Reservoir inversion method applicable to various karst reservoir types

Abstract

The present invention discloses a reservoir inversion method applicable to various karst reservoir types, comprising: S100. high-resolution processing of seismic data to obtain a high-resolution seismic data volume; S200. karst fracture-cavern body carving based on the high-resolution seismic data volume; S300. karst body model established based on the karst fracture-cavern body; S400. seismic inversion using the karst body model to obtain reservoir inversion parameters. The karst body model obtained by the method of the present invention is suitable for various medium types.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration, and in particular relates to a reservoir inversion method applicable to various karst reservoir types. Background Art

[0002] As one of the most important carbonate reservoirs in the global oil and gas basins, karst reservoirs have always been a hot topic of research at home and abroad. In recent years, large marine oil and gas fields discovered in the Ordos Basin, Tarim Basin and Sichuan Basin in China are mostly related to paleokarst. Due to the large scale of karst reservoir development, good physical properties and huge exploration potential, it has received more and more attention in recent years, and a series of breakthroughs have been made in the exploration and development of karst reservoirs.

[0003] At present, there are mainly the following exploration methods in karst reservoir exploration and research:

[0004] The first method is the geological genesis method, which is to restore the karst paleo-geomorphology through the impression method or the residual thickness method, and then select the favorable karst reservoir development area based on the differences in karst paleo-geomorphology and the reservoir development laws of different karst geomorphological units. This method is theoretically reliable and has a wide range of applications. It is suitable for the optimization of karst reservoir target areas in the exploration stage, but its disadvantage is that it is too macroscopic and not conducive to fine reservoir prediction in the development stage. In addition, this method is relatively qualitative and lacks research accuracy.

[0005] The second method is the seismic attribute prediction method. This method is based on the structural information of the karst reservoir distribution, especially the fracture and cave development characteristics, and uses seismic attributes to describe the fracture and cave development law, thereby completing the karst reservoir prediction. This method has a good prediction effect for fracture and cave reservoirs, but it is not sensitive to weathering crust karst reservoirs and karst reservoirs with unknown fractures and caves. In addition, this method is also based on qualitative prediction, and it is difficult to quantitatively predict reservoir properties and thickness.

[0006] The third method is the inversion method, which can effectively identify reservoir porosity and reservoir thickness with high accuracy and relative quantification. However, the inversion theory is generally based on the layered medium model, while the karst reservoir is a non-layered medium with strong lateral heterogeneity. Therefore, the conventional inversion modeling method is not conducive to the characterization of karst reservoir heterogeneity. Summary of the invention

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a reservoir inversion method applicable to various karst reservoir types.

[0008] The object of the present invention is achieved through the following technical solutions: a reservoir inversion method applicable to various karst reservoir types, comprising:

[0009] S100. Perform high-resolution processing on the seismic data to obtain a high-resolution seismic data volume;

[0010] S200. Carving karst fracture and cave bodies based on the high-resolution seismic data body;

[0011] S300. Establishing a karst body model based on the karst fracture-cavity body;

[0012] S400. Perform seismic inversion using the karst body model to obtain reservoir inversion parameters.

[0013] Preferably, the S100 includes the following steps:

[0014] S110. Perform spectrum analysis on the seismic data to obtain the effective frequency of the seismic data;

[0015] S120. Performing spectral decomposition on the seismic data to obtain a time-frequency spectrum within an effective frequency range;

[0016] S130. Taking each time-frequency spectrum as a fundamental signal, respectively calculating the harmonic signal of each fundamental signal within the effective frequency range;

[0017] S140. Superimposing the harmonic signal onto the corresponding fundamental signal to obtain a superimposed signal;

[0018] S150. Perform inverse transformation on the stacked signal to obtain a high-resolution seismic data volume.

[0019] Preferably, the S200 includes the following steps:

[0020] S210. Establishing a stratigraphic framework by calibrating the marker layer through well seismic;

[0021] S220. Establishing a karst reservoir seismic response model within the stratigraphic framework according to the single well calibration;

[0022] S230. Based on the seismic response mode of the karst reservoir, use seismic attributes to identify the planar karst fracture-cave reservoir and profile characteristics;

[0023] S240. Complete the carving of the karst fracture-cave body based on the planar karst fracture-cave reservoir and profile features.

[0024] Preferably, the S300 includes the following steps:

[0025] S310. Establishing a layered model framework by linear interpolation within the stratum framework using the layered medium model;

[0026] S320. Establishing a layered inversion model by interpolating well velocity within the layered model framework;

[0027] S330. Filling the karst fracture-cavity reservoir velocity parameters inside the boundary of the karst fracture-cavity body to establish a non-layered medium model;

[0028] S340. Inlaying the non-layered medium model into the layered inversion model to obtain a karst body model.

[0029] The beneficial effects of the present invention are:

[0030] (1) The current inversion modeling methods are generally based on layered media, and lack response targeting and application scenarios for non-layered media such as karst fracture-cave bodies and volcanic rock reservoirs, resulting in reduced inversion accuracy. The inversion method of the present invention adds seismic attributes that can effectively characterize karst fracture-cave reservoirs into the inversion modeling, effectively solving the inversion modeling problem of non-layered processing;

[0031] (2) As for the difficulty in identifying karst reservoirs, the present invention improves the identification accuracy of karst reservoirs by performing high-resolution processing on seismic data and combining seismic attributes to perform karst fracture and cave carving;

[0032] (3) The method of the present invention solves the problem of complex medium modeling in the inversion process of complex karst reservoirs (such as weathering crusts and karst reservoir types with unclear fracture-cavity structure information), and helps to solve the problem of quantitative prediction of karst reservoir physical properties and gas content by using the inversion method;

[0033] (4) The present invention adopts different modeling methods for different reservoir types, and then performs model fusion to obtain karst models suitable for various media types;

[0034] (5) For karst reservoirs with no obvious cave information in seismic data, the present invention uses chaotic attributes combined with automatic tracking methods to carve out the karst shape, providing a framework for karst reservoir modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of an embodiment of a reservoir inversion method applicable to various karst reservoir types in the present invention;

[0036] Figure 2 The frequency scanning result of the original seismic data in one case;

[0037] Figure 3 A calibration diagram for the composite record of variable subwaves in one case;

[0038] Figure 4 This is the original full stack section through the Y8 well in a case study;

[0039] Figure 5 This is a full stacked cross section after treatment through the Y8 well in one case;

[0040] Figure 6 Interpretation of the sequence stratigraphic framework for a case study;

[0041] Figure 7Predicting karst planimetric distribution maps for seismic attributes in one case;

[0042] Figure 8 Predicting karst profiles for seismic attributes in one case;

[0043] Fig. 9 This is a cross-section diagram of the layered and non-layered fusion model in a case;

[0044] Fig.10 This is a prestack density inversion profile of a conventional layered model in one case;

[0045] Fig.11 This is a pre-stack density inversion profile of the karst body model of the present invention in one case. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1-Figure 11 , this embodiment provides a reservoir inversion method applicable to various karst reservoir types:

[0048] like Figure 1 As shown in the figure, the reservoir inversion method is applicable to various karst reservoir types, including:

[0049] S100. Perform high-resolution processing on the seismic data to obtain a high-resolution seismic data volume.

[0050] Since the karst reservoir is thin and discontinuous in lateral distribution, seismic data are required to have high vertical and lateral resolution. High-resolution processing of seismic data mainly solves the identification of thin karst reservoirs in weathering crust, and helps to highlight the appearance of karst fracture-cave reservoirs. Due to the unique structural information of karst reservoirs, it is particularly important to maintain the low frequency of seismic data. Therefore, high-resolution processing cannot lose low frequency.

[0051] For example, the high-resolution processing method of harmonic high-frequency recovery with low-frequency retention is used to carry out high-resolution processing with the resolution requirement of variable wavelet forward modeling as the target.

[0052] In one embodiment, the S100 includes the following steps:

[0053] S110. Perform spectrum analysis on the seismic data to obtain the effective frequency of the seismic data.

[0054] Specifically, frequency division scanning is performed on the seismic data to determine the effective frequency of the seismic data. Through the effective frequency scanning and analysis, the resolution of the seismic data is determined on the one hand, and the frequency improvement direction of the seismic data is determined on the other hand.

[0055] S120. Perform spectral decomposition on the seismic data to obtain a time-frequency spectrum within an effective frequency range.

[0056] S130. Take each time-frequency spectrum as a fundamental signal and calculate the harmonic signal of each fundamental signal within the effective frequency range.

[0057] S140. Superimpose the harmonic signal onto the corresponding fundamental signal to obtain a superimposed signal.

[0058] S150. Perform inverse transformation on the stacked signal to obtain a high-resolution seismic data volume.

[0059] S200. Carving karst fracture-cave bodies based on the high-resolution seismic data body.

[0060] In one embodiment, the S200 includes the following steps:

[0061] S210. Establish a stratigraphic framework by calibrating the marker layers using well seismic data.

[0062] Specifically, through well-seismic calibration, the marker layer is determined, the marker layer tracking interpretation is completed, and the stratigraphic framework is established.

[0063] S220. Establish a karst reservoir seismic response model within the stratigraphic framework based on single well calibration.

[0064] S230. Based on the seismic response pattern of the karst reservoir, use seismic attributes to identify the planar karst fracture-cave reservoir and profile characteristics.

[0065] S240. Complete the carving of the karst fracture-cave body based on the planar karst fracture-cave reservoir and profile features.

[0066] S300. Establishing a karst body model based on the karst fracture-cavern body.

[0067] In one embodiment, the S300 includes the following steps:

[0068] S310. Use the layered medium model to linearly interpolate within the stratum framework to establish a layered model framework.

[0069] S320. Establish a layered inversion model by interpolating well velocities within the layered model framework.

[0070] S330. Fill the karst fracture-cavity reservoir velocity parameters inside the boundary of the karst fracture-cavity body to establish a non-layered medium model.

[0071] Specifically, the velocity parameters of the karst fracture-cave reservoirs in different well areas are obtained, the velocity parameters of the karst fracture-cave reservoirs are filled inside the boundaries of the karst fracture-cave bodies, and a non-layered medium model is established.

[0072] S340. Inlaying the non-layered medium model into the layered inversion model to obtain a karst body model.

[0073] S400. Perform seismic inversion using the karst body model to obtain reservoir inversion parameters.

[0074] The following is a case using the solution of this embodiment.

[0075] Taking the Maokou Formation in the YB area as an example, the distribution pattern of karst reservoirs in this area is as follows: a set of thin weathering crust karst reservoirs is developed at the top of the Maokou Formation, and a set of karst fracture-cave reservoirs is developed in the middle. For the thin reservoirs at the top, the resolution of conventional seismic data is difficult to identify, and for the karst fracture-cave reservoirs in the middle, the fracture-cave characteristics are not obvious, and the lateral heterogeneity is strong.

[0076] like Figure 2 As shown in the figure, taking the Y area as an example, through frequency scanning analysis, the effective frequency of the seismic data of the Wujiaping-Maokou formation section in this area is 5-60Hz, among which the high-frequency band (45Hz) and above are severely attenuated, and the high-frequency energy is relatively weak. Therefore, for this set of data, high-frequency expansion processing is needed to improve the identification of thin layers and karst reservoirs.

[0077] like Figure 3 As shown in the figure, taking Well Y7 as an example, the Wujiaping Formation Section 1 has a 9.7-meter limestone and a 4.6-meter tuff reservoir; the Mao 3 Section reservoir is 23.7 meters thick. The Wu Section 1 reservoir is 11.6 meters away from the Mao 3 Section reservoir. The forward modeling results show that the effective frequency of seismic data is 5 to 80 Hz, the Wu Section 1 bottom boundary has a response, and the 5 to 90 Hz interface is clear. Therefore, the seismic data frequency requirement for karst reservoir identification in the YB area is 5 to 90 Hz.

[0078] Figure 4 and Figure 5 This is a comparison diagram before and after high-resolution processing in the YB area. After processing, both the layered karst reservoir and the karst fracture-cave reservoir can be identified, and the features are clear, which is conducive to calibration and interpretation.

[0079] like Figure 6 As shown, taking the YB area as an example, the target strata are the Wujiaping Formation and the Maokou Formation. Therefore, through well-seismic calibration, the bottom boundaries of the Wu 2nd Member, the Wu 1st Member, the Mao 32nd Sub-Member and the Mao 3rd Member are identified as marker layers. Therefore, the interpretation of the marker layers is completed through tracking interpretation in the entire area.

[0080] Figure 7 To characterize the chaotic property plane prediction results of karst fracture-cavity bodies, Figure 7 The worm-like distribution geological body is the karst fracture-cave body. Figure 8 To characterize the chaotic properties of karst fracture-cavity bodies, Figure 8 The bean-shaped geological body is a karst fracture cave. The three-dimensional karst fracture cave body carving is completed by integrating the plane and section distribution results.

[0081] The karst body model established based on karst fracture-cavity bodies is as follows: Fig. 9 shown. Fig.10 For the pre-stack density inversion profile using the conventional layered model, Fig.11 The pre-stack density inversion profile of the karst body model in this embodiment is adopted. By comparison, it can be seen that the inversion result of this embodiment has a higher resolution, and both layered and karst fracture-cave reservoirs can be effectively identified, and the accuracy is greatly improved.

[0082] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. Reservoir inversion methods applicable to various karst reservoir types, It is characterized in that include: S100. Perform high-resolution processing on the seismic data to obtain a high-resolution seismic data volume; S200. Carving karst fracture and cave bodies based on the high-resolution seismic data body; S300. Establishing a karst body model based on the karst fracture-cavity body; S400. Performing seismic inversion using the karst body model to obtain reservoir inversion parameters; The S100 includes the following steps: S110. Perform spectrum analysis on the seismic data to obtain the effective frequency of the seismic data; S120. Performing spectral decomposition on the seismic data to obtain a time-frequency spectrum within an effective frequency range; S130. Taking each time-frequency spectrum as a fundamental signal, respectively calculating the harmonic signal of each fundamental signal within the effective frequency range; S140. Superimposing the harmonic signal onto the corresponding fundamental signal to obtain a superimposed signal; S150. Inversely transforming the stacked signal to obtain a high-resolution seismic data volume; The S200 includes the following steps: S210. Establishing a stratigraphic framework by calibrating the marker layer through well seismic; S220. Establishing a karst reservoir seismic response model within the stratigraphic framework according to the single well calibration; S230. Based on the seismic response mode of the karst reservoir, use seismic attributes to identify the planar karst fracture-cave reservoir and profile characteristics; S240. Carving out the karst fracture-cave body based on the planar karst fracture-cave reservoir and profile characteristics; The S300 includes the following steps: S310. Establishing a layered model framework by linear interpolation within the stratum framework using the layered medium model; S320. Establishing a layered inversion model by interpolating well velocity within the layered model framework; S330. Filling the karst fracture-cavity reservoir velocity parameters inside the boundary of the karst fracture-cavity body to establish a non-layered medium model; S340. embedding the non-layered medium model into the layered inversion model to obtain a karst body model; For karst reservoirs with insignificant cave information in seismic data, chaotic attributes combined with automatic tracking methods are used to carve out the karst shape, providing a framework for karst reservoir modeling.

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