are divided into positive indicators and negative indicators. S3. Based on the normalized results of the sand control index after normalization processing obtained in step S1, and based on the positive and negative indicators of each sand control method obtained in step S2, calculate the applicability index UI and failure risk index FRI of each sand control method. Based on the applicability index UI and failure risk index FRI, calculate the comprehensive fit index CSI, and select the sand control method with the highest comprehensive fit index CSI value as the optimal sand control method. The availability index PAI mentioned in step S1 i To quantitatively reflect the overall availability of parameters in engineering applications by weighting and synthesizing acquisition path, acquisition cost, and acquisition stability factor, the calculation method is as follows: （1） Where i is the i-th sand control index, PAI i A is the availability index. i To obtain the path factor, B i To obtain the cost factor, C i To obtain the stability factor; The path factor A is obtained i It is used to characterize whether parameters can be obtained directly through conventional data processes, reflecting the complexity of the parameter acquisition path; The acquisition cost factor B i This is used to characterize whether acquiring parameters requires additional engineering work or increases economic and time costs, reflecting the engineering cost level of parameter acquisition. The calculation method is as follows: （2） In the formula, Additional engineering costs required to obtain sand control indicators; This represents the maximum acceptable additional cost among similar parameters. The acquisition of stability factor C i This method is used to characterize the data integrity and stability of parameters during multi-well, multi-time-period applications, reflecting the reliability of continuous parameter acquisition. The calculation method is as follows: （3） In the formula, The number of wells or time periods for which sand control indicators are missing in historical well data; This refers to the number of wells or time periods missing from historical well data; The suitability index UI mentioned in step S3 is used to quantify the compatibility between well conditions and sand control methods. The calculation method is as follows: （6） In the formula, w i f is the weight coefficient of the i-th positive indicator, which is dimensionless; i Let be the normalized value of the i-th positive indicator, which is dimensionless. The Failure Risk Index (FRI) is used to measure the risk of failure or diminished effectiveness of the sand control method under well conditions. The calculation method is as follows: （7） In the formula, v j g is the weight coefficient of the j-th negative indicator, which is dimensionless; j Let be the normalized value of the j-th negative indicator, which is dimensionless. The Comprehensive Suitability Index (CSI) is directly used to guide on-site process selection and technology decisions. The calculation method is as follows: （8） In the formula, UI is the suitability index, FRI is the failure risk index, and α and β are the weighting coefficients of the suitability index UI and the failure risk index FRI, respectively.

2. The method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs according to claim 1, characterized in that, Step S1 describes the screening of sand control indicators during the sand control process, using the PAI of the sand control indicators. i A value of at least 0.75 is used as a screening criterion.

3. The method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs according to claim 1, characterized in that, The sand control methods described in step S2 include: chemical sand fixation, chemical cementation of artificial well walls, independent mechanical screen sand control, expansion screen sand control, conventional extrusion filling sand control, high-speed water filling sand control, high-saturation extrusion filling, micro-saturation extrusion filling, dual-stage multi-stage plug extrusion filling, early screen circulation filling, late screen circulation filling, conventional fracturing filling sand control, micro-fracturing filling for increased production sand control, fiber composite screenless fracturing filling sand control, composite fracturing filling, circulating filling sand suppressant composite sand control, extrusion filling sand suppressant composite sand control, mechanical screen chemical composite sand control, mechanical screen artificial well wall composite sand control, multi-stage composite filling sand control, and integrated water control and sand control.

4. The method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs according to claim 1, characterized in that, Step S2 involves calculating the data sequences corresponding to each sand control index selected in step S1 from the historical operation data, and the effective period t of each sand control method included in the historical operation data. f The correlation coefficients between data sequences are used to screen out the main sand control indicators of different sand control methods, and the main sand control indicators of different sand control methods are classified into positive indicators or negative indicators. For each sand control method, the weight coefficients of positive and negative indicators are assigned according to the magnitude of the absolute value of the correlation coefficient.

5. The method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs according to claim 4, characterized in that, Step S2 is as follows: S21. Obtain historical operation data corresponding to each sand control method, and use the Shapiro-Wilk normality test method to test the sand control index data sequence and sand control validity period data sequence contained therein. When the significance level α=0.05, if the p value of the corresponding data sequence is not less than 0.05, it is considered to follow a normal distribution; otherwise, it is considered not to follow a normal distribution. S22. For data sets where both the sand control index and the sand control validity period follow a normal distribution, calculate the sand control index and the sand control validity period t after screening in step S1. f The Pearson correlation coefficient between them; for data sets where either the sand control index or the sand control effectiveness period does not satisfy a normal distribution, calculate the correlation coefficient between each sand control index and the sand control effectiveness period t. f Spearman's rank correlation coefficient between them; S23. Determine whether the absolute value of the Pearson correlation coefficient or Spearman rank correlation coefficient calculated in step S22 is ≥0.

2. Select the indicators that meet the above conditions as the main sand control indicators of the sand control method, and determine whether they are positive or negative indicators based on the value of the correlation coefficient. A positive correlation coefficient indicates a positive indicator; a negative correlation coefficient indicates a negative indicator. S24. For each sand control method, the positive or negative indicators are allocated proportionally according to the absolute value of the correlation coefficient, and then normalized so that the sum of the weight coefficients of all positive indicators and the sum of the weight coefficients of all negative indicators in each sand control method are 1.

6. The method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs according to claim 5, characterized in that, Step S22 is calculated according to the following formula: The Pearson correlation coefficient r p It is used to measure the degree of linear correlation between two continuous variables, is dimensionless, and is calculated as follows: （4） In the formula, X represents the value of the sand control index in the historical operation data, and Y represents the value of the sand control validity period in the historical operation data. This is the average value of all sand control indicators in this set of historical operation data. This is the average effective period of all sand control measures in this group of historical operational data; The Spearman rank correlation coefficient r s The ranking system is used to measure the degree of monotonic correlation between two variables. It is a nonparametric correlation index based on ranking. The ranking refers to the ordinal number assigned to each data point after sorting a set of data from smallest to largest or largest to smallest. The calculation method is as follows: （5） In the formula, R x R represents the level of sand control index in historical operation data. y This refers to the level of sand control effectiveness in historical operational data. This is the average value of all sand control index levels in this group of historical operation data. This is the average value of the effective sand control period levels for all data in this group from historical operational data.

7. The method for selecting sand control methods for oil and gas wells in loose sandstone reservoirs according to claim 1, characterized in that, 0＜α≤1，0＜β≤1。

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