Method, device and computing equipment for determining the lower limit of effective reservoir physical properties

Abstract

The present application discloses a method, device and computing equipment for determining the lower limit of effective reservoir physical properties. The method includes: obtaining a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target formation; calculating the first effective porosity of the measurement point based on the first transverse relaxation time spectrum of the measurement point; extracting the second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point, and calculating the second effective porosity of the measurement point based on the second transverse relaxation time spectrum; establishing a first intersection diagram of the first effective porosity and the second effective porosity based on the first effective porosity and the second effective porosity of different measurement points; determining the inflection point of the first intersection diagram, and using the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target formation. This solution can improve the accuracy of determining the lower limit of effective reservoir physical properties, lower the data threshold, and improve the efficiency of determining the lower limit of effective reservoir physical properties.

Description

Technical Field

[0001] The present application relates to the technical field of physical property analysis, and in particular to a method, apparatus, computing device, computer storage medium, and computer program product for determining the lower limit of effective reservoir physical properties. Background Art

[0002] An effective reservoir is a rock formation that has the capacity to store oil and gas and can be economically extracted using existing technology. The lower limit of an effective reservoir's physical properties is the critical value of a reservoir's physical properties used to determine whether it is economically viable. When key reservoir parameters such as porosity and permeability fall below this critical value, it is determined that the reservoir is unlikely to produce industrially valuable oil and gas flows, or that the cost of extraction is too high to be economically viable.

[0003] The commonly used methods for determining the lower limit of effective reservoir properties in existing technologies include static methods and dynamic methods:

[0004] The parameters used in the static method are experimental results and empirical values. Since it does not take actual production factors into consideration, the lower limit values ​​of physical properties obtained by the static method in some scenarios do not match the actual situation, and the accuracy of the determination of the lower limit values ​​of physical properties is insufficient.

[0005] Dynamic methods use parameters derived from actual production processes. Existing dynamic methods typically include displacement pressure experiments, testing methods, oil testing methods, shale content methods, and drilling fluid invasion methods. However, the implementation of existing dynamic methods relies on oil testing or long-term production data, resulting in a high data threshold. Summary of the Invention

[0006] In view of the above problems, the present application is proposed to provide a method, apparatus, computing device, computer storage medium and computer program product for determining an effective lower limit of reservoir physical properties that overcomes the above problems or at least partially solves the above problems.

[0007] According to a first aspect of the present application, a method for determining the lower limit of effective reservoir physical properties is provided, comprising:

[0008] Acquiring a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target formation;

[0009] For any measurement point, calculating a first effective porosity of the measurement point according to a first transverse relaxation time spectrum of the measurement point;

[0010] For any measurement point, extracting a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point, and calculating a second effective porosity of the measurement point based on the second transverse relaxation time spectrum;

[0011] Establishing a first intersection graph of the first effective porosity and the second effective porosity according to the first effective porosity and the second effective porosity at different measurement points;

[0012] An inflection point of the first cross-plot is determined, and a first effective porosity corresponding to the inflection point is used as a lower limit of the effective porosity of the target formation.

[0013] In an optional embodiment, the method further includes: acquiring neutron logging data of a target measurement point obtained by performing neutron logging on the target formation, and acquiring density logging data of the target measurement point obtained by performing density logging on the target formation; and calculating a third effective porosity of the target measurement point based on the neutron logging data and the density logging data;

[0014] Then, for any measurement point, calculating the first effective porosity of the measurement point according to the first transverse relaxation time spectrum of the measurement point includes: calculating the initial effective porosity of any measurement point according to the first transverse relaxation time spectrum of any measurement point; calculating the offset value of the third effective porosity and the initial effective porosity of the target measurement point; and using the offset value to correct the initial effective porosity of each measurement point to obtain the first effective porosity of each measurement point.

[0015] In an optional embodiment, the extracting of the second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measuring point includes: preprocessing the first transverse relaxation time spectrum of the measuring point; performing wavelet decomposition on the preprocessed first transverse relaxation time spectrum to decompose the first transverse relaxation time spectrum into multiple sub-spectra; using a pre-trained recognition model to identify the sub-spectrum type of each sub-spectrum; wherein the sub-spectrum type is an invading mud type or a reservoir fluid type; reconstructing the sub-spectrum whose sub-spectrum type is a reservoir fluid type into a reservoir fluid spectrum; and obtaining the second transverse relaxation time spectrum of the invading mud after removing the reservoir fluid spectrum from the first transverse relaxation time spectrum of the measuring point.

[0016] In an optional embodiment, the identifying the sub-spectrum type of each sub-spectrum by using a pre-trained recognition model includes:

[0017] For any sub-spectrum, extract the sub-spectrum features of the sub-spectrum, and input the sub-spectrum features into the recognition model;

[0018] Obtaining the confidence of each sub-spectrum type output by the recognition model, and identifying the target sub-spectrum type with the highest confidence;

[0019] If the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy proportion is less than the proportion threshold, the target sub-spectrum type is used as the sub-spectrum type of the sub-spectrum;

[0020] The sub-spectrum features include at least one of the following features: peak amplitude, relaxation time, peak area ratio, wavelet coefficient energy, power spectrum density, skewness, kurtosis, and variance.

[0021] In an optional implementation, determining the inflection point of the first intersection graph includes:

[0022] Performing denoising on the data points in the first intersection graph;

[0023] Perform data fitting on the denoised data points, and determine candidate inflection points based on the data fitting results;

[0024] performing clustering processing on the denoised data points to generate high-density clusters and low-density clusters, determining the boundary sections between the high-density clusters and the low-density clusters, and eliminating candidate inflection points outside the boundary sections;

[0025] The inflection point of the first intersection graph is determined according to the remaining candidate inflection points.

[0026] In an optional embodiment, the method further includes: splitting the target formation into multiple intervals;

[0027] Then, the obtaining of the first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target formation includes: obtaining the first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target layer;

[0028] The taking the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target formation includes: taking the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target layer segment.

[0029] According to a second aspect of the present application, a device for determining a lower limit of an effective reservoir property is provided, comprising:

[0030] an acquisition module, configured to acquire a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on a target formation;

[0031] a calculation module configured to calculate, for any measurement point, a first effective porosity of the measurement point based on a first transverse relaxation time spectrum of the measurement point; and, for any measurement point, extract a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point, and calculate a second effective porosity of the measurement point based on the second transverse relaxation time spectrum;

[0032] a cross-plot generating module, configured to establish a first cross-plot of the first effective porosity and the second effective porosity according to the first effective porosity and the second effective porosity at different measurement points;

[0033] The lower limit determination module is configured to determine an inflection point of the first cross-plot, and use a first effective porosity corresponding to the inflection point as a lower limit of the effective porosity of the target formation.

[0034] In an optional embodiment, the acquisition module is used to: acquire neutron logging data of a target measurement point obtained by performing neutron logging on the target formation, and acquire density logging data of a target measurement point obtained by performing density logging on the target formation;

[0035] The calculation module is used to: calculate the third effective porosity of the target measurement point based on the neutron logging data and the density logging data; calculate the initial effective porosity of any measurement point based on the first transverse relaxation time spectrum of any measurement point; calculate the offset value between the third effective porosity and the initial effective porosity of the target measurement point; and use the offset value to correct the initial effective porosity of each measurement point to obtain the first effective porosity of each measurement point.

[0036] In an optional embodiment, the calculation module is used to: pre-process the first transverse relaxation time spectrum of the measurement point;

[0037] performing wavelet decomposition on the preprocessed first transverse relaxation time spectrum to decompose the first transverse relaxation time spectrum into a plurality of sub-spectra;

[0038] Using a pre-trained recognition model to identify the sub-spectrum type of each sub-spectrum; wherein the sub-spectrum type is an invading mud type or a reservoir fluid type;

[0039] Reconstructing a sub-spectrum whose sub-spectrum type is a reservoir fluid type into a reservoir fluid spectrum;

[0040] The second transverse relaxation time spectrum of the invading mud is obtained by removing the reservoir fluid spectrum from the first transverse relaxation time spectrum of the measuring point.

[0041] In an optional embodiment, the calculation module is used to: for any sub-spectrum, extract the sub-spectrum features of the sub-spectrum, and input the sub-spectrum features into the recognition model; obtain the confidence of each sub-spectrum type output by the recognition model, and identify the target sub-spectrum type with the highest confidence; if the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy proportion is less than the proportion threshold, then use the target sub-spectrum type as the sub-spectrum type of the sub-spectrum;

[0042] The sub-spectrum features include at least one of the following features: peak amplitude, relaxation time, peak area ratio, wavelet coefficient energy, power spectrum density, skewness, kurtosis, and variance.

[0043] In an optional embodiment, the lower limit determination module is used to: denoise the data points in the first intersection diagram; perform data fitting on the denoised data points, and determine candidate inflection points based on the data fitting results; cluster the denoised data points to generate high-density clusters and low-density clusters, and determine the boundary sections of the high-density clusters and low-density clusters, and eliminate candidate inflection points located outside the boundary sections; determine the inflection points of the first intersection diagram based on the remaining candidate inflection points.

[0044] In an optional embodiment, the apparatus further comprises: a division module for dividing the target formation into a plurality of intervals;

[0045] The acquisition module is used to: acquire a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target layer;

[0046] The lower limit determination module is used to: use the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target layer.

[0047] According to a third aspect of the present application, 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 via the communication bus;

[0048] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for determining the lower limit of effective reservoir physical properties.

[0049] According to a fourth aspect of the present application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for determining the lower limit of effective reservoir physical properties.

[0050] According to a fifth aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for determining the lower limit of effective reservoir physical properties.

[0051] The method, apparatus, computing device, computer storage medium, and computer program product provided in the present application determine the effective reservoir property lower limits based on actual nuclear magnetic resonance logging data, so that the determined physical property lower limits match the actual logging conditions, thereby improving the accuracy of determining the physical property lower limits. Moreover, the embodiments of the present application can obtain the effective reservoir property lower limits based solely on nuclear magnetic resonance logging data, thereby lowering the data threshold, increasing the scope of application of the present solution, and improving the efficiency of determining the physical property lower limits.

[0052] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0054] Figure 1 A schematic flow chart of a method for determining the lower limit of effective reservoir physical properties provided in Example 1 of the present application is shown;

[0055] Figure 2 A schematic flow chart of a method for extracting a second transverse relaxation time spectrum provided in Example 1 of the present application is shown;

[0056] Figure 3 A schematic diagram of a process for identifying sub-spectrum types provided in Example 1 of the present application is shown;

[0057] Figure 4 A schematic diagram of a process for obtaining a second transverse relaxation time spectrum provided in Example 1 of the present application is shown;

[0058] Figure 5 A schematic flow chart of a method for identifying an inflection point of a first cross-graph provided in the first embodiment of the present application is shown;

[0059] Figure 6 A schematic diagram of an inflection point in a first intersection diagram provided in Example 1 of the present application is shown;

[0060] Figure 7 A cross-plot of permeability and pressure flow provided in Example 1 of the present application is shown;

[0061] Figure 8 A schematic diagram of a second intersection diagram provided in Example 1 of the present application is shown;

[0062] Figure 9 A schematic flow chart of a method for determining the lower limit of effective reservoir physical properties provided in Example 2 of the present application is shown;

[0063] Figure 10 A schematic structural diagram of a device for determining the lower limit of effective reservoir physical properties provided in Example 3 of the present application is shown;

[0064] Figure 11A structural diagram of a computing device provided in Example 4 of the present application is shown. DETAILED DESCRIPTION

[0065] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0066] Example 1

[0067] Figure 1 FIG. 1 shows a flow chart of a method for determining the lower limit of effective reservoir physical properties provided in Example 1 of the present application. Figure 1 As shown, the method specifically includes the following steps:

[0068] Step S101 : obtaining a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on a target formation.

[0069] The target formation is the formation for which the lower limit analysis of effective reservoir properties is to be performed. Nuclear Magnetic Resonance Logging (NMR) is performed on the target formation. Based on the NMR logging data, transverse relaxation time spectra (T2 spectra) are obtained at different measurement points in the target formation. These T2 spectra are referred to as first T2 spectra. Different measurement points correspond to different logging depths.

[0070] Step S102 : For any measurement point, calculate a first effective porosity of the measurement point according to a first transverse relaxation time spectrum of the measurement point.

[0071] For each measurement point, the effective porosity of the measurement point is calculated based on the first transverse relaxation time spectrum of the measurement point. The effective porosity is referred to as the first effective porosity of the measurement point.

[0072] In an optional embodiment, the first effective porosity can be obtained by determining a T2 cutoff value for the current target formation through core centrifugation experiments, mercury injection experiments, or manual experience. The T2 cutoff value is the critical T2 time value for distinguishing between movable fluid and bound fluid. The first transverse relaxation time spectrum is then integrated starting from the T2 cutoff value and normalized after integration to obtain the first effective porosity.

[0073] In an optional embodiment, to improve the accuracy of determining the first effective porosity, this embodiment further performs neutron logging and density logging on one or more target measurement points in the target formation, thereby obtaining neutron logging data for the target measurement points obtained through neutron logging of the target formation, and density logging data for the target measurement points obtained through density logging of the target formation. A third effective porosity for the target measurement point is further calculated based on the neutron logging data and density logging data. For example, the effective porosity for the target measurement point can be determined based on the neutron logging data and density logging data of the target measurement point in combination with a neutron-density crossplot. This effective porosity is referred to herein as the third effective porosity.

[0074] And the initial effective porosity of any measuring point is calculated according to the first transverse relaxation time spectrum of any measuring point, that is, the effective porosity directly calculated according to the first transverse relaxation time spectrum is used as the initial effective porosity; and the offset value of the third effective porosity and the initial effective porosity of the target measuring point is calculated, for example, the difference between the two can be used as the offset value; finally, the initial effective porosity of each measuring point is corrected by using the offset value to obtain the first effective porosity of each measuring point, for example, the first effective porosity of the measuring point = the initial effective porosity of the measuring point + the offset value.

[0075] Further optionally, the core at the target measurement point can be extracted, and the effective porosity of the core can be detected through core experiments. The effective porosity is used as the third effective porosity at the target measurement point, and the offset is determined based on the third effective porosity. The initial effective porosity of each measurement point is corrected using the offset to obtain the first effective porosity of each measurement point.

[0076] Step S103 : for any measurement point, extracting a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point.

[0077] The first transverse relaxation time spectrum at each measurement point is the total transverse relaxation time spectrum. This first transverse relaxation time spectrum is typically a superposition of the transverse relaxation time spectrum of the reservoir fluid and the transverse relaxation time spectrum of the invading mud. This step extracts the transverse relaxation time spectrum corresponding to the invading mud from the first transverse relaxation time spectrum at each measurement point. This transverse relaxation time spectrum corresponding to the invading mud is called the second transverse relaxation time spectrum.

[0078] In an optional embodiment, in order to realize the automatic extraction of the second transverse relaxation time spectrum and improve the extraction accuracy and extraction efficiency of the second transverse relaxation time spectrum, the following method can be used: Figure 2 The steps shown are to extract the second transverse relaxation time spectrum:

[0079] Step S1031 : pre-processing the first transverse relaxation time spectrum of the measurement point.

[0080] In order to improve the extraction accuracy of the second transverse relaxation time spectrum, the first transverse relaxation time spectrum of the measurement point is first preprocessed, wherein the preprocessing includes but is not limited to: noise filtering and / or baseline correction, etc.

[0081] The noise filtering process specifically uses a corresponding filtering algorithm to filter out the noise in the first transverse relaxation time spectrum to improve the signal-to-noise ratio of the subsequent processing signal. For example, the first transverse relaxation time spectrum can be smoothed by using a Savitzky-Golay filter to retain the signal trend of the first transverse relaxation time spectrum and suppress high-frequency noise. Among them, the Savitzky-Golay filter is to fit the data points with a polynomial within a window, and then use the value of the fitting polynomial to replace the data point at the center of the window to achieve the filtering effect. Specifically, the following formula 1 can be used for filtering:

[0082] (Formula 1)

[0083] in, represents the filtered data point; represents the raw data points of the first transverse relaxation time spectrum; represents the filter coefficient; N represents the window width (which is usually an odd number, for example, it can be 5~15), N=2m+1.

[0084] Baseline correction is specifically designed to eliminate baseline offsets and enhance spectral peak identifiability. For example, asymmetric least squares methods, such as AsLS (Asymmetric Least Squares), can be used for baseline correction.

[0085] In short, this embodiment does not limit the specific preprocessing method.

[0086] Step S1032 : performing wavelet decomposition on the pre-processed first transverse relaxation time spectrum to decompose the first transverse relaxation time spectrum into a plurality of sub-spectra.

[0087] The first transverse relaxation time spectrum of the measurement point is subjected to wavelet decomposition to obtain a plurality of signal spectra, which are sub-spectra of the first transverse relaxation time spectrum.

[0088] During the specific implementation process, the wavelet basis and the number of decomposition layers used in the wavelet decomposition process are determined. Specifically, the wavelet basis and the number of decomposition layers used can be dynamically determined based on the characteristics of the first transverse relaxation time spectrum and the energy entropy minimization criterion. For example, when the first transverse relaxation time spectrum is relatively simple, the wavelet basis Daubechies-3 and the number of decomposition layers 3 can be used; when the first transverse relaxation time spectrum is relatively complex, the wavelet basis Symlet-6 and the number of decomposition layers 5 can be used. In short, those skilled in the art can select a suitable wavelet basis and the number of decomposition layers according to the actual characteristics of the first transverse relaxation time spectrum, and the embodiments of the present application do not limit the specific contents of the wavelet basis and the number of decomposition layers.

[0089] Taking a five-layer decomposition as an example, the first layer decomposition is as follows: decomposing the first transverse relaxation time spectrum into a low-frequency approximation coefficient A1 and a high-frequency detail coefficient D1; the second layer decomposition is as follows: decomposing A1 into a low-frequency approximation coefficient A2 and a high-frequency detail coefficient D2; the third layer decomposition is as follows: decomposing A2 into a low-frequency approximation coefficient A3 and a high-frequency detail coefficient D3; the fourth layer decomposition is as follows: decomposing A3 into a low-frequency approximation coefficient A4 and a high-frequency detail coefficient D4; and the fifth layer decomposition is as follows: decomposing A4 into a low-frequency approximation coefficient A5 and a high-frequency detail coefficient D5, ultimately obtaining a low-frequency approximation coefficient A5 and high-frequency detail coefficients D1, D2, D3, D4, and D5. Typically, the invading mud signal manifests as a low-frequency, long-relaxation component (such as the low-frequency approximation coefficient A5), while the reservoir fluid signal is concentrated in a high-frequency, short-relaxation region (such as the high-frequency detail coefficients D1-D3).

[0090] Step S1033: Use the pre-trained recognition model to identify the sub-spectrum type of each sub-spectrum.

[0091] Wherein, the sub-spectrum type is an invasion mud type or a reservoir fluid type. If the sub-spectrum type of the sub-spectrum is an invasion mud type, it indicates that the sub-spectrum corresponds to an invasion mud signal; if the sub-spectrum type of the sub-spectrum is a reservoir fluid type, it indicates that the sub-spectrum corresponds to a reservoir fluid signal.

[0092] During the specific implementation process, a recognition model is pre-built based on a machine learning algorithm. For example, the recognition model can be constructed and trained based on a random forest algorithm (RF), a support vector machine algorithm (SVM), or cross-validation hyperparameter optimization. During the specific training process, transverse relaxation time spectra from different actual measurement data can be obtained, and invasion mud signals and reservoir fluid signals can be extracted from the transverse relaxation time spectra. Corresponding labels are assigned to the different signals. Sample data is then generated based on the different signals and their corresponding labels, and the recognition model is trained using this sample data. Training terminates when the corresponding training termination conditions are met, resulting in a trained recognition model. Optionally, the recognition model can be incrementally learned. Specifically, new sample data is generated when new transverse relaxation time spectra are available, and the recognition model is trained using this new sample data to update model parameters, optimize the recognition model, and expand its applicability to formations with diverse geological conditions.

[0093] Further optional, for any sub-spectrum, you can use Figure 3 The steps shown identify the sub-spectrum type of the sub-spectrum:

[0094] S10331: Extract sub-spectrum features of the sub-spectrum.

[0095] To improve the accuracy of subspectral type identification, different types of subspectral features can be extracted in this step. These types can include: time domain type, frequency domain type, and statistical type. The subspectral features extracted in this step can be divided into time domain features, frequency domain features, and statistical features.

[0096] Specifically, the subspectral features include at least one of the following features: peak amplitude, relaxation time, peak area ratio, wavelet coefficient energy, power spectral density, skewness, kurtosis, and variance. Peak amplitude, relaxation time, and peak area ratio are time domain features, wavelet coefficient energy and power spectral density are frequency domain features, and skewness, kurtosis, and variance are statistical features.

[0097] S10332: Input the sub-spectrum features into the recognition model, obtain the confidence of each sub-spectrum type output by the recognition model, and identify the target sub-spectrum type with the highest confidence.

[0098] The sub-spectrum features of the sub-spectrum are input into the recognition model. After processing by the recognition model, the confidence level that the sub-spectrum is of the invading mud type and the confidence level that the sub-spectrum is of the reservoir fluid type are output. The sub-spectrum type with the highest confidence level is used as the target sub-spectrum type of the sub-spectrum.

[0099] S10333, determine whether the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy proportion is less than the proportion threshold; if so, execute step S10334.

[0100] In order to avoid excessive signal stripping and reduce signal noise, a confidence threshold and a proportion threshold are set in this embodiment. For example, the confidence threshold may be 0.8, and the proportion threshold may be 10%.

[0101] Optionally, the confidence threshold can be dynamically adjusted. Specifically, a sliding time window can be used to obtain the classification results within the current window (e.g., the most recent 10 first transverse relaxation time spectra). Based on these classification results, the original confidence threshold can be adjusted to obtain the latest confidence threshold. For example, if the proportion of invading mud type sub-spectra in the classification results within the current window is too low (lower than expected), the original confidence threshold can be increased or decreased by a certain step size to obtain a new confidence threshold.

[0102] S10334: Set the target sub-spectrum type as the sub-spectrum type of the sub-spectrum.

[0103] If the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy ratio is less than the ratio threshold, the target sub-spectrum type is used as the sub-spectrum type of the sub-spectrum. The sub-spectrum energy ratio is the ratio of the energy of the sub-spectrum to the energy of the first transverse relaxation time spectrum.

[0104] Therefore, the sub-spectrum type of each sub-spectrum can be obtained through steps S10331 to S10334.

[0105] Step S1034: reconstruct the sub-spectrum whose sub-spectrum type is the reservoir fluid type into a reservoir fluid spectrum.

[0106] All sub-spectra of reservoir fluid type are reconstructed, and all sub-spectra of invading mud type are set to zero, finally obtaining the reservoir fluid spectrum. Thus, the pure reservoir fluid response is reconstructed through inverse wavelet transform.

[0107] Step S1035 : removing the reservoir fluid spectrum from the first transverse relaxation time spectrum of the measurement point to obtain a second transverse relaxation time spectrum of the invading mud.

[0108] The transverse relaxation time spectrum obtained by removing the reservoir fluid spectrum reconstructed in step S1034 from the first transverse relaxation time spectrum is the second transverse relaxation time spectrum of the invading mud.

[0109] By executing steps S1031 to S1035 , the second transverse relaxation time spectrum of the invading mud can be accurately and automatically separated from the first transverse relaxation time spectrum.

[0110] like Figure 4 As shown, the original first transverse relaxation time spectrum is obtained through data acquisition, and the corrected first transverse relaxation time spectrum is obtained by correction processing (such as wavelet transform, clutter filtering, etc.) of the original first transverse relaxation time spectrum. Based on the corrected first transverse relaxation time spectrum, the second transverse relaxation time spectrum of the intruding mud is stripped out.

[0111] Step S104 : calculating a second effective porosity at the measurement point according to a second transverse relaxation time spectrum at the measurement point.

[0112] The effective porosity of the measuring point is calculated based on the second transverse relaxation time spectrum of the measuring point. The effective porosity is referred to as the second effective porosity of the measuring point.

[0113] In an optional embodiment, the second effective porosity may be calculated using the following formula 2:

[0114] (Formula 2)

[0115] Where MSIG represents the second effective porosity; T min represents the minimum transverse relaxation time of the second transverse relaxation time spectrum; T max represents the maximum transverse relaxation time of the second transverse relaxation time spectrum; represents the envelope area of ​​the second transverse relaxation time spectrum.

[0116] Step S105 : establishing a first intersection graph of the first effective porosity and the second effective porosity according to the first effective porosity and the second effective porosity at different measurement points.

[0117] For each measurement point, the first effective porosity and the second effective porosity of the measurement point are treated as a data point, and a first cross-plot is then plotted based on each data point. For example, the first effective porosity can be used as the abscissa and the second effective porosity as the ordinate to construct the first cross-plot. That is, the first cross-plot contains many data points, with the abscissa of each data point being the first effective porosity and the ordinate being the second effective porosity.

[0118] Step S106 : determining the inflection point of the first cross-plot, and taking the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target formation.

[0119] During drilling fluid logging, when physical invasion dominates, drilling mud infiltrates into the pores and fractures of the formation rock due to pressure differentials. This process is also known as filtration. This process partially displaces existing fluids (such as groundwater, oil, and gas) in the formation pores. Reservoir fluids have a certain degree of flow exchange capacity, and the degree to which the signal is affected by invasion is positively correlated with physical properties. This is reflected in the first crossplot as a positive correlation between the first and second effective porosity. In this stage, the reservoir has economic production value. However, when the formation is dense, the reservoir fluid has no or negligible flow exchange capacity. In this case, capillary invasion dominates. In small pores and fractures, mud can migrate upward or downward along the capillaries due to its surface tension. When capillary invasion dominates, the degree to which the signal is affected by invasion is linearly stable. This is reflected in the first crossplot as the second effective porosity remains stable as the first effective porosity increases. In this case, the reservoir is generally considered uneconomical.

[0120] The switching point between physical invasion dominance and capillary invasion dominance is the critical point corresponding to the lower limit of the effective reservoir physical property, which is represented by the inflection point in the first crossplot. Therefore, this step identifies the inflection point of the first crossplot, and the first effective porosity corresponding to this inflection point is used as the lower limit of the effective porosity.

[0121] In an optional embodiment, the inflection point of the first intersection graph can be specifically Figure 5 The steps shown identify:

[0122] S1061: Perform denoising processing on the data points in the first intersection graph.

[0123] The corresponding denoising algorithm (such as sliding average algorithm, Savitzky-Golay filter, etc.) is used to denoise the data points in the first intersection map to reduce noise interference.

[0124] S1062: Perform data fitting on the denoised data points, and determine candidate inflection points based on the data fitting results.

[0125] The candidate inflection points can be determined in the following ways:

[0126] Determination method 1: Divide the data points into multiple data segments (e.g., every N data points is divided into one data segment), and perform a linear fit on each data segment. The slope of each data point can be determined based on the linear fit results. For any data point, calculate the slope change rate of that data point compared to the previous data point. The slope change rate corresponding to each data point is obtained using the following formula 3:

[0127] (Formula 3)

[0128] in, Indicates the rate of change of the slope corresponding to data point i+1; represents the slope of data point i+1; Represents the slope of data point i; data point i and data point i+1 are two adjacent data points, and the horizontal coordinate of data point i+1 is greater than the horizontal coordinate of data point i.

[0129] If the slope change rate corresponding to a data point exceeds a first threshold, the data point is determined as a candidate inflection point. The first threshold can be determined dynamically, for example, by calculating the average of the slope change rates of the first 10% and determining twice the average as the first threshold.

[0130] Method 2: Fit a polynomial to the data points and calculate the second-order derivative or curvature of the data points based on the polynomial fit results. Specifically, the data points can be divided into multiple data segments using a sliding window or fixed segmentation method. A quadratic polynomial is fitted to each data segment, and the second-order derivative or curvature of each data point can be calculated based on the fitting results.

[0131] The curvature can be calculated using Formula 4:

[0132] (Formula 4)

[0133] in, represents the curvature of data point i; represents the second-order derivative of data point i; represents the first derivative of data point i.

[0134] If the absolute value of the second-order derivative or curvature of a data point is greater than a second threshold, the data point is determined to be a candidate inflection point. This second threshold can be determined dynamically. For example, the variance or standard deviation of the second-order derivative of the data point within the sliding window (the window length is typically 5% * the total number of data points) in which the data point currently resides is determined, and a preset multiple (e.g., 3) of this variance or standard deviation is used as the second threshold.

[0135] In addition, further optionally, the first threshold, the second threshold, the window length, etc. may also be manually calibrated.

[0136] S1063 , clustering the denoised data points to generate high-density clusters and low-density clusters, determining the boundary segments between the high-density clusters and the low-density clusters, and eliminating candidate inflection points outside the boundary segments.

[0137] Through the analysis of a large amount of data, it was found that in the section where the second effective porosity does not change with the first effective porosity, the data points are relatively concentrated; in the section where the second effective porosity changes in a positive correlation with the first effective porosity, the data points are relatively dispersed. Based on this characteristic, this step uses a clustering processing algorithm to cluster the data points after denoising (such as using the DBSCAN clustering algorithm). After clustering, the data clusters with high data point density are regarded as high-density clusters, and the data clusters with low data point density are regarded as low-density clusters. The inflection points of the first directed graph should be distributed in the boundary area of ​​the two clusters, so this step further determines the boundary section of the two clusters. Specifically, there will be overlap between the data clusters generated by the DBSCAN clustering algorithm. The overlapping part of the high-density cluster and the low-density cluster, or the area obtained by expanding outward by a preset proportion with the overlapping part as the center, is the boundary area of ​​the two clusters.

[0138] In an optional embodiment, after obtaining the data clusters, the cluster separation degree may be calculated. If the cluster separation degree meets the preset conditions, the clustering is terminated; if the cluster separation degree does not meet the preset conditions, the clustering parameters are readjusted to perform clustering processing.

[0139] The candidate inflection points outside the boundary section are further eliminated to obtain the remaining candidate inflection points.

[0140] S1064: Determine the inflection point of the first intersection graph based on the remaining candidate inflection points.

[0141] For example, the remaining candidate inflection points can be visually annotated in the first intersection graph, and the curvature strength of each candidate inflection point can be displayed for manual reference in determining the final inflection point; the candidate inflection point with the largest curvature or curvature strength (for example, the normalized value of the curvature can be used as the curvature strength) can also be directly used as the inflection point of the first intersection graph. Figure 6 As shown in the figure, the first cross-plot is plotted with the first effective porosity as the horizontal axis and the second effective porosity as the vertical axis. The first cross-plot contains many data points. When the first effective porosity is less than 15 (corresponding to Figure 6 In the middle A area), the second effective porosity does not increase with the increase of the first effective porosity; after the first effective porosity is greater than 15 (corresponding to Figure 6 (Area B in the middle) shows that the second effective porosity increases as the first effective porosity increases. This indicates that the inflection point of the first crossplot is located near the first effective porosity = 15.

[0142] In an optional embodiment, after obtaining the effective porosity lower limit of the target formation, other physical property lower limits of the target formation can also be determined based on the effective porosity lower limit, such as the nuclear magnetic total porosity lower limit, the nuclear magnetic movable porosity lower limit, and / or the permeability lower limit.

[0143] Further optionally, the permeability lower limit may include a KSDR (Permeability from Schlumberger-Doll Research Model) lower limit and / or a KTIM (Permeability from Timur Model) lower limit.

[0144] In the specific implementation process, the permeability (including KSDR and / or KTIM) of any measurement point in the target formation can be determined based on the NMR logging data. Optionally, in order to improve the permeability accuracy, the mobility of the effective pressure measurement point of the target formation is also obtained, and the permeability obtained based on the NMR logging data is corrected using the effective pressure measurement point mobility. Subsequently, the lower limit of the physical property is determined based on the corrected permeability. Figure 7 As shown in Figure 2, different types of nuclear magnetic permeability are obtained based on nuclear magnetic resonance logging data, such as Figure 7 The KSDR, KTIM, KSDR-overpressure, and KTIM-overpressure data are used to obtain the measured pressure mobility at the measurement point in the target formation, and then a cross-plot of the nuclear magnetic permeability and measured mobility is constructed. Ideally, the data points should be distributed along a straight line with a slope of 1, but in reality, the data points will deviate from this line. Therefore, during the calibration process, permeability can be corrected using linear, nonlinear, or segmented calibration methods. This embodiment does not limit the specific calibration algorithm.

[0145] Furthermore, a second cross-plot of the first effective porosity and permeability is constructed. The permeability corresponding to the lower limit of the effective porosity is determined from the second cross-plot, and the corresponding permeability is used as the lower limit of the permeability. Figure 8 As shown, Figure 8 The middle horizontal line corresponds to the lower limit of effective porosity. The permeability corresponding to the point on the horizontal line is the permeability corresponding to the lower limit of effective porosity. The statistical parameters (such as minimum value) in the permeability corresponding to the lower limit of effective porosity can be used as the lower limit of permeability.

[0146] It can be seen that the method for determining the effective reservoir physical property lower limit provided in the embodiment of the present application determines the effective reservoir physical property lower limit based on actual nuclear magnetic resonance logging data, so that the determined physical property lower limit matches the actual logging conditions, thereby improving the accuracy of determining the physical property lower limit; moreover, the embodiment of the present application can obtain the effective reservoir physical property lower limit based only on nuclear magnetic resonance logging data, thereby lowering the data threshold, increasing the scope of application of this solution, and improving the efficiency of determining the physical property lower limit.

[0147] Example 2

[0148] Figure 9 A flow chart of a method for determining the lower limit of effective reservoir physical properties provided in Example 2 of the present application is shown.

[0149] like Figure 9 As shown, the method specifically includes the following steps:

[0150] Step S901: Divide the target stratum into multiple layers.

[0151] In actual implementation, many formations exhibit significant vertical heterogeneity, meaning that reservoir properties (porosity, permeability, shale content, etc.) vary significantly at different depths or levels. To improve the accuracy of determining the effective reservoir property lower limits, this embodiment first divides the target formation into multiple segments based on their vertical heterogeneity, and then determines the effective reservoir property lower limits for each segment. For example, this can be based on differences in geological characteristics (such as changes in sedimentary facies, diagenetic intensity, and lithologic combinations), logging response characteristics (such as differences in nuclear magnetic resonance T2 spectral morphology and breakpoints in conventional logging curves), and dynamic production data (such as test oil production, segmentation characteristics of pressure buildup curves, and zoning patterns of drilling fluid loss or formation test permeability). When segmenting the target formation based on logging data, different types of logging data can be obtained and preprocessed. Segmentation can then be performed based on this preprocessed data. The preprocessing may specifically include data alignment (such as depth alignment of different well logging data) and outlier removal (such as using a box plot or the 3σ principle to filter outliers).

[0152] In an optional embodiment, the target stratum may be divided by combining one or more of the following division methods:

[0153] Division method 1: Static division. Specifically, for highly homogeneous target formations, a fixed thickness division method can be used. For example, the target formation can be divided into multiple segments at fixed depth intervals (e.g., 5 meters). Alternatively, various well logging curves can be obtained for the target formation, such as natural gamma ray curves, resistivity curves, porosity curves, spontaneous potential curves, permeability curves, and T2 spectrum characteristic value curves (e.g., peak position, peak area, peak width, etc.). Segmentation points can be determined based on the sudden changes in the logging curves. For example, a natural gamma ray value of less than 60 can be classified as a sandstone segment, or a resistivity sudden change of 10 Ω·m can be used as a segmentation point.

[0154] Partitioning method 2: Dynamic partitioning. Specifically, well logging data for the target formation, such as natural gamma ray logging and resistivity logging data, can be obtained. Clustering (e.g., K-means clustering or hierarchical clustering) can be performed on the logging data at different depths, with each cluster identified as a segment. Alternatively, a partitioning model can be pre-trained using a machine learning algorithm (e.g., random forest or LSTM) to extract a sequence of well logging signatures for the target formation. This sequence includes logging characteristics at different logging depths, such as T2 spectral features (e.g., peak position, peak area, peak width, etc.). The extracted logging signature sequence is then fed into the partitioning model to generate segmentation results and corresponding confidence levels.

[0155] Method 3: Comprehensive classification. Specifically, geological data, well logging data, and dynamic data can be combined to determine stratification weights using entropy weighting or principal component analysis. For example, empirical data from an expert rule base (e.g., "low GR + high resistivity + porosity >8%)" can be used to assist in automatic stratification.

[0156] In an optional embodiment, after adopting the above-mentioned division method, the layer thickness of each segment is determined. If the layer thickness of a layer segment is less than a preset layer thickness threshold (such as 0.5 meters), the layer segment is merged with the adjacent layer segment. The divided layer segments can also be adjusted in combination with the address rules, such as avoiding forced segmentation in continuous sandstone.

[0157] Step S902: determine the target layer segment.

[0158] Select one of the layers for which the lower limit of effective reservoir properties has not yet been determined as the target layer.

[0159] Step S903, obtaining a first transverse relaxation time spectrum of any measuring point obtained by performing nuclear magnetic resonance logging on the target layer; for any measuring point, calculating a first effective porosity of the measuring point based on the first transverse relaxation time spectrum of the measuring point; for any measuring point, extracting a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measuring point, and calculating a second effective porosity of the measuring point based on the second transverse relaxation time spectrum; establishing a first cross-plot of the first effective porosity and the second effective porosity based on the first effective porosity and the second effective porosity of different measuring points; determining an inflection point of the first cross-plot, and using the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target layer.

[0160] The process of determining the lower limit of effective porosity and other physical property limits of the target layer can refer to the relevant description in Example 1 and will not be repeated here.

[0161] Step S904, determine whether the current layer segment has been analyzed; if so, execute step S905; if not, execute step S902.

[0162] If the effective reservoir property lower limits have been determined for all the currently segmented layers, step S905 is executed; if there are still layers for which the effective reservoir property lower limits have not been determined, step S902 is executed to take the layers for which the physical property lower limits have not been determined as the target layers.

[0163] Step S905: Visually display the lower limits of physical properties of each layer.

[0164] Specifically, different layer segmentation data (such as reservoir classification and depth range) and the physical property lower limits of each layer (such as different types of porosity lower limits and permeability lower limits, etc.) can be displayed in three-dimensional or two-dimensional formats in combination with well trajectory data.

[0165] In an optional embodiment, after obtaining the lower limits of the physical properties of each layer segment, if the lower limit of the physical property of a layer segment undergoes a sudden change (such as exceeding a preset proportion of the lower limit of the physical property of an adjacent layer segment), step S901 is re-executed to re-segment the target formation, or to determine whether the lower limit of the physical property and the data are abnormal.

[0166] It can be seen that the method for determining the lower limit of effective reservoir physical properties provided in the embodiment of the present application divides the target formation into multiple layers, and determines the lower limit of effective reservoir physical properties for each layer respectively, thereby further improving the accuracy of determining the lower limit of effective reservoir physical properties.

[0167] Example 3

[0168] Figure 10 FIG. 1 shows a schematic diagram of a structure of an effective reservoir property lower limit determination device provided in Example 3 of the present application. Figure 10 As shown, the apparatus 1000 includes: an acquisition module 1010 , a calculation module 1020 , a cross-graph generation module 1030 , and a lower limit determination module 1040 .

[0169] An acquisition module 1010 is configured to acquire a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on a target formation;

[0170] A calculation module 1020 is configured to calculate, for any measurement point, a first effective porosity of the measurement point based on a first transverse relaxation time spectrum of the measurement point; extract, for any measurement point, a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point, and calculate a second effective porosity of the measurement point based on the second transverse relaxation time spectrum;

[0171] A cross-plot generating module 1030 is configured to establish a first cross-plot of the first effective porosity and the second effective porosity based on the first effective porosity and the second effective porosity at different measurement points;

[0172] The lower limit determination module 1040 is configured to determine an inflection point of the first cross-plot, and use a first effective porosity corresponding to the inflection point as a lower limit of the effective porosity of the target formation.

[0173] In an optional embodiment, the acquisition module 1010 is configured to: acquire neutron logging data of a target measurement point obtained by performing neutron logging on the target formation, and acquire density logging data of a target measurement point obtained by performing density logging on the target formation;

[0174] The calculation module 1020 is configured to calculate a third effective porosity of a target measurement point based on the neutron logging data and the density logging data;

[0175] The initial effective porosity of any measuring point is calculated based on the first transverse relaxation time spectrum of any measuring point; the third effective porosity of the target measuring point and the offset value of the initial effective porosity are calculated; and the initial effective porosity of each measuring point is corrected using the offset value to obtain the first effective porosity of each measuring point.

[0176] In an optional embodiment, the calculation module 1020 is configured to: pre-process the first transverse relaxation time spectrum of the measurement point;

[0177] performing wavelet decomposition on the preprocessed first transverse relaxation time spectrum to decompose the first transverse relaxation time spectrum into a plurality of sub-spectra;

[0178] Using a pre-trained recognition model to identify the sub-spectrum type of each sub-spectrum; wherein the sub-spectrum type is an invading mud type or a reservoir fluid type;

[0179] Reconstructing a sub-spectrum whose sub-spectrum type is a reservoir fluid type into a reservoir fluid spectrum;

[0180] The second transverse relaxation time spectrum of the invading mud is obtained by removing the reservoir fluid spectrum from the first transverse relaxation time spectrum of the measuring point.

[0181] In an optional embodiment, the calculation module 1020 is configured to: for any sub-spectrum, extract sub-spectrum features of the sub-spectrum, and input the sub-spectrum features into the recognition model;

[0182] Obtaining the confidence of each sub-spectrum type output by the recognition model, and identifying the target sub-spectrum type with the highest confidence;

[0183] If the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy proportion is less than the proportion threshold, the target sub-spectrum type is used as the sub-spectrum type of the sub-spectrum;

[0184] The sub-spectrum features include at least one of the following features: peak amplitude, relaxation time, peak area ratio, wavelet coefficient energy, power spectrum density, skewness, kurtosis, and variance.

[0185] In an optional implementation, the lower limit determination module 1040 is configured to: perform denoising on the data points in the first intersection graph;

[0186] Perform data fitting on the denoised data points, and determine candidate inflection points based on the data fitting results;

[0187] performing clustering processing on the denoised data points to generate high-density clusters and low-density clusters, determining the boundary sections between the high-density clusters and the low-density clusters, and eliminating candidate inflection points outside the boundary sections;

[0188] The inflection point of the first intersection graph is determined according to the remaining candidate inflection points.

[0189] In an optional embodiment, the apparatus further comprises: a division module (not shown in the figure), configured to divide the target formation into a plurality of intervals;

[0190] The acquisition module 1010 is used to: acquire a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target layer;

[0191] The lower limit determination module 1040 is configured to use the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target layer.

[0192] It can be seen that the effective reservoir physical property lower limit determination device provided in the embodiment of the present application determines the effective reservoir physical property lower limit based on actual nuclear magnetic resonance logging data, so that the determined physical property lower limit matches the actual logging conditions, thereby improving the accuracy of determining the physical property lower limit; moreover, the embodiment of the present application can obtain the effective reservoir physical property lower limit based only on nuclear magnetic resonance logging data, thereby lowering the data threshold, increasing the scope of application of this solution, and improving the efficiency of determining the physical property lower limit.

[0193] Example 4

[0194] Figure 11 The schematic diagram of the structure of a computing device provided in the fourth embodiment of the present application is shown. The specific embodiments of the present application do not limit the specific implementation of the computing device.

[0195] like Figure 11 As shown, the computing device may include a processor 1102 , a communication interface 1104 , a memory 1106 , and a communication bus 1108 .

[0196] Processor 1102, communication interface 1104, and memory 1106 communicate with each other via communication bus 1108. Communication interface 1104 is used to communicate with other devices, such as client devices or other server network elements. Processor 1102 is used to execute program 1110, which may specifically perform the steps described in the embodiment of the method for determining the lower limit of effective reservoir properties for a computing device.

[0197] Specifically, the program 1110 may include program codes, which include computer operation instructions.

[0198] Processor 1102 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in a computing device 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.

[0199] Memory 1106 is used to store program 1110. Memory 1106 may include high-speed RAM memory, or may also include non-volatile memory, such as at least one disk memory. Program 1110 may be specifically used to enable processor 1102 to perform the operations in the above method embodiment.

[0200] Example 5

[0201] Embodiment 5 of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, which enables a processor to perform operations corresponding to the method for determining the lower limit of effective reservoir properties in any of the above method embodiments.

[0202] Example 6

[0203] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, which can enable a processor to perform operations corresponding to the method for determining the lower limit of effective reservoir properties in any of the above method embodiments.

[0204] In summary, according to the computing device, computer storage medium, and computer program product provided in this embodiment, the effective reservoir physical property lower limits are determined based on actual nuclear magnetic resonance logging data, so that the determined physical property lower limits match the actual logging conditions, thereby improving the accuracy of determining the physical property lower limits. Moreover, the embodiment of the present application can obtain the effective reservoir physical property lower limits based solely on nuclear magnetic resonance logging data, thereby lowering the data threshold, increasing the scope of application of this solution, and improving the efficiency of determining the physical property lower limits.

[0205] The algorithm or demonstration provided here are not inherently relevant to any particular computer, virtual system or other equipment. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the present application.

[0206] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0207] Similarly, it should be understood that in order to streamline the present application and facilitate understanding of one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed approach should not be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present application.

[0208] Those skilled in the art will appreciate that the modules in the devices of the embodiments can be adaptively modified and installed in one or more devices different from the embodiments. The modules, units, or components in the embodiments can be combined into a single module, unit, or component, and furthermore, they can be divided into multiple sub-modules, sub-units, or sub-components. All features disclosed in this specification (including the accompanying claims, abstract, and drawings), and all processes or units of any method or device disclosed therein, can be combined in any combination, unless at least some of such features and / or processes or units are mutually exclusive. 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.

[0209] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0210] The various component embodiments of the present application 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 appreciate that in practice, a microprocessor or digital signal processor (DSP) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present application. The present application can also be implemented as a device or apparatus program (e.g., a computer program or computer program product) for performing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium or 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.

[0211] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim 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 may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. A method for determining the lower limit of effective reservoir physical properties, characterized in that: include: Acquiring a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target formation; For any measurement point, calculating a first effective porosity of the measurement point according to a first transverse relaxation time spectrum of the measurement point; For any measurement point, extracting a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point, and calculating a second effective porosity of the measurement point based on the second transverse relaxation time spectrum; Establishing a first intersection graph of the first effective porosity and the second effective porosity according to the first effective porosity and the second effective porosity at different measurement points; determining an inflection point of the first cross-plot, and using a first effective porosity corresponding to the inflection point as a lower limit of the effective porosity of the target formation; The step of extracting the second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measuring point includes: Preprocessing the first transverse relaxation time spectrum of the measurement point; performing wavelet decomposition on the preprocessed first transverse relaxation time spectrum to decompose the first transverse relaxation time spectrum into a plurality of sub-spectra; For any sub-spectrum, extract the sub-spectrum features of the sub-spectrum, and input the sub-spectrum features into a pre-trained recognition model; obtain the confidence of each sub-spectrum type output by the recognition model, and identify the target sub-spectrum type with the highest confidence; if the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy ratio is less than the ratio threshold, use the target sub-spectrum type as the sub-spectrum type of the sub-spectrum; wherein the sub-spectrum type is an intrusion mud type or a reservoir fluid type; the sub-spectrum features include time domain features, frequency domain features, and statistical features; The sub-spectrum of the reservoir fluid type is reconstructed into a reservoir fluid spectrum; and the second transverse relaxation time spectrum of the invading mud is obtained after the reservoir fluid spectrum is removed from the first transverse relaxation time spectrum of the measurement point.

2. The method according to claim 1, characterized in that The method further comprises: Acquiring neutron logging data of a target measurement point obtained by performing neutron logging on the target formation, and acquiring density logging data of the target measurement point obtained by performing density logging on the target formation; and calculating a third effective porosity of the target measurement point based on the neutron logging data and the density logging data; Then, for any measurement point, calculating the first effective porosity of the measurement point according to the first transverse relaxation time spectrum of the measurement point includes: The initial effective porosity of any measuring point is calculated based on the first transverse relaxation time spectrum of any measuring point; the third effective porosity of the target measuring point and the offset value of the initial effective porosity are calculated; and the initial effective porosity of each measuring point is corrected using the offset value to obtain the first effective porosity of each measuring point.

3. The method according to claim 1, characterized in that The sub-spectrum features include at least one of the following features: peak amplitude, relaxation time, peak area ratio, wavelet coefficient energy, power spectrum density, skewness, kurtosis, and variance.

4. The method according to any one of claims 1 to 3, characterized in that Determining the inflection point of the first intersection graph includes: Performing denoising on the data points in the first intersection graph; Perform data fitting on the denoised data points, and determine candidate inflection points based on the data fitting results; performing clustering processing on the denoised data points to generate high-density clusters and low-density clusters, determining the boundary sections between the high-density clusters and the low-density clusters, and eliminating candidate inflection points outside the boundary sections; The inflection point of the first intersection graph is determined according to the remaining candidate inflection points.

5. The method according to any one of claims 1 to 3, characterized in that The method further includes: splitting the target formation into a plurality of intervals; Then, the obtaining of the first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target formation includes: obtaining the first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on the target layer; The taking the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target formation includes: taking the first effective porosity corresponding to the inflection point as the lower limit of the effective porosity of the target layer segment.

6. A device for determining the lower limit of effective reservoir physical properties, characterized in that: include: an acquisition module, configured to acquire a first transverse relaxation time spectrum of any measurement point obtained by performing nuclear magnetic resonance logging on a target formation; a calculation module configured to calculate, for any measurement point, a first effective porosity of the measurement point based on a first transverse relaxation time spectrum of the measurement point; and, for any measurement point, extract a second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measurement point, and calculate a second effective porosity of the measurement point based on the second transverse relaxation time spectrum; a cross-plot generating module, configured to establish a first cross-plot of the first effective porosity and the second effective porosity according to the first effective porosity and the second effective porosity at different measurement points; a lower limit determination module, configured to determine an inflection point of the first cross-plot, and use a first effective porosity corresponding to the inflection point as a lower limit of the effective porosity of the target formation; The step of extracting the second transverse relaxation time spectrum of the invading mud from the first transverse relaxation time spectrum of the measuring point includes: Preprocessing the first transverse relaxation time spectrum of the measurement point; performing wavelet decomposition on the preprocessed first transverse relaxation time spectrum to decompose the first transverse relaxation time spectrum into a plurality of sub-spectra; For any sub-spectrum, extract the sub-spectrum features of the sub-spectrum, and input the sub-spectrum features into a pre-trained recognition model; obtain the confidence of each sub-spectrum type output by the recognition model, and identify the target sub-spectrum type with the highest confidence; if the confidence of the target sub-spectrum type is greater than the confidence threshold and the sub-spectrum energy ratio is less than the ratio threshold, use the target sub-spectrum type as the sub-spectrum type of the sub-spectrum; wherein the sub-spectrum type is an intrusion mud type or a reservoir fluid type; the sub-spectrum features include time domain features, frequency domain features, and statistical features; The sub-spectrum of the reservoir fluid type is reconstructed into a reservoir fluid spectrum; and the second transverse relaxation time spectrum of the invading mud is obtained after the reservoir fluid spectrum is removed from the first transverse relaxation time spectrum of the measurement point.

7. A computing device, characterized in that include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the method for determining the lower limit of effective reservoir physical properties according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the method for determining the lower limit of effective reservoir physical properties according to any one of claims 1 to 5.

9. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the method for determining the lower limit of effective reservoir physical properties according to any one of claims 1 to 5.

Images