Reservoir effectiveness evaluation method and device based on acoustic logging data

Abstract

The present application discloses a reservoir effectiveness evaluation method and device based on acoustic logging data, which relates to the field of acoustic logging. The method includes: calculating the formation Stoneley wave amplitude and dipole shear wave spectrum energy for array acoustic logging data collected in a bare hole; analyzing the reservoir effectiveness based on the Stoneley wave amplitude and dipole shear wave spectrum energy, integrating electrical imaging logging and other data; establishing a reservoir effectiveness evaluation plate with the Stoneley wave amplitude as the horizontal coordinate and the dipole shear wave spectrum energy as the vertical coordinate, and dividing the plate into a preset number of reservoir effectiveness evaluation intervals; after processing the array acoustic logging data corresponding to the well to be evaluated, the reservoir effectiveness is evaluated according to the area where the Stoneley wave amplitude and the dipole shear wave spectrum energy are located in the plate. Therefore, the present application can effectively improve the accuracy of tight reservoir effectiveness evaluation and improve the evaluation efficiency, and solve the problems of complex evaluation process, long time consumption and poor universality of the current commonly used reservoir evaluation technology.

Description

Technical Field

[0001] The present application relates to the field of acoustic logging, and in particular to a reservoir effectiveness evaluation method and device based on acoustic logging data. Background Art

[0002] In recent years, with the continuous deepening of exploration and development, tight reservoirs have become a key area for increasing oil and gas reserves and production in my country. Actual exploration has found that under a tight background, effective reservoirs with relatively good physical properties and oil and gas content often have relatively developed fractures. Therefore, the effectiveness of fractures has become the key to evaluating the effectiveness of tight reservoirs.

[0003] At present, reservoir effectiveness evaluation mainly uses conventional logging, imaging logging, array acoustic wave, geomechanics and other methods to comprehensively evaluate reservoir effectiveness, which has the disadvantages of complex evaluation process, long time consumption and poor universality. Therefore, a method that can quickly and accurately evaluate reservoir effectiveness is needed. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a reservoir effectiveness evaluation method and device based on acoustic logging data that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of an embodiment of the present application, a reservoir effectiveness evaluation method based on acoustic logging data is provided, the method comprising:

[0006] Acquire array acoustic logging data corresponding to a target depth interval in an open hole, wherein the array acoustic logging data includes: monopole full-wave waveform data and dipole shear wave waveform data;

[0007] Performing data processing on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum;

[0008] Reservoir effectiveness analysis is performed based on the target Stoneley wave amplitude, target dipole shear wave spectrum energy and electrical imaging logging data corresponding to the target depth interval in the open hole, and multiple reservoir effectiveness evaluation results are obtained, wherein the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, dipole shear wave spectrum energy interval range;

[0009] A reservoir effectiveness evaluation chart is established with the Stoneley wave amplitude as the abscissa and the dipole shear wave spectrum energy as the ordinate, and the reservoir effectiveness evaluation chart is divided into a preset number of reservoir effectiveness evaluation intervals according to multiple reservoir effectiveness evaluation results;

[0010] The array acoustic logging data corresponding to the target depth interval in the well to be evaluated is obtained, and the array acoustic logging data is processed to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated. The target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated are projected onto the reservoir effectiveness evaluation plate to determine the reservoir effectiveness level corresponding to the well to be evaluated.

[0011] Optionally, calculating the target Stoneley wave amplitude based on the unipolar full-wave waveform data further comprises:

[0012] Processing the monopolar full-wave waveform data using a first preset processing method to calculate the Stoneley wave time difference;

[0013] The Stoneley wave arrival time is calculated based on the Stoneley wave time difference, borehole fluid velocity, and acoustic logging instrument structure data;

[0014] The target Stoneley wave amplitude is calculated based on the Stoneley wave arrival time and monopolar full-wave waveform data.

[0015] Optionally, the array acoustic logging data is received by N receivers in the acoustic logging instrument;

[0016] According to the Stoneley wave arrival time and the monopolar full-wave waveform data, the target Stoneley wave amplitude is calculated further including:

[0017] The root mean square method is used to calculate the amplitude of the monopolar full-wave waveform data received on the i-th receiver at any depth point, and the root mean square Stoneley wave amplitude in the monopolar full-wave waveform data received on the i-th receiver is obtained;

[0018] The root mean square Stoneley wave amplitudes in the monopolar full-wave waveform data received on N receivers are added and averaged to obtain the average Stoneley wave amplitude at the depth point;

[0019] According to the maximum average Stoneley wave amplitude in the target depth interval, the average Stoneley wave amplitude of each depth point in the target depth interval is normalized to obtain the target Stoneley wave amplitude.

[0020] Optionally, calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum further includes:

[0021] The dipole shear wave spectrum at any depth point is added and averaged to obtain the dipole shear wave average spectrum corresponding to the depth point;

[0022] Calculate the average energy of the dipole shear wave spectrum corresponding to the depth point within the preset frequency band according to the dipole shear wave average spectrum;

[0023] According to the maximum average energy of the dipole shear wave spectrum in the target depth interval, the average energy of the dipole shear wave spectrum at each depth point in the target depth interval is normalized to obtain the target dipole shear wave spectrum energy.

[0024] Optionally, calculating the average energy of the dipole shear wave spectrum corresponding to the depth point within the preset frequency band according to the dipole shear wave average spectrum further includes:

[0025] The average energy of the dipole shear wave spectrum is calculated using the following formula:

[0026]

[0027]

[0028] in, is the average energy of the dipole shear wave spectrum at any depth point; f2 is the upper limit frequency of the preset frequency band; f1 is the lower limit frequency of the preset frequency band; is the average spectrum of dipole shear waves at any depth point; is the dipole shear wave waveform data received on the i-th receiver, N is the total number of receivers, and FFT{·} represents the dipole shear wave spectrum obtained by fast Fourier transforming the waveform in brackets {·};

[0029] According to the maximum dipole shear wave spectrum average energy in the target depth interval, the dipole shear wave spectrum average energy at each depth point in the target depth interval is normalized to obtain the target dipole shear wave spectrum energy further including:

[0030] The target dipole shear wave spectrum energy is calculated according to the following formula:

[0031]

[0032] in, It is the maximum value of the average energy of the dipole shear wave spectrum within the target depth interval; is the target dipole shear wave spectrum energy.

[0033] Optionally, the reservoir effectiveness level includes: a first reservoir effectiveness level, a second reservoir effectiveness level, and a third reservoir effectiveness level;

[0034] The reservoir effectiveness evaluation interval corresponding to the first reservoir effectiveness level is: Stoneley wave amplitude : <0.23, dipole shear wave spectrum energy : 0.28 <0.7 interval;

[0035] The reservoir effectiveness evaluation interval corresponding to the second reservoir effectiveness level is: Stoneley wave amplitude : <0.38, dipole shear wave spectrum energy : 0.12 <0.28 interval;

[0036] The reservoir effectiveness evaluation interval corresponding to the third reservoir effectiveness level is: Stoneley wave amplitude : <0.6, dipole shear wave spectrum energy : 0.1 The interval is <0.2.

[0037] According to another aspect of an embodiment of the present application, a reservoir effectiveness evaluation device based on acoustic logging data is provided, the device comprising:

[0038] An acquisition module is adapted to acquire array acoustic logging data corresponding to a target depth interval in a naked hole, wherein the array acoustic logging data includes: monopolar full-wave waveform data and dipole shear wave waveform data;

[0039] A data processing module is suitable for performing data processing on array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum;

[0040] The reservoir effectiveness analysis module is suitable for performing reservoir effectiveness analysis based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole, and obtaining multiple reservoir effectiveness evaluation results, wherein the reservoir effectiveness evaluation results include: the reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, the dipole shear wave spectrum energy interval range;

[0041] Establishing a module, suitable for establishing a reservoir effectiveness evaluation chart with Stoneley wave amplitude as the horizontal coordinate and dipole shear wave spectrum energy as the vertical coordinate, and dividing the reservoir effectiveness evaluation chart into a preset number of reservoir effectiveness evaluation intervals according to multiple reservoir effectiveness evaluation results;

[0042] The evaluation module is suitable for obtaining array acoustic logging data corresponding to the target depth interval in the well to be evaluated, and triggering the data processing module to process the array acoustic logging data to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, and projecting the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated onto the reservoir effectiveness evaluation plate to determine the reservoir effectiveness level corresponding to the well to be evaluated.

[0043] According to another aspect of the embodiment of the present application, there is provided a computing device, including: 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;

[0044] 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 reservoir effectiveness evaluation method based on acoustic logging data.

[0045] According to another aspect of the embodiment of the present application, a computer storage medium is provided, in which at least one executable instruction is stored, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned reservoir effectiveness evaluation method based on acoustic logging data.

[0046] According to another 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 reservoir effectiveness evaluation method based on acoustic logging data.

[0047] According to the technical solution provided in the present application, array acoustic logging data corresponding to a target depth interval in an open hole is obtained, wherein the array acoustic logging data includes: monopole full-wave waveform data and dipole shear wave waveform data; data processing is performed on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum; reservoir effectiveness analysis is performed based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole, and multiple reservoir effectiveness evaluation results are obtained, wherein the reservoir effectiveness The evaluation results include: the reservoir effectiveness level and the corresponding Stoneley wave amplitude range and dipole shear wave spectrum energy range; using the Stoneley wave amplitude as the horizontal coordinate and the dipole shear wave spectrum energy as the vertical coordinate, a reservoir effectiveness evaluation chart is established, and the reservoir effectiveness evaluation chart is divided into a preset number of reservoir effectiveness evaluation intervals according to multiple reservoir effectiveness evaluation results; the array acoustic logging data corresponding to the target depth interval in the well to be evaluated is obtained, and the array acoustic logging data is processed to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, and the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated are projected onto the reservoir effectiveness evaluation chart to determine the reservoir effectiveness level corresponding to the well to be evaluated. Therefore, the present application can solve the problems of complex evaluation process, long time consumption and poor universality of the currently commonly used reservoir evaluation technology, and realize reservoir effectiveness evaluation by comprehensively utilizing the Stoneley wave amplitude and dipole shear wave spectrum energy, which can effectively improve the accuracy of tight reservoir effectiveness evaluation and improve the evaluation efficiency. The method of this application has good application effect and high applicability, and at the same time provides a new idea for the effectiveness evaluation of unconventional reservoirs.

[0048] 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

[0049] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0050] Figure 1 A schematic diagram of a process of a reservoir effectiveness evaluation method based on acoustic logging data according to an embodiment of the present application is shown;

[0051] Figure 2 This is a schematic diagram of the Stoneley wave amplitude and dipole shear wave spectrum energy in an offshore open hole well;

[0052] Figure 3 yes Figure 2 Electrical imaging of the middle well section 1-3;

[0053] Figure 4 It is a reservoir effectiveness evaluation chart established by using the Stoneley wave amplitude and dipole shear wave spectrum energy of the above-mentioned open hole well;

[0054] Figure 5 This is a diagram of the reservoir effectiveness treatment results of a well to be evaluated offshore;

[0055] Figure 6 yes Figure 5 Electro-imaging diagram of X310;

[0056] Figure 7 A structural block diagram of a reservoir effectiveness evaluation device based on acoustic logging data according to an embodiment of the present application is shown;

[0057] Figure 8 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0059] Figure 1 FIG. 1 shows a flow chart of a reservoir effectiveness evaluation method based on acoustic logging data according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0060] Step S101, acquiring array acoustic logging data corresponding to a target depth interval in a naked hole, wherein the array acoustic logging data includes: monopole full-wave waveform data and dipole shear wave waveform data.

[0061] Specifically, an acoustic logging instrument can be used to perform array acoustic logging in a target depth interval in an open hole to obtain array acoustic logging data. The target depth interval can be the entire open hole section or a partial depth interval in the open hole, for example, the 1000-2000 meter interval. This is only an example and does not have any limiting effect. The array acoustic logging data is array acoustic full wave series data, which is time series data. The array acoustic logging data includes: monopole full wave waveform data and dipole shear wave waveform data. In addition, it may also include longitudinal wave waveform data, etc. Among them, the monopole full wave waveform data includes Stoneley wave waveform data.

[0062] The acoustic logging instrument includes multiple receivers, and the common number of receivers ranges from 4 to 24. Each receiver receives an independent time series of array acoustic full wave train data. For example, the present application may use an acoustic logging instrument with 8 receivers.

[0063] Step S102, performing data processing on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude based on the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band based on the dipole shear wave spectrum.

[0064] Specifically, after obtaining the monopole full-wave waveform data, the target Stoneley wave amplitude can be calculated based on the monopole full-wave waveform data. For example, the monopole full-wave waveform data can be processed using a first preset processing method to calculate the Stoneley wave time difference; then, the Stoneley wave arrival time is calculated based on the Stoneley wave time difference, the borehole fluid velocity, and the acoustic logging instrument structure data; finally, the target Stoneley wave amplitude is calculated based on the Stoneley wave arrival time and the monopole full-wave waveform data. Here, the Stoneley wave arrival time of the Stoneley wave waveform data contained in the monopole full-wave waveform data received on each receiver at each depth point within the target depth interval is calculated, and then the target Stoneley wave amplitude is calculated based on the monopole full-wave waveform data received on each receiver at each depth point within the target depth interval and the corresponding Stoneley wave arrival time.

[0065] For example, the STC method can be used to process monopolar full-wave waveform data to obtain Stoneley wave time difference, and then the Stoneley wave arrival time is calculated in combination with the acoustic logging instrument structure data and the borehole fluid velocity. More specifically, a time window is taken from the first wave train (referring to the monopolar full-wave waveform data received by the first receiver), and then this time window is moved to scan the entire wave train. Relative to the time position of the first track, the position of the time window on the wave train of different receivers is different (determined by the slowness). The data in the time window is superimposed using the array correlation function to calculate the coherence value. For all slowness values, the coherence values ​​at all time positions are calculated to obtain a time-slowness coherence diagram, whose peak corresponds to the arrival time and slowness of the wave in the array, and the time difference information of different wave modes (such as Stoneley waves) is obtained. Then, the Stoneley wave arrival time is calculated based on parameters such as the borehole fluid velocity, Stoneley wave time difference and acoustic logging instrument structure.

[0066] Then, the root mean square method is used to calculate the root mean square Stoneley wave amplitude for the monopolar full-wave waveform data received on any receiver at each depth point in the target depth interval. Then, the average value of the N root mean square Stoneley wave amplitudes at each depth point is calculated and the average value is normalized to obtain the target Stoneley wave amplitude.

[0067] Specifically, the root mean square method is used to calculate the amplitude of the monopolar full-wave waveform data received on the i-th receiver at any depth point to obtain the root mean square Stoneley wave amplitude in the monopolar full-wave waveform data received on the i-th receiver. Then, the root mean square Stoneley wave amplitudes in the monopolar full-wave waveform data received on N receivers are added and averaged to obtain the average Stoneley wave amplitude at the depth point. The specific calculation formula is as follows:

[0068]

[0069]

[0070] in, is the root mean square Stoneley wave amplitude in the monopolar full-wave waveform data received on the i-th receiver in the sonic logging tool; It was Stoneleigh Wave time; is the calculated Stoneley wave termination window position, usually ; (t) is the unipolar full-wave waveform data received on the i-th receiver; is the time variable, N is the total number of receivers participating in the Stoneley wave amplitude calculation, usually 8, is the average Stoneley wave amplitude at that depth point. The above formula can be used to calculate the average Stoneley wave amplitude corresponding to each depth point in the target depth range.

[0071] According to the maximum average Stoneley wave amplitude in the target depth interval, the average Stoneley wave amplitude of each depth point in the target depth interval is normalized to obtain the target Stoneley wave amplitude. For example, the target Stoneley wave amplitude can be calculated by normalizing using the following formula:

[0072]

[0073] in, is the normalized target Stoneley wave amplitude, is the maximum value of the average Stoneley wave amplitude in the target depth interval, that is, the maximum average Stoneley wave amplitude is selected from all depth points. It is the absolute value of the average Stoneley wave amplitude, which is mainly affected by the borehole environment, formation lithology and physical properties. Due to the large differences in lithology and physical properties in different blocks, the absolute values ​​of the Stoneley wave amplitude vary greatly, resulting in poor universality in establishing evaluation maps using its absolute value. In comparison, the target Stoneley wave amplitude after normalization has a value range of 0-1, which has strong universality and is more conducive to establishing evaluation maps.

[0074] Of course, in addition to the above methods, other methods can also be used to calculate the Stoneley wave amplitude, which is not specifically limited here.

[0075] The dipole shear wave amplitude is easily affected by non-formation factors such as borehole environment and receiver consistency, and needs to be corrected in field applications, with poor universality. In contrast, the dipole shear wave frequency energy mainly utilizes the low-frequency dipole shear wave information. The low-frequency dipole shear wave has stronger radial penetration ability and is almost unaffected by non-formation factors such as borehole environment and receiver consistency. It can better reflect the real formation information. Therefore, the present invention selects the dipole shear wave spectrum energy for reservoir effectiveness evaluation. The dipole shear wave waveform data received on each receiver at each depth within the target depth interval is Fourier transformed to obtain the corresponding dipole shear wave spectrum. For example, Fourier transform can be performed using discrete Fourier transform algorithm (DFT) or fast Fourier transform algorithm (FFT) and other methods.

[0076] Then, the target dipole shear wave spectrum energy within the preset frequency band can be calculated based on the dipole shear wave spectrum. Specifically, the dipole shear wave spectrum at any depth point is first added and averaged to obtain the dipole shear wave average spectrum corresponding to the depth point; then, the average energy of the dipole shear wave spectrum corresponding to the depth point within the preset frequency band is calculated based on the dipole shear wave average spectrum; finally, based on the maximum dipole shear wave spectrum average energy within the target depth interval, the average energy of the dipole shear wave spectrum at each depth point within the target depth interval is normalized to obtain the target dipole shear wave spectrum energy.

[0077] For example, the average spectrum of dipole shear waves at a certain depth can be calculated using the following formula:

[0078]

[0079] in, is the dipole shear wave waveform data received on the ith receiver at a certain depth point, N is the total number of receivers, and FFT{·} represents the dipole shear wave spectrum obtained by fast Fourier transforming the waveform in brackets {·}.

[0080] Then, the average energy of the dipole shear wave spectrum at a certain depth point can be calculated using the following formula:

[0081]

[0082] Among them, f2 is the upper limit frequency of the preset frequency band; f1 is the lower limit frequency of the preset frequency band; the preset frequency band can be set according to actual needs, for example, the preset frequency band can be 500-2000Hz, among which f2 is 2000Hz and f1 is 500Hz. is the average spectrum of dipole shear waves at a certain depth point; is the average energy of the dipole shear wave spectrum at a certain depth point; the above formula can be used to obtain the average energy of the dipole shear wave spectrum corresponding to each depth point in the target depth range.

[0083] Finally, the following formula is used for normalization processing to calculate the normalized target dipole shear wave spectrum energy:

[0084]

[0085] in, It is the maximum value of the average energy of the dipole shear wave spectrum within the target depth interval; is the target dipole shear wave spectrum energy after normalization.

[0086] Step S103, performing reservoir effectiveness analysis based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole well, and obtaining multiple reservoir effectiveness evaluation results, wherein the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, dipole shear wave spectrum energy interval range.

[0087] Reservoir effectiveness analysis is performed based on the target Stoneley wave amplitude, target dipole shear wave spectrum energy and electrical imaging logging data corresponding to the target depth interval in the open hole. The main focus here is on analyzing the relationship between reservoir fracture development and the target Stoneley wave amplitude and target dipole shear wave spectrum energy, so as to determine multiple reservoir effectiveness evaluation results, among which the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range and dipole shear wave spectrum energy interval range.

[0088] Figure 2 This is a schematic diagram of the Stoneley wave amplitude and dipole shear wave spectrum energy of an offshore open hole well. Figure 3 yes Figure 2 The electrical imaging diagram of well sections 1-3. The effectiveness of reservoir fractures is comprehensively judged based on the relationship between the change of target Stoneley wave amplitude, target dipole shear wave spectrum energy and the response of the open hole electrical imaging logging data (electrical imaging, conventional logging data). Comprehensive analysis shows that in well section 1, the resistivity curves at different detection depths are not differentiated, only some induced fractures exist on the electrical imaging, the Stoneley wave amplitude is large, and the dipole shear wave spectrum energy is weak, indicating that the reservoir fractures in this well section are not developed and the reservoir effectiveness is poor; in well section 2, the resistivity curves at different detection depths are differentiated to a certain extent, and there are a small number of high-conductivity fractures on the electrical imaging, the Stoneley wave amplitude is large, and the dipole shear wave spectrum energy is weak, indicating that the reservoir fractures in this well section are well developed and the reservoir effectiveness is good; in well section 3, the resistivity curves at different detection depths are obviously differentiated, and there are several high-conductivity fractures on the electrical imaging, the Stoneley wave amplitude is small, and the dipole shear wave spectrum energy is strong, indicating that the reservoir fractures in this well section are developed and the reservoir effectiveness is good. Through effectiveness analysis, the effectiveness evaluation results of each reservoir can be determined, wherein the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude range, dipole shear wave spectrum energy range.

[0089] For example, the reservoir effectiveness level includes: a first reservoir effectiveness level, a second reservoir effectiveness level, and a third reservoir effectiveness level;

[0090] The Stoneley wave amplitude range and dipole shear wave spectrum energy range corresponding to the first reservoir effectiveness level are: Stoneley wave amplitude : <0.23, dipole shear wave spectrum energy : 0.28 <0.7 interval, where the first reservoir effectiveness level can also be called: good reservoir effectiveness;

[0091] The Stoneley wave amplitude range and dipole shear wave spectrum energy range corresponding to the second reservoir effectiveness level are: Stoneley wave amplitude : <0.38, dipole shear wave spectrum energy : 0.12 <0.28 interval, the second reservoir effectiveness level can also be called: medium reservoir effectiveness;

[0092] The Stoneley wave amplitude range and dipole shear wave spectrum energy range corresponding to the third reservoir effectiveness level are: Stoneley wave amplitude : <0.6, dipole shear wave spectrum energy : 0.1 In the interval <0.2, the third reservoir effectiveness level can also be called: poor reservoir effectiveness.

[0093] The number of reservoir effectiveness levels can be set to other numbers according to actual needs, which is not specifically limited here. For example, the reservoir effectiveness levels can be set to 2 or 4.

[0094] Based on the above understanding, reservoir effectiveness evaluation can be achieved through the Stoneley wave amplitude and dipole shear wave spectrum energy.

[0095] Step S104, establishing a reservoir effectiveness evaluation chart with Stoneley wave amplitude as the horizontal coordinate and dipole shear wave spectrum energy as the vertical coordinate, and dividing the reservoir effectiveness evaluation chart into a preset number of reservoir effectiveness evaluation intervals according to multiple reservoir effectiveness evaluation results.

[0096] With the Stoneley wave amplitude as the horizontal coordinate and the dipole shear wave spectrum energy as the vertical coordinate, a reservoir effectiveness evaluation chart is established, so that the reservoir effectiveness of a well can be intuitively evaluated through the reservoir effectiveness evaluation chart. Figure 4 In order to utilize the Stoneley wave amplitude and dipole shear wave spectrum energy of the above-mentioned open hole well, a reservoir effectiveness evaluation chart is established. Then, according to multiple reservoir effectiveness evaluation results, the reservoir effectiveness evaluation chart is divided into a preset number of reservoir effectiveness evaluation intervals. Taking the reservoir effectiveness being divided into three levels as an example, the reservoir effectiveness evaluation chart is divided into three reservoir effectiveness evaluation intervals according to the Stoneley wave amplitude and dipole shear wave spectrum energy, and the Stoneley wave amplitude is defined. : <0.23, dipole shear wave spectrum energy : 0.28 The interval <0.7 indicates good reservoir effectiveness; Definition of Stoneley wave amplitude : <0.38, dipole shear wave spectrum energy : 0.12 The interval <0.28 indicates medium reservoir effectiveness; Stoneley wave amplitude : <0.6, dipole shear wave spectrum energy : 0.1 The interval <0.2 indicates poor reservoir effectiveness.

[0097] Step S105, obtaining array acoustic logging data corresponding to the target depth interval in the well to be evaluated, performing data processing on the array acoustic logging data to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, projecting the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated onto a reservoir effectiveness evaluation plate, and determining the reservoir effectiveness level corresponding to the well to be evaluated.

[0098] The well to be evaluated refers to a well that needs to be evaluated for reservoir effectiveness, and may be a well that has not been subjected to logging operations. Array acoustic logging is performed on the target depth interval in the well to be evaluated to obtain array acoustic logging data corresponding to the target depth interval in the well to be evaluated, wherein the array acoustic logging data corresponding to the target depth interval in the well to be evaluated also includes: monopolar full-wave waveform data and dipole shear wave waveform data. The array acoustic logging data is processed to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated. The specific processing process can be referred to step S102, which will not be repeated here.

[0099] After calculating the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated are projected onto the reservoir effectiveness evaluation plate to determine the reservoir effectiveness evaluation interval where they are projected, so as to determine the reservoir effectiveness level corresponding to the well to be evaluated. For example, assuming that the target Stoneley wave amplitude corresponding to the target depth interval in the well to be evaluated is 0.2, 0.15, 0.18, and the target dipole shear wave spectrum energy is 0.5, 0.6, 0.45, then after projecting it onto the reservoir effectiveness evaluation plate, it will fall within the reservoir effectiveness evaluation interval corresponding to the first reservoir effectiveness level, then it can be determined that the reservoir effectiveness level corresponding to the well to be evaluated is the first reservoir effectiveness level. It should be noted that only some data are simply listed here, there may be a lot of actual data, and the values ​​are only for example.

[0100] Figure 5 This is a diagram showing the reservoir effectiveness processing results of an offshore well to be evaluated. Figure 6 yes Figure 5 The electrical imaging image of X310 in the middle. The reservoir lithology of this well is mainly sandstone. The processing results of the Stoneley wave amplitude and dipole shear wave spectrum energy in the well section of the dotted line frame in the figure are projected onto the reservoir effectiveness evaluation plate, as shown in Figure 5As shown in the black square icon, the result of this well section is located in the area with good reservoir effectiveness. To further verify the accuracy of the judgment result, the electrical imaging and conventional logging data of this well show that the resistivity of this well section is significantly reduced, and there is a certain differentiation phenomenon. The electrical imaging shows that there are several high-conductivity fractures, and the field test of this well section has good oil and gas display. In summary, the reservoir effectiveness of this well section is good, and the correctness and effectiveness of the reservoir effectiveness evaluation method based on acoustic logging data of the present invention are further explained.

[0101] This application is based on the logging response characteristics that effective cracks existing in tight reservoirs will cause the near-wellbore acoustic wave amplitude (especially Stoneley waves) to decrease and the dipole shear wave spectrum energy to move to low frequencies. The application mainly uses the Stoneley wave amplitude and the dipole shear wave spectrum energy to establish a reservoir effectiveness evaluation chart, and evaluates the reservoir effectiveness according to the area where the Stoneley wave amplitude and the dipole shear wave energy spectrum energy of a single well are located in the chart.

[0102] According to the reservoir effectiveness evaluation method based on acoustic logging data provided by the embodiment of the present application, array acoustic logging data corresponding to the target depth interval in the open hole is obtained, wherein the array acoustic logging data includes: monopole full-wave waveform data and dipole shear wave waveform data; data processing is performed on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum; reservoir effectiveness analysis is performed based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole, and multiple reservoir effectiveness evaluation results are obtained. Among them, the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, dipole shear wave spectrum energy interval range; with Stoneley wave amplitude as the horizontal coordinate and dipole shear wave spectrum energy as the vertical coordinate, a reservoir effectiveness evaluation chart is established, and the reservoir effectiveness evaluation chart is divided into a preset number of reservoir effectiveness evaluation intervals according to multiple reservoir effectiveness evaluation results; the array acoustic logging data corresponding to the target depth interval in the well to be evaluated is obtained, and the array acoustic logging data is processed to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, and the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated are projected onto the reservoir effectiveness evaluation chart to determine the reservoir effectiveness level corresponding to the well to be evaluated. Therefore, the present application can solve the problems of complex evaluation process, long time consumption and poor universality of the currently commonly used reservoir evaluation technology, and realize reservoir effectiveness evaluation by comprehensively utilizing the Stoneley wave amplitude and dipole shear wave spectrum energy, which can effectively improve the accuracy of tight reservoir effectiveness evaluation and improve the evaluation efficiency. The method of this application has good application effect and high applicability, and at the same time provides a new idea for the effectiveness evaluation of unconventional reservoirs.

[0103] Figure 7 FIG. 4 shows a structural block diagram of a reservoir effectiveness evaluation device based on acoustic logging data according to an embodiment of the present application. Figure 7 As shown, the device comprises:

[0104] The acquisition module 701 is adapted to acquire array acoustic logging data corresponding to a target depth interval in a naked hole, wherein the array acoustic logging data includes: monopolar full-wave waveform data and dipole shear wave waveform data;

[0105] The data processing module 702 is adapted to perform data processing on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopolar full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum;

[0106] The reservoir effectiveness analysis module 703 is suitable for performing reservoir effectiveness analysis based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole, and obtaining multiple reservoir effectiveness evaluation results, wherein the reservoir effectiveness evaluation results include: the reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, the dipole shear wave spectrum energy interval range;

[0107] Establishing module 704, adapted to establish a reservoir effectiveness evaluation chart with Stoneley wave amplitude as the horizontal coordinate and dipole shear wave spectrum energy as the vertical coordinate, and divide the reservoir effectiveness evaluation chart into a preset number of reservoir effectiveness evaluation intervals according to a plurality of reservoir effectiveness evaluation results;

[0108] The evaluation module 705 is suitable for obtaining the array acoustic logging data corresponding to the target depth interval in the well to be evaluated, and triggering the data processing module to process the array acoustic logging data to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, and projecting the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated onto the reservoir effectiveness evaluation plate to determine the reservoir effectiveness level corresponding to the well to be evaluated.

[0109] Optionally, the data processing module is further adapted to: process the monopolar full-wave waveform data using a first preset processing method to calculate the Stoneley wave time difference;

[0110] The Stoneley wave arrival time is calculated based on the Stoneley wave time difference, borehole fluid velocity, and acoustic logging instrument structure data;

[0111] The target Stoneley wave amplitude is calculated based on the Stoneley wave arrival time and monopolar full-wave waveform data.

[0112] Optionally, the array acoustic logging data is received by N receivers in the acoustic logging instrument;

[0113] The data processing module is further adapted to: calculate the wave amplitude of the monopolar full-wave waveform data received on the i-th receiver at any depth point using the root mean square method to obtain the root mean square Stoneley wave amplitude in the monopolar full-wave waveform data received on the i-th receiver;

[0114] The root mean square Stoneley wave amplitudes in the monopolar full-wave waveform data received on N receivers are added and averaged to obtain the average Stoneley wave amplitude at the depth point;

[0115] According to the maximum average Stoneley wave amplitude in the target depth interval, the average Stoneley wave amplitude of each depth point in the target depth interval is normalized to obtain the target Stoneley wave amplitude.

[0116] Optionally, the data processing module is further adapted to: add and average the dipole shear wave spectra at any depth point to obtain the dipole shear wave average spectrum corresponding to the depth point;

[0117] Calculate the average energy of the dipole shear wave spectrum corresponding to the depth point within the preset frequency band according to the dipole shear wave average spectrum;

[0118] According to the maximum average energy of the dipole shear wave spectrum in the target depth interval, the average energy of the dipole shear wave spectrum at each depth point in the target depth interval is normalized to obtain the target dipole shear wave spectrum energy.

[0119] Optionally, the data processing module is further adapted to: calculate the average energy of the dipole shear wave spectrum using the following formula:

[0120]

[0121]

[0122] in, is the average energy of the dipole shear wave spectrum at any depth point; f2 is the upper limit frequency of the preset frequency band; f1 is the lower limit frequency of the preset frequency band; is the average spectrum of dipole shear waves at any depth point; is the dipole shear wave waveform data received on the i-th receiver, N is the total number of receivers, and FFT{·} represents the dipole shear wave spectrum obtained by fast Fourier transforming the waveform in brackets {·};

[0123] The target dipole shear wave spectrum energy is calculated according to the following formula:

[0124]

[0125] in, It is the maximum value of the average energy of the dipole shear wave spectrum within the target depth interval; is the target dipole shear wave spectrum energy.

[0126] Optionally, the reservoir effectiveness level includes: a first reservoir effectiveness level, a second reservoir effectiveness level, and a third reservoir effectiveness level;

[0127] The reservoir effectiveness evaluation interval corresponding to the first reservoir effectiveness level is: Stoneley wave amplitude : <0.23, dipole shear wave spectrum energy : 0.28 <0.7 interval;

[0128] The reservoir effectiveness evaluation interval corresponding to the second reservoir effectiveness level is: Stoneley wave amplitude : <0.38, dipole shear wave spectrum energy : 0.12 <0.28 interval;

[0129] The reservoir effectiveness evaluation interval corresponding to the third reservoir effectiveness level is: Stoneley wave amplitude : <0.6, dipole shear wave spectrum energy : 0.1 The interval is <0.2.

[0130] According to the reservoir effectiveness evaluation device based on acoustic logging data provided by the embodiment of the present application, array acoustic logging data corresponding to the target depth interval in the open hole is obtained, wherein the array acoustic logging data includes: monopole full-wave waveform data and dipole shear wave waveform data; data processing is performed on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain the corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum; reservoir effectiveness analysis is performed based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole, and multiple reservoir effectiveness evaluation results are obtained. Among them, the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, dipole shear wave spectrum energy interval range; with Stoneley wave amplitude as the horizontal coordinate and dipole shear wave spectrum energy as the vertical coordinate, a reservoir effectiveness evaluation chart is established, and the reservoir effectiveness evaluation chart is divided into a preset number of reservoir effectiveness evaluation intervals according to multiple reservoir effectiveness evaluation results; the array acoustic logging data corresponding to the target depth interval in the well to be evaluated is obtained, and the array acoustic logging data is processed to obtain the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated, and the target Stoneley wave amplitude and target dipole shear wave spectrum energy corresponding to the well to be evaluated are projected onto the reservoir effectiveness evaluation chart to determine the reservoir effectiveness level corresponding to the well to be evaluated. Therefore, the present application can solve the problems of complex evaluation process, long time consumption and poor universality of the currently commonly used reservoir evaluation technology, and realize reservoir effectiveness evaluation by comprehensively utilizing the Stoneley wave amplitude and dipole shear wave spectrum energy, which can effectively improve the accuracy of tight reservoir effectiveness evaluation and improve the evaluation efficiency. This application has good application effect and high applicability, and also provides new ideas for the effectiveness evaluation of unconventional reservoirs.

[0131] An embodiment 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 execute operations corresponding to the reservoir effectiveness evaluation method based on acoustic logging data in any of the above method embodiments.

[0132] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, which enables a processor to execute operations corresponding to the reservoir effectiveness evaluation method based on acoustic logging data in any of the above method embodiments.

[0133] Figure 8 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the computing device.

[0134] like Figure 8 As shown, the computing device may include: a processor 802 , a communication interface 804 , a memory 806 , and a communication bus 808 .

[0135] The processor 802 , the communication interface 804 , and the memory 806 communicate with each other via a communication bus 808 .

[0136] The communication interface 804 is used to communicate with other devices such as clients or other servers.

[0137] The processor 802 is used to execute the program 810, and specifically can execute the relevant steps in the above-mentioned embodiment of the reservoir effectiveness evaluation method based on acoustic logging data.

[0138] Specifically, the program 810 may include program codes, which include computer operation instructions.

[0139] The processor 802 may be a central processing unit (CPU), or 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 the 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.

[0140] The memory 806 is used to store the program 810. The memory 806 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0141] Program 810 can be specifically used to enable processor 802 to execute the reservoir effectiveness evaluation method based on acoustic logging data in any of the above-mentioned method embodiments. The specific implementation of each step in program 810 can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned reservoir effectiveness evaluation method embodiment based on acoustic logging data, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described equipment and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0142] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. 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 such systems. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is for the purpose of disclosing the best mode of implementation of the application.

[0143] 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.

[0144] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims, the inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present application.

[0145] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0146] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0147] The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present application. The present application may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0148] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A reservoir effectiveness evaluation method based on acoustic logging data, characterized in that: The method comprises: Acquiring array acoustic logging data corresponding to a target depth interval in a bare hole, wherein the array acoustic logging data comprises: monopole full-wave waveform data and dipole shear wave waveform data; Performing data processing on the array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain a corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum; Reservoir effectiveness analysis is performed based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole well to obtain multiple reservoir effectiveness evaluation results, wherein the reservoir effectiveness evaluation results include: reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, dipole shear wave spectrum energy interval range; Establishing a reservoir effectiveness evaluation chart with Stoneley wave amplitude as the abscissa and dipole shear wave spectrum energy as the ordinate, and dividing the reservoir effectiveness evaluation chart into a preset number of reservoir effectiveness evaluation intervals according to a plurality of reservoir effectiveness evaluation results; Acquire array acoustic logging data corresponding to a target depth interval in the well to be evaluated, perform data processing on the array acoustic logging data to obtain a target Stoneley wave amplitude and a target dipole shear wave spectrum energy corresponding to the well to be evaluated, project the target Stoneley wave amplitude and the target dipole shear wave spectrum energy corresponding to the well to be evaluated onto the reservoir effectiveness evaluation plate, and determine a reservoir effectiveness level corresponding to the well to be evaluated.

2. The reservoir effectiveness evaluation method based on acoustic logging data according to claim 1, characterized in that: The calculating the target Stoneley wave amplitude according to the monopolar full-wave waveform data further comprises: Processing the monopolar full-wave waveform data using a first preset processing method to calculate the Stoneley wave time difference; Calculating the Stoneley wave arrival time according to the Stoneley wave time difference, borehole fluid velocity, and acoustic logging instrument structure data; The target Stoneley wave amplitude is calculated based on the Stoneley wave arrival time and the monopolar full-wave waveform data.

3. The reservoir effectiveness evaluation method based on acoustic logging data according to claim 2 is characterized in that: The array acoustic logging data is received by N receivers in the acoustic logging instrument; The calculating the target Stoneley wave amplitude according to the Stoneley wave arrival time and the monopolar full-wave waveform data further comprises: The root mean square method is used to calculate the amplitude of the monopolar full-wave waveform data received on the i-th receiver at any depth point, and the root mean square Stoneley wave amplitude in the monopolar full-wave waveform data received on the i-th receiver is obtained; The root mean square Stoneley wave amplitudes in the monopolar full-wave waveform data received on N receivers are added and averaged to obtain the average Stoneley wave amplitude at the depth point; According to the maximum average Stoneley wave amplitude in the target depth interval, the average Stoneley wave amplitude of each depth point in the target depth interval is normalized to obtain the target Stoneley wave amplitude.

4. The reservoir effectiveness evaluation method based on acoustic logging data according to any one of claims 1 to 3, characterized in that: The step of calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum further comprises: The dipole shear wave spectrum at any depth point is added and averaged to obtain the dipole shear wave average spectrum corresponding to the depth point; Calculating the average energy of the dipole shear wave spectrum corresponding to the depth point within a preset frequency band according to the dipole shear wave average spectrum; According to the maximum average energy of the dipole shear wave spectrum in the target depth interval, the average energy of the dipole shear wave spectrum at each depth point in the target depth interval is normalized to obtain the target dipole shear wave spectrum energy.

5. The reservoir effectiveness evaluation method based on acoustic logging data according to claim 4 is characterized in that: The step of calculating the average energy of the dipole shear wave spectrum corresponding to the depth point within the preset frequency band according to the dipole shear wave average spectrum further comprises: The average energy of the dipole shear wave spectrum is calculated using the following formula: in, is the average energy of the dipole shear wave spectrum at any depth point; f2 is the upper limit frequency of the preset frequency band; f1 is the lower limit frequency of the preset frequency band; is the angular frequency; is the average spectrum of dipole shear waves at any depth point; is the dipole shear wave waveform data received on the i-th receiver, N is the total number of receivers, and FFT{·} represents the dipole shear wave spectrum obtained by fast Fourier transforming the waveform in brackets {·}; According to the maximum dipole shear wave spectrum average energy in the target depth interval, the dipole shear wave spectrum average energy at each depth point in the target depth interval is normalized to obtain the target dipole shear wave spectrum energy further including: The target dipole shear wave spectrum energy is calculated according to the following formula: in, It is the maximum value of the average energy of the dipole shear wave spectrum within the target depth interval; is the target dipole shear wave spectrum energy.

6. The reservoir effectiveness evaluation method based on acoustic logging data according to any one of claims 1 to 3, characterized in that: The reservoir effectiveness level includes: the first reservoir effectiveness level, the second reservoir effectiveness level, and the third reservoir effectiveness level; The reservoir effectiveness evaluation interval corresponding to the first reservoir effectiveness level is: Stoneley wave amplitude : <0.23, dipole shear wave spectrum energy : 0.28 <0.7 interval; The reservoir effectiveness evaluation interval corresponding to the second reservoir effectiveness level is: Stoneley wave amplitude : <0.38, dipole shear wave spectrum energy : 0.12 <0.28 interval; The reservoir effectiveness evaluation interval corresponding to the third reservoir effectiveness level is: Stoneley wave amplitude : <0.6, dipole shear wave spectrum energy : 0.1 The interval is <0.

2.

7. A reservoir effectiveness evaluation device based on acoustic logging data, characterized in that: The device comprises: An acquisition module, adapted to acquire array acoustic logging data corresponding to a target depth interval in a naked hole, wherein the array acoustic logging data comprises: monopolar full-wave waveform data and dipole shear wave waveform data; A data processing module is suitable for performing data processing on array acoustic logging data, wherein the data processing includes: calculating the target Stoneley wave amplitude according to the monopole full-wave waveform data, performing Fourier transform on the dipole shear wave waveform data to obtain a corresponding dipole shear wave spectrum, and calculating the target dipole shear wave spectrum energy within a preset frequency band according to the dipole shear wave spectrum; The reservoir effectiveness analysis module is suitable for performing reservoir effectiveness analysis based on the target Stoneley wave amplitude, the target dipole shear wave spectrum energy and the electrical imaging logging data corresponding to the target depth interval in the open hole well, and obtaining multiple reservoir effectiveness evaluation results, wherein the reservoir effectiveness evaluation results include: the reservoir effectiveness level and the corresponding Stoneley wave amplitude interval range, the dipole shear wave spectrum energy interval range; An establishment module is adapted to establish a reservoir effectiveness evaluation chart with Stoneley wave amplitude as the horizontal coordinate and dipole shear wave spectrum energy as the vertical coordinate, and divide the reservoir effectiveness evaluation chart into a preset number of reservoir effectiveness evaluation intervals according to a plurality of reservoir effectiveness evaluation results; The evaluation module is suitable for acquiring array acoustic logging data corresponding to a target depth interval in the well to be evaluated, and triggering a data processing module to process the array acoustic logging data to obtain a target Stoneley wave amplitude and a target dipole shear wave spectrum energy corresponding to the well to be evaluated, and projecting the target Stoneley wave amplitude and the target dipole shear wave spectrum energy corresponding to the well to be evaluated onto the reservoir effectiveness evaluation plate to determine the reservoir effectiveness level corresponding to the well to be evaluated.

8. 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 reservoir effectiveness evaluation method based on acoustic logging data as described in any one of claims 1-6.

9. A computer storage medium, characterized in that: The computer storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the reservoir effectiveness evaluation method based on acoustic logging data as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables the processor to execute operations corresponding to the reservoir effectiveness evaluation method based on acoustic logging data as described in any one of claims 1 to 6.

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