Method for determining boundary value of main control factors for well selection and layer selection of sidetrack horizontal well

Abstract

The invention relates to the technical field of water drive oilfield development and adjustment, in particular to a sidetrack horizontal well selection and layer selection main control factor threshold value determination method, which comprises the steps of considering m sidetrack effect influence factors, determining the change range of each influence factor, determining b main control factors according to a global sensitivity analysis method, k values of each main control factor of which the threshold value needs to be determined are taken in a change range, and b * k * n schemes are designed by using an orthogonal test; calculating the ratio of cumulative oil increase in different schemes to limit cumulative oil increase; calculating probability cumulative distribution of cumulative oil increase / limit cumulative oil increase; determining all main control factor Ab threshold values under different feasibility probability conditions; and all main control factor boundaries of sidetracking well selection and layer selection are determined. According to the method for determining the boundary value of the main control factors for well selection and layer selection of the sidetrack horizontal well, the main control factors influencing the sidetrack drilling effect serve as entry points, numerical simulation and an uncertainty analysis method are comprehensively applied, parameter boundaries are clearly evaluated, well selection and layer selection standards of classified reservoirs are established, and basis and guidance are provided for well selection and layer selection of sidetrack drilling of an oil field.

Description

Technical Field

[0001] The present invention relates to the technical field of waterflooding oilfield development adjustment, and particularly relates to a method for determining the boundary values of the main control factors for well and layer selection of sidetrack horizontal wells. Background Art

[0002] The horizontal sidetracking technology can achieve real-time tracking and adjustment during drilling and long-distance horizontal drilling, and can locate layers and directions to tap various types of remaining oil such as fault blockage and incomplete local well patterns. However, there are problems in the application of the sidetracking technology, such as large differences in the effects of the implemented wells and difficulties in well and layer selection. The main reasons are as follows: First, the horizontal sidetracking technology with MWD logging while drilling is a new technology in China, and there is still a blank in reservoir well and layer selection; second, the sidetracking effect is affected by multiple factors such as formation pressure and remaining oil saturation, and the correlation between each single factor and the production increase effect of the sidetracked well is poor, making it difficult to determine the boundaries of well and layer selection; third, the length of the horizontal section of the sidetracked well affects both the production increase effect and economic benefits of the sidetracking, and comprehensive consideration is required. The effects of some sidetracked wells are poor, and it is urgent to determine the boundaries of the main control factors affecting the sidetracking effect, establish well and layer selection criteria for classified reservoirs, and guide on-site well and layer selection for sidetracking. Summary of the Invention

[0003] (1) Technical Problems to be Solved

[0004] The present invention provides a method for determining the boundary values of the main control factors for well and layer selection of sidetrack horizontal wells to overcome the problem in the prior art that it is difficult to determine the boundaries of well and layer selection, resulting in large differences in the effects of sidetrack horizontal wells.

[0005] (2) Technical Solutions

[0006] To solve the above problems, the present invention provides a method for determining the boundary values of the main control factors for well and layer selection of sidetrack horizontal wells:

[0007] Step S1: Consider a total of m influencing factors of the sidetracking effect, clarify the change range of each influencing factor, and according to the global sensitivity analysis method, determine that the main control factors are b. Take k values for each main control factor whose boundary values need to be determined within the change range, and use the orthogonal experimental design to design b×k×n schemes;

[0008] Step S2: Calculate the ratio of the cumulative incremental oil of different schemes to the ultimate cumulative incremental oil;

[0009] Step S3: Calculate the probability cumulative distribution of the cumulative incremental oil / ultimate cumulative incremental oil;

[0010] Step S4: Determine the boundary values of all main control factors A under different feasibility probability conditions b Boundary values;

[0011] Step S5: Determine the boundaries of all main control factors for well and layer selection of sidetracking.

[0012] Preferably, the specific steps for using the orthogonal experimental design to generate b×k×n solutions in step S1 include:

[0013]

[0014]

[0015] L nm : The value of the nth solution for the mth influencing factor; A bk : The kth value of the bth main control factor.

[0016] Preferably, step S2 specifically includes:

[0017]

[0018] When the main control factor A1 takes A 11 , the result is {x 111 x 112 x 113 x 114 …x 11n}

[0019] When the main control factor A1 takes A 12 , the result is {x 121 x 122 x 123 x 124 …x 12n}

[0020] …

[0021] When the main control factor A1 takes A 1k , the result is {x 1k1 x 1k2 x 1k3 x 1k4 …x 1kn}

[0022] When the main control factor A2 takes A 21 , the result is {x 211 x 212 x 213 x 214 …x 21n}

[0023] When the main control factor A2 takes A 22 , the result is {x 221 x 222 x 223 x 224 …x 22n}

[0024] …

[0025] The main control factor A2 takes A 2k The result is {x 2k1 x 2k2 x 2k3 x 2k4 …x 2kn}

[0026] …

[0027] The main control factor takes A b Take A b1 The result is {x b11 x b12 x b13 x b14 …x b1n}

[0028] The main control factor takes A b Take A b2 The result is {x b21 x b22 x b23 x b24 …x b2n}

[0029] …

[0030] The main control factor takes A b Take A bk The result is {x bk1 x bk2 x bk3 x bk4 …x bkn}

[0031] Among them:

[0032] A b : Represents the bth main control factor;

[0033] Q bkn : The main control factor A b When the value is A bk The incremental oil production value of the nth plan;

[0034] Q' bkn : The main control factor A b When the value is A bk The ultimate incremental oil production value of the nth plan;

[0035] x bkn : The main control factor A b When the value is A bk The x value of the nth plan.

[0036] Preferably, the step S3 specifically includes:

[0037]

[0038]

[0039]

[0040] When the main control factor A b Take A bk At this time, according to the array composed of x obtained in step 2 bkn According to the above three formulas, calculate the probability cumulative distribution Φ corresponding to x bkn ; bkn ;

[0041] When the main control factor A1 takes A 11 The result is {Φ 111 Φ 112 Φ 113 Φ 114 …Φ 11n}

[0042] When the main control factor A1 takes A 12 The result is {Φ 121 Φ 122 Φ 123 Φ 124 …Φ 12n}

[0043] …

[0044] When the main control factor A1 takes A 1k The result is {Φ 1k1 Φ 1k2 Φ 1k3 Φ 1k4 …Φ 1kn}

[0045] When the main control factor A2 takes A 21 The result is {Φ 211 Φ 212 Φ 213 Φ 214 …Φ 21n}

[0046] When the main control factor A2 takes A 22 The result is {Φ 221 Φ 222 Φ 223 Φ 224 …Φ 22n}

[0047] …

[0048] When the main control factor A2 takes A 2k The result is {Φ 2k1 Φ 2k2 Φ 2k3 Φ 2k4…Φ 2kn}

[0049] …

[0050] The main control factor is taken as A b Take A b1 The result is {Φ b11 Φ b12 Φ b13 Φ b14 …Φ b1n}

[0051] The main control factor is taken as A b Take A b2 The result is {Φ b21 Φ b22 Φ b23 Φ b24 …Φ b2n}

[0052] …

[0053] Main control factor A b Take A bk The result is {Φ bk1 Φ bk2 Φ bk3 Φ bk4 …Φ bkn}

[0054] Wherein:

[0055] Φ bkn : The main control factor A b Take A bk When, the probability cumulative distribution of the nth scheme;

[0056] μ bk : The main control factor A b Take A bk When, x bkn The arithmetic mean of the distribution;

[0057] σ bk : The main control factor A b Take A bk When, x bkn The standard deviation of the distribution;

[0058] n: The main control factor A b Take A bk When, x bkn The number of values.

[0059] Preferably, the step S4 specifically includes:

[0060] Draw the cumulative probability distribution diagram of the main control factor incremental oil / ultimate incremental oil value, and arrange the values in the x bkn The values in the array are arranged in ascending order, and use points (xbkn , Φ bkn ) Plot curve graphs, with one graph for each main control factor, a total of b graphs, and each graph includes k curves.

[0061] The main control factor A1 takes A 11 {Points (x 111 , Φ 111 ) Points (x 112 , Φ 112 ) … Points (x 11n , Φ 11n )}

[0062] The main control factor A1 takes A 12 {Points (x 121 , Φ 121 ) Points (x 122 , Φ 122 ) … Points (x 12n , Φ 12n )}

[0063] …

[0064] The main control factor A1 takes A 1k {Points (x 1k1 , Φ 1k1 ) Points (x 1k2 , Φ 1k2 ) … Points (x 1kn , Φ 1kn )}

[0065] The main control factor A2 takes A 21 {Points (x 211 , Φ 211 ) Points (x 212 , Φ 212 ) … Points (x 21n , Φ 21n )}

[0066] The main control factor A2 takes A 22 {Points (x 221 , Φ 221 ) Points (x 222 , Φ 222 ) … Points (x 22n , Φ 22n )}

[0067] …

[0068] The main control factor A2 takes A 2k {Points (x 2k1 , Φ 2k1 ) Points (x 2k2 , Φ 2k2 ) … Points (x 2kn , Φ 2kn )}

[0069] …

[0070] Main control factor A b Take A b11 {Point (x b11 , Φ b11 ). Point (x b12 , Φ b12 ). … Point (x b1n , Φ b1n )}

[0071] Main control factor A b Take A b12 {Point (x b21 , Φ b21 ). Point (x b22 , Φ b22 ). … Point (x b2n , Φ b2n )}

[0072] …

[0073] Main control factor A b Take A b1k {Point (x bk1 , Φ bk1 ). Point (x bk2 , Φ bk2 ). … Point (x bkn , Φ bkn )}

[0074] In the cumulative incremental oil / ultimate cumulative incremental oil probability distribution diagram of the main control factor A b , determine the A values of the curves where the points (x = 1, Φ = 0.1), (x = 1, Φ = 0.2), (x = 1, Φ = 0.3), (x = 1, Φ = 0.4), (x = 1, Φ = 0.5) are located, and obtain the boundary values A' of the main control factor A bk under different probabilities b ; (X = 1 is the economic break-even point) 0.1 b A' 0.2 b A' 0.3 b A' 0.4 b A' 0.5 b ; (X = 1 is the economic break-even point)

[0075] p = 1 - Φ (Formula 4)

[0076] p: Economic feasibility probability

[0077] A' 0.1 b: The cumulative probability distribution of x ≤ 1 is 0.1, that is, when the economic feasibility is 90%, the main control factor A b Boundary value;

[0078] A' 0.2 b : The cumulative probability distribution of x ≤ 1 is 0.2, that is, when the economic feasibility is 80%, the main control factor A b Boundary value;

[0079] A' 0.3 b : The cumulative probability distribution of x ≤ 1 is 0.3, that is, when the economic feasibility is 70%, the main control factor A b Boundary value;

[0080] A' 0.4 b : The cumulative probability distribution of x ≤ 1 is 0.4, that is, when the economic feasibility is 60%, the main control factor A b Boundary value;

[0081] A' 0.5 b : The cumulative probability distribution of x ≤ 1 is 0.5, that is, when the economic feasibility is 50%, the main control factor A b Boundary value;

[0082] Using the chart, determine the boundary values of all main control factors A1, A2, A3... A b at different feasibility probabilities.

[0083] Preferably, the step S5 specifically includes:

[0084] According to the boundary values A' 0.1 1, A' 0.1 2, A' 0.1 3... A' 0.1 b , in the following schemes, remove the schemes that do not meet the conditions, and according to step S3, determine the cumulative probability distribution of incremental oil / ultimate incremental oil and the feasibility probability p' 0.1 :

[0085]

[0086]

[0087] Similarly, calculate the feasibility probability p' 0.2 , p' 0.3 , p’ 0.4 , p’ 0.5 ;

[0088] p' 0.1: The multi - factor feasibility probability when the single - factor feasibility probability is 90%;

[0089] p' 0.2 : The multi - factor feasibility probability when the single - factor feasibility probability is 80%;

[0090] p' 0.3 : The multi - factor feasibility probability when the single - factor feasibility probability is 70%;

[0091] p' 0.4 : The multi - factor feasibility probability when the single - factor feasibility probability is 60%;

[0092] p' 0.5 : The multi - factor feasibility probability when the single - factor feasibility probability is 50%.

[0093] Taking the single - factor feasibility probability as the abscissa and the multi - factor feasibility probability as the ordinate, draw a curve. The specific point coordinates are as follows:

[0094] {Point (90%, p' 0.1 ), Point (80%, p' 0.2 ), (70%, p' 0.3 ), (60%, p' 0.4 ), (50%, p' 0.5 )}

[0095] Among p' 0.1 , p' 0.2 , p' 0.3 , p' 0.4 , p' 0.5 : Take the abscissa value where the multi - factor feasibility probability ≥ 90% and the increase of the multi - factor feasibility probability becomes gentle as the single - factor feasibility probability increases as the final single - factor feasibility probability, so as to determine the boundaries of all main control factors for sidetracking well selection and layer selection.

[0096] (3) Beneficial effects

[0097] The present invention provides a method for determining the boundary values of the main control factors for sidetracking horizontal well selection and layer selection. This method takes the main control factors affecting the sidetracking effect as the starting point, comprehensively applies numerical simulation and uncertainty analysis methods, clarifies the evaluation parameter boundaries, and establishes the criteria for well selection and layer selection for classified reservoirs, providing a basis and guidance for oilfield sidetracking well selection and layer selection. Brief description of the drawings

[0098] Figure 1 Is the cumulative distribution of the incremental oil / ultimate incremental oil value under different sand body widths;

[0099] Figure 2 Is the cumulative distribution of the incremental oil / ultimate incremental oil value under different effective thicknesses;

[0100] Figure 3 is the cumulative distribution of the incremental oil / ultimate incremental oil values under different formations;

[0101] Figure 4 is the comparison of the cumulative distributions between single - boundary - value control and multi - boundary - value control;

[0102] Figure 5 is the probability relationship between single - boundary - value control and multi - boundary - value control;

[0103] Figure 6 is the schematic flow chart of the method for determining the boundary values of the main control factors for well and layer selection in sidetracking horizontal wells in the embodiments of the present invention. Detailed implementation mode

[0104] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0105] As Figure 6 shown, the present invention provides a method for determining the boundary values of the main control factors for well and layer selection in sidetracking horizontal wells, including the following steps:

[0106] Step S1: Consider m influencing factors affecting the sidetracking effect, clarify the change ranges of each influencing factor, determine b main control factors according to the global sensitivity analysis method, take k values for each main control factor whose boundary values need to be determined within the change range, and use the orthogonal experimental design to generate b×k×n schemes;

[0107] Step S2: Calculate the ratio of the incremental oil to the ultimate incremental oil for different schemes;

[0108] Step S3: Calculate the probability cumulative distribution of incremental oil / ultimate incremental oil;

[0109] Step S4: Determine the boundary values of all main control factors A under different feasibility probability conditions b Boundary values;

[0110] Step S5: Determine the boundary values of all main control factors for well and layer selection in sidetracking.

[0111] Preferably, the use of orthogonal experimental design to generate b×k×n schemes in step S1 specifically includes:

[0112]

[0113]

[0114] L nm: Value of the nth solution for the mth influencing factor; A bk : Value of the kth for the bth main control factor.

[0115] Preferably, the step S2 specifically includes:

[0116]

[0117] When the main control factor A1 takes A 11 the result is {x 111 x 112 x 113 x 114 …x 11n}

[0118] When the main control factor A1 takes A 12 the result is {x 121 x 122 x 123 x 124 …x 12n}

[0119] …

[0120] When the main control factor A1 takes A 1k the result is {x 1k1 x 1k2 x 1k3 x 1k4 …x 1kn}

[0121] When the main control factor A2 takes A 21 the result is {x 211 x 212 x 213 x 214 …x 21n}

[0122] When the main control factor A2 takes A 22 the result is {x 221 x 222 x 223 x 224 …x 22n}

[0123] …

[0124] When the main control factor A2 takes A 2k the result is {x 2k1 x 2k2 x 2k3 x 2k4 …x 2kn}

[0125] …

[0126] When the main control factor takes Ab Take A b1 The result is {x b11 x b12 x b13 x b14 …x b1n}

[0127] The main control factor takes A b Take A b2 The result is {x b21 x b22 x b23 x b24 …x b2n}

[0128] …

[0129] The main control factor takes A b Take A bk The result is {x bk1 x bk2 x bk3 x bk4 …x bkn}

[0130] Among them:

[0131] A b : Represents the bth main control factor;

[0132] Q bkn : The main control factor A b The value is A bk When, the incremental oil value of the nth scheme;

[0133] Q' bkn : The main control factor A b The value is A bk When, the ultimate incremental oil value of the nth scheme;

[0134] x bkn : The main control factor A b The value is A bk When, the x value of the nth scheme.

[0135] Preferably, the step S3 specifically includes:

[0136]

[0137]

[0138]

[0139] When the main control factor A b Take A bk When, according to the x obtained from step 2 bknAn array is formed, and according to the above three formulas, x is calculated. bkn The corresponding probability cumulative distribution Φ bkn ;

[0140] When the main control factor A1 takes A 11 the result is {Φ 111 Φ 112 Φ 113 Φ 114 …Φ 11n}

[0141] When the main control factor A1 takes A 12 the result is {Φ 121 Φ 122 Φ 123 Φ 124 …Φ 12n}

[0142] …

[0143] When the main control factor A1 takes A 1k the result is {Φ 1k1 Φ 1k2 Φ 1k3 Φ 1k4 …Φ 1kn}

[0144] When the main control factor A2 takes A 21 the result is {Φ 211 Φ 212 Φ 213 Φ 214 …Φ 21n}

[0145] When the main control factor A2 takes A 22 the result is {Φ 221 Φ 222 Φ 223 Φ 224 …Φ 22n}

[0146] …

[0147] When the main control factor A2 takes A 2k the result is {Φ 2k1 Φ 2k2 Φ 2k3 Φ 2k4 …Φ 2kn}

[0148] …

[0149] When the main control factor takes A b takes A b1 the result is {Φ b11 Φ b12 Φ b13 Φ b14…Φ b1n}

[0150] The main control factor is taken as A b Take A b2 When the result is {Φ b21 Φ b22 Φ b23 Φ b24 …Φ b2n}

[0151] …

[0152] Main control factor A b Take A bk When the result is {Φ bk1 Φ bk2 Φ bk3 Φ bk4 …Φ bkn}

[0153] Wherein:

[0154] Φ bkn : The main control factor A b Take A bk When, the cumulative probability distribution of the nth scheme;

[0155] μ bk : The main control factor A b Take A bk When, x bkn The arithmetic mean of the distribution;

[0156] σ bk : The main control factor A b Take A bk When, x bkn The standard deviation of the distribution;

[0157] n: The main control factor A b Take A bk When, x bkn The number of values.

[0158] Preferably, the step S4 specifically includes:

[0159] Draw the cumulative probability distribution diagram of the main control factor incremental oil / ultimate incremental oil value, arrange the values in the x bkn array in ascending order, and use the points (x bkn , Φ bkn ) to draw a curve diagram. There is one diagram for each main control factor, a total of b diagrams, and each diagram includes k curves.

[0160] The main control factor A1 takes A 11 {Points (x 111 , Φ 111 ) Points (x 112 , Φ112 )… point (x 11n , Φ 11n )}

[0161] The main control factor A1 takes A 12 {point (x 121 , Φ 121 ) point (x 122 , Φ 122 )… point (x 12n , Φ 12n )}

[0162] …

[0163] The main control factor A1 takes A 1k {point (x 1k1 , Φ 1k1 ) point (x 1k2 , Φ 1k2 )… point (x 1kn , Φ 1kn )}

[0164] The main control factor A2 takes A 21 {point (x 211 , Φ 211 ) point (x 212 , Φ 212 )… point (x 21n , Φ 21n )}

[0165] The main control factor A2 takes A 22 {point (x 221 , Φ 221 ) point (x 222 , Φ 222 )… point (x 22n , Φ 22n )}

[0166] …

[0167] The main control factor A2 takes A 2k {point (x 2k1 , Φ 2k1 ) point (x 2k2 , Φ 2k2 )… point (x 2kn , Φ 2kn )}

[0168] …

[0169] The main control factor A b takes A b11 {point (x b11 , Φ b11 ) point (x b12 , Φ b12 )… point (x b1n , Φb1n )}

[0170] Main control factor A b Take A b12 {Point (x b21 , Φ b21 ) Point (x b22 , Φ b22 ) … Point (x b2n , Φ b2n )}

[0171] …

[0172] Main control factor A b Take A b1k {Point (x bk1 , Φ bk1 ) Point (x bk2 , Φ bk2 ) … Point (x bkn , Φ bkn )}

[0173] In the cumulative probability distribution diagram of incremental oil / ultimate incremental oil of the main control factor A, determine the A values of the curves where the points (x = 1, Φ = 0.1), (x = 1, Φ = 0.2), (x = 1, Φ = 0.3), (x = 1, Φ = 0.4), (x = 1, Φ = 0.5) are located, and obtain the boundary values A' of the main control factor A under different probabilities b , A' bk , A' b , A' 0.1 b , A' 0.2 b , A' 0.3 b , A' 0.4 b , A' 0.5 b ; (X = 1 is the economic equilibrium point)

[0174] p = 1 - Φ (Formula 4)

[0175] p: Economic feasibility probability;

[0176] A' 0.1 b : When the cumulative probability distribution of x ≤ 1 is 0.1, that is, when the economic feasibility is 90%, the boundary value of the main control factor A b ;

[0177] A' 0.2 b : When the cumulative probability distribution of x ≤ 1 is 0.2, that is, when the economic feasibility is 80%, the boundary value of the main control factor A b ;

[0178] ​​​​​​​​​​​​A' 0.3 b : When the cumulative probability distribution of x ≤ 1 is 0.3, that is, when the economic feasibility is 70%, the boundary value of the main control factor A b ;

[0179] A' 0.4 b : When the cumulative probability distribution of x ≤ 1 is 0.4, that is, when the economic feasibility is 60%, the boundary value of the main control factor A b ;

[0180] A' 0.5 b : When the cumulative probability distribution of x ≤ 1 is 0.5, that is, when the economic feasibility is 50%, the boundary value of the main control factor A b ;

[0181] Use the chart to determine the boundary values of all main control factors A1, A2, A3... A b at different feasibility probabilities.

[0182] Preferably, the step S5 specifically includes:

[0183] According to the boundary values A' 0.1 1, A' 0.1 2, A' 0.1 3... A' 0.1 b , in the following schemes, remove the schemes that do not meet the conditions, and according to step S3, determine the cumulative probability distribution of incremental oil / ultimate incremental oil and the feasibility probability p' 0.1 :

[0184]

[0185]

[0186] Similarly, calculate the feasibility probability p' 0.2 , p' 0.3 , p’ 0.4 , p’ 0.5 ;

[0187] p' 0.1 : When the single-factor feasibility probability is 90%, the multi-factor feasibility probability;

[0188] p' 0.2 : When the single-factor feasibility probability is 80%, the multi-factor feasibility probability;

[0189] p' 0.3 : When the single-factor feasibility probability is 70%, the multi-factor feasibility probability;

[0190] p'0.4 : When the single - factor feasibility probability is 60%, the multi - factor feasibility probability;

[0191] p' 0.5 : When the single - factor feasibility probability is 50%, the multi - factor feasibility probability.

[0192] Taking the single - factor feasibility probability as the abscissa and the multi - factor feasibility probability as the ordinate, draw a curve. The specific point coordinates are as follows:

[0193] {Point (90%, p' 0.1 ), Point (80%, p' 0.2 ), (70%, p' 0.3 ), (60%, p' 0.4 ), (50%, p' 0.5 )}

[0194] Among p' 0.1 , p' 0.2 , p' 0.3 , p' 0.4 , p' 0.5 , take the multi - factor feasibility probability ≥ 90%, and the abscissa value where the multi - factor feasibility probability increases gently as the single - factor feasibility probability increases as the final single - factor feasibility probability, so as to determine the boundaries of all main control factors for sidetracking well and layer selection.

[0195] The following describes the specific steps of the present invention in detail in combination with the actual object:

[0196] Step S1: Consider a total of m influencing factors on the sidetracking effect, clarify the change range of each influencing factor. According to the global sensitivity analysis method, determine that the main control factors are b. Take k values for each main control factor whose boundary value needs to be determined within the change range, and use the orthogonal experiment design to design b×k×n schemes.

[0197] Taking the narrow - channel sand in the Putaohua oil layer with production but no injection as the object of sidetracking well, determine that the main control factors are the width of the channel sand body, the effective thickness, and the formation pressure before sidetracking. According to the reservoir geology, dynamic and other data, determine the influencing factors and their change ranges, as shown in Table 1.

[0198] Table 1 Influencing factors and their parameter change ranges

[0199] Parameter Numerical range Width of channel sand body (m) 80-400 Formation pressure (MPa) 5-15 Effective thickness (m) 1.5-5 Length of horizontal section (m) 100-200 Residual oil saturation (%) 0.5-0.7 Porosity (%) 0.15-0.25 Permeability (mD) 30-300

[0200] Using the orthogonal experiment design method, a total of 1872 schemes are designed. The specific parameter values of some schemes are shown in Table 2, Table 3, and Table 4.

[0201] Table 2 Specific parameter table of sidetracking well schemes for the boundary analysis of the width of the channel sand body

[0202]

[0203]

[0204] Table 3 Specific Parameter Table of Sidetracking Plan for Effective Thickness Boundary Analysis

[0205]

[0206]

[0207] Table 4 Specific Parameter Table of Sidetracking Plan for Formation Pressure Boundary Analysis

[0208]

[0209]

[0210] Step S2: Calculate the ratio of incremental oil to ultimate incremental oil for different plans.

[0211] For each sidetracking plan, due to different horizontal section lengths and different sidetracking costs, the input cost of each sidetracking plan is calculated according to the market price standard of horizontal sidetracking well engineering. Under the condition of an oil price of 50 $ / bbl and an input-output ratio of 1:1, the ultimate incremental oil of each plan is calculated. Using numerical simulation, the sidetracking production increase effect of the plan is predicted, and the ratio of incremental oil to ultimate incremental oil is calculated, as shown in Tables 5, 6, and 7.

[0212] Table 5 Calculation Results of Sidetracking Plan for Sand Body Width Boundary Analysis

[0213]

[0214]

[0215] Table 6 Calculation Results of Sidetracking Plan for Effective Thickness Boundary Analysis

[0216]

[0217]

[0218] Table 7 Calculation Results of Sidetracking Plan for Formation Pressure Boundary Analysis

[0219]

[0220]

[0221] Step S3: Calculate the probability cumulative distribution of incremental oil / ultimate incremental oil.

[0222] According to the calculation results in Table 5, six arrays of the incremental oil / ultimate incremental oil values are obtained when the sand body widths are 80m, 120m, 170m, 220m, 300m, and 350m respectively. According to Formula 2 and Formula 3, the arithmetic mean and standard deviation of each array are obtained, and the incremental oil / ultimate incremental oil value x is calculated according to Formula 1. kn The corresponding cumulative distribution value Φ kn Similarly, according to Table 6 and Table 7, when the effective thickness and formation pressure are different, the arrays, their arithmetic means, standard deviations, and cumulative distribution values are calculated, and the results are shown in Table 8 and Table 9.

[0223] Table 8 Arithmetic means and standard deviations of incremental oil / ultimate incremental oil under different main controlling factors

[0224]

[0225]

[0226] Table 9 Probability cumulative distribution of incremental oil and ultimate incremental oil for different main controlling factor schemes

[0227]

[0228] Step S4: Determine all main controlling factors A under different feasibility probability conditions b Boundary values.

[0229] Using the (X n , Φ n ) values in Table 9, draw the cumulative distribution chart of incremental oil / ultimate incremental oil. First, the X n values calculated for the scheme with a sand body width of 80m are arranged in ascending order. Taking X n as the horizontal axis and Φ n as the vertical axis, draw a curve. Similarly, draw the remaining curves and establish a chart, such as Figure 1 , Figure 2 , Figure 3 , to determine the boundary values of the main controlling factors under different feasibility probabilities, as shown in Table 10.

[0230] Table 10 Boundary values of main controlling factors under different feasibility probabilities

[0231]

[0232]

[0233] Step S5: Determine the boundaries of all main controlling factors for sidetracking well selection and layer selection.

[0234] According to the sand body width, effective thickness, and formation pressure limit value in Table 10, under the joint limitation of the three main control factor limits, the plans that do not meet the conditions are removed. According to the method in Step 3, recalculate the probability cumulative distribution of incremental oil / ultimate incremental oil under the limitation of the three main control factor limits, and draw the probability relationship diagram of single limit value control and multi-limit value control. See Figure 5 。

[0235] When the feasibility probability under the single factor limit value reaches 70%, under the limitation of the three main control factor limits, the feasibility probability increases by 93%. See Figure 4 。From Figure 5 It can be seen that as the single factor feasibility probability value increases, the increase in the multi-factor feasibility probability slows down. Therefore, determine the limits for well and layer selection for sidetracking in the narrow channel sand of the Putaohua oil layer without energy replenishment: the developed width of the channel sand body ≥ 170 m, the effective thickness ≥ 2.5 m, and the formation pressure before sidetracking 7.7 MPa.

[0236] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those of ordinary skill in the relevant technical field can also make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention.

Claims

1. A method for determining the boundary values of the main controlling factors for well and layer selection in sidetracking horizontal wells, characterized in that: Step S1: Consider m influencing factors affecting the sidetracking effect, clarify the variation ranges of each influencing factor, determine b main controlling factors according to the global sensitivity analysis method, take k values for each main controlling factor whose boundary values need to be determined within the variation range, and use the orthogonal experimental design to design b×k×n schemes; Step S2: Calculate the ratio of the cumulative incremental oil of different schemes to the ultimate cumulative incremental oil; Step S3: Calculate the probability cumulative distribution of cumulative incremental oil / ultimate cumulative incremental oil; Step S4: Determine all main control factors A under different feasible probability conditions b Boundary values Step S5: Determine the boundary values of all main controlling factors for well and layer selection in sidetracking.

2. The method for determining the boundary value of the main control factors for well and layer selection in sidetrack horizontal wells based on data analysis according to claim 1, characterized in that The specific steps of using the orthogonal experimental design to design b×k×n schemes in Step S1 include: A bk L n2 L n3 L n4 … L nm Wherein: L nm : The value of the nth solution for the mth influencing factor; A bk : The k-th value of the b-th main control factor.

3. The method for determining the boundary value of the main controlling factors for well and layer selection in sidetracking horizontal wells based on data analysis according to claim 2, wherein, The specific steps of Step S2 include: The main control factor A1 takes A 11 The result is {x 111 x 112 x 113 x 114 … x 11n} The main control factor A1 takes A 12 The result is {x 121 x 122 x 123 x 124 … x 12n} … The main control factor A1 takes A 1k The result is {x 1k1 x 1k2 x 1k3 x 1k4 … x 1kn} The main control factor A2 takes A 21 The result is {x 211 x 212 x 213 x 214 … x 21n} The main control factor A2 takes A 22 The result is {x 221 x 222 x 223 x 224 … x 22n} … The main control factor A2 takes A 2k The result is {x 2k1 x 2k2 x 2k3 x 2k4 … x 2kn} … The main control factor is taken as A b Take A b1 The result is {x b11 x b12 x b13 x b14 … x b1n} The main control factor is taken as A b Take A b2 The result is {x b21 x b22 x b23 x b24 … x b2n} … The main control factor takes A b Take A bk The result is {x bk1 x bk2 x bk3 x bk4 … x bkn} Wherein: A b : represents the b-th main control factor; Q bkn : Master control factor A b When the value is A bk the incremental oil value of the nth plan; Q' bkn : Main control factor A b When the value is A bk the ultimate cumulative incremental oil value of the nth plan; x bkn : Master control factor A b The value is A bk When, the x value of the nth solution 4. The method for determining the boundary value of the main controlling factors for well and layer selection in sidetrack horizontal wells based on data analysis according to claim 3, characterized in that, The specific steps of Step S3 include: When the main control factor A b Take A bk At this time, according to the array composed of x bkn obtained in step 2, according to the above three formulas, calculate the probability cumulative distribution Φ bkn corresponding to x bkn ; The main control factor A1 takes A 11 The result is {Φ 111 Φ 112 Φ 113 Φ 114 … Φ 11n} The main control factor A1 takes A 12 The result is {Φ 121 Φ 122 Φ 123 Φ 124 … Φ 12n} … The main control factor A1 takes A 1k The result is {Φ 1k1 Φ 1k2 Φ 1k3 Φ 1k4 … Φ 1kn} The main control factor A2 takes A 21 The result is {Φ 211 Φ 212 Φ 213 Φ 214 … Φ 21n} The main control factor A2 takes A 22 The result is {Φ 221 Φ 222 Φ 223 Φ 224 … Φ 22n} … The main control factor A2 takes A 2k The result is {Φ 2k1 Φ 2k2 Φ 2k3 Φ 2k4 … Φ 2kn} … The main control factor takes A b Take A b1 The result is {Φ b11 Φ b12 Φ b13 Φ b14 … Φ b1n} The main control factor is taken as A b Take A b2 The result is {Φ b21 Φ b22 Φ b23 Φ b24 … Φ b2n} … Main control factor A b Take A bk The result is {Φ bk1 Φ bk2 Φ bk3 Φ bk4 … Φ bkn} Wherein: Φ bkn : Main control factor A b Take A bk At this time, the probability cumulative distribution of the nth scheme; μ bk : Main control factor A b Take A bk When, x bkn Arithmetic mean of the distribution; σ bk : The main control factor A b Take A bk When, x bkn The standard deviation of the distribution; n: Main control factor A b Take A bk When bkn The number of values of x 5. The method for determining the boundary value of the main controlling factors for well and layer selection in sidetracking horizontal wells based on data analysis according to claim 4, characterized in that The specific steps of Step S4 include: Draw the cumulative probability distribution graph of the incremental oil value and the ultimate incremental oil value of the main control factors, and arrange the values in the x bkn array in ascending order. Use the points (x bkn , Φ bkn ) to draw a curve graph. There is one graph for each main control factor, with a total of b graphs, and each graph includes k curves. The main control factor A1 takes A 11 {point (x 111 , Φ 111 ), point (x 112 , Φ 112 ), …, point (x 11n , Φ 11n )} The main control factor A1 takes A 12 {point (x 121 , Φ 121 ) point (x 122 , Φ 122 )... point (x 12n , Φ 12n )} … The main control factor A1 takes A 1k {point (x 1k1 , Φ 1k1 ) point (x 1k2 , Φ 1k2 )... point (x 1kn , Φ 1kn )} The main control factor A2 takes A 21 {point (x 211 , Φ 211 ), point (x 212 , Φ 212 ), …, point (x 21n , Φ 21n )} The main control factor A2 takes A 22 {point (x 221 , Φ 221 ) point (x 222 , Φ 222 )… point (x 22n , Φ 22n )} … The main control factor A2 takes A 2k {point (x 2k1 , Φ 2k1 ) point (x 2k2 , Φ 2k2 )… point (x 2kn , Φ 2kn )} … Master control factor A b Take A b11 {Point (x b11 , Φ b11 ) Point (x b12 , Φ b12 )…Point (x b1n , Φ b1n )} Master control factor A b Take A b12 {Point (x b21 , Φ b21 ) Point (x b22 , Φ b22 )… Point (x b2n , Φ b2n )} … Master control factor A b Take A b1k {Point (x bk1 , Φ bk1 ), Point (x bk2 , Φ bk2 ), …, Point (x bkn , Φ bkn )} In the cumulative probability distribution diagram of the incremental oil / ultimate incremental oil of the main control factor A b Determine the A value of the curve where the points (x = 1, Φ = 0.1), (x = 1, Φ = 0.2), (x = 1, Φ = 0.3), (x = 1, Φ = 0.4), and (x = 1, Φ = 0.5) are located, and obtain the boundary value A' of the main control factor A under different probabilities bk b 0.1 b A' 0.2 b A' 0.3 b A' 0.4 b A' 0.5 b ; (X = 1 is the economic equilibrium point)​​ p = 1 - Φ (Formula 4) p: Economic feasibility probability; A' 0.1 b : The cumulative probability distribution for x ≤ 1 is 0.1, i.e., when the economic feasibility is 90%, the threshold value of the main control factor A b ; A' 0.2 b : When the cumulative probability distribution of x ≤ 1 is 0.2, that is, when the economic feasibility is 80%, the main control factor A b 's threshold value; A' 0.3 b : When the cumulative probability distribution of x ≤ 1 is 0.3, that is, when the economic feasibility is 70%, the boundary value of the main control factor A b ; A' 0.4 b : When the cumulative probability distribution of x ≤ 1 is 0.4, that is, when the economic feasibility is 60%, the boundary value of the main control factor A b ; A' 0.5 b : When the cumulative probability distribution of x ≤ 1 is 0.5, that is, when the economic feasibility is 50%, the boundary value of the main control factor A b ; Using the chart, determine all the main control factors A1, A2, A3... A b Boundary values at different feasibility probabilities.

6. The method for determining the boundary values of the main control factors for well and layer selection in sidetracking horizontal wells based on data analysis according to claim 5, characterized in that, The steps of Step S5 include: According to the boundary value A' 0.1 1. A' 0.1 2. A' 0.1 3... A' 0.1 b , in the following solutions, eliminate the solutions that do not meet the conditions, and according to step S3, determine the probability cumulative distribution of the cumulative incremental oil / ultimate cumulative incremental oil and the feasibility probability p' 0.1 : … A' 0.1 2L n2 L n3 L n4 …L nm … Similarly, under the restriction of the multi-factor limit value, the feasibility probability p' 0.2 , p' 0.3 , p′ 0.4 , p′ 0.5 ; p' 0.1 : The multi-factor feasibility probability when the single-factor feasibility probability is 90%; p' 0.2 : The multi-factor feasibility probability when the single-factor feasibility probability is 80%; p' 0.3 : The multi-factor feasibility probability when the single-factor feasibility probability is 70%; p' 0.4 : The multi-factor feasibility probability when the single-factor feasibility probability is 60%; p' 0.5 : The multi-factor feasibility probability when the single-factor feasibility probability is 50%. Taking the single-factor feasibility probability as the abscissa and the multi-factor feasibility probability as the ordinate, draw a curve, and the specific point coordinates are as follows: {Point (90%, p' 0.1 ), Point (80%, p' 0.2 ), (70%, p' 0.3 ), (60%, p' 0.4 ), (50%, p' 0.5 )} At p' 0.1 、p' 0.2 、p' 0.3 、p' 0.4 、p' 0.5 Among them, the abscissa value at which the multi-factor feasibility probability is ≥90% and the increase in the multi-factor feasibility probability becomes gentle as the single-factor feasibility probability increases is taken as the final single-factor feasibility probability, so as to determine the boundaries of all main control factors for sidetracking well and layer selection.

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