A method and system for optimizing drilling wellbore trajectory in shale formations

Abstract

The present invention provides a method and system for optimizing the drilling trajectory of a shale formation, relating to the technical field of petroleum exploration and development. The method comprises: obtaining a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit of the shale formation as a first curve; obtaining a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation as a second curve; based on the first and second curves, obtaining a difference change between the potential benefit and the development cost; based on the difference change, determining the direction of the maximum difference change to optimize the drilling trajectory of the shale formation; wherein the direction of the maximum difference change is the optimal drilling trajectory. The technical solution provided by the present invention can ensure that the optimized drilling trajectory has the greatest development benefit.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration and development, and in particular to a method and system for optimizing shale formation drilling wellbore trajectory. Background Art

[0002] Sweet spots in shale formations are areas with good reservoir geology and amenability to fracturing. These sweet spots are crucial for shale development, as identifying them can help reduce shale exploration and development costs and increase the productivity of gas-bearing shale formations.

[0003] Existing technology, from an economic evaluation perspective, defines the "sweet spot" in shale formations as areas with favorable flow characteristics and reservoir properties, with a particular focus on the cost of fracturing engineering interventions. Existing technology suggests that the "sweet spot" in shale formations consists of two components: reservoir quality and completion quality. Good reservoir quality corresponds to favorable formation physical properties, high hydrocarbon abundance, and high organic matter content, while good completion quality corresponds to greater formation brittleness, greater fracturing potential, lower engineering intervention costs, and improved flow properties after fracturing.

[0004] Existing technologies also use reservoir quality parameters to evaluate the gas recoverability of shale formations. The usual practice is to use geological statistical methods to describe the "sweet spots": parameters such as brittle mineral content, porosity, thermal maturity, organic matter content, net thickness, burial depth, gas content and water saturation of shale formations. However, these parameters do not distinguish between geological sweet spot parameters and engineering sweet spot parameters, resulting in a single evaluation standard and inaccurate evaluation results.

[0005] In summary, the existing technology does not analyze the recoverability of the shale formation from the perspective of the overall development benefits of the shale formation, so that the obtained shale formation drilling wellbore trajectory does not have the maximum development benefits. Summary of the Invention

[0006] In response to the above-mentioned problems in the prior art, the present application proposes a method and system for optimizing the drilling wellbore trajectory in shale formations, which can maximize the development benefits of the optimized drilling wellbore trajectory.

[0007] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0008] In a first aspect, an embodiment of the present invention provides a method for optimizing a drilling trajectory in a shale formation, the method comprising:

[0009] Obtaining a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit of the shale formation as a first curve;

[0010] obtaining a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation as a second curve;

[0011] Obtaining a difference change between the potential benefit and the development cost based on the first curve and the second curve;

[0012] The azimuth of the maximum difference change is determined based on the difference change to optimize the drilling wellbore trajectory of the shale formation; wherein the azimuth of the maximum difference change is the optimal drilling wellbore trajectory.

[0013] Preferably, the first curve is obtained in the following manner:

[0014] Obtaining the geological sweet spot coefficient at each well in the shale formation and the initial gas production of each well;

[0015] Based on the geological sweet spot coefficient at each well and the initial gas production of each well, constructing a first regression equation reflecting the relationship between the geological sweet spot coefficient and the initial gas production;

[0016] The first curve is obtained based on the first regression equation and an existing expression reflecting the relationship between the initial gas production and the potential benefit.

[0017] Preferably, the first regression equation constructed is:

[0018]

[0019] Wherein, y1 is the initial gas production; X G is the geological sweet spot coefficient; a1 and b1 are constants determined by the regression algorithm;

[0020] The expression reflecting the relationship between the initial gas production and the potential benefit is:

[0021]

[0022] E0=y1·p

[0023] Wherein, E is the potential income; p is the oil and gas price; y1 is the initial gas production; d1 is the gas production attenuation rate; d2 is the discount rate; and m is the mining life.

[0024] Preferably, the geological sweet spot coefficient at each well in the shale formation is obtained in the following manner:

[0025] Obtain multiple main geological sweet spot parameters at the drilling location;

[0026] Obtaining the weight of each of the main geological sweet spot parameters;

[0027] The geological sweet spot coefficient at each well in the shale formation is obtained based on the value of each of the main geological sweet spot parameters and the weight of each of the main geological sweet spot parameters.

[0028] Preferably, the obtaining of a plurality of main geological sweet spot parameters at the drilling location includes:

[0029] Using well logging interpretation or experimental testing to obtain multiple geological parameters at the drilling site;

[0030] Calculating a correlation coefficient between each of the geological parameters and the initial gas production of the well as a first correlation coefficient;

[0031] Based on the absolute value of the first correlation coefficient, a geological parameter whose absolute value of the first correlation coefficient exceeds a first preset threshold is selected from the multiple geological parameters as the main geological sweet spot parameter.

[0032] Preferably, obtaining the weight of each of the main geological sweet spot parameters includes:

[0033] Calculating a complex correlation coefficient between each of the main geological sweet spot parameters and other parameters of the main geological sweet spot parameters except the main geological sweet spot parameter as a first complex correlation coefficient;

[0034] Based on the first complex correlation coefficient and the correlation coefficient between each of the main geological sweet spot parameters and the initial gas production, a weight of each of the main geological sweet spot parameters is obtained.

[0035] Preferably, the weight of each of the main geological sweet spot parameters is calculated using the following expression:

[0036]

[0037]

[0038] Among them, W i ' is the weight of the i-th main geological sweet spot parameter; n is the number of the main geological sweet spot parameters; R i is the correlation coefficient between the i-th main geological sweet spot parameter and the initial gas production; CR i is the complex correlation coefficient between the i-th main geological sweet spot parameter and the other parameters.

[0039] Preferably, the second curve is obtained in the following manner:

[0040] Obtaining the engineering sweet spot coefficient at each well in the shale formation and the development cost of each well;

[0041] constructing a second regression equation reflecting the relationship between the engineering sweet spot coefficient and the development cost based on the engineering sweet spot coefficient at each well and the development cost of each well;

[0042] Based on the second regression equation, the second curve is obtained.

[0043] Preferably, the second regression equation constructed is:

[0044] y2=a2+b2(c-dln(X E )) e

[0045] Wherein, y2 is the development cost; X E is the engineering sweet spot coefficient; a2 is the known drilling and completion cost; b2, c, d, and e are constants determined by the regression algorithm.

[0046] Preferably, the engineering sweet spot coefficient at each well in the shale formation is obtained in the following manner:

[0047] Obtain multiple main engineering sweet spot parameters at the drilling location;

[0048] Obtaining the weight of each of the main engineering sweet spot parameters;

[0049] The engineering sweet spot coefficient at each well in the shale formation is obtained based on the value of each main engineering sweet spot parameter and the weight of each main engineering sweet spot parameter.

[0050] Preferably, the step of obtaining a plurality of main engineering sweet spot parameters at the drilling location includes:

[0051] Using well logging interpretation or experimental testing to obtain multiple engineering parameters at the drilling site;

[0052] Calculating the correlation coefficient between each of the engineering parameters and the sand carrying ratio of the drilling well as a second correlation coefficient;

[0053] Based on the absolute value of the second correlation coefficient, an engineering parameter whose absolute value of the second correlation coefficient exceeds a second preset threshold is selected from the multiple engineering parameters as the main engineering sweet spot parameter.

[0054] Preferably, obtaining the weight of each of the main engineering sweet spot parameters includes:

[0055] Calculating a complex correlation coefficient between each of the main engineering sweet spot parameters and other parameters of the main engineering sweet spot parameters except the main engineering sweet spot parameter as a second complex correlation coefficient;

[0056] Based on the second complex correlation coefficient and the correlation coefficient between each of the main engineering sweet spot parameters and the sand carrying ratio, the weight of each of the main engineering sweet spot parameters is obtained.

[0057] In a second aspect, an embodiment of the present invention provides a system for optimizing a drilling trajectory in a shale formation, the system comprising:

[0058] a first curve acquisition module, configured to acquire a relationship curve between a geological sweet spot coefficient of the shale formation and a potential benefit of the shale formation as a first curve;

[0059] a second curve acquisition module, configured to acquire a relationship curve between an engineering sweet spot coefficient of the shale formation and a development cost of the shale formation as a second curve;

[0060] A difference variation acquisition module, configured to obtain a difference variation between the potential benefit and the development cost based on the first curve and the second curve;

[0061] An optimization module is used to determine the azimuth of the maximum difference change based on the difference change to optimize the drilling wellbore trajectory of the shale formation; wherein the azimuth of the maximum difference change is the optimal drilling wellbore trajectory.

[0062] In a third aspect, an embodiment of the present invention provides a storage medium having program code stored thereon. When the program code is executed by a processor, the method for optimizing the wellbore trajectory of shale formation drilling as described in any one of the above embodiments is implemented.

[0063] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, a method for optimizing the wellbore trajectory of shale formation drilling is implemented as described in any one of the above embodiments.

[0064] The embodiments of the present invention provide a method, device, storage medium, and electronic device for optimizing the drilling trajectory of a shale formation. The method obtains a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit as a first curve, obtains a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost as a second curve, and obtains the difference change between the potential benefit and the development cost based on the first and second curves. The orientation of the maximum difference change is determined based on the difference change to find the optimal drilling trajectory. It can be seen that compared with the prior art, the present invention comprehensively considers the impact of the geological sweet spot of the shale formation on the potential benefit and the impact of the engineering sweet spot on the development cost. The method of optimizing the drilling trajectory of the shale formation based on the difference change between the potential benefit and the development cost can analyze the recoverability of the formation from the perspective of the overall development benefit of the shale formation, so that the optimized drilling trajectory has the maximum development benefit. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The scope of the present invention can be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings, which include:

[0066] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0067] Figure 2 is a potential profit curve of a shale formation under different geological sweet spot coefficients in an embodiment of the present invention;

[0068] Figure 3 : is the development cost curve of the shale formation under different engineering sweet spot coefficients in the embodiment of the present invention;

[0069] Figure 4 Schematic diagram of the relationship between the potential revenue curve and the development cost curve of a shale formation in an embodiment of the present invention;

[0070] Figure 5 Graph showing the relationship between the geological sweet spot coefficient and the initial gas production in an embodiment of the present invention;

[0071] Figure 6 Schematic diagram of the available benefit sweet spots in an embodiment of the present invention;

[0072] Figure 7 2 is a system structure diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to make the objectives, technical solutions and advantages of the present invention clearer, the implementation method of the present invention will be described in detail below with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0074] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0075] Example 1

[0076] Key elements for the successful development of shale formations include drilling long horizontal well sections and large-scale fracturing. However, economic considerations are also crucial for shale gas development. Horizontal well drilling is primarily intended to increase the drainage area of ultra-low porosity and permeability shale formations. When employing large-scale fracturing, the engineering sweet spot parameters of the shale formation must be evaluated. A good engineering sweet spot is characterized by ease of fracturing construction and low fracturing costs. Determining the weights of the engineering sweet spot parameters facilitates the evaluation of each parameter's contribution to fracturing scale and cost. The sweet spot parameters of horizontal formations can be derived from seismic data interpretation. The potential revenue and development cost are further calculated using the aforementioned steps. The difference between the potential revenue and development cost yields the recoverable sweet spot of the shale formation. The area along the wellbore that varies along the wellbore is called the recoverable sweet spot. The drilling trajectory is optimized based on the formation's recoverable sweet spot. The optimal drilling trajectory is the direction that maximizes the recoverable sweet spot; drilling in this direction maximizes development efficiency and benefits.

[0077] Based on the above principles and concepts, the embodiment of the present invention provides a method for optimizing the wellbore trajectory of shale formation drilling, such as Figure 1 As shown, the method described in this embodiment includes:

[0078] Step S101, obtaining a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit of the shale formation as a first curve;

[0079] In this embodiment, the first curve is obtained in the following manner: obtaining the geological sweet spot coefficient at each well in the shale formation and the initial gas production of each well; constructing a first regression equation reflecting the relationship between the geological sweet spot coefficient and the initial gas production based on the geological sweet spot coefficient at each well and the initial gas production of each well; and obtaining the first curve based on the first regression equation and an existing expression reflecting the relationship between the initial gas production and the potential benefit.

[0080] In this embodiment, the geological sweet spot coefficient at each well corresponds to the initial gas production of the well. Based on this correspondence, a regression algorithm can be used to construct a regression equation between the geological sweet spot coefficient and the initial gas production, and this regression equation is used as the first regression equation. The first regression equation constructed in this embodiment is:

[0081]

[0082] Wherein, y1 is the initial gas production; X G is the geological sweet spot coefficient; a1 and b1 are constants determined by the regression algorithm.

[0083] In this embodiment, the expression reflecting the relationship between the initial gas production and the potential revenue is:

[0084]

[0085] E0=y1·p Formula (3)

[0086] Wherein, E is the potential income; p is the oil and gas price; y1 is the initial gas production; d1 is the gas production attenuation rate; d2 is the discount rate; and m is the mining life.

[0087] Based on the above formulas (1), (2) and (3), the relationship between the geological sweet spot coefficient and potential benefits can be obtained:

[0088]

[0089] Based on this relationship, a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefits of the shale formation can be drawn, that is, the first curve mentioned above, such as Figure 2 shown.

[0090] Figure 2 The multiple curves in represent the potential profit curves under different mining years t, that is, each curve represents the potential profit of each mining year, and the curves arranged from top to bottom correspond to the mining years from the 1st to the 20th year. Figure 2 It can be seen that in the same curve, the larger the geological sweet spot coefficient, the greater the potential benefit of the shale formation; when the geological sweet spot coefficient is the same, the larger the mining year, the smaller the potential benefit of the shale formation.

[0091] In this embodiment, the geological sweet spot coefficient of each drilling location in the shale formation is obtained in the following manner: obtaining multiple main geological sweet spot parameters of the drilling location; obtaining the weight of each of the main geological sweet spot parameters; and obtaining the geological sweet spot coefficient of each drilling location in the shale formation based on the numerical value of each main geological sweet spot parameter and the weight of each main geological sweet spot parameter.

[0092] In this embodiment, obtaining multiple main geological sweet spot parameters at a certain drilling site includes: obtaining multiple geological parameters at the drilling site by using a logging interpretation method or an experimental testing method; calculating the correlation coefficient between each of the geological parameters and the initial gas production of the drilling site as a first correlation coefficient; and based on the absolute value of the first correlation coefficient, selecting a geological parameter from the multiple geological parameters whose absolute value of the first correlation coefficient exceeds a first preset threshold as the main geological sweet spot parameter.

[0093] Specifically, the absolute values of the first correlation coefficients are sorted, and the geological parameters corresponding to the correlation coefficients whose absolute values exceed a first preset threshold (for example, exceeding a value of 0.35) are selected as the main geological sweet spot parameters.

[0094] The correlation coefficient method is to determine the importance and role of each indicator based on the strength of the collinearity between each indicator and the output indicator. Suppose the indicator items X1, X2,…, X n , if the indicator X p The larger the correlation coefficient with the output indicator Y, the better p The stronger the collinearity between the output indicator Y, the more the output indicator Y is affected by the indicator X. p The greater the impact of indicator X p Correlation coefficient R with indicator Y p Calculated according to the following expression:

[0095]

[0096] Among them, COV(X p ,Y) is X p Covariance with Y, D(X p ) is X p The variance of , D(Y) is the variance of Y.

[0097] In this embodiment, obtaining the weight of each of the main geological sweet spot parameters includes: calculating the complex correlation coefficient between each of the main geological sweet spot parameters and other parameters of the main geological sweet spot parameters except the main geological sweet spot parameter as a first complex correlation coefficient; and obtaining the weight of each of the main geological sweet spot parameters based on the first complex correlation coefficient and the correlation coefficient between each of the main geological sweet spot parameters and the initial gas production.

[0098] In this embodiment, the above-mentioned multiple correlation coefficient is calculated using the independence weight coefficient method. The independence weight coefficient method determines the weight of each indicator according to the strength of collinearity between each indicator and other indicators. n , if the indicator X k and index items X1, X2,…, X nThe larger the multiple correlation coefficient of other indicators in X is, the k The stronger the collinearity relationship between X and other indicators, the easier it is to be represented by the linear combination of other indicators, and the more repeated information there is. k The weight should be smaller.

[0099] That is, if the indicator X k Multiple correlation coefficient CR with other indicators k The larger the index X is, the k The smaller the weight of indicator X k The multiple correlation coefficient CR between the above indicators k Calculated according to the following expression:

[0100]

[0101] in, are index items X1, X2,…, X n The average value of For X k expectations.

[0102] In this embodiment, the weight of each major geological sweet spot parameter is calculated using the following expression:

[0103]

[0104]

[0105] Among them, W i ' is the weight of the i-th main geological sweet spot parameter; n is the number of the main geological sweet spot parameters; R i is the correlation coefficient between the parameters of the i-th main geological sweet spot and the initial gas production; CR i is the multiple correlation coefficient between the i-th main geological sweet spot parameter and other parameters in the main geological sweet spot parameters.

[0106] Specifically, for a well in a shale formation, the steps to obtain its geological sweet spot coefficient are as follows:

[0107] Step 1: Obtain multiple geological parameters of the well by interpreting seismic data or conducting experimental tests, including: shale content, silica content, carbon content, organic matter content, kerogen content, porosity, water saturation, and pore pressure;

[0108] Step 2: Obtain the open flow rate of the initial gas production test of the well, that is, the initial gas production;

[0109] Step 3: Based on the data obtained in the above steps, the correlation coefficient R between each geological parameter and the initial gas production is calculated using the above formula (5):i , according to the correlation coefficient R i The absolute value of the geological parameters with a high correlation with the initial gas production is selected as the main geological sweet spot parameters;

[0110] Step 4: Calculate the multiple correlation coefficient CR between each main geological sweet spot parameter and other parameters in the main geological sweet spot parameters except the main geological sweet spot parameter by using the above formula (6): i ;

[0111] Step 5: Calculate the weight W of each major geological sweet spot parameter using the above formulas (7) and (8): i ';

[0112] Step 6: Calculate the geological sweet spot coefficient at the drilling location using the following expression:

[0113]

[0114] Among them, X G is the geological sweet spot coefficient of the drilling location; n is the number of main geological sweet spot parameters of the drilling location; W i ' is the weight of the main geological sweet spot parameter at the drilling location i; X i is the value of the main geological sweet spot parameter at the i-th drilling location.

[0115] Step S102, obtaining a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation as a second curve;

[0116] In this embodiment, the second curve is obtained in the following manner: obtaining the engineering sweet spot coefficient at each well in the shale formation and the development cost of each well; constructing a second regression equation reflecting the relationship between the engineering sweet spot coefficient and the development cost based on the engineering sweet spot coefficient at each well and the development cost of each well; and obtaining the second curve based on the second regression equation.

[0117] In this embodiment, the engineering sweet spot coefficient at each drilling location corresponds to the development cost of the drilling location. Based on this correspondence, a regression algorithm can be used to construct a regression equation between the engineering sweet spot coefficient and the development cost, and this regression equation is used as the second regression equation. The second regression equation constructed in this embodiment is:

[0118] y2=a2+b2(c-dln(X E )) e Formula (10)

[0119] Wherein, y2 is the development cost; X Eis the engineering sweet spot coefficient; a2 is the known drilling and completion cost; b2, c, d, and e are constants determined by the regression algorithm.

[0120] Based on the above formula (10), a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation can be drawn, that is, the above second curve, as shown in Figure 3 shown.

[0121] from Figure 3 It can be seen that the larger the engineering sweet spot coefficient is, the lower the development cost of the shale formation is.

[0122] In this embodiment, the engineering sweet spot coefficient of each drilling well in the shale formation is obtained in the following manner: obtaining multiple main engineering sweet spot parameters of the drilling well; obtaining the weight of each of the main engineering sweet spot parameters; and obtaining the engineering sweet spot coefficient of each drilling well in the shale formation based on the numerical value of each of the main engineering sweet spot parameters and the weight of each of the main engineering sweet spot parameters.

[0123] In this embodiment, multiple main engineering sweet spot parameters at a certain drilling site are obtained, including: obtaining multiple engineering parameters at the drilling site by means of well logging interpretation or experimental testing; calculating the correlation coefficient between each of the engineering parameters and the sand carrying ratio of the drilling site as a second correlation coefficient; and based on the absolute value of the second correlation coefficient, selecting an engineering parameter from the multiple engineering parameters whose absolute value of the second correlation coefficient exceeds a second preset threshold as the main engineering sweet spot parameter.

[0124] Specifically, the absolute values of the second correlation coefficients are sorted, and the engineering parameters corresponding to the correlation coefficients whose absolute values exceed a second preset threshold (for example, exceeding a value of 0.25) are selected as the main engineering sweet spot parameters.

[0125] In this embodiment, obtaining the weight of each of the main engineering sweet spot parameters includes: calculating the complex correlation coefficient between each of the main engineering sweet spot parameters and other parameters of the main engineering sweet spot parameters except the main engineering sweet spot parameter as the second complex correlation coefficient; based on the second complex correlation coefficient and the correlation coefficient between each of the main engineering sweet spot parameters and the sand carrying ratio, obtaining the weight of each of the main engineering sweet spot parameters.

[0126] In this embodiment, the weight of each main engineering sweet spot parameter is calculated using the following expression:

[0127]

[0128]

[0129] Among them, W' Eiis the weight of the i-th main engineering sweet spot parameter; n0 is the number of the main engineering sweet spot parameters; R Ei is the correlation coefficient between the i-th main engineering sweet spot parameter and the sand carrying ratio; CR Ei is the complex correlation coefficient between the i-th main engineering sweet spot parameter and other parameters in the main engineering sweet spot parameters.

[0130] Specifically, for a well in a shale formation, the steps for obtaining its engineering sweet spot coefficient are as follows:

[0131] Step 1: Obtain multiple engineering parameters of the well by interpreting seismic data through logging, or through experimental testing, including: shale content, silica content, carbon content, brittleness index, fracture pressure, pore pressure, and stress difference coefficient;

[0132] Step 2: Obtain the total amount of sand added during the fracturing of the shale formation at the drilling site F sand and total injection volume F fluid , calculate the sand carrying ratio

[0133] Step 3: Based on the data obtained in the above steps, the correlation coefficient between each engineering parameter and the sand carrying ratio is calculated by the above formula (5). According to the absolute value of the correlation coefficient, the engineering parameter with a high correlation with the sand carrying ratio is selected as the main engineering sweet spot parameter;

[0134] Step 4: Calculate the complex correlation coefficient between each main engineering sweet spot parameter and other parameters in the main engineering sweet spot parameters except the main engineering sweet spot parameter using the above formula (6);

[0135] Step 5: Calculate the weight W' of each main engineering sweet spot parameter using the above formula (11) and formula (12) Ei ;

[0136] Step 6: Calculate the engineering sweet spot coefficient at the drilling location using the following expression:

[0137]

[0138] Among them, X E is the engineering sweet spot coefficient of the drilling location; n0 is the number of main engineering sweet spot parameters of the drilling location; W' Ei is the weight of the main engineering sweet spot parameter at the drilling location i; X Ei is the value of the main engineering sweet spot parameter at the i-th drilling location.

[0139] Step S103, obtaining a difference change between the potential benefit and the development cost based on the first curve and the second curve;

[0140] In this embodiment, the difference between the potential benefit and the development cost can be expressed as:

[0141] Z=E-y2

[0142] Where E is the potential income of the shale formation and y2 is the development cost of the shale formation.

[0143] In this embodiment, the geological sweet spot coefficient of a shale formation determines its potential revenue, while the engineering sweet spot coefficient of a shale formation determines its development cost. The difference between the potential revenue and the development cost represents the development benefit of the shale formation and is an effective indicator for measuring whether the shale formation is worth developing. This value is called the recoverable benefit. Based on the potential revenue determined by the geological sweet spot coefficient, the development cost determined by the engineering sweet spot coefficient, and the relationship between the two, the marginal recoverability of the sweet spot is evaluated. The sweet spot where the potential revenue and development cost of a shale formation are equal is called the marginal recoverable sweet spot. For example, Figure 4 Typically, shale formations do not have a marginal sweet spot, but the difference between the potential revenue curve and the development cost curve represents the development benefit. A larger marginal sweet spot indicates a smaller development benefit and a greater development difficulty. A smaller marginal sweet spot indicates a greater development benefit and a lower development difficulty.

[0144] During the development of shale formations, the geological sweet spot and potential benefits reflected by the geological sweet spot coefficient change with the change of wellbore trajectory. The engineering sweet spot and development cost reflected by the engineering sweet spot coefficient also change with the change of wellbore trajectory. Therefore, the difference between potential benefits and development costs also changes. This difference change is called the recoverable benefit sweet spot, and the area along the wellbore that changes is called the recoverable benefit sweet spot area.

[0145] Step S104, determining the azimuth of the maximum difference change based on the difference change to optimize the drilling wellbore trajectory of the shale formation; wherein the azimuth of the maximum difference change is the optimal drilling wellbore trajectory.

[0146] In this embodiment, the drilling trajectory is optimized according to the recoverable sweet spot of the formation. The optimal drilling trajectory direction is the direction of the maximum value of the recoverable sweet spot, that is, the direction of the above-mentioned maximum difference change (or the direction of the maximum value of the difference change). Drilling in this direction can achieve the maximum development efficiency and benefits.

[0147] An embodiment of the present invention provides a method for optimizing the drilling trajectory of a shale formation, which obtains a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit as a first curve, obtains a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost as a second curve, and obtains the difference change between the potential benefit and the development cost based on the first and second curves. Based on the difference change, the direction of the maximum difference change is determined to find the optimal drilling trajectory. It can be seen that compared with the existing technology, the present invention comprehensively considers the impact of the geological sweet spot of the shale formation on the potential benefit and the impact of the engineering sweet spot on the development cost. The method of optimizing the drilling trajectory of the shale formation based on the difference change between the potential benefit and the development cost can analyze the recoverability of the formation from the perspective of the overall development benefit of the shale formation, so that the optimized drilling trajectory has the maximum development benefit.

[0148] Example 2

[0149] This embodiment takes a certain actual shale formation as an example to describe in detail a method for optimizing a drilling trajectory in the shale formation. The method includes:

[0150] Step S201: By performing logging interpretation on seismic data or experimental testing, multiple geological parameters are obtained at each well, including: shale content Vs, silica content Vq, carbon content Vc, organic matter content TOC, kerogen content Vker, porosity POR, water saturation S g and pore pressure P f , and obtain the initial gas production P of each well D , the initial gas production P D It is expressed by the actual gas production after fracturing;

[0151] Step S202: For each well, calculate each of the above geological parameters and the initial gas production P D The correlation coefficient between them is taken as the first correlation coefficient. According to the absolute value of the first correlation coefficient, the geological parameters with a high correlation with the initial gas production are selected as the main geological sweet spot parameters.

[0152] In this embodiment, the pore pressure P is found by calculating the first correlation coefficient. f , organic matter content TOC, porosity POR, water saturation S g and kerogen content Vker affect the initial gas production P D The influence of the initial gas production P is significant, and the correlation coefficients are all greater than 0.35. The clay content Vs, silica content Vq and carbon content Vc have a significant impact on the initial gas production P. D The influence of is small, and the correlation coefficients are all less than 0.15, as shown in Table 1:

[0153] Table 1

[0154]

[0155] In Table 1, the first correlation coefficient is ranked, and the pore pressure P is selected. f , organic matter content TOC, porosity POR, water saturation S g The five geological parameters of kerogen content Vker are the main influencing parameters of shale formation geological sweet spots, and their influence on the initial gas production is as follows: pore pressure P f > Organic matter content TOC > Porosity POR > Water saturation S g >Kerogen content Vker.

[0156] Step S203, calculating the complex correlation coefficient between each main geological sweet spot parameter and other parameters of the main geological sweet spot parameters except the main geological sweet spot parameter as the first complex correlation coefficient, and calculating the weight of each main geological sweet spot parameter based on the first complex correlation coefficient and the correlation coefficient between each main geological sweet spot parameter and the initial gas production;

[0157] Specifically, the weight of each major geological sweet spot parameter is calculated using the following expression:

[0158]

[0159]

[0160] Among them, W i ' is the weight of the i-th main geological sweet spot parameter; n is the number of the main geological sweet spot parameters; R i is the correlation coefficient between the parameters of the i-th main geological sweet spot and the initial gas production; CR i is the multiple correlation coefficient between the i-th main geological sweet spot parameter and other parameters in the main geological sweet spot parameters.

[0161] In this embodiment, the weights of each major geological sweet spot parameter calculated according to the above method are shown in Table 2:

[0162] Table 2

[0163] parameter TOC (%) POR (%) Vker (%) Sg(%) <![CDATA[P f (g / cm 3 )]]> Sorting 2 3 5 4 1 <![CDATA[1 / CR i ]]> 0.703 0.633 0.545 0.317 0.319 <![CDATA[Ri / CR i ]]> 0.393 0.264 0.194 0.127 0.182 <![CDATA[Weight W i ’]]> 0.339 0.228 0.167 0.110 0.157

[0164] Step S204: Calculate the geological sweet spot coefficient at each drilling location using the following expression:

[0165]

[0166] Among them, X Gis the geological sweet spot coefficient of the drilling location; n is the number of main geological sweet spot parameters of the drilling location; W i ' is the weight of the main geological sweet spot parameter at the drilling location i; X i is the value of the main geological sweet spot parameter at the i-th drilling location.

[0167] Step S205: Based on the geological sweet spot coefficient of each well and the initial gas production of each well, construct a first regression equation reflecting the relationship between the geological sweet spot coefficient and the initial gas production; based on the first regression equation and an existing expression reflecting the relationship between the initial gas production and the potential benefit, obtain the first curve.

[0168] In this embodiment, the geological sweet spot coefficient at each well corresponds to the initial gas production of the well. Based on this correspondence, a regression algorithm can be used to construct a regression equation between the geological sweet spot coefficient and the initial gas production, and this regression equation is used as the first regression equation. In this embodiment, the first regression equation constructed based on the geological sweet spot coefficients in Tables 1 and 2 and their corresponding initial gas production is:

[0169]

[0170] Among them, y1 is the initial gas production; X G is the geological sweet spot coefficient.

[0171] The curve graph based on the regression equation reflecting the relationship between the geological sweet spot coefficient and the initial gas production is as follows: Figure 5 shown.

[0172] from Figure 5 As can be seen from the figure, the larger the geological sweet spot coefficient, the higher the initial gas production; the smaller the geological sweet spot coefficient, the lower the initial gas production. The geological sweet spot coefficient and initial gas production have an exponential relationship, and the correlation between them reaches 0.76, which is much better than the evaluation results of existing technologies using a single parameter.

[0173] In this embodiment, the expression reflecting the relationship between the initial gas production and the potential revenue is:

[0174]

[0175] E0=y1·p Formula (3)

[0176] Wherein, E is the potential income; p is the oil and gas price; y1 is the initial gas production; d1 is the gas production attenuation rate; d2 is the discount rate; and m is the mining life.

[0177] In this embodiment, d1 = -0.15, d2 = 0.09.

[0178] Based on the above formulas (1), (2) and (3), the relationship between the geological sweet spot coefficient and potential benefits can be obtained:

[0179]

[0180] Based on this relationship, a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefits of the shale formation can be drawn, that is, the first curve mentioned above, such as Figure 2 shown.

[0181] Step S206: By performing logging interpretation on the seismic data or by experimental testing, multiple engineering parameters of each well are obtained, including: shale content Vs, silica content Vq, carbon content Vc, brittleness index Brit, fracture pressure P p , pore pressure P f and stress difference coefficient Δσ, and obtain the sand carrying ratio P of each well;

[0182] Step S207: For each well, calculate the correlation coefficient between each of the above engineering parameters and the sand carrying ratio P as the second correlation coefficient. Based on the absolute value of the second correlation coefficient, select the engineering parameter with a higher correlation with the sand carrying ratio as the main engineering sweet spot parameter;

[0183] In this embodiment, through the calculated second correlation coefficient, it is found that the mud content Vs, brittleness index Brit, carbon content Vc and stress difference coefficient Δσ have a greater impact on the sand carrying ratio P, and their correlation coefficients are all greater than 0.25, while the silica content Vq, fracture pressure P p and pore pressure P f The influence on the sand carrying ratio P is small, and the correlation coefficients are all less than 0.25, as shown in Table 3:

[0184] Table 3

[0185]

[0186] In Table 3, the second correlation coefficients are ranked, and the four engineering parameters, namely, mud content Vs, brittleness index Brit, carbon content Vc, and stress difference coefficient Δσ, are selected as the main influencing parameters of shale formation geological sweet spots. Their influence on the sand carrying ratio is as follows: brittleness index Brit > mud content Vs > stress difference coefficient Δσ > carbon content Vc.

[0187] Step S208, calculating the complex correlation coefficient between each main engineering sweet spot parameter and other parameters of the main engineering sweet spot parameters except the main engineering sweet spot parameter as the second complex correlation coefficient, and calculating the weight of each main engineering sweet spot parameter based on the second complex correlation coefficient and the correlation coefficient between each main engineering sweet spot parameter and the sand carrying ratio;

[0188] Specifically, the weight of each major engineering sweet spot parameter is calculated using the following expression:

[0189]

[0190]

[0191] Among them, W' Ei is the weight of the i-th main engineering sweet spot parameter; n0 is the number of the main engineering sweet spot parameters; R Ei is the correlation coefficient between the i-th main engineering sweet spot parameter and the sand carrying ratio; CR Ei is the complex correlation coefficient between the i-th main engineering sweet spot parameter and other parameters in the main engineering sweet spot parameters.

[0192] In this embodiment, the weights of each main engineering sweet spot parameter calculated according to the above method are shown in Table 4:

[0193] Table 4

[0194]

[0195]

[0196] Step S209: Calculate the engineering sweet spot coefficient at each drilling location using the following expression:

[0197]

[0198] Among them, X E is the engineering sweet spot coefficient of the drilling location; n0 is the number of main engineering sweet spot parameters of the drilling location; W' Ei is the weight of the main engineering sweet spot parameter at the drilling location i; X Ei is the value of the main engineering sweet spot parameter at the i-th drilling location.

[0199] Step S210: Based on the engineering sweet spot coefficient at each well and the development cost of each well, construct a second regression equation reflecting the relationship between the engineering sweet spot coefficient and the development cost; and obtain the second curve based on the second regression equation.

[0200] In this embodiment, the engineering sweet spot coefficient at each drilling location corresponds to the development cost of the drilling location. Based on this correspondence, a regression algorithm can be used to construct a regression equation between the engineering sweet spot coefficient and the development cost, and this regression equation is used as the second regression equation. The second regression equation constructed in this embodiment is:

[0201] y2=a2+b2(c-dln(X E )) e=4000+5000×(0.63-3.39ln(X E )) 3 Formula (10)

[0202] Wherein, y2 is the development cost; X E is the engineering sweet spot coefficient.

[0203] Based on the above formula (10), a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation can be drawn, that is, the above second curve, as shown in Figure 3 shown.

[0204] Step S211, obtaining a difference change between the potential benefit and the development cost based on the first curve and the second curve;

[0205] Step S212, determining the azimuth of the maximum difference change based on the difference change to optimize the drilling wellbore trajectory of the shale formation; wherein the azimuth of the maximum difference change is the optimal drilling wellbore trajectory.

[0206] In this embodiment, the relationship between the first curve and the second curve obtained based on the above data is shown in FIG. Figure 4 and Figure 6 As shown. Figure 4 and Figure 6 In the figure, the "potential benefit curve" represents the total potential benefit over the mining life. As can be seen from the figure, the potential benefit curve increases with increasing geological sweet spot coefficients, while the development cost curve decreases with increasing engineering sweet spot coefficients. The difference between potential benefit and development cost is used to determine the recoverable benefit sweet spot of a shale formation. The area along the wellbore is called the recoverable benefit sweet spot. The drilling trajectory is optimized based on the formation's recoverable benefit sweet spot. The optimal drilling trajectory is the direction of the maximum recoverable benefit sweet spot, i.e., the direction of the maximum difference change.

[0207] The impact of geological sweet spot parameters on initial gas production varies, and the impact of engineering sweet spot parameters on the sand-carrying ratio during shale formation fracturing also varies. By studying these parameters and initial gas production or sand-carrying ratio, key parameters are optimized and weighted for each engineering sweet spot parameter. The geological sweet spot reflects initial gas production and potential revenue, while the engineering sweet spot reflects the scale and cost of fracturing. During shale formation development, the geological sweet spot and potential revenue change with the wellbore trajectory, and the engineering sweet spot and development cost also change with the wellbore trajectory. Therefore, the difference between potential revenue and development cost also changes. This change is called the recoverable benefit sweet spot, and the area along the wellbore where this changes is called the recoverable benefit sweet spot zone. To achieve maximum development efficiency and benefits, the optimal drilling trajectory is the direction that maximizes the recoverable benefit sweet spot.

[0208] An embodiment of the present invention provides a method for optimizing the drilling trajectory of a shale formation, which obtains a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit as a first curve, obtains a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost as a second curve, and obtains the difference change between the potential benefit and the development cost based on the first and second curves. Based on the difference change, the direction of the maximum difference change is determined to find the optimal drilling trajectory. It can be seen that compared with the existing technology, the present invention comprehensively considers the impact of the geological sweet spot of the shale formation on the potential benefit and the impact of the engineering sweet spot on the development cost. The method of optimizing the drilling trajectory of the shale formation based on the difference change between the potential benefit and the development cost can analyze the recoverability of the formation from the perspective of the overall development benefit of the shale formation, so that the optimized drilling trajectory has the maximum development benefit.

[0209] Example 3

[0210] Corresponding to the above method embodiment, the present invention also provides a system for optimizing the wellbore trajectory of shale formation drilling, such as Figure 7 As shown, the system includes:

[0211] A first curve acquisition module 301 is configured to acquire a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit of the shale formation as a first curve;

[0212] A second curve acquisition module 302 is configured to acquire a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation as a second curve;

[0213] A difference variation acquisition module 303 is configured to obtain a difference variation between the potential benefit and the development cost based on the first curve and the second curve;

[0214] The optimization module 304 is configured to determine the azimuth of the maximum difference variation based on the difference variation to optimize the drilling trajectory of the shale formation; wherein the azimuth of the maximum difference variation is the optimal drilling trajectory.

[0215] The working principle, workflow and other contents related to the specific implementation of the above system can be found in the specific implementation of the method for optimizing shale formation drilling well trajectory provided by the present invention, and the same technical contents will not be described in detail here.

[0216] Example 4

[0217] According to an embodiment of the present invention, a storage medium is further provided, on which program code is stored. When the program code is executed by a processor, the method for optimizing the wellbore trajectory of shale formation drilling as described in any of the above embodiments is implemented.

[0218] Example 5

[0219] According to an embodiment of the present invention, an electronic device is also provided, which includes a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, the method for optimizing the wellbore trajectory of shale formation drilling as described in any of the above embodiments is implemented.

[0220] The embodiments of the present invention provide a method, device, storage medium, and electronic device for optimizing the drilling trajectory of a shale formation. The method obtains a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit as a first curve, obtains a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost as a second curve, and obtains the difference change between the potential benefit and the development cost based on the first and second curves. The orientation of the maximum difference change is determined based on the difference change to find the optimal drilling trajectory. It can be seen that compared with the prior art, the present invention comprehensively considers the impact of the geological sweet spot of the shale formation on the potential benefit and the impact of the engineering sweet spot on the development cost. The method of optimizing the drilling trajectory of the shale formation based on the difference change between the potential benefit and the development cost can analyze the recoverability of the formation from the perspective of the overall development benefit of the shale formation, so that the optimized drilling trajectory has the maximum development benefit.

[0221] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features.

[0222] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present invention.

[0223] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0224] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0225] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A method for optimizing a drilling trajectory in a shale formation, characterized in that: The method comprises: Obtaining a relationship curve between the geological sweet spot coefficient of the shale formation and the potential benefit of the shale formation as a first curve; obtaining a relationship curve between the engineering sweet spot coefficient of the shale formation and the development cost of the shale formation as a second curve; Obtaining a difference change between the potential benefit and the development cost based on the first curve and the second curve; Determining the azimuth of the maximum difference change based on the difference change to optimize the drilling trajectory of the shale formation; wherein the azimuth of the maximum difference change is the optimal drilling trajectory; The first curve is obtained in the following manner: Obtaining the geological sweet spot coefficient at each well in the shale formation and the initial gas production of each well; Based on the geological sweet spot coefficient at each well and the initial gas production of each well, constructing a first regression equation reflecting the relationship between the geological sweet spot coefficient and the initial gas production; Obtaining the first curve based on the first regression equation and an existing expression reflecting the relationship between the initial gas production and the potential benefit; The first regression equation constructed is: in, is the initial gas production; is the geological sweet spot coefficient; 、 The constant determined by the regression algorithm; The expression reflecting the relationship between the initial gas production and the potential benefit is: Wherein, E is the potential benefit; is the price of oil and gas; is the initial gas production; is the gas production attenuation rate; is the discount rate; m is the mining life.

2. The method for optimizing shale formation drilling wellbore trajectory according to claim 1, characterized in that: The geological sweet spot coefficient at each well in the shale formation is obtained in the following manner: Obtain multiple main geological sweet spot parameters at the drilling location; Obtaining the weight of each of the main geological sweet spot parameters; The geological sweet spot coefficient at each well in the shale formation is obtained based on the value of each of the main geological sweet spot parameters and the weight of each of the main geological sweet spot parameters.

3. The method for optimizing shale formation drilling wellbore trajectory according to claim 2, characterized in that: The method of obtaining multiple main geological sweet spot parameters at the drilling location includes: Using well logging interpretation or experimental testing to obtain multiple geological parameters at the drilling site; Calculating a correlation coefficient between each of the geological parameters and the initial gas production of the well as a first correlation coefficient; Based on the absolute value of the first correlation coefficient, a geological parameter whose absolute value of the first correlation coefficient exceeds a first preset threshold is selected from the multiple geological parameters as the main geological sweet spot parameter.

4. The method for optimizing shale formation drilling wellbore trajectory according to claim 3, characterized in that: The obtaining of the weight of each of the main geological sweet spot parameters includes: Calculating a complex correlation coefficient between each of the main geological sweet spot parameters and other parameters of the main geological sweet spot parameters except the main geological sweet spot parameter as a first complex correlation coefficient; Based on the first complex correlation coefficient and the correlation coefficient between each of the main geological sweet spot parameters and the initial gas production, a weight of each of the main geological sweet spot parameters is obtained.

5. The method for optimizing shale formation drilling wellbore trajectory according to claim 4, characterized in that: The weight of each of the main geological sweet spot parameters is calculated using the following expression: ， in, is the weight of the i-th main geological sweet spot parameter; n is the number of the main geological sweet spot parameters; is the correlation coefficient between the i-th main geological sweet spot parameter and the initial gas production; is the complex correlation coefficient between the i-th main geological sweet spot parameter and the other parameters.

6. The method for optimizing shale formation drilling wellbore trajectory according to claim 1, characterized in that: The second curve is obtained in the following way: Obtaining the engineering sweet spot coefficient at each well in the shale formation and the development cost of each well; constructing a second regression equation reflecting the relationship between the engineering sweet spot coefficient and the development cost based on the engineering sweet spot coefficient at each well and the development cost of each well; Based on the second regression equation, the second curve is obtained.

7. The method for optimizing shale formation drilling wellbore trajectory according to claim 6, characterized in that: The second regression equation constructed is: in, for said development costs; is the engineering sweet spot coefficient; is the known cost of drilling and completion; 、 、 、 is a constant determined by the regression algorithm.

8. The method for optimizing shale formation drilling wellbore trajectory according to claim 6, characterized in that: The engineering sweet spot coefficient at each well in the shale formation is obtained in the following manner: Obtain multiple main engineering sweet spot parameters at the drilling location; Obtaining the weight of each of the main engineering sweet spot parameters; The engineering sweet spot coefficient at each well in the shale formation is obtained based on the value of each main engineering sweet spot parameter and the weight of each main engineering sweet spot parameter.

9. The method for optimizing shale formation drilling wellbore trajectory according to claim 8, characterized in that: The method of obtaining a plurality of main engineering sweet spot parameters at the drilling location includes: Using well logging interpretation or experimental measurement to obtain multiple engineering parameters at the drilling site; Calculating the correlation coefficient between each of the engineering parameters and the sand carrying ratio of the drilling well as a second correlation coefficient; Based on the absolute value of the second correlation coefficient, an engineering parameter whose absolute value of the second correlation coefficient exceeds a second preset threshold is selected from the multiple engineering parameters as the main engineering sweet spot parameter.

10. The method for optimizing shale formation drilling wellbore trajectory according to claim 9, characterized in that: Obtaining the weight of each of the main engineering sweet spot parameters includes: Calculating a complex correlation coefficient between each of the main engineering sweet spot parameters and other parameters of the main engineering sweet spot parameters except the main engineering sweet spot parameter as a second complex correlation coefficient; Based on the second complex correlation coefficient and the correlation coefficient between each of the main engineering sweet spot parameters and the sand carrying ratio, the weight of each of the main engineering sweet spot parameters is obtained.

11. A system for optimizing drilling wellbore trajectory in shale formations, characterized in that: The system comprises: a first curve acquisition module, configured to acquire a relationship curve between a geological sweet spot coefficient of the shale formation and a potential benefit of the shale formation as a first curve; a second curve acquisition module, configured to acquire a relationship curve between an engineering sweet spot coefficient of the shale formation and a development cost of the shale formation as a second curve; A difference variation acquisition module, configured to obtain a difference variation between the potential benefit and the development cost based on the first curve and the second curve; an optimization module, configured to determine an orientation of a maximum difference variation based on the difference variation, so as to optimize a drilling wellbore trajectory of the shale formation; wherein the orientation of the maximum difference variation is an optimal drilling wellbore trajectory; The first curve acquisition module is used for: Obtaining the geological sweet spot coefficient at each well in the shale formation and the initial gas production of each well; Based on the geological sweet spot coefficient at each well and the initial gas production of each well, constructing a first regression equation reflecting the relationship between the geological sweet spot coefficient and the initial gas production; Obtaining the first curve based on the first regression equation and an existing expression reflecting the relationship between the initial gas production and the potential benefit; The first regression equation constructed is: in, is the initial gas production; is the geological sweet spot coefficient; 、 The constant determined by the regression algorithm; The expression reflecting the relationship between the initial gas production and the potential benefit is: Wherein, E is the potential benefit; is the price of oil and gas; is the initial gas production; is the gas production attenuation rate; is the discount rate; m is the mining life.

12. A storage medium having program code stored thereon, characterized in that: When the program code is executed by a processor, the method for optimizing the wellbore trajectory for drilling in a shale formation according to any one of claims 1 to 10 is implemented.

13. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores program code that can be run on the processor. When the program code is executed by the processor, the method for optimizing the wellbore trajectory of shale formation drilling is implemented as described in any one of claims 1 to 10.

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