A method and system for predicting mud loss before drilling based on analogy of main control factors

Abstract

The embodiment of the present invention discloses a method and system for predicting mud loss before drilling based on the analogy of main control factors, which analyzes the data of the loss area, determines the main control factor set affecting the loss and the mud loss amount at the loss position; obtains the correlation between each factor in the main control factor set and the mud loss amount through correlation analysis; obtains the value of each main control factor in the area to be predicted, and obtains the mud loss amount prediction result of the area to be predicted based on the obtained correlation between each factor and the mud loss amount. The mud loss prediction before drilling in complex well conditions is realized, and the formation is predicted for well loss before drilling, which provides guidance for drilling, avoids the drilling loss layer section, and improves the drilling success rate.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of drilling technology, and in particular to a method and system for predicting mud loss before drilling based on analogy of main control factors. Background Art

[0002] During the drilling production process, when drilling in fracture-developed zones and / or karst-developed zones, well leakage often occurs. If drilling is carried out in a formation prone to well leakage, it is a more complicated drilling operation. Drilling in the above-mentioned formations is very likely to induce major downhole accidents such as blowouts and drill jams. More importantly, drilling engineering accidents directly affect the discovery and protection of oil and gas layers, resulting in a decline in the effectiveness of exploration and development. The commonly used technical means at present are mainly to test and analyze well leakage through well logging, find the location of the leakage layer, and determine the properties of the leakage layer channel. That is, drilling work is carried out first, and only when well leakage is encountered can it be known that the drilling formation is a drilling leakage layer section. This method cannot predict well leakage in the formation before drilling, and thus cannot provide guidance for drilling to avoid drilling leakage layers, so the drilling success rate of this method is low. Summary of the invention

[0003] To this end, an embodiment of the present invention provides a pre-drilling mud loss prediction method based on the analogy of the main controlling factors to solve the problem that the existing method cannot predict the well leakage of the formation before drilling, and thus cannot provide guidance for drilling to avoid the drilling leakage layer section, resulting in a low drilling success rate.

[0004] In order to achieve the above purpose, the embodiment of the present invention provides the following technical solutions:

[0005] According to a first aspect of an embodiment of the present invention, a method for predicting mud loss before drilling based on analogy of main control factors is proposed, the method comprising:

[0006] Analyze the data of the leakage area to determine the main controlling factors affecting the leakage and the amount of mud loss at the leakage location;

[0007] Through correlation analysis, the correlation between each factor in the main control factor set and the mud loss is obtained;

[0008] The values ​​of the main control factors of the area to be predicted are obtained, and the mud loss prediction result of the area to be predicted is obtained according to the correlation between the obtained factors and the mud loss.

[0009] Furthermore, the main controlling factors include fault zone throw, distance from the fault, horizontal stress difference and fracture development coefficient.

[0010] Furthermore, the data of the leakage area is analyzed to determine the main control factors affecting the leakage and the amount of mud loss at the leakage location, including:

[0011] Combined with the lithological combination characteristics and thickness of each formation in the leakage area, Landmark software was used to calibrate the seismic reflection layer and divide the formation. The overall 8×8 and local 4×4 densities were used to finely interpret the three-dimensional seismic data of the study area, and the distribution morphology of the fault zone in the plane and vertical direction was finely characterized. The relationship between the leakage position and the fault was analyzed to determine the influence of the fault zone fault distance and the distance from the fault on the leakage.

[0012] Furthermore, the data of the leakage area is analyzed to determine the main control factors affecting the leakage and the amount of mud loss at the leakage location, including:

[0013] Establishing geological model: The simulation scope is determined according to the scope of the study area in Landmark software; the fault zones determined in the Landmark study area are exported and digitized; the digitized faults and the scope of the study area are imported into ANSYS software to establish the geological model; different mechanical parameters are assigned to different blocks of stress field simulation according to the rock mechanical parameter results calculated from the well logging data of the study area;

[0014] Finite element meshing: For the study area, the plane solid95 unit is used to mesh the solid geological model to obtain the finite element model;

[0015] Finite element numerical simulation: constraints and stress loads are imposed on the boundaries of the model, and the structural stress field of the interface of the fault zone in the study area is numerically simulated under the above model constraints and loading conditions. The stress distribution results obtained by simulation are used to obtain the horizontal stress difference.

[0016] Furthermore, the data of the leakage area is analyzed to determine the main control factors affecting the leakage and the amount of mud loss at the leakage location, including:

[0017] The tensile rupture coefficient I is obtained by using the Griffith rupture criterion to predict the tensile rupture. Γ ;

[0018] The shear fracture coefficient I is obtained by using the Mohr-Coulomb fracture criterion to predict shear fracture. n ;

[0019] Use the crack development coefficient I F To characterize the degree of development of structural fractures:

[0020] I F =aI Γ +bI n

[0021] Where a and b represent the proportion of tensile fractures and shear fractures in the rock to the structural fractures, respectively.

[0022] Furthermore, the correlation between each factor in the main control factor set and the mud loss is obtained through correlation analysis, including:

[0023] The correlation coefficient between each factor and the mud loss is calculated, and the correlation formula between each factor and the mud loss is obtained.

[0024] Furthermore, the values ​​of the main control factors of the area to be predicted are obtained, and according to the correlation between the obtained factors and the mud loss, the mud loss prediction result of the area to be predicted is obtained, which specifically includes:

[0025] According to the correlation between each factor and mud loss, the mud loss corresponding to each main control factor in the area to be predicted is calculated;

[0026] According to the obtained correlation coefficient between each factor and mud loss, the weight of each factor is determined;

[0027] The weighted average value is calculated based on the mud loss corresponding to each main control factor in the area to be predicted and the weight of each factor to obtain the final mud loss prediction value.

[0028] According to a second aspect of an embodiment of the present invention, a pre-drilling mud loss prediction system based on main control factor analogy is proposed, the system comprising:

[0029] The main control factor set determination module is used to analyze the data of the leakage area, determine the main control factor set affecting the leakage and the mud loss at the leakage location;

[0030] A correlation determination module is used to obtain the correlation between each factor in the main control factor set and the mud loss through correlation analysis;

[0031] The prediction result acquisition module is used to obtain the values ​​of the main control factors of the area to be predicted, and obtain the mud loss prediction result of the area to be predicted based on the correlation between the obtained factors and the mud loss.

[0032] The embodiments of the present invention have the following advantages:

[0033] The embodiment of the present invention proposes a method and system for predicting mud loss before drilling based on the analogy of the main control factors. The method analyzes the data of the leakage area to determine the main control factor set that affects the leakage and the mud loss at the leakage location; obtains the correlation between each factor in the main control factor set and the mud loss through correlation analysis; obtains the value of each main control factor in the area to be predicted, and obtains the mud loss prediction result of the area to be predicted based on the obtained correlation between each factor and the mud loss. The method realizes the prediction of mud loss before drilling in complex well conditions, provides guidance for drilling by predicting the well loss of the formation before drilling, avoids the drilling loss layer section, and improves the drilling success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0035] Figure 1 A flowchart of a method for predicting mud loss before drilling based on analogy of main control factors provided in Example 1 of the present invention;

[0036] Figure 2 The fault zone T in the study area in the method for predicting mud loss before drilling based on analogy of main controlling factors provided in Example 1 of the present invention is 6 3 Interface geological model and grid division diagram;

[0037] Figure 3 The fault zone T in the study area in the method for predicting mud loss before drilling based on analogy of main controlling factors provided in Example 1 of the present invention is 6 3 Distribution diagram of the plane of the principal stress in the middle of the interface;

[0038] Figure 4 A schematic diagram of micro-crack stress and a schematic diagram of expansion in a method for predicting mud loss before drilling based on analogy of main control factors provided in Example 1 of the present invention;

[0039] Figure 5 The correlation between the amount of mud loss and the distance from the fault in a method for predicting mud loss before drilling based on analogy of main control factors provided in Example 1 of the present invention;

[0040] Figure 6 The correlation between the mud loss amount and the fault distance in a method for predicting mud loss before drilling based on the analogy of the main control factors provided in Example 1 of the present invention;

[0041] Figure 7 The correlation between the mud loss amount and the horizontal stress difference in a pre-drilling mud loss prediction method based on the analogy of the main control factors provided in Example 1 of the present invention;

[0042] Figure 8 The present invention provides a method for predicting mud loss before drilling based on analogy of main controlling factors, which provides a correlation between the amount of mud loss and the fracture development coefficient. DETAILED DESCRIPTION

[0043] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1

[0045] like Figure 1 As shown, this embodiment proposes a method for predicting mud loss before drilling based on analogy of main control factors, and the method includes:

[0046] S100, analyzing the data of the leakage area to determine the main control factor set affecting the leakage and the mud loss amount at the leakage location.

[0047] In this embodiment, the main controlling factors include the fault zone throw (the fault throw refers to the vertical distance between the upper and lower plates of the fault), the distance from the fault (the distance between the leakage position and the fault), the horizontal stress difference and the fracture development coefficient.

[0048] 1. Analysis of the influence of fault zone offset (fault offset refers to the vertical distance between the upper and lower plates of the fault) and distance from the fault (the distance between the leakage location and the fault) on leakage

[0049] Combined with the lithological combination characteristics and thickness of each formation in the leakage area, Landmark software was used to calibrate the seismic reflection layer and divide the formation. The overall 8×8 and local 4×4 densities were used to finely interpret the three-dimensional seismic data of the study area, and the distribution morphology of the fault zone in the plane and vertical direction was finely characterized. The relationship between the leakage position and the fault was analyzed to determine the influence of the fault zone fault distance and the distance from the fault on the leakage.

[0050] Specifically, based on the existing data, combined with the lithologic combination characteristics and thickness of each stratum in the Tazhong North Slope area of ​​the Tarim Basin, Landmark software was used to calibrate the seismic reflection layer and stratum division, and each reflection layer was calibrated and tracked. The three-dimensional seismic data of the study area were finely interpreted using the overall 8×8 and local 4×4 density, and the distribution morphology of the fault zone in the plane and in the vertical direction was finely portrayed. It was concluded that the negative flower-shaped branch normal faults of the Silurian system are very developed, with a right-step en echelon distribution, strong strike-slip extension, and severe stratum fragmentation. By analyzing the relationship between the leakage position and the fault, it was concluded that the amount of mud leakage increases with the increase of the fault distance, and the frequency and amount of leakage are the highest when the distance to the fault is closer. The statistics of mud leakage and the distance from the wellbore to the nearby fault show that the frequency of leakage is the highest when the wellbore is within 150 meters from the fault, followed by 150 meters to 300 meters. The distance between the wellbore and the fault exceeds 300 meters, and the possibility of leakage is the lowest.

[0051] 2. Analysis of the impact of horizontal stress difference on leakage

[0052] Experimental tests and logging data calculations were used to determine the rock mechanical parameters of the target layer and the magnitude of the geostress of key wells in the north slope area of ​​Tazhong, Tarim Basin. In addition, well wall collapse, elliptical boreholes, induced fractures and GPS data were used to determine the direction of geostress. Based on the interpretation of three-dimensional seismic data, a geological model was established. Based on the geological model, finite element numerical simulation was carried out in the study area to clarify the planar distribution law of the current geostress in the study area, including the maximum, minimum and intermediate principal stresses. The stress field simulation results were compared and analyzed with the actual logging data interpretation results to further verify the rationality of the geological model and simulation results.

[0053] The essence of the finite element method is to discretize the continuous geological body into a finite number of units, assign corresponding rock mechanics parameters such as elastic modulus and Poisson's ratio to each unit, impose constraints and stress loads on the boundaries of the model, and calculate the displacement, strain, stress and other variables at each node based on the finite element linear algebraic equations. The specific idea is to first establish a geological model based on the results of structural analysis, the current landform and fault system diagram, and establish a mechanical model based on rock mechanics parameter tests. Secondly, according to the regional stress field background and logging data, determine the direction of the current ground stress, calculate the magnitude of the current ground stress, and finally use ANSYS software to solve and calculate to obtain the current regional triaxial principal stress distribution characteristics.

[0054] The specific steps are as follows:

[0055] Establishing geological model: The geological model established this time is a two-dimensional plane model, and the simulation range is determined according to the scope of the study area in the Landmark software; the fault zones determined in the Landmark study area are exported and digitized; the digitized faults and the scope of the study area are imported into the ANSYS software to establish the geological model; the mechanical properties of different structural units on the plane are different, and generally the strength of the fault zone is weaker than that of normal sedimentary strata; different mechanical parameters are assigned to different blocks of the stress field simulation according to the rock mechanical parameter results calculated from the well logging data in the study area;

[0056] Finite element meshing: After determining the mechanical parameters, different mesh unit types are selected according to the strain characteristics of different rocks to divide the geological model to obtain a finite element model. For the study area, plane solid95 units are selected to mesh the solid geological model to obtain a finite element model, such as Figure 2 As shown in the figure, solid95 unit constructs a three-dimensional finite element model through 8 nodes. Each node has three degrees of freedom in the x / y / z directions. The unit has plasticity, creep, expansion, stress hardening, large deformation and large strain capabilities, which are closest to the actual geological body. The fracture interface model of the study area is divided into 20,926 units, and 40,187 nodes are involved in the calculation. In order to more accurately reflect the differences between fault blocks and fault zones, the mesh length of the fault zone is relatively small compared to the inside of the fault block. After the mesh is divided, the mesh is checked, and the aspect ratio check, balance deviation check, maximum torsion angle check, etc. are all within the acceptable range, and the mesh quality is good.

[0057] Finite element numerical simulation:

[0058] Constraints and stress loads are imposed on the boundaries of the model. On the basis of establishing the geological model and determining various rock mechanics parameters, the model can be loaded and solved according to the direction of the current stress field. The vertical direction of the model is the Z axis, which is vertically upward. The overall coordinate X axis points to the east and west, and the Y axis points to the north and south. In order to meet the requirements of finite element analysis, the constraints imposed on the model are: constraints are imposed on the bottom surface of the model's lower plate to fix its displacement in the Z direction, which can reduce the deformation of the lower plate basement rock. Because it is a two-dimensional plane model, the boundary force only includes horizontal tectonic forces. According to the current stress state of the study area, it is finally determined to apply extrusion stress on the northeast-southwest and northwest-southeast boundaries of the model;

[0059] Under the above model constraints and loading conditions, the tectonic stress field of the fault zone interface in the study area was numerically simulated, and the stress distribution results obtained by simulation are as follows: Figure 3 As shown, the intermediate stress diagram obtained is the horizontal stress difference diagram.

[0060] 3. Analysis of the influence of fracture development coefficient on leakage

[0061] Developed natural fractures are closely related to mud loss. Therefore, predicting the degree of development of natural fractures in the Silurian sandstone in the northern slope of Tazhong, Tarim Basin is of great significance for exploring the mechanism of mud loss. As early as the 1990s, predecessors summarized a set of methods for predicting the degree of development of rock fractures using rock fracture criteria. Under stress, tensile fractures and shear fractures mainly occur inside the rock. The Griffith fracture criterion is used to predict tensile fractures, and the Mohr-Coulomb fracture criterion is used to predict shear fractures, and the planar distribution of fractures in the Silurian formation in the study area is comprehensively analyzed.

[0062] (1) Using the Griffith rupture criterion to predict tensile rupture, we can obtain the tensile rupture coefficient I Γ

[0063] Griffith believes that there are always many small cracks or defects that can be approximated as ellipses in actual materials. Under the action of external force, concentrated tangential tensile stress will be generated around the elliptical crack. Once the concentrated tangential tensile stress at a certain point on the top of the crack exceeds the cohesive strength of the brittle material, the material will begin to expand in a certain direction at that point and cause fracture. Figure 4 shown.

[0064] The plane fracture criterion expression of Griffith strength theory is:

[0065] When σ1+3σ3≥0,

[0066] (σ 1 -σ 3 ) 2 +8(σ 1 +σ 3 )σ Γ =0

[0067]

[0068] When σ1+3σ3<0,

[0069] σ 3 =-σ Γ

[0070] Where, σ1 and σ3 are the maximum horizontal principal stress and the minimum horizontal principal stress, MPa; β is the rupture direction, °; ​​σ Γ is the tensile stress strength, MPa. The tensile rupture coefficient I Γ Characterizes the development of rock tensile fractures, I Γ =σ Г / σ Г ′.

[0071] When the tensile stress σ ΓExceeding the tensile strength of rock Γ ', that is, I Γ ≥1, the rock undergoes tensile fracture.

[0072] (2) Using the Mohr-Coulomb rupture criterion to predict shear rupture, the shear rupture coefficient I is obtained. n ;

[0073] The Mohr-Coulomb fracture criterion holds that the shear fracture of rock along a fracture surface is not only related to the shear stress value T on the fracture surface, but also to the normal stress value σ on the fracture surface, that is, the shear failure on the plane is the result of the combined effect of the shear stress T on the surface and the normal stress σ. The Mohr-Coulomb shear fracture criterion expression is:

[0074]

[0075] where σ and T can be expressed in terms of principal stresses:

[0076]

[0077]

[0078] Where C is the cohesion of rock, MPa; is the internal friction angle of the rock, °; θ is the angle between the shear fracture surface and the minimum principal stress σ3, that is, the angle between the normal direction of the shear fracture surface and the maximum principal stress σ1, °. The shear fracture coefficient In is used to characterize the development of rock shear cracks, I n =τ n / τ n ′. When the shear stress τ n Exceeds the shear strength of rock τ n ', that is, I n ≥1, the rock undergoes shear failure.

[0079] (3) Use the crack development coefficient I F To characterize the degree of development of structural fractures

[0080] The northern slope of Tazhong in the Tarim Basin has not only one type of fracture, but also tensile fracture and shear fracture coexisting. F :

[0081] I F =aI Γ +bI n

[0082] Where a and b represent the proportion of tensile fractures and shear fractures in the rock to the structural fractures, respectively.

[0083] The fracture development coefficient I of the Silurian strata in the fault zone of the study area was calculated using the above method. F Plane distribution. Fracture development coefficient I of Silurian strata in the fault zone F The distribution ranges from 0.61 to 2.69. F When I is less than 1, it can be considered that the rock has not been fractured. F When >1, the rock will be fractured to different degrees. F The larger the value, the greater the probability of rock fracture. F The larger the value, the higher the classification, and the easier it is for the rock to break. It reflects that the distance from the well to the fracture, the stress difference, and the degree of fracture development are the main factors leading to high leakage.

[0084] Crack development degree judgment standard table

[0085] Fracture development level A B C Comprehensive crack rupture rate I I＞1.4 1.0＜I＜1.4 I＜1.0 Degree of crack development development More developed Underdevelopment

[0086] S200. The correlation between each factor in the main control factor set and the mud loss is obtained through correlation analysis.

[0087] Specifically, the correlation coefficient between each factor and the mud loss is calculated to obtain the correlation relationship between each factor and the mud loss.

[0088] Based on 6 completed wells, the horizontal distance of the lost section from the wellbore fracture (distance to the fracture), the fault distance of the wellbore fracture, the horizontal stress difference of the lost section and the fracture development coefficient I F In addition, the relationship between the Silurian mud loss of the six completed wells and the distance to the fault, fault throw, horizontal stress difference and fracture development coefficient I was analyzed. F (Right now Figure 7 The correlation between the stress heterogeneity coefficient in Figure 5 , 6 As shown in Figure 7, it can be seen that the correlation between mud loss and distance from the fault is poor (R 2 =0.0473), indicating that the reliability of predicting mud loss by distance from fault is low; mud loss and fault distance show a positive correlation, and the correlation coefficient (R 2 )0.6584, the correlation is y=11.946x-274.5; the mud loss is negatively correlated with the horizontal stress difference, and the correlation coefficient (R 2 )0.6854, the correlation is y=-15.78x+898.63; mud loss and fracture development coefficient I F There is a negative correlation, and the correlation coefficient (R 2 )0.833, and the correlation is y=-918.92x+1675.9.

[0089] S300, obtaining the values ​​of the main control factors of the area to be predicted, and obtaining the mud loss prediction result of the area to be predicted based on the correlation between the obtained factors and the mud loss.

[0090] According to the correlation between each factor and mud loss, the mud loss corresponding to each main control factor in the area to be predicted is calculated;

[0091] According to the obtained correlation coefficient between each factor and mud loss, the weight of each factor is determined;

[0092] The weighted average value is calculated based on the mud loss corresponding to each main control factor in the area to be predicted and the weight of each factor to obtain the final mud loss prediction value.

[0093] Specifically, the weight of each factor is assigned according to the calculated correlation coefficient. If the correlation coefficient is large and the correlation is high, a higher weight is assigned, and if the correlation is low, a lower weight is assigned. Based on the above-obtained correlation formula between mud loss and fault distance, horizontal stress difference and fracture development coefficient (the distance factor from the fault has a low correlation and is ignored), the mud loss at the target depth of Well 6 and Well 7 was predicted respectively. The predicted mud loss range of the Silurian system in Well 6 is 144-435m 3 The average leakage is predicted to be 314m 3 The predicted mud loss range of the Silurian system in Well 7 is 84-233m 3 The average leakage is predicted to be 169m 3 , the effect is good and very close.

[0094] The present embodiment proposes a method for predicting mud loss before drilling based on the analogy of the main controlling factors, which can realize the prediction of mud loss before drilling in complex well conditions. By predicting the well leakage of the formation before drilling, it can provide guidance for drilling, avoid the drilling leakage layer section, and improve the drilling success rate.

[0095] Example 2

[0096] Corresponding to the above-mentioned embodiment 1, this embodiment proposes a pre-drilling mud loss prediction system based on the analogy of the main control factors, and the system includes:

[0097] The main control factor set determination module is used to analyze the data of the leakage area, determine the main control factor set affecting the leakage and the mud loss at the leakage location;

[0098] A correlation determination module is used to obtain the correlation between each factor in the main control factor set and the mud loss through correlation analysis;

[0099] The prediction result acquisition module is used to obtain the values ​​of the main control factors of the area to be predicted, and obtain the mud loss prediction result of the area to be predicted based on the correlation between the obtained factors and the mud loss.

[0100] The functions performed by the various components in the pre-drilling mud loss prediction system based on the analogy of the main control factors provided by the embodiment of the present invention have been introduced in detail in the above embodiment 1, so they will not be described in detail here.

[0101] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for predicting mud loss before drilling based on analogy of main controlling factors, characterized in that: The method comprises: Analyze the data of the leakage area to determine the main controlling factors affecting the leakage and the amount of mud loss at the leakage location; Through correlation analysis, the correlation between each factor in the main control factor set and the mud loss is obtained; Obtaining the values ​​of the main control factors of the area to be predicted, and obtaining the mud loss prediction result of the area to be predicted based on the correlation between the obtained factors and the mud loss; The main controlling factors include fault zone displacement, distance from the fault, horizontal stress difference and fracture development coefficient; Among them, the fault zone throw refers to the vertical distance between the upper and lower plates of the fault, and the distance from the fault is the distance from the leakage position to the fault; The fracture development coefficient is the degree of development of structural fractures: I F =aI Γ +bI n Among them, I F is the fracture development coefficient, Ir is the tensile fracture coefficient, In is the shear fracture coefficient, and a and b represent the proportion of tensile fractures and shear fractures in the rock to the structural fractures respectively.

2. The method for predicting mud loss before drilling based on analogy of main controlling factors according to claim 1, characterized in that: Analyze the data of the leakage area to determine the main controlling factors affecting the leakage and the amount of mud loss at the leakage location, including: Combined with the lithological combination characteristics and thickness of each formation in the leakage area, Landmark software was used to calibrate the seismic reflection layer and divide the formation. The overall 8×8 and local 4×4 densities were used to interpret the three-dimensional seismic data of the study area, and the distribution morphology of the fault zone in the plane and vertical direction was characterized. The relationship between the leakage position and the fault was analyzed to determine the influence of the fault zone fault distance and the distance from the fault on the leakage.

3. The method for predicting mud loss before drilling based on analogy of main controlling factors according to claim 2, characterized in that: Analyze the data of the leakage area to determine the main controlling factors affecting the leakage and the amount of mud loss at the leakage location, including: Establishing geological model: The simulation scope is determined according to the scope of the study area in Landmark software; the fault zones determined in the Landmark study area are exported and digitized; the digitized faults and the scope of the study area are imported into ANSYS software to establish the geological model; different mechanical parameters are assigned to different blocks of stress field simulation according to the rock mechanical parameter results calculated from the well logging data of the study area; Finite element meshing: For the study area, the plane solid95 unit is used to mesh the solid geological model to obtain the finite element model; Finite element numerical simulation: constraints and stress loads are applied to the boundaries of the model, and the structural stress field of the fault zone interface in the study area is numerically simulated. The stress distribution results obtained by simulation are used to obtain the horizontal stress difference.

4. The method for predicting mud loss before drilling based on analogy of main controlling factors according to claim 1, characterized in that: Analyze the data of the leakage area to determine the main controlling factors affecting the leakage and the amount of mud loss at the leakage location, including: The tensile rupture coefficient I is obtained by using the Griffith rupture criterion to predict the tensile rupture. Γ ; The shear fracture coefficient I is obtained by using the Mohr-Coulomb fracture criterion to predict shear fracture. n ; Use the crack development coefficient I F To characterize the degree of development of structural fractures: I F =aI Γ +bI n Where a and b represent the proportion of tensile fractures and shear fractures in the rock to the structural fractures, respectively.

5. The method for predicting mud loss before drilling based on analogy of main controlling factors according to claim 1, characterized in that: Through correlation analysis, the correlation between each factor in the main control factor set and the mud loss is obtained, including: The correlation coefficient between each factor and the mud loss is calculated, and the correlation formula between each factor and the mud loss is obtained.

6. The method for predicting mud loss before drilling based on analogy of main controlling factors according to claim 5, characterized in that: Obtain the values ​​of the main control factors of the area to be predicted, and obtain the mud loss prediction results of the area to be predicted based on the correlation between the obtained factors and the mud loss, which specifically includes: According to the correlation between each factor and mud loss, the mud loss corresponding to each main control factor in the area to be predicted is calculated; According to the obtained correlation coefficient between each factor and mud loss, the weight of each factor is determined; The weighted average value is calculated based on the mud loss corresponding to each main control factor in the area to be predicted and the weight of each factor to obtain the final mud loss prediction value.

7. A pre-drilling mud loss prediction system based on analogy of main control factors, characterized in that: The system comprises: The main control factor set determination module is used to analyze the data of the leakage area, determine the main control factor set affecting the leakage and the mud loss at the leakage location; A correlation determination module is used to obtain the correlation between each factor in the main control factor set and the mud loss through correlation analysis; The prediction result acquisition module is used to obtain the values ​​of the main control factors of the area to be predicted, and obtain the prediction result of the mud loss in the area to be predicted based on the correlation between the obtained factors and the mud loss; The main controlling factors include fault zone displacement, distance from the fault, horizontal stress difference and fracture development coefficient; Among them, the fault zone throw refers to the vertical distance between the upper and lower plates of the fault, and the distance from the fault is the distance from the leakage position to the fault; The fracture development coefficient is the degree of development of structural fractures: I F =aI Γ +bI n Among them, I F is the fracture development coefficient, Ir is the tensile fracture coefficient, In is the shear fracture coefficient, and a and b represent the proportion of tensile fractures and shear fractures in the rock to the structural fractures respectively.

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