Array sensing wellbore correction method, device, storage medium and processor

Abstract

The present invention discloses an array induction borehole correction method, device, storage medium, and processor. The method comprises: obtaining measurement signals obtained by each subarray of an array induction logging instrument; determining borehole environment parameters corresponding to the borehole environment of a target formation, and using a multidimensional space interpolation method to obtain target borehole geometry factors of the borehole environment from a borehole geometry factor library based on the borehole environment parameters; establishing a tomographic matrix equation based on a radial formation model and the measurement signal, solving the tomographic matrix equation to obtain a radial conductivity distribution of the target formation; determining a measurement value expression based on the radial formation model and the radial conductivity distribution, and determining a borehole correction formula using the target borehole geometry factors corresponding to the borehole environment; determining the measurement value of the corrected target formation based on the measurement value expression and the borehole correction formula, and completing the borehole correction. The present invention solves the technical problem of borehole correction errors caused by existing borehole correction methods ignoring mud intrusion.

Description

Technical Field

[0001] The present invention relates to the technical field of well logging data processing, and in particular to an array sensing wellbore correction method, device, storage medium and processor. Background Art

[0002] The measurement signals of each sub-array of array induction logging instruments are significantly affected by the wellbore environment. If this influence cannot be correctly corrected, the wellbore correction residual will be propagated or even amplified during subsequent data processing such as soft focusing, resulting in reduced reliability of the final measurement curve.

[0003] Existing array induction borehole correction methods often provide ineffective results. Field engineers and processors frequently need to repeatedly adjust borehole correction control parameters to obtain satisfactory final logging curves, but the resulting measurement results are often suboptimal. When invasion is present, the invasion response is misinterpreted as a borehole effect and "corrected." This reduces the curve difference after borehole correction, reducing the curve's ability to analyze invasion radially and affecting the determination of true formation resistivity.

[0004] In view of the above problems, the present invention proposes an effective solution. Summary of the Invention

[0005] The embodiments of the present invention provide an array sensing wellbore correction method, device, storage medium and processor to at least solve the technical problem of wellbore correction errors caused by ignoring mud invasion in existing wellbore correction methods.

[0006] According to one aspect of an embodiment of the present invention, an array induction wellbore correction method is provided, comprising: obtaining measurement signals obtained by each subarray of an array induction logging instrument, wherein the measurement signals include at least a conductivity curve group; determining wellbore environment parameters corresponding to the wellbore environment of a target formation, and using a multidimensional space interpolation method to obtain target wellbore geometry factors of the wellbore environment from a wellbore geometry factor library based on the wellbore environment parameters, wherein the wellbore environment parameters include: wellbore diameter cal, mud conductivity σ m , formation conductivity σ tand eccentricity ecc, the above-mentioned wellbore geometry factor library is a pre-established wellbore geometry factor database, and the above-mentioned wellbore geometry factor is a function of the above-mentioned wellbore environment parameters; based on the radial formation model and the above-mentioned measurement signal, a tomographic matrix equation is established, and the above-mentioned tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation, wherein the above-mentioned radial formation model is used to characterize the conductivity change of the above-mentioned target formation; according to the above-mentioned radial formation model and the above-mentioned radial conductivity distribution, a measurement value expression is determined, and the above-mentioned target wellbore geometry factor corresponding to the above-mentioned wellbore environment is used to determine the wellbore correction formula; based on the above-mentioned measurement value expression and the above-mentioned wellbore correction formula, the measurement value of the above-mentioned target formation after correction is determined to complete the wellbore correction.

[0007] Optionally, before adopting the multidimensional space interpolation method to obtain the target wellbore geometry factor of the wellbore environment from the wellbore geometry factor library according to the wellbore environment parameters, the method further includes: determining the variation range of the wellbore diameter, and determining a preset number of discrete points of the wellbore diameter based on the variation range of the wellbore diameter to form a first-dimensional discrete point set; determining the variation range of the mud conductivity, and determining a preset number of discrete points of the mud conductivity based on the variation range of the mud conductivity to form a second-dimensional discrete point set; determining the variation range of the formation conductivity, and determining a preset number of discrete points of the mud conductivity based on the variation range of the formation conductivity to form a second-dimensional discrete point set. The present invention relates to a method for determining a preset number of discrete points of formation conductivity based on a variation range of the conductivity to form a third-dimensional discrete point set; determining a variation range of the eccentricity, and based on the variation range of the eccentricity, determining a preset number of discrete points of the eccentricity to form a fourth-dimensional discrete point set; combining the first-dimensional discrete point set, the second-dimensional discrete point set, the third-dimensional discrete point set, and the fourth-dimensional discrete point set to calculate the wellbore geometry factors of each of the above-mentioned subarrays, storing the wellbore geometry factors of all the above-mentioned subarrays according to a preset storage rule, and constructing the above-mentioned wellbore geometry factor library.

[0008] Optionally, the above-mentioned determination of the wellbore environment parameters corresponding to the wellbore environment of the target formation adopts a multi-dimensional space interpolation method to obtain the target wellbore geometry factor of the above-mentioned wellbore environment from the wellbore geometry factor library according to the above-mentioned wellbore environment parameters, including: determining the value of the wellbore environment parameter corresponding to the above-mentioned wellbore environment of the above-mentioned target formation; obtaining the closest target discrete point from the above-mentioned wellbore geometry factor library according to the value of the above-mentioned wellbore environment parameter; and determining the above-mentioned target wellbore geometry factor based on the above-mentioned first-dimensional discrete point set, the above-mentioned second-dimensional discrete point set, the above-mentioned third-dimensional discrete point set and the above-mentioned fourth-dimensional discrete point set using the above-mentioned multi-dimensional space interpolation method.

[0009] Optionally, based on the radial formation model and the above-mentioned measurement signals, a tomographic matrix equation is established, and the above-mentioned tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation. The above-mentioned method also includes: dividing the above-mentioned target formation into multiple layers in the horizontal radial direction, wherein the wellbore is the innermost layer, and the formations other than the above-mentioned wellbore are the invasion flushing zone, the invasion transition zone and the original formation; and using the above-mentioned wellbore, the above-mentioned invasion flushing zone, the above-mentioned invasion transition zone and the above-mentioned original formation to construct the above-mentioned radial formation model.

[0010] Optionally, based on the radial formation model and the above-mentioned measurement signals, a tomographic matrix equation is established, and the above-mentioned tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation, including: determining the wellbore geometry factor of the above-mentioned each subarray and the radial geometry factor of the above-mentioned each subarray; based on the above-mentioned radial formation model and the above-mentioned measurement signals, the above-mentioned wellbore geometry factor and the above-mentioned radial geometry factor, establishing the above-mentioned tomographic matrix equation, wherein the left-hand side term of the above-mentioned tomographic matrix equation is the above-mentioned measurement signal of the above-mentioned each subarray, the unknown quantity of the above-mentioned tomographic matrix equation is the conductivity difference between radially adjacent layers, the coefficient matrix of the above-mentioned tomographic matrix equation is composed of the above-mentioned wellbore geometry factor of the above-mentioned each subarray, the above-mentioned radial geometry factor and a constant, and the constraint conditions of the above-mentioned tomographic matrix equation are determined according to the above-mentioned wellbore environment parameters; solving the above-mentioned tomographic matrix equation to obtain the above-mentioned radial conductivity distribution of the above-mentioned target formation.

[0011] Optionally, the above-mentioned measurement value expression and the above-mentioned wellbore correction formula are used to determine the corrected measurement value of the above-mentioned target formation and complete the wellbore correction, including: using the above-mentioned measurement value expression and the above-mentioned wellbore correction formula to process the above-mentioned measurement signal to determine the target formation conductivity and the target wellbore mud conductivity of the above-mentioned target formation; and applying the above-mentioned target formation conductivity and the above-mentioned target wellbore mud conductivity to perform wellbore correction on the target measurement value.

[0012] According to another aspect of an embodiment of the present invention, an array induction wellbore correction device is provided, characterized in that it includes: an acquisition module for acquiring measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals include at least a conductivity curve group; a first determination module for determining wellbore environment parameters corresponding to the wellbore environment of the target formation, and using a multi-dimensional space interpolation method to obtain the target wellbore geometry factor of the wellbore environment from a wellbore geometry factor library based on the wellbore environment parameters, wherein the wellbore environment parameters include: well diameter cal, mud conductivity σ m , formation conductivity σ tand eccentricity ecc, the above-mentioned wellbore geometry factor library is a pre-established wellbore geometry factor database, and the above-mentioned wellbore geometry factor is a function of the above-mentioned wellbore environment parameters; a processing module is used to establish a tomographic matrix equation based on the radial formation model and the above-mentioned measurement signal, and solve the above-mentioned tomographic matrix equation to obtain the radial conductivity distribution of the above-mentioned target formation, wherein the above-mentioned radial formation model is used to characterize the conductivity change of the above-mentioned target formation; a second determination module is used to determine the measurement value expression according to the above-mentioned radial formation model and the above-mentioned radial conductivity distribution, and to determine the wellbore correction formula using the above-mentioned target wellbore geometry factor corresponding to the above-mentioned wellbore environment; a correction module is used to determine the measurement value of the above-mentioned target formation after correction based on the above-mentioned measurement value expression and the above-mentioned wellbore correction formula to complete the wellbore correction.

[0013] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium stores a plurality of instructions. The instructions are suitable for being loaded by a processor and executing any one of the above-mentioned array sensing wellbore correction methods.

[0014] According to another aspect of an embodiment of the present invention, a processor is further provided. The processor is used to run a program, wherein the program is configured to execute any one of the above-mentioned array sensing wellbore correction methods when running.

[0015] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any one of the above-mentioned array sensing wellbore correction methods.

[0016] In an embodiment of the present invention, a measurement signal obtained by measuring an array induction logging instrument is obtained, wherein the measurement signal includes at least: a resistivity curve group; wellbore environmental parameters are determined, a wellbore geometry factor library is established based on the wellbore environmental parameters, and a target wellbore geometry factor of a target formation is determined based on the wellbore geometry factor library; a radial formation model is used to process the measurement signal to determine a measurement value expression, and a radial formation model is used to process the measurement signal and the target wellbore geometry factor to determine a wellbore correction formula; based on the measurement value expression and the wellbore correction formula, the measurement value of the target formation after correction is determined, and the wellbore correction is completed, thereby achieving the purpose of constructing a new adaptive wellbore environmental parameter solving algorithm, thereby realizing the technical effect of accurately obtaining the true conductivity of the formation, and further solving the technical problem of wellbore correction errors caused by ignoring mud invasion in the existing wellbore correction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of an array sensing wellbore correction method according to an embodiment of the present invention;

[0019] Figure 2 1 is a schematic diagram of an optional probe structure of an array induction logging instrument according to an embodiment of the present invention;

[0020] Figure 3 is an optional typical array induction logging tool logging curve diagram according to an embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of an optional array induction instrument for well logging according to an embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of an optional array sensing wellbore geometry factor curve according to an embodiment of the present invention;

[0023] Figure 6 is a schematic diagram of an optional radial two-layer model according to an embodiment of the present invention;

[0024] Figure 7 is a schematic diagram of an optional radial three-layer model according to an embodiment of the present invention;

[0025] Figure 8 According to the embodiment of the present invention, m >σ xo >σ t Schematic diagram of apparent conductivity after model correction;

[0026] Figure 9 According to the embodiment of the present invention, t >σ xo ≥σ m Schematic diagram of apparent conductivity after model correction when ;

[0027] Figure 10 According to the embodiment of the present invention, t >σ m >σ xo Schematic diagram of apparent conductivity after model correction when ;

[0028] Figure 11 According to the embodiment of the present invention, m ≥σ t >σ xo Schematic diagram of apparent conductivity after model correction;

[0029] Figure 12 is a schematic diagram of an optional intrusion transition path according to an embodiment of the present invention;

[0030] Figure 13 1 is a schematic diagram of a radial cross section of conductivity represented by an array sensing curve before and after a borehole correction according to an optional embodiment of the present invention;

[0031] Figure 14 A schematic structural diagram of a wellbore correction device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Example 1

[0035] According to an embodiment of the present invention, an embodiment of an array sensing wellbore correction method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] In the prior art, the array sensing borehole correction method has two defects: (1) the model for establishing borehole correction is composed of the borehole plus an infinite uniform formation, which is inconsistent with the actual formation and the original intention of array sensing design; (2) in adaptive borehole correction, the "inherent correlation" between the measurement values ​​of the array sensing sub-arrays is poor, which is only a qualitative concept and has no quantitative description.

[0037] To address the theoretical flaws and application pain points of existing array sensing borehole correction methods, the concept of tomography can be applied. Multiple step functions are introduced to approximate arbitrary radial one-dimensional formations. Approximate processing is then performed using the Born method to derive a mathematical expression with an internal relationship. This expression is then applied to the borehole environmental parameters for adaptive solution. Reconstructing the borehole correction formula based on the invasion model avoids the phenomenon of "correcting" the invasion effect as a borehole effect, maintaining the curve differences after borehole correction, and improving the ability of the curve to analyze invasion radially.

[0038] Figure 1 FIG. 1 is a flow chart of an array sensing wellbore correction method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0039] Step S102, obtaining measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals at least include: a conductivity curve group;

[0040] Step S104, determining the borehole environment parameters corresponding to the target formation, and using a multi-dimensional space interpolation method to obtain the target borehole geometry factors of the above-mentioned borehole environment from the borehole geometry factor library according to the above-mentioned borehole environment parameters, wherein the above-mentioned borehole environment parameters include: well diameter cal, mud conductivity σ m , formation conductivity σ t and eccentricity ecc, the wellbore geometry factor library is a pre-established wellbore geometry factor database, and the wellbore geometry factor is a function of the wellbore environment parameters;

[0041] Step S106: establishing a tomographic matrix equation based on the radial formation model and the measurement signal, and solving the tomographic matrix equation to obtain the radial conductivity distribution of the target formation, wherein the radial formation model is used to characterize the conductivity change of the target formation;

[0042] Step S108, determining a measurement value expression based on the radial formation model and the radial conductivity distribution, and determining a wellbore correction formula using the target wellbore geometry factor corresponding to the wellbore environment;

[0043] Step S110 , based on the above measurement value expression and the above borehole correction formula, the measured value of the above target formation after correction is determined to complete the borehole correction.

[0044] In an embodiment of the present invention, the array induction wellbore correction method of steps S102 to S110 is executed by an array induction wellbore correction system, wherein the system is used to obtain measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals at least include: a conductivity curve group; determine the wellbore environment parameters corresponding to the wellbore environment of the target formation, and use a multi-dimensional space interpolation method to obtain the target wellbore geometry factors of the wellbore environment from the wellbore geometry factor library based on the wellbore environment parameters, wherein the wellbore environment parameters include: well diameter cal, mud conductivity σ m , formation conductivity σ t and eccentricity ecc, the above-mentioned wellbore geometry factor library is a pre-established wellbore geometry factor database, and the above-mentioned wellbore geometry factor is a function of the above-mentioned wellbore environment parameters; based on the radial formation model and the above-mentioned measurement signal, a tomographic matrix equation is established, and the above-mentioned tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation, wherein the above-mentioned radial formation model is used to characterize the conductivity change of the above-mentioned target formation; according to the above-mentioned radial formation model and the above-mentioned radial conductivity distribution, a measurement value expression is determined, and the above-mentioned target wellbore geometry factor corresponding to the above-mentioned wellbore environment is used to determine the wellbore correction formula; based on the above-mentioned measurement value expression and the above-mentioned wellbore correction formula, the measurement value of the above-mentioned target formation after correction is determined to complete the wellbore correction.

[0045] It should be noted that the array induction logging tool includes multiple sub-measurement arrays, and its functional feature is to obtain a curve group of multi-radial detection depth for describing the invasion profile. The obtained curve group may include, but is not limited to: conductivity curve group, resistivity curve group, etc. Figure 2 The following diagram shows the structure of an array induction logging tool probe. The probe consists of multiple (5, 6, 7, or more) subarrays (e.g., A1 through A6 in the figure). Each subarray consists of a transmitting coil and a series-connected set of receiving coils (at least two coils: a main receiving coil and a shielded receiving coil). The subarrays share a single transmitting coil.

[0046] As an optional embodiment, a series of signal processing is performed on the original measurement data of multiple sub-arrays of the array induction logging instrument to obtain the final measurement signal; the above series of signal processing includes: calibration processing, temperature influence correction, skin effect correction, wellbore correction, software focusing, resolution matching and radial inversion processing.

[0047] As an optional embodiment, after obtaining the measurement signal obtained by the array induction logging instrument, the obtained measurement signal can be pre-processed, for example, missing data processing, error data processing, etc. Figure 3The typical array induction logging tool logging curve diagram shown in the figure is that the raw measurement signal of the array induction logging tool undergoes data processing steps such as preprocessing, skin effect correction, borehole correction and software focusing, and a resistivity curve group with multiple vertical resolutions (1 foot, 2 feet, 4 feet or 0.5 feet) and multiple radial detection depths (10 inches, 20 inches, 30 inches, 60 inches, 90 inches and / or 120 inches) can be obtained.

[0048] In an optional embodiment, before obtaining the target wellbore geometry factor of the wellbore environment from the wellbore geometry factor library according to the wellbore environment parameters using a multidimensional space interpolation method, the method further includes: determining the variation range of the wellbore diameter, and determining a preset number of discrete points of the wellbore diameter based on the variation range of the wellbore diameter to form a first-dimensional discrete point set; determining the variation range of the mud conductivity, and determining a preset number of discrete points of the mud conductivity based on the variation range of the mud conductivity to form a second-dimensional discrete point set; determining the variation range of the formation conductivity, and determining a preset number of discrete points of the mud conductivity based on the variation range of the mud conductivity to form a second-dimensional discrete point set. The variation range of the formation conductivity determines a preset number of discrete points of the formation conductivity to form a third-dimensional discrete point set; the variation range of the above-mentioned eccentricity is determined, and based on the variation range of the above-mentioned eccentricity, a preset number of discrete points of the eccentricity are determined to form a fourth-dimensional discrete point set; the above-mentioned first-dimensional discrete point set, the above-mentioned second-dimensional discrete point set, the above-mentioned third-dimensional discrete point set, and the above-mentioned fourth-dimensional discrete point set are combined and calculated to obtain the wellbore geometry factors of the above-mentioned sub-arrays, and the wellbore geometry factors of all the above-mentioned sub-arrays are stored according to preset storage rules to construct the above-mentioned wellbore geometry factor library.

[0049] In an embodiment of the present invention, the borehole geometry factor value of a subarray of an array sensing instrument is determined by the values ​​of the four borehole environment parameters. To facilitate obtaining the geometry factor values ​​of each subarray of an array sensing instrument corresponding to a specific borehole environment, a borehole geometry factor library can be pre-established.

[0050] It should be noted that if Figure 4 The schematic diagram of array induction tool logging is shown in Figure 1. t is the formation conductivity (its reciprocal is the formation resistivity R t ), σ m is the wellbore mud conductivity (its reciprocal is the mud resistivity R m ), cal is the well diameter, d tool The diameter of the tool, x is the distance between the tool and the well wall. For convenience, the relative eccentricity of the tool, ecc, is introduced: When the instrument is centered, the relative eccentricity is 0; when the instrument is completely attached to the well wall, the relative eccentricity is 1.

[0051] In the embodiment of the present invention, according to the principle of induction logging, the array induction wellbore effect is controlled by four environmental parameters, namely: wellbore diameter cal, mud conductivity σ m (or its reciprocal, mud resistivity R m ), the eccentricity of the tool in the wellbore ecc, and the formation conductivity σ t (or its reciprocal, formation resistivity R t ). The above four environmental parameters are determined as the above wellbore environmental parameters.

[0052] As an optional embodiment, the variation range of the above four wellbore environmental parameter values ​​is determined, the number of discrete points is determined based on the variation range, and four discrete point sets are formed. The discrete point sets are combined, and then all combinations are calculated to obtain the wellbore geometry factor of each subarray. The wellbore geometry factors of all subarrays corresponding to all combinations are stored in a certain manner to construct the above-mentioned wellbore geometry factor library. For example, a discrete point is set every 0.5 inches from the well diameter range of 4 inches to 32 inches, resulting in a total of 57 discrete points; a discrete point is set every 0.05 inches from the eccentricity range of 0 to 0.95, resulting in a total of 20 discrete points; 25 discrete points are selected from the mud conductivity range of 0.0001 S / m to 1000 S / m; and 20 discrete points are selected from the formation conductivity range of 0.0001 S / m to 100 S / m. There are a total of 57x20x25x20 discrete points in the discrete point set of the above four dimensions. For the array sensing instrument with 7 subarrays, there are 57x20x25x20x7 wellbore geometry factor values ​​in the wellbore geometry factor library.

[0053] Optionally, for quick and easy borehole calibration, the borehole geometry factor can be calculated in advance based on the array induction instrument probe coil system structure and other relevant structural parameters: m ,σ t ) is discretized, and the geometric factors of each sub-array are calculated and stored for all discrete combination points to form a wellbore geometric factor library. Figure 5 The following is a schematic diagram of the array sensing borehole geometry factor curve, corresponding to Rt = 100Ohmm, Rm = 0.1Ohmm, and eccentricity Ecc equal to 0.0, 0.5, 0.75, 0.85, 0.90, and 0.95, respectively. It can be concluded from the figure that: (1) the borehole geometry factor of the three shorter sub-arrays is more sensitive to the wellbore environmental parameters and is relatively reliable; (2) relative to the conductivity variable (σ m ,σ t ), the wellbore geometry factor is more sensitive to the geometric variables (ecc, cal).

[0054] It's important to note that in wells with high contrast between mud and formation conductivity (e.g., a mud / formation conductivity ratio greater than 100), the majority of shallow-surface subarray readings (subarrays A1, A2, and A3) originate from the wellbore. In large boreholes with high formation resistivity, the wellbore geometry factor can exceed 0.6, and the measured signal is almost entirely contributed by the wellbore. The measurement error in the total reading can be greater than the formation signal, making over- or under-correction more likely. Therefore, prior to synthetic focusing, the influence of the wellbore environment must be minimized.

[0055] In an optional embodiment, the above-mentioned determination of the wellbore environment parameters corresponding to the wellbore environment of the target formation adopts a multi-dimensional space interpolation method to obtain the target wellbore geometry factor of the above-mentioned wellbore environment from the wellbore geometry factor library according to the above-mentioned wellbore environment parameters, including: determining the value of the wellbore environment parameter corresponding to the above-mentioned wellbore environment of the above-mentioned target formation; obtaining the closest target discrete point from the above-mentioned wellbore geometry factor library according to the value of the above-mentioned wellbore environment parameter; and determining the above-mentioned target wellbore geometry factor based on the above-mentioned first-dimensional discrete point set, the above-mentioned second-dimensional discrete point set, the above-mentioned third-dimensional discrete point set and the above-mentioned fourth-dimensional discrete point set using the above-mentioned multi-dimensional space interpolation method.

[0056] As an optional embodiment, the values ​​of the four wellbore environmental parameters corresponding to the above-mentioned target formation are determined; and the target discrete point closest to the values ​​of the four wellbore environmental parameters corresponding to the target formation is obtained from the above-mentioned wellbore geometric factor library, and the target wellbore geometric factor of the above-mentioned target formation is determined from the above-mentioned wellbore geometric factor library based on the above-mentioned four wellbore environmental parameters using a spatial interpolation method.

[0057] In an optional embodiment, based on the radial formation model and the above-mentioned measurement signals, a tomographic matrix equation is established, and before the above-mentioned tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation, the above-mentioned method further includes: dividing the above-mentioned target formation into multiple layers in the radial direction, wherein the wellbore is the innermost layer, and the formations other than the above-mentioned wellbore are the invasion flushing zone, the invasion transition zone and the original formation; and constructing the above-mentioned radial formation model using the above-mentioned wellbore, the above-mentioned invasion flushing zone, the above-mentioned invasion transition zone and the above-mentioned original formation.

[0058] In an optional embodiment, a tomographic matrix equation is established based on the radial formation model and the above-mentioned measurement signals, and the tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation, including: determining the wellbore geometry factor and the radial geometry factor of the above-mentioned subarrays; establishing the above-mentioned tomographic matrix equation based on the above-mentioned radial formation model and the above-mentioned measurement signals, the above-mentioned wellbore geometry factor and the above-mentioned radial geometry factor, wherein the left-hand side term of the above-mentioned tomographic matrix equation is the above-mentioned measurement signal of the above-mentioned subarray, the unknown quantity of the above-mentioned tomographic matrix equation is the conductivity difference between radially adjacent layers, the coefficient matrix of the above-mentioned tomographic matrix equation is composed of the above-mentioned wellbore geometry factor, the above-mentioned radial geometry factor and a constant of the above-mentioned subarray, and the constraint conditions of the above-mentioned tomographic matrix equation are determined according to the above-mentioned wellbore environment parameters; and solving the above-mentioned tomographic matrix equation to obtain the above-mentioned radial conductivity distribution of the above-mentioned target formation.

[0059] In the embodiment of the present invention, the array sensing raw measurement data satisfies a certain relationship, and the trend formed between the fuzzy sub-arrays can be used to determine whether a sub-array response is significantly abnormal. The trend formed between sub-arrays is controlled by two factors: the radial variation of formation conductivity and the radial geometry factor of the sub-array. After skin correction, it can be assumed that the measurements of each sub-array meet the Born approximation. Under the Born approximation, when the invasion depth is D, the response of sub-array No. i can be expressed as follows: a (i, D) = σ t +Δσ(D)*GF(i, D); where Δσ(D) is a step function with a step amplitude of σ xo -σ t , the step position is D; GF(i, D) is the radial integral geometric factor when the radial depth of the i-th subarray is D. When the radial variation of the formation conductivity is not a single step, the concept of radial tomography can be used as follows. Figure 7 The step function Δσ is approximated by the multiple radial steps shown in the figure. l (r l ) represents the lth step, and the step amplitude is Δσ l =σ l -σ l-1 , the step position is r l After introducing the step function, the conductivity σ of the formation can be expressed as:

[0060] σ=σ0+Δσ1(r1)+Δσ2(r2)+…+Δσ K (r K )

[0061] Alternatively, the measurement value of the i-th subarray using the Born approximation can be expressed as follows:

[0062] σ a(i)=σ0+Δσ1*GF(i,r1)+…+Δσ i *GF(i,r i )+…+Δσ k *GF(i,r k )

[0063] Optionally, the array sensing subarray measurement values ​​of radial multi-step formations can be described in matrix form, thereby obtaining a general formula describing the inherent quantitative correlation of the array sensing subarray measurement values:

[0064]

[0065] Where M is the number of subarrays; K is the number of analysis layers; and the tomographic coefficient G is ij corresponds to the radial integral geometry factor, and G i0 =1.

[0066] As an optional embodiment, after skin correction, the measurement value of each sub-array of the array sensor has good linear characteristics. The sub-array measurement value is equal to the sum of the contributions of each area in the space around the instrument, which can be expressed as: a =∫∫∫g j σ j dv j Among them, g j It is called spatial volume unit dv j The differential geometry factor satisfies the normalization condition, that is, ∫∫∫g j dv j = 1. The differential geometric factor is a function of the spatial coordinates. In the cylindrical coordinate system, the independent variables of the geometric factor are z, r,

[0067] Optionally, the radial integral geometric factor GF(i, D) corresponding to any radial depth D can be obtained by selecting N pre-selected radial depths D j The geometric factor GF(i, D j ) interpolation to approximate the calculation, that is:

[0068]

[0069] Optionally, substituting the above formula into the measured value expression of the i-th subarray obtained after Born approximation, we can obtain:

[0070]

[0071] Among them, record a M+1 =σ0; j=1,…,N;G(i,j)=GF(i,D j ); Write the above formula into matrix form, and we get:

[0072]

[0073] It should be noted that [a1 a2 … a M a M+1 ] T is a trend relationship vector that describes the relationship trend between the measurement values ​​of each subarray. Under the Born approximation, this formula can approximate the subarray measurement values ​​of any radial one-dimensional formation. The left-hand side of the tomographic matrix equation is the measurement signal of each subarray, the unknown quantity of the tomographic matrix equation is the conductivity difference between radially adjacent layers, and the coefficient matrix of the tomographic matrix equation consists of the wellbore geometry factor, radial geometry factor, and constant of each subarray. The constraints of the tomographic matrix equation are determined based on the wellbore environmental parameters.

[0074] Optionally, in order to analyze the borehole effect of array induction measurement, the borehole geometry factor G can be introduced bh , used to express the weight of the contribution of the borehole medium to the measurement signal. Consider the borehole as a For a cylinder with the z direction extending from -∞ to +∞, then: Introducing radial integral geometric factor G r , expressing the weight of the contribution of a cylinder with an infinite height (from -∞ to +∞ in the z direction) and an outer radius of r to the measurement signal, then: visible, Same as the wellbore effect, the wellbore geometry factor G bh The four wellbore environmental parameters (ecc, cal, σ m ,σ t ) is determined by the geometric factor. After the geometric factor is introduced, the logging response in some special cases can be explicitly expressed. For example, in an infinite uniform formation model with wells and no invasion, the apparent conductivity measured by the i-th subarray can be expressed as:

[0075] σ a (i)=σ m G bh (i)+σ t [1-G bh (i)].

[0076] As an optional embodiment, the existing array sensing borehole correction methods are based on the following Figure 6 The radial two-layer model diagram shown in the figure, "wellbore + infinite formation", can be called a (radial) two-layer model. In this case, according to the geometric factor theory, the basic logging response relationship of each subarray is:

[0077] σ a (i)=σ m G bh(i)+σ t [1-G bh (i)]

[0078] Where i represents the subarray number, from 1 to M. The larger i is, the greater the detection depth of the subarray is; G bh (i) is the wellbore geometry factor of subarray i; σ a (i) is the measured apparent conductivity of subarray i; σ t is the original formation conductivity.

[0079] Alternatively, in the two-layer model, wellbore correction is to “replace” the wellbore fluid with a conductivity of σ t The corresponding wellbore correction formula is:

[0080] σ bhc (i)=σ a (i)-(σ m -σ t )G bh (i).

[0081] Among them, σ bhc (i) is the measured value of subarray i after wellbore correction.

[0082] Alternatively, array induction logging tools can describe the invasion profile, i.e., the radial variation of resistivity or conductivity, by measuring multiple radial resistivity curves at different depths (10 in-, 20 in-, 30 in-, 60 in-, 90 in-, or 120 in-) from shallow to deep, thereby accurately obtaining the true resistivity Rt of the formation. The formation model is an invasion model, which can be simplified into a (radial) three-layer model, such as Figure 7 The schematic diagram of the radial three-layer model shown in Figure 1 is as follows: xo is the intrusion zone diameter. According to the geometric factor theory, the measured value of the subarray can be expressed as:

[0083] σ a (i)=σ m G bh (i)+σ xo [G xo (i)-G bh (i)]+σ t [1-G xo (i)].

[0084] Among them, σ xo G is the conductivity of the intrusion zone; xo (i) is the radial integral geometric factor of the invasion zone of subarray i, which is a function of the invasion zone radius. xo Invasion zone formation, the wellbore correction is realized, and the corresponding wellbore correction formula is:

[0085] σ bhc (i)=σ a (i)-(σ m -σ xo )G bh (i).

[0086] Optionally, when intrusion does not exist, σ xo =σ t , the three-layer model can be transformed into a two-layer model, and the wellbore correction formula also changes accordingly.

[0087] As an optional embodiment, when there is drag reduction intrusion, σ m >σ xo >σ t Due to the existence of wellbore and invasion, the relationship between the original measurement data is: a (1)>…>σ a (i)>…>σ a (M), such as Figure 8 The σ shown m >σ xo >σ t Schematic diagram of apparent conductivity after model correction. The result of wellbore correction based on the formula of the three-layer model (taking invasion into account) is an ideal result. When the wellbore correction is performed using the formula based on the two-layer model (not taking invasion into account), due to σ t Lower than σ xo ,(σ m -σ t )>(σ m -σ xo ), overcorrection occurs. As the number of subarrays increases, the amount of overcorrection gradually decreases. This results in a smaller difference between the subarray curves, resulting in a smaller intrusion difference in the final curve.

[0088] As an optional embodiment, when there is resistance-increasing intrusion, σ xo <σ t If the wellbore correction is performed using the formula based on the two-layer model (without considering invasion), the result will be m , σ t and σ xo There are three changes in the relationship between them: The first one: σ t >σ xo ≥σ m At this time, (σ m -σ xo ) is negative, (σ m -σ t ) is also negative, and |σ m -σ t |>|σm -σ xo |, so there is also an over-correction, which results in a smaller intrusion difference in the final curve, such as Figure 9 The σ shown t >σ xo ≥σ m Schematic diagram of apparent conductivity after model correction. The second type: σ t >σ m >σ xo At this time (σ m -σ xo ) is positive, but since (σ m -σ t ) is negative, the wellbore correction direction is in the opposite direction, making the invasion difference of the final curve smaller, such as Figure 10 The σ shown t >σ m >σ xo Schematic diagram of apparent conductivity after model correction. m ≥σ t >σ xo , at this time (σ m -σ t ) and (σ m -σ xo ) are all positive or zero, since (σ m -σ xo )>(σ m -σ t ), so the borehole correction is insufficient, making the invasion difference of the final curve smaller, such as Figure 11 The σ shown m ≥σ t >σ xo Schematic diagram of apparent conductivity after model correction.

[0089] It should be noted that when using the well correction formula based on the two-layer model, the invasion difference of the correction result curve is always smaller. In addition, the commonly used well correction formula is: It can be understood as follows: the conductivity "generated" by all strata except the wellbore is "deduced" to the wellbore; the conductivity "generated" by all strata is the result after wellbore correction. If the strata conform to the two-layer model, the result after wellbore correction is equal to σ t ; However, if there is intrusion, that is, a three-layer model, the corrected result of this formula is:

[0090] In an optional embodiment, the above-mentioned measurement value expression and the above-mentioned wellbore correction formula are used to determine the corrected measurement value of the above-mentioned target formation and complete the wellbore correction, including: using the above-mentioned measurement value expression and the above-mentioned wellbore correction formula to process the above-mentioned measurement signal to determine the target formation conductivity and the target wellbore mud conductivity of the above-mentioned target formation; and applying the above-mentioned target formation conductivity and the above-mentioned target wellbore mud conductivity to perform wellbore correction on the target measurement value.

[0091] In an embodiment of the present invention, based on the radial invasion model, an expression for the subarray measurement values ​​and a borehole correction formula are derived; starting from the multi-order formation model, the born approximation is applied to deduce a general formula describing the intrinsic quantitative correlation of the array sensing subarray measurement values; based on the intrinsic correlation between the array sensing subarray measurement values, the parameters required for borehole correction are solved; and finally, the borehole correction formula based on the radial invasion model is applied to perform borehole correction.

[0092] As an optional embodiment, during the solution process, the only parameters that need to be determined experimentally in the program are the weights used to calculate the fitting residuals. We need to focus on the first four shallow subarrays, so their corresponding weights are set to 1.0, and the weights of the other subarrays can be gradually reduced. The program has four optional wellbore correction modes, and their functions are described in the following table:

[0093] Table 1

[0094] Wellbore correction mode Functional Description conventional <![CDATA[Given σ m , Cal, and Ecc, adaptively solve for σ xo and σ t > Adaptive mud seeking <![CDATA[Given Cal and Ecc, adaptively solve for σ m , σ xo and σ t > Adaptive well diameter calculation <![CDATA[Given σ m and Ecc, adaptively solve for Cal, σ xo and σ t > Adaptive solution for eccentricity <![CDATA[Given σ m and Cal, adaptively solve for Ecc, σ xo and σ t >

[0095] As an optional embodiment, by applying the above-mentioned adaptive solution procedure, the curve difference can reliably describe the invasion profile, thereby more accurately obtaining the true resistivity Rt of the formation.

[0096] It should also be noted that the radial three-layer model is a great improvement over the two-layer model because it includes the invasion zone, but it still has a gap with the actual radial variation of formation conductivity. Figure 12 The schematic diagram of the invasion transition path shown in the figure shows a radial section of the formation that is closer to the actual one. It only represents four possible paths of the transition zone from the wellbore flushing zone to the original formation, while there are infinite possible actual paths.

[0097] The above method, based on the interconnected relationships between array sensing subarray measurement curves, applies tomography concepts and introduces multiple step functions to approximate arbitrary radial one-dimensional formations. Based on geometric factor theory, a mathematical expression for this interconnected relationship is derived and applied to adaptive borehole correction. Based on the invasion model, a new borehole correction formula is determined. Applying this interconnected relationship between array sensing subarray measurement curves and the new borehole correction formula accurately solves for the parameters required for borehole correction, avoiding mistaking invasion effects for borehole effects and "correcting" them. After borehole correction, the curve differences remain unchanged, preserving the ability of the curves to analyze invasion radially.

[0098] Through the above steps, a new adaptive wellbore environmental parameter solution algorithm can be constructed by applying the inline relationship between the array sensing sub-array measurement curves. The accuracy of the adaptive solution result is greatly improved. The target wellbore correction formula is obtained based on the invasion model. This avoids the invasion effect being "corrected" as a wellbore effect. The curve difference will not become smaller after the wellbore correction, and the ability of the curve to analyze the invasion radially is maintained. Figure 13 The following diagram shows the conductivity radial profile represented by the array sensing curve before and after borehole correction. Before borehole correction, the conductivity of the borehole medium is generally significantly different from that of the formation (invasion flushing zone, invasion transition zone, and original formation), as shown in (a). At this time, the measurement values ​​of each sub-array of array sensing are the response of the model in (a). After borehole correction, it is equivalent to "taking the conductivity of σ as the value". m The wellbore medium is replaced with a conductivity of σ xo In this case, the measurement values ​​of each array sensing subarray are the responses of model (b). This solves the technical problems of existing array sensing wellbore correction methods, such as the discrepancy between the model and the actual formation and the low intrinsic correlation between the measurement values ​​of array sensing subarrays.

[0099] Example 2

[0100] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing the above array sensing wellbore correction method. Figure 14 A structural diagram of a wellbore correction device according to an embodiment of the present invention is shown in FIG. Figure 14 As shown, the wellbore correction device includes: an acquisition module 140, a first determination module 142, a processing module 144, a second determination module 146 and a correction module 148, wherein:

[0101] The acquisition module 140 is configured to acquire measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals at least include: a conductivity curve group;

[0102] The first determination module 142 is used to determine the borehole environment parameters corresponding to the borehole environment of the target formation, and uses a multi-dimensional space interpolation method to obtain the target borehole geometry factors of the above-mentioned borehole environment from the borehole geometry factor library according to the above-mentioned borehole environment parameters, wherein the above-mentioned borehole environment parameters include: well diameter cal, mud conductivity σ m , formation conductivity σ t and eccentricity ecc, the wellbore geometry factor library is a pre-established wellbore geometry factor database, and the wellbore geometry factor is a function of the wellbore environment parameters;

[0103] a processing module 144 for establishing a tomographic matrix equation based on a radial formation model and the measurement signal, and solving the tomographic matrix equation to obtain a radial conductivity distribution of the target formation, wherein the radial formation model is used to characterize conductivity changes of the target formation;

[0104] A second determination module 146 is configured to determine a measurement value expression based on the radial formation model and the radial conductivity distribution, and to determine a wellbore correction formula using the target wellbore geometry factor corresponding to the wellbore environment;

[0105] The correction module 148 is used to determine the corrected measurement value of the target formation based on the measurement value expression and the wellbore correction formula to complete the wellbore correction.

[0106] It should be noted that the acquisition module 140, first determination module 142, processing module 144, second determination module 146, and correction module 148 correspond to steps S102 to S110 in Example 1. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in Example 1. It should be noted that the above modules, as part of the device, can be run in a computer terminal.

[0107] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in Example 1 and will not be repeated here.

[0108] The above-mentioned wellbore correction device can also include a processor and a memory. The above-mentioned acquisition module 140, first determination module 142, processing module 144, second determination module 146 and correction module 148 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0109] The processor includes a core, which retrieves the corresponding program unit from memory. There can be one or more cores. Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0110] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program is executed, the device containing the non-volatile storage medium is controlled to execute any of the above-mentioned array sensing wellbore correction methods.

[0111] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0112] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: obtaining measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals at least include: a conductivity curve group; determining wellbore environment parameters corresponding to the wellbore environment of the target formation, and using a multi-dimensional space interpolation method to obtain target wellbore geometry factors of the wellbore environment from a wellbore geometry factor library based on the wellbore environment parameters, wherein the wellbore environment parameters include: well diameter cal, mud conductivity σ m , formation conductivity σ t and eccentricity ecc, the above-mentioned wellbore geometry factor library is a pre-established wellbore geometry factor database, and the above-mentioned wellbore geometry factor is a function of the above-mentioned wellbore environment parameters; based on the radial formation model and the above-mentioned measurement signal, a tomographic matrix equation is established, and the above-mentioned tomographic matrix equation is solved to obtain the radial conductivity distribution of the above-mentioned target formation, wherein the above-mentioned radial formation model is used to characterize the conductivity change of the above-mentioned target formation; according to the above-mentioned radial formation model and the above-mentioned radial conductivity distribution, a measurement value expression is determined, and the above-mentioned target wellbore geometry factor corresponding to the above-mentioned wellbore environment is used to determine the wellbore correction formula; based on the above-mentioned measurement value expression and the above-mentioned wellbore correction formula, the measurement value of the above-mentioned target formation after correction is determined to complete the wellbore correction.

[0113] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: determining a first variation range of the above-mentioned well diameter, and determining a first preset number of discrete points based on the above-mentioned first variation range to form a first-dimensional discrete point set; determining a second variation range of the above-mentioned mud conductivity, and determining a second preset number of discrete points based on the above-mentioned second variation range to form a second-dimensional discrete point set; determining a third variation range of the above-mentioned formation conductivity, and determining a third preset number of discrete points based on the above-mentioned third variation range to form a third-dimensional discrete point set; determining a fourth variation range of the above-mentioned eccentricity, and determining a fourth preset number of discrete points based on the above-mentioned fourth variation range to form a fourth-dimensional discrete point set; performing combined calculations on the above-mentioned first-dimensional discrete point set, the above-mentioned second-dimensional discrete point set, the above-mentioned third-dimensional discrete point set, and the above-mentioned fourth-dimensional discrete point set to obtain the wellbore geometry factors of the above-mentioned sub-arrays, storing the wellbore geometry factors of all the above-mentioned sub-arrays according to preset storage rules, and constructing the above-mentioned wellbore geometry factor library.

[0114] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: determining the value of the wellbore environment parameter corresponding to the wellbore environment of the above-mentioned target formation; obtaining the closest target discrete point from the above-mentioned wellbore geometry factor library based on the value of the above-mentioned wellbore environment parameter; and determining the above-mentioned target wellbore geometry factor based on the above-mentioned first-dimensional discrete point set, the above-mentioned second-dimensional discrete point set, the above-mentioned third-dimensional discrete point set, and the above-mentioned fourth-dimensional discrete point set using the above-mentioned multi-dimensional space interpolation method.

[0115] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: dividing the above-mentioned target formation into multiple layers in the horizontal radial direction, wherein the wellbore is the innermost layer, and the formations other than the above-mentioned wellbore are the invasion flushing zone, the invasion transition zone and the original formation; and using the above-mentioned wellbore, the above-mentioned invasion flushing zone, the above-mentioned invasion transition zone and the above-mentioned original formation to construct the above-mentioned radial formation model.

[0116] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: determining the wellbore geometry factor and the radial geometry factor of each of the above-mentioned subarrays; establishing the above-mentioned tomographic matrix equation based on the above-mentioned radial formation model and the above-mentioned measurement signal, the above-mentioned wellbore geometry factor and the above-mentioned radial geometry factor, wherein the left-hand side term of the above-mentioned tomographic matrix equation is the above-mentioned measurement signal of each of the above-mentioned subarrays, the unknown quantity of the above-mentioned tomographic matrix equation is the conductivity difference between radially adjacent layers, the coefficient matrix of the above-mentioned tomographic matrix equation is composed of the above-mentioned wellbore geometry factor, the above-mentioned radial geometry factor and a constant of the above-mentioned subarrays, and the constraint conditions of the above-mentioned tomographic matrix equation are determined according to the above-mentioned wellbore environment parameters; solving the above-mentioned tomographic matrix equation to obtain the above-mentioned radial conductivity distribution of the above-mentioned target formation.

[0117] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: use the above-mentioned measurement value expression and the above-mentioned wellbore correction formula to process the above-mentioned measurement signal to determine the target formation conductivity and target wellbore mud conductivity of the above-mentioned target formation; apply the above-mentioned target formation conductivity and the above-mentioned target wellbore mud conductivity to perform wellbore correction on the target measurement value.

[0118] According to an embodiment of the present application, an embodiment of a processor is further provided. Optionally, in this embodiment, the processor is used to run a program, wherein when the program is run, any one of the above array sensing wellbore correction methods is executed.

[0119] According to an embodiment of the present application, an embodiment of an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any one of the above-mentioned array sensing wellbore correction methods.

[0120] According to an embodiment of the present application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is suitable for executing a program that initializes any one of the above-mentioned array sensing wellbore correction method steps.

[0121] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0122] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0124] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0125] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0127] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An array sensing wellbore correction method, characterized in that: include: Acquiring measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals at least include: a conductivity curve group; Determine the borehole environment parameters corresponding to the target formation, and use multi-dimensional space interpolation method to obtain the target borehole geometry factor of the borehole environment from the borehole geometry factor library according to the borehole environment parameters, wherein the borehole environment parameters include: wellbore diameter , mud conductivity , formation conductivity and eccentricity , the wellbore geometry factor library is a pre-established wellbore geometry factor database, and the wellbore geometry factor is a function of the wellbore environment parameter; Based on the radial formation model and the measurement signal, a tomographic matrix equation is established, and the tomographic matrix equation is solved to obtain the radial conductivity distribution of the target formation, wherein the radial formation model is used to characterize the conductivity change of the target formation; Determining a measurement value expression based on the radial formation model and the radial conductivity distribution, and determining a wellbore correction formula using the target wellbore geometry factor corresponding to the wellbore environment; Based on the measurement value expression and the wellbore correction formula, determining the measured value of the target formation after correction, and completing the wellbore correction; Before obtaining the target wellbore geometry factor of the wellbore environment from the wellbore geometry factor library based on the wellbore environment parameters using a multidimensional spatial interpolation method, the method further includes: determining a variation range of the wellbore diameter, and determining a preset number of discrete points of the wellbore diameter based on the variation range of the wellbore diameter to form a first-dimensional discrete point set; determining a variation range of the mud conductivity, and determining a preset number of discrete points of the mud conductivity based on the variation range of the mud conductivity to form a second-dimensional discrete point set; determining a variation range of the formation conductivity, and determining a preset number of discrete points of the formation conductivity based on the variation range of the formation conductivity to form a third-dimensional discrete point set; determining a variation range of the eccentricity, and determining a preset number of discrete points of the eccentricity based on the variation range of the eccentricity to form a fourth-dimensional discrete point set; combining the first-dimensional discrete point set, the second-dimensional discrete point set, the third-dimensional discrete point set, and the fourth-dimensional discrete point set to calculate the wellbore geometry factor of each subarray, storing the wellbore geometry factors of all the subarrays according to a preset storage rule, and constructing the wellbore geometry factor library; Wherein, when the radial formation model is a two-layer model, the expression of the wellbore correction formula is: ,in, for i The borehole-corrected measurements of the subarray, i Indicates the subarray number, from 1 to M, i The larger the subarray, the greater the detection depth. For the measured i The apparent conductivity of the sub-array, It is represented as the borehole geometry factor of the i-th subarray.

2. The method according to claim 1, characterized in that The determining of the borehole environment parameters corresponding to the borehole environment of the target formation, and obtaining the target borehole geometry factor of the borehole environment from a borehole geometry factor library according to the borehole environment parameters using a multi-dimensional space interpolation method, comprises: determining a value of a wellbore environment parameter corresponding to the wellbore environment of the target formation; According to the value of the wellbore environmental parameter, the closest target discrete point is obtained from the wellbore geometric factor library; The target wellbore geometry factor is determined by using the multidimensional space interpolation method based on the first dimensional discrete point set, the second dimensional discrete point set, the third dimensional discrete point set, and the fourth dimensional discrete point set.

3. The method according to claim 1, characterized in that Before establishing a tomographic matrix equation based on the radial formation model and the measurement signal and solving the tomographic matrix equation to obtain the radial conductivity distribution of the target formation, the method further includes: Dividing the target formation into multiple layers in the radial direction, wherein the wellbore is the innermost layer, and the formations outside the wellbore are the invasion and flushing zone, the invasion transition zone, and the original formation; The radial formation model is constructed using the wellbore, the invasion flushing zone, the invasion transition zone and the original formation.

4. The method according to claim 1, wherein Based on the radial formation model and the measurement signal, a tomographic matrix equation is established, and the tomographic matrix equation is solved to obtain the radial conductivity distribution of the target formation, including: Determining a borehole geometry factor of each subarray and a radial geometry factor of each subarray; The tomographic matrix equation is established based on the radial formation model and the measurement signal, the wellbore geometry factor, and the radial geometry factor, wherein the left-hand side term of the tomographic matrix equation is the measurement signal of each subarray, the unknown quantity of the tomographic matrix equation is the conductivity difference between radially adjacent layers, the coefficient matrix of the tomographic matrix equation is composed of the wellbore geometry factor, the radial geometry factor, and a constant of each subarray, and the constraint conditions of the tomographic matrix equation are determined according to the wellbore environment parameters; The tomographic matrix equation is solved to obtain the radial conductivity distribution of the target formation.

5. The method according to any one of claims 1 to 4, characterized in that The step of determining the corrected measured value of the target formation based on the measured value expression and the wellbore correction formula to complete the wellbore correction includes: Processing the measurement signal using the measurement value expression and the wellbore correction formula to determine a target formation conductivity and a target wellbore mud conductivity of the target formation; The target formation conductivity and the target wellbore mud conductivity are used to perform wellbore correction on the target measurement value.

6. An array sensing wellbore correction device, characterized in that: include: An acquisition module is used to acquire measurement signals obtained by each sub-array of the array induction logging instrument, wherein the measurement signals at least include: a conductivity curve group; A first determination module determines wellbore environment parameters corresponding to the wellbore environment of the target formation, and uses a multidimensional spatial interpolation method to obtain target wellbore geometry factors of the wellbore environment from a wellbore geometry factor library based on the wellbore environment parameters, wherein the wellbore environment parameters include: well diameter, mud conductivity, formation conductivity, and eccentricity. The wellbore geometry factor library is a pre-established database of wellbore geometry factors, and the wellbore geometry factors are functions of the wellbore environment parameters. a processing module, configured to establish a tomographic matrix equation based on a radial formation model and the measurement signal, and solve the tomographic matrix equation to obtain a radial conductivity distribution of the target formation, wherein the radial formation model is used to characterize a conductivity change of the target formation; a second determination module, configured to determine a measurement value expression based on the radial formation model and the radial conductivity distribution, and to determine a wellbore correction formula using the target wellbore geometry factor corresponding to the wellbore environment; A correction module, configured to determine a corrected measurement value of the target formation based on the measurement value expression and the wellbore correction formula, thereby completing the wellbore correction; Wherein, the device is also used for: Before obtaining the target wellbore geometry factor of the wellbore environment from the wellbore geometry factor library based on the wellbore environment parameters using the multidimensional spatial interpolation method, the processing module is further configured to: determine the variation range of the wellbore diameter, and determine a preset number of discrete points of the wellbore diameter based on the variation range of the wellbore diameter to form a first-dimensional discrete point set; determine the variation range of the mud conductivity, and determine a preset number of discrete points of the mud conductivity based on the variation range of the mud conductivity to form a second-dimensional discrete point set; determine the variation range of the formation conductivity, and determine a preset number of discrete points of the formation conductivity based on the variation range of the formation conductivity to form a third-dimensional discrete point set; determine the variation range of the eccentricity, and determine a preset number of discrete points of the eccentricity based on the variation range of the eccentricity to form a fourth-dimensional discrete point set; combine the first-dimensional discrete point set, the second-dimensional discrete point set, the third-dimensional discrete point set, and the fourth-dimensional discrete point set to calculate the wellbore geometry factor of each subarray, store the wellbore geometry factors of all the subarrays according to a preset storage rule, and construct the wellbore geometry factor library; Wherein, when the radial formation model is a two-layer model, the expression of the wellbore correction formula is: ,in, for i The borehole-corrected measurements of the subarray, i Indicates the subarray number, from 1 to M, i The larger the subarray, the greater the detection depth. For the measured i The apparent conductivity of the sub-array, It is represented as the borehole geometry factor of subarray i.

7. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions and a wellbore geometry factor database, wherein the instructions are suitable for being loaded by a processor and executing the array sensing wellbore correction method according to any one of claims 1 to 5.

8. A processor, characterized in that: The processor is used to run a program, wherein the program is configured to execute the array sensing wellbore correction method according to any one of claims 1 to 5 when running.

9. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program and a wellbore geometry factor database, and the processor is configured to run the computer program to execute the array sensing wellbore correction method according to any one of claims 1 to 5.

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