Method for predicting stratum drilling fluid leakage-prone position by using logging data

Abstract

The invention discloses a method for predicting a stratum drilling fluid easy-to-leak position by using logging data, which relates to the field of petroleum and natural gas exploration and development, and is characterized in that the stratum drilling fluid easy-to-leak position is predicted through five or more sample adjacent well target stratum rock mineral components, lithology, fracture and pore morphological characteristics and rock mechanics experimental tests; the rock tensile strength performance, the stratum leakage channel and the reservoir space characteristics of the target stratum are revealed, and rock mechanical parameters and ground stress are obtained; on the basis of fine division of small layers of a target stratum, performing thinning or interpolation processing on rock mechanical parameters, ground stress and stratum pressure data of a sample adjacent well, and determining a prediction weight according to a distance between the adjacent well and a prediction well to realize prediction calculation of target stratum data of the prediction well; and calculating and predicting collapse pressure, leakage pressure and fracture pressure of the target stratum of the well, and correcting the collapse pressure, the leakage pressure and the fracture pressure according to the mode so as to predict the stratum drilling fluid leakage-prone position before drilling. According to the method, the drilling fluid easy-to-leak position can be subjected to pre-drilling fine prediction, and quality-improving and efficiency-improving drilling of the complex easy-to-leak stratum is achieved.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas exploration and development, and in particular relates to a method for predicting formation drilling fluid leakage prone layers by using well logging data. Background Art

[0002] Drilling fluid loss is a frequent and complex downhole incident in oil and gas drilling operations. According to statistics, global economic losses due to lost circulation exceed $2 billion annually. All time spent addressing drilling fluid loss, from the start to the end, is considered non-productive time, accounting for 5% to 15% of total oil and gas drilling time in my country. Downhole drilling fluid loss incidents not only impact drilling efficiency and cause economic losses to companies, but if not properly addressed, they can also lead to other complex drilling safety incidents such as blowouts, collapses, and stuck wells. Therefore, developing predictive technology for identifying zones prone to drilling fluid loss in proposed wells will effectively support wellbore optimization and drilling parameter design, significantly reducing drilling fluid loss. This is of vital practical significance for reducing drilling costs, improving drilling efficiency, and ensuring drilling safety.

[0003] Drilling fluid loss is a complex result of multiple factors, including geological conditions and drilling techniques. Seismic data contains a wealth of fracture-related information and is widely used to predict the location of leaky formations. Current methods for identifying leaky formations using seismic data include coherence volume technology, shear wave splitting technology, longitudinal wave AVO (Amplitude Various Offset) technology, and AVA (Amplitude Various Angle) technology. Although domestic researchers have attempted to use raw seismic data to predict the location of leaky formations, this method can only identify large fracture zones, such as faults and major fractures. In practical applications, this method still has significant shortcomings. Using mud logging and well logging data to evaluate drilling fluid loss-prone zones involves measuring wellbore physical properties, such as density and acoustic velocity, using logging instruments. These properties are then converted into rock mechanical parameters using specific theoretical models. This method is fast and efficient, but mud logging and well logging data only reflect local geological characteristics around the wellbore wall and cannot fully reflect the mechanical properties of the entire formation. Furthermore, since this method is performed during or after drilling, it lacks predictive power and is therefore of limited value for optimizing wellbore structure and designing drilling parameters for new wells. In recent years, researchers at home and abroad have explored the nonlinear correlation between geology and construction effects on lost circulation risk using machine learning and neural networks. However, lost circulation risk prediction based on machine learning and neural networks requires extensive geological data and field data. A reliable prediction model must be trained on data from multiple wells with fluid loss. Furthermore, these models are only applicable to the formations in the study area, limiting their application to new wellbore areas where relatively few development wells are currently operating.

[0004] In order to achieve safe, high-quality and rapid drilling in complex formations, there is an urgent need for pre-drilling prediction of leaky layers. The present invention provides a method for predicting formation drilling fluid leaky layers using logging data. This method is based on indoor core experiments and logging interpretation, clarifies the mineral composition and natural fracture development characteristics, rock mechanics parameters, ground stress and formation pressure characteristics of the drilled target formation, divides the formation into small layers, and uses thinning or interpolation mapping to construct the rock mechanics parameters, ground stress and formation pressure profiles of the predicted well, and then calculates the formation leakage pressure, formation fracture pressure and formation collapse pressure and performs mode difference correction. Finally, combined with the target formation leakage channel and reservoir space characteristics, pre-drilling prediction of formation drilling fluid leaky layers is achieved. The theoretical basis for the method of using logging data to predict formation drilling fluid leaky layers is as follows:

[0005] 1. Calculation formula of rock dynamic elastic modulus and dynamic Poisson's ratio based on longitudinal and shear wave velocities

[0006] The calculation formulas for the rock longitudinal and shear wave velocities are as follows:

[0007]

[0008] Where: V p is the rock longitudinal wave velocity, m / s; V s is the rock shear wave velocity, m / s; L is the length of the rock sample, mm; t p is the termination time of the longitudinal sound wave propagation in the specimen, ms; t p0 is the initial time when the longitudinal sound wave propagates in the specimen, ms; Δt p is the propagation time of the longitudinal sound wave in the specimen, ms; t s is the termination time of the shear wave propagation in the specimen, ms; t s0 is the initial time when the shear wave propagates in the specimen, ms; Δt s is the propagation time of the shear wave in the specimen, ms.

[0009] The dynamic elastic modulus and Poisson's ratio of rock are calculated by the following formula:

[0010]

[0011] Where: E d is the dynamic elastic modulus of rock, GPa; μ d is the dynamic Poisson's ratio of rock; ρ is the volume density of rock, g / cm 3 .

[0012] 2. Calculation formula for rock tensile strength

[0013] The tensile strength of rock refers to the maximum stress that rock can withstand under tension. The Brazilian splitting method for measuring rock tensile strength is a standard recommended by the International Society for Rock Mechanics. When a symmetrical circular disk specimen is subjected to a concentrated load P, according to elasticity theory, the stress state at any point (x, y) on the loading diameter of the disk specimen is:

[0014]

[0015]

[0016] Where: σ x is the stress in the x direction acting on the loading diameter of the disk specimen, MPa; σ y is the stress in the y direction acting on the loading diameter of the disk specimen, MPa; P is the load, kN; D is the specimen diameter, cm; π is the circumference of a circle, which is generally taken as 3.14.

[0017] The stress at the center of the specimen (y=0) is:

[0018]

[0019] Equations (7) and (8) show that the compressive stress at the center of the disk specimen is three times the tensile stress. However, since the tensile strength of rock is much lower than its compressive strength, failure occurs at the center once the tensile stress reaches the tensile strength of the specimen. It is generally believed that tensile stress plays a dominant role in fracture. Therefore, equation (7) can be used to calculate the tensile strength of rock.

[0020] 3. Calculation formula for rock compressive strength

[0021] In the triaxial test of rock, according to the "Test Procedures for Physical and Mechanical Properties of Rock Part 20: Triaxial Compression Strength Test of Rock" (DZ / T 0276.20-2015), the axial stress under different confining pressures is calculated using the following formula:

[0022]

[0023] Where: σ p is the axial stress when the sample is broken, MPa; A is the cross-sectional area of ​​the sample, cm 2 Under uniaxial test conditions, the calculated axial stress at the time of sample failure is the uniaxial compressive strength of the rock. The triaxial compressive strength is usually calculated using the differential stress S c Indicates that:

[0024] S c =σ p -σ c (10)

[0025] Where: σ c is the confining pressure, MPa.

[0026] 4. Calculation formula for rock static elastic modulus and static Poisson's ratio

[0027] The static elastic modulus of rock is the ratio of the axial stress increment to the axial strain increment in the elastic deformation stage of the sample stress-strain curve obtained from the triaxial compression mechanics experiment of rock, that is:

[0028]

[0029] Where: ΔP is the axial load increment during the elastic deformation stage, MPa; ΔL is the axial deformation increment during the elastic deformation stage, mm.

[0030] The static Poisson's ratio of rock is the ratio of the radial strain increment to the axial strain increment during the elastic deformation phase of the stress-strain curve of the rock specimen obtained from a triaxial compression test. Under conventional rock triaxial compression test conditions, the circumferential deformation increment during the elastic deformation phase is usually used instead of the radial strain increment for calculation, using the following formula:

[0031]

[0032] Where: ΔC is the circumferential deformation increment in the elastic deformation stage, mm.

[0033] 5. Rock acoustic emission test ground stress calculation formula

[0034] The Kaiser effect of rock can be used to measure the three-dimensional stress values ​​of rock in the formation. Based on the analysis and acquisition of the test stress in each direction, according to the spatial relationship of the stress components, the stress vectors in the three directions can be used to calculate the magnitude and direction of the horizontal in-situ stress at the test point through corresponding transformation. The calculation formulas for vertical in-situ stress, maximum horizontal in-situ stress, and minimum horizontal in-situ stress are:

[0035]

[0036] Where: σ v is the vertical ground stress, MPa; σ H is the maximum horizontal ground stress, MPa; σ h is the minimum horizontal ground stress, MPa; σ 丄 is the stress value of the Kaiser effect point in the vertical direction of the core, MPa; P P is the formation pressure, MPa; σ0°, σ 45 °、σ 90 ° is the stress value of the Kaiser effect point in the core at three horizontal directions of 0°, 45°, and 90°, MPa; φ is the angle between the axis of the rock sample and the direction of the maximum horizontal principal stress, °.

[0037] 6. Mohr-Coulomb strength criterion

[0038] The failure of rock is mainly shear failure. The force resisting failure on the shear surface is the rock shear strength, which is equal to the cohesive force of the rock itself resisting shear failure and the friction generated by the normal force on the shear surface. The shear strength criterion in the plane is:

[0039]

[0040] Where: τ is the shear strength of rock, MPa; C is the cohesion of rock, MPa; σ is the normal stress, MPa; is the internal friction angle of rock, °.

[0041] The Mohr-Coulomb strength criterion for rock under triaxial stress conditions, expressed using maximum and minimum principal stresses, is:

[0042]

[0043] Where: σ1 represents the maximum principal stress, MPa; σ3 represents the minimum principal stress, MPa.

[0044] Considering the formation pressure P P When , formula (18) is expressed in terms of effective stress:

[0045]

[0046] Where: α is the effective stress coefficient, 0<α<1, for shale formations, α=0.5~0.6, for sandstone α=0.7~0.9, and for high permeability formations α=1.

[0047] 7. Calculation formula for dynamic mechanical parameters of rock

[0048] The calculation formula of the dynamic uniaxial compressive strength of rock is as follows:

[0049] S c =0.0026×E d ×(1-V sh )+0.008×E×V sh (20)

[0050] Where: S c is the dynamic uniaxial compressive strength of the rock sample, MPa; V sh is the mud content of the rock sample, %.

[0051] The calculation formula of rock dynamic shear strength is as follows:

[0052]

[0053] Where: S s is the dynamic shear strength of the rock sample, MPa.

[0054] The calculation formula of rock dynamic tensile strength is as follows:

[0055] S t =1.263+0.386×S s (twenty two)

[0056] Where: S t is the dynamic tensile strength of the rock sample, MPa.

[0057] 8. Formation pressure calculation model

[0058] The Eaton method uses interval velocity, the "Dc" index, sonic logging, resistivity logging, density logging, and shale density to evaluate formation pressure. The principle is that the relationship between the ratio of the actual value of the compaction parameter to the normal trend value and the formation pressure is determined by the change in the overburden pressure gradient. The general calculation model of the Eaton method is as follows:

[0059] P P =P0-(P0-P W )(Q / Q′) c (twenty three)

[0060] Where: P0 is the overlying formation pressure, MPa; P W is the hydrostatic pressure of the formation water (generally ρ w =1g / cm 3 ~1.07g / cm 3 , fresh water is 1g / cm 3 , saline is 1.05g / cm 3 ), MPa; c is the compaction index; Q and Q' are the selected logging or drilling parameters, which are acoustic transit time, resistivity, interval velocity, and Dc index, and satisfy L / L'<1. When the selected parameters increase with depth, Q / Q' represents the ratio of the measured value to the standard value, and vice versa. Therefore, when selecting acoustic transit time logging data, since acoustic transit time gradually decreases with increasing depth, Q / Q' represents the ratio of the standard acoustic transit time to the measured acoustic transit time, and thus:

[0061] P P =P0-(P0-P W )(Δt n / Δt c ) c (twenty four)

[0062] Where: Δt c is the measured value of acoustic time difference logging, μs / ft; Δt n is the acoustic time difference on the normal compaction trend line, μs / ft.

[0063] The key of Eaton method based on acoustic travel time to calculate formation pressure is the construction of normal compaction trend line and compaction index c. For compaction index c, the formation conditions are different in different areas, so directly using this empirical coefficient will inevitably cause large error in prediction results. Therefore, it is necessary to recalculate the value of c, and the calculation formula of c is as follows:

[0064]

[0065] 9. Dynamic stress calculation model

[0066] The dynamic vertical ground stress calculation formula is as follows:

[0067]

[0068] In the formula, H is the formation depth, m; ρ(z) is the formation density at the depth z, g / cm 3 ; g is the acceleration of gravity, m / s 2 , generally 9.8 m / s 2 .

[0069] The dynamic horizontal ground stress calculation formula is as follows:

[0070]

[0071] In the formula, ε h and ε H are two orthogonal principal strain components of the rock unit in the horizontal direction, dimensionless.

[0072] 10. Formation collapse pressure calculation model

[0073] When the liquid column pressure in the wellbore is lower than the formation collapse pressure, the wellbore wall rock will produce shear failure. If it is plastic rock, plastic flow will be produced to the well to cause the reduction of diameter, and brittle rock will cause collapse and blockage to cause the expansion of well diameter and sticking of drill pipe. When the liquid column pressure in the wellbore is greater than the formation collapse pressure, the stress and the wellbore liquid column pressure produce stress condition lower than the shear strength of the rock, and the wellbore wall rock will not be damaged, and the wellbore wall remains stable.

[0074] According to the theory of elasticity, under the action of three-dimensional ground stress, the wellbore stress in cylindrical coordinate system is represented as:

[0075]

[0076] In the formula, σ r is the wellbore radial stress in cylindrical coordinate system, MPa; σ θ is the wellbore circumferential stress in cylindrical coordinate system, MPa; σ z is the wellbore axial stress in cylindrical coordinate system, MPa; P m is the drilling fluid column pressure, MPa.

[0077] When the liquid column pressure in the wellbore is equal to the formation collapse pressure, substitute formula (28) into formula (19) to obtain the following formula for calculating the formation collapse pressure:

[0078]

[0079] Where: P b is the formation collapse pressure, MPa; η is the nonlinear stress correction coefficient of the wellbore rock, generally taken as 0.95; the calculation formula of K is as follows:

[0080]

[0081] 11. Formation fracture pressure calculation model

[0082] At present, there are many methods for calculating formation fracture pressure using logging data. The improved Tan model method comprehensively considers the formation pressure, overburden pressure and rock tensile strength and is widely used. Its expression is as follows:

[0083]

[0084] Where: P f is the formation fracture pressure, MPa; μ b is the tectonic stress coefficient; μ is the Poisson's ratio; S t is the tensile strength of rock, MPa.

[0085] 12. Formation leakage pressure calculation model

[0086] When the formation is a fractured formation or a porous formation, the drilling fluid flowing into the formation will no longer require the rock to undergo tensile damage, that is, the formation loss pressure does not need to consider the rock tensile strength. The formation loss pressure calculation model is obtained according to formula (31):

[0087] Summary of the Invention

[0088] The present invention aims to solve the problem that drilling fluid leakage-prone layers are difficult to predict effectively before drilling. To this end, a method for predicting drilling fluid leakage-prone layers in formations using well logging data is proposed.

[0089] The technical solution adopted in the present invention is as follows:

[0090] Step 1.1. Based on the interpretation of logging data from the target formations of five or more sample adjacent wells, construct logging interpretation profiles of the corrected rock mechanical parameters and in-situ stresses of the target formations of the sample adjacent wells. The rock mechanical parameters include tensile strength, uniaxial compressive strength, triaxial compressive strength, elastic modulus, and Poisson's ratio. Also, construct logging interpretation profiles of the formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure of the target formations of the five or more sample adjacent wells.

[0091] Step 1.2: Based on rock mineral composition testing, electron microscopy scanning, cast thin section identification, and imaging logging interpretation and analysis of the target formations in five or more sampled adjacent wells, the lithology, fracture development, and pore development of the target formations in the sampled adjacent wells are determined. The tensile strength properties of the rock, the formation leakage pathways, and the reservoir space characteristics of the target formations in the sampled adjacent wells are revealed from the perspective of rock lithology and structural characteristics.

[0092] Step 1.3: Based on the logging interpretation profiles of the target formation's rock mechanical parameter correction values ​​and ground stress correction values ​​from five or more sample adjacent wells, as well as the logging interpretation profiles of formation pressure, formation collapse pressure, formation loss pressure, and formation fracture pressure, combined with the natural gamma ray curves, resistivity curves, and acoustic wave curves of the target formation from the sample adjacent wells, perform fine sub-layer division of the target formation;

[0093] Step 1.4: Based on the results of the fine sub-stratum division of the target formation, the rock mechanical parameter correction values, ground stress correction values, and formation pressure data obtained by interpreting the target formation acoustic logging data of 5 or more sample adjacent wells are used to perform data thinning or interpolation processing;

[0094] Step 1.5: Based on the distances between 5 or more sample adjacent wells and the predicted well, obtain the prediction weight value of each sample adjacent well;

[0095] Step 1.6: Based on the prediction weights of five or more sample adjacent wells, the rock mechanical parameter correction values, in-situ stress correction values, and formation pressure of the target formation of the five or more sample adjacent wells are used to perform a weighted average calculation to obtain the rock mechanical parameters, in-situ stress, and formation pressure of the target formation of the predicted well;

[0096] Step 1.7: Construct a well logging interpretation profile of formation collapse pressure, formation loss pressure, and formation fracture pressure of the target formation of the prediction well based on the rock mechanical parameters, ground stress, and formation pressure of the target formation of the prediction well;

[0097] Step 1.8, using the mode difference to correct the well logging interpretation profile of the formation collapse pressure, formation loss pressure, and formation fracture pressure of the target formation of the predicted well;

[0098] Step 1.9: Based on the corrected formation collapse pressure, formation loss pressure, and formation breakdown pressure logging interpretation profiles of the target formation of the prediction well, combined with the characteristics of the loss channel and reservoir space of the target formation, determine the drilling fluid leakage-prone layer of the target formation of the prediction well.

[0099] Furthermore, the specific steps of step 1.1 are as follows:

[0100] Step 1.1.5. Based on the interpretation of acoustic logging data of the target formations from five or more sample adjacent wells, calculate the dynamic rock mechanical parameters and dynamic geostress logging interpretation values ​​of the target formations in the sample adjacent wells. Based on the dynamic and static rock mechanical parameters and geostress conversion model of the target formations in the sample adjacent wells, correct the dynamic rock mechanical parameters and dynamic geostress logging interpretation values ​​of the target formations in the sample adjacent wells to obtain the logging interpretation profiles of the corrected rock mechanical parameters and geostress values ​​of the target formations in the sample adjacent wells.

[0101] Step 1.1.6: Utilize the logging interpretation profiles of the target formation's rock mechanical parameter correction values ​​and ground stress correction values ​​from five or more sample adjacent wells, and based on the calculation models for formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure, calculate and construct the logging interpretation profiles of the target formation's formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure from the sample adjacent wells.

[0102] Further, the specific steps of step 1.2 are as follows:

[0103] Step 1.2.1. Use an X-ray diffractometer to test downhole rock samples from the target formation in five or more adjacent wells. Determine the rock mineral composition by observing the intensity of characteristic peaks in the rock mineral-specific X-ray spectra.

[0104] Step 1.2.2: Obtain fresh sections of downhole rock samples from the target formation in five or more adjacent wells. Use a scanning electron microscope to observe and identify the microscopic fracture and pore morphology of the fresh sections.

[0105] Step 1.2.3: Prepare thin cast sections of downhole rock samples from the target formation in five or more adjacent wells. Identify the thin cast sections using a polarizing microscope to determine the rock lithology and microscopic fracture and pore morphology.

[0106] Step 1.2.4: Use acoustic imaging logging technology to obtain high-resolution imaging logging interpretation images of the target formation from five or more sampled adjacent wells. Based on the imaging logging interpretation images, reveal the development characteristics and specific strata of natural fractures and dissolution pores;

[0107] Step 1.2.5: Based on the rock mineral composition, micro-scale fracture and pore morphology, rock lithology, meso-scale fracture and pore morphology, natural fracture and solution pore development characteristics, and specific strata, reveal the tensile strength properties, leakage pathways, and reservoir space characteristics of the target formation rock.

[0108] Further, the specific steps of step 1.3 are as follows:

[0109] Step 1.3.1. Conduct preliminary stratification of the target formation based on the logging interpretation profiles of the target formation's rock mechanical parameter correction values, in-situ stress correction values, and formation pressure logging interpretation profiles from five or more sampled adjacent wells;

[0110] Step 1.3.2: Analyze the target formation logging data from five or more adjacent wells, extract the mutation characteristic points of the natural gamma ray logging curve to determine the lithologic interface; identify the oil-water interface and gas-bearing display interval through resistivity logging curve morphology analysis, and determine the fluid properties; use the sonic transit time logging data to determine the lithologic and porosity changes by comparing the sonic transit time range with the standard lithologic data; identify the pressure anomaly zones and determine the pressure characteristic changes based on the logging interpretation profile of the formation collapse pressure, formation loss pressure, and formation fracture pressure; further, based on the preliminary stratification of the target formation, perform a fine subdivision of the target formation.

[0111] Further, the specific steps of step 1.4 are as follows:

[0112] Step 1.4.1. Based on the target formation thickness and original data sampling interval of 5 or more sampled adjacent wells, calculate the rock mechanical parameter correction value, ground stress correction value, and formation pressure data volume of each sublayer of the target formation in the sampled adjacent wells. The formula is as follows:

[0113]

[0114] Where: n sl H is the rock mechanics parameter correction value, ground stress correction value and formation pressure data of the lth layer of the target formation in the sample adjacent well, l is an integer; sl is the thickness of the first layer, m; N is the raw data sampling interval, m, generally 0.125 m;

[0115] Step 1.4.2: Calculate the total data volume of rock mechanical parameter corrections, ground stress corrections, and formation pressure for the target formation of 5 or more adjacent wells using the following formula:

[0116]

[0117] Where: n s is the total data volume of rock mechanics parameter correction values, ground stress correction values ​​and formation pressure of the target formation of the sample adjacent well, an integer; L is the total number of layers, an integer;

[0118] Step 1.4.3, calculating the thickness H of each small layer of the target formation of the prediction well tl The thickness H of each small layer of the target formation of the sample adjacent well sl The ratio m l , and the formula is as follows:

[0119]

[0120] Step 1.4.4, when m l <1, the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the sample adjacent well are thinned out, the data extraction interval number is calculated, and the formula is as follows:

[0121]

[0122] In the formula: k rl is the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data extraction interval number, which is an integer;

[0123] The rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the target formation of the sample adjacent well are extracted every k rl interval, and the extracted data is the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the prediction well mapped from the sample adjacent well;

[0124] Step 1.4.5, when m>1, the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the sample adjacent well are interpolated, the adjacent data interpolation number is calculated, and the formula is as follows:

[0125] k il =round(m l -1)

[0126] In the formula: k il is the adjacent data interpolation number, which is an integer;

[0127] The adjacent data of the rock mechanics parameter correction value, the ground stress correction value and the formation pressure of each small layer of the target formation of the sample adjacent well are linearly interpolated for k il data, and the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the prediction well mapped from the sample adjacent well are obtained.

[0128] Further, the specific steps of step 1.5 are as follows:

[0129] Step 1.5.1, according to the layout map of the sample adjacent well and the prediction well, the distance between the sample adjacent well and the prediction well is calculated;

[0130] Step 1.5.2: Calculate the prediction weight of each sample adjacent well based on the distance between the predicted well and five or more sample adjacent wells. The formula is as follows:

[0131]

[0132] Where: q k is the predicted weight value of the kth sample adjacent well, dimensionless; x k is the distance between the sample adjacent well and the prediction well, m;

[0133] Step 1.5.3: Normalize the predicted weights of 5 or more adjacent wells using the following formula:

[0134]

[0135] Where: p k is the predicted weight value of the kth sample adjacent well after normalization, dimensionless.

[0136] Further, the specific steps of step 1.6 are as follows:

[0137] Step 1.6.1. Based on the rock mechanical parameter correction values, ground stress correction values, and formation pressure data of the sub-layer corresponding to the prediction well mapped from 5 or more sample adjacent wells, use the normalized prediction weight values ​​of each sample adjacent well to calculate the predicted contribution of each sample adjacent well to the target formation rock mechanical parameters, ground stress, and formation pressure of the prediction well. The formula is as follows:

[0138] w k =s k p k

[0139] Where: w k Refers to the predicted components of the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well by the kth sample adjacent well; s k Refers to the rock mechanical parameters, ground stress and formation pressure of the target formation in the kth sample adjacent well;

[0140] Step 1.6.2: Based on the predicted components of the rock mechanical parameters, in-situ stress, and formation pressure of the target formation of the prediction well from five or more sample adjacent wells, calculate the rock mechanical parameters, in-situ stress, and formation pressure of the target formation of the prediction well by summing them up. The formula is as follows:

[0141] s=∑w k

[0142] Where: s refers to the rock mechanical parameters, ground stress and formation pressure of the target formation of the predicted well.

[0143] Further, the specific steps of step 1.7 are as follows:

[0144] Step 1.7.1. Based on the predicted in-situ stress and formation pressure of the target well, perform stress coordinate axis transformation, establish a cylindrical coordinate stress distribution model centered on the wellbore axis, and obtain the maximum and minimum principal stresses in the cylindrical coordinate system.

[0145] Step 1.7.2: Based on the rock mechanical parameters of the target formation of the prediction well, the formation pressure, the maximum principal stress and the minimum principal stress in the cylindrical coordinate system around the well, and the calculation model of the formation collapse pressure, formation loss pressure, and formation breakdown pressure, calculate and construct the logging interpretation profile of the formation collapse pressure, formation loss pressure, and formation breakdown pressure of the target formation of the prediction well.

[0146] Further, the specific steps of step 1.8 are as follows:

[0147] Step 1.8.1: Perform weighted averaging of the modes of formation collapse pressure, formation loss, and formation fracture pressure for the target formation in five or more adjacent wells. The formula is as follows:

[0148] z s =∑z k p k

[0149] Where: z s Refers to the mode of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation after weighted average processing, MPa; k Refers to the mode of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation of the kth sample adjacent well, MPa;

[0150] Step 1.8.2: Using the weighted average mode of the target formation's formation collapse pressure, formation loss pressure, and formation breakdown pressure, and based on the well logging interpretation profile data of the target formation's formation collapse pressure, formation loss pressure, and formation breakdown pressure in the prediction well, calculate and construct a profile of the corrected formation collapse pressure, formation loss pressure, and formation breakdown pressure in the target formation of the prediction well. The formula is as follows:

[0151] Df jz =Df+(z s -z)

[0152] Where: Df jz It refers to the correction value of formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well, MPa; Df refers to the logging interpretation value of formation collapse pressure, formation loss pressure or formation breakdown pressure of the target formation of the prediction well, MPa; z is the mode of formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well.

[0153] Further, the specific steps of step 1.9 are as follows:

[0154] Step 1.9.1. Based on the corrected profiles of formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure of the target formation of the predicted well, find areas of low formation loss pressure and formation breakdown pressure in the target formation of the predicted well, and areas where the difference between the formation pressure and formation collapse pressure relative to the formation loss pressure and formation breakdown pressure is small;

[0155] Step 1.9.2: Based on the characteristics of the target formation's leakage pathways and reservoir spaces, determine whether leakage pathways and reservoir spaces are developed in areas of low formation leakage pressure and formation breakdown pressure in the target formation of the prediction well, and in areas where the difference between formation pressure and formation collapse pressure relative to the formation leakage pressure and formation breakdown pressure is small. If leakage pathways and reservoir spaces are developed, the formation is determined to be a risky formation with fracture leakage. If the formation breakdown pressure is low but leakage pathways and reservoir spaces are not developed, the formation is determined to be a risky formation with fracture leakage.

[0156] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0157] 1. In the present invention, the formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well are calculated by adopting a series of optimized calculation and correction methods including proportional mapping of small layer thickness, proportional conversion of inter-well distance, weighted average operation of basic parameter distance and mode difference correction, thereby improving the calculation accuracy of the formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well.

[0158] 2. The present invention fully considers the characteristics of formation leakage channels and reservoir spaces, and combines the precise prediction of formation collapse pressure, formation leakage pressure and formation fracture pressure to accurately determine the type of drilling fluid loss, providing a reliable basis for the prevention and control of formation drilling fluid loss.

[0159] 3. In the present invention, by utilizing logging data from 5 or more adjacent wells and combining it with the results of rock physics and mechanics experiments, it is possible to predict the drilling fluid leakage-prone layers of the new target formation before drilling, and provide guidance for proactively preventing formation drilling fluid loss before drilling. Compared with traditional post-drilling evaluation and control methods, the post-drilling formation drilling fluid loss control is transformed into pre-drilling prevention, which can better reduce the risk of drilling fluid loss in oil and gas drilling and achieve quality-enhanced and efficient drilling of new wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0160] Figure 1 Flowchart of a method for predicting formation drilling fluid leakage zones using well logging data

[0161] Figure 2 This is the data diagram of the Maokou Formation sub-layer division in the case well area

[0162] Figure 3 Schematic diagram of the Maokou Formation sub-layer division in the case well area

[0163] Figure 4 Comparison of predicted values ​​of rock mechanical parameters of the Maokou Formation in the example well and the values ​​interpreted by logging

[0164] Figure 5 This is the coincidence curve between the predicted values ​​of rock mechanical parameters of the Maokou Formation and the logging interpretation values.

[0165] Figure 6 Comparison chart of the predicted values ​​and logging interpretation values ​​of the Maokou Formation formation collapse pressure, formation loss pressure, and formation fracture pressure of the example well

[0166] Figure 7 This is the coincidence curve of the predicted values ​​of the Maokou Formation formation collapse pressure, formation loss pressure, and formation fracture pressure and the logging interpretation values.

[0167] Figure 8 Comparison chart of the predicted correction values ​​and logging interpretation values ​​of the Maokou Formation formation collapse pressure, formation loss pressure, and formation fracture pressure in the example well

[0168] Figure 9 Coincidence curve of the predicted correction value and the logging interpretation value of the Maokou Formation formation collapse pressure, formation loss pressure, and formation fracture pressure in the example well

[0169] Figure 10 The final prediction results of the Maokou Formation formation collapse pressure, formation loss pressure, and formation fracture pressure and the loss risk analysis diagram are shown in the example well. DETAILED DESCRIPTION

[0170] 1. Based on the interpretation of logging data of target formations from 5 or more sample adjacent wells, construct the target formations of sample adjacent wells.

[0171] The rock mechanical parameter correction value and ground stress correction value logging interpretation profile of the layer, rock mechanical parameters include tensile strength, uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio; and construct the formation pressure, formation collapse pressure, formation loss pressure and formation fracture pressure logging interpretation profile of the target formation of 5 or more sample adjacent wells. The specific process is as follows:

[0172] (1) The rock samples were tested according to the acoustic time difference experiment operation procedures, and the core length (L) and the termination time of the longitudinal wave propagation in the specimen (t p ), the termination time of the shear wave propagation in the specimen (t s ), the initial moment of longitudinal sound wave propagation in the specimen (t p0 ) and the initial moment of shear wave propagation in the specimen (t s0 ), and the longitudinal and shear wave velocities of the rock in the target formation of 5 or more adjacent wells are calculated by formula (1) and formula (2).

[0173] (2) Through rock tensile strength tests, rock uniaxial compressive strength tests, and rock triaxial compressive strength tests, the experimental values ​​of rock mechanical parameters of downhole rock samples from target formations in 5 or more adjacent wells are obtained. The rock mechanical parameters include tensile strength, uniaxial compressive strength, triaxial compressive strength, elastic modulus, and Poisson's ratio. The calculation formulas are shown in Formula (7), Formula (9), Formula (10), Formula (11), and Formula (12), respectively.

[0174] (3) Through the rock acoustic emission ground stress test experiment, the ground stress experimental values ​​of the downhole rock samples of the target formation in 5 or more sample adjacent wells are obtained. The calculation formulas are shown in formulas (13), (14) and (15).

[0175] (4) Based on the rock acoustic wave velocity of the downhole rock samples of the target formation of 5 or more sample adjacent wells, the dynamic uniaxial compressive strength, dynamic shear strength, dynamic tensile strength, dynamic elastic modulus, dynamic Poisson's ratio and dynamic in-situ stress of the downhole rock of the target formation of the sample adjacent wells are calculated. The calculation formulas are shown in formula (20), formula (21), formula (22), formula (3), formula (4), formula (26) and formula (27) respectively. Combined with the experimental values ​​of rock mechanical parameters and in-situ stress obtained from experimental tests, the dynamic and static rock mechanical parameters and in-situ stress conversion model of the target formation of the sample adjacent wells are constructed.

[0176] (5) Based on the interpretation of acoustic logging data of target formations from 5 or more sample adjacent wells, the dynamic uniaxial compressive strength, dynamic shear strength, dynamic tensile strength, dynamic elastic modulus, dynamic Poisson's ratio, and dynamic geostress logging interpretation values ​​of the target formations from the sample adjacent wells are calculated. Based on the dynamic and static rock mechanics parameters and geostress conversion model of the target formations from the sample adjacent wells, the dynamic rock mechanics parameters and dynamic geostress logging interpretation values ​​of the target formations from the sample adjacent wells are corrected to obtain the logging interpretation profiles of the corrected rock mechanics parameters and geostress correction values ​​of the target formations from the sample adjacent wells.

[0177] (6) Using the logging interpretation profiles of the target formation rock mechanical parameter correction values ​​and the ground stress correction values ​​of 5 or more sample adjacent wells, based on the calculation models of formation pressure, formation collapse pressure, formation loss pressure and formation fracture pressure, see formula (24), formula (29), formula (32) and formula (31), the logging interpretation profiles of the target formation of the sample adjacent wells are calculated and constructed.

[0178] 2. Based on the mineral composition testing, electron microscope scanning, cast thin section identification and imaging logging interpretation analysis of the target formations in 5 or more sample adjacent wells, the lithology, fracture development and occurrence, and pore development of the target formations in the sample adjacent wells are clarified. From the perspective of rock lithology and structural characteristics, the tensile strength properties, formation leakage channels and reservoir space characteristics of the target formations in the sample adjacent wells are revealed. The specific methods are as follows:

[0179] (1) Using an X-ray diffractometer, test 5 or more sample adjacent well target formation downhole rock samples, and determine the rock mineral composition by observing the characteristic peak intensity in the specific X-ray pattern of the rock mineral.

[0180] (2) Cut the fresh section of 5 or more sample adjacent well target formation downhole rock samples, and use FEI Quanta650FEG field emission scanning electron microscope to observe the fresh section of the rock sample obtained by cutting, and obtain the microscale crack and pore morphology characteristics of the fresh section of the rock sample.

[0181] (3) Select the part with obvious surface structure characteristics and developed pores, holes and cracks in the rock sample of 5 or more sample adjacent well target formation, cut it into a diameter of not more than 25mm and a thickness of 2mm to 3.5mm. Use polarized light microscope to identify the cast thin section, and determine the rock lithology, microscale crack and pore morphology characteristics.

[0182] (4) Use acoustic imaging logging technology to obtain high-resolution imaging logging interpretation image of 5 or more sample adjacent well target formation, and reveal the development characteristics and specific layer of natural fractures and dissolution pores according to the imaging logging interpretation image.

[0183] (5) According to the rock mineral composition, microscale crack and pore morphology characteristics, rock lithology, microscale crack and pore morphology characteristics, development characteristics and specific layer of natural fractures and dissolution pores, reveal the tensile strength performance, leakage channel and reservoir space characteristics of the target formation rock.

[0184] 3. According to the rock mechanics parameter correction value and geostress correction value logging interpretation profile of 5 or more sample adjacent well target formation, and the formation pressure, formation collapse pressure, formation leakage pressure and formation fracture pressure logging interpretation profile, combined with the sample adjacent well target formation natural gamma curve, resistivity curve, acoustic curve, the target formation small layer is finely divided. The specific method is as follows:

[0185] (1) According to the rock mechanics parameter correction value and geostress correction value logging interpretation profile, formation pressure logging interpretation profile of 5 or more sample adjacent well target formation, the target formation is preliminarily stratified.

[0186] (2) Analyze the logging curve data of the target formation of 5 or more sample adjacent wells, extract the mutation characteristic points of the natural gamma logging curve to determine the lithologic interface; identify the oil-water interface and gas-bearing display layer through resistivity logging curve morphology analysis, and determine the fluid properties; use the sonic time difference logging data to determine the lithologic and porosity changes by comparing the sonic time difference range of the standard lithologic properties; identify the pressure anomaly zone and determine the pressure characteristic changes based on the logging interpretation profile of the formation collapse pressure, formation leakage pressure and formation fracture pressure; further, conduct a fine division of the target formation into small layers based on the preliminary stratification of the target formation.

[0187] 4. Based on the results of the fine division of the target formation, the rock mechanical parameter correction values, ground stress correction values ​​and formation pressure data obtained by interpreting the target formation acoustic logging data of 5 or more sample adjacent wells are used to perform data thinning or interpolation. The specific methods are as follows:

[0188] (1) Based on the target formation thickness of 5 or more sampled adjacent wells and the original data sampling interval, the rock mechanics parameter correction value, ground stress correction value and formation pressure data volume of each sublayer of the target formation of the sample adjacent wells are calculated as follows:

[0189]

[0190] Where: n sl H is the rock mechanics parameter correction value, ground stress correction value and formation pressure data of the lth layer of the target formation in the sample adjacent well, l is an integer; sl is the thickness of the first layer, m; N is the original data sampling interval, m, generally 0.125m.

[0191] (2) Calculate the total data volume of rock mechanical parameter correction values, ground stress correction values, and formation pressure of the target formation of 5 or more sample adjacent wells using the following formula:

[0192]

[0193] Where: n s is the total data volume of rock mechanics parameter correction values, ground stress correction values ​​and formation pressure of the target formation of the sample adjacent wells, an integer; L is the total number of layers, an integer.

[0194] (3) Calculate the thickness H of each sub-layer of the target formation of the prediction well tl Thickness H of each sub-layer of target formation in 5 or more adjacent wells sl Ratio m l , the formula is as follows:

[0195]

[0196] (4) When m lWhen m < 1, the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the sample adjacent well are thinned, and the data extraction interval number is calculated, and the formula is as follows:

[0197]

[0198] In the formula, k rl is the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data extraction interval number, and is an integer.

[0199] The rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the target formation of the sample adjacent well are extracted every k rl data, and the extracted data is the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the sample adjacent well mapped in the corresponding small layer of the prediction well.

[0200] (5) When m > 1, the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the sample adjacent well are interpolated, the adjacent data interpolation number is calculated, and the formula is as follows:

[0201] k il = round(m l -1) (37)

[0202] In the formula, k il is the adjacent data interpolation number, and is an integer.

[0203] The adjacent data of the rock mechanics parameter correction value, the ground stress correction value and the formation pressure of each small layer of the target formation of the sample adjacent well are linearly interpolated for k il data, and the rock mechanics parameter correction value, the ground stress correction value and the formation pressure data of the corresponding small layer of the sample adjacent well mapped in the corresponding small layer of the prediction well are obtained.

[0204] 5. According to the distance between the 5 and more sample adjacent wells and the prediction well, the prediction weight value of each sample adjacent well is obtained. The specific method is as follows:

[0205] (1) According to the layout of the 5 and more sample adjacent wells and the prediction well, the distance between the sample adjacent well and the prediction well is calculated.

[0206] (2) According to the distance between the 5 and more sample adjacent wells and the prediction well, the prediction weight value of each sample adjacent well is calculated, and the formula is as follows:

[0207]

[0208] In the formula, q k is the prediction weight value of the kth sample adjacent well, and is dimensionless; x k is the distance between the sample adjacent well and the prediction well, and m.

[0209] (3) The predicted weight values ​​of 5 or more sample adjacent wells are normalized using the following formula:

[0210]

[0211] Where: p k is the predicted weight value of the kth sample adjacent well after normalization, dimensionless.

[0212] 6. Based on the prediction weights of 5 or more sample adjacent wells, the rock mechanical parameter correction values, ground stress correction values ​​and formation pressure of the target formation of 5 or more sample adjacent wells are used to perform weighted average calculation to obtain the rock mechanical parameters, ground stress and formation pressure of the target formation of the predicted well. The specific method is as follows:

[0213] (1) Based on the rock mechanical parameter correction values, ground stress correction values ​​and formation pressure data of 5 or more sample adjacent wells mapped to the corresponding small layer of the prediction well, the predicted weight value after normalization of each sample adjacent well is used to calculate the predicted component of the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well of each sample adjacent well. The formula is as follows:

[0214] w k =s k p k (40)

[0215] Where: w k Refers to the predicted components of the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well by the kth sample adjacent well; s k Refers to the rock mechanical parameters, ground stress and formation pressure of the target formation in the kth sample adjacent well;

[0216] (2) Based on the predicted components of the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well from 5 or more sample adjacent wells, the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well are calculated by summing them up. The formula is as follows:

[0217] s=∑w k (41)

[0218] Where: s refers to the rock mechanical parameters, ground stress and formation pressure of the target formation of the predicted well.

[0219] 7. Based on the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well, construct the formation collapse pressure, formation loss pressure and formation fracture pressure logging interpretation profile of the target formation of the prediction well. The specific method is as follows:

[0220] (1) According to the predicted ground stress and formation pressure of the target well, the stress coordinate axis is transformed, and a cylindrical coordinate stress distribution model centered on the wellbore axis is established to obtain the maximum principal stress and minimum principal stress of the cylindrical coordinate system.

[0221] (2) According to the rock mechanical parameters of the target formation of the prediction well, the formation pressure, the maximum principal stress and the minimum principal stress of the cylindrical coordinate system around the well, and based on the calculation model of formation collapse pressure, formation loss pressure and formation fracture pressure, the formation collapse pressure, formation loss pressure and formation fracture pressure logging interpretation profile of the target formation of the prediction well are calculated and constructed.

[0222] 8. Use the mode difference to correct the well logging interpretation profile of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation of the predicted well. The specific method is as follows:

[0223] (1) The mode values ​​of formation collapse pressure, formation loss and formation fracture pressure of target formations in 5 or more sample adjacent wells are weighted averaged respectively, and the formula is as follows:

[0224] z s =∑z k p k (42)

[0225] Where: z s Refers to the mode of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation after weighted average processing, MPa; k Refers to the mode of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation of the kth sample adjacent well, MPa.

[0226] (2) Using the mode of the formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation after weighted average processing, and based on the well logging interpretation profile data of the formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well, the formation collapse pressure, formation loss pressure and formation breakdown pressure correction value profile of the target formation of the prediction well is calculated and constructed. The formula is as follows:

[0227] Df jz =Df+(z s -z) (43)

[0228] Where: Df jz It refers to the correction value of formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well, MPa; Df refers to the logging interpretation value of formation collapse pressure, formation loss pressure or formation breakdown pressure of the target formation of the prediction well, MPa; z is the mode of formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well.

[0229] 9. Based on the corrected formation collapse pressure, formation loss pressure and formation fracture pressure logging interpretation profile of the target formation of the prediction well, combined with the leakage channel and reservoir space characteristics of the target formation, determine the drilling fluid leakage-prone layer of the target formation of the prediction well. The specific method is as follows:

[0230] (1) Based on the correction value profiles of formation pressure, formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well, find the areas with low formation loss pressure and formation breakdown pressure of the target formation of the prediction well, as well as the areas with small differences between formation pressure and formation collapse pressure relative to formation loss pressure and formation breakdown pressure.

[0231] (2) Based on the characteristics of the target formation leakage channels and reservoir space, in the areas with low formation leakage pressure and formation fracture pressure of the target formation of the prediction well, and in the areas where the difference between the formation pressure and formation collapse pressure relative to the formation leakage pressure and formation fracture pressure is small, it is judged whether the formation leakage channels and reservoir space are developed. If the formation leakage channels and reservoir space are developed, it is determined to be a fracture leakage formation risk layer. If the formation fracture pressure is low and the formation leakage channels and reservoir space are not developed, it is determined to be a fracture leakage formation risk layer.

[0232] Implementation Cases

[0233] The predicted formation in this example is the Maokou Formation in a well area in the Sichuan Basin. The prediction well is selected as PS16 in the well area, and the sample adjacent wells are PS21, PY1, PS7, PS9, PS17, and PS8 in the well area. The specific steps to implement the case are as follows:

[0234] Step 1: Complete logging data from the prediction wells and adjacent sample wells, along with 75 downhole Maokou Formation rock samples, were collected. Through logging data interpretation, dynamic rock mechanical parameters and dynamic in-situ stress data for the Maokou Formation were obtained for Wells PS21, PY1, PS7, PS9, PS17, and PS8. Using a static rock mechanical parameter-to-in-situ stress dynamic conversion model constructed using rock experimental results, the dynamic rock mechanical parameters and dynamic in-situ stresses were corrected to obtain the corrected rock mechanical parameter and in-situ stress log interpretation profiles for each well. Furthermore, using the corrected dynamic rock mechanical parameters and dynamic in-situ stresses for each well, the formation pressure, formation collapse pressure, formation loss pressure, and formation fracture pressure profiles for the Maokou Formation were calculated.

[0235] Step 2: Rock mineral composition testing, electron microscopy scanning, and cast thin section identification were performed on rock samples from the Maokou Formation in Wells PS21, PY1, PS7, PS9, PS17, and PS8. The imaging logging data were used to interpret and analyze the development characteristics of the formation fractures to reveal the tensile strength properties of the rocks in the Maokou Formation, the formation leakage channels, and the reservoir space characteristics.

[0236] Step 3: Using the well logging interpretation profiles of the Maokou Formation rock mechanical parameter correction values ​​and ground stress correction values ​​of Wells PS21, PY1, PS7, PS9, PS17 and PS8, as well as the formation pressure, formation collapse pressure, formation loss pressure and formation fracture pressure profiles of the Maokou Formation, combined with the natural gamma curves, resistivity curves and acoustic wave curves of the Maokou Formation of these 6 wells, the Maokou Formation is divided into 7 small layers. The division results are shown in Figure 2 and Figure 3 .

[0237] Step 4: Based on the 7 sub-layers divided into the Maokou Formation in Wells PS21, PY1, PS7, PS9, PS17 and PS8, data thinning or interpolation processing is performed on the rock mechanics parameter correction values, ground stress correction values ​​and formation pressure data obtained by interpreting the acoustic logging data of the Maokou Formation in these 6 wells.

[0238] Step 5: Based on the distances from Wells PS21, PY1, PS7, PS9, PS17, and PS8 to Well PS16, the distance ratios of Wells PS21, PY1, PS7, PS9, PS17, PS8, and PS16 are 1:1.3:1.4:1.5:2.3:3.2. The predicted weights of these six sample neighboring wells are 0.2308, 0.1776, 0.1649, 0.1539, 0.1004, and 0.072, respectively.

[0239] Step 6: Based on the predicted weight values ​​of Wells PS21, PY1, PS7, PS9, PS17, and PS8, and using the rock mechanical parameter correction values ​​and ground stress correction values ​​of the target formation, the logging interpretation profile and formation pressure profile, the weighted average calculation is performed to obtain the rock mechanical parameters, ground stress, and formation pressure of the Maokou Formation in Well PS16. The comparison between the predicted values ​​of rock mechanical parameters and the logging interpretation values ​​of the Maokou Formation in Well PS16 is shown in Figure 4 The coincidence curve between the predicted values ​​of rock mechanical parameters and the logging interpretation values ​​of the Maokou Formation in Well PS16 is shown in Figure 5 .from Figure 4 and Figure 5 It can be seen that the average coincidence rate between the predicted values ​​of the rock mechanical parameters of the Maokou Formation in Well PS16 obtained by weighted average calculation and the well logging interpretation values ​​is greater than 92%, indicating that this method has a good effect on the prediction results of the rock mechanical parameters of the target formation.

[0240] Step 7: Using the rock mechanical parameters, in-situ stress and formation pressure of the Maokou Formation in Well PS16, combined with the calculation model of formation collapse pressure, formation loss pressure and formation breakdown pressure, the formation collapse pressure, formation loss pressure and formation breakdown pressure profile of the Maokou Formation in Well PS16 was constructed. The comparison between the predicted values ​​of formation collapse pressure, formation loss pressure and formation breakdown pressure of the Maokou Formation in Well PS16 and the well logging interpretation values ​​is shown in Figure 6 The coincidence rate curve of the predicted values ​​of formation collapse pressure, formation loss pressure and formation fracture pressure of PS16 Maokou Formation and the well logging interpretation values ​​is shown in Figure 7 .from Figure 6 and Figure 7 It can be seen that the average coincidence rate between the predicted results of formation collapse pressure, formation loss pressure and formation fracture pressure of the Maokou Formation in Well PS16 and the well logging interpretation values ​​is greater than 84%. Overall, the coincidence rate of each value is relatively stable.

[0241] Step 8: Perform weighted averaging on the mode of formation collapse pressure, formation loss pressure and formation breakdown pressure of the Maokou Formation in Wells PS21, PY1, PS7, PS9, PS17 and PS8, and calculate the mode of formation collapse pressure, formation loss pressure and formation breakdown pressure of the Maokou Formation after weighted averaging. Then, according to the difference in the mode, the predicted correction values ​​of formation collapse pressure, formation loss pressure and formation breakdown pressure of the Maokou Formation in Well PS16 are obtained. The comparison between the predicted correction values ​​of formation collapse pressure, formation loss pressure and formation breakdown pressure of the Maokou Formation in Well PS16 and the well logging interpretation values ​​is shown in Figure 8 The curve of the coincidence rate between the predicted correction value and the logging interpretation value of the formation collapse pressure, formation loss pressure and formation fracture pressure of the Maokou Formation in Well PS16 is shown in Figure 9 .from Figure 8 and Figure 9 It can be seen that the average coincidence rate between the predicted correction values ​​of the formation collapse pressure, formation loss pressure and formation breakdown pressure of the Maokou Formation in Well PS16 and the logging interpretation values ​​has increased to 97%, and the overall prediction results are highly consistent with the actual logging calculation values.

[0242] Step 9: Based on the corrected formation collapse pressure, formation loss pressure and formation fracture pressure profile of the Maokou Formation in Well PS16, combined with the characteristics of the loss channel and reservoir space of the Maokou Formation in Well PS16, the loss layer was predicted before drilling. The results are shown in Figure 10 .analyze Figure 10 It was found that there was one obvious leakage risk point in the Maokou Formation of Well PS16, located at 6050.75 m, and the predicted fracture pressure equivalent density was 2.37 g / cm 3 The actual on-site logging data shows that the equivalent density at the bottom of the well at 6050.75m exceeds 2.39g / cm 3The actual leakage points are consistent with the leakage risk horizons predicted by the method, and the method has high reliability in predicting the easy-leakage horizons of the formation.

[0243] The above description is only the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting formation drilling fluid leakage prone layers using well logging data, characterized in that: The method comprises the following steps: Step 1.

1. Based on the interpretation of logging data from the target formations of five or more sample adjacent wells, construct logging interpretation profiles of the corrected rock mechanical parameters and in-situ stresses of the target formations of the sample adjacent wells. The rock mechanical parameters include tensile strength, uniaxial compressive strength, triaxial compressive strength, elastic modulus, and Poisson's ratio. Also, construct logging interpretation profiles of the formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure of the target formations of the five or more sample adjacent wells. Step 1.2: Based on rock mineral composition testing, electron microscopy scanning, cast thin section identification, and imaging logging interpretation and analysis of the target formations in five or more sampled adjacent wells, the lithology, fracture development, and pore development of the target formations in the sampled adjacent wells are determined. The tensile strength properties of the rock, the formation leakage pathways, and the reservoir space characteristics of the target formations in the sampled adjacent wells are revealed from the perspective of rock lithology and structural characteristics. Step 1.3: Based on the logging interpretation profiles of the target formation's rock mechanical parameter correction values ​​and ground stress correction values ​​from five or more sample adjacent wells, as well as the logging interpretation profiles of formation pressure, formation collapse pressure, formation loss pressure, and formation fracture pressure, combined with the natural gamma ray curves, resistivity curves, and acoustic wave curves of the target formation from the sample adjacent wells, perform fine sub-layer division of the target formation; Step 1.4: Based on the results of the fine sub-stratum division of the target formation, the rock mechanical parameter correction values, ground stress correction values, and formation pressure data obtained by interpreting the target formation acoustic logging data of 5 or more sample adjacent wells are used to perform data thinning or interpolation processing; Step 1.5: Based on the distances between 5 or more sample adjacent wells and the predicted well, obtain the prediction weight value of each sample adjacent well; Step 1.6: Based on the prediction weights of five or more sample adjacent wells, the rock mechanical parameter correction values, in-situ stress correction values, and formation pressure of the target formation of the five or more sample adjacent wells are used to perform a weighted average calculation to obtain the rock mechanical parameters, in-situ stress, and formation pressure of the target formation of the predicted well; Step 1.7: Construct a well logging interpretation profile of formation collapse pressure, formation loss pressure, and formation fracture pressure of the target formation of the prediction well based on the rock mechanical parameters, ground stress, and formation pressure of the target formation of the prediction well; Step 1.8, using the mode difference to correct the well logging interpretation profile of the formation collapse pressure, formation loss pressure, and formation fracture pressure of the target formation of the predicted well; Step 1.9: Determine the drilling fluid leakage-prone layer in the target formation of the prediction well based on the corrected formation collapse pressure, formation loss pressure, and formation breakdown pressure logging interpretation profile of the target formation, combined with the characteristics of the loss channel and reservoir space of the target formation; The specific steps of step 1.4 are as follows: Step 1.4.

1. Based on the target formation thickness and original data sampling interval of 5 or more sampled adjacent wells, calculate the rock mechanical parameter correction value, ground stress correction value, and formation pressure data volume of each sublayer of the target formation in the sampled adjacent wells. The formula is as follows: Where: n sl H is the rock mechanics parameter correction value, ground stress correction value and formation pressure data of the lth layer of the target formation in the sample adjacent well, l is an integer; sl is the thickness of the first layer, m; N is the raw data sampling interval, m, generally 0.125 m; Step 1.4.2: Calculate the total data volume of rock mechanical parameter corrections, ground stress corrections, and formation pressure for the target formation of 5 or more adjacent wells using the following formula: Where: n s is the total data volume of rock mechanics parameter correction values, ground stress correction values ​​and formation pressure of the target formation of the sample adjacent well, an integer; L is the total number of layers, an integer; Step 1.4.3: Calculate the thickness H of each sub-layer of the target formation of the prediction well tl Thickness H of each sub-layer of target formation in 5 or more adjacent wells sl Ratio m l , the formula is as follows: Step 1.4.4, when m l When <1, the rock mechanical parameter correction values, ground stress correction values ​​and formation pressure data of the corresponding small layers of the sample adjacent wells are thinned out, and the number of data extraction intervals is calculated. The formula is as follows: Where: k rl is the interval number for extracting the correction values ​​of rock mechanics parameters, ground stress correction values ​​and formation pressure data, an integer; Corrected values ​​of rock mechanics parameters, ground stress and formation pressure data of target formations in adjacent wells are calculated every k interval. rl One data is extracted, and the extracted data is the rock mechanics parameter correction value, ground stress correction value and formation pressure data of the sample adjacent well mapped to the corresponding small layer of the prediction well; Step 1.4.5: When m > 1, interpolate the rock mechanics parameter correction values, ground stress correction values, and formation pressure data of the corresponding small layers of the sample adjacent wells to calculate the adjacent data interpolation number. The formula is as follows: k il =round(m l -1) Where: k il is the interpolation number of adjacent data, an integer; The adjacent data of rock mechanical parameter correction value, ground stress correction value and formation pressure of each layer of the target formation of the sample adjacent well are k- il The data are linearly interpolated to obtain the rock mechanics parameter correction value, ground stress correction value and formation pressure data of the sample adjacent well mapped to the corresponding small layer of the prediction well.

2. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.1 are as follows: Step 1.1.

1. Obtain rock acoustic wave velocities of downhole rock samples from target formations in five or more adjacent wells through a rock acoustic wave transit time test experiment; Step 1.1.2: Obtain experimental values ​​of rock mechanical parameters for downhole rock samples from target formations in five or more adjacent wells through rock tensile strength tests, rock uniaxial compressive strength tests, and rock triaxial compressive strength tests. Rock mechanical parameters include tensile strength, uniaxial compressive strength, triaxial compressive strength, elastic modulus, and Poisson's ratio. Step 1.1.3: Obtain experimental in-situ stress values ​​of downhole rock samples from target formations in five or more adjacent wells through rock acoustic emission in-situ stress testing experiments; Step 1.1.

4. Calculate the dynamic rock mechanical parameters and dynamic in-situ stresses of downhole rock samples from target formations in five or more adjacent wells based on rock acoustic wave velocities. Combined with experimental values ​​of rock mechanical parameters and in-situ stresses obtained from experimental testing, construct a conversion model of dynamic and static rock mechanical parameters and in-situ stresses for the target formations in the adjacent wells. Step 1.1.

5. Based on the interpretation of acoustic logging data of the target formations from five or more sample adjacent wells, calculate the dynamic rock mechanical parameters and dynamic geostress logging interpretation values ​​of the target formations in the sample adjacent wells. Based on the dynamic and static rock mechanical parameters and geostress conversion model of the target formations in the sample adjacent wells, correct the dynamic rock mechanical parameters and dynamic geostress logging interpretation values ​​of the target formations in the sample adjacent wells to obtain the logging interpretation profiles of the corrected rock mechanical parameters and geostress values ​​of the target formations in the sample adjacent wells. Step 1.1.6: Utilize the logging interpretation profiles of the target formation's rock mechanical parameter correction values ​​and ground stress correction values ​​from five or more sample adjacent wells, and based on the calculation models for formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure, calculate and construct the logging interpretation profiles of the target formation's formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure from the sample adjacent wells.

3. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.2 are as follows: Step 1.2.

1. Use an X-ray diffractometer to test downhole rock samples from the target formation in five or more adjacent wells. Determine the rock mineral composition by observing the intensity of characteristic peaks in the rock mineral-specific X-ray spectra. Step 1.2.2: Obtain fresh downhole rock samples from the target formation from five or more adjacent wells. Use a scanning electron microscope to observe and identify the microscopic fracture and pore morphology of the fresh rock samples. Step 1.2.3: Prepare thin cast sections of downhole rock samples from the target formation in five or more adjacent wells. Identify the thin cast sections using a polarizing microscope to determine the rock lithology and microscopic fracture and pore morphology. Step 1.2.4: Use acoustic imaging logging technology to obtain high-resolution imaging logging interpretation images of the target formation from five or more sampled adjacent wells. Based on the imaging logging interpretation images, reveal the development characteristics and specific strata of natural fractures and dissolution pores; Step 1.2.5: Based on the rock mineral composition, micro-scale fracture and pore morphology, rock lithology, meso-scale fracture and pore morphology, natural fracture and solution pore development characteristics, and specific strata, reveal the tensile strength properties, leakage pathways, and reservoir space characteristics of the target formation rock.

4. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.3 are as follows: Step 1.3.

1. Conduct preliminary stratification of the target formation based on the logging interpretation profiles of the target formation's rock mechanical parameter correction values, in-situ stress correction values, and formation pressure logging interpretation profiles from five or more sampled adjacent wells; Step 1.3.2: Analyze the target formation logging data from five or more adjacent wells, extract the mutation characteristic points of the natural gamma ray logging curve to determine the lithologic interface; identify the oil-water interface and gas-bearing display interval through resistivity logging curve morphology analysis, and determine the fluid properties; use the sonic transit time logging data to determine the lithologic and porosity changes by comparing the sonic transit time range with the standard lithologic data; identify the pressure anomaly zones and determine the pressure characteristic changes based on the logging interpretation profile of the formation collapse pressure, formation loss pressure, and formation fracture pressure; further, based on the preliminary stratification of the target formation, perform a fine subdivision of the target formation.

5. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.5 are as follows: Step 1.5.

1. Calculate the distance between the sample adjacent wells and the predicted wells based on the layout of five or more sample adjacent wells and the predicted wells; Step 1.5.2: Calculate the prediction weight of each sample adjacent well based on the distance between the predicted well and five or more sample adjacent wells. The formula is as follows: Where: q k is the predicted weight value of the kth sample adjacent well, dimensionless; x k is the distance between the sample adjacent well and the prediction well, m; Step 1.5.3: Normalize the predicted weights of 5 or more adjacent wells using the following formula: Where: p k is the predicted weight value of the kth sample adjacent well after normalization, dimensionless.

6. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.6 are as follows: Step 1.6.

1. Based on the rock mechanical parameter correction values, ground stress correction values, and formation pressure data of the sub-layer corresponding to the prediction well mapped from 5 or more sample adjacent wells, use the normalized prediction weight values ​​of each sample adjacent well to calculate the predicted contribution of each sample adjacent well to the target formation rock mechanical parameters, ground stress, and formation pressure of the prediction well. The formula is as follows: w k =s k p k Where: w k Refers to the predicted components of the rock mechanical parameters, ground stress and formation pressure of the target formation of the prediction well by the kth sample adjacent well; s k Refers to the rock mechanical parameters, ground stress and formation pressure of the target formation in the kth sample adjacent well; Step 1.6.2: Based on the predicted components of the rock mechanical parameters, in-situ stress, and formation pressure of the target formation of the prediction well from five or more sample adjacent wells, calculate the rock mechanical parameters, in-situ stress, and formation pressure of the target formation of the prediction well by summing them up. The formula is as follows: s=∑w k Where: s refers to the rock mechanical parameters, ground stress and formation pressure of the target formation of the predicted well.

7. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.7 are as follows: Step 1.7.

1. Based on the predicted in-situ stress and formation pressure of the target well, perform stress coordinate axis transformation, establish a cylindrical coordinate stress distribution model centered on the wellbore axis, and obtain the maximum and minimum principal stresses in the cylindrical coordinate system. Step 1.7.2: Based on the rock mechanical parameters of the target formation of the prediction well, the formation pressure, the maximum principal stress and the minimum principal stress in the cylindrical coordinate system around the well, and the calculation model of the formation collapse pressure, formation loss pressure, and formation breakdown pressure, calculate and construct the logging interpretation profile of the formation collapse pressure, formation loss pressure, and formation breakdown pressure of the target formation of the prediction well.

8. The method for predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.8 are as follows: Step 1.8.1: Perform weighted averaging of the modes of formation collapse pressure, formation loss, and formation breakdown pressure for the target formation in five or more adjacent wells. The formula is as follows: z s =∑z k p k Where: z s Refers to the mode of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation after weighted average processing, MPa; k Refers to the mode of the formation collapse pressure, formation loss pressure and formation fracture pressure of the target formation of the kth sample adjacent well, MPa; Step 1.8.2: Using the weighted average mode of the target formation's formation collapse pressure, formation loss pressure, and formation breakdown pressure, and based on the well logging interpretation profile data of the target formation's formation collapse pressure, formation loss pressure, and formation breakdown pressure in the prediction well, calculate and construct a profile of the corrected formation collapse pressure, formation loss pressure, and formation breakdown pressure in the target formation of the prediction well. The formula is as follows: Df jz =Df+(z s -z) Where: Df jz It refers to the correction value of formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well, MPa; Df refers to the logging interpretation value of formation collapse pressure, formation loss pressure or formation breakdown pressure of the target formation of the prediction well, MPa; z is the mode of formation collapse pressure, formation loss pressure and formation breakdown pressure of the target formation of the prediction well.

9. The method of predicting formation drilling fluid leakage prone layers using well logging data according to claim 1, characterized in that: The specific steps of step 1.9 are as follows: Step 1.9.

1. Based on the corrected profiles of formation pressure, formation collapse pressure, formation loss pressure, and formation breakdown pressure of the target formation of the predicted well, find areas of low formation loss pressure and formation breakdown pressure in the target formation of the predicted well, and areas where the difference between the formation pressure and formation collapse pressure relative to the formation loss pressure and formation breakdown pressure is small; Step 1.9.2: Based on the characteristics of the target formation's leakage pathways and reservoir spaces, determine whether leakage pathways and reservoir spaces are developed in areas of low formation leakage pressure and formation breakdown pressure in the target formation of the prediction well, and in areas where the difference between formation pressure and formation collapse pressure relative to the formation leakage pressure and formation breakdown pressure is small. If leakage pathways and reservoir spaces are developed, the formation is determined to be a risky formation with fracture leakage. If the formation breakdown pressure is low but leakage pathways and reservoir spaces are not developed, the formation is determined to be a risky formation with fracture leakage.

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