Method and device for detecting cement in a through-casing density logging environment

Abstract

The application discloses a method and device for detecting cement in a through casing density logging environment, and the method comprises the following steps: constructing a plurality of cement density constraint models; obtaining energy spectrum data of a density measurement after a well casing is detected, and dividing the energy spectrum data by a sliding window to obtain energy spectrum data of a plurality of sliding windows; determining a reference model based on the plurality of cement density constraint models; obtaining energy spectrum data of a sliding window in sequence, and inverting the energy spectrum data according to the reference model to obtain inverted formation density and inverted cement density of the sliding window; calculating and determining the probability of each cement density constraint model of each cement invasion material combined with solid cement in the sliding window according to the inverted cement density, updating the cement density constraint model with the highest probability as the reference model, inverting the energy spectrum data of the next sliding window, and performing inversion on the energy spectrum data of all sliding windows until the inversion of the energy spectrum data of all sliding windows is completed to obtain the inverted formation density and the inverted cement density of all sliding windows.

Description

Technical Field

[0001] This invention relates to the field of well logging technology, specifically to a method and apparatus for detecting cementing cement under a casing density logging environment. Background Technology

[0002] Cementing engineering refers to the installation of casing in the well and the pouring of cement into the wellbore and tubing to bond the cement, casing, and formation, preventing borehole collapse, isolating the formation, sealing leaky zones / oil and gas layers, and providing a stable passage for drilling. Ensuring the integrity and sealing of the casing also effectively protects groundwater resources and prevents environmental pollution. The method of logging after installing casing and pouring cement into the wellbore and tubing is called through-casing logging. Compared to traditional logging, through-casing logging can prevent accidents and provide a more stable logging environment. By monitoring the condition and position of the casing, operators can better control operational risks and ensure the safety of the operation. Post-casing logging can reduce operational difficulty, avoid potential engineering risks, and shorten the construction period.

[0003] However, in through-casing density measurement, the cement density changes with the thickness, introducing uncertainties and affecting the accuracy of existing through-casing density measurement methods, thus increasing the complexity of the measurement. Therefore, there is an urgent need for a method to detect cementing cement in through-casing density logging environments that can be used for formation density measurement. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method and apparatus for detecting cementing in a casing density logging environment that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for detecting cementing in a casing density logging environment is provided, the method comprising:

[0006] Based on cementing cement and cement intrusion materials, multiple cement density constraint models are constructed.

[0007] The energy spectrum data of the density measurement behind the casing of the well to be tested is obtained, and the energy spectrum data is divided into sliding windows to obtain energy spectrum data of multiple sliding windows; among them, there is overlapping data between two adjacent sliding windows;

[0008] A baseline model was determined based on multiple cement density constraint models.

[0009] The energy spectrum data of a sliding window is acquired sequentially. The energy spectrum data is inverted according to the baseline model to obtain the inverted formation density and inverted cement density of the sliding window. Based on the inverted cement density, the probability of each cement density constraint model that combines each cement intrusion material with solid cement in the sliding window is calculated. The cement density constraint model with the highest probability is updated as the baseline model. Based on the updated baseline model, the energy spectrum data of the next sliding window is inverted until the energy spectrum data of all sliding windows are inverted. The obtained inverted formation density and inverted cement density of all sliding windows are used as the formation density and cement density of the over-casing density logging environment.

[0010] According to another aspect of the present invention, a device for detecting cementing in a casing density logging environment is provided, comprising:

[0011] The constraint construction module is suitable for constructing multiple cement density constraint models based on cementing cement and cement intrusion materials.

[0012] The window partitioning module is suitable for acquiring the energy spectrum data of density measurement behind the casing of the well to be tested. It divides the energy spectrum data into sliding windows to obtain energy spectrum data of multiple sliding windows; among them, there is overlapping data between two adjacent sliding windows.

[0013] The initial baseline module is suitable for determining a baseline model based on multiple cement density constraint models.

[0014] The inversion module is suitable for sequentially acquiring the energy spectrum data of a sliding window, inverting the energy spectrum data according to the benchmark model, and obtaining the inverted formation density and inverted cement density of the sliding window. Based on the inverted cement density, the probability of each cement density constraint model combining each cement intrusion material with solid cement within the sliding window is calculated and determined. The cement density constraint model with the highest probability is updated as the benchmark model. Based on the updated benchmark model, the energy spectrum data of the next sliding window is inverted until the energy spectrum data of all sliding windows are inverted. The obtained inverted formation density and inverted cement density of all sliding windows are used as the formation density and cement density of the over-casing density logging environment.

[0015] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0016] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for detecting cementing under the over-casing density logging environment.

[0017] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform an operation corresponding to the above-described method for detecting cementing under a casing density logging environment.

[0018] According to another aspect of the present invention, a computer program product is provided, including at least one executable instruction, the executable instruction causing a processor to perform operations corresponding to the above-described method for detecting cementing under casing density logging conditions.

[0019] The method and apparatus for detecting cementing in a casing density logging environment provided by the present invention utilizes cementing and cement intrusion material to construct multiple cement density constraint models, divides multiple windows based on energy spectrum data, and uses window information combined with the cement density constraint threshold of the cement density constraint model to perform inversion to determine formation density and cement density, thereby solving the cement uncertainty problem in casing density measurement and helping to improve the accuracy of casing density measurement.

[0020] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0022] Figure 1 A flowchart of a method for detecting cementing cement in a casing density logging environment according to an embodiment of the present invention is shown;

[0023] Figure 2 A schematic diagram of density logging through casing is shown;

[0024] Figure 3 A schematic diagram of the energy deposition spectrum is shown;

[0025] Figure 4 A schematic diagram of formation density and cement density curves in a casing density logging environment is shown.

[0026] Figure 5 A schematic diagram of a device for detecting cementing cement under a casing density logging environment according to an embodiment of the present invention is shown.

[0027] Figure 6 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0028] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0029] Figure 1 A flowchart illustrating a method for detecting cementing in a casing density logging environment according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, the method includes the following steps:

[0030] Step S101: Construct multiple cement density constraint models based on cementing cement and cement intrusion materials.

[0031] Variations in cement density and uncertainties introduced by material intrusion can significantly impact the accuracy of formation density measurements. During through-casing gamma density logging, since gamma attenuation is primarily related to the density of the medium, this embodiment characterizes the uncertainty of cement bonding quality or cement contamination using variations in average cement density to analyze the cement's inherent uncertainties.

[0032] like Figure 2 As shown, the cement is located between the outer wall of the casing and the inner wall of the formation. When constructing the cement density constraint model, it can be regarded as a collection of solid cement and cement intrusion material. In this embodiment, the cement intrusion material and solid cement are assumed to be uniformly mixed. Therefore, the density of the cement is the mixed density of solid cement and cement intrusion material. The initial density constraint model can be constructed using a homogeneous mixing model as follows:

[0033] ρ cement =k·ρ fluid +(1-k)·ρ cement_r

[0034] Where, ρ fluid and ρ cement_r , where are the densities of the cement intrusion material and the cementing density, respectively, and k is a preset weight.

[0035] Different cement density constraint models can be obtained based on different cement intrusion substances, thus determining different cement density constraint thresholds. Cement intrusion substances include, for example, methane, water, and drilling mud. Here, the cement intrusion substance is a single substance, meaning it is only methane, only water, or only drilling mud. Substituting the density of the cement intrusion substance and the density of the cementing cement into the initial density constraint model yields different cement density constraint thresholds. Specifically, the density of solid cement can be represented by data such as 1.3 g / cm³. 3 1.6g / cm 3 1.9g / cm 3 The density of methane is 0.2 g / cm³. 3 The density of water is 1.0 g / cm³. 3 The mud density is 1.3 g / cm³. 3 Based on the cement density constraint model, the upper constraint limit corresponds to the maximum density of solid cement, while the lower constraint limit corresponds to the highest proportion of cement intrusion material. Taking water as the cement intrusion material as an example, the cement density constraint threshold corresponding to the water intrusion model is 1.0–1.9 g / cm³. 3 It can be used in situations where there is no specific well condition information or very little information.

[0036] Based on the union of cement density constraint thresholds from all cement density constraint models, the possible range of overall cement density variation can be determined to be 0.2–1.9 g / cm³. 3 .

[0037] Step S102: Obtain the energy spectrum data of the density measurement behind the well casing to be tested, and divide the energy spectrum data into sliding windows to obtain energy spectrum data of multiple sliding windows.

[0038] In casing gamma density logging, Cs-137 particles with an energy of 0.662 MeV are used as the particle source, emitted from the source chamber at a specific angle. The emitted gamma particles react with various media in the casing well environment via the photoelectric effect and Compton effect. After the reaction, a small portion of the particles carrying information about the corresponding media return to the detector of the density instrument, thus forming a gamma density distribution. Figure 3 The energy deposition spectrum is shown. The attenuation process of gamma rays can be described as follows:

[0039]

[0040] Where I and I0 are the intensity / quantity of the emitted and incident gamma rays, respectively, n is the type of material passing through, and μ i and x i These are the attenuation coefficient and attenuation distance of the i-th medium, respectively.

[0041] In density logging, the focus is on the Compton effect. When gamma rays have moderate energy, they interact with the outer electrons of atoms, transferring some energy to them and causing them to escape in one direction—these are called Compton electrons. The gamma rays, having lost some energy, scatter in another direction. When gamma rays pass through the formation, they are absorbed due to the Compton effect. The strength of the formation's absorption of gamma rays depends on the number of electrons per unit volume in the rock, i.e., the electron density. The electron density is related to the formation density; therefore, the density of the lithology can be determined by measuring the intensity of the gamma rays. The Compton effect causes a decrease in gamma ray energy as they pass through matter; this decrease can be expressed using the Compton absorption coefficient.

[0042]

[0043] Where σ is the Compton scattering cross section (probability) of each electron, ρ is the medium density, Z is the atomic number, A is the molecular weight, and N is the molecular weight. A Let Z be Avogadro's constant. Based on the elements in the sedimentary rock, the Z / A ratio can be as low as 0.5. The density of the medium can then be determined based on the relationship between ∑ and ρ in the above formula.

[0044] Based on the above-mentioned through-casing logging, energy spectrum data for density measurement can be obtained. This data can be used to determine the particle count, energy information, energy threshold, and other parameters from the density instrument. Figure 3 The energy spectrum shape shown can be used to divide the energy spectrum data into sliding windows according to a preset window length, resulting in multiple sliding window energy spectrum data. The overlapping data between two adjacent sliding windows is determined according to a preset overlap threshold. For example, if the step size of each sliding window is t, the overlap rate between two adjacent sliding windows can range from 50% to 80%, meaning that two adjacent sliding windows, such as k and k+1, contain the same overlapping data with an overlap rate of 50%. The above is an example; the specific settings should be determined according to the implementation situation and are not limited here.

[0045] Furthermore, to ensure data accuracy, after acquiring the energy spectrum data of the density measurement behind the well casing, preprocessing can be performed on the energy spectrum data. Preprocessing includes removing invalid data such as outlier detection and filtering, reducing the impact on subsequent inversion and improving accuracy.

[0046] Step S103: Determine the benchmark model based on multiple cement density constraint models.

[0047] The baseline model is used for subsequent inversion. Here, a model can be selected from multiple cement density constraint models based on the actual well conditions. Since the energy spectrum data is limited in the first sliding window calculation, any model can be chosen as the baseline model. Subsequent calculations for each sliding window can be corrected based on probability, with minimal impact on the overall inversion calculation, which can be ignored.

[0048] Step S104: Sequentially acquire the energy spectrum data of one sliding window, invert the energy spectrum data according to the benchmark model to obtain the inverted formation density and inverted cement density of the sliding window; based on the inverted cement density, calculate and determine the probability of each cement density constraint model that combines each cement intrusion material with solid cement in the sliding window, update the cement density constraint model with the highest probability as the benchmark model, and invert the energy spectrum data of the next sliding window according to the updated benchmark model until the inversion of the energy spectrum data of all sliding windows is completed, and use the obtained inverted formation density and inverted cement density of all sliding windows as the formation density and cement density of the over-casing density logging environment.

[0049] In this embodiment, the energy dispersive spectroscopy (EDS) data includes cement windows and density windows. Cement window counts primarily reflect cement density, while density window counts reflect the core of through-casing density measurement: formation density. For through-casing density inversion, the interplay of various factors results in poor inversion performance when using windows alone. Therefore, this embodiment uses a comprehensive algorithm to handle the influence of cement and formation, establishing corresponding forward and inversion mathematical models based on the cement and density windows of the EDS data using the gamma-ray transport principle. The model input includes formation and cement information, and the output is the corresponding window counts, as shown below:

[0050]

[0051] Where, ρ b ρ is the density of the formation. c Let N be the density of cement, ln(N) be the logarithm of the window count, and F(ρ) be the density of cement. b ,ρ c ) is the combined influence function of cement density and formation density, and a1-a4 are multiple preset coefficients.

[0052] Based on the above model, inversion can be performed. During inversion, energy spectrum data of a sliding window is obtained sequentially from top to bottom according to the logging depth. The cement density constraint threshold of the selected benchmark model is substituted into the sliding window of the energy spectrum data to perform inversion, and the inverted formation density and inverted cement density of the sliding window can be obtained.

[0053] Based on the cement density obtained from the inversion, the probabilities of cement density constraint models formed by different cement intrusion substances combined with solid cement within the sliding window are calculated. Specifically, assuming that the probability of the cement density constraint model formed by each cement intrusion substance combined with solid cement is the same, and that the cement density within the sliding window follows a Gaussian distribution, a probability calculation model based on Bayesian classifier theory is constructed:

[0054]

[0055] Among them, Byes * Let w(k) be the energy spectrum data within the k-th sliding window, w(k+1) be the energy spectrum data within the (k+1)-th sliding window, and w(k)∩w(k+1) be the intersection information of the energy spectrum data of two adjacent sliding windows k and k+1. Let ρ be the optimal Bayesian classifier. c Let P(C) be the cement density within the intersection of adjacent sliding windows k and k+1. i |ρ c ) represents the probability after the event, C i The cement density constraint thresholds are for different types of cement density, including cement density constraint models for different cement intrusion substances combined with solid cement.

[0056] For the post-probability, P(C) i |ρ c )≈P(C i )P(ρ c |C i )

[0057] Among them, P(C i ) is C i The probability, P(ρ) c |C i ) is C i In class P(C) i The class conditional probability of ).

[0058]

[0059] Transforming the above formula, we get:

[0060]

[0061] Where, ρ cj The cement density is obtained by inversion at a point in the intersection of the sliding window w(k)∩w(k+1). They belong to category C respectively i The average and variance of the cement density.

[0062] For multiple cement density constraint models, based on the inverted cement density from the overlapping data in two adjacent sliding windows, the aforementioned Bayesian optimal classifier can be used to determine the probability of each cement density constraint model formed by the combination of each cement intrusion substance with solid cement within the sliding window.

[0063] After calculating the probabilities, the cement density constraint model with the highest probability is updated as the new baseline model. Based on the updated baseline model, the energy spectrum data for the next sliding window is obtained, and the energy spectrum data for the next sliding window is inverted. This process of probability calculation and inversion is repeated until the energy spectrum data for all sliding windows is inverted. The inverted formation density and inverted cement density of all sliding windows are used as the formation density and cement density in the through-casing density logging environment. The curves of formation density and cement density in the through-casing density logging environment are shown below. Figure 4 As shown.

[0064] Furthermore, the formation density can be used to determine the corresponding porosity, and combined with other geological curves, detailed well logging information can be obtained, thereby guiding oil development and geological exploration.

[0065] The method for detecting cementing in a casing density logging environment provided by the present invention utilizes cementing and cement intrusion materials to construct multiple cement density constraint models, divides multiple windows based on energy spectrum data, and uses window information combined with the cement density constraint threshold of the cement density constraint model to perform inversion to determine formation density and cement density, thereby solving the cement uncertainty problem in casing density measurement and helping to improve the accuracy of casing density measurement.

[0066] Figure 5 A schematic diagram of the structure of the cement testing device for well cementing under through-casing density logging conditions provided in an embodiment of the present invention is shown. Figure 5 As shown, the device includes:

[0067] The constraint construction module 510 is suitable for constructing multiple cement density constraint models based on cementing cement and cement intrusion materials.

[0068] The window partitioning module 520 is suitable for acquiring the energy spectrum data of the density measurement behind the well casing to be measured, and dividing the energy spectrum data into sliding windows to obtain energy spectrum data of multiple sliding windows; among them, there is overlapping data between two adjacent sliding windows;

[0069] The initial baseline module 530 is suitable for determining a baseline model based on multiple cement density constraint models.

[0070] The inversion module 540 is adapted to sequentially acquire the energy spectrum data of a sliding window, invert the energy spectrum data according to the benchmark model, and obtain the inverted formation density and inverted cement density of the sliding window. Based on the inverted cement density, the probability of each cement density constraint model combining each cement intrusion material with solid cement in the sliding window is calculated and determined. The cement density constraint model with the highest probability is updated as the benchmark model. Based on the updated benchmark model, the energy spectrum data of the next sliding window is inverted until the energy spectrum data of all sliding windows are inverted. The obtained inverted formation density and inverted cement density of all sliding windows are used as the formation density and cement density of the over-casing density logging environment.

[0071] Optionally, the constraint building module 510 is further adapted to:

[0072] Construct a density-constrained initial model;

[0073] Different cement density constraint models are obtained based on different cement intrusion substances, and different cement density constraint thresholds are determined; cement intrusion substances include: methane, water, and mud.

[0074] Optionally, the window partitioning module 520 is further adapted to:

[0075] Acquire the energy spectrum data of the density measurement behind the casing of the well to be tested, and preprocess the energy spectrum data; the preprocessing includes removing invalid data based on outliers and / or filtering;

[0076] The preprocessed energy spectrum data is divided into sliding windows according to a preset window length to obtain energy spectrum data of multiple sliding windows; among them, the overlapping data between two adjacent sliding windows is determined according to a preset overlap threshold.

[0077] Optionally, the energy dispersive spectral data includes cement window and density window.

[0078] Alternatively, the inversion module 540 is further adapted to:

[0079] An inversion model is constructed based on the cement window and density window of the energy spectrum data from the sliding window.

[0080] The energy spectrum data are inverted based on the cement density constraint thresholds of the inversion model and the baseline model to obtain the inverted formation density and inverted cement density of the sliding window.

[0081] Alternatively, the inversion module 540 is further adapted to:

[0082] Based on the inverted cement density from the overlapping data in two adjacent sliding windows, and using a Bayesian optimal classifier, the probability of each cement density constraint model combining each cement intrusion substance with solid cement within the sliding window is determined.

[0083] The descriptions of the above modules refer to the corresponding descriptions in the method embodiments, and will not be repeated here.

[0084] This invention also provides a non-volatile computer storage medium storing at least one executable instruction that can perform the operation corresponding to the cementing detection method under the over-casing density logging environment in any of the above method embodiments.

[0085] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the cementing detection method under the over-casing density logging environment in any of the above method embodiments.

[0086] Figure 6 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.

[0087] like Figure 6 As shown, the computing device may include: a processor 602, a communication interface 604, a memory 606, and a communication bus 608.

[0088] in:

[0089] The processor 602, communication interface 604, and memory 606 communicate with each other via communication bus 608.

[0090] Communication interface 604 is used to communicate with other network elements such as clients or other servers.

[0091] The processor 602 is used to execute program 610, which can specifically execute the relevant steps in the above embodiment of the method for detecting cementing cement under the over-casing density logging environment.

[0092] Specifically, program 610 may include program code that includes computer operation instructions.

[0093] Processor 602 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.

[0094] Memory 606 is used to store program 610. Memory 606 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0095] Specifically, program 610 can be used to cause processor 602 to execute the cementing detection method under the over-casing density logging environment in any of the above method embodiments. The specific implementation of each step in program 610 can be found in the corresponding steps and units described in the above embodiments of cementing detection under the over-casing density logging environment, 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 above-described equipment and modules can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0096] The algorithms or 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, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the present invention.

[0097] 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.

[0098] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following 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.

[0099] 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.

[0100] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0101] 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 embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of 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.

[0102] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, 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. Embodiments of the present 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. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for detecting cementing in a casing density logging environment, characterized in that the method... include: Based on cementing cement and cement intrusion materials, multiple cement density constraint models are constructed. The energy spectrum data of the density measurement behind the casing of the well to be tested is obtained, and the energy spectrum data is divided into sliding windows to obtain energy spectrum data of multiple sliding windows; among them, there is overlapping data between two adjacent sliding windows; A baseline model was determined based on multiple cement density constraint models. The energy spectrum data of a sliding window is acquired sequentially. The energy spectrum data is inverted according to the baseline model to obtain the inverted formation density and inverted cement density of the sliding window. Based on the inverted cement density, the probability of each cement density constraint model that combines each cement intrusion material with solid cement in the sliding window is calculated and determined. The cement density constraint model with the highest probability is updated as the baseline model. Based on the updated baseline model, the energy spectrum data of the next sliding window is inverted until the energy spectrum data of all sliding windows are inverted. The obtained inverted formation density and inverted cement density of all sliding windows are used as the formation density and cement density of the over-casing density logging environment.

2. The method according to claim 1, characterized in that, The construction of multiple cement density constraint models based on cementing cement and cement intrusion materials further includes: Construct a density-constrained initial model; Different cement density constraint models are obtained based on different cement intrusion substances, and different cement density constraint thresholds are determined; the cement intrusion substances include: methane, water, and mud.

3. The method according to claim 1, characterized in that, The step of acquiring the energy spectrum data of the density measurement behind the casing of the well to be tested, and dividing the energy spectrum data into sliding windows to obtain energy spectrum data of multiple sliding windows, further includes: Acquire the energy spectrum data of the density measurement behind the casing of the well to be tested, and preprocess the energy spectrum data; the preprocessing includes removing invalid data based on outliers and / or filtering; The preprocessed energy spectrum data is divided into sliding windows according to a preset window length to obtain energy spectrum data of multiple sliding windows; wherein, the overlapping data between two adjacent sliding windows is determined according to a preset overlap threshold.

4. The method according to claim 1, characterized in that, The energy spectrum data includes cement window and density window.

5. The method according to claim 4, characterized in that, The step of sequentially acquiring energy spectrum data for a sliding window, and then inverting the energy spectrum data according to the baseline model to obtain the inverted formation density and inverted cement density of the sliding window further includes: An inversion model is constructed based on the cement window and density window of the energy spectrum data from the sliding window; The energy spectrum data is inverted based on the cement density constraint threshold of the inversion model and the benchmark model to obtain the inverted formation density and inverted cement density of the sliding window.

6. The method according to claim 1, characterized in that, The step of calculating and determining the probability of each cement density constraint model combining each cement intrusion substance with solid cement within the sliding window based on the inverted cement density further includes: Based on the inverted cement density from the overlapping data in two adjacent sliding windows, and using a Bayesian optimal classifier, the probability of each cement density constraint model formed by the combination of each cement intrusion substance with solid cement within the sliding window is determined.

7. A device for detecting cementing in a casing density logging environment, characterized in that, The device includes: The constraint construction module is suitable for constructing multiple cement density constraint models based on cement and cement intrusion materials. The window partitioning module is suitable for acquiring the energy spectrum data of the density measurement behind the well casing, and dividing the energy spectrum data into sliding windows to obtain energy spectrum data of multiple sliding windows; wherein, there is overlapping data between two adjacent sliding windows; The initial baseline module is suitable for determining a baseline model based on multiple cement density constraint models. The inversion module is adapted to sequentially acquire the energy spectrum data of a sliding window, invert the energy spectrum data according to the benchmark model, and obtain the inverted formation density and inverted cement density of the sliding window; based on the inverted cement density, calculate and determine the probability of each cement density constraint model that combines each cement intrusion material with solid cement within the sliding window, update the cement density constraint model with the highest probability as the benchmark model, and invert the energy spectrum data of the next sliding window according to the updated benchmark model, until the inversion of the energy spectrum data of all sliding windows is completed, and use the obtained inverted formation density and inverted cement density of all sliding windows as the formation density and cement density of the over-casing density logging environment.

8. A computing device, characterized in that, include: 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 method for detecting cementing in a casing density logging environment as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction, which causes the processor to perform the operation corresponding to the cementing detection method under the casing density logging environment as described in any one of claims 1-6.

10. A computer program product comprising at least one executable instruction that causes a processor to perform an operation corresponding to the method for detecting cementing in a casing density logging environment as described in any one of claims 1-6.

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