A fracture prediction method based on sand body combination type and distribution

Abstract

The present application relates to a kind of fracture prediction methods based on sand body combination type and distribution, the fracture prediction method includes: according to well logging interpretation and subzone division, in combination with existing core data, determine sand body combination type;Then according to sand body combination type, draw well correlation profile, identify the sand body combination type contained in the object to be measured;Afterwards, the fracture development density of different sand body combination type is calculated according to the data of known single well;Finally, the fracture development number of the sand body combination type contained in the object to be measured is calculated according to the data obtained.The fracture prediction method provided by the present application can accurately predict the fracture distribution law of single well imaging logging data and no seismic data, and is simple, fast, and can reduce the uncertainty of interwell fracture prediction.

Description

Technical Field

[0001] This invention relates to the field of geological engineering, and specifically to a method for predicting fractures based on sand body combination type and distribution. Background Technology

[0002] Currently, fracture prediction mainly relies on qualitative and semi-quantitative methods, including: ① Inter-well interpolation method: This method uses wellpoint imaging logging interpretation of fracture data as a basis and predicts the distribution of fractures between wells using mathematical interpolation techniques; ② Curvature method: This method uses wellpoint fracture data as a basis and uses curvature attributes such as maximum principal curvature, minimum principal curvature, and Gaussian curvature as constraints, employing stochastic statistical methods to predict the distribution of fractures between wells; ③ Rock fracture method: This method mainly uses rock fracture criteria and rock mechanics parameters to establish a stress field, using the stress field as a constraint to predict the distribution of fractures in the study area; ④ Seismic method: This method uses post-stack 3D seismic attributes such as amplitude, frequency, and phase as data as a basis and identifies fractures by judging the morphology of seismic reflection waves; ⑤ Fractal dimension method: This method calculates the fractal dimension of fractures by performing fractal processing on the fractures to predict the distribution of fractures; ⑥ Discretized fracture network modeling method: This method constructs an overall fracture model by forming fracture network groups composed of various fracture fragments distributed in 3D space, achieving a realistic and detailed description of the fracture system from its geometric morphology to its seepage behavior.

[0003] CN106842299A discloses a method for quantitative prediction of fractures based on seismic attributes. The method includes: acquiring three-dimensional seismic data and well logging data of the target layer, and obtaining a structural interpretation of the target layer based on these two sets of data; denoising the three-dimensional seismic data; calculating the coherence attributes of the denoised three-dimensional seismic data volume to obtain a coherence attribute volume; calculating the curvature attributes of the denoised three-dimensional seismic data volume to obtain a curvature attribute volume; performing value range correction on the coherence attribute volume; calculating a fracture prediction attribute volume based on the curvature attribute volume and the corrected coherence attribute volume; and predicting the spatial distribution information of underground fractures based on the fracture prediction attribute volume.

[0004] CN110954958A discloses a method and system for predicting fractures and faults. The method may include: performing frequency division processing on post-stack seismic data; obtaining an optimal dominant frequency data volume through forward modeling of seismic wavelets at different frequencies; obtaining a corresponding dip angle data volume based on the optimal dominant frequency data volume; performing fault enhancement attribute calculation based on dip angle control to obtain a fault enhancement attribute volume; calculating the maximum curvature value based on dip angle control to identify the planar distribution characteristics of fractures; and obtaining a quantitative relationship between fractures, faults, and the maximum curvature value based on the maximum curvature value of the fault and the maximum curvature value of the fracture.

[0005] However, the above methods have certain limitations: ① The above methods are based on data and are limited by single-well imaging logging data or seismic data; ② They mainly consider the prediction of structural fractures under stress, without considering the control effect of sand body combination type and distribution on fractures; ③ It is difficult to accurately predict inter-well fractures, and interpolation and random simulation are the main methods; ④ Fracture prediction methods are complex and cumbersome, requiring a lot of manpower and resources, and the results are highly uncertain.

[0006] Therefore, it is of great significance to provide a crack prediction method based on the combination type and distribution of sand bodies. Summary of the Invention

[0007] To address the above problems, the present invention aims to provide a fracture prediction method based on sand body combination type and distribution. Compared with the prior art, the fracture prediction method provided by the present invention can accurately predict the fracture distribution pattern when there is little single-well imaging logging data or no seismic data. It is also simple, fast, and reduces the uncertainty of inter-well fracture prediction.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] This invention provides a crack prediction method based on sand body combination type and distribution, the crack prediction method comprising the following steps:

[0010] (1) Perform logging interpretation and sub-layer division on the object to be measured and at least one single well in sequence to identify lithology and single sand bodies;

[0011] (2) Based on the existing core data, and according to the lithology and single sand bodies identified in step (1), draw and classify them in sequence to obtain the sand body combination type;

[0012] (3) Draw a well-to-well comparison profile based on the sand body combination type obtained in step (2), identify the sand body combination type contained in the object to be tested, and obtain the bottom depth and top depth of the sand body combination type contained in the object to be tested and the single well based on the logging curve on the well-to-well comparison profile.

[0013] (4) Perform imaging logging on the single well described in step (1) to obtain the number of fractures developed in the sand body combination type of the single well;

[0014] (5) Based on the number of fractures developed in step (4) and the bottom and top depths of the sand body combination type in a single well as described in step (3), calculate and statistically analyze them sequentially to obtain the fracture development density of different sand body combination types.

[0015] (6) Based on the crack development density obtained in step (5) and the bottom and top depths of the sand body combination type of the object to be tested obtained in step (3), calculate the number of cracks developed in the sand body combination type of the object to be tested.

[0016] The fracture prediction method provided by this invention addresses the limitations of existing fracture prediction methods, such as the influence of lithology on fracture development, the difficulty of predicting fractures between wells, and the complexity of prediction methods. This invention analyzes the control effect of sand body combination types and their distribution on fractures, and forms a fracture prediction method based on sand body combination types and distribution. This method can accurately predict the fracture distribution patterns in wells with limited logging data or no seismic data. The prediction method is simple and fast, reducing the uncertainty of inter-well fracture prediction.

[0017] Preferably, the well logging interpretation in step (1) includes well logging interpretation using well logging curves.

[0018] In this invention, the term "small layer division" refers to dividing a single sand body into individual small layers to achieve the function of identifying the single sand body.

[0019] Preferably, the logging curve in step (1) includes any one or at least two of natural gamma, resistivity, or sonic transit time, wherein typically, but not limitedly, it includes natural gamma and resistivity or natural gamma, resistivity, and sonic transit time.

[0020] In this invention, the existing core data includes data on lithology, structural components, sedimentary structures, and reservoir properties from existing studies.

[0021] Preferably, the drawing in step (2) includes: drawing a sand body combination pattern recognition chart containing lithology, natural gamma curves and resistivity curves.

[0022] Preferably, the division in step (2) includes: dividing different sand body combination types according to the curve shape in the sand body combination pattern recognition map.

[0023] Preferably, the sand body combination type in step (2) includes any one or at least two of the following: box-shaped, toothed box-shaped, bell-shaped, wide-finger-shaped, or funnel-shaped, wherein typically but not limitedly includes box-shaped and toothed box-shaped or toothed box-shaped, bell-shaped, and wide-finger-shaped.

[0024] In this invention, the box shape refers to the smooth and flat curve shape of the natural gamma and resistivity logging curves in the sand body combination pattern recognition chart.

[0025] In this invention, the toothed box shape refers to the curve shape in which the natural gamma and resistivity logging curves in the sand body combination pattern recognition chart exhibit a low-amplitude toothed appearance.

[0026] In this invention, the bell shape refers to the natural gamma logging curve showing a low-to-high curve shape and the resistivity logging curve showing a high-to-low curve shape in the sand body combination pattern recognition chart.

[0027] In this invention, the term "wide finger shape" refers to the high-amplitude toothed curve shape of the natural gamma and resistivity logging curves in the sand body combination pattern recognition chart.

[0028] In this invention, the funnel shape refers to the natural gamma logging curve showing a high-low curve shape at the bottom and the resistivity logging curve showing a low-high curve shape at the top in the sand body combination pattern recognition chart.

[0029] In this invention, the sand body combination type also includes composite shape, which is: excluding box shape, toothed box shape, bell shape, wide finger shape and funnel shape, the remaining sand body combination types are collectively referred to as composite shape.

[0030] In this invention, based on the sand body combination type in step (2), the principle of "similar morphology and isochronous comparison" is adopted in the horizontal direction to draw a well-connected comparison profile. The "similar morphology and isochronous comparison" refers to dividing sand body combinations with similar curve morphology into the same unit based on natural gamma and resistivity logging curves in strata formed in the same geological deposition period.

[0031] Preferably, the object to be measured in step (3) includes inter-well and / or gas wells without imaging logging.

[0032] In this invention, the fracture prediction method is applicable not only to single wells but also to inter-well systems.

[0033] Preferably, the method for calculating the crack development density in step (5) includes:

[0034]

[0035] Among them, N, D2, D1 and F are all characteristic parameters of the same sand body combination type;

[0036] N represents the number of cracks developed, in units of cracks;

[0037] D2 represents the bottom depth of the sand body assemblage type, in meters;

[0038] D1 represents the top depth of the sand body assemblage type, in meters;

[0039] F represents the crack development density, with units of cracks per meter.

[0040] In this invention, the crack development density calculated in step (5) is statistically analyzed and classified according to the sand body combination type to obtain the crack development density corresponding to different sand body combination types, which is generally a numerical range.

[0041] Preferably, the method for calculating the number of cracks in the object under test in step (6) includes:

[0042] Nx=F×(D 2x -D 1x );

[0043] Among them, Nx, D 2x D 1x Both F and Nx are characteristic parameters of the same sand body assemblage type. 2x and D 1x Here are the feature parameters of the object to be tested, and F is obtained from the statistics in step (5);

[0044] Nx represents the number of cracks developed, in units of cracks;

[0045] The D 2x Indicates the bottom depth of the sand body assemblage type, in meters;

[0046] The D 1x Indicates the top depth of the sand body assemblage type, in meters.

[0047] As a preferred embodiment of the present invention, the crack prediction method includes the following steps:

[0048] (1) Well logging interpretation is performed on the object to be measured and at least one single well using logging curves, and then the sub-layers are divided to identify lithology and single sand bodies;

[0049] The logging curves include any one or at least two of natural gamma, resistivity, or sonic transit time.

[0050] (2) Based on the existing core data, according to the lithology and single sand bodies identified in step (1), draw a sand body combination pattern recognition chart containing lithology, natural gamma curve and resistivity curve, and classify different sand body combination types according to the curve shape in the sand body combination pattern recognition chart.

[0051] The sand body combination type includes any one or at least two of the following: box-shaped, toothed box-shaped, bell-shaped, wide finger-shaped, or funnel-shaped.

[0052] (3) Draw a well-to-well comparison profile based on the sand body combination type obtained in step (2), identify the sand body combination type contained in the object to be tested, and obtain the bottom depth and top depth of the sand body combination type contained in the object to be tested and the single well based on the logging curve on the well-to-well comparison profile; the object to be tested includes inter-well and / or gas wells without imaging logging;

[0053] (4) Perform imaging logging on the single well described in step (1) to obtain the number of fractures developed in the sand body combination type of the single well;

[0054] (5) Calculate the fracture development density based on the number of fractures developed in step (4) and the bottom and top depths of the sand body combination type contained in the single well as described in step (3). The calculation method for the fracture development density includes:

[0055]

[0056] Among them, N, D2, D1 and F are all characteristic parameters of the same sand body combination type;

[0057] N represents the number of cracks developed, in units of cracks;

[0058] D2 represents the bottom depth of the sand body assemblage type, in meters;

[0059] D1 represents the top depth of the sand body assemblage type, in meters;

[0060] F represents the crack development density, in units of cracks / meter;

[0061] The calculated crack development density was statistically analyzed to obtain the crack development density corresponding to different sand body combination types according to the sand body combination type.

[0062] (6) Based on the crack development density obtained in step (5) and the bottom and top depths of the sand body combination type of the object to be tested obtained in step (3), calculate the number of cracks developed in the sand body combination type of the object to be tested;

[0063] The method for calculating the number of cracks developed in the object under test includes:

[0064] Nx=F×(D 2x -D 1x );

[0065] Among them, Nx, D 2x D 1x Both F and Nx are characteristic parameters of the same sand body assemblage type. 2x and D 1x Here are the feature parameters of the object to be tested, and F is obtained from the statistics in step (5);

[0066] Nx represents the number of cracks developed, in units of cracks;

[0067] The D 2x Indicates the bottom depth of the sand body assemblage type, in meters;

[0068] The D 1x Indicates the top depth of the sand body assemblage type, in meters.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] (1) The fracture prediction method provided by the present invention can predict multiple blocks and can predict the fracture development of gas wells without imaging logging or between wells with relatively accurate results; for gas wells without imaging logging, the fracture prediction method can predict the development characteristics of fractures in layers.

[0071] (2) The fracture prediction method provided by the present invention takes into account the control effect of lithology on fracture development, making the fracture prediction results more accurate; and the fracture prediction method constrained by lithology combination makes the inter-well fracture prediction results more reliable.

[0072] (3) The crack prediction method provided by the present invention is fast, convenient and universally operable, requires little data, is easy to obtain data, and has a wide range of applications. Attached Figure Description

[0073] Figure 1 This is a flowchart of the crack prediction method based on sand body combination type and distribution described in Embodiment 1 of the present invention;

[0074] Figure 2 This is the box-shaped sand body combination pattern recognition diagram described in Embodiment 1 of the present invention;

[0075] Figure 3 This is the pattern recognition diagram of the toothed box-shaped sand body combination described in Embodiment 1 of the present invention;

[0076] Figure 4 This is the bell-shaped sand body combination pattern recognition diagram described in Embodiment 1 of the present invention;

[0077] Figure 5 This is the wide-finger-shaped sand body combination pattern recognition diagram described in Embodiment 1 of the present invention;

[0078] Figure 6 This is the pattern recognition diagram of the funnel-shaped sand body combination described in Embodiment 1 of the present invention. Detailed Implementation

[0079] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0080] Example 1

[0081] This embodiment provides a fracture prediction method based on sand body combination type and distribution, applied to the BZ3-1X well in the Bozi 3 gas reservoir. The Bozi 3 gas reservoir is located within the Kelasu tectonic belt of the Tarim Basin and is a typical ultra-deep fractured tight sandstone gas reservoir with seven producing wells. Well BZ3-1X experienced a blowout in June 2020 and was shut down in April 2021, producing gas for 289 days with a cumulative production of 1.792 billion cubic meters. The calculated unobstructed flow rate of this well is 12.5-15.84 million cubic meters per day, seven times that of other wells in the block, and significantly higher than adjacent wells. Due to the blowout, imaging logging was not performed on well BZ3-1X, and the quality of the surrounding seismic data was poor, making it impossible to predict the fracture development characteristics using traditional methods.

[0082] The flowchart of the crack prediction method provided in this embodiment is as follows: Figure 1 As shown, it includes the following steps:

[0083] (1) Well logging interpretation was performed on BZ3-1X well and single wells such as BZ3-2X, BZ3-3X, BZ301 and BZ302 using logging curves such as natural gamma, resistivity and sonic transit time. Then, the layers were divided to identify the lithology and single sand bodies.

[0084] (2) Based on the existing core data, according to the lithology and single sand bodies identified in step (1), draw a sand body combination pattern recognition chart containing lithology, natural gamma curve and resistivity curve, and classify different sand body combination types according to the curve shape in the sand body combination pattern recognition chart.

[0085] The sand body combination types include box-shaped, toothed box-shaped, bell-shaped, wide-finger-shaped, and funnel-shaped, and their corresponding sand body combination pattern recognition diagrams are as follows: Figure 2-6 As shown; apart from box-shaped, toothed box-shaped, bell-shaped, wide finger-shaped and funnel-shaped, the remaining sand body combination type is composite;

[0086] (3) Draw a well-to-well comparison profile based on the sand body combination type obtained in step (2), identify the sand body combination type contained in well BZ3-1X, and obtain the bottom depth and top depth of the sand body combination type contained in well BZ3-1X and single wells such as BZ3-2X, BZ3-3X, BZ301 and BZ302 based on the logging curves on the well-to-well comparison profile.

[0087] (4) Perform imaging logging on the single wells BZ3-2X, BZ3-3X, BZ301 and BZ302 mentioned in step (1) to obtain the number of fractures developed in the sand body combination type of the single well;

[0088] (5) Calculate the fracture development density based on the number of fractures developed in step (4) and the bottom and top depths of the sand body combination type contained in the single well as described in step (3). The calculation method for the fracture development density includes:

[0089]

[0090] Among them, N, D2, D1 and F are all characteristic parameters of the same sand body combination type;

[0091] N represents the number of cracks developed, in units of cracks;

[0092] D2 represents the bottom depth of the sand body assemblage type, in meters;

[0093] D1 represents the top depth of the sand body assemblage type, in meters;

[0094] F represents the crack development density, in units of cracks / meter;

[0095] The fracture development density corresponding to the sand body combination type of single wells BZ3-2X, BZ3-3X, BZ301 and BZ302 was calculated using the above method, and the results were statistically analyzed. According to the sand body combination type, the fracture development density range corresponding to different sand body combination types was obtained, as shown in Table 1.

[0096] Table 1

[0097]

[0098] In Table 1, " / " indicates that the data is not available.

[0099] (6) Based on the fracture development density obtained in step (5) and the bottom and top depths of the sand body combination type in well BZ3-1X obtained in step (3), calculate the number of fractures developed in the sand body combination type in well BZ3-1X. The results are shown in Table 2.

[0100] The method for calculating the number of fractures in the BZ3-1X well includes:

[0101] Nx=F×(D 2x -D 1x );

[0102] Among them, Nx, D 2x D 1x Both F and Nx are characteristic parameters of the same sand body assemblage type. 2x and D 1x The characteristic parameters of well BZ3-1X are given by F, which is obtained from the statistics in step (5).

[0103] Nx represents the number of cracks developed, in units of cracks;

[0104] The D 2x Indicates the bottom depth of the sand body assemblage type, in meters;

[0105] The D 1x Indicates the top depth of the sand body assemblage type, in meters.

[0106] Table 2

[0107] 1 Toothed box 11-27 2 bell-shaped 3-7 3 Funnel-shaped 1-3 4 Wide finger shape 2-5 5 Composite 5-13 6 box 5-24 7 box 28-34 total 55-114

[0108] The fracture prediction method provided by this invention successfully predicted the development characteristics of peri-well fractures and the development pattern of inter-well fractures in the BZ3-1X well. The prediction method is fast, convenient and accurate, and the prediction results provide support for geological research and productivity evaluation of the BZ3-1X well.

[0109] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A crack prediction method based on sand body combination type and distribution, characterized in that, The crack prediction method includes the following steps: (1) Perform logging interpretation and sub-layer division on the object to be tested and at least one single well in sequence to identify lithology and single sand bodies; (2) Based on the existing core data, the lithology and single sand bodies identified in step (1) are drawn and divided in sequence to obtain the sand body combination type; The drawing includes: drawing a sand body combination pattern recognition chart containing lithology, natural gamma curves, and resistivity curves; The division includes: identifying different sand body combination types based on the curve shape in the sand body combination pattern recognition map; The sand body combination type includes any one or at least two of the following: box-shaped, toothed box-shaped, bell-shaped, wide finger-shaped, or funnel-shaped. (3) Draw a well-to-well comparison profile based on the sand body combination type obtained in step (2), identify the sand body combination type contained in the object to be tested, and obtain the bottom depth and top depth of the sand body combination type contained in the object to be tested and the single well based on the logging curve on the well-to-well comparison profile. (4) Perform imaging logging on the single well described in step (1) to obtain the number of fractures developed in the sand body combination type of the single well; (5) Based on the number of fractures developed in step (4) and the bottom and top depths of the sand body combination type in a single well as described in step (3), calculate and statistically analyze them sequentially to obtain the fracture development density of different sand body combination types. The method for calculating the crack development density includes: ； Among them, N, D2, D1 and F are all characteristic parameters of the same sand body combination type; N represents the number of cracks developed, in units of cracks; D2 represents the bottom depth of the sand body assemblage type, in meters; D1 represents the top depth of the sand body assemblage type, in meters; F represents the crack development density, in units of cracks / meter; (6) Based on the crack development density obtained in step (5) and the bottom and top depths of the sand body combination type of the object to be tested obtained in step (3), calculate the number of cracks developed in the sand body combination type of the object to be tested; The method for calculating the number of cracks developed in the object under test includes: ； Among them, Nx, D 2x D 1x Both F and Nx are characteristic parameters of the same sand body assemblage type. 2x and D 1x Here are the feature parameters of the object to be tested, and F is obtained from the statistics in step (5); Nx represents the number of cracks developed, in units of cracks; The D 2x Indicates the bottom depth of the sand body assemblage type, in meters; The D 1x Indicates the top depth of the sand body assemblage type, in meters.

2. The crack prediction method according to claim 1, characterized in that, The objects to be measured in step (3) include inter-well and / or gas wells without imaging logging.

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