A multi-factor decision intelligent comprehensive well selection and layer selection decision method

Abstract

The present application relates to a kind of multi-factor decision intelligent comprehensive well selection decision-making method, well selection decision-making method comprehensively uses water absorption capacity decision, oil layer heterogeneity decision, injection dynamic decision and multi-well group resistance and capacitance model well selection decision-making, obtains comprehensive decision factor, selects the well of comprehensive decision factor greater than average to carry out profile control and driving measure;Layer selection decision-making method, comprehensively uses water absorption profile decision, small layer PI decision, small layer tracer decision and resistance and capacitance model advantage channel identification method, etc., select the small layer of decision factor above average, the small layer of decision factor value maximum is optimized.The present application considers resistance and capacitance model well selection or layer selection decision-making method, identifies interwell connectivity coefficient and average permeability, the oil-water well connectivity coefficient of each small layer, realizes well layer optimization;Multi-parameter fuzzy evaluation model decision-making technology is used to form multi-factor comprehensive decision-making method, eliminates the interference that the reliability of each method used data is not completely same, makes profile control well selection more scientific and reasonable.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas field development, and particularly relates to a multi-factor decision intelligent comprehensive well selection and layer selection decision method. BACKGROUND

[0002] At present, when injection wells for profile control and flooding are selected, various well selection factors are affected, through investigation of the influencing factors, factors affecting profile control and flooding are classified and arranged, which generally include the water well pressure drop curve after the injection well is closed, the apparent water injection index per meter in the injection profile of the injection well, the calculated formation permeability variation coefficient and water absorption variation coefficient, the water cut, the remaining reserves of the well group and the remaining oil recovery degree of the formation, etc. The above factors respectively represent the heterogeneity of the production layer, the water absorption capacity of the injection well and the corresponding oil well dynamic situation.

[0003] In view of the complexity of well selection and layer selection, a multi-parameter fuzzy evaluation model decision technology is used to form a multi-factor comprehensive decision method, which can eliminate the interference of the incomplete reliability of the data used by each method, so that the profile control well is selected scientifically and reasonably. However, there is no complete well selection and layer selection method at home and abroad at present. SUMMARY

[0004] The problem to be solved by the present application is to provide a well selection and layer selection decision method; in particular, a multi-factor comprehensive decision method is used to consider various factors comprehensively, so as to scientifically and reasonably select a profile control well. A multi-factor decision intelligent comprehensive well selection and layer selection decision method.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is: a multi-factor decision intelligent comprehensive well selection and layer selection decision method, characterized by comprising the following steps,

[0006] S1: obtaining target well layer screening use data;

[0007] S2: calculating a multi-factor well selection decision factor according to the data;

[0008] S3: calculating a comprehensive decision factor according to the multi-factor well selection decision factor, and selecting a well with a comprehensive decision factor greater than an average value for profile control and flooding measures;

[0009] S4: calculating a layer selection decision value of a to-be-evaluated injection well, and selecting a small layer with the layer selection decision value above the average value and the largest value for optimization.

[0010] Further, the S1 comprises the following steps,

[0011] S11: collecting reservoir geological information of a target oil field or a target block;

[0012] S12: establishing single well information of the target block;

[0013] S13: Establishing the single-well sublayer information;

[0014] S14: Establishing the block well group information, inputting the daily and monthly production data of water wells and oil wells, and inputting the design parameters of adjacent wells;

[0015] S15: Carrying out the profile control and flooding construction on the well group, inputting the profile control conditions of the well group in the system for years, and simulating the formation geology by the system to form new geological conditions.

[0016] Further, the S2 comprises the following steps,

[0017] S21: Calculating the water absorption capacity decision factor;

[0018] S22: Calculating the oil layer heterogeneity decision factor;

[0019] S23: Calculating the injection dynamic decision factor;

[0020] S24: Calculating the multi-well group resistance model well selection decision factor.

[0021] Further, the S21 comprises the following steps,

[0022] S211: After data processing, obtaining the pressure drop curve of the water injection well, the water absorption index per meter, and the apparent water absorption index per meter;

[0023] S212: After normalization processing of the pressure drop curve, the water absorption index per meter, and the apparent water absorption index per meter, performing weight scoring;

[0024] S213: Calculating the specific value of the water absorption capacity decision factor of the water injection well;

[0025] S214: For the well whose specific value of the water absorption capacity decision factor of the water injection well is greater than the average value, performing profile control and flooding measures.

[0026] Further, the S22 comprises the following steps,

[0027] S221: After data processing, obtaining the permeability variation coefficient of the oil layer and the water absorption variation coefficient of the water injection well;

[0028] S222: After normalization processing of the permeability variation coefficient and the water absorption variation coefficient, performing weight scoring;

[0029] S223: Calculating the specific value of the oil layer heterogeneity decision factor;

[0030] S224: For the oil well with greater data in the calculation result of the oil layer heterogeneity, performing operation.

[0031] Further, the S23 comprises the following steps,

[0032] S231: After data processing, the daily fluid production, the average water cut, the remaining reserves and the recovery degree of the oil well corresponding to the injection well are obtained;

[0033] S232: After normalization processing, the daily fluid production, the average water cut, the remaining reserves and the recovery degree are weighted and scored;

[0034] S233: The specific value of the injection dynamic decision factor is calculated;

[0035] S234: The oil well with greater data in the specific value of the injection dynamic decision factor is operated.

[0036] Further, the S24 comprises the following steps,

[0037] S241: Based on the established water drive resistance model, the interwell connectivity coefficient and the average permeability are inverted;

[0038] S242: An advantage channel identification model is established to make intelligent well selection and layer selection decisions.

[0039] Further, the S3 comprises the following steps,

[0040] S31: The injection dynamic decision factor, the oil layer heterogeneity decision factor, the injection dynamic decision factor and the multi-well group resistance model well selection decision factor are normalized and matrix processed;

[0041] S32: The comprehensive decision factor is obtained;

[0042] S33: The well with a comprehensive decision factor greater than the average value is subjected to profile control and flooding measures.

[0043] Further, the S4 comprises the following steps,

[0044] S41: A water absorption profile decision method is used to directly calculate the water absorption intensity of each small layer, obtain a water absorption profile decision factor, and select a small layer with a water absorption intensity greater than the average value as a profile control and flooding preferred small layer;

[0045] S42: A small layer PI decision method is used to provide small layer pressure drop data, obtain a small layer PI decision factor, and select a small layer by PI decision;

[0046] S43: A tracer decision method is used to calculate the tracer concentration variation coefficient and the tracer concentration variation coefficient of the water injection well small layer by using tracer test data, obtain a tracer decision factor, and select a layer with a tracer decision factor greater than the average value to realize small layer optimization;

[0047] S44: Using the multi-well group capacity resistance model decision method, the oil-water well connectivity coefficients of each small layer are identified, the multi-well group capacity resistance model decision factor is obtained, the layer position with the connectivity coefficient greater than the average value is selected, and the small layer optimization is realized.

[0048] Further, S45, the water absorption profile decision factor, the PI decision factor, the tracer concentration variation coefficient decision factor and the capacity resistance model dominant channel identification decision factor are normalized, respectively, to obtain a matrix, and then weighted scoring is performed to calculate a multi-factor fuzzy decision factor.

[0049] The present application has the advantages and positive effects that:

[0050] 1. In the well and layer selection decision of the present application, the capacity resistance model well selection and layer selection decision method is considered, the interwell connectivity coefficient and the average permeability, the oil-water well connectivity coefficient of each small layer are identified, and the well and layer optimization is realized.

[0051] 2. The multi-parameter fuzzy evaluation model decision technology is used to form a multi-factor comprehensive decision method, various factors are considered comprehensively, the interference of the reliability of the data used by each method is eliminated, and the profile control well is selected scientifically and reasonably. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is the overall flowchart of the embodiment of the present application.

[0053] Figure 2 is the connectivity coefficient field map of the embodiment NmIV of the present application. DETAILED DESCRIPTION

[0054] The technical solutions of the present application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0055] The embodiments of the present application will be further described in combination with the drawings:

[0056] As shown in Figure 1 A multi-factor decision intelligent comprehensive well and layer selection decision method includes the following steps.

[0057] S1: Obtain the data used for target well layer screening, wherein the data types include: small layer permeability, oil well production dynamic data, water well production dynamic data, oil-water well production test data, and the oil-water well production test data includes wellhead pressure drop test, water absorption profile, liquid production profile, water absorption index test, etc.

[0058] Specifically, S1 includes the following steps,

[0059] S11: Collecting reservoir geological information of the target oilfield or target block, and specific parameters include reservoir depth, reservoir area, oil layer pressure, geothermal gradient, formation temperature, formation water PH value, formation water salinity, crude oil volume coefficient, surface crude oil density, injection well control area, whether there are natural fractures, whether there are bottom water, well spacing and row spacing, etc.

[0060] S12: Establishing single well information of the target block, and specific parameters include production layer of each well, geological reserves, well radius, water viscosity, crude oil viscosity, original saturation pressure, permeability, effective porosity, effective thickness, surface crude oil density, original volume coefficient, formation irreducible water saturation, etc.

[0061] S13: Establishing single well layer information, and specific parameters include layer number, porosity, permeability, oil saturation, layer top depth, layer bottom depth, oil layer thickness, oil phase permeability, water phase permeability, etc.

[0062] S14: Establishing block well group information, inputting production daily report and monthly report data of water wells and oil wells, inputting adjacent well design parameters, and specific parameters include injection pump displacement, injection pressure gradient, tubing diameter, injection well control radius, borehole radius, residual resistance coefficient, adjacent well plugging rate, etc. Interwell data of well group oil wells are inputted, and parameters include oil viscosity, water viscosity, oil phase permeability, actual recovery rate, formation pore throat radius standard deviation, etc., which reflect interlayer heterogeneity.

[0063] S15: Carrying out profile adjustment and flooding construction on the well group, inputting profile adjustment situation of the well group in previous years into the system, and the system simulates the formation geology to form new geological conditions.

[0064] S2: Calculating multi-factor well selection decision factors according to the data, and S2 includes the following steps.

[0065] S21: Calculating water absorption capacity decision factors, and S21 includes the following steps.

[0066] S211: Assuming that m wells are to be diagnosed whether profile adjustment is needed, and after data processing, the decision factors of injection well pressure drop curve IPI, per meter water absorption index Kh, and per meter apparent water absorption index Ks are obtained.

[0067] S212: After normalization processing of the water absorption capacity index matrix:

[0068]

[0069] S213: The weights of Ks, Kh, and IPI are scored as A1=[a1, a2, a3], and the water absorption capacity evaluation result is B1=A1xCij.

[0070] S214: After the calculation of the above formula, the specific value of the water injection well's water absorption capacity decision factor is obtained, and the wells with water absorption capacity decision factor greater than the average value are subjected to profile control and flooding measures.

[0071] Specifically, PI decision can be obtained by PI parameter calculation decision and pressure drop curve decision.

[0072] The data part of the PI parameter calculation method comes from injection performance and pressure drop curve data, and formation and fluid physical property parameters also affect well selection, so these related parameters are needed to make decisions.

[0073] The calculation formula of the parameter calculation method is:

[0074]

[0075] Among them:

[0076] q is the daily injection rate of the injection well, unit: m 3 / d; μ is the dynamic viscosity of the fluid, unit: m Pa·s; K is the formation permeability, unit: μm 2 ; h is the formation thickness, unit: m; re is the injection well control radius, unit: m; φ is the porosity, unit: %; C is the comprehensive compressibility, unit: Pa -1 ; t is the shut-in time, unit: s; pm is the wellhead pressure when the formation starts to absorb water, unit: MPa.

[0077] Before decision, the related parameters such as dynamic viscosity of fluid, injection well control radius, porosity, and comprehensive compressibility are needed, the formation permeability and formation thickness come from layer data, the daily injection rate of injection well is taken from injection performance data, the shut-in time and the wellhead pressure when the formation starts to absorb water come from pressure drop curve data. In order to compare the PI values of injection wells with each other, the PI values of each injection well should be changed to the same condition.

[0078]

[0079] According to the formula, the wells with PI value less than the critical value of the decision factor are selected for profile control.

[0080] The data part of the wellhead pressure drop decision comes from the pressure drop curve data.

[0081] The calculation formula is:

[0082]

[0083]

[0084] According to the formula, the wells with PI value less than the critical value of the decision factor are selected for profile control.

[0085] In order to eliminate the influence of the reservoir pressure relief ability on the pressure drop curve, and reflect the dynamic change of the reservoir permeability before and after the formation of the dominant channel, the following dimensionless PI value is proposed:

[0086]

[0087] DPI = PI / (k * h)

[0088] k i is the original permeability of the reservoir, with the unit of μm 2 ;

[0089] h is the effective thickness of the reservoir, with the unit of m;

[0090] q is the injection rate of the injection well, with the unit of m 3 / d;

[0091] μ is the viscosity of the fluid in the reservoir, with the unit of mPa·s;

[0092] PI is the PI value, with the unit of MPa.

[0093] Therefore, after calculating the size of the PI value according to the formula, the dimensionless PI value can be calculated by the above formula. It can be obtained that:

[0094]

[0095] There are two key technologies in the profile control technology: one is the discrimination of the profile control sufficiency, and the other is the establishment of the plugging agent series.

[0096] There are two standards for the discrimination of the profile control sufficiency: after profile control, the injection pressure of the injection well is greatly improved under the injection allocation condition; after profile control, the fullness calculated from the wellhead pressure drop curve of the injection well falls within the range of 0.7-0.95.

[0097]

[0098] FD is the fullness; P0 is the injection pressure of the injection well before shut-in, with the unit of MPa.

[0099] The apparent injectivity index per meter is calculated from the injectivity index data.

[0100] The apparent injectivity index per meter of the i th well is

[0101]

[0102] wherein:

[0103] qi is the i th injection rate or the water absorption rate, with the unit of m 3 ;

[0104] h is the thickness of the layer of the i th well, and the unit is m;

[0105] P1 is the wellhead injection pressure, and the unit is Mpa;

[0106] J1i is the apparent water injection index per meter of the i th well.

[0107] The water injection index per meter is calculated from the production data:

[0108] The water injection index per meter of the i th well is

[0109]

[0110] Wherein:

[0111] qi is the injection amount and water absorption amount of the i th well, and the unit is m 3 ;

[0112] h is the thickness of the layer of the i th well, and the unit is m;

[0113] P2 is the flowing pressure, and the unit is Mpa;

[0114] J2i is the water injection index per meter of the i th well.

[0115] S22: calculating the reservoir heterogeneity decision factor, S22 includes the following steps,

[0116] S221: after data processing, the permeability variation coefficient Kv of the reservoir and the water absorption variation coefficient Wv of the injection well are obtained.

[0117] S222: the permeability variation coefficient Kv and the water absorption variation coefficient Wv matrix are normalized,

[0118]

[0119] S223: scoring the weight of Kv and Wv, A2=[a1,a2], and the calculation result of the reservoir heterogeneity is B2=A2xCij.

[0120] S224: the oil well with larger data in the calculation result of the reservoir heterogeneity is operated.

[0121] Specifically, the permeability variation coefficient calculation process is as follows,

[0122] The average permeability Ki and the variation coefficient Vi of the i th well are calculated by the following formula:

[0123]

[0124]

[0125] The membership degree of the i th well Vi is:

[0126]

[0127] For the profiled injection well, the water absorption profile is used to calculate the water absorption variation coefficient, and the water absorption or injection percentage of the i th well and the j th layer is calculated by the following formula:

[0128]

[0129] In the formula, Wij is the water absorption or injection percentage of the i th well and the j th layer; qwij is the water absorption or injection of the i th well and the j th layer.

[0130] The average water absorption percentage of the i th well is:

[0131]

[0132] In the formula, is the average water absorption percentage of the i th well; n is the number of layers of the i th well.

[0133] The water absorption variation coefficient of the i th well is:

[0134]

[0135] S23: calculating the injection dynamic decision factor, S23 includes the following steps,

[0136] S231: assuming that m wells participate in decision-making, through preprocessing, the daily liquid production R, the average water cut f, the remaining reserves N and the recovery degree R of the decision factor corresponding to each injection well and the oil well can be obtained.

[0137] S232: after the index matrix established by the production dynamic of the well group is normalized:

[0138]

[0139] S233: scoring the weight of Ql, f, Ns and R: A3 = [a1, a2, a3, a4], and the calculation result of the oil well connected with the injection well: B3 = A3 × Cij.

[0140] S234: the oil well with larger data in the calculation result of the production dynamic analysis of the oil well connected with the injection well is operated.

[0141] S24: calculating the multi-well group resistance model well selection decision factor, S24 includes the following steps.

[0142] S241: based on the established water drive resistance model, the interwell connectivity coefficient and the average permeability are inverted.

[0143] S242: Establishing the dominant channel identification model, making intelligent well selection and layer selection decisions, and outputting the well layer screening basis for profile control and flooding.

[0144] The above several decision-making methods consider single factors, and the reliability of the data used by each method is not completely the same, so multiple factors should be considered comprehensively to make scientific and reasonable selection of profile control wells.

[0145] S3: According to the multi-factor well selection decision factor, calculate the comprehensive decision factor, and select the well with a comprehensive decision factor greater than the average value for profile control and flooding measures; S3 includes the following steps,

[0146] S31: In the influencing factors of water injection well profile control, the value of each factor is different, and the value of the factor directly calculated according to the original value will have a great influence on the final decision. Therefore, before well selection and layer selection, the water absorption capacity decision factor, the oil layer heterogeneity decision factor, the injection dynamic decision factor, and the multi-well group resistance model well selection decision factor need to be normalized to reduce the influence caused by the different value ranges of different factors.

[0147] There are three forms of factor normalization: the larger the value, the better, select the ascending half trapezoidal distribution, the smaller the value, the smaller, select the descending half trapezoidal distribution, and the more moderate, the better, select the moderate distribution. By analyzing various factors affecting water injection well profile control, it can be found that, except for PI value and the producing degree of connected oil wells, which need to be processed by descending half trapezoidal, other influencing factors are processed by ascending half trapezoidal. The specific processing formula is as follows:

[0148] Ascending half trapezoidal distribution simplified formula:

[0149]

[0150] Descending half trapezoidal distribution simplified formula:

[0151]

[0152] In the formula, a1 is the minimum value of the single factor in all wells in the block, and a2 is the maximum value of the single factor in all wells in the block.

[0153] S32: The water absorption capacity well selection decision, the oil layer heterogeneity well selection decision, the production dynamic analysis of the oil well connected with the water injection well, and the resistance model dominant channel identification are weighted respectively, and the combined water absorption capacity, heterogeneity, surrounding oil well production dynamic, and dominant channel comprehensive decision FZ, i.e. multi-factor fuzzy decision factor, is calculated.

[0154] The multi-factor is comprehensively evaluated to obtain the fuzzy decision factor FZ of the selected profile control well.

[0155] FZ = A × F

[0156] Wherein A=(A1, A2, A3, A4) represents the weight of water injection well's water absorption capacity, reservoir heterogeneity, surrounding oil well production performance and advantage channel identification of capacity-resistance model.

[0157]

[0158] Wherein: i in F(i, j) represents the i-th factor, and j represents the j-th well. The calculation result obtained through the above calculation is the multi-factor comprehensive decision factor of each water injection well. The well with the multi-factor comprehensive decision factor greater than the average value is implemented profile control.

[0159] S33: The multi-factor decision result is as follows: the water absorption capacity decision factor, the reservoir heterogeneity decision factor, the oil well performance decision factor, the advantage channel identification decision factor of capacity-resistance model, and the multi-factor decision factor are outputted. The well with the multi-factor decision factor greater than the average value needs to be profiled.

[0160] S4: Calculate the selected layer decision value of the water injection well to be evaluated, and select the smallest layer with the largest value above the average value as the preferred layer. S4 includes the following steps,

[0161] S41: Since the water injection profile is a method that directly reflects the water absorption capacity of each layer, calculate the water injection intensity of each layer, and select the layer with the largest water injection intensity as the profiled and driven preferred layer.

[0162] Input the water injection profile data of the well group, and the input parameters are: layer number, relative suction amount, water injection thickness, injection pressure, etc. For profiled water injection wells, if the water injection profile is used for decision-making, the water injection intensity calculation formula of the i-th well in the j-th layer is:

[0163]

[0164] qwij is the water injection amount or injection amount of the i-th well in the j-th layer;

[0165] h is the thickness of the i-th well in the j-th layer.

[0166] According to the formula, select the layer with Kij greater than the average value for profile control, and the layer with the largest Kij value is preferred.

[0167] S42: Select the PI decision method of the layer, provide the pressure drop data of the layer, and select the layer by the PI decision, which is the same as the PI decision in the well selection decision. The input data include water well layer name, time, wellhead pressure, test time, etc. The calculation result can obtain PI value, corrected PI value, dimensionless PI value, and FD value.

[0168] S43: using the tracer concentration data and tracer velocity data of each layer of the corresponding oil well, calculating the tracer concentration variation coefficient and tracer velocity variation coefficient of the water well layer, and performing profile control and flooding measures on the layer with a decision factor greater than the average value, and optimizing the layer with the largest decision factor;

[0169] Input the tracer parameters of the water well and each layer of the corresponding oil well of the well group, the tracer injection concentration of each layer of the water well, the connection between each layer of the oil well and the water well, the tracer production concentration of each layer of the oil well, and the tracer velocity.

[0170] For the profile control injection well, tracer is injected in layers, and each layer of the oil well is developed to obtain the tracer concentration of each layer of the oil well. The tracer concentration percentage of the jth layer of the ith oil well, i.e. the recovery rate, is calculated as follows:

[0171]

[0172] In the formula, Coij is the tracer concentration of the jth layer of the ith well; Cwj is the tracer concentration of the jth layer of the water well.

[0173] The average tracer concentration percentage of the jth layer is:

[0174]

[0175] In the formula, is the average tracer concentration percentage of the jth layer; n is the number of oil wells in the jth layer;

[0176] Aij is the tracer concentration percentage of the jth layer of the ith oil well. The tracer concentration variation coefficient of the jth layer is:

[0177]

[0178] The decision factor of the jth layer is:

[0179]

[0180] In the formula, Fv(i) is the tracer concentration variation coefficient decision factor of the jth layer.

[0181] For the tracer decision-making process, not only the concentration variation coefficient of the tracer, but also the difference in tracer time of the oil well is considered, which is represented by the oil well tracer velocity variation coefficient.

[0182] The average oil well tracer velocity percentage of the jth layer is:

[0183]

[0184] In the formula, is the average oil well tracer velocity of the jth layer;

[0185] n is the number of oil wells in the jth layer;

[0186] Bij is the tracer velocity of the ith oil well in the jth layer.

[0187] The coefficient of variation of the tracer velocity of the oil wells in the jth layer is:

[0188]

[0189] where xi is the coefficient of variation of the tracer velocity of the jth layer.

[0190] The decision factor of the tracer velocity of the oil wells in the jth layer is:

[0191]

[0192] where Fx(i) is the decision factor of the tracer velocity of the jth layer.

[0193] The decision factor of the jth layer is:

[0194] F(i) = AFv(i) + BFx(i) where Fx(i) is the tracer decision factor of the jth layer.

[0195] A and B are weight values, which are derived from the overall properties of the block, A is 0.7 and B is 0.3.

[0196] According to the formula, the value of F(i) is calculated, and the wells with F(i) greater than the average value are selected for profile control.

[0197] S44: Select the advantage channel identification method of the capacity resistance model to identify the oil-water well connectivity coefficient of each small layer, select the layer with the largest connectivity coefficient, and realize the optimization of small layers, as shown in Figure 2 The method is the same as the well selection decision of the capacity resistance model of the multi-well group.

[0198] S45: The four decision factors of the water injection profile decision, i.e. water injection intensity, PI decision, tracer concentration coefficient decision, and capacity resistance model advantage channel identification decision, i.e. connectivity coefficient, are normalized to obtain the matrix:

[0199]

[0200] Wherein: F(i, m) indicates the ith factor and m indicates the mth well.

[0201] FZ = A x F

[0202] Where A = (A1, A2, A3, A4) represents the weight of the water injection profile decision, PI decision, tracer decision, and capacity resistance model advantage channel identification decision. The comprehensive decision FZ multi-factor fuzzy decision factor is calculated to determine whether adjustment is needed in each range, as shown in the following table,

[0203] Decision factor 0-0.5 0.5-0.7 0.7-1 Judgment No profile adjustment needed Profile adjustment possible Profile adjustment needed

[0204] The present application has the advantages and positive effects that:

[0205] 1. The present application considers the well and layer selection decision method of the capacity resistance model, identifies the interwell connectivity coefficient and average permeability, and the oil and water well connectivity coefficient of each small layer, and realizes the optimization of well and layer.

[0206] 2. The present application adopts the multi-parameter fuzzy evaluation model decision technology to form a multi-factor comprehensive decision method, comprehensively considers various factors, eliminates the interference of the incomplete reliability of the data used by each method, and makes the scientific and reasonable selection of the profile control well.

[0207] The above has carried out the detailed description to one embodiment of the present application, but the content described is only the preferred embodiment of the present application, and cannot be considered to limit the implementation range of the present application. Any equivalent change and improvement made according to the application range of the present application should still belong to the patent coverage range of the present application.

Claims

1. A multi-factor decision intelligent comprehensive well selection and layer selection decision method, characterized in that: It comprises the following steps, S1: obtaining target well layer screening data; S2: calculating multi-factor well selection decision factors according to the data, the S2 comprising the following steps, S21: calculating water absorption capacity decision factor; S22: calculating oil layer heterogeneity decision factor; S23: calculating injection dynamic decision factor; S24: calculating multi-well group capacity resistance model well selection decision factor; S3: calculating comprehensive decision factor according to the multi-factor well selection decision factor, and selecting wells with the comprehensive decision factor greater than the average value for profile control and flooding measures; S4: calculating selected layer decision value of the water injection well to be evaluated, and selecting the smallest layer with the selected layer decision value above the average value and the largest value for optimization, the S4 comprising the following steps, S41: directly calculating the water absorption intensity of each small layer by using the water absorption profile decision method to obtain the water absorption profile decision factor, and selecting the small layer with the water absorption intensity greater than the average value as the profile control and flooding optimized small layer; S42: providing small layer pressure drop data by using the small layer PI decision method to obtain the small layer PI decision factor, and selecting the small layer by the PI decision; S43: using the tracer decision method, calculating the tracer concentration variation coefficient and tracer velocity variation coefficient of the small layer of the water injection well by using the tracer test data to obtain the tracer decision factor, and selecting the layer with the tracer decision factor greater than the average value to realize the optimization of the small layer; S44: using the multi-well group capacity resistance model decision method to identify the oil-water well connectivity coefficient of each small layer to obtain the multi-well group capacity resistance model decision factor, and selecting the layer with the connectivity coefficient greater than the average value to realize the optimization of the small layer.

2. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1, characterized in that: The S1 comprises the following steps, S11: collecting reservoir geological information of the target oilfield or target block; S12: establishing single well information of the target block; S13: establishing small layer information of the single well; S14: establishing block well group information, inputting production daily and monthly report data of the water injection well and the oil well, and inputting adjacent well design parameters; S15: performing profile control and flooding construction on the well group, inputting profile control conditions of the well group in previous years into the system, and simulating the formation geology by the system to form new geological conditions.

3. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1 or 2, characterized in that: The S21 comprises the following steps, S211: obtaining the pressure drop curve, the water absorption index per meter, and the apparent water absorption index per meter of the water injection well after data processing; S212: performing weight scoring after normalizing the pressure drop curve, the water absorption index per meter, and the apparent water absorption index per meter; S213: calculating the specific value of the water absorption capacity decision factor of the water injection well; S214: performing profile control and flooding measures on the well with the specific value of the water absorption capacity decision factor of the water injection well greater than the average value.

4. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1 or 2, characterized in that: The S22 comprises the following steps, S221: obtaining the permeability variation coefficient of the oil layer and the water absorption variation coefficient of the water injection well after data processing; S222: performing weight scoring after normalizing the permeability variation coefficient and the water absorption variation coefficient; S223: calculating the specific value of the oil layer heterogeneity decision factor; S224: performing operation on the oil well with the greater data in the calculation result of the oil layer heterogeneity.

5. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1 or 2, characterized in that: The S23 comprises the following steps, S231: After data processing, the daily fluid production, average water cut, remaining reserves and recovery degree of the oil well corresponding to the water injection well are obtained; S232: After normalization processing, the daily fluid production, average water cut, remaining reserves and recovery degree are weighted and scored; S233: The specific value of the injection dynamic decision factor is calculated; S234: The oil well with greater data in the specific value of the injection dynamic decision factor is operated.

6. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1 or 2, characterized in that: The S24 includes the following steps, S241: Based on the established water drive resistance model, the interwell connectivity coefficient and average permeability are inverted; S242: The dominant channel identification model is established to make intelligent well selection and layer selection decisions.

7. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1 or 2, characterized in that: The S3 includes the following steps, S31: The calculated water injection capacity decision factor, the oil layer heterogeneity decision factor, the injection dynamic decision factor and the multi-well group resistance model well selection decision factor are normalized and matrix processed; S32: The comprehensive decision factor is calculated; S33: The wells with a comprehensive decision factor greater than the average value are profile adjusted and driven.

8. The multi-factor decision intelligent comprehensive well and layer selection decision method according to claim 1 or 2, characterized in that: It also includes S45, the water injection profile decision factor, the PI decision factor, the tracer decision factor and the resistance model decision factor are respectively normalized, the matrix is weighted and scored, and the multi-factor fuzzy decision factor is calculated.

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