Establishing a fracture characterization method and system related to productivity

Abstract

The present invention proposes a method and system for establishing fracture characterization related to production capacity. The method includes: obtaining seismic fracture attribute data A, core fracture data B, logging fracture data C and exploration drilling production capacity data D; based on the core fracture data B and the logging fracture data C, screening the seismic fracture attribute data A to obtain seismic fracture attribute data A1; based on the seismic fracture attribute data A1 and the exploration drilling production capacity data D, respectively establishing fracture characterization and exploration well production relationship E for different seismic layers. The present invention follows the exploration integration principle combining seismic, logging, drilling and geology, uses seismic data to select fracture characterization technology results with fine characterization capabilities from a large number of fracture characterization method data, combines the core fracture related data of drilling, logging and fracture related data, and then combines the statistical data of drilling test oil production to establish an effective fracture characterization method with a corresponding relationship with production.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas geophysical exploration, and more specifically, relates to a method, system and electronic equipment for establishing fracture characterization related to productivity. Background Art

[0002] Whether in the oil and gas exploration stage or the development stage, the study of fractures is one of the important sweet spots related to reservoir targets. As the effective storage space and main seepage channel of oil and gas in the reservoir, fractures control the occurrence and production capacity of oil and gas. Favorable fracture development zones often indicate the existence of high oil and gas production areas. In tight sandstone and volcanic rock reservoirs, there is even a saying that "there is no reservoir without seams". It can be seen that the fracture system plays a vital role in the enrichment and accumulation of oil and gas. At the same time, among the many hot issues in the research on fractures, how to effectively evaluate the impact of fractures on production capacity is directly related to the final production of oil and gas and the huge actual single well drilling engineering costs of millions or even tens of millions.

[0003] As early as the 1970s, foreign countries began to use seismic methods to detect fractured oil and gas reservoirs. It has gone through several development stages, including shear wave exploration, multi-wave and multi-component exploration, and longitudinal wave fracture detection (Roberts A., First Break, 2001). In particular, great progress has been made in longitudinal wave seismic fracture detection (Zheng Duoming, China Petroleum Exploration, 2011). The development of fracture prediction is often developed on the basis of the development of rock physics equivalent model research. At present, foreign countries have developed rock physics models for anisotropic media, two-phase media, and two-phase anisotropic media.

[0004] Fracture descriptions can be made at different scales: (1) The description of large-scale fractures of the order of hundreds of meters in reservoir numerical models is mainly based on tectonic stress field analysis combined with geometric post-stack seismic attributes (such as coherence, curvature, etc.). (2) For small- and medium-scale fractures of the order of tens of meters expected in the exploration phase, they are mainly based on pre- and post-stack seismic geometric attributes or physical laws: coherence, curvature, ant tracking or their derivative technologies of post-stack seismic data can identify relatively large-scale fractures (greater than 1 / 8 wavelength), but for the prediction of smaller-scale fractures (1 / 100-1 / 8 wavelength), it is necessary to use pre-stack seismic data and corresponding methods to study them. (3) As for extremely small-scale fractures of the order of centimeters, they can only be described through drilling and logging data. (Zhang Yanling, Progress in Geophysics, 2005).

[0005] In recent years, with the discovery and development of a large number of fractured oil and gas reservoirs at home and abroad, researchers have conducted a lot of research work on the core issue of fracture characterization in reservoirs, and have made great progress. They have proposed geological methods for field outcrop description, laboratory core analysis methods, logging methods, and seismic exploration methods. Although drilling, logging, and core data have a relatively detailed and accurate description of fractures, their characterization range is limited to the well point of the well, which is still a one-sided view. The seismic exploration method, with its advantages of relatively wide exploration range and relatively low cost, occupies an important position in fracture system identification, fracture reservoir prediction and detailed description. At present, the commonly used fracture reservoir prediction and identification technologies mainly include seismic attribute technology and coherence technology developed based on seismic anisotropy (Ekanem AM, Geophysical Prospecting, 2013). Seismic attribute technologies such as amplitude attributes, coherence body technology, curvature body technology, etc., with their relatively simple and practical advantages, have played an important role in the well site deployment in the early stage of fracture reservoir exploration. However, these technologies are all indirect inferences of the distribution of fracture system development zones, and can only qualitatively predict the relative degree of fracture development but not the fracture orientation and density. The identification and description of fracture systems using related technologies developed based on seismic anisotropy have made great progress in the past three decades. Many technologies have been proposed to estimate fracture orientation and density, and have achieved good results in practical applications. These technologies utilize the azimuthal anisotropy of velocity and attenuation when seismic waves propagate in anisotropic media, mainly including shear wave splitting analysis technology, reflected longitudinal wave amplitude variation with offset (AVO, Amplitude Versus Offset) analysis technology, reflected longitudinal wave amplitude variation with offset and azimuth (AVOZ, Amplitude Versus Offset and Azimuth) analysis technology, AVO gradient azimuth analysis technology, and longitudinal wave normal time difference velocity variation with azimuth analysis technology. These technologies are used to estimate fracture development orientation and density, and their prediction results are directly related to the characteristics of the fracture system.

[0006] In the oil and gas exploration stage, although fractures are a major reference sweet spot factor, the research on the indicative role of fractures on production capacity is rarely carried out in the exploration stage. In the general exploration stage, only a relatively favorable area of ​​fracture sweet spots is provided. However, there is little research on how to select new well sites based on fractures in the favorable area. At present, the research on the impact of fractures on production capacity is mostly concentrated in the development stage.

[0007] At present, there are two basic seepage theories in the application of fracture-capacity models in the oilfield development stage: the equivalent continuous medium model (Warren JE, 1963) and the discrete medium model (Kasap E, 1989). Wang Yuchuan et al. (2011) established a single well productivity evaluation method for fracture-cavity carbonate reservoirs by using linear regression and combining actual data; Zhao Yanyan et al. (2010) established an oil well production model for series fracture-cavity reservoirs; Xiong Wei (2011) established an unstable channeling model for isolated caves in bedrock; Liu Jianjun et al. (2006) established an equivalent permeability model for parallel fracture reservoirs; Liu Hong et al. (2012) established a mathematical model for large-scale cave-cavity reservoirs; Kang Zhijiang et al. (2005, 2010) established a mathematical model for cave-cavity coupled two-phase flow in fracture-cavity reservoirs; Xu Xuan et al. (2010) established an oil well productivity equation for matrix cave fracture reservoirs.

[0008] The indicative effect of cracks on production capacity requires close integration of data research and statistics by personnel from the three parties of seismology, well logging and geology during the exploration stage. In other words, integrated research of seismology, well logging and geology is required. In actual work, there is generally little overlap in research ideas and division of labor among the three, which has led to slow progress and insufficient integration of this integrated research of seismology, well logging and geology.

[0009] In the exploration stage, in recent years, with the deepening of the integration of oil and gas exploration and development, it is necessary to carry out research on the relationship between fractures and production capacity in the exploration stage. In the development stage, there are a lot of well location data for the study area. The exploration stage is before the development stage. Although there are not as many well location data as in the development stage, many well data can be directly applied to conduct detailed statistical research on fractures and production capacity. However, because the ultimate goal of exploration must also be implemented on the exploration well location, when deploying risk wells, pre-exploration wells, and evaluation wells for exploration, there are some corresponding contents of studying fractures in the corresponding basic exploration data. Such as three-dimensional seismic data and well logging, logging, and core results data of a few known wells. In addition, the exploration stage will also deploy horizontal well fracturing tasks with a cost of millions or even tens of millions of yuan. Therefore, how to effectively use these existing fracture basic data in the exploration data (three-dimensional seismic and existing well data, which is different from the production data of many wells used in the development stage), and then improve the drilling success rate and save a lot of economic costs when deploying exploration drilling with higher costs, is the main purpose of the present invention. Summary of the invention

[0010] At present, in the oil and gas exploration stage, fracture characterization in the sweet spot technology research is limited to providing the relative development area of ​​fractures, and lacks the characterization technology to establish the correlation between effective fractures and production. The present invention provides a set of such process technology methods. Following the exploration integration principle combining seismic, logging, drilling and geology, in the current historical stage of oil and gas exploration with low oil prices and high costs, it provides effective solutions to assist in efficient exploration, high-quality exploration, cost reduction and efficiency improvement.

[0011] According to one aspect of the present invention, a method for establishing fracture characterization related to productivity is provided, comprising:

[0012] Obtain seismic fracture attribute data A, core fracture data B, logging fracture data C and exploration drilling productivity data D;

[0013] Based on the core fracture data B and the logging fracture data C, the seismic fracture attribute data A is screened to obtain the seismic fracture attribute data A1;

[0014] Based on the seismic fracture attribute data A1 and the exploration drilling productivity data D, the fracture characterization and exploration well production relationship E are established for different seismic layers.

[0015] Furthermore, the obtaining of earthquake crack attribute data A1 includes:

[0016] Each type of seismic fracture attribute data A is compared with the core fracture data B, and each type of seismic fracture attribute is compared with the well logging fracture data C. The seismic fracture attribute data A that has a high degree of consistency with both the core fracture data B and the well logging fracture data C is selected as the seismic fracture attribute data A1 that characterizes the fracture.

[0017] Furthermore, the method further comprises:

[0018] Extract the core fracture data B and the logging fracture data C that are consistent with the seismic fracture attribute data A1 as core fracture data B1 and logging fracture data C1 respectively;

[0019] Extract the core fracture data B and the logging fracture data C that are inconsistent with the seismic fracture attribute data A1 as core fracture data B2 and logging fracture data C2 respectively;

[0020] Satisfies: B=B1∪B2, C=C1∪C2.

[0021] Furthermore, the method further comprises setting a consistency value M between the seismic fracture attribute data A and the core fracture data B and the logging fracture data C, wherein the value range of M is 0%-100%.

[0022] Furthermore, the liquid production of different wells in each layer is counted using the exploration drilling productivity data D, and the result is matched with the fracture attribute characterization value at the well point in the seismic fracture attribute data A to form a fracture characterization and exploration well production relationship E;

[0023] The relationship E between fracture characterization and exploration well production is used to guide well location deployment.

[0024] Furthermore, the consistency value M is used as a credibility parameter of the relationship E between the fracture characterization and the production of the exploration well, and together with the relationship E between the fracture characterization and the production of the exploration well, guides the deployment of new well locations.

[0025] Furthermore, in the fracture characterization and exploration well production relationship E, the range of fracture attribute characterization values ​​corresponding to wells with higher fluid production is Q. When fracture factors need to be considered when deploying well locations, points within the Q value range are selected for well deployment.

[0026] Further, obtaining the seismic fracture attribute data A includes obtaining seismic layer data and marking the depth section corresponding to the target layer;

[0027] When obtaining the core fracture data B, logging fracture data C and exploration drilling productivity data D, data of the depth section corresponding to the seismic layer are counted.

[0028] According to another aspect of the present invention, there is provided a system for establishing fracture characterization related to productivity, comprising:

[0029] An acquisition unit, which acquires seismic fracture attribute data A, core fracture data B, well logging fracture data C, and exploration drilling productivity data D;

[0030] A screening unit screens the seismic fracture attribute data A based on the core fracture data B and the logging fracture data C to obtain the seismic fracture attribute data A1;

[0031] The characterization unit establishes the relationship E between fracture characterization and exploration well production for different seismic layers based on the seismic fracture attribute data A1 and the exploration drilling productivity data D.

[0032] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0033] A memory storing executable instructions;

[0034] A processor runs the executable instructions in the memory to implement the method for establishing fracture characterization related to productivity.

[0035] The method for establishing fracture characterization related to productivity of the present invention has the following technical effects:

[0036] (1) Intuitively and quantitatively characterize the relationship between fractures and the oil and gas production capacity of existing wells, and provide fracture sweet spots for the deployment of new wells.

[0037] (2) A fracture sweet spot reliability factor that provides well placement reliability when deploying new well locations.

[0038] (3) More comprehensive integrated seismic, logging and geological exploration efforts will make the deployment of new well sites more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components in the exemplary embodiments of the present disclosure.

[0040] Figure 1 The flowchart of the method for establishing fracture characterization related to productivity of the present invention is shown.

[0041] Figure 2 A schematic diagram is provided for establishing a relationship between fracture sweet spots and exploration well productivity according to an embodiment of the present invention.

[0042] Figure 3 Herein, a, b, c, and d are respectively four seismic attributes characterizing fractures in the same target layer according to an embodiment of the present invention.

[0043] Figure 4 This is a picture of the seismic target layer section in the Wb21 well according to an embodiment of the present invention, wherein a is the coring section 1113.21-1114.00m, b is the coring section 1095.11-1095.65m, and fractures are developed in the core.

[0044] Figure 5 This is the core section 1032.79-1035.69 m in the Wb58 well according to an embodiment of the present invention, and no fractures are developed in the core of the target layer.

[0045] Figure 6 This is the Wb59 well according to an embodiment of the present invention, the coring section is 1075-1094m, and no fractures are developed in the core of the target layer section.

[0046] Figure 7 Wherein a and b respectively represent the amplified curvature and the likelihoodhood earthquake crack attributes according to an embodiment of the present invention.

[0047] Figure 8: The target layer section in the WB70 well according to an embodiment of the present invention, wherein a1 is the imaging logging result of the target layer section one, a2 is the likelyhood seismic attribute enlarged map of the target layer section one, b1 is the imaging logging result of the target layer section two, b2 is the likelyhood seismic attribute enlarged map of the target layer section two, c1 is the imaging logging result of the target layer section three, c2 is the likelyhood seismic attribute enlarged map of the target layer section three, the well seismic fractures in the target layer section one are not developed, the well seismic fractures in the target layer sections two and three are relatively developed, and the well-seismic consistency is high.

[0048] Fig. 9 The target well 3 (WMB3) is deployed in the target trap in combination with the fracture sweet spot according to an embodiment of the present invention, wherein a is the position of the target well point 3 in the likelihoodhood attribute, and b is the position of the new well point in the target trap. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0050] The method of the present invention follows the exploration integration principle of combining seismic, logging, drilling and geology. From the fracture characterization method data of numerous seismic data, fracture characterization technology results with fine characterization capabilities (such as maximum likelihood fractures and fusion fracture multi-scale characterization methods) are selected. The preferred seismic attributes have the characteristics of being consistent with the core fractures and logging fracture data of the drilling. Then, the seismic fracture data is combined with the statistical data of the drilling test oil production in the oil and gas exploration stage to establish a corresponding relationship table between fractures and production, and at the same time, reliable parameters for fracture prediction are obtained. The obtained corresponding relationship table is used to guide the selection of well points for new well locations. This improves the success rate of oil and gas exploration and provides a powerful auxiliary solution for efficient exploration, high-quality exploration, and cost reduction and efficiency improvement.

[0051] Reference Figure 1 As shown, the method for establishing fracture characterization related to productivity of the present invention comprises:

[0052] Obtain seismic fracture attribute data A, core fracture data B, logging fracture data C and exploration drilling productivity data D;

[0053] Based on the core fracture data B and the logging fracture data C, the seismic fracture attribute data A is screened to obtain the seismic fracture attribute data A1;

[0054] Based on the seismic fracture attribute data A1 and the exploration drilling productivity data D, the fracture characterization and exploration well production relationship E are established for different seismic layers.

[0055] More specifically, preparing seismic fracture attribute data A includes: obtaining or using existing seismic fracture attribute data that can characterize fracture attributes, such as coherence attributes, curvature attributes, frequency division attributes, AFE (Auto Fault Enhanced) attributes, likelihood maximum likelihood attributes, prestack fracture characterization attributes, fracture fusion characterization attributes, etc. These attributes can be obtained by conventional technology calculation. These data are used as seismic fracture attribute data A. At the same time, seismic technicians are required to provide layer data and mark the depth segment corresponding to the target layer. When preparing data B, data C and data D, it is only necessary to count the data of the corresponding seismic layer depth segment.

[0056] Preparing core fracture data B includes: even in the exploration stage, the core will be drilled, and the data obtained from drilling will have relevant photos, thin slices and other information. This step requires technicians to collect as much actual core data related to the fracture description as possible. Statistics on the degree of development of core fractures corresponding to the seismic layer segment, such as the depth of a certain layer within a certain range of meters, the total length of the coring section, the depth range of the fracture development section in the core section of the corresponding layer, the angle, length, number of fracture development, and the degree of fracture opening. For example, it is necessary to make a description as detailed as possible, preferably statistically into a table or segmented picture atlas that can be intuitively applied, so as to facilitate the comparison with the selected fracture seismic attributes in the following steps. The workload of the description operation of this step may be large, especially when the number of coring wellheads is large and the fractures in some layers are relatively developed, the workload will be relatively large. This method of statistically characterizing the core fractures of the exploration well into a table or a segmented core atlas is one of the key points of the present invention, and the statistical table or atlas results are used as core fracture data B.

[0057] Preparation of logging fracture data C includes: Since 1990, with the emergence of technologies such as FMI imaging logging, in the logging process of oil and gas exploration, there are more and more methods for describing fractures, and the accuracy is getting higher and higher. This step requires logging personnel to select all the most intuitive and accurate descriptions of core fractures from the numerous logging fracture interpretation results, and compile fracture characterization statistical tables or atlases as logging fracture data C. It should be noted that, on the one hand, when optimizing logging data, the most intuitive and accurate results such as FMI are needed to describe core fractures. Some logging fracture interpretation results are subjectively inferred through simple logging curves, and such interpretation results are not needed.

[0058] Preparation of exploration drilling productivity data D includes: during the exploration well drilling process, the corresponding production data of the target layer section will be counted. If the corresponding layer section has not been artificially added with sand and liquid, the direct liquid production can be used as the final net liquid production result. If sand and liquid are added, it is necessary to subtract the amount of liquid added from the total liquid production to calculate the net liquid production of the formation. Each section of each well is counted separately to obtain a net liquid production statistical table. Taking the layer as the unit, the net liquid production results of the corresponding layer sections of each layer in different drillings are counted. If there are several layers, several tables are counted as exploration drilling productivity data D. This step is relatively labor-intensive, especially the need to summarize the net liquid results of each layer section from the existing drilling results statistical data report. In some more detailed drilling statistical results, the exploration drilling productivity data D is ready-made, while others require technical personnel to count them themselves.

[0059] Next, the seismic attribute data are optimized and sorted again. The data selection criteria and process of this step are the most important invention points of the present invention. It is necessary to use the core fracture data B and the logging fracture data C to select the seismic attribute data with consistent statistical results from the numerous seismic fracture attribute data A as the seismic fracture attribute data A1.

[0060] The preferred method is as follows: each type of seismic fracture attribute data A is compared with the core fracture data B, and each type of seismic fracture attribute data A is compared with the well logging fracture data C. Which seismic fracture attribute data A has the best fit with the data B and C is selected as the final seismic data characterizing the fracture, as the seismic fracture attribute data A1. Preferably, the size of the data fit percentage value can be quantitatively counted as the parameter M (M size range 0%-100%). At the same time, the well section statistics of the wells in the core fracture data B and the well logging fracture data C that are consistent with the seismic fracture attribute data A1 are extracted respectively as the core fracture data B1 and the well logging fracture data C1. The well sections of the wells that are inconsistent with the seismic fracture attribute data A1 are extracted as the core fracture data B2 and the well logging fracture data C2. B=B1∪B2 C=C1∪C2. That is, the core fracture data B is divided into B1 and B2, and the well logging fracture data C is divided into C1 and C2.

[0061] The preferred earthquake crack attribute data A1 of the present invention needs to have the following characteristics:

[0062] Characteristic 1: The image can withstand magnification, and after magnification, it is still a fracture strip, which will not submerge the well point representation; some seismic attributes, such as coherence, AFE, curvature and other attributes in some areas, will appear relatively coarse when compared with the well depth section of the corresponding well point after magnification, and even their representation of fractures is larger than the surface element of tens of square meters. These representation methods that cannot withstand image magnification are difficult to represent the fracture development degree at the well point, so they will be abandoned.

[0063] Feature 2: The seismic crack attribute data A1 is highly consistent with data B and C.

[0064] Characteristic 3: The dimensional range of this type of attribute is relatively uniform and correlated with the size and development of the cracks. For example, the size range of the likelyhood attribute is between (0, 1). A value of 0 means that there is no crack development, and a value close to 0 means that there are small cracks, while a value close to 1 reflects a larger fault.

[0065] It is found in practical application experience that the seismic attributes that meet the above three characteristics, under the current fracture characterization technology conditions, the general seismic fracture attribute data A1 are likelihoodhood type or ant tracking type seismic attributes.

[0066] Next, based on the seismic fracture attribute data A1 and the exploration drilling productivity data D, fracture characterization and exploration well production relationship E are established for different seismic layers. Result of establishing fracture and exploration well production relationship: fracture characterization and liquid production relationship tables are established for different seismic layers.

[0067] Specifically, the exploration drilling productivity data D is a statistical table of the liquid production of different wells in each layer. This table can be added with a column to form a new table E, which is the fracture attribute characterization value at the corresponding well point. The value is read from the seismic data A. Table E reflects the final result of the relationship between fractures and exploration well production.

[0068] Preferably, the well-seismic coincidence rate percentage parameter M can be used as the credibility parameter of the relationship between fracture characterization and exploration well production E. The two are combined to jointly guide the deployment of new well locations.

[0069] Furthermore, the relationship E between fracture characterization and exploration well production can be used to guide well site deployment. When deploying well sites, there are many actual reference factors, involving a wide range of subjects and reference factors such as earthquakes, geology, oil accumulation, sedimentation, reservoirs, and structures. Based on these factors, a possible well site area will be selected from the existing exploration area, and the area is generally represented by a target closure. The selection of the target closure is a routine basic work. The present invention provides the well point location based on the relationship between the reservoir and the fracture when selecting a point within the closure range for drilling a well, based on the basic selection of the target closure range.

[0070] Within the target trap range, the size of the fracture characterization value of each point can be seen. In the fracture characterization and exploration well production relationship E, the fracture characterization value corresponding to the well with higher fluid production will have an approximate value range, and this value range is set to Q. When deploying well locations, when fracture factors need to be considered after other factors have been considered, points within the Q value range can be selected for well deployment. Count the wells within the target trap range or at the same time count the wells in the adjacent areas, calculate the corresponding M value, and use it as the reliability factor for well deployment based on the fracture factor.

[0071] The above steps in the method of the present invention are only technical descriptions of the process and each process of the present invention. It should be noted that there is no logical sequence in the steps of obtaining seismic fracture attribute data A, core fracture data B, logging fracture data C and exploration drilling productivity data D, and relevant technicians can work at the same time during execution.

[0072] To facilitate understanding of the solution and effects of the embodiments of the present invention, a specific application example is given below. Those skilled in the art should understand that this example is only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0073] Figure 2 A schematic diagram of the relationship between the sweet spot of a fracture and the production capacity of an exploration well is given. In this embodiment, it will be demonstrated how to apply a variety of materials such as seismic, well logging, geology, and drilling, and how to use the principle of exploration integration to screen out seismic attributes that effectively characterize fractures based on well logging and core data, and then use this seismic attribute to compare and analyze with drilling production capacity. Establish the relationship between the sweet spot of a fracture and the production capacity of an existing exploration well. Then, using this relationship, deploy new well locations, improve the success rate of drilling, improve the quality of drilling, and provide a strong basis for efficient exploration and continuously promote cost reduction and efficiency improvement in oil and gas exploration.

[0074] This embodiment will describe how to establish a statistical table of fractures and existing drilling production, and use this table to guide the deployment of new wells.

[0075] Step 1: Prepare seismic data

[0076] In a certain oil and gas exploration block in the country, the fracture attribute data characterizing a certain target layer are as follows. Figure 3 As shown in the figure, four seismic attributes characterizing the fractures in the same target layer are: a-Likelyhood attribute hierarchical characterization of fractures, b-AFE fracture attribute, c-curvature attribute hierarchical characterization of fractures, and d-coherence attribute.

[0077] Step 2: Prepare core fracture data and find seismic attributes that match the core fracture data. Figure 4 This is a picture of the seismic target layer section in the Wb21 well according to an embodiment of the present invention, wherein a is the coring section 1113.21-1114.00m, b is the coring section 1095.11-1095.65m, and fractures are developed in the core. Figure 5 This is the core section 1032.79-1035.69 m in the Wb58 well according to an embodiment of the present invention, and no fractures are developed in the core of the target layer. Figure 6 This is the Wb59 well according to an embodiment of the present invention, the coring section is 1075-1094m, and no fractures are developed in the core of the target layer section.

[0078] Depend on Figure 3 and Figure 4 Figure 5 Figure 6 After comparing the core data, it was found that only the curvature and fracture attributes were consistent with the core data. Based on the above-mentioned characteristic 2, the other two seismic attributes were eliminated. Figure 7 Two earthquake attributes are selected and amplified. According to characteristic 1, after the curvature attribute is amplified, the cracks are already characterized as rods or blocks. The likelihoodhood earthquake crack attribute is more resistant to amplification and still presents linear characteristics, so the curvature attribute is discarded. It is also found that the likelihoodhood earthquake crack attribute meets characteristic 3. Its value range is (0, 1), 0 represents no cracks, places close to 0 represent small cracks, and places close to 1 reflect larger faults. Therefore, the likelihoodhood earthquake crack attribute is finally selected from several earthquake crack attributes.

[0079] Step 3: Prepare logging data: The existing data are imaging logging fracture characterization data of different target layers in the same well. Figure 8The target layer in the WB70 well according to an embodiment of the present invention, wherein a1 is the imaging logging result of the target layer one, a2 is the enlarged map of the likelihoodhood seismic attribute of the target layer one, b1 is the imaging logging result of the target layer two, b2 is the enlarged map of the likelihoodhood seismic attribute of the target layer two, c1 is the imaging logging result of the target layer three, c2 is the enlarged map of the likelihoodhood seismic attribute of the target layer three, the target layer one has no well-developed seismic fractures, the target layer two and layer three have relatively developed well-developed seismic fractures, and the well-seismic agreement is relatively high. Figure 8 It can be found that the fracture characterization results of seismic and logging data are consistent. Based on the evidence of the core data in the previous step, the reliability of the likelihood seismic attribute is once again proved from the perspective of FMI imaging logging.

[0080] Step 4:

[0081] (1) The screening of seismic attributes has been analyzed and compared in steps 1, 2, and 3 of the example above and will not be repeated here.

[0082] (2) Establishment of parameter M:

[0083] Table 1 is a statistical table of the consistency of fracture characterization between seismic data and core and well logging data. There are 13 points in all the existing core and imaging logging sections in the three target layers in the target area, and 11 points match. The well-seismic matching degree in the table is 85%, so the M value is 85%.

[0084]

[0085] Table 1

[0086] Step 5: In this embodiment, Table 2 is a table showing the relationship between the magnitude of seismic fracture characterization values ​​and the production of existing wells, reflecting the relationship E between fracture characterization and the production of exploration wells.

[0087]

[0088]

[0089] Table 2 (sorted from high to low by liquid production)

[0090] Table 2 shows a total of 39 well sections. The thickness of each target layer in seismic analysis generally ranges from a few meters to tens of meters. Therefore, when drilling, each seismic layer is further subdivided into many small layers for oil testing. 2It represents the second sub-layer in the third sub-layer after the second sub-layer of the seismic target layer is subdivided into three sub-layers. The seismic crack characterization value is divided into five levels from (0, 1). Level 0: The seismic crack characterization value is 0, indicating that the cracks are not developed. Level 1: The seismic crack characterization value is (0, 0.35], indicating that small cracks are developed. Level 2: The seismic crack characterization value is (0.35, 0.65], indicating that medium-scale cracks are developed. Level 3: The seismic crack characterization value is (0.65, 0.9], indicating that larger-scale cracks are developed. Level 4: The seismic crack characterization value is (0.9, 1], indicating larger-scale fault-level cracks, which are usually called faults.

[0091] Step 6: From Table 2, we can see that wells with higher production, in the likelyhood attribute characterization, are generally located on micro-scale fractures or in areas where fractures are not well developed. Therefore, when selecting new locations for well placement in the target trap, referring to the fracture factor, we can select locations where small-scale fractures are well developed or where fractures are not well developed in the likelyhood attribute. Fig. 9 As shown, the target well 3 is the well location to be deployed, and its reliability is 85%. Fig. 9 The target well 3 (WMB3) is deployed in the target trap in combination with the fracture sweet spot according to an embodiment of the present invention, wherein a is the position of the target well point 3 in the likelihoodhood attribute, and b is the position of the new well point in the target trap.

[0092] Following the above steps, we completed a deployment task of selecting new well sites with potential high production based on seismic, geological, logging, and known drilling data, following the principle of integration, and referring to fracture sweet spots.

[0093] The present invention follows the exploration integration principle of combining seismic, logging, drilling and geology, and uses seismic data to select fracture characterization technical results with fine characterization capabilities (such as maximum likelihood fractures or fusion fracture multi-scale characterization methods, etc.) from a large number of fracture characterization method data, combined with drilling core fracture related data (such as lithology pictures, fracture identification descriptions), logging and fracture related data (capable of characterizing fractures, such as FMI (Fullbore Formation MicroImager) imaging logging), and then combined with the statistical data of drilling test oil production, an effective fracture characterization method corresponding to the production is established.

[0094] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for establishing fracture characterization related to productivity, characterized in that: include: Obtain seismic fracture attribute data A, core fracture data B, logging fracture data C and exploration drilling productivity data D; Based on the core fracture data B and the logging fracture data C, the seismic fracture attribute data A is screened to obtain the seismic fracture attribute data A1; Based on the seismic fracture attribute data A1 and the exploration drilling production capacity data D, a fracture characterization and exploration well production relationship E is established for different seismic layers; It also includes setting a consistency value M between the seismic fracture attribute data A and the core fracture data B and the logging fracture data C, where the value range of M is 0%-100%; the consistency value M is used as a credibility parameter of the relationship E between fracture characterization and exploration well production, and together with the relationship E between fracture characterization and exploration well production, guides new well site deployment.

2. The method for establishing fracture characterization related to productivity according to claim 1, characterized in that: The obtaining of earthquake crack attribute data A1 includes: Each type of seismic fracture attribute data A is compared with the core fracture data B, and each type of seismic fracture attribute is compared with the well logging fracture data C. The seismic fracture attribute data A that has a high degree of consistency with both the core fracture data B and the well logging fracture data C is selected as the seismic fracture attribute data A1 that characterizes the fracture.

3. The method for establishing fracture characterization related to productivity according to claim 2, characterized in that: Further including: Extract the core fracture data B and the logging fracture data C that are consistent with the seismic fracture attribute data A1 as core fracture data B1 and logging fracture data C1 respectively; Extract the core fracture data B and the logging fracture data C that are inconsistent with the seismic fracture attribute data A1 as core fracture data B2 and logging fracture data C2 respectively; Satisfies: B=B1∪B2, C=C1∪C2.

4. The method for establishing fracture characterization related to productivity according to claim 1, characterized in that: The liquid production of different wells in each layer is counted using the exploration drilling productivity data D, and the result is matched with the fracture attribute characterization value at the well point in the seismic fracture attribute data A to form a fracture characterization and exploration well production relationship E; The relationship E between fracture characterization and exploration well production is used to guide well location deployment.

5. The method for establishing fracture characterization related to productivity according to claim 4, characterized in that: The range of fracture attribute characterization values ​​corresponding to wells with higher fluid production in the fracture characterization and exploration well production relationship E is Q. When fracture factors need to be considered when deploying well locations, points within the Q value range are selected for well deployment.

6. The method for establishing fracture characterization related to productivity according to claim 1, characterized in that: Acquiring the seismic fracture attribute data A includes acquiring seismic layer data and marking the depth section corresponding to the target layer; When obtaining the core fracture data B, logging fracture data C and exploration drilling productivity data D, data of the depth section corresponding to the seismic layer are counted.

7. A fracture characterization system for establishing productivity-related fractures, characterized in that: include: An acquisition unit, which acquires seismic fracture attribute data A, core fracture data B, well logging fracture data C, and exploration drilling productivity data D; A screening unit screens the seismic fracture attribute data A based on the core fracture data B and the logging fracture data C to obtain the seismic fracture attribute data A1; A characterization unit, based on the seismic fracture attribute data A1 and the exploration drilling productivity data D, establishes a fracture characterization and exploration well production relationship E for different seismic layers; The consistency value setting unit is used to set the consistency value M of the seismic fracture attribute data A with the core fracture data B and the logging fracture data C, and the value range of M is 0%-100%; the consistency value M is used as a credibility parameter of the relationship E between the fracture characterization and the exploration well production, and together with the relationship E between the fracture characterization and the exploration well production, guides the new well location deployment.

8. The system for establishing fracture characterization related to productivity according to claim 7, characterized in that: The obtaining of earthquake crack attribute data A1 includes: Each type of seismic fracture attribute data A is compared with the core fracture data B, and each type of seismic fracture attribute is compared with the well logging fracture data C. The seismic fracture attribute data A that has a high degree of consistency with both the core fracture data B and the well logging fracture data C is selected as the seismic fracture attribute data A1 that characterizes the fracture.

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