Stratum pore pressure while drilling estimation method based on Bayesian theory

Abstract

The invention discloses a method for estimating formation pore pressure while drilling based on the Bayesian theory, which comprises the following steps of: collecting well logging information of a target well, logging-while-drilling information and adjacent well information of the same block of the target well; according to the logging information and the logging-while-drilling information, predicting a formation pressure interval profile containing confidence of a target well, and determining the prior probability of formation pressure at any well depth position; according to the data of the adjacent well, calculating a formation pressure interval profile containing confidence of the adjacent well, and determining a likelihood function of formation pressure at any well depth position; and the prior probability and the likelihood function of the formation pressure of the same horizon are selected and substituted into a Bayesian formula, and the updated formation pressure posterior probability is obtained. According to the method, while-drilling updating and correction of the formation pressure interval profile containing the confidence coefficient are realized, so that formation pressure parameters are obtained more accurately; more accurate formation pressure information is provided for drilling risk assessment, drilling risks caused by unclear understanding of formation pressure are reduced, and drilling efficiency is effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of complex formation drilling petroleum, and in particular relates to a formation pore pressure while drilling estimation method based on Bayesian theory. Background Art

[0002] Formation pressure refers to the pressure acting on the fluid in the pores of the rock, also known as formation pore pressure. It is a basic formation parameter that reflects the fluid conditions in the formation, rock types, rock mechanical properties, and geological structures. Formation pressure information is the basis for ensuring the safe, efficient, and economical implementation of drilling operations. Accurate estimation of formation pressure helps drilling operators select appropriate drilling fluid mud density, prevent well kicks, blowouts, and other safety accidents caused by abnormally high pressure, and reduce drilling operation time. At the same time, estimating pressure information helps design a reasonable wellbore structure and save engineering construction costs while ensuring safety. Therefore, formation pressure estimation is of great significance in the fields of oil and gas resource exploration, oil and gas field development, and drilling engineering.

[0003] At present, there are many methods for estimating formation pressure, which can be divided into the following categories according to the drilling construction sequence: pre-drilling pressure prediction, pressure monitoring while drilling, post-drilling logging interpretation and actual pressure measurement. Pre-drilling pressure prediction mainly uses seismic data to obtain seismic layer velocity through data processing, and establishes a theoretical relationship model between seismic layer velocity and formation pressure to calculate formation pressure. Commonly used methods include Fillippone method and single-point prediction method. Pressure monitoring while drilling is a method that uses logging data while drilling and related engineering data to monitor formation pressure in real time. Commonly used methods include dc index method, σ index method and standardized drilling speed method. Post-drilling logging interpretation is the most commonly used and generally considered the most accurate method. Its commonly used methods mainly include equivalent depth method, Eaton method and effective stress method. Actual pressure measurement is to use instruments to directly measure formation pressure. Its commonly used methods are drill pipe test and repeated formation test.

[0004] However, due to the complex spatial variability of the geological body itself, different understandings of the causes of pressure can lead to different theoretical estimation models, and the prediction data (seismic, logging, well logging data) itself has fixed errors, so the estimation results of formation pressure are very uncertain. When drilling operations rely on the above uncertain formation pressure information to make plans, it leads to deviations in drilling operations and even serious drilling accidents.

[0005] Therefore, in order to take into account the influence of various uncertain factors on the formation pressure estimation, the present invention aims to provide a formation pore pressure estimation method while drilling based on Bayesian theory. Summary of the invention

[0006] The problem to be solved by the present invention is to provide a method for estimating formation pore pressure while drilling based on Bayesian theory. The method updates the target well formation pressure estimation result through a formation pressure interval profile containing confidence and through adjacent well data, thereby improving the accuracy of formation pressure estimation and providing scientific and effective technical support for oil and gas field exploration and development and drilling risk assessment.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for estimating formation pore pressure while drilling based on Bayesian theory, comprising the following steps:

[0008] S1: Collect well logging data, logging while drilling data of the target well and data of adjacent wells in the same block of the target well;

[0009] S2: predicting the formation pressure interval profile of the target well with confidence based on the well logging data and the logging while drilling data, and determining the prior probability of the formation pressure at any well depth;

[0010] S3: Based on the adjacent well data, calculate the confidence interval profile of the adjacent well formation pressure, and determine the likelihood function of the formation pressure at any well depth;

[0011] S4: Select the prior probability and likelihood function of the formation pressure at the same layer and substitute them into the Bayesian formula to obtain the updated posterior probability of the formation pressure.

[0012] Further, the S2 comprises the following steps:

[0013] S21: calculating the formation pressure of the entire well section of the target well based on the effective stress method formation pressure prediction model;

[0014] S22: Substitute the formation pressure Pp of the entire well section calculated by the effective stress method into the DC index method formula, inversely calculate the Eaton index at any well depth position along the vertical direction, and construct an analysis sample library of the Eaton index;

[0015] S23: The Eaton index at any well depth is converted from a single sample into a set of 2n+1 small samples by using the sliding window method, and the normal information diffusion method is selected for probability distribution fitting to establish the probability density function and cumulative probability density function of the Eaton index at any well depth;

[0016] S24: Selecting a pure mudstone section as a normal compaction trend line construction interval, and defining a starting well section and an ending well section within the interval, wherein the starting well section contains n sample points, and the ending well section contains m sample points;

[0017] S25: Selecting the nth sample point in the starting well section and the mth sample point in the ending well section respectively, constructing n×m normal compaction trend lines, and constructing an analysis sample library of the slope and intercept of the normal compaction trend line;

[0018] S26: performing probability statistical analysis on the slope and intercept of the normal compaction trend line, selecting the normal information diffusion method for probability distribution fitting, and establishing a probability density function and a cumulative probability density function of the normal compaction trend line;

[0019] S27: Generate a random number sample XN that meets the probability density function of the characteristic parameters of the Eaton index and the normal compaction trend line through Monte Carlo simulation, where N is the number of Monte Carlo simulations;

[0020] S28: Substitute the random number samples generated in S27 into the DC index calculation model to calculate the formation pressure sample set P at any well depth position. prnd , select normal distribution to fit the formation pressure to obtain the prior probability density function f(Pp) and cumulative probability density function F(Pp) of the formation pressure;

[0021] S29: Take the pressure values ​​of the cumulative probability density of formation pressure J1=5%, J2=95% at any well depth, connect the points along the vertical well depth to form a line, and obtain the formation pressure curve with a cumulative probability of |J2-J1|=90% for the entire well section of the target well, that is, the formation pressure interval profile with a confidence level of 90%.

[0022] Furthermore, in S21, the effective stress method formation pressure prediction model is as follows:

[0023] P P =S v -100.674e -2.5782μ

[0024] Where: P P is the formation pressure, in MPa; S v is the overburden pressure, in MPa; μ is Poisson’s ratio, dimensionless;

[0025] Calculate the overburden formation pressure S by using the logging data v And the Poisson's ratio μ, the formula is as follows:

[0026]

[0027] Where: ρ is the average density of the overlying rock layer, in g / cm 3 ρ b is the formation density, in g / cm 3 ; H0 is the starting depth of the target well, in m; H is the ending depth of the target well, in m; Δts is the shear wave time difference, in us / m; Δt P is the longitudinal wave time difference, the unit is us / m.

[0028] Furthermore, in S22, the calculation model of the Eaton index is as follows:

[0029]

[0030] Where: n is the Eaton index; d c The measured d c Index; d cn The normal compaction trend line of the formation corresponds to d c Index; P h is the hydrostatic pressure, in MPa; d c The index is calculated through drilling data, and its calculation formula is as follows:

[0031]

[0032] Where: T is drilling time, unit is min / m; N is rotary table speed, unit is r / min; W is drilling pressure, unit is KN; D b is the drill diameter, in mm; G h is the hydrostatic pressure gradient, in g / cm 3 ρ ECD is the circulating equivalent drilling fluid density, in g / cm 3 .

[0033] Furthermore, in S23, the calculation model of the normal information diffusion method is as follows:

[0034]

[0035] Where: n is the sample size; h is the diffusion coefficient; x i is the observed sample;

[0036] When the sample volume n is different, the diffusion coefficient h is calculated by the following formula:

[0037]

[0038] Where: b is the maximum value of the sample, b = max{x i}; a is the minimum value of the sample, a=min{x i}; σ is the sample standard deviation; ζ is the correlation coefficient, which is determined by the sample size.

[0039] Furthermore, in S28, the random sample set P of formation pressure at any well depth position prnd The calculation formula is as follows:

[0040]

[0041] Where: nrnd is the random number sample set of Eaton index; d cnrnd A sample set of random numbers for the normal compaction trend line.

[0042] Furthermore, in S28, the prior probability density function and cumulative probability density function formulas of the target well formation pressure are as follows:

[0043]

[0044] Where: μ is the sample mean; σ is the sample standard deviation.

[0045] Further, the S3 comprises the following steps:

[0046] S31: According to the geological stratification of the block where the target well is located, the information of neighboring wells in the same stratum as the target well is screened;

[0047] S32: According to S2, the likelihood function L(X1|P) of the formation pressure of the target layer is obtained through the information of adjacent wells.

[0048] Further, the S4 comprises the following steps:

[0049] S41: The target well formation pressure prior probability f(P p ) and the likelihood function L(X 1 |P)Substitute into the Bayesian formula;

[0050] S42: Obtain the posterior probability g(P|X 1 ) to update the formation pressure of the target well.

[0051] Furthermore, the Bayesian formula is as follows:

[0052]

[0053] Where: p(X) is the marginal probability, that is, the normalization coefficient.

[0054] The advantages and positive effects of the present invention are:

[0055] The present invention combines the effective stress method and the DC index method, quantitatively characterizes the uncertainty of the Eaton index and the normal compaction trend line in the formation pressure estimation process through mathematical statistical analysis, and establishes the formation pressure prediction interval profile with confidence for the target well. Further considering the information of neighboring wells in the same block, based on the Bayesian theory, the drilling-time update and correction of the formation pressure interval profile with confidence is realized, so as to obtain the formation pressure parameters more accurately. It provides more accurate formation pressure information for drilling risk assessment, reduces the drilling risk caused by unclear understanding of formation pressure, and effectively improves drilling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention.

[0057] Figure 2 It is a schematic diagram of the sliding window method processing according to an embodiment of the present invention.

[0058] Figure 3 , Figure 4 It is a schematic diagram of the sample construction of characteristic parameters of a normal compaction trend line according to an embodiment of the present invention.

[0059] Figure 5 It is the formation pressure of the entire well section calculated by the effective stress method in the X-1 well in the specific embodiment of the present invention.

[0060] Figure 6 This is the Eaton index diagram at 3100m in the X-1 well in a specific embodiment of the present invention.

[0061] Figure 7 , Figure 8 It is a probability density distribution diagram of characteristic parameters of the normal compaction trend line at 3100 m of the X-1 well in a specific embodiment of the present invention.

[0062] Fig. 9 It is a statistical diagram of random number samples of Eaton index at 3100m of X-1 well in a specific embodiment of the present invention.

[0063] Fig.10 , Fig.11 It is a random number simulation sample statistical chart of the normal compaction trend line at 3100m of the X-1 well in a specific embodiment of the present invention.

[0064] Fig.12 It is the probability density distribution and cumulative probability density distribution diagram of the formation pressure at 3100m of the X-1 well in the specific embodiment of the present invention.

[0065] Fig.13 It is a cross-sectional diagram of the confidence formation pressure interval of the X-1 well in a specific embodiment of the present invention.

[0066] Fig.14It is a cross-sectional diagram of the confidence formation pressure interval of the X-2 well in a specific embodiment of the present invention.

[0067] Fig.15 It is a posterior probability distribution diagram of the formation pressure at 3100 m in a specific embodiment of the present invention.

[0068] Fig.16 It is a cross-sectional diagram of the formation pressure interval containing confidence after Bayesian updating in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0069] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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.

[0070] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0071] like Figure 1 As shown, a method for estimating formation pore pressure while drilling based on Bayesian theory includes the following steps.

[0072] S1: Collect the well logging data, logging while drilling data of the target well and the data of the neighboring wells in the same block of the target well.

[0073] Specifically, the logging data include the target well's sonic logging curve, density logging curve, natural gamma logging, etc. The logging while drilling data include the target well's drilling time, rotary table speed, drilling pressure, drill bit diameter, and circulating equivalent drilling fluid density.

[0074] S2: Based on the well logging data and the logging while drilling data, the formation pressure interval profile of the target well is predicted with confidence, and the prior probability of the formation pressure at any well depth is determined. Specifically, S2 includes the following steps:

[0075] S21: According to the collected logging and mud logging interpretation results of the target well, or the acoustic wave velocity, uniaxial compression and triaxial compression experiments, the basic parameters of the target well such as the longitudinal wave time difference Δtp, the shear wave time difference Δts, the static Poisson's ratio μs, the dc index dc, and the overburden formation pressure Sv are determined. Based on the effective stress method formation pressure prediction model, the formation pressure of the entire well section of the target well is calculated.

[0076] Specifically, the overburden pressure S v and Poisson's ratio μ, as follows:

[0077]

[0078] Where: ρ is the average density of the overlying rock layer, in g / cm 3 ρ b is the formation density, in g / cm 3 ; H0 is the starting depth of the target well, in m; H is the ending depth of the target well, in m; Δt s is the shear wave time difference, in us / m; Δt P is the longitudinal wave time difference, the unit is us / m.

[0079] The formation pressure of the drilling formation is calculated by the effective stress method formation pressure prediction model:

[0080] P P =S v -100.674e -2.5782μ Formula (3)

[0081] Where: P P is the formation pressure, in MPa; S v is the overburden pressure, in MPa; μ is Poisson’s ratio, dimensionless.

[0082] S22: Substitute the formation pressure Pp of the entire well section calculated by the effective stress method into the DC index method formula, inversely calculate the Eaton index at any well depth position along the vertical direction, and construct an analysis sample library of the Eaton index.

[0083] Specifically, the calculation model of the Eaton index is as follows:

[0084]

[0085] Where: n is the Eaton index; d c The measured d c Index; d cn The normal compaction trend line of the formation corresponds to d c Index; P h is the hydrostatic pressure, in MPa; d c The index is calculated through drilling data, and its calculation formula is as follows:

[0086]

[0087] Where: T is drilling time, unit is min / m; N is rotary table speed, unit is r / min; W is drilling pressure, unit is KN; D b is the drill diameter, in mm; G h is the hydrostatic pressure gradient, in g / cm 3 ρ ECD is the circulating equivalent drilling fluid density, in g / cm 3 .

[0088] S23: Figure 2 As shown in the figure, the Eaton index at any well depth is converted from a single sample into a set of 2n+1 small samples through the sliding window method, and the normal information diffusion method is selected for probability distribution fitting to establish the probability density function and cumulative probability density function of the Eaton index at any well depth.

[0089] Specifically, the probability density function calculation formula is as follows:

[0090]

[0091] The cumulative probability density function calculation formula is as follows:

[0092]

[0093] Where: n is the sample size; h is the diffusion coefficient; x i is the observed sample.

[0094] When the sample volume n is different, the diffusion coefficient h is calculated by the following formula:

[0095]

[0096] Where: b is the maximum value of the sample, b = max{x i}; a is the minimum value of the sample, a=min{x i}; σ is the sample standard deviation; ζ is the correlation coefficient, which is determined by the sample size, as shown in Table 1.

[0097] Table 1 ζ values ​​corresponding to different sample sizes

[0098]

[0099] S24: Quantitatively characterize the uncertainty of the normal compaction trend line. Select the pure mudstone section as the normal compaction trend line construction interval, and define the starting well section and the ending well section in the interval. The starting well contains n sample points, and the ending well section contains m sample points, such as Figure 3 , Figure 4 shown.

[0100] S25: Select the nth sample point in the starting well section and the mth sample point in the ending well section respectively, construct n×m normal compaction trend lines, and construct an analysis sample library of the slope and intercept of the normal compaction trend line.

[0101] S26: Perform probability statistical analysis on the slope and intercept of the normal compaction trend line, select the normal information diffusion method for probability distribution fitting, and establish a probability density function and a cumulative probability density function of the normal compaction trend line.

[0102] S27: Generate a random number sample XN that conforms to the probability density functions of the characteristic parameters of the Eaton index and the normal compaction trend line through Monte Carlo simulation, where N is the number of Monte Carlo simulations.

[0103] S28: Substitute the random number samples generated in S27 into the DC index calculation model to calculate the formation pressure sample set P at any well depth position. prnd , select normal distribution to fit the formation pressure and obtain the prior probability density function f(Pp) and cumulative probability density function F(Pp) of the formation pressure.

[0104] The random sample set P of formation pressure at any well depth prnd The calculation formula is as follows:

[0105]

[0106] Where: nrnd is the random number sample set of Eaton index; d cnrnd A sample set of random numbers for the normal compaction trend line.

[0107] In S28, the prior probability density function f(Pp) of the target well formation pressure is given by the following formula:

[0108]

[0109] The cumulative probability density function F(Pp) of the target well formation pressure is as follows:

[0110]

[0111] Where: μ1 is the mean value of the target well formation pressure samples; σ1 is the standard deviation of the target well formation pressure samples.

[0112] S29: Take the pressure values ​​of the cumulative probability density of formation pressure J1=5%, J2=95% at any well depth, connect the points along the vertical well depth to form a line, and obtain the formation pressure curve with a cumulative probability of |J2-J1|=90% for the entire well section of the target well, that is, the formation pressure interval profile with a confidence level of 90%.

[0113] S3: Based on the data of adjacent wells, calculate the confidence interval profile of the adjacent wells, and determine the likelihood function of the formation pressure at any well depth. Specifically, S3 includes the following steps:

[0114] S31: According to the geological stratification of the block where the target well is located, the information of neighboring wells in the same layer as the target well is screened, including well logging data, mud logging data, etc.

[0115] S32: According to S2, the likelihood function L(X1|P) of the formation pressure of the target layer is obtained through the information of the adjacent wells, and a formation pressure interval profile with a confidence level of 90% of the adjacent wells is established.

[0116]

[0117] Where: μ2 is the mean value of the formation pressure samples of adjacent wells; σ2 is the standard deviation of the formation pressure samples of adjacent wells.

[0118] S4: Select the prior probability and likelihood function of the formation pressure at the same layer and substitute them into the Bayesian formula to obtain the updated posterior probability of the formation pressure. Specifically, S4 includes the following steps:

[0119] S41: The target well formation pressure prior probability f(P p ) and the likelihood function L(X 1 |P) into the Bayesian formula.

[0120] S42: Obtain the posterior probability g(P|X 1 ) to update the formation pressure of the target well.

[0121] The Bayesian formula is as follows:

[0122]

[0123] Where: p(X) is the marginal probability, that is, the normalization coefficient.

[0124] The present invention is specifically described below by taking the X-1 well as an example:

[0125] S1: Collect the acoustic logging curve, density logging curve, natural gamma logging data of Well X-1 and the logging data while drilling such as drilling time, rotary table speed, drilling pressure, drill bit diameter and circulating equivalent drilling fluid density, as well as the drilling data of Well X-2 in the same block.

[0126] S2: Based on the well logging data and logging while drilling data, the prior probability of the formation pressure at any depth of well X-1 is calculated, and the formation pressure interval profile of X-1 with confidence is established.

[0127] S21: Calculate the formation pressure of the entire well section of Well X-1 by using the effective stress method, such as Figure 5 shown.

[0128] S22: Substitute the formation pressure Pp obtained by effective stress calculation of Well X-1 into the calculation formula of the DC index method, inversely calculate the Eaton index, and obtain a sample analysis library of the Eaton index.

[0129] S23: The Eaton index is processed by the sliding window method to construct a small sample set of the Eaton index at any well depth, and the probability density function f1(x) and cumulative probability density function F1(x) of the Eaton index are constructed by the normal information diffusion method.

[0130] S24: Select the pure mudstone section of Well X-1 as the structural interval of the normal compaction trend line.

[0131] S25: constructing an analysis sample library of the slope and intercept of the normal compaction trend line according to the method mentioned in the present invention.

[0132] S26: The probability density functions f2(x), f3(x) and the cumulative probability density functions F2(x), F3(x) of the normal compaction trend line (slope and intercept) are constructed by the normal information diffusion method, and the analysis is performed at the position of 3100m in the X-1 well, as shown in Figure 2. Figure 6-8 shown.

[0133] S27: Through MATLAB programming, using the Monte Carlo simulation method, generate random number samples XN that conform to the probability density functions of the characteristic parameters (slope and intercept) of the Eaton index and the normal compaction trend line, and set the number of simulations N = 104 times, such as Figure 9-11 shown.

[0134] S28: Substitute the generated random number samples of each parameter back into the DC index method (Formula 9) to obtain a formation pressure random sample set P prnd , select normal distribution to fit the formation pressure, and obtain the probability density function f(Pp) and cumulative probability density function F(Pp) of the formation pressure at 3100m, as shown in Fig.12 shown.

[0135] S29: Take the pressure values ​​of the formation pressure cumulative probability density J1=5% and J2=95% at any well depth, connect the points along the vertical well depth to form a line, and obtain the formation pressure curve with the cumulative probability of |J2-J1|=90% for the entire well section of well X-1, that is, the formation pressure interval profile with a confidence level of 90%, such as Fig.13 shown.

[0136] S3: Based on the well logging data and logging while drilling data of the adjacent well X-2, the formation pressure interval profile with a confidence level of 90% is constructed in the same way, such as Fig.14 shown.

[0137] S4: Select the formation pressure at 3100m of Well X-1 as the prior probability N(42.27,2.422) of the formation pressure at the depth of the well, select the formation pressure at 3100m of Well X-2 as the likelihood function N(54.76,3.062) of the formation pressure at the depth of the well, substitute the prior probability and likelihood function into the Bayesian formula, and calculate the posterior probability of the formation pressure at 3100m as N(46.87,1.062), as follows: Fig.15 shown.

[0138] Finally, the formation pressure information of well X-1 is used as the prior probability, and the formation pressure information of well X-2 is used as the likelihood function. The formation pressure estimation result at any depth is updated by the Bayesian formula, and the updated confidence formation pressure interval profile based on the Bayesian Bayesian theory is obtained, as shown in Fig.16 shown.

[0139] The updated posterior probability of formation pressure integrates the formation pressure prediction information of the target well and the formation pressure prediction information of the adjacent wells. It is continuously revised and updated based on the previous predictions using the data of adjacent wells, which ensures the accuracy of the local formation pressure estimation to the greatest extent and can provide more reasonable formation pressure information for drilling operations.

[0140] The advantages and positive effects of the present invention are:

[0141] The present invention combines the effective stress method and the DC index method, quantitatively characterizes the uncertainty of the Eaton index and the normal compaction trend line in the formation pressure estimation process through mathematical statistical analysis, and establishes the formation pressure prediction interval profile with confidence for the target well. Further considering the information of neighboring wells in the same block, based on the Bayesian theory, the drilling-time update and correction of the formation pressure interval profile with confidence is realized, so as to obtain the formation pressure parameters more accurately. It provides more accurate formation pressure information for drilling risk assessment, reduces the drilling risk caused by unclear understanding of formation pressure, and effectively improves drilling efficiency.

[0142] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for estimating formation pore pressure while drilling based on Bayesian theory, characterized by: The following steps are included: S1: Collect well logging data, logging while drilling data of the target well and data of adjacent wells in the same block of the target well; S2: predicting the formation pressure interval profile of the target well with confidence based on the well logging data and the logging while drilling data, and determining the prior probability of the formation pressure at any well depth; S3: Based on the adjacent well data, calculate the confidence interval profile of the adjacent well formation pressure, and determine the likelihood function of the formation pressure at any well depth; S4: Select the prior probability and likelihood function of the formation pressure at the same layer and substitute them into the Bayesian formula to obtain the updated posterior probability of the formation pressure.

2. The method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 1, characterized in that: The S2 comprises the following steps, S21: calculating the formation pressure of the entire well section of the target well based on the effective stress method formation pressure prediction model; S22: Substitute the formation pressure Pp of the entire well section calculated by the effective stress method into the DC index method formula, inversely calculate the Eaton index at any well depth position along the vertical direction, and construct an analysis sample library of the Eaton index; S23: The Eaton index at any well depth is converted from a single sample into a set of 2n+1 small samples by using the sliding window method, and the normal information diffusion method is selected for probability distribution fitting to establish the probability density function and cumulative probability density function of the Eaton index at any well depth; S24: Selecting a pure mudstone section as a normal compaction trend line construction interval, and defining a starting well section and an ending well section within the interval, wherein the starting well section contains n sample points, and the ending well section contains m sample points; S25: Selecting the nth sample point in the starting well section and the mth sample point in the ending well section respectively, constructing n×m normal compaction trend lines, and constructing an analysis sample library of the slope and intercept of the normal compaction trend line; S26: performing probability statistical analysis on the slope and intercept of the normal compaction trend line, selecting the normal information diffusion method for probability distribution fitting, and establishing a probability density function and a cumulative probability density function of the normal compaction trend line; S27: Generate a random number sample XN that meets the probability density function of the characteristic parameters of the Eaton index and the normal compaction trend line through Monte Carlo simulation, where N is the number of Monte Carlo simulations; S28: Substitute the random number samples generated in S27 into the DC index calculation model to calculate the formation pressure sample set P at any well depth position. prnd , select normal distribution to fit the formation pressure to obtain the prior probability density function f(Pp) and cumulative probability density function F(Pp) of the formation pressure; S29: Take the pressure values ​​of the cumulative probability density of formation pressure J1=5%, J2=95% at any well depth, connect the points along the vertical well depth to form a line, and obtain the formation pressure curve with a cumulative probability of |J2-J1|=90% for the entire well section of the target well, that is, the formation pressure interval profile with a confidence level of 90%.

3. The method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 2, characterized in that: In S21, the effective stress method formation pressure prediction model is as follows: P P =S v -100.674e -2.5782μ Where: P P is the formation pressure, in MPa; S v is the overburden pressure, in MPa; μ is Poisson’s ratio, dimensionless; Calculate the overburden formation pressure S by using the logging data v And the Poisson's ratio μ, the formula is as follows: Where: ρ is the average density of the overlying rock layer, in g / cm 3 ρ b is the formation density, in g / cm 3 ; H0 is the starting depth of the target well, in m; H is the ending depth of the target well, in m; Δt s is the shear wave time difference, in us / m; Δt P is the longitudinal wave time difference, unit is us / m.

4. A method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 2 or 3, characterized in that: In S22, the calculation model of the Eaton index is as follows: Where: n is the Eaton index; d c The measured d c Index; d cn The normal compaction trend line of the formation corresponds to d c Index; P h is the hydrostatic pressure, in MPa; d c The index is calculated through drilling data, and its calculation formula is as follows: Where: T is drilling time, unit is min / m; N is rotary table speed, unit is r / min; W is drilling pressure, unit is KN; D b is the drill diameter, in mm; G h is the hydrostatic pressure gradient, in g / cm 3 ; ρ ECD is the circulating equivalent drilling fluid density, in g / cm 3 .

5. A method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 2 or 3, characterized in that: In S23, the calculation model of the normal information diffusion method is as follows: Where: n is the sample size; h is the diffusion coefficient; x i is the observed sample; When the sample volume n is different, the diffusion coefficient h is calculated by the following formula: Where: b is the maximum value of the sample, b = max{x i }; a is the minimum value of the sample, a=min{x i }; σ is the sample standard deviation; ζ is the correlation coefficient, which is determined by the sample size.

6. A method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 2 or 3, characterized in that: In S28, the random sample set P of formation pressure at any well depth position prnd The calculation formula is as follows: Where: nrnd is the random number sample set of Eaton index; d cnrnd A sample set of random numbers for the normal compaction trend line.

7. A method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 2 or 3, characterized in that: In S28, the prior probability density function and cumulative probability density function formulas of the target well formation pressure are as follows: Where: μ is the sample mean; σ is the sample standard deviation.

8. A method for estimating formation pore pressure while drilling based on Bayesian theory according to any one of claims 1 to 3, characterized in that: The S3 comprises the following steps, S31: According to the geological stratification of the block where the target well is located, the information of neighboring wells in the same stratum as the target well is screened; S32: According to S2, the likelihood function L(X1|P) of the formation pressure of the target layer is obtained through the information of adjacent wells.

9. A method for estimating formation pore pressure while drilling based on Bayesian theory according to any one of claims 1 to 3, characterized in that: The S4 comprises the following steps, S41: The target well formation pressure prior probability f(P p ) and the likelihood function L(X 1 |P)Substitute into the Bayesian formula; S42: Obtain the posterior probability g(PX 1 ) to update the formation pressure of the target well.

10. The method for estimating formation pore pressure while drilling based on Bayesian theory according to claim 9, characterized in that: The Bayesian formula is as follows: Where: p(X) is the marginal probability, that is, the normalization coefficient.

Images