A quantitative identification method and device for the boundary of ultra-deep fault-controlled karst reservoirs

Abstract

The present invention discloses a method and device for quantitatively identifying the boundary of a super-deep fault-controlled karst reservoir body, including: characterizing effective seismic attributes of the karst reservoir body boundary to obtain an attribute fusion body; determining the depth of a well based on the characteristics of the karst reservoir body boundary from drilling data; determining effective attribute values based on the well depth and in combination with the attribute fusion body; establishing a geological model of the super-deep fault-controlled karst reservoir body boundary by combining drilling data; conducting seismic forward modeling based on the geological model to establish a boundary quantitative identification template based on forward modeling, and obtaining a quantitative identification threshold value; combining the effective attribute values with the quantitative identification threshold value to achieve intelligent quantitative identification. The present invention is based on the analysis of effective seismic attributes and the determination of effective attribute values, and then establishes a geological model for the typical super-deep fault-controlled karst reservoir body boundary in the target area. Through forward modeling analysis, the effective attribute values are adjusted, ensuring the reliability of the quantitative identification of the karst reservoir body boundary.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas geophysical exploration, and more specifically, relates to a method and device for quantitatively identifying the boundary of a super-deep fault-controlled karst reservoir body. Background Art

[0002] The main types of reservoir spaces in the Shunbei area of the Tarim Basin are large caves and fracture surface cavities formed by dissolution and transformation along deep major fault zones. Secondly, there are inter-gravel seams, intra-gravel seams, high-angle structural fractures and associated dissolution pores in the fault fracture zones. The reservoir is controlled by the main strike-slip fault zone of the source, with strong lateral heterogeneity but good vertical connectivity. In this trap, due to the infiltration of multi-stage karst water along the fault or the upwelling of local hydrothermal fluids, the fractures in the fracture zone are dissolved and transformed, and the physical properties of the reservoir gradually decrease from the inside to the outside. The overlying marl and mudstone caps and the lateral dense limestone form seals and barriers, becoming the boundaries of the karst reservoir body.

[0003] Currently, in oil and gas resource geophysical exploration, there is no quantitative identification technology for the boundary of such karst reservoir bodies. A similar one is the boundary characterization of the karst fracture-cavity type reservoir in Tahe. The boundary of this type of reservoir body is often relatively clear due to being controlled by surface karst, and its response characteristics on seismic profiles show clear bead-shaped reflections; in practical applications, the reservoir boundary can be obtained through seismic wave impedance inversion combined with the interpretation of logging porosity templates; Guo Haihua (2017) based on the relationship between the porosity and wave impedance curve changes in the reservoir sections of the drilled wells in Tahe, used a random algorithm to statistically obtain that when the effective porosity of the Middle-Lower Ordovician carbonate rocks in the Tazhong area is greater than or equal to 2%, the corresponding inverted wave impedance value is the boundary threshold value of the effective carbonate rock fracture-cavity reservoir, and used this threshold value to quantitatively carve the effective reservoir; Feng Xukui (in 2016) analyzed the problems existing in the traditional method and proposed corresponding improvement measures: the porosity has realized the transformation from linear prediction to non-linear prediction, the establishment of the spatial velocity field has realized the transformation from using single information to multi-information constraint, and the calculation of geological reserves has realized the transformation from the reservoir carving method to the reservoir geological modeling method; by comprehensively applying the porosity prediction technology based on multi-attributes, the spatial velocity field establishment technology based on multi-information, and the reserve calculation method based on reservoir geological modeling, the accuracy of quantitative description of carbonate rock reservoirs in the Tarim Basin has been improved.

[0004] The seismic response characteristics of the ultra-deep fault-controlled karst reservoir bodies are different from those in the Tahe area. The main response characteristics on the seismic section are the comprehensive response of chaotic reflection, weak reflection and bead-like reflection. During the development of the ultra-deep reservoir in Shunbei, most wells often experience blowout or leakage as soon as they enter the reservoir section, and complete logging data cannot be obtained. As a result, in the study of identifying the boundary of ultra-deep karst reservoir bodies, there is a lack of acoustic curves for seismic wave impedance inversion and porosity curve results along the well, and thus the boundary of ultra-deep fault-controlled karst reservoir bodies cannot be calibrated.

[0005] With the improvement of deep drilling capabilities and the continuous increase in the national demand for the development of ultra-deep oil and gas resources, the prediction and reserve evaluation technologies for ultra-deep carbonate fault-controlled karst reservoir bodies have been booming in recent years. Due to the characteristics of large burial depth of the target layer, lack of logging data, complex seismic response characteristics, etc., the difficulty of identifying ultra-deep reservoirs is very high. Summary of the Invention

[0006] In view of this, the embodiments of the present invention provide a quantitative identification method and device for the boundary of ultra-deep fault-controlled karst reservoir bodies, which at least solve the technical problem of high difficulty in identifying ultra-deep reservoirs in the prior art.

[0007] In a first aspect, the embodiments of the present invention provide a quantitative identification method for the boundary of ultra-deep fault-controlled karst reservoir bodies, including:

[0008] Characterize the effective seismic attributes of the karst reservoir body boundary to obtain an attribute fusion body;

[0009] Determine the depth of the well based on the characteristics of the karst reservoir body boundary in the drilling data;

[0010] Based on the depth of the well and in combination with the attribute fusion body, determine the effective attribute value;

[0011] Establish a geological model for the boundary of ultra-deep fault-controlled karst reservoir bodies by combining the drilling data;

[0012] Carry out seismic forward modeling based on the geological model, establish a boundary quantitative identification template based on the forward modeling, and obtain a quantitative identification threshold value;

[0013] Combine the effective attribute value with the quantitative identification threshold value to achieve intelligent quantitative identification.

[0014] Optionally, the characterizing the effective seismic attributes of the karst reservoir body boundary to obtain an attribute fusion body includes:

[0015] By characterizing the effective seismic attributes, the single effective seismic attribute is intelligently fused to obtain the attribute fusion body for qualitatively identifying the karst reservoir body boundary in the study area.

[0016] Optionally, the intelligent fusion method is to connect the single effective seismic attribute in series through neural network technology.

[0017] Optionally, based on the depth of the well and in combination with the attribute fusion body, an effective attribute value is determined, including:

[0018] Based on the determination of the depth of the well and the well trajectory position at the boundary of the karst reservoir body, and in combination with the qualitative identification result of the boundary of the karst reservoir body in the attribute fusion body, the effective attribute value at the boundary of the fault-controlled karst reservoir body is determined.

[0019] Optionally, seismic forward modeling is carried out based on the geological model, a boundary quantization identification template based on the forward modeling is established, and a quantization identification threshold value is obtained, including:

[0020] Based on the geological model, the seismic forward modeling is carried out, the effective seismic attributes are extracted from the offset seismic forward data, the geological model is projected onto the seismic profiles of the effective seismic attributes and the attribute fusion body, and by adjusting the effective attribute value of the boundary of the karst reservoir body, the quantization identification threshold value of the boundary of the fault-controlled karst reservoir body is determined.

[0021] Optionally, the depth of the well is determined based on the characteristics of the boundary of the karst reservoir body in the drilling data, including:

[0022] The drilling data includes the drilling time curve, hydrocarbon detection and cuttings logging data. Based on the characteristics that the drilling speed changes and a response occurs on the drilling time curve and the hydrocarbon detection shows an anomaly after the rock formation outside the drilling enters the boundary of the karst reservoir body, the depth of the well for the boundary of the karst reservoir body is determined.

[0023] In a second aspect, an embodiment of the present invention further provides a quantitative identification device for the boundary of a ultra-deep fault-controlled karst reservoir body, including:

[0024] A fusion device, configured to characterize the effective seismic attributes of the boundary of the karst reservoir body and obtain an attribute fusion body;

[0025] A determination device, configured to determine the depth of the well based on the characteristics of the boundary of the karst reservoir body in the drilling data;

[0026] A combination device, configured to determine an effective attribute value based on the depth of the well and in combination with the attribute fusion body;

[0027] An acquisition device, configured to establish a geological model of the boundary of the ultra-deep fault-controlled karst reservoir body by combining the drilling data;

[0028] A quantization identification device, configured to carry out seismic forward modeling based on the geological model, establish a boundary quantization identification template based on the forward modeling, and obtain a quantization identification threshold value;

[0029] A combined device is used to combine the effective attribute value with the quantization recognition threshold value to achieve intelligent quantization recognition.

[0030] The fusion device is further used to: connect the single effective seismic attribute in series through an intelligent algorithm to form the attribute fusion body capable of qualitatively identifying the boundary of the karst reservoir body in the study area.

[0031] Optionally, the intelligent algorithm is neural network technology.

[0032] Optionally, the quantization recognition device is further used to:

[0033] Based on the geological model, conduct the seismic forward modeling, extract the effective seismic attributes from the offset seismic forward data, project the geological model onto the seismic profile of the effective seismic attributes and the attribute fusion body, and determine the quantization recognition threshold value of the fault-controlled karst reservoir body boundary by adjusting the effective seismic attributes of the karst reservoir body boundary.

[0034] Advantages of the present invention:

[0035] By combining the existing geological and geophysical exploration data, and through the intelligent quantitative recognition of the karst reservoir body boundary carried out by the boundary quantitative recognition analysis results of drilling achievements, forward modeling and the attribute fusion body, on the one hand, it can support the subsequent quantitative carving of the karst reservoir body boundary and meet the requirements of oilfield reserve calculation; on the other hand, the quantitative recognition of the fault-controlled karst reservoir body boundary in ultra-deep layers can guide the development drilling direction and provide geological and geophysical exploration references for the guidance of new wells.

[0036] Furthermore, using neural network technology, the calculation of the fusion body of effective seismic attributes is realized, laying a foundation for the intelligent recognition of the fault-controlled karst reservoir body attribute boundary.

[0037] Furthermore, using the characteristic that the drilling speed changes when the drilling encounters the karst reservoir body in the drilling data for well-seismic calibration to determine the boundary of the ultra-deep fault-controlled karst reservoir body.

[0038] Furthermore, based on the geological model that conforms to geological characteristics, conduct seismic forward modeling to establish a boundary quantization recognition template, which can realize the correction of the karst reservoir body boundary recognition, and the calculation results are more reliable and accurate, and can also provide a basis for the quantization recognition of the karst reservoir body boundary in the well-free area.

[0039] Other features and advantages of the present invention will be described in detail in the subsequent specific implementation section. Brief Description of the Drawings

[0040] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail with reference to the accompanying drawings, in which, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0041] Figure 1 The flowchart of a method for quantitatively identifying the boundary of a super-deep fault-controlled karst reservoir body in the first embodiment is shown;

[0042] Figure 2a The schematic diagram of seismic texture attributes in the second embodiment is shown;

[0043] Figure 2b The schematic diagram of Gaussian curvature attributes in the second embodiment is shown;

[0044] Figure 2c The schematic diagram of seismic tensor attributes in the second embodiment is shown;

[0045] Figure 3 The schematic cross-sectional view of the multi-attribute fusion body of neural network technology in the second embodiment is shown;

[0046] Figure 4 The schematic diagram showing the depth of the measurement well in the second embodiment is shown;

[0047] Figure 5a - 5e The schematic diagram of the quantitative identification template for the boundary of the fault-controlled karst reservoir body established based on forward modeling in the second embodiment is shown;

[0048] Figure 6 The schematic cross-sectional view of the identification of the boundary of the super-deep fault-controlled karst reservoir body in the first embodiment is shown. Detailed implementation manners

[0049] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0050] The embodiment of the present invention provides a method for quantitatively identifying the boundary of a super-deep fault-controlled karst reservoir body, including:

[0051] Characterize the effective seismic attributes of the karst reservoir body boundary and obtain an attribute fusion body;

[0052] Specifically, aiming at the boundary characteristics of the super-deep fault-controlled karst reservoir body, carry out the comprehensive application of multi-attribute prediction and inversion of the karst reservoir body in the target layer. By selecting a single effective seismic attribute and using the corresponding mathematical relationship that conforms to the seismic geological conditions of the target area, through an intelligent algorithm, such as neural network technology, the single effective seismic attributes are strung together to form an attribute fusion body that can qualitatively identify the boundary of the karst reservoir body in the study area.

[0053] Determine the well depth based on the characteristics of the karst reservoir boundary from drilling data;

[0054] Specifically, the available drilling data includes the drilling time curve, hydrocarbon detection, and cuttings logging data. After the drill bit passes through the surrounding rock formation and enters the karst reservoir boundary, the drilling rate will change, resulting in an obvious response on the drilling time curve and abnormal hydrocarbon detection. By utilizing this characteristic, the well depth of the karst reservoir boundary can be determined.

[0055] Determine the effective attribute value based on the well depth and in combination with the attribute fusion body;

[0056] Specifically, based on the determination of the well depth of the karst reservoir boundary and the well trajectory position, and in combination with the qualitative identification result of the karst reservoir boundary of the attribute fusion body, determine the effective attribute value at the fault-controlled karst reservoir boundary. The effective attribute value serves as the preliminary criterion for identifying the karst reservoir boundary in the undrilled area.

[0057] Establish a geological model of the ultra-deep fault-controlled karst reservoir boundary by combining the drilling data;

[0058] Based on the understanding of the ultra-deep fault-controlled karst reservoir in the target area from drilling and geological investigations, and in combination with other drilling data, establish a geological model of the typical ultra-deep fault-controlled karst reservoir boundary for the target area, which is used for subsequent seismic forward modeling.

[0059] Carry out seismic forward modeling based on the geological model, establish a boundary quantitative identification template based on the forward modeling, and obtain the quantitative identification threshold value;

[0060] Carry out seismic forward modeling based on the geological model. The forward modeling parameters refer to the parameters of seismic data acquisition and processing of actual seismic data. Extract the effective seismic attributes from the migrated forward seismic data, project the geological model onto the seismic profile of the attribute fusion body, and determine the final quantitative identification threshold value of the effective seismic attributes of the fault-controlled karst reservoir boundary by adjusting the effective attribute value of the karst reservoir boundary.

[0061] Combine the effective attribute value with the quantitative identification threshold value to achieve intelligent quantitative identification.

[0062] By combining the existing geological and geophysical data, and carrying out intelligent quantitative identification of the karst reservoir boundary through the drilling results, the boundary quantitative identification analysis results of forward modeling, and the attribute fusion body, on the one hand, it can support the subsequent quantitative carving of the karst reservoir boundary and meet the requirements of oilfield reserve calculation; on the other hand, the quantitative identification of the ultra-deep fault-controlled karst reservoir boundary can guide the development drilling direction and provide geological and geophysical references for the guidance of new drilling.

[0063] Optionally, the effective seismic attributes for depicting the boundary of the karst reservoir body, and obtaining an attribute fusion body, include:

[0064] By depicting the effective seismic attributes, the single effective seismic attribute is intelligently fused to obtain the attribute fusion body with the ability to qualitatively identify the boundary of the karst reservoir body in the study area.

[0065] Optionally, the way of the intelligent fusion is to connect the single effective seismic attribute in series through neural network technology.

[0066] Specifically, using neural network technology to realize the calculation of the fusion body of effective seismic attributes lays a foundation for the intelligent identification of the property boundary of fault-controlled karst reservoir bodies.

[0067] Optionally, based on the depth of the well and in combination with the attribute fusion body, determining the effective attribute value includes:

[0068] Based on the determination of the depth of the well and the well trajectory position of the karst reservoir body boundary, and in combination with the qualitative identification result of the karst reservoir body boundary of the attribute fusion body, the effective attribute value at the boundary of the fault-controlled karst reservoir body is determined.

[0069] Optionally, based on the geological model, seismic forward modeling is carried out to establish a boundary quantization identification template based on the forward modeling, and obtain a quantization identification threshold value, including:

[0070] Based on the geological model, the seismic forward modeling is carried out, the effective seismic attributes are extracted from the offset seismic forward data, the geological model is projected onto the seismic profiles of the effective seismic attributes and the attribute fusion body, and by adjusting the effective attribute value of the karst reservoir body boundary, the quantization identification threshold value of the fault-controlled karst reservoir body boundary is determined.

[0071] Specifically, by carrying out seismic forward modeling based on a geological model that conforms to geological characteristics to establish a boundary quantization identification template for forward modeling, the correction of the identification of the karst reservoir body boundary can be realized, and the calculation results are more reliable and accurate, and it can also provide a basis for the quantization identification of the karst reservoir body boundary in the well-free area.

[0072] Optionally, determining the depth of the well based on the characteristics of the karst reservoir body boundary of the drilling data includes:

[0073] The drilling data includes the drilling time curve, hydrocarbon detection, and cuttings logging data. Based on the characteristics that the drilling speed changes and the drilling time curve responds and hydrocarbon detection shows abnormalities after the rock formation outside the drilling enters the karst reservoir body boundary, the depth of the well for the karst reservoir body boundary is determined.

[0074] The boundary of ultra-deep fault-controlled karst reservoirs is determined by taking advantage of the characteristics of the drilling rate change when the karst reservoirs are encountered in drilling data, which reduces the difficulty of identifying the boundary of ultra-deep fault-controlled karst reservoirs.

[0075] Example 1

[0076] In this example, the Shunbei Oil and Gas Field in the Tarim Basin is taken as an example. The surface is a desert-covered area, and the target formation depth is greater than 7000m.

[0077] As Figure 1 shown, this example provides a quantitative identification method for the boundary of ultra-deep fault-controlled karst reservoirs. The method includes the following steps:

[0078] S1. Characterize the effective seismic attributes of the karst reservoir boundary

[0079] Aiming at the boundary characteristics of ultra-deep fault-controlled karst reservoirs, the comprehensive application of multi-attribute prediction and inversion of the karst reservoirs in the target formation is carried out. By selecting a single effective seismic attribute and using the corresponding mathematical relationship that conforms to the seismic geological conditions of the target area, through an intelligent algorithm, such as neural network technology, the single effective seismic attributes are connected in series to form an attribute fusion body that can qualitatively identify the boundary of the fault-controlled karst reservoirs in the study area.

[0080] S2. Determine the well depth according to the characteristics of the karst reservoir boundary

[0081] For the boundary of ultra-deep fault-controlled karst reservoirs, it is easy to have blowout and loss during drilling, resulting in the inability to log. The drilling data includes the drilling time curve, hydrocarbon component detection and cuttings logging data. When the drilling passes through the surrounding rock layers and enters the karst reservoir, the drilling rate changes, resulting in an obvious response on the drilling time curve and abnormal hydrocarbon detection. This characteristic is used to judge the well depth of the karst reservoir boundary.

[0082] S3. Determine the effective attribute value

[0083] Based on the fine calibration of the drilling, according to the determination of the well depth and well trajectory position of the karst reservoir boundary, combined with the qualitative identification result of the karst reservoir boundary by the attribute fusion body, the effective attribute value at the boundary of the fault-controlled karst reservoir is determined. This value is used as the preliminary standard for identifying the karst reservoir boundary in the un-drilled area.

[0084] S4. Establish a geological model of ultra-deep fault-controlled karst reservoirs

[0085] According to the understanding of the ultra-deep fault-controlled karst reservoirs in the target area from drilling and geological investigations, combined with other research data, a geological model of the typical ultra-deep fault-controlled karst reservoir boundary for the target area is established for subsequent seismic forward modeling.

[0086] S5. Establish a boundary quantization recognition template

[0087] Based on the geological model, seismic forward modeling is carried out. The forward modeling parameters refer to the parameters of seismic acquisition and processing of actual seismic data. Effective seismic attributes are extracted from the forward seismic data obtained by migration. The geological model is projected onto the seismic profile of the attribute fusion body. By adjusting the effective attribute values of the karst reservoir body boundary, the quantization recognition threshold value of the effective seismic attributes of the final fault-controlled karst reservoir body boundary is determined.

[0088] S6. Realize intelligent quantization recognition

[0089] Combine the effective attribute values with the quantization recognition threshold value to realize intelligent quantization recognition.

[0090] By combining the existing geological and geophysical data, through the intelligent quantitative recognition of the karst reservoir body boundary carried out by the boundary quantitative recognition analysis results of drilling results and forward modeling and the attribute fusion body, on the one hand, it can support the subsequent quantitative carving of the karst reservoir body boundary and meet the needs of oilfield reserve calculation; on the other hand, the quantitative recognition of the ultra-deep fault-controlled karst reservoir body boundary can guide the development drilling direction and provide geological and geophysical references for the orientation of new wells.

[0091] Embodiment 2

[0092] This embodiment takes the research area of Shunbei as an example for specific illustration:

[0093] S1. Characterize the effective seismic attributes of the karst reservoir body boundary

[0094] Refer to Figure 2a - 2c , first conduct multi-attribute analysis and optimization of effective seismic attributes for the fault-controlled karst reservoir body in the research area, and determine that seismic texture, Gaussian curvature attribute, and seismic tensor attribute have good effects on characterizing the karst reservoir body boundary. Through neural network technology, the single effective seismic attributes are fused. The closer the fused effective seismic attributes are to the inside of the karst reservoir body, the higher the attribute value.

[0095] S2. Determine the well depth according to the characteristics of the karst reservoir body boundary

[0096] From the drilling time curve and the total hydrocarbon content curve of mud logging of Well SHB1-10H, it can be seen that when the drilling encounters the karst reservoir body boundary, not only will the drilling time curve change significantly, but the total hydrocarbon content will also increase rapidly. The obvious changes from high to low of both occur at the drilling depth of 7530m. Using this feature, the well depth of the fault-controlled karst reservoir body boundary can be discriminated, as Figure 3 shown.

[0097] S3. Determine the effective attribute value

[0098] By calibrating the attribute value of the multi-attribute fusion body calculated by the neural network method at a depth of 7530 m along the well trajectory, the attribute value is T. Then, the fracture-controlled karst reservoir body has an attribute value of the attribute fusion body greater than T, and T is the effective attribute value of the karst reservoir body boundary.

[0099] S4. Establish a geological model of ultra-deep fracture-controlled karst reservoir bodies

[0100] Based on the understanding of the ultra-deep fracture-controlled karst reservoir bodies in the target area from drilling and geological investigations, combined with other research data, establish a geological model of the typical ultra-deep fracture-controlled karst reservoir body boundary in the target area for subsequent seismic forward modeling.

[0101] S5. Establish a boundary quantization identification template

[0102] Refer to Figure 5a - 5e , carry out seismic forward modeling based on the geological model, and according to the overlay analysis of the forward model and the effective seismic attributes, based on the effective attribute value T calibrated by Well SHB1-10H, adjust it to T0, so as to determine the quantization identification threshold value of the effective seismic attributes of the fracture-controlled karst reservoir body boundary finally.

[0103] S6. Realize intelligent quantization identification

[0104] Combine the effective attribute value with the quantization identification threshold value to realize intelligent quantization identification.

[0105] Embodiment 3

[0106] This embodiment provides a quantitative identification device for the boundary of ultra-deep fracture-controlled karst reservoir bodies, including:

[0107] A fusion device for depicting the effective seismic attributes of the karst reservoir body boundary and obtaining an attribute fusion body;

[0108] A determination device for determining the depth of the well based on the characteristics of the karst reservoir body boundary in the drilling data;

[0109] A combination device for determining an effective attribute value based on the depth of the well and in combination with the attribute fusion body;

[0110] An acquisition device for establishing a geological model of the ultra-deep fracture-controlled karst reservoir body boundary by combining the drilling data;

[0111] A quantization identification device for carrying out seismic forward modeling based on the geological model, establishing a boundary quantization identification template based on the forward modeling, and obtaining a quantization identification threshold value;

[0112] A combination device for combining the effective attribute value with the quantization identification threshold value to realize intelligent quantization identification.

[0113] The present invention combines existing geological and geophysical exploration data, and conducts intelligent quantitative identification of the boundaries of karst reservoirs through drilling results, boundary quantitative identification and analysis results of forward modeling, and attribute fusion bodies. On the one hand, it can support the subsequent quantitative carving of the boundaries of karst reservoirs to meet the requirements of oilfield reserve calculation; on the other hand, the quantitative identification of the boundaries of ultra-deep fault-controlled karst reservoirs can guide the direction of development drilling and provide geological and geophysical references for the guidance of new drilling.

[0114] The fusion device is also used to: connect the single effective seismic attribute in series through an intelligent algorithm to form the attribute fusion body capable of qualitatively identifying the boundaries of karst reservoirs in the study area.

[0115] Optionally, the intelligent algorithm is neural network technology.

[0116] Using neural network technology, the calculation of the fusion body of effective seismic attributes is realized, laying a foundation for the intelligent identification of the attribute boundaries of fault-controlled karst reservoirs.

[0117] Optionally, the quantitative identification device is also used to:

[0118] Based on the geological model, the seismic forward modeling is carried out, the effective seismic attributes are extracted from the migrated seismic forward data, the geological model is projected onto the seismic profiles of the effective seismic attributes and the attribute fusion body, and the effective seismic attributes of the karst reservoir boundary are adjusted to determine the quantitative identification threshold of the fault-controlled karst reservoir boundary.

[0119] For the detailed description of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, and details are not described herein again.

[0120] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments.

Claims

1. A quantitative identification method for the boundary of ultra-deep fault-controlled karst reservoirs, characterized in that Comprising: Effective seismic attributes for delineating the boundaries of karst reservoirs, obtaining an attribute fusion body, including: By delineating effective seismic attributes, fusing the single effective seismic attribute through intelligent fusion to obtain the attribute fusion body that can qualitatively identify the boundaries of karst reservoirs in the study area; determining the depth of the well based on the characteristics of the boundaries of karst reservoirs in drilling data, including: The drilling data includes drilling time curves, hydrocarbon detection, and cuttings logging data. Based on the fact that after the rock formation outside the well enters the boundary of the karst reservoir, the drilling speed changes, resulting in a response on the drilling time curve and an anomaly in hydrocarbon detection, the depth of the well is determined for the boundary of the karst reservoir; Based on the depth of the well and in combination with the attribute fusion body, determining effective attribute values, including: Based on the determination of the depth of the well and the well trajectory position at the boundary of the karst reservoir, and in combination with the qualitative identification result of the boundary of the karst reservoir by the attribute fusion body, determining the effective attribute values at the boundary of the fault-controlled karst reservoir; By combining the drilling data, establishing a geological model of the boundary of the ultra-deep fault-controlled karst reservoir; Based on the geological model, conducting seismic forward modeling, establishing a boundary quantitative identification template based on the forward modeling, and obtaining a quantitative identification threshold value, including: Based on the geological model, conducting the seismic forward modeling, extracting the effective seismic attributes from the offset seismic forward data, projecting the geological model onto the seismic profile of the effective seismic attributes and the attribute fusion body, and determining the quantitative identification threshold value of the boundary of the fault-controlled karst reservoir by adjusting the effective seismic attributes of the boundary of the karst reservoir; Combining the effective attribute values with the quantitative identification threshold value to achieve intelligent quantitative identification.

2. The quantitative identification method for the boundary of an ultra-deep fault-controlled karst reservoir according to claim 1, wherein The intelligent fusion method is to realize the fusion connection of the single effective seismic attribute through neural network technology.

3. A quantitative identification device for the boundary of ultra-deep fault-controlled karst reservoirs, characterized in that, Including: A fusion device for delineating effective seismic attributes of the boundary of a karst reservoir and obtaining an attribute fusion body, including: By delineating effective seismic attributes, fusing the single effective seismic attribute through intelligent fusion to obtain the attribute fusion body that can qualitatively identify the boundaries of karst reservoirs in the study area; A determination device for determining the depth of the well based on the characteristics of the boundary of the karst reservoir in drilling data, including: The drilling data includes drilling time curves, hydrocarbon detection, and cuttings logging data. Based on the fact that after the rock formation outside the well enters the boundary of the karst reservoir, the drilling speed changes, resulting in a response on the drilling time curve and an anomaly in hydrocarbon detection, the depth of the well is determined for the boundary of the karst reservoir; A combination device for determining effective attribute values based on the depth of the well and in combination with the attribute fusion body, including: Based on the determination of the depth of the well and the well trajectory position at the boundary of the karst reservoir, and in combination with the qualitative identification result of the boundary of the karst reservoir by the attribute fusion body, determining the effective attribute values at the boundary of the fault-controlled karst reservoir; An acquisition device for establishing a geological model of the boundary of ultra-deep fault-controlled karst reservoirs by combining the drilling data; A quantitative identification device for carrying out seismic forward modeling based on the geological model, establishing a boundary quantitative identification template based on the forward modeling, and obtaining a quantitative identification threshold value, including: Carrying out the seismic forward modeling based on the geological model, extracting the effective seismic attributes from the offset seismic forward data, projecting the geological model onto the seismic profiles of the effective seismic attributes and the attribute fusion body, and determining the quantitative identification threshold value of the boundary of the fault-controlled karst reservoir by adjusting the effective seismic attributes of the karst reservoir boundary; A joint device for combining the effective attribute value with the quantitative identification threshold value to achieve intelligent quantitative identification.

4. The quantitative identification device for the boundary of ultra-deep fault-controlled karst reservoirs according to claim 3, characterized in that The intelligent fusion method is to achieve fusion connection of the single effective seismic attribute through neural network technology.

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