Method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy

Abstract

The invention relates to a method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy. The method comprises the following steps: constructing a dynamic modulus and first static modulus state conversion model; combining the dynamic modulus and first static modulus conversion model with the porosity and dynamic modulus relationship to construct a porosity and first static modulus model; a stratum model is constructed, and a second static modulus is set according to the range of 10-80 GPa; creating a PDC drill bit model, and fixing a reference point; surface-surface contact is adopted, a drill bit-stratum action model is set, and an eight-node reduction integral unit is adopted for grid division; rock breaking simulation is carried out, and the drilling pressure, the rotating speed, the torque and the mechanical drilling speed under different second static moduli are extracted. Calculating a mechanical specific energy value by using the drilling pressure, the rotating speed, the torque and the mechanical drilling speed; regression fitting is carried out on the obtained mechanical specific energy value in an index form, and a mechanical specific energy and second static modulus relation model is constructed; enabling the first static modulus to be equal to the second static modulus, and constructing a porosity and mechanical specific energy relation model; and collecting and filtering the while-drilling engineering parameters, substituting the while-drilling engineering parameters into the porosity and mechanical specific energy relation model, and determining the porosity value of the reservoir in real time. The method has the advantages of low cost and continuous data points, and the porosity can be quantitatively monitored in real time in the while-drilling process.

Description

Technical Field

[0001] The present disclosure relates to the field of oil and gas drilling and logging, and in particular to a method for predicting the porosity of tight reservoirs based on drilling mechanical specific energy. Background Art

[0002] With the deepening of oil and gas exploration, deep and ultra-deep layers will be one of the main battlefields for domestic offshore exploration in the future. It is also a practical field for implementing the "Oil and Gas Reserve and Production Increase Project". The real-time quantitative prediction of reservoir porosity while drilling is a key task in tight reservoir exploration operations.

[0003] The existing reservoir porosity evaluation methods mainly include: indoor experimental method (such as patent CN201410612599.4), seismic data method (such as patents CN202211291421.5; CN201911256155.0; CN202210071281.4; CN201711212942.6; CN202310348069.2; CN202310034418.3; CN202211211184.7; CN2017 10876544.8; CN202010452820.X; CN202010946137.1; CN202110569164.6; CN202311798749.0; CN201911421493.5), and well logging data evaluation methods (as shown in patents CN202210267185.7; CN201710747940.0; CN202111562006.4; and CN201810116777.2). These existing methods are used in deep and ultra-deep offshore exploration, but each has the following problems: Laboratory core porosity tests require on-site coring, resulting in high costs and discontinuities, making it impossible to predict porosity while drilling. Seismic data methods are pre-drilling predictions, but due to their multi-solution nature, they suffer from low interpretation precision and insufficient prediction accuracy. The logging data evaluation method is a post-drilling prediction method that uses logging instruments to measure porosity, which has the disadvantage of hysteresis.

[0004] Different from these mainstream methods, researchers have also used logging engineering parameter characteristics to quantitatively interpret reservoir properties. As shown in patent CN202210298234.3, this method has achieved certain results in sandstone, volcanic rock, and carbonate rock, enabling porosity determination while drilling. However, this method also requires extensive field coring and laboratory core testing, which has the disadvantages of being difficult, costly, and discontinuous. Summary of the Invention

[0005] In order to solve the technical problems existing in the background technology, the present disclosure proposes a method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy. This method can establish a relationship model between porosity and the first static modulus through logging data; establish a relationship model between mechanical specific energy and the second static modulus through finite element numerical simulation; make the first static modulus and the second static modulus equal, and construct a relationship model between mechanical specific energy and porosity, so that the porosity can be quantitatively determined in real time based on the logging parameters while drilling. This method does not require a large amount of on-site coring for indoor core experiments. Through logging data and finite element numerical simulation, it has the advantages of low cost and continuous data points. It can perform real-time quantitative monitoring of porosity during the drilling process, which will be a very beneficial supplement to regional exploration operation decision-making.

[0006] The present disclosure first provides a method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy, comprising measuring a dynamic modulus and a first static modulus, and:

[0007] Constructing a dynamic modulus and first static modulus state conversion model;

[0008] Combining the dynamic modulus and first static modulus conversion model with the porosity and dynamic modulus relationship to construct a porosity and first static modulus model;

[0009] Construct a formation model and set the second static modulus in the range of 10-80 GPa;

[0010] Create a PDC drill bit model and fix the reference point;

[0011] Surface-to-surface contact was used to set the drill bit-formation interaction model, and 8-node reduced integration elements were used for meshing.

[0012] Conduct rock-breaking simulations to extract WOB, rotational speed, torque, and ROP under different second static moduli. Calculate mechanical specific energy using WOB, rotational speed, torque, and ROP.

[0013] The obtained mechanical specific energy values were subjected to exponential regression fitting to construct a relationship model between mechanical specific energy and the second static modulus.

[0014] The first static modulus and the second static modulus are set equal to each other, and a relationship model between porosity and mechanical specific energy is constructed;

[0015] The drilling-while-drilling parameters are collected and filtered, and substituted into the porosity and mechanical specific energy relationship model to determine the reservoir porosity value in real time; the drilling-while-drilling parameters include bit pressure, rotation speed, mechanical drilling speed and torque value.

[0016] Furthermore, the porosity and first static modulus model is constructed according to the following path:

[0017] (1) Drill and coring of tight reservoirs, measure the longitudinal wave time difference, shear wave time difference and density data of cylindrical core columns, and calculate the dynamic elastic modulus E of the corresponding core d ;

[0018] (2) Loading axial and radial extensometers on several cylindrical cores, carrying out compression tests, measuring stress-strain curves, and calculating the corresponding first static elastic modulus E s ;

[0019] (3) The static modulus and dynamic modulus of the corresponding cylindrical core are plotted in the rectangular coordinate system, with the static modulus E s is the vertical axis, E d As the horizontal axis, linear regression fitting is performed, and the slope is used as the coefficient a value and the intercept is used as the b value to establish the first static modulus and dynamic modulus conversion model:

[0020] E s =aln(E d )-b;

[0021] (4) Using the P-wave time difference logging information, S-wave time difference logging information, density logging information and neutron porosity information of the tight reservoir section, the dynamic modulus values and porosity values φ at different measuring points are calculated, and a porosity and dynamic modulus relationship model is established. The porosity and first static modulus model is established by connecting the first static modulus and the dynamic modulus conversion model, and the coefficients c and d are determined:

[0022] φ=-cE s +d.

[0023] Furthermore, the construction of the relationship model between the mechanical specific energy and the second static modulus is performed according to the following path:

[0024] (1) The formation is modeled as a cylinder, and the second static modulus with different gradients is set according to the arithmetic progression of the logging data to cover the distribution range of the first static modulus in the target work area;

[0025] (2) Create a PDC drill bit model, define the cutting teeth as discrete rigid bodies, and fix them at a reference point;

[0026] (3) Surface-to-surface contact is adopted between the rock and the cutting tooth surface of the drill bit. The friction coefficient of each contact surface is set. The rock body and the drill bit model both use an 8-node reduced integration unit with hourglass control.

[0027] (4) Extract the first principal stress σ1 and the third principal stress σ3 of the rock in front of the cutting teeth and substitute them into the Mohr-Coulomb strength criterion. When the first principal stress σ1, the third principal stress σ3, the rock cohesion C and the rock internal friction angle φ meet the following equation, the rock is cut and damaged, and the rock body mesh is deleted.

[0028]

[0029] (5) Carry out rock breaking simulation of PDC drill bits with different second static moduli, record the bit weight on bit (WOB), speed (RPM), torque (T), and mechanical penetration rate (ROP), and substitute them into the formula to calculate the mechanical specific energy (MSE) value;

[0030]

[0031] Where: WOB is the bit pressure, A b is the drill bit area, RPM is the rotational speed, T is the torque, ROP is the mechanical penetration rate, and MSE is the mechanical specific energy;

[0032] (6) Plot the mechanical specific energy and the second static elastic modulus values corresponding to the rock breaking simulation results in a rectangular coordinate system, use exponential regression fitting to establish a relationship model between the mechanical specific energy and the second static modulus, and determine the coefficients f1 and f2;

[0033]

[0034] Furthermore, the second static modulus with different gradients is set according to the arithmetic progression of the logging data so that the selected data points can cover the distribution range of the first static modulus and do not change the fitting trend of the first static modulus curve;

[0035] The arithmetic progression table is described as: n =e1+(n-1)·f;

[0036] Where, e n is the nth term of the sequence; e1 is the first term of the sequence; f is the common difference; n is the number of terms;

[0037] The tolerance f is calculated using a decimal notation method with 10 as the base, the first term e1 being 10 GPa, and the number of terms n ranging from 1 to 8.

[0038] Furthermore, the construction of the porosity and mechanical specific energy relationship model includes:

[0039] (1) Combining the relationship model between mechanical specific energy and the second static modulus, and the relationship model between porosity and the first static modulus, making the first static modulus and the second static modulus equal, the relationship model between porosity and mechanical specific energy is established by combining the two, and the coefficients g and h are determined;

[0040] φ=-gMSE+h

[0041] Where: g is the slope of the linear fitting curve of porosity φ and mechanical specific energy MSE, h is the intercept of the ordinate porosity φ;

[0042] (2) By collecting data points (MSE1, φ1), (MSE2, φ2), ..., (MSE n ,φ n ), calculate the slope g and intercept h;

[0043] The calculation formula for the slope g is:

[0044] The calculation formula for the intercept h is:

[0045] Where n is the number of data points; ∑MSE·φ is the sum of all products of MSE and φ; ∑MSE is the sum of all MSE values; ∑φ is the sum of all φ values; ∑MSE 2 is the sum of the squares of all MSE values;

[0046] (3) The calculated g and h are substituted into the porosity and mechanical specific energy relationship model φ = -gMSE + h to obtain a linear model.

[0047] Furthermore, the mean μ and standard deviation σ of the drilling parameters are calculated using normal distribution, and values less than (μ-3σ) and greater than (μ+3σ) are eliminated to eliminate erroneous or distorted data generated during pump stop, tripping, and circulation operations.

[0048] At least one of the above technical solutions adopted in one or more embodiments of this specification can achieve the following beneficial effects:

[0049] First, the technical solution provided in the present disclosure establishes a relationship model between porosity and the first static modulus through logging data; establishes a relationship model between mechanical specific energy and the second static modulus through finite element numerical simulation; makes the first static modulus and the second static modulus equal, and constructs a relationship model between mechanical specific energy and porosity, thereby realizing real-time quantitative prediction of porosity based on while-drilling logging parameters.

[0050] Secondly, when the technical solution provided in the present disclosure is implemented, there is no need for a large number of on-site coring for indoor core experiments. Compared with patent CN202210298234.3, which requires a large number of on-site coring for indoor core experiments to predict reservoir porosity, this solution has the advantages of low cost and continuous data points. It can perform real-time quantitative monitoring of porosity during the drilling process, which will be a very beneficial supplement to regional exploration operation decisions.

[0051] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0052] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0054] Figure 1 : Flow chart of reservoir porosity prediction method based on PDC rock breaking simulation and logging mechanical specific energy.

[0055] Figure 2 : Relationship diagram between dynamic modulus and static modulus.

[0056] Figure 3 : Data anomaly detection and correction diagram.

[0057] Figure 4 : Data weighting and smoothing filtering graph.

[0058] Figure 5 : Normal distribution histogram.

[0059] Figure 6 :Real-time prediction results of reservoir porosity while drilling for well NB19-6-5d.

[0060] Figure 7 :Real-time prediction results of reservoir porosity while drilling for well NB22-1-3.

[0061] Figure 8 :Real-time prediction results of reservoir porosity while drilling for well NB22-1-4. DETAILED DESCRIPTION

[0062] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0063] In addition, in order to better illustrate the present disclosure, numerous specific details are provided in the following detailed description. Those skilled in the art will understand that the present disclosure can be practiced without certain specific details. In some examples, methods and means well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure.

[0064] The specific implementation is mainly completed from the following four aspects:

[0065] Aspect 1: Based on reservoir physical properties, P-wave and S-wave time difference experiments, and rock mechanics parameter experiments, a relationship model between reservoir porosity and the first static modulus is established, which is characterized by comprising the following steps:

[0066] (1) Drill and core the tight reservoir and process it into cylindrical core blocks with a diameter of 25 mm and a height of 50 mm. Measure the P-wave time difference, S-wave time difference and density data, and calculate the dynamic modulus E of the corresponding core. d .

[0067] (2) For n cylindrical cores, load axial and radial extensometers, conduct compression tests, measure stress-strain curves, and calculate the corresponding first static modulus E s .

[0068] (3) Plot the first static modulus and dynamic modulus of the corresponding cylindrical core into the rectangular coordinate system, with the first static modulus E s is the vertical axis, E d With θ as the horizontal axis, linear regression fitting is performed to establish the first static modulus and dynamic modulus conversion model.

[0069] E s =6.9137ln(E d )-3.2498;

[0070] (4) Using the P-wave time difference logging information, S-wave time difference logging information, density logging information, neutron porosity information, etc. of the tight reservoir section, the dynamic modulus values and porosity values φ at different measuring points are calculated, and a porosity and dynamic modulus relationship model is established. The porosity and first static modulus relationship model is established by connecting the conversion model of the first static modulus and the dynamic modulus.

[0071] φ=-4.0576E s +93.058.

[0072] Aspect 2: Using PDC rock breaking simulation, the characteristics of parameters such as bit pressure, torque, and drilling time of different formations under the action of different PDC drill bits are obtained, the mechanical specific energy value is calculated, and a relationship model between mechanical specific energy and the second static modulus is established, which is characterized by including the following steps:

[0073] (1) The formation is modeled as a cylinder with a radius of 350 mm and a thickness of 200 mm. The second static modulus with different gradients is set according to the distribution range of the first static modulus of the field logging data. Due to the excessive number of data points and the small intervals between the first static modulus data points, the analysis is too complicated and the experimental cycle is too long. Therefore, the first static modulus data points are further optimized, and the second static modulus with different gradients is set in the form of an arithmetic progression to ensure that the selected data points can cover the distribution range of the first static modulus and do not change the fitting trend of the first static modulus curve. The tolerance of the arithmetic progression is set to 10, the first item is 10 GPa, the eighth item is 80 GPa, and the second static modulus is set to 10 GPa, 20 GPa, 30 GPa, 40 GPa, 50 GPa, 60 GPa, 70 GPa, and 80 GPa, respectively.

[0074] (2) A PDC drill bit model is created based on commonly used drill bit types, crown profiles, and cutting structures. The strain and stress parameters of the cutting teeth on the drill bit are not considered, and the cutting teeth are defined as discrete rigid bodies and fixed at a reference point.

[0075] (3) Surface-to-surface contact is adopted between the rock and the cutting tooth surface of the drill bit. Considering the friction between the cutting tooth surface and the rock chips and between the flank and the cutting surface, the friction coefficient of each contact surface is set. Both the rock body and the drill bit model use an 8-node reduced integration element with hourglass control. The mesh of the cut rock body is refined.

[0076] (4) Carry out rock breaking simulation of PDC drill bits with different static elastic moduli, record the values of WOB, RPM, torque T, and displacement S, and calculate the values of ROP and MSE.

[0077]

[0078] (5) The mechanical specific energy and the second static elastic modulus values corresponding to the rock breaking simulation results are plotted in a rectangular coordinate system, and an exponential regression fitting is used to establish a relationship model between the mechanical specific energy and the second static modulus.

[0079]

[0080] Aspect three: Combine the relationship model between porosity and the first static modulus, and the relationship model between mechanical specific energy and the second static modulus to construct a relationship model between porosity and mechanical specific energy.

[0081] The relationship model between mechanical specific energy and the second static modulus, as well as the relationship model between porosity and the first static modulus, are combined to construct a relationship model between porosity and mechanical specific energy by making the first static modulus and the second static modulus equal.

[0082] φ=-0.0034MSE+13.35.

[0083] Aspect 4: Using well site logging data, calculating the mechanical specific energy value in real time while drilling, and using the relationship model between porosity and mechanical specific energy, quantitatively predicting the reservoir porosity value in real time, characterized by including the following steps:

[0084] (1) Engineering data such as drilling pressure, rotation speed, torque, and drilling time are collected while drilling, and filtered to calculate the mean μ and standard deviation σ of each engineering parameter. Values less than (μ-3σ) and greater than (μ+3σ) are eliminated to eliminate erroneous or distorted data generated by operations such as stopping the pump, tripping the drill, and circulating the drill.

[0085] (2) Using the screened and filtered data, the mechanical specific energy value is calculated in real time while drilling, and combined with the relationship model between porosity and mechanical specific energy, the formation porosity value is quantitatively predicted in real time.

[0086] Figure 1 This is a flowchart of the method for predicting the porosity of tight reservoirs based on drilling mechanical specific energy. Figure 1 , describing the specific implementation of the present invention.

[0087] 1. Based on reservoir physical properties, P-wave and S-wave time difference experiments, and rock mechanics parameter experiments, a relationship model between reservoir porosity and the first static modulus is established, which is characterized by including the following steps:

[0088] Step 1 (101): Taking the DH-1 oilfield as an example, the tight reservoir was cored and processed into cylindrical cores with a diameter of 25 mm and a height of 50 mm. The longitudinal wave time difference, shear wave time difference and density data were measured, and the dynamic modulus E of the corresponding core was calculated. d .

[0089] Step 2 (102): Load n cylindrical cores with axial and radial extensometers, conduct compression tests, measure stress-strain curves, and calculate the corresponding first static modulus E s .

[0090] Step 3 (103): Plot the static modulus and dynamic modulus of the corresponding cylindrical core into a rectangular coordinate system, such as Figure 2 As shown, linear regression fitting is performed to establish a first static modulus and dynamic modulus conversion model.

[0091] E s =6.9137ln(E d )-3.2498;

[0092] Step 4 (104): Calculate the dynamic modulus and porosity values φ at different measuring points using the P-wave time difference logging information, S-wave time difference logging information, density logging information, neutron porosity information, etc. of the tight reservoir section, establish a porosity and dynamic modulus relationship model, and first establish a porosity and first static modulus relationship model using the static modulus and dynamic modulus conversion model as a link.

[0093] φ=-4.0576E s +93.058.

[0094] Second, using PDC rock breaking simulation, the characteristics of parameters such as bit pressure, torque, and drilling time of different formations under the action of different PDC drill bits are obtained, the mechanical specific energy value is calculated, and a relationship model between mechanical specific energy and the second static modulus is established, which is characterized by including the following steps:

[0095] Step 1 (201): The formation is modeled as a cylinder with a radius of 350 mm and a thickness of 200 mm. The second static modulus with different gradients is set according to the distribution range of the first static modulus of the field logging data. Due to the excessive number of first static modulus data points and the small intervals, the analysis is too complicated and the experimental cycle is too long. Therefore, the first static modulus data points are further optimized and the second static modulus with different gradients is set in the form of an arithmetic progression to ensure that the selected data points can cover the distribution range of the first static modulus and do not change the fitting trend of the first static modulus curve. The tolerance of the arithmetic progression is set to 10, the first item is 10 GPa, the eighth item is 80 GPa, and the second static modulus is set to 10 GPa, 20 GPa, 30 GPa, 40 GPa, 50 GPa, 60 GPa, 70 GPa, and 80 GPa, respectively.

[0096] Step 2 (202): Create a PDC drill bit model based on commonly used drill bit types, crown profiles, and cutting structures, such as Figure 3 As shown in Figure 2, the strain and stress parameters of the cutting teeth on the drill bit are not taken into consideration. Instead, the cutting teeth are defined as discrete rigid bodies and fixed at a reference point.

[0097] Step 3 (203): Surface-to-surface contact is adopted between the rock and the cutting tooth surface of the drill bit. Considering the friction between the cutting tooth surface and the rock chips and between the flank and the cutting surface, the friction coefficient of each contact surface is set. Both the rock body and the drill bit model use an 8-node reduced integration element with hourglass control. The mesh of the cut rock body is refined.

[0098] Step 4 (204): Carry out rock breaking simulation of PDC drill bits with different static elastic moduli, record the values of WOB, RPM, torque T, and displacement S, and calculate the values of ROP and MSE.

[0099]

[0100] Step 5 (205): Plot the mechanical specific energy and the second static elastic modulus values corresponding to the rock breaking simulation results in a rectangular coordinate system, and use the regression fitting method to establish a relationship model between the mechanical specific energy and the second static modulus.

[0101]

[0102] 3. The relationship model between porosity and the first static modulus, as well as the relationship model between mechanical specific energy and the second static modulus, are combined to construct a relationship model between porosity and mechanical specific energy.

[0103] (301): Combining the relationship model between mechanical specific energy and the second static modulus, and the relationship model between porosity and the first static modulus, the first static modulus and the second static modulus are made equal, and the two are combined to establish the relationship model between porosity and mechanical specific energy.

[0104] φ=-0.0034MSE+13.35.

[0105] 4. Using well site logging data, calculating the mechanical specific energy value in real time while drilling, and using the relationship model between porosity and mechanical specific energy, quantitatively predicting the reservoir porosity value in real time, characterized by including the following steps:

[0106] Step 1 (401): Collect engineering data such as drilling pressure, rotation speed, torque, drilling time, etc. while drilling, and perform filtering processing to calculate the mean μ and standard deviation σ of each engineering parameter, and eliminate values less than (μ-3σ) and greater than (μ+3σ) to eliminate erroneous or distorted data generated by operations such as stopping the pump, drilling, and circulation.

[0107] (1) Data anomaly inspection and error correction

[0108] Due to the differences in drilling conditions, complexity, logging tools and other conditions, the original data may have certain errors. The 3σ method is used to check and correct the original data.

[0109] The distribution density of random error is:

[0110]

[0111] The probability that the error is distributed between δ1 and δ2 is:

[0112]

[0113] The probability of random errors occurring within the range [μ-3δ, μ+3δ] is 0.9973, and abnormal values such as abnormal drilling pressure and rotation speed outside the range are eliminated.

[0114] (2) Data weighting and smoothing filtering

[0115] The SG method performs least square fitting on the given data samples and high-order polynomials to obtain the data weighted weight value, which achieves the goal of more effectively retaining the signal change information while filtering and smoothing.

[0116]

[0117] 2M+1 point data, the P-order polynomial fitting error is:

[0118]

[0119] make

[0120] Obtain coefficient a o , a1, a p Determine the best-fit polynomial.

[0121] Step 2 (402): Calculate the mechanical specific energy value in real time while drilling using the screened and filtered data, and quantitatively predict the formation porosity value in real time by combining the relationship model between porosity and mechanical specific energy.

[0122] 6. Data obtained from field trials

[0123] This method was experimentally applied in three exploration wells in the NB block (NB19-6-5d, NB22-1-3, and NB22-1-4). The predicted porosity matched the actual porosity at a rate of over 82%, demonstrating that the real-time reservoir porosity prediction method based on while-drilling logging parameters can quickly, accurately, and in real time predict reservoir porosity. This method provides a basis and support for regional exploration decision-making.

[0124] 1. Well NB19-6-5d

[0125] The predicted porosity of Well NB19-6-5d is highly consistent with the actual porosity, with a fluctuation range of 80.15% to 86.35% and an average of 82.42%. The data collected while drilling for Well NB19-6-5d are shown in Table 1. The results of the real-time prediction of reservoir porosity while drilling for Well NB19-6-5d are shown in Table 1. Figure 6 As shown in (attached to the specification).

[0126] Table 1 Data collected while drilling for Well NB19-6-5d

[0127]

[0128]

[0129]

[0130] 2. NB22-1-3 Well

[0131] The predicted porosity of Well NB22-1-3 has a high consistency with the actual porosity, with a fluctuation range of 80.03%-93.50% and an average of 85.02%. The data collected while drilling for Well NB22-1-3 are shown in Table 2. The real-time prediction results of reservoir porosity while drilling for Well NB22-1-3 are shown in Table 2. Figure 7 shown.

[0132] Table 2 Data collected while drilling for Well NB22-1-3

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] 3. Well NB22-1-4

[0141] The predicted porosity of Well NB22-1-4 is highly consistent with the actual porosity, with a fluctuation range of 80.03%-93.50% and an average of 85.02%. The data collected while drilling for Well NB22-1-4 are shown in Table 3. The real-time prediction results of reservoir porosity while drilling for Well NB22-1-4 are shown in Table 3. Figure 8 As shown in the figure, the scattered points at the end of the figure are the actual porosity, the line is the predicted porosity, and the overlapping part of the scattered points and the line is the porosity compliance rate. The closer the scattered points are to the end point of the line, the higher the porosity compliance rate.

[0142] Table 3 Data collected while drilling for Well NB22-1-4

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] The above description is only an illustrative embodiment of the present invention and is not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principle of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy, comprising measuring dynamic modulus and first static modulus, characterized in that The method further comprises: Constructing a dynamic modulus and first static modulus state conversion model; Combining the dynamic modulus and first static modulus conversion model with the porosity and dynamic modulus relationship to construct a porosity and first static modulus model; Construct a formation model and set the second static modulus in the range of 10-80 GPa; Create a PDC drill bit model and fix the reference point; Surface-to-surface contact was used to set the drill bit-formation interaction model, and 8-node reduced integration elements were used for meshing. Conduct rock breaking simulations to extract the drilling pressure, rotational speed, torque, and mechanical penetration rate under different second static moduli; calculate the mechanical specific energy value using the drilling pressure, rotational speed, torque, and mechanical penetration rate; The obtained mechanical specific energy values were subjected to exponential regression fitting to construct a relationship model between mechanical specific energy and the second static modulus. The first static modulus and the second static modulus are set equal to each other, and a relationship model between porosity and mechanical specific energy is constructed; The drilling-while-drilling parameters are collected and filtered, and substituted into the porosity and mechanical specific energy relationship model to determine the reservoir porosity value in real time; the drilling-while-drilling parameters include bit pressure, rotation speed, mechanical drilling speed and torque value.

2. The method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy according to claim 1, characterized in that: The porosity and first static modulus model is constructed as follows: (1) Drill and coring of tight reservoirs, measure the longitudinal wave time difference, shear wave time difference and density data of cylindrical core columns, and calculate the dynamic elastic modulus E of the corresponding core d ; (2) Loading axial and radial extensometers on several cylindrical cores, carrying out compression tests, measuring stress-strain curves, and calculating the corresponding first static elastic modulus E s ; (3) The static modulus and dynamic modulus of the corresponding cylindrical core are plotted in the rectangular coordinate system, with the static modulus E s is the vertical axis, E d As the horizontal axis, linear regression fitting is performed, and the slope is used as the coefficient a value and the intercept is used as the b value to establish the first static modulus and dynamic modulus conversion model: HAVE BEEN s =aln(E d )-b; (4) Using the P-wave time difference logging information, S-wave time difference logging information, density logging information and neutron porosity information of the tight reservoir section, the dynamic modulus values and porosity values φ at different measuring points are calculated, and a porosity and dynamic modulus relationship model is established. The porosity and first static modulus model is established by connecting the first static modulus and the dynamic modulus conversion model, and the coefficients c and d are determined: φ=-cE s +d.

3. The method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy according to claim 2, characterized in that: The construction of the relationship model between the mechanical specific energy and the second static modulus is carried out according to the following path: (1) The formation is modeled as a cylinder, and the second static modulus with different gradients is set according to the arithmetic progression of the logging data to cover the distribution range of the first static modulus in the target work area; (2) Create a PDC drill bit model, define the cutting teeth as discrete rigid bodies, and fix them at a reference point; (3) Surface-to-surface contact is adopted between the rock and the cutting tooth surface of the drill bit. The friction coefficient of each contact surface is set. The rock body and the drill bit model both use an 8-node reduced integration unit with hourglass control. (4) Extract the first principal stress σ1 and the third principal stress σ3 of the rock in front of the cutting teeth and substitute them into the Mohr-Coulomb strength criterion. When the first principal stress σ1, the third principal stress σ3, the rock cohesion C and the rock internal friction angle φ meet the following equation, the rock is cut and damaged, and the rock body mesh is deleted. (5) Carry out rock breaking simulation of PDC drill bits with different second static moduli, record the bit weight on bit (WOB), speed (RPM), torque (T), and mechanical penetration rate (ROP), and substitute them into the formula to calculate the mechanical specific energy (MSE) value; Where: WOB is the bit pressure, A b is the drill bit area, RPM is the rotational speed, T is the torque, ROP is the mechanical penetration rate, and MSE is the mechanical specific energy; (6) Plot the mechanical specific energy and the second static elastic modulus values corresponding to the rock breaking simulation results in a rectangular coordinate system, use exponential regression fitting to establish a relationship model between the mechanical specific energy and the second static modulus, and determine the coefficients f1 and f2; 4. The method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy according to claim 3, characterized in that: The second static modulus with different gradients is set according to the arithmetic progression of the logging data so that the selected data points can cover the distribution range of the first static modulus and do not change the fitting trend of the first static modulus curve; The arithmetic progression table is described as: n =e1+(n-1)·f; Where, e n is the nth term of the sequence; e1 is the first term of the sequence; f is the common difference; n is the number of terms; The tolerance f is calculated using a decimal notation method with 10 as the base, the first term e1 being 10 GPa, and the number of terms n ranging from 1 to 8.

5. The method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy according to claim 4, characterized in that: The method of constructing a porosity and mechanical specific energy relationship model comprises: (1) Combining the relationship model between mechanical specific energy and the second static modulus, and the relationship model between porosity and the first static modulus, making the first static modulus and the second static modulus equal, the relationship model between porosity and mechanical specific energy is established by combining the two, and the coefficients g and h are determined; φ=-gMSE+h Where: g is the slope of the linear fitting curve of porosity φ and mechanical specific energy MSE, h is the intercept of the ordinate porosity φ; (2) By collecting data points (MSE1, φ1), (MSE2, φ2), ..., (MSE n ,φ n ), calculate the slope g and intercept h; The calculation formula for the slope g is: The calculation formula for the intercept h is: Where n is the number of data points; ∑MSE·φ is the sum of all products of MSE and φ; ∑MSE is the sum of all MSE values; ∑φ is the sum of all φ values; ∑MSE 2 is the sum of the squares of all MSE values; (3) The calculated g and h are substituted into the porosity and mechanical specific energy relationship model φ = -gMSE + h to obtain a linear model.

6. The method for determining reservoir porosity based on PDC rock breaking simulation and logging mechanical specific energy according to claim 5, characterized in that: The mean μ and standard deviation σ of the drilling parameters are calculated using normal distribution, and values less than (μ-3σ) and greater than (μ+3σ) are eliminated to eliminate erroneous or distorted data generated during pump stop, tripping, and circulation operations.

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