A thin reservoir identification method and apparatus
By acquiring well control processing parameters from well logging data and 3D seismic data volumes, a wave impedance model was constructed and iteratively corrected, solving the problem of insufficient accuracy in thin reservoir identification and achieving higher accuracy in thin reservoir identification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2021-12-24
- Publication Date
- 2026-05-08
AI Technical Summary
Existing thin reservoir identification methods are insufficient to meet the production needs of oil and gas exploration due to problems such as uneven distribution of well numbers and low lateral resolution, especially the lack of accuracy in thin reservoir identification.
By acquiring well control processing parameters from well logging data, such as amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity, and combining them with three-dimensional seismic data volume, a wave impedance model is constructed. The model is then modified using a model optimization iterative algorithm until the fit with the well logging data reaches a predetermined standard, and the thin reservoir identification result is obtained through inversion.
It improves the accuracy and resolution of thin reservoir identification, makes full use of the advantages of well logging and seismic data, overcomes the limitations of existing technologies, and provides more accurate thin reservoir identification results.
Smart Images
Figure CN116338775B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of geophysical exploration, and more specifically, to a method and apparatus for identifying thin reservoirs. Background Technology
[0002] Thin reservoirs have become one of the key targets of oil and gas exploration in my country in recent years, and improving the ability to identify thin underground reservoirs is of great significance to oil and gas exploration.
[0003] In actual oil and gas exploration, the mainstream thin-layer identification methods at present are mainly geostatistical inversion, phase control frequency division inversion, and spectral inversion. The core idea is to improve the thin-layer identification effect from the perspective of high-precision inversion, and they have achieved good results in actual production. However, due to some limitations of geostatistical methods, such as the requirement for a large number of wells with a relatively uniform distribution, low lateral resolution, and unsatisfactory variogram fitting effect, existing thin-layer identification methods still cannot meet production needs. Summary of the Invention
[0004] This application provides a method and apparatus for identifying thin reservoirs, aiming to improve the shortcomings of geostatistical inversion and enhance the accuracy of thin reservoir identification.
[0005] In a first aspect, embodiments of this application provide a thin reservoir identification method, the method comprising:
[0006] Acquire logging data from all sample oil wells within the target area, process the logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity;
[0007] Based on the well control processing parameters, a three-dimensional seismic data volume is obtained;
[0008] Obtain seismic data for the target area;
[0009] Based on the three-dimensional seismic data volume and the seismic data, a wave impedance model of the target area is constructed;
[0010] The wave impedance model is corrected using a model optimization iterative algorithm until the fit between the seismic record and well logging data obtained by forward modeling of the wave impedance model reaches a predetermined fit.
[0011] The wave impedance model inversion result is the thin reservoir identification result.
[0012] Optionally, processing the logging data to obtain well control processing parameters includes:
[0013] Obtain the amplitude attenuation law of the downlink direct wave in the zero-biased VSP and near-biased W-VSP from the well logging data;
[0014] Based on the amplitude attenuation law of the downlink direct wave of the zero-bias VSP and near-bias W-VSP, the true amplitude recovery Tar factor is obtained by linear fitting and statistical analysis.
[0015] The VSP data in the well logging data is obtained, and the absorption attenuation Q factor is obtained by the spectral ratio method;
[0016] Obtain the VSP velocity, Walkaway-VSP, and 3D-VSP initial arrival information of all oil wells in the logging data;
[0017] An anisotropic velocity model is established by scanning anisotropic parameters using the first arrival information of the VSP velocity, Walkaway-VSP, and 3D-VSP.
[0018] Based on the anisotropic velocity model, anisotropic velocities are obtained.
[0019] Optionally, acquiring the three-dimensional seismic data volume based on the well control processing parameters includes:
[0020] Based on the framework layers of the target region, the absorption attenuation Q factor and the anisotropic values in the anisotropic velocity model are extracted to establish a well-layer dual-constraint Q and anisotropic field.
[0021] The well control parameters, the well formation double constraint Q, and the anisotropic field are preprocessed to obtain a three-dimensional seismic data volume.
[0022] Optionally, the preprocessing of the well control parameters, the well double-constraint Q, and the anisotropic field to obtain the three-dimensional seismic data volume includes:
[0023] The well control processing parameters, well double constraint Q, and anisotropic field are subjected to tomographic static correction, spherical diffusion compensation, absorption attenuation Q compensation, predicted deconvolution, and pre-stack time migration velocity analysis to obtain the initial three-dimensional seismic data volume.
[0024] Based on the initial 3D seismic data volume, combined with well logging data and lithology tests, the low-resolution portion of the initial 3D seismic data volume is removed to obtain the 3D seismic data volume.
[0025] Optionally, constructing the wave impedance model of the target area based on the three-dimensional seismic data volume and seismic data includes:
[0026] Two oil wells were selected as sample wells within the target area;
[0027] Using the target stratum relative to the target area as a time window, the well logging waveform characteristics of the sample well in the three-dimensional seismic data volume and the seismic waveform amplitude characteristics in the seismic data are obtained;
[0028] The high-frequency inversion components are obtained by comparing the characteristics of the well logging waveform and the amplitude characteristics of the seismic waveform.
[0029] Based on the aforementioned high-frequency inversion components, the target region is divided into zones and classified.
[0030] Obtain the wave impedance curves of each oil well and calculate the wave impedance value of each oil well;
[0031] Based on the zoning classification results, the wave impedance values of each oil well are interpolated into each zone using an interpolation method to establish a zoning wave impedance model for each zone.
[0032] By merging the wave impedance models of each region, the wave impedance model of the target region is obtained.
[0033] Optionally, the step of comparing the well logging waveform characteristics and the seismic waveform amplitude characteristics to obtain high-frequency inversion components includes:
[0034] The well logging waveform features are decomposed into frequency bands to obtain the first frequency bands after the well logging waveform features are decomposed.
[0035] The similarity of the earthquake waveform amplitude features with the first frequency band is compared to determine the initial frequency bands whose similarity exceeds the preset similarity.
[0036] The initial frequency band is decomposed to obtain the individual second frequency bands after the initial frequency band is decomposed.
[0037] The similarity of the earthquake waveform amplitude features with the second frequency band is compared to determine the high-frequency inversion components whose similarity exceeds the preset similarity.
[0038] Secondly, embodiments of this application provide a thin reservoir identification device, including: a well logging data acquisition module, a processing module, a seismic data acquisition module, a model building module, and a model optimization module;
[0039] The well logging data acquisition module is used to acquire well logging data of all sample oil wells in the target area, process the well logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity;
[0040] The processing module is used to acquire a three-dimensional seismic data volume based on the well control processing parameters;
[0041] The seismic data acquisition module is used to acquire seismic data of the target area;
[0042] The model building module constructs a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data.
[0043] The model optimization module is used to correct the wave impedance model using a model optimization iterative algorithm until the fitting degree between the seismic record and well logging data obtained by forward modeling of the wave impedance model reaches a predetermined fitting degree.
[0044] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the thin reservoir identification method as described in any one of the first aspects.
[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the thin reservoir identification method as described in any one of the first aspects.
[0046] Beneficial effects: This application first obtains well logging data, then processes the well logging data to generate a three-dimensional seismic data volume, and then combines the seismic data of the target area with the three-dimensional seismic data volume to generate a wave impedance model. The wave impedance model is then forward-modeled until the seismic results obtained from the forward modeling of the wave impedance model have the best fit with the well logging data. Finally, the wave impedance model is inverted, and the inversion result is the thin reservoir identification result. In this application, well logging data and seismic data are fully utilized, the resolution of the seismic data is improved, and the accuracy of thin reservoir identification is improved. Attached Figure Description
[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 This is a flowchart of the identification method proposed in an embodiment of this application;
[0049] Figure 2 This application proposes a method for obtaining the true amplitude recovery Tar factor through linear fitting statistical analysis in one embodiment.
[0050] Figure 3 This is the thin reservoir identification result of the identification method proposed in one embodiment of this application;
[0051] Figure 4 This is a schematic diagram of the structure of an identification device proposed in an embodiment of this application. Detailed Implementation
[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0053] Example 1
[0054] Reference Figure 1 The flowchart illustrates the steps of a thin reservoir identification method according to an embodiment of the present invention, as follows: Figure 1 As shown, this identification method specifically includes:
[0055] Step S101: Obtain logging data of all sample oil wells in the target area, process the logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity;
[0056] In geophysical exploration, well logging is an important method and technology for exploring and developing oil and gas fields. The logging data obtained can reflect the stratigraphic characteristics of the target area, and the data obtained can supplement the insufficient seismic data, making the basic data for thin reservoir identification more complete.
[0057] Well logging data is obtained through well logging methods. In this embodiment, some well control processing parameters from the well logging data are mainly needed, namely amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity.
[0058] Step S102: Based on the well control processing parameters, obtain the three-dimensional seismic data volume;
[0059] The 3D seismic data volume combines all the features of the well control processing parameters. Through subsequent processing of the 3D seismic data volume, it can more accurately reflect the formation information.
[0060] Step S103: Obtain seismic data for the target area;
[0061] In this embodiment, it is necessary to combine well logging data and seismic data of the target area to improve the accuracy of thin reservoir identification through the complementarity between the two different types of data.
[0062] Step S104: Based on the three-dimensional seismic data volume and the seismic data, construct the wave impedance model of the target area;
[0063] In geophysical exploration, well logging data and seismic data have different advantages and disadvantages. Well logging data has advantages such as certain time-depth relationship, accurate formation information near the well, rich and intuitive VSP wavefield, and direct description of reservoir properties, but disadvantages such as uneven spatial distribution of information and lack of macroscopic capabilities. Seismic data has advantages such as flexible and uniform observation system, large imaging aperture and strong spatial observation capability, but disadvantages such as uncertain time-depth relationship, lack of direct formation information, and wavefield that cannot directly describe reservoir properties.
[0064] This embodiment constructs a wave impedance model for the target area by combining the three-dimensional seismic data volume obtained from processing well logging data with seismic data. This wave impedance model has the advantages of both well logging and seismic data, and the disadvantages of both are also complemented in the wave impedance model. When identifying thin reservoirs using this wave impedance model, higher accuracy can be achieved.
[0065] Step S105: The wave impedance model is corrected using a model optimization iterative algorithm until the fitting degree between the seismic record and well logging data obtained by forward modeling of the wave impedance model reaches a predetermined fitting degree.
[0066] After obtaining the wave impedance model, the wave impedance model is corrected using an optimal iterative method. When the seismic record obtained by the forward modeling of the wave impedance model can achieve the best fit with the well logging data, it indicates that the forward modeling results of the wave impedance model can reflect the actual seismic situation; then the accuracy of the thin reservoir identification results obtained by the inversion of the wave impedance model reaches the best.
[0067] The wave impedance model inversion result is the thin reservoir identification result;
[0068] If the forward modeling results of the wave impedance model are consistent with the results of well logging data, then by inverting the wave impedance model, the thin reservoir identification results can be obtained, and these thin reservoir identification results are more accurate than those obtained by conventional techniques that only use seismic data.
[0069] In this embodiment, well logging data is first acquired, then processed to generate a three-dimensional seismic data volume. This data is then combined with seismic data of the target area to generate a wave impedance model. Forward modeling is performed on the wave impedance model until the seismic results obtained from the forward modeling achieve the best fit with the well logging data. Finally, the wave impedance model is inverted, and the inversion result is the thin reservoir identification result. This embodiment fully utilizes both well logging and seismic data, improving the resolution of the seismic data and thus enhancing the accuracy of thin reservoir identification.
[0070] Example 2
[0071] Reference Figure 1 The diagram illustrates a flowchart of a thin reservoir identification method according to an embodiment of this application. Figure 1 As shown, the identification method specifically includes:
[0072] Step S101: Obtain logging data of all sample oil wells in the target area, process the logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity;
[0073] The step of processing the logging data to obtain well control processing parameters includes:
[0074] Obtain the amplitude attenuation law of the downlink direct wave in the zero-biased VSP and near-biased W-VSP from the well logging data;
[0075] Based on the amplitude attenuation law of the downlink direct wave of the zero-bias VSP and near-bias W-VSP, the true amplitude recovery Tar factor is obtained by linear fitting and statistical analysis.
[0076] like Figure 2 , Figure 2 An example of obtaining the true amplitude recovery Tar factor through linear fitting statistics is shown. When obtaining the true amplitude recovery Tar factor, both spherical diffusion and absorption attenuation will cause amplitude energy attenuation. The absorption attenuation is small at low frequencies and large at high frequencies. Therefore, the Tar value obtained from the original data is more affected by absorption attenuation than the true value. The Tar obtained from the high-frequency data BF (40-60-80-100) is larger, while the Tar obtained from the low-frequency data BF (3-6-25-30) is the smallest (1.67), which is closest to the spherical diffusion factor.
[0077] Figure 2 In this context, linear fitting is performed using the following formula:
[0078] y = kx + b;
[0079] Right now Figure 1 The straight line in the figure, and the Tar value of the true amplitude recovery Tar factor is -k.
[0080] The VSP data in the well logging data is obtained, and the absorption attenuation Q factor is obtained by the spectral ratio method;
[0081] Assuming the amplitude spectrum of the seismic signal decays exponentially with time, the quality factor Q can be expressed and calculated using the following formula:
[0082]
[0083] In the formula, a1(f) is the amplitude spectrum within the reference time window; a2(f) is the amplitude spectrum within the sliding time window.
[0084] We can obtain:
[0085]
[0086] Obtain the VSP velocity, Walkaway-VSP, and 3D-VSP initial arrival information of all oil wells in the logging data;
[0087] An anisotropic velocity model is established by scanning anisotropic parameters using the first arrival information of the VSP velocity, Walkaway-VSP, and 3D-VSP.
[0088] VSP technology studies the vertical changes of geological profiles by distributing detectors in a vertical direction, thus obtaining more obvious and intuitive kinematic and dynamic characteristics of waves. Vertical seismic profile VSP data has a high signal-to-noise ratio and a wide signal frequency range, which can accurately observe the direction of wave particle motion and study wave properties and stratigraphic lithology.
[0089] In conventional techniques, the above parameters are obtained from 3D seismic data, while in this embodiment they are obtained from well logging data. Since the basic data of well logging data is obtained in the well, it is more accurate than seismic data, thereby improving the thin reservoir identification results.
[0090] Step S102: Based on the well control processing parameters, obtain the three-dimensional seismic data volume;
[0091] After obtaining the above basic data, multiple well control processing parameters are combined by establishing a three-dimensional seismic data volume. The three-dimensional seismic data volume can reflect the basic information of the formation obtained through well logging data.
[0092] Based on the well control processing parameters, the acquisition of the three-dimensional seismic data volume includes:
[0093] Based on the framework layers of the target region, the absorption attenuation Q factor and the anisotropic values in the anisotropic velocity model are extracted to establish a well-layer dual-constraint Q and anisotropic field.
[0094] The frame hierarchy of the target region can be passed through
[0095] The well control parameters, the well formation double constraint Q, and the anisotropic field are preprocessed to obtain a three-dimensional seismic data volume.
[0096] The preprocessing of the well control parameters, well double-constraint Q, and anisotropic field to obtain a three-dimensional seismic data volume includes:
[0097] The well control processing parameters, well double constraint Q, and anisotropic field are subjected to tomographic static correction, spherical diffusion compensation, absorption attenuation Q compensation, predicted deconvolution, and pre-stack time migration velocity analysis to obtain the initial three-dimensional seismic data volume.
[0098] When processing well control parameters, well double-constraint Q, and anisotropic fields, the techniques used are all existing technologies. By processing the well control parameters through techniques such as tomographic static correction, spherical diffusion compensation, absorption attenuation Q compensation, predictive deconvolution, and pre-stack time migration velocity analysis, the data required in this embodiment, namely the initial three-dimensional seismic data volume, can be obtained.
[0099] Based on the initial 3D seismic data volume, combined with well logging data and lithology tests, the low-resolution portion of the initial 3D seismic data volume is removed to obtain the 3D seismic data volume.
[0100] The initial 3D seismic data volume contains a portion of data that does not conform to the actual situation, namely the low-resolution portion of the initial 3D seismic data volume. In this processing step, this portion of data needs to be removed. The removal is done using well logging data and lithological testing. Well logging data is obtained from actual stratigraphic measurements, while lithological testing can provide relatively accurate test results for geological analysis. By combining and analyzing the data from well logging and lithological testing, we can identify data with large deviations in the initial 3D seismic data volume. By removing this portion of data, we can obtain a high-resolution 3D seismic data volume.
[0101] Step S103: Obtain seismic data for the target area;
[0102] Conventional techniques can be used to acquire seismic data for the target area. By combining the seismic data and well logging data in subsequent steps, more accurate thin reservoir identification results can be obtained.
[0103] Step S104: Based on the three-dimensional seismic data volume and the seismic data, construct the wave impedance model of the target area;
[0104] The wave impedance model of the target area can reflect the stratigraphic information of the target area, the forward modeling results of the wave impedance model can reflect the seismic results of the target area, and the inversion modeling results of the wave impedance model can reflect the reservoir conditions of the target area.
[0105] The construction of the wave impedance model for the target area based on the three-dimensional seismic data volume and seismic information includes:
[0106] Two oil wells were selected as sample wells within the target area;
[0107] When selecting sample wells, the seismic waveforms of all oil wells in the target area are compared and ranked with the seismic waveforms of known wells. Oil wells with high similarity to known wells and close spatial distance are selected as effective statistical samples.
[0108] Using the target stratum relative to the target area as a time window, the well logging waveform characteristics of the sample well in the three-dimensional seismic data volume and the seismic waveform amplitude characteristics in the seismic data are obtained;
[0109] When comparing the characteristics of well logging waveforms and the amplitude characteristics of seismic waveforms, it is necessary to select the characteristics of well logging waveforms and the amplitude characteristics of seismic waveforms of the target formation in the target area. If the characteristics are of the non-target formation, they are less correlated with the thin reservoir predicted in this embodiment. Therefore, the target formation relative to the target area is used as the time window.
[0110] The high-frequency inversion components are obtained by comparing the characteristics of the well logging waveform and the amplitude characteristics of the seismic waveform.
[0111] include:
[0112] The well logging waveform features are decomposed into frequency bands to obtain the first frequency bands after the well logging waveform features are decomposed.
[0113] The first frequency band is a larger frequency band. When dividing it, the logging waveform characteristics are first divided on a large scale. For example, if the logging frequency includes 0-1000Hz, the logging frequency is divided into five first frequency bands: 0-1000Hz, 0-800Hz, 0-600Hz, 0-400Hz and 0-200Hz.
[0114] The similarity of the earthquake waveform amplitude features with the first frequency band is compared to determine the initial frequency bands whose similarity exceeds the preset similarity.
[0115] After decomposing the well logging waveform features on a large scale, the seismic waveform features are compared with multiple first frequency bands, and the first frequency band with higher similarity is determined as the initial frequency band.
[0116] The initial frequency band is decomposed to obtain the individual second frequency bands after the initial frequency band is decomposed.
[0117] If, after comparison, 0-200Hz is selected as the initial frequency band, then the initial frequency band is further divided into 0-50Hz, 0-80Hz, 0-100Hz, 0-120Hz, 0-150Hz, 0-180Hz, 0-200Hz, etc., to decompose the logging waveform characteristics at a smaller scale.
[0118] The similarity of the earthquake waveform amplitude features with the second frequency band is compared to determine the high-frequency inversion components whose similarity exceeds the preset similarity.
[0119] The initial frequency band is one of the first frequency bands, with a relatively high logging frequency. Decomposing the initial frequency band further yields a second frequency band with a lower logging frequency. Therefore, using this second frequency band as a high-frequency inversion component results in greater accuracy. However, in existing technologies, the determination of high-frequency components in geological inversion mainly employs a trial-and-error method, which suffers from high computational difficulty and significant randomness in the results. In this embodiment, by decomposing the logging frequency into frequency bands of different scales and comparing the logging waveform with the seismic waveform, the frequency band with the highest similarity is selected as the high-frequency band. This results in a higher similarity of the obtained high-frequency band, leading to more reliable inversion results and thus more accurate thin reservoir identification.
[0120] Based on the aforementioned high-frequency inversion components, the target region is divided into zones and classified.
[0121] When classifying the target area, the high-frequency inversion components obtained by the above method are used as the basis. They are compared with the seismic waveforms and well logging waveforms of each oil well. Oil wells with high similarity between the seismic waveforms and well logging waveforms and the high-frequency inversion components are classified into one category. The areas where these oil wells are located are classified into the same zone. The formations where the oil wells in the same zone are located have the same type of seismic facies.
[0122] Obtain the wave impedance curves of each oil well and calculate the wave impedance value of each oil well;
[0123] When obtaining the acoustic impedance curves of each oil well, it is necessary to use the acoustic and density curves of each oil well, which are obtained through seismic data.
[0124] Based on the zoning classification results, the wave impedance values of each oil well are inserted into each zone using an interpolation method to establish a zoning wave impedance model for each zone.
[0125] By interpolating the wave impedance values of each oil well into the partitioned wave impedance model, the partitioned wave impedance model can become closer and closer to the actual formation conditions. In this step, the interpolation method used is Kriging interpolation.
[0126] By merging the wave impedance models of each region, the wave impedance model of the target region is obtained.
[0127] Step S105: The wave impedance model is corrected using a model optimization iterative algorithm until the seismic record obtained by forward modeling of the wave impedance model achieves the best fit with the measured record.
[0128] The wave impedance model inversion result is the thin reservoir identification result.
[0129] The wave impedance model is corrected by using an optimal iterative method. When the seismic record obtained by the forward modeling of the wave impedance model can achieve the best fit with the well logging data, it indicates that the forward modeling results of the wave impedance model can reflect the actual seismic situation. In this case, the accuracy of the thin reservoir identification results obtained by the inversion of the wave impedance model reaches the best.
[0130] like Figure 3 As shown, Figure 3 The thin reservoir identification results obtained by the method of this embodiment are shown;
[0131] exist Figure 3 In the image, the left side shows the thin reservoir identification results obtained using conventional techniques, while the right side shows the thin reservoir identification results obtained using the method described in this embodiment of the oil production method; through... Figure 3 It can be seen that the identification results obtained by the method of this embodiment are more accurate than those obtained by conventional techniques in the Longtan Formation and Changxing Formation.
[0132] In this embodiment, well logging data is first acquired, then processed to generate a three-dimensional seismic data volume. The seismic data of the target area and the three-dimensional seismic data volume are then combined to generate a wave impedance model. The wave impedance model is then forward modeled until the seismic results obtained from the forward modeling of the wave impedance model have the best fit with the well logging data. Finally, the wave impedance model is inverted, and the inversion result is the thin reservoir identification result.
[0133] In this embodiment, well logging data and seismic data are fully utilized, and the method for determining high-frequency inversion components is improved. By maximizing the use of existing VSP well logging data during seismic data processing, "well point data" and surface seismic data are integrated and jointly analyzed and processed, ultimately providing high-fidelity, high-resolution, and high signal-to-noise ratio "three-high" seismic data, providing a reliable basis for subsequent exploration and development plans; the resolution of seismic data is improved, thereby enhancing the accuracy of thin reservoir identification.
[0134] Example 3
[0135] Based on the same inventive concept Figure 4 The diagram shown is a structural schematic of a thin reservoir identification device, with reference to... Figure 4 As shown, the identification device may include: a well logging data acquisition module, a processing module, a seismic data acquisition module, a model building module, and a model optimization module;
[0136] The well logging data acquisition module is used to acquire well logging data of all sample oil wells in the target area, process the well logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity;
[0137] The processing module is used to acquire a three-dimensional seismic data volume based on the well control processing parameters;
[0138] The seismic data acquisition module is used to acquire seismic data of the target area;
[0139] The model building module constructs a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data.
[0140] The model optimization module is used to correct the wave impedance model using a model optimization iterative algorithm until the fitting degree between the seismic record and well logging data obtained by forward modeling of the wave impedance model reaches a predetermined fitting degree.
[0141] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.
[0142] Example 4
[0143] Based on the same inventive concept, Embodiment 4 of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the thin reservoir identification method as described in any one of Embodiments 1 and 2.
[0144] Example 5
[0145] Based on the same inventive concept, Embodiment 5 of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the thin reservoir identification method as described in any one of Embodiments 1 and 2.
[0146] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0148] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0151] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.
[0152] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.
[0153] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for identifying thin reservoirs, characterized in that, The method includes: Acquire logging data from all sample oil wells within the target area, process the logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity; Based on the well control processing parameters, a three-dimensional seismic data volume is obtained; Obtain seismic data for the target area; Based on the three-dimensional seismic data volume and the seismic data, a wave impedance model of the target area is constructed; The wave impedance model is corrected using a model optimization iterative algorithm until the fit between the seismic record and well logging data obtained by forward modeling of the wave impedance model reaches a predetermined fit. The wave impedance model inversion result is the thin reservoir identification result; The construction of the wave impedance model for the target area based on the three-dimensional seismic data volume and seismic information includes: Two oil wells were selected as sample wells within the target area; Using the target stratum relative to the target area as a time window, the well logging waveform characteristics of the sample well in the three-dimensional seismic data volume and the seismic waveform amplitude characteristics in the seismic data are obtained; The high-frequency inversion components are obtained by comparing the characteristics of the well logging waveform and the amplitude characteristics of the seismic waveform. Based on the aforementioned high-frequency inversion components, the target region is divided into zones and classified. Obtain the wave impedance curves of each oil well and calculate the wave impedance value of each oil well; Based on the zoning classification results, the wave impedance values of each oil well are interpolated into each zone using an interpolation method to establish a zoning wave impedance model for each zone. By merging the wave impedance models of each region, the wave impedance model of the target region is obtained.
2. The identification method according to claim 1, characterized in that, The process of processing the logging data to obtain well control processing parameters includes: Obtain the amplitude attenuation law of the downlink direct wave in the zero-biased VSP and near-biased W-VSP from the well logging data; Based on the amplitude attenuation law of the downlink direct wave of the zero-bias VSP and near-bias W-VSP, the true amplitude recovery Tar factor is obtained by linear fitting and statistical analysis. The VSP data in the well logging data is obtained, and the absorption attenuation Q factor is obtained by the spectral ratio method; Obtain the VSP velocity, Walkaway-VSP, and 3D-VSP initial arrival information of all oil wells in the logging data; An anisotropic velocity model is established by scanning anisotropic parameters using the first arrival information of the VSP velocity, Walkaway-VSP, and 3D-VSP. Based on the anisotropic velocity model, anisotropic velocities are obtained.
3. The identification method according to claim 2, characterized in that, The acquisition of the three-dimensional seismic data volume based on the well control processing parameters includes: Based on the framework layers of the target region, the absorption attenuation Q factor and the anisotropic values in the anisotropic velocity model are extracted to establish a well-layer dual-constraint Q and anisotropic field. The well control parameters, the well formation double constraint Q, and the anisotropic field are preprocessed to obtain a three-dimensional seismic data volume.
4. The identification method according to claim 3, characterized in that, The preprocessing of the well control parameters, well double-constraint Q, and anisotropic field to obtain a three-dimensional seismic data volume includes: The well control processing parameters, well double constraint Q, and anisotropic field are subjected to tomographic static correction, spherical diffusion compensation, absorption attenuation Q compensation, predicted deconvolution, and pre-stack time migration velocity analysis to obtain the initial three-dimensional seismic data volume. Based on the initial 3D seismic data volume, combined with well logging data and lithology tests, the low-resolution portion of the initial 3D seismic data volume is removed to obtain the 3D seismic data volume.
5. The identification method according to claim 1, characterized in that, The comparison of the well logging waveform characteristics and the seismic waveform amplitude characteristics to obtain high-frequency inversion components includes: The well logging waveform features are decomposed into frequency bands to obtain the first frequency bands after the well logging waveform features are decomposed. The similarity of the earthquake waveform amplitude features with the first frequency band is compared to determine the initial frequency bands whose similarity exceeds the preset similarity. The initial frequency band is decomposed to obtain the individual second frequency bands after the initial frequency band is decomposed. The similarity of the earthquake waveform amplitude features with the second frequency band is compared to determine the high-frequency inversion components whose similarity exceeds the preset similarity.
6. A thin reservoir identification device, characterized in that, include: The system includes modules for well logging data acquisition, processing, seismic data acquisition, model building, and model optimization. The well logging data acquisition module is used to acquire well logging data of all sample oil wells in the target area, process the well logging data, and obtain well control processing parameters; the well control processing parameters include amplitude recovery Tar factor, absorption attenuation Q factor, and anisotropic velocity; The processing module is used to acquire a three-dimensional seismic data volume based on the well control processing parameters; The seismic data acquisition module is used to acquire seismic data of the target area; The model building module constructs a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data. The model optimization module is used to correct the wave impedance model using a model optimization iterative algorithm until the fitting degree between the seismic record and well logging data obtained by forward modeling of the wave impedance model reaches a predetermined fitting degree. The model building module includes: Two oil wells were selected as sample wells within the target area; Using the target stratum relative to the target area as a time window, the well logging waveform characteristics of the sample well in the three-dimensional seismic data volume and the seismic waveform amplitude characteristics in the seismic data are obtained; The high-frequency inversion components are obtained by comparing the characteristics of the well logging waveform and the amplitude characteristics of the seismic waveform. Based on the aforementioned high-frequency inversion components, the target region is divided into zones and classified. Obtain the wave impedance curves of each oil well and calculate the wave impedance value of each oil well; Based on the zoning classification results, the wave impedance values of each oil well are interpolated into each zone using an interpolation method to establish a zoning wave impedance model for each zone. By merging the wave impedance models of each region, the wave impedance model of the target region is obtained.
7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the thin reservoir identification method as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the thin reservoir identification method as described in any one of claims 1 to 5.