A post-casing density inversion calculation method, device, equipment and storage medium

Abstract

The application discloses a casing-after-density inversion calculation method and device, equipment and a storage medium. According to the technical scheme provided by the application, the nuclear radiation particle transport process of the downhole gamma ray is simulated, a detector count ratio matrix is constructed, and a target function is constructed according to the detector count ratio matrix; the detector is used to detect the gamma ray of the casing well, and actual detection data of the detector is obtained; the actual detection data is substituted into the detector count ratio matrix to obtain a first count ratio function, and standard detection data under a standard condition is substituted into the detector count ratio matrix to obtain a second count ratio function; the target function is solved by using the first count ratio function and the second count ratio function, and the formation density and the cement density are determined. According to the application, the standard detection data and the actual detection data can be used to perform inversion based on the target function, and the formation density and the cement density can be accurately obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of offshore oil well exploration, and particularly relates to a casing-after density inversion calculation method, device, equipment and storage medium. BACKGROUND

[0002] In the oil drilling technology, different media have different absorption and scattering abilities of gamma rays, and the attenuation caused by the Compton effect and the formation bulk density have a significant relationship. Therefore, in the open hole well, the relationship between the formation density and the gamma ray detector count rate is constructed to invert the change of the formation density. As more and more deep wells are drilled, and the well conditions are relatively more complex, casing-after measurement is needed.

[0003] However, in the prior art, the casing-after density count based on casing-after measurement is still in the initial stage. In the casing-after measurement, the measurement of the rays will be affected by the casing cement, so that the information obtained by the detection instrument in the casing-after measurement is insufficient, and it is difficult to obtain accurate density values in a complex environment; and the uncertainty of the cementing process, such as uneven distribution of cement quality, makes it difficult for the detection instrument to detect and collect signals to be systematically corrected and quantitatively evaluated. In addition, the casing-after density inversion algorithm in the prior art assumes that the composition and density of the cement are determined, which does not match the actual exploration and exploitation situation, and cannot meet the demand of inverting the formation density. SUMMARY

[0004] In view of the above problems, the present application is proposed to provide a casing-after density inversion calculation method and a corresponding casing-after density inversion calculation device, equipment and storage medium which overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of the present application, a casing-after density inversion calculation method is provided, comprising:

[0006] By simulating the nuclear radiation particle transport process of the downhole gamma rays, a detector count ratio matrix is constructed, and a target function is constructed according to the detector count ratio matrix;

[0007] The detector is used to detect gamma rays of the casing well to obtain actual detection data of the detector;

[0008] The actual detection data is substituted into the detector count ratio matrix to obtain a first count ratio function, and the standard detection data under the standard condition is substituted into the detector count ratio matrix to obtain a second count ratio function;

[0009] The first count ratio function and the second count ratio function are used to solve the target function to determine the formation density and the cement density.

[0010] In the scheme, the constructing the detector count ratio matrix by simulating the nuclear radiation particle transport process of the downhole gamma ray further includes:

[0011] The total dose received by the detector is determined by simulating the nuclear radiation particle transport process of the downhole gamma ray by the Monte Carlo method;

[0012] The post-casing model is determined according to the total dose received by the detector;

[0013] The detector count ratio matrix is constructed by using the post-casing model.

[0014] In the scheme, the total dose received by the detector is determined by simulating the nuclear radiation particle transport process of the downhole gamma ray by the Monte Carlo method further includes:

[0015] In the case of infinite medium, point source and uniform medium composition, the total dose received by the detector at a distance r from the source is:

[0016]

[0017] Where D is the total dose received by the detector at a distance r from the source; E is the energy; S(r s ,E) is the radiation source at energy E in space r s ; V is the volume of the detector; μ is the absorption coefficient; G(r s ,r) is the non-collision index point kernel in infinite homogeneous medium.

[0018] In the scheme, when the number of detectors is 4, the detector count ratio matrix is represented as:

[0019]

[0020] Where R is the detector count ratio matrix; R 1ρ ~R 4ρ is the density window count ratio of the four detectors; R 1c ~R 4c is the cement window count ratio of the four detectors; R st is the count ratio value in standard environment; ρ c is the cement density; ρ f is the formation density; F is the approximate mass attenuation coefficient of cement and formation; k1~k5 are environmental parameters; b is a constant coefficient obtained by the least square method.

[0021] In the scheme, the objective function is represented as:

[0022]

[0023] Limited to

[0024] wherein, (p f * , p c * ) is the optimal solution; W is a weight matrix of each detector energy window; R' is a second count ratio function obtained according to standard detection data under standard conditions; R" is a first count ratio function obtained according to actual detection data of the detector; p min is a formation density calculated when the cement is completely filled, and p max is a formation density calculated when the cement filling space is all gas.

[0025] In the above scheme, the step of solving the objective function by using the first count ratio function and the second count ratio function to determine the formation density and the cement density further includes:

[0026] The step of solving the objective function by using the first count ratio function and the second count ratio function to determine the formation density and the cement density further includes:

[0027] According to another aspect of the present application, there is provided a casing-after-density inversion calculation device, comprising a construction module, a detection module, a substitution module and a calculation module, wherein:

[0028] The construction module is configured to construct a detector count ratio value matrix by simulating a nuclear radiation particle transport process of a downhole gamma ray, and construct an objective function according to the detector count ratio value matrix;

[0029] The detection module is configured to perform gamma ray detection on a casing well by using a detector to obtain actual detection data of the detector;

[0030] The substitution module is configured to substitute the actual detection data into the detector count ratio value matrix to obtain a first count ratio function, and substitute standard detection data under standard conditions into the detector count ratio value matrix to obtain a second count ratio function;

[0031] The calculation module is configured to solve the objective function by using the first count ratio function and the second count ratio function to determine the formation density and the cement density.

[0032] In the above scheme, the calculation module further includes:

[0033] The calculation module is configured to solve the objective function by using the first count ratio function and the second count ratio function to determine the formation density and the cement density.

[0034] According to another aspect of the present application, a computing device is provided, comprising a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface are in communication with each other through the communication bus;

[0035] The memory is configured to store at least one executable instruction, and the executable instruction is configured to enable the processor to perform operations corresponding to the post-casing density inversion calculation method.

[0036] According to another aspect of the present application, a computer storage medium is provided, and the computer storage medium stores at least one executable instruction, and the executable instruction is configured to enable the processor to perform operations corresponding to the post-casing density inversion calculation method.

[0037] According to the technical scheme provided by the present application, by simulating the nuclear radiation particle transport process of the downhole gamma ray, a detector count ratio matrix is constructed, and a target function is constructed according to the detector count ratio matrix; the detector is used to detect the gamma ray of the casing well to obtain actual detection data of the detector; the actual detection data is substituted into the detector count ratio matrix to obtain a first count ratio function, and standard detection data under a standard condition is substituted into the detector count ratio matrix to obtain a second count ratio function; the first count ratio function and the second count ratio function are used to solve the target function to determine the formation density and the cement density. Thus, the problem of insufficient information obtained by the detection instrument during post-casing measurement in the prior art is solved, and the formation density and the cement density can be more accurately obtained in a complex environment.

[0038] The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0039] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become apparent to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be limiting on the present application. Moreover, the same reference symbols are used to represent the same components throughout the drawings. In the drawings:

[0040] Figure 1 A flowchart of a post-casing density inversion calculation method according to an embodiment of the present application is shown;

[0041] Figure 2 A flowchart of constructing a target function and a density inversion method according to an embodiment of the present application is shown;

[0042] Figure 3 A schematic diagram of gamma rays passing through a shielding wall is shown according to an embodiment of the present application;

[0043] Figure 4 A schematic diagram of cementing in a casing is shown according to an embodiment of the present application;

[0044] Figure 5 A schematic diagram of cement-water and cement-gas model simulation results is shown according to an embodiment of the present application;

[0045] Figure 6 A structural block diagram of a casing post-density inversion calculation device is shown according to an embodiment of the present application;

[0046] Figure 7 A structural schematic diagram of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0047] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0048] Figure 1 A flowchart of a casing post-density inversion calculation method according to an embodiment of the present application is shown, as shown in Figure 1 The method includes the following steps:

[0049] Step S101, a detector count ratio matrix is constructed by simulating the nuclear radiation particle transport process of downhole gamma rays, and a target function is constructed according to the detector count ratio matrix.

[0050] Specifically, the total dose received by the detector is determined by simulating the nuclear radiation particle transport process of downhole gamma rays by the Monte Carlo method; the casing post-model is determined according to the total dose received by the detector; and the detector count ratio matrix is constructed using the casing post-model.

[0051] Step S102, gamma ray detection of the casing well is performed using the detector to obtain actual detection data of the detector.

[0052] Step S103, the actual detection data is substituted into the detector count ratio matrix to obtain a first count ratio function, and the standard detection data under standard conditions is substituted into the detector count ratio matrix to obtain a second count ratio function.

[0053] Step S104, solving the target function by using the first count ratio function and the second count ratio function to determine the formation density and the cement density.

[0054] Specifically, since the target function is constructed based on the detector count ratio value matrix, the first count ratio function and the second count ratio function obtained based on the actual detection data and the standard detection data are further substituted into the target function, and the target function is solved after being combined.

[0055] According to the casing density inversion calculation method provided in the embodiment, the nuclear radiation particle transport process of the downhole gamma ray is simulated to construct a detector count ratio value matrix, and a target function is constructed according to the detector count ratio value matrix; the casing well is detected by the detector to obtain actual detection data of the detector; the actual detection data is substituted into the detector count ratio value matrix to obtain a first count ratio function, and standard detection data under standard conditions is substituted into the detector count ratio value matrix to obtain a second count ratio function; the target function is solved by using the first count ratio function and the second count ratio function to determine the formation density and the cement density. According to the actual detection data and the standard detection data obtained by the detector, the first count ratio function and the second count ratio function are obtained, and the target function is further solved, and finally the accurate formation density and cement density are obtained. The scheme solves the target function according to the difference between the measured data and the standard data, reduces the influence of the unknown downhole condition on the calculation of the formation density and the cement density, and effectively improves the calculation accuracy of the formation density and the cement density.

[0056] Figure 2 A flowchart of a method for constructing a target function and density inversion according to an embodiment of the application is shown in FIG. 1. Figure 2 As shown in FIG. 1, the method comprises the following steps:

[0057] Step S201, simulating the nuclear radiation particle transport process of the downhole gamma ray by the Monte Carlo method to determine the total dose received by the detector.

[0058] Specifically, the total dose received by the detector is the dose received by the detector at the selected point of interest of the shielding wall, wherein the distance between the point of interest and the source is r, and there is a shielding wall between the point of interest and the source; that is, the dose received by the detector at the point of interest is the dose of the gamma ray emitted from the source and reaching the point of interest through the shielding wall.

[0059] The gamma ray emitted from the source passes through the shielding wall as shown in FIG. 2. Figure 3 Figure 3 ​A schematic diagram of gamma rays penetrating a shield wall is shown according to one embodiment of the present application. Wherein r is the distance between a point of interest and the source, θ is the angle of incidence, and t is the shield wall thickness.

[0060] Because of the gamma rays as the radiation signal, in the range of 1 Kev to about 20 Mev, the results of the radiation transport calculation are presented in the form of the buildup factor, the attenuation factor, the albedo (or called the reflection factor) and the pencil kernel function, therefore, regardless of the photon source and the attenuation medium, the energy spectrum of the total photon flux Φ(r, E) at a point of interest on the detector can be divided into two fluxes:

[0061] The non-scattered flux Φ 0 (r, E) is composed of those photons which travel from the source to the point of interest without experiencing any interaction in the attenuation medium;

[0062] The scattered flux Φ s (r, E) is the source photons which are scattered once or more times, and the secondary photons (the incident photons interact with the matter, and the photons scattered by the absorbed body are generated at the same time).

[0063] Therefore, the dose at the point of interest, i.e. the detector response D(r) can also be divided into the main non-scattered dose D 0 (r) and the secondary scattered dose D s (r). The detector response D(r) is the superposition of the two photon signals, and under the condition of this wide beam, the detector response D(r) is expressed as:

[0064]

[0065] Wherein D(r) is the dose at the point of interest which is away from the source r; D 0 (r) is the non-scattered dose, μ is the linear absorption coefficient, t is the shield wall thickness, and θ is the angle of incidence.

[0066] In order to consider the accumulation of flux, an additional term called the buildup factor B(r) is introduced in formula (1), and the buildup factor B(r) is defined as the ratio of the total dose to the non-scattered dose:

[0067]

[0068] Wherein E is the energy, η(E) is the irradiation effect conversion coefficient, Φ 0 (r, E) is the non-scattered flux, and Φ s (r, E) is the scattered flux.

[0069] It can be seen that the result of formula 2 depends on the non-scattered flux Φ 0 (r, E) and the scattered flux Φ s(r, E), further, it is known that the response depends only on the source and the attenuating medium, and not on the type of response or reaction. And the conversion factor η(E) depends only on the type of response (dose).

[0070] In summary, the total dose at a point of interest requires the combination of the properties of the source, the properties of the attenuating medium, the properties of the response, and the buildup factor. Thus, in the case of an infinite medium, a point source, and a homogeneous medium, the total dose received by a point of interest at a distance r from the source by a detector is:

[0071]

[0072]

[0073] where D is the total dose received by a point of interest at a distance r from the source by a detector; E is the energy; S(r s , E) is the fluence at a point in space r s with energy E; V is the volume of the detector; μ is the absorption coefficient; G(r s , r) is the collisionless exponential point in an infinite homogeneous medium (also known as the Green's function); B is the buildup factor; η(E) is the conversion factor for the irradiation effect; and |r s - r| is the distance between a point in space r s with energy E and r. The Green's function G(r s , r) can be expressed by equation 4.

[0074] At step S202, a casing model is determined according to the structure of the cased well, the cementation condition, and the mixing condition.

[0075] Specifically, in the cased well, the particles of the reaction formation need to pass through the cement layer twice to reach the detector, and the mass attenuation coefficients μ m of the cement and the formation are similar (counting using the same energy window), thus, the linear absorption coefficient μ can be written as:

[0076] μ = μ m ρ = F(ρ f , ρ c )· ρ equation 5

[0077] where F is the approximate mass attenuation coefficient of the cement and the formation, ρ is the density of the corresponding medium, ρ c is the density of the cement, and ρ f is the density of the formation.

[0078] Given that the volume of the detector is constant, and the source is a single energy source, and assuming that the formation is an infinite medium, the casing model can be simplified as:

[0079]

[0080] Where N is the number of particles obtained by the detector, and N0 is the number of particles after the sleeve decays.

[0081] Step S203: Based on the obtained post-model, construct the detector counting ratio matrix and finally derive the objective function.

[0082] Specifically, based on the assessment of actual well logging, the cement bonding is usually not perfect; there may be gaps in the cement, and these gaps may contain either liquid or gas. Figure 4 As shown. Figure 4 A schematic diagram illustrating cement bonding in a casing according to an embodiment of the present invention is shown. Figure 4 As shown in the cross-sectional diagram of this casing well, 401 represents cement, 402 represents voids within the cement, 403 represents the casing, and 404 represents the well fluid. This situation can cause a change in the overall density of the cement, thus affecting the final inversion of the cement density. Therefore, to reduce this influence, two models are set up in the theoretical model based on the volume fraction of cement mixed with water or gas: a cement-water model and a cement-gas model.

[0083] The volume fraction is shown in Formula 7:

[0084]

[0085] The volume proportions in the two models are shown in Table 1:

[0086] Solid volume fraction q Remaining component volume fraction Cement-water 0，0.2，0.5，0.8，1 1-q Cement-gas 0，0.2，0.5，0.8，1 1-q

[0087] Table 1

[0088] Simulations were performed on the two models described above, and the simulation results are as follows: Figure 5 As shown. Figure 5 A schematic diagram illustrating simulation results of a cement-water and cement-air model according to an embodiment of the present invention is shown. The horizontal axis represents the solid volume percentage, and the vertical axis represents the apparent density. According to... Figure 5 The simulation results show that the apparent density decreases by approximately 0.1 g / cm³ when 10% gas is mixed in, which is similar to the result when 50% liquid (water) is mixed in. This demonstrates that the impact of gas mixing is significant and cannot be ignored; therefore, gas correction needs to be incorporated into the algorithm design.

[0089] Specifically, the density window count ratio and cement window count ratio are obtained through detectors. The logarithm of the detector count ratio is taken, and a second-order approximation is performed. When the number of detectors is 4, the detector count ratio matrix is ​​represented as follows:

[0090]

[0091] Where R is the detector count ratio matrix; R 1ρ ~R 4ρ The density window count ratio for the four detectors; R 1c ~R 4c The cement window count ratio for the four detectors; R st The count ratio under standard conditions; ρ c ρ is the density of cement. f is the formation density; F is the approximate mass attenuation coefficient of cement and formation; k1~k5 are environmental parameters; b is a constant coefficient obtained by the least squares method (the coefficients of each detector and each energy window are different).

[0092] Preferably, the four detectors are referred to as BSP, SSP, MSP, and LSP detectors, respectively; the density window count ratio corresponding to the BSP detector is R. 1ρ The corresponding cement window count ratio is R. 1c The density window count ratio corresponding to the SSP detector is R. 2ρ The corresponding cement window count ratio is R. 2c The density window count ratio corresponding to the MSP detector is R. 3ρ The corresponding cement window count ratio is R. 3c The density window count ratio corresponding to the LSP detector is R. 4ρ The corresponding cement window count ratio is R. 4c .

[0093] Based on the detector counting ratio matrix, the error function is set as the objective function:

[0094]

[0095] Where, (ρ f * ,ρ c * ) represents the optimal solution; W is the weight matrix of each detector energy window; R′ is the second count ratio function obtained based on standard detection data under standard conditions; R″ is the first count ratio function obtained based on the actual detection data of the detector; ρ min ρ is the formation density calculated when cement is completely filled. max The formation density calculated when the cement-filled space is entirely filled with gas.

[0096] Due to ρ f Let ρ be the density of the formation. Therefore, assuming the overall density is the same, when the cement is completely filled, the cement density is the highest, and the formation density is the minimum value ρ. min When the cement-filled space is entirely filled with gas, the density of the cement-filled space is at its minimum, and the formation density is at its maximum value ρ. maxFurthermore, based on known parameters, the density of cement filling a space entirely of gas is 0.2 g / cm³. 3 That is, the density ρ of cement c The minimum value is not less than 0.2 g / cm³. 3 .

[0097] Step S204: Using the first counting ratio function and the second counting ratio function, solve the objective function to determine the formation density and cement density.

[0098] Specifically, the initial solution of the objective function is obtained by using the first counting ratio function and the second counting ratio function, and then iterative calculations are performed within the constraints to determine the formation density and cement density.

[0099] Preferably, WR′-R″ in Formula 9 is represented as G, where G is the cost function corresponding to Formula 9.

[0100] Preferably, the formation density and cement density are calculated by inversion using the Newton-Raphson iteration method. The density window count ratio R measured by the MSP detector is then used. 3ρ The cement window count ratio R of the SSP detector 2c Solving the system of equations 8 simultaneously yields the initial solutions (ρ) for the formation density and cement density. f0 ,ρ c0 Then, using formula 10, the formation density and cement density that minimize G are obtained within the specified limits. The iterative process is as follows:

[0101]

[0102] Where G is the cost function corresponding to Formula 9, l1 and l2 are the set iteration step sizes, and i is the number of iterations.

[0103] This scheme simulates the nuclear radiation particle transport process of downhole gamma rays, constructs a detector count ratio matrix, and builds an objective function based on the detector count ratio matrix. It then uses a detector to perform gamma ray detection on the casing well, obtaining actual detection data. Substituting this actual detection data into the detector count ratio matrix yields a first count ratio function, and substituting standard detection data under standard conditions into the detector count ratio matrix yields a second count ratio function. Using the first and second count ratio functions, the objective function is solved to determine the formation density and cement density. By utilizing the technical solution provided by this invention, based on the actual detection data and standard detection data obtained by the detector, the first and second count ratio functions are obtained, and the objective function is further solved to finally obtain accurate formation density and cement density. In practical applications, this technology can model multiple parameters for open-hole wells, calculate the response relationship between the energy window count rate and various density logging parameters based on Monte Carlo numerical models, establish a response database for multiple density logging parameters, and solve the nonlinear overdetermined equations using the Newton-Raphson iteration method by combining the difference between the measured energy spectrum and the corresponding energy window count rate in the standard condition energy spectrum to inversely calculate the formation density. It can also simulate downhole conditions by establishing models based on actual cement sheath model holes of different thicknesses and densities, fitting the cement sheath absorption coefficient, and determining the influence coefficient of the cement sheath on the gamma logging count rate, providing a basis for correcting the results of re-logging old wells. Furthermore, it can eliminate the influence of formation density and cement sheath density (i.e., cement density) on the calculated casing wall thickness by using the near and far detector count rates, and establish a casing wall thickness calculation formula using regression algorithms. It can also obtain the response relationships of four gamma detectors with respect to casing, cement sheath, and formation; based on these response relationships, a set of nonlinear forward equations for the measurements of the four detectors is established; and using inversion methods, the casing thickness, cement sheath density, cement sheath thickness, and formation density values ​​are obtained respectively, thereby realizing density logging in casing wells. This scheme solves the objective function based on the differences between measured data and standard data, reducing the impact of unknown downhole conditions on the calculation of formation density and cement density, and greatly improving the accuracy of inversion calculations for formation density and cement density.

[0104] Figure 6 A structural block diagram of a post-cabinet density inversion calculation apparatus according to an embodiment of the present invention is shown, as follows: Figure 6 As shown, the device includes: a construction module 601, a detection module 602, a substitution module 603, and a calculation module 604. Among them,

[0105] The construction module 601 is used to construct a detector counting ratio matrix by simulating the nuclear radiation particle transport process of downhole gamma rays, and to construct an objective function based on the detector counting ratio matrix.

[0106] Specifically, the Monte Carlo method is used to simulate the nuclear radiation particle transport process of downhole gamma rays to determine the total dose received by the detector; based on the total dose received by the detector, the back-end model is determined; and using the back-end model, the detector count ratio matrix is ​​constructed.

[0107] Specifically, the step of simulating the nuclear radiation particle transport process of downhole gamma rays using the Monte Carlo method to determine the total dose received by the detector further includes:

[0108] In the case of an infinitely large medium, a point source, and a homogeneous medium composition, the total dose received by the detector at the point of interest at a distance r from the source is:

[0109]

[0110] Where D is the total dose received by the detector at the point of interest at a distance r from the source; E is the energy; S(r s E) is the space r s A radiation source with energy E; V is the detector volume; μ is the absorption coefficient; G(r) s ,r) is the collision-free index point kernel in an infinitely homogeneous medium.

[0111] When the number of detectors is 4, the detector counting ratio matrix is ​​represented as follows:

[0112]

[0113] Where R is the detector count ratio matrix; R 1ρ ~R 4ρ The density window count ratio for the four detectors; R 1c ~R 4c The cement window count ratio for the four detectors; R st The count ratio under standard conditions; ρ c ρ is the density of cement. f denoted as ρ, where ρ is the formation density; F is the approximate mass attenuation coefficient of cement and formation; k1 to k5 are environmental parameters; and b is a constant coefficient obtained by the least squares method.

[0114] The objective function is expressed as:

[0115]

[0116] Restricted

[0117] Where, (ρ f * ,ρ c *) represents the optimal solution; W is the weight matrix of each detector energy window; R′ is the second count ratio function obtained based on standard detection data under standard conditions; R″ is the first count ratio function obtained based on the actual detection data of the detector; ρ min ρ is the formation density calculated when cement is completely filled. max The formation density calculated when the cement-filled space is entirely filled with gas.

[0118] The detection module 602 is used to perform gamma ray detection on the casing well using a detector to obtain the actual detection data of the detector.

[0119] The substitution module 603 is used to substitute the actual detection data into the detector counting ratio matrix to obtain a first counting ratio function, and to substitute the standard detection data under standard conditions into the detector counting ratio matrix to obtain a second counting ratio function.

[0120] The calculation module 604 is used to solve the objective function using the first counting ratio function and the second counting ratio function to determine the formation density and cement density.

[0121] Specifically, the initial solution of the objective function is obtained by using the first counting ratio function and the second counting ratio function, and then iterative calculations are performed within the constraints to determine the formation density and cement density.

[0122] According to the casing density inversion calculation device provided in this embodiment, a detector count ratio matrix is ​​constructed by simulating the nuclear radiation particle transport process of downhole gamma rays, and an objective function is constructed based on the detector count ratio matrix; gamma ray detection is performed on the casing well using a detector to obtain the actual detection data of the detector; the actual detection data is substituted into the detector count ratio matrix to obtain a first count ratio function, and standard detection data under standard conditions is substituted into the detector count ratio matrix to obtain a second count ratio function; the objective function is solved using the first count ratio function and the second count ratio function to determine the formation density and cement density. Using the technical device provided by this invention, the response relationship between the detector window count rate and various density logging parameters can be determined based on the Monte Carlo numerical simulation model according to different conditions in actual casing wells. Without prior knowledge of cement density, formation density, cement density, and cement thickness can be obtained by inverting the detector count. At the same time, by incorporating cement-water and cement-gas models based on the quality problem of cement bonding into the formation density correction algorithm, and by using actual detection data and standard detection data to solve the nonlinear overdetermined equation system, the accuracy of the formation density inversion results is greatly improved.

[0123] The present invention also provides a non-volatile computer storage medium storing at least one executable instruction that can execute the overlay density inversion calculation method in any of the above method embodiments.

[0124] Figure 7 The diagram illustrates the structure of a computing device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0125] like Figure 7 As shown, the computing device may include: a processor 702, a communications interface 704, a memory 706, and a communications bus 708.

[0126] in:

[0127] The processor 702, communication interface 704, and memory 706 communicate with each other via communication bus 708.

[0128] The communication interface 704 is used to communicate with other network elements such as clients or other servers.

[0129] The processor 702 is used to execute program 710, which can specifically execute the relevant steps in the above-described embodiment of the density inversion calculation method.

[0130] Specifically, program 710 may include program code that includes computer operation instructions.

[0131] The processor 702 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0132] Memory 706 is used to store program 710. Memory 706 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0133] Specifically, program 710 can be used to cause processor 702 to execute the post-cabinet density inversion calculation method in any of the above method embodiments. The specific implementation of each step in program 710 can be found in the corresponding steps and units described in the above-described post-cabinet density inversion calculation method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the aforementioned method embodiments, and will not be repeated here.

[0134] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0135] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0136] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0137] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0138] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0139] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0140] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for calculating density inversion after overlay, comprising: By simulating the nuclear radiation particle transport process of gamma rays downhole, a detector counting ratio matrix is ​​constructed, and an objective function is constructed based on the detector counting ratio matrix. The casing well is subjected to gamma ray detection using a detector to obtain the actual detection data of the detector; wherein, the presence of liquid or gas in the gaps of the cement in the casing well causes a change in the overall density of the cement; Substituting the actual detection data into the detector count ratio matrix yields the first count ratio function; substituting the standard detection data under standard conditions into the detector count ratio matrix yields the second count ratio function. Using the first counting ratio function and the second counting ratio function, the initial solution of the objective function is obtained, and then iterative calculations are performed within the constraints to determine the formation density and cement density; The objective function is expressed as: in, This represents the optimal solution; W is the weight matrix for each detector's energy window. This is the second count ratio function obtained based on standard detection data under standard conditions; This is the first count ratio function obtained based on the actual detection data of the detector; The density of the formation is calculated when the cement is completely filled. The formation density calculated when the cement-filled space is entirely filled with gas; The density of cement, Formation density; Among them, Represented as Using the measured density window count ratio of the MSP detector Cement window count ratio compared to SSP detector By solving the detector count ratio matrix, the initial solutions for the formation density and cement density are obtained. Then, through iteration, within the constraints, the result is found to be... The minimum density of the aforementioned formation and cement; The iterative process is as follows: in, The cost function corresponding to the objective function. and The set iteration step size, This represents the number of iterations.

2. The method according to claim 1, characterized in that, The construction of the detector counting ratio matrix by simulating the nuclear radiation particle transport process of downhole gamma rays further includes: The total dose received by the detector was determined by simulating the nuclear radiation particle transport process of downhole gamma rays using the Monte Carlo method. The model after the overlay is determined based on the total dose received by the detector; Using the aforementioned model, a detector counting ratio matrix is ​​constructed.

3. The method according to claim 2, characterized in that, The step of simulating the nuclear radiation particle transport process of downhole gamma rays using the Monte Carlo method to determine the total dose received by the detector further includes: In the case of an infinitely large medium, a point source, and a homogeneous medium composition, the total dose received by the detector at a distance r from the source is: in, This represents the total dose received by the detector at a distance r from the source; For energy; For space A radiation source with energy E; V is the detector volume; The absorption coefficient; B represents the collision-free exponential point kernel in an infinitely homogeneous medium; B is the accumulation factor. This is the radiation effect conversion coefficient.

4. The method according to claim 1, characterized in that, When the number of detectors is 4, the detector counting ratio matrix is ​​represented as follows: Where R is the detector count ratio matrix; The density window count ratio for the four detectors; The cement window count ratio for the four detectors; This is the count ratio under standard conditions; This refers to the density of cement. Formation density; This is the approximate mass attenuation coefficient for cement and strata. For environmental parameters; These are constant coefficients obtained by the least squares method.

5. A density inversion calculation device after overlay, comprising: The module consists of a construction module, a detection module, a substitution module, and a calculation module; among them, The construction module is used to construct a detector counting ratio matrix by simulating the nuclear radiation particle transport process of downhole gamma rays, and to construct an objective function based on the detector counting ratio matrix. The detection module is used to perform gamma ray detection on the casing well using a detector to obtain the actual detection data of the detector; wherein, the cement in the casing well contains liquid or gas in the gaps, causing the overall density of the cement to change; The substitution module is used to substitute the actual detection data into the detector counting ratio matrix to obtain a first counting ratio function, and to substitute the standard detection data under standard conditions into the detector counting ratio matrix to obtain a second counting ratio function. The calculation module is used to solve the initial solution of the objective function using the first counting ratio function and the second counting ratio function, and then perform iterative calculations within a limited range to determine the formation density and cement density. The objective function is expressed as: in, This represents the optimal solution; W is the weight matrix for each detector's energy window. This is the second count ratio function obtained based on standard detection data under standard conditions; This is the first count ratio function obtained based on the actual detection data of the detector; The density of the formation is calculated when the cement is completely filled. The formation density calculated when the cement-filled space is entirely filled with gas; The density of cement, Formation density; Among them, Represented as Using the measured density window count ratio of the MSP detector Cement window count ratio compared to SSP detector By solving the detector count ratio matrix, the initial solutions for the formation density and cement density are obtained. Then, through iteration, within the constraints, the result is found to be... The minimum density of the aforementioned formation and cement; The iterative process is as follows: in, The cost function corresponding to the objective function. and The set iteration step size, This represents the number of iterations.

6. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the nested density inversion calculation method as described in any one of claims 1-4.

7. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the back-end density inversion calculation method as described in any one of claims 1-4.

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