CN-116338775-B - Thin reservoir identification method and device

CN116338775BCN 116338775 BCN116338775 BCN 116338775BCN-116338775-B

Abstract

The embodiment of the application provides a thin reservoir identification method and device, and belongs to the field of geophysical exploration. The identification method comprises the steps of obtaining well logging data of all sample oil extraction wells in a target area, processing the well logging data, obtaining well control processing parameters, wherein the well control processing parameters comprise an amplitude recovery Tar factor, an absorption attenuation Q factor and an anisotropy speed, obtaining a three-dimensional seismic data body based on the well control processing parameters, obtaining seismic data of the target area, constructing a wave impedance model of the target area based on the three-dimensional seismic data body and the seismic data, and adopting a model optimization iterative algorithm to modify the wave impedance model until the fitting degree of the seismic records obtained by forward modeling of the wave impedance model and the well logging data reaches a preset fitting degree, wherein a wave impedance model inversion result is a thin reservoir identification result. The application aims to improve the identification precision of a thin reservoir.

Inventors

LIU WEI
NING HONGXIAO
CAI ZHIDONG

Assignees

中国石油天然气集团有限公司
中国石油集团东方地球物理勘探有限责任公司

Dates

Publication Date: 20260508
Application Date: 20211224

Claims (8)

1. A method of thin reservoir identification, the method comprising: Acquiring logging data of all sample oil production wells in a target area, and processing the logging data to acquire well control processing parameters, wherein the well control processing parameters comprise an amplitude recovery Tar factor, an absorption attenuation Q factor and an anisotropic speed; acquiring a three-dimensional seismic data volume based on the well control processing parameters; acquiring seismic data of the target area; Constructing a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data; Correcting the wave impedance model by adopting a model optimization iterative algorithm until the fitting degree of the seismic record obtained by forward modeling of the wave impedance model and logging data reaches a preset fitting degree; the wave impedance model inversion result is a thin reservoir identification result; The constructing a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data includes: selecting two oil extraction wells as sample wells in the target area; Taking a target stratum opposite to the target area as a time window, and acquiring logging waveform characteristics of the sample well in the three-dimensional seismic data volume and seismic waveform amplitude characteristics in the seismic data; comparing the logging waveform characteristics with the seismic waveform amplitude characteristics to obtain high-frequency inversion components; classifying the target area in a partition mode based on the high-frequency inversion component; Acquiring a wave impedance curve of each oil production well, and calculating a wave impedance value of each oil production well; Inserting the wave impedance value of each oil well into each zone by adopting an interpolation method according to the zonal classification result, and establishing a zonal wave impedance model of each zone; And merging the subarea wave impedance models of the areas to obtain the wave impedance model of the target area.
2. An identification method as claimed in claim 1, wherein said processing said log data to obtain well control processing parameters comprises: Acquiring the amplitude attenuation rule of the zero bias VSP and near bias W-VSP downlink direct waves in the logging data; based on the amplitude attenuation rule of the zero-bias VSP and near-bias W-VSP downlink direct waves, obtaining a true amplitude recovery Tar factor through linear fitting statistics; VSP data in the logging data are obtained, and an absorption attenuation Q factor is obtained through a spectral ratio method; acquiring the VSP speed, walkaway-VSP and first arrival information of 3D-VSP of all the oil production wells in the logging data; Scanning anisotropic parameters through the first arrival information of the VSP speed, walkaway-VSP and 3D-VSP, and establishing an anisotropic speed model; And acquiring the anisotropic speed based on the anisotropic speed model.
3. The method of claim 2, wherein the acquiring a three-dimensional seismic data volume based on the well control processing parameters comprises: Extracting the absorption attenuation Q factor and the anisotropy value in the anisotropic speed model according to the frame layer of the target area, and establishing a well layer double constraint Q and an anisotropic field; and preprocessing the well control processing parameters, the well layer double constraint Q and the anisotropic field to obtain a three-dimensional seismic data volume.
4. A method of identification as claimed in claim 3 wherein said preprocessing of said well control processing parameters, well double constraints Q and anisotropy fields to obtain a three-dimensional seismic data volume comprises: Performing chromatographic static correction, spherical diffusion compensation, absorption attenuation Q compensation, predictive deconvolution and prestack time migration velocity analysis on the well control processing parameters, the well layer double constraint Q and the anisotropic field to obtain an initial three-dimensional seismic data volume; And removing a low resolution part in the initial three-dimensional seismic data body based on the initial three-dimensional seismic data body by combining logging data and lithology tests to obtain a three-dimensional seismic data body.
5. An identification method according to claim 1, wherein said comparing said log waveform characteristics with seismic waveform amplitude characteristics to obtain high frequency inversion components comprises: Performing frequency band decomposition on the logging waveform characteristics to obtain first frequency bands after the logging waveform characteristics are decomposed; Respectively comparing the seismic waveform amplitude characteristics with the first frequency band in similarity, and determining an initial frequency band with similarity exceeding preset similarity; performing frequency band decomposition on the primary frequency band to obtain second frequency bands after the primary frequency band is decomposed; and respectively carrying out similarity comparison on the seismic waveform amplitude characteristics and the second frequency band, and determining a high-frequency inversion component with similarity exceeding the preset similarity.
6. The thin reservoir identification device is characterized by comprising a logging data acquisition module, a processing module, a seismic data acquisition module, a model construction module and a model optimization module; The logging information acquisition module is used for acquiring logging information of all sample oil production wells in a target area, processing the logging information and acquiring well control processing parameters; the well control processing parameters comprise an amplitude recovery Tar factor, an absorption attenuation Q factor and an anisotropic speed; the processing module is used for acquiring a three-dimensional seismic data volume based on the well control processing parameters; The seismic data acquisition module is used for acquiring the seismic data of the target area; The model construction module is used for constructing 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 for correcting the wave impedance model by adopting a model optimization iterative algorithm until the fitting degree of the seismic record obtained by forward modeling of the wave impedance model and logging data reaches a preset fitting degree; The model building module comprises: selecting two oil extraction wells as sample wells in the target area; Taking a target stratum opposite to the target area as a time window, and acquiring logging waveform characteristics of the sample well in the three-dimensional seismic data volume and seismic waveform amplitude characteristics in the seismic data; comparing the logging waveform characteristics with the seismic waveform amplitude characteristics to obtain high-frequency inversion components; classifying the target area in a partition mode based on the high-frequency inversion component; Acquiring a wave impedance curve of each oil production well, and calculating a wave impedance value of each oil production well; Inserting the wave impedance value of each oil well into each zone by adopting an interpolation method according to the zonal classification result, and establishing a zonal wave impedance model of each zone; And merging the subarea wave impedance models of the areas to obtain the wave impedance model of the target area.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the thin reservoir identification method of any of claims 1 to 5.
8. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the thin reservoir identification method according to any of claims 1 to 5.

Description

Thin reservoir identification method and device Technical Field The embodiment of the application relates to the field of geophysical exploration, in particular to a thin reservoir identification method and device. Background Thin reservoirs are one of the key targets of oil and gas exploration in China in recent years, and improving the identification capability of the underground thin reservoirs has important significance for oil and gas exploration. In actual oil and gas exploration work, the main thin layer identification method in the current stage mainly comprises geostatistical inversion, phased frequency division inversion and spectrum inversion, the core idea is to improve the thin reservoir identification effect from the high-precision inversion angle, and good effect is achieved in actual production, but due to limitations of the geostatistical method, for example, the number of wells is required to be more, the distribution is required to be uniform as much as possible, the transverse resolution is low, and the variation function fitting effect is not ideal, and the existing thin reservoir identification method still cannot meet the production requirement. Disclosure of Invention The embodiment of the application provides a thin reservoir identification method and device, which aim to perfect the defect of geostatistical inversion and improve the thin reservoir identification precision. In a first aspect, an embodiment of the present application provides a thin reservoir identification method, the method including: Acquiring logging data of all sample oil production wells in a target area, and processing the logging data to acquire well control processing parameters, wherein the well control processing parameters comprise an amplitude recovery Tar factor, an absorption attenuation Q factor and an anisotropic speed; acquiring a three-dimensional seismic data volume based on the well control processing parameters; acquiring seismic data of the target area; Constructing a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data; Correcting the wave impedance model by adopting a model optimization iterative algorithm until the fitting degree of the seismic record obtained by forward modeling of the wave impedance model and logging data reaches a preset fitting degree; The wave impedance model inversion result is a thin reservoir identification result. Optionally, the processing the logging data, obtaining well control processing parameters includes: Acquiring the amplitude attenuation rule of the zero bias VSP and near bias W-VSP downlink direct waves in the logging data; based on the amplitude attenuation rule of the zero-bias VSP and near-bias W-VSP downlink direct waves, obtaining a true amplitude recovery Tar factor through linear fitting statistics; VSP data in the logging data are obtained, and an absorption attenuation Q factor is obtained through a spectral ratio method; acquiring the VSP speed, walkaway-VSP and first arrival information of 3D-VSP of all the oil production wells in the logging data; Scanning anisotropic parameters through the first arrival information of the VSP speed, walkaway-VSP and 3D-VSP, and establishing an anisotropic speed model; And acquiring the anisotropic speed based on the anisotropic speed model. Optionally, the acquiring the three-dimensional seismic data volume based on the well control processing parameter includes: Extracting the absorption attenuation Q factor and the anisotropy value in the anisotropic speed model according to the frame layer of the target area, and establishing a well layer double constraint Q and an anisotropic field; and preprocessing the well control processing parameters, the well layer double constraint Q and the anisotropic field to obtain a three-dimensional seismic data volume. Optionally, the preprocessing the well control processing parameter, the well layer double constraint Q and the anisotropic field to obtain a three-dimensional seismic data volume includes: Performing chromatographic static correction, spherical diffusion compensation, absorption attenuation Q compensation, predictive deconvolution and prestack time migration velocity analysis on the well control processing parameters, the well layer double constraint Q and the anisotropic field to obtain an initial three-dimensional seismic data volume; And removing a low resolution part in the initial three-dimensional seismic data body based on the initial three-dimensional seismic data body by combining logging data and lithology tests to obtain a three-dimensional seismic data body. Optionally, the constructing a wave impedance model of the target area based on the three-dimensional seismic data volume and the seismic data includes: selecting two oil extraction wells as sample wells in the target area; Taking a target stratum opposite to the target area as a time window, and acquiring logging waveform characterist