Method and device for constructing post-stack fracture prediction model combining well logging and seismic data

Abstract

This specification relates to the fields of geophysics and hydraulic fracturing engineering, and in particular to a method and device for constructing a post-stack fracture prediction model by combining well and seismic data. The method includes: constructing an initial fracture seismic facies model of the study area based on post-stack seismic data and drilling information; obtaining fracture density interpretation data of sample wells and determining fracture seismic attributes of the sample wells; sampling the fracture seismic attributes into the initial seismic facies model to obtain a fracture seismic facies model constrained by fracture density; obtaining hydraulic fracturing pressure drop data of the sample wells and sampling the hydraulic fracturing pressure drop data into the fracture seismic facies model constrained by fracture density to obtain a fracture prediction model. This specification uses the static fracture density results of imaging logging or multi-pole acoustic logging data to constrain the post-stack seismic fracture facies model, and dynamically constrains the seismic fracture prediction model based on the hydraulic fracturing pressure drop to improve the prediction accuracy of natural fractures and achieve the prediction and evaluation of effective fractures.

Description

Technical Field

[0001] This specification relates to the fields of geophysics and hydraulic fracturing engineering, and in particular to a method and device for constructing a post-stack fracture prediction model combining well and seismic data. Background Art

[0002] Natural fractures in complex reservoirs such as glutenite bodies, carbonate fractured reservoirs, tight sandstones, and shale oils are developed to varying degrees, and the degree of fracture development to a certain extent determines the production of a single well. However, it is difficult to accurately predict the fracture development density, development characteristics, and distribution law only based on post-stack seismic data.

[0003] The prior art mentions a similar post-stack seismic fracture prediction method. A relationship is established between the fracture zone with a set fracture density and the variance volume attribute values at different window lengths by obtaining the post-stack seismic data volume of the work area, the structural interpretation horizons of the research intervals, the geology and logging data describing the fracture development characteristics. Then, the variance volume of the work area with the set fracture density is calculated, and the fracture variance attribute volume of the work area within the set fracture density range is obtained. Fracture prediction is carried out based on the obtained fracture prediction variance attribute volume of the work area. This method realizes the quantitative prediction of medium - large scale fracture zones. However, the prior art only predicts the fracture zone based on post-stack seismic data and fracture development characteristics, and the obtained prediction results still have the problem of inaccuracy. Summary of the Invention

[0004] To solve the problems in the prior art, the embodiments of this specification provide a method and device for constructing a post-stack fracture prediction model combining well and seismic data.

[0005] The embodiments of this specification provide a method for constructing a post-stack fracture prediction model combining well and seismic data. The method includes: constructing an initial fracture seismic facies model of the study area according to post-stack seismic data and drilling information; obtaining the fracture density interpretation data of the sample wells and determining the fracture seismic attributes of the sample wells; sampling the fracture seismic attributes into the initial seismic facies model to obtain a fracture seismic facies model constrained by fracture density; obtaining the hydraulic fracturing pressure drop data of the sample wells and sampling the hydraulic fracturing pressure drop data into the fracture seismic facies model constrained by fracture density to obtain a fracture prediction model.

[0006] According to one aspect of the embodiments of this specification, the drilling information includes at least one of lost circulation, well kick, or total hydrocarbon display.

[0007] According to one aspect of the embodiments of the present specification, constructing an initial model of fracture seismic facies based on post-stack seismic data and drilling information includes: obtaining post-stack seismic data subjected to denoising processing; respectively extracting seismic three-dimensional curvature, coherence, maximum likelihood volume, and ant volume attribute volumes from the post-stack seismic data; using the drilling information in the actual drilling process to calibrate the fracture seismic attributes of the study area, and selecting the fracture seismic attributes with a matching degree greater than a preset threshold as fracture seismic facies; establishing an initial model of the fracture seismic facies corresponding to the study area according to the seismic interpretation horizons and fault interpretation information of the post-stack seismic data.

[0008] According to one aspect of the embodiments of the present specification, establishing an initial model of the fracture seismic facies corresponding to the study area according to the seismic interpretation horizons and fault interpretation information of the post-stack seismic data includes: performing structural interpretation on the post-stack seismic data to determine the seismic interpretation horizons and fault interpretation information; establishing a grid model using the seismic interpretation horizons and the fault interpretation information, and inputting the fracture seismic facies into the grid model to obtain an initial model of the fracture seismic facies.

[0009] According to one aspect of the embodiments of the present specification, sampling fracture seismic attributes into the initial model of the seismic facies to obtain a fracture seismic facies model constrained by fracture density includes: normalizing the fracture density data of the sample well to obtain the fracture density of the known sample well; simulating the fracture density of the point to be estimated according to the initial model of the fracture seismic facies and the fracture density of the known sample well; sampling the fracture density of the known sample well and the fracture density of the point to be estimated into the initial model of the fracture seismic facies according to the vertical resolution of the sample well and the resolution of the initial model of the fracture seismic facies to obtain a fracture seismic facies model constrained by fracture density.

[0010] According to one aspect of the embodiments of the present specification, determining the fracture density of the point to be estimated as the fracture density to be simulated, and simulating the fracture density of the point to be estimated according to the initial fracture seismic facies model and the fracture density of the known sample wells includes: determining the weight of the fracture density to be simulated at the first adjacent point according to the covariance between the fracture density to be simulated between the first adjacent point and the second adjacent point, the covariance between the fracture density to be simulated and the fracture density of the known sample wells between the first adjacent point and the point to be estimated, and the covariance between the fracture density to be simulated between the point to be estimated and the second adjacent point; determining the weight of the fracture density of the known sample well at the point to be estimated according to the covariance between the fracture density of the known sample well and the fracture density to be simulated between the first adjacent point and the second adjacent point, the variance of the fracture density of the known sample well at the point to be estimated, and the covariance between the fracture density of the known sample well and the fracture density to be simulated between the point to be estimated; determining the co-Kriging mean of the fracture density to be simulated at the point to be estimated according to the weight of the fracture density to be simulated at the first adjacent point, the weight of the fracture density of the known sample well at the point to be estimated, the variable value of the fracture density to be simulated at the first adjacent point, and the variable value of the fracture density of the known sample well at the point to be estimated; and simulating the fracture density of the point to be estimated according to the co-Kriging means of the fracture density to be simulated at all points to be estimated in the initial seismic facies model.

[0011] According to one aspect of the embodiments of the present specification, obtaining the hydraulic fracturing pressure data of the sample well and sampling the hydraulic fracturing pressure data into the fracture seismic facies model constrained by the fracture density includes: extracting the pressure drop data from the hydraulic fracturing pressure construction curve of the sample well and normalizing it to obtain the normalized pressure drop data; determining the points corresponding to the normalized pressure drop data as the known points; using the Gaussian random co-Kriging simulation method to simulate the pressure drop data of the unknown points; and sampling the pressure drop data of the unknown points and the known points into the fracture seismic facies model constrained by the fracture density to construct a fracture prediction model.

[0012] The embodiments of the present specification further provide a method and apparatus for constructing a post-stack fracture prediction model by combining well and seismic data. The apparatus includes: a model construction unit for constructing an initial fracture seismic facies model of the study area according to the post-stack seismic data and the drilling information; a fracture seismic attribute determination unit for obtaining the fracture density interpretation data of the sample well and determining the fracture seismic attributes of the sample well; a fracture seismic facies model determination unit for sampling the fracture seismic attributes into the initial seismic facies model to obtain a fracture seismic facies model constrained by the fracture density; and a fracture prediction model determination unit for obtaining the hydraulic fracturing pressure drop data of the sample well and sampling the hydraulic fracturing pressure drop data into the fracture seismic facies model constrained by the fracture density to obtain a fracture prediction model.

[0013] An embodiment of this specification also provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for constructing a post-stack fracture prediction model combining well logging and seismic data is implemented.

[0014] An embodiment of this specification also provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the method for constructing a post-stack fracture prediction model combining well logging and seismic data.

[0015] This specification utilizes the advantages of high accuracy and high reliability in the interpretation of fracture density in the longitudinal direction of the wellbore using imaging logging and multi-pole acoustic logging data, as well as the advantages of post-stack three-dimensional seismic data in fracture prediction and dynamic fracture identification of sudden drops in hydraulic fracturing pressure. It effectively combines the advantages of logging, seismic, and hydraulic fracturing to perform fracture prediction combining well logging and seismic data. This method can improve the accuracy and reliability of fracture prediction and achieve the prediction and evaluation of effective fractures. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The following shows a flowchart of a method for constructing a post-stack fracture prediction model combining well logging and seismic data according to an embodiment of this specification;

[0018] Figure 2 The following shows a flowchart of a method for determining the correlation between an initial three-dimensional geological model and real gravity anomaly data according to an embodiment of this specification;

[0019] Figure 3 The following shows a flowchart of a method for adjusting an initial three-dimensional geological model according to an embodiment of this specification;

[0020] Figure 4 The following is a flowchart of a method for constructing an initial three-dimensional geological model according to an embodiment of this specification;

[0021] Figure 5 The following shows a flowchart of a method for determining the thermal physical parameters of a target area according to an embodiment of this specification;

[0022] Figure 6 The following shows a flowchart of a method for setting temperature boundaries for a three-dimensional geological model according to an embodiment of this specification;

[0023] Figure 7 The following is a schematic structural diagram of a post-stack fracture prediction model construction device according to an embodiment of the present specification;

[0024] Figure 8 The following is a fracture prediction map of a post-stack ant body according to an embodiment of the present specification;

[0025] Figure 9 The following is an interpretation diagram of the fracture density of WellB single-well imaging logging according to an embodiment of the present specification;

[0026] Figure 10 The following is a planar map of seismic fracture prediction constrained by the fracture density of imaging logging according to an embodiment of the present specification;

[0027] Figure 11 The following is a diagram of the sudden drop in pressure in the fracture development section of WellA by hydraulic fracturing according to an embodiment of the present specification;

[0028] Figure 12A The following is a high-precision planar map of fracture prediction constrained by the static fracture density of imaging logging and the dynamic pressure sudden drop of hydraulic fracturing according to an embodiment of the present specification;

[0029] Figure 12B The following is a high-precision cross-sectional view of fracture prediction constrained by the static fracture density of imaging logging and the dynamic pressure sudden drop of hydraulic fracturing according to an embodiment of the present specification;

[0030] Figure 13 The following is a schematic structural diagram of a computer device according to an embodiment of the present specification.

[0031] Explanation of the reference symbols in the drawings:

[0032] 701, model construction unit;

[0033] 702, fracture seismic attribute determination unit;

[0034] 703, fracture seismic facies model determination unit;

[0035] 704, fracture prediction model determination unit;

[0036] 1302, computer device;

[0037] 1304, processor;

[0038] 1306, memory;

[0039] 1308, drive mechanism;

[0040] 1310, input / output module;

[0041] 1312, input device;

[0042] 1314, output device;

[0043] 1316. Presentation device;

[0044] 1318. Graphical user interface;

[0045] 1320. Network interface;

[0046] 1322. Communication link;

[0047] 1324. Communication bus. Detailed implementation manners

[0048] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0049] It should be noted that the terms "first", "second", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of this specification described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0050] This specification provides method operation steps as described in the embodiments or flowcharts, but based on routine or non-creative labor, it may include more or fewer operation steps. The order of steps listed in the embodiments is only one way among the execution orders of numerous steps and does not represent the only execution order. When the actual system or device product is executed, it can be executed in the order shown in the embodiments or the drawings or executed in parallel.

[0051] It should be noted that the method for constructing the post-stack fracture prediction model combining well and seismic in this specification can be used in the field of oil and gas field exploration and development. This specification does not limit the application fields of the method and device for constructing the post-stack fracture prediction model combining well and seismic.

[0052] Figure 1The flowchart of a method for constructing a post-stack fracture prediction model combining wellbore and seismic analysis according to an embodiment of the present specification is shown, which specifically includes the following steps:

[0053] Step 101, constructing an initial model of fracture seismic phases in the study area based on post-stack seismic data and drilling information.

[0054] In this step, multiple seismic records are stacked according to a certain algorithm to obtain post-stack three-dimensional seismic data. Through stacking, the signal of the seismic data can be enhanced, making the features that may have been masked by noise more obvious. Furthermore, the post-stack seismic data is subjected to denoising processing such as smoothing or filtering to eliminate the influence of noise on the effective signal as much as possible, thereby improving the signal-to-noise ratio, enhancing the readability and analysis effect of seismic data, better understanding and interpretation of seismic data, and more accurately evaluating the properties and structure of underground rocks.

[0055] In practical applications, it is difficult to effectively identify fractures based on a single seismic attribute, and the fracture scales identified by different seismic attributes are limited, making it impossible to effectively identify the development of fractures at different scales. Therefore, the drilling information generated by the sample wells during the drilling process is combined with the post-stack seismic data to construct an initial model of the fracture seismic phase corresponding to the study area, which can effectively predict the degree of fracture development in the study area.

[0056] Specifically, drilling information includes: well leakage, well kick, full hydrocarbon display and other common situations in drilling projects. Among them, well leakage refers to a situation in which various working fluids (including drilling fluid, cement slurry, completion fluid and other fluids) directly enter the formation under the action of pressure difference during downhole operations such as drilling, cementing, testing or well repair. Well leakage includes three types: permeability loss, fracture loss and cave loss. Most drilling processes have leakage to varying degrees. Severe well leakage will cause the pressure in the well to drop, affecting normal drilling, causing well wall instability, inducing formation fluid to flow into the wellbore and may cause blowout.

[0057] Among them, a kick refers to the phenomenon that underground fluid or mud suddenly rushes into the wellbore due to pressure. During the drilling process, if the underground pressure exceeds the pressure in the wellbore, a kick may occur. The full hydrocarbon display means that the hydrocarbon component content is continuously monitored and presented to geologists in the form of a full hydrocarbon curve, which plays a very important role in immediately understanding the gas content of the formation. Especially in high-pressure, low-permeability fractured formations, the full hydrocarbon curve shows a high value display feature.

[0058] Step 102, obtaining fracture density interpretation data of the sample wells, and determining fracture seismic attributes of the sample wells.

[0059] In this step, the fracture density interpretation data of the sample well includes: single-well imaging logging or multipole acoustic logging fracture density interpretation. For example, obtain the single-well imaging logging or multipole acoustic logging fracture density interpretation of WellB, as shown in Figure 9 as follows.

[0060] Furthermore, normalize the fracture density interpretation data of the sample well and scale the fracture density interpretation data to the range of 0-1. Specifically, the normalization process is as follows:

[0061] B_norm = (B - B_min) / (B_max - B_min), where B_norm represents the normalized fracture density, and B_max and B_min represent the maximum and minimum fracture densities interpreted by imaging logging or multipole acoustic logging, respectively.

[0062] Step 103: Sample the fracture seismic attributes into the initial seismic facies model to obtain a fracture seismic facies model constrained by fracture density.

[0063] In this step, sample the fracture seismic attributes on the well into the grid model. Since the well trajectory length and scale ratio of the actual well are not consistent with the length and initial ratio of the grid model, it is necessary to sample the fracture seismic attributes on the sample well into the grid model according to a certain ratio. For example, the sampling rate of the fracture density interpretation data of WellB in the longitudinal direction of the wellbore is 0.125 m / point, that is, the wellbore trajectory is divided into points at intervals of 0.125 m. The grid model is divided into points at 1 m / point in the longitudinal direction. If there are 5 fractures at the 3000 m position and 4 fractures at the 3000.5 m position in the longitudinal direction of WellB wellbore, when sampling the fracture densities at these two positions into the grid model according to the division ratio of the grid model, that is, the fracture density value in the range of 3000-3001 m in the grid model is 4.5 fractures, so as to sample all the fracture seismic attributes on the sample well into the grid model.

[0064] Step 104: Obtain the hydraulic fracturing pressure drop data of the sample well, and sample the hydraulic fracturing pressure drop data into the fracture seismic facies model constrained by fracture density to obtain a fracture prediction model.

[0065] In this step, the hydraulic fracturing pressure drop data of the sample well is collected from the hydraulic fracturing pressure construction curve of the sample well. For example, according to the hydraulic fracturing pressure construction curve of WellA well, extract the well section with a pressure drop and the pressure values before and after the pressure drop, so as to obtain the hydraulic fracturing pressure drop data.

[0066] Similarly, normalize the pressure drop values of the well sections with a pressure drop in the hydraulic fracturing pressure data and scale the pressure drop values to the range of 0-1.

[0067] The normalization formula is as follows:

[0068] S_norm = (S - S_min) / (S_max - S_min); S_norm represents the normalized sudden drop value of hydraulic fracturing pressure; S_max and S_min represent the maximum and minimum values of the sudden drop value of hydraulic fracturing pressure respectively.

[0069] According to the principle of the same fracture density data, the normalized sudden drop data of hydraulic fracturing pressure are sampled into the fracture seismic facies model constrained by fracture density to obtain a fracture prediction model.

[0070] This specification aims at natural fractures in glutenite bodies, carbonate fractured reservoirs, tight sandstones, and shale oil reservoirs. By taking advantage of the high precision and reliability of imaging logging or multi-pole acoustic logging data in interpreting fracture density longitudinally in the wellbore, combined with the fact that post-stack three-dimensional seismic data can better reflect fracture information and the sudden drop of hydraulic fracturing pressure can dynamically reflect fracture information. The organic combination of the three can improve the accuracy and reliability of fracture prediction and achieve the prediction and evaluation of effective fractures.

[0071] Figure 2 The following shows a flowchart of a method for constructing an initial fracture seismic model in an embodiment of this specification, which specifically includes the following steps:

[0072] Step 201, obtain post-stack seismic data after denoising processing.

[0073] In this step, the post-stack seismic data after denoising processing are post-stack seismic data after interpretive preprocessing. Through interpretive preprocessing, the signal-to-noise ratio of the post-stack seismic data can be effectively improved, and the recognition accuracy for fracture identification of the current reservoir can be significantly improved.

[0074] Step 202, extract seismic three-dimensional curvature, coherence, maximum likelihood volume, and ant volume attributes from the post-stack seismic data respectively.

[0075] In this step, seismic attribute extraction is performed on the post-stack seismic data, and relevant geometric forms and characteristics of seismic waves are derived through mathematical transformation. Seismic attributes are related to the geometric forms of waveform-level seismic horizons. Among them, seismic attributes include at least one of high-precision coherence, maximum likelihood, maximum curvature, and ant body tracking.

[0076] In the embodiments of this specification, fracture feature enhancement processing is performed on post-stack seismic data, and the fault ant body attribute volume is extracted using the ant tracking algorithm. Specifically, a large number of electronic ants are scattered in the three-dimensional seismic data volume, and fracture conditions are set. According to the preset fracture conditions, the fracture traces in the seismic data are traced to interpret these electronic ants, and the fracture data obtained by tracking the electronic ants is integrated into the seismic attribute volume data, so as to link the information of the seismic attributes with the positions of the fractures.

[0077] In the embodiments of this specification, the three-dimensional seismic curvature attribute volume of the seismic data can be calculated according to the post-stack seismic data volume. The three-dimensional seismic curvature attribute reflects the degree of bending of the underground geological body in three-dimensional space. By measuring the curvature change of the seismic reflection interface, the deformation characteristics of the underground rock formation, such as folds, faults, and fractures, can be revealed. In this specification, the three-dimensional grid division is performed on the seismic data volume, and the curvature value is calculated at each grid point to calculate the curvature attribute of the seismic reflection interface.

[0078] In the embodiments of this specification, the seismic coherence attribute volume is extracted by measuring the similarity degree of seismic attributes between adjacent seismic traces. The seismic coherence attribute is a technique that reflects the continuity or fracture characteristics of the underground geological body. Areas with high coherence attributes indicate good continuity of the geological body, while areas with low coherence attributes may indicate fractures, cracks, or lithology changes. Specifically, the coherence algorithm is used to calculate the coherence attributes between adjacent seismic traces.

[0079] In the embodiments of this specification, a suitable statistical model is established according to the characteristics of the seismic data, and the maximum likelihood method principle is used to process the seismic data to obtain the maximum likelihood body attribute volume.

[0080] In summary, the extraction of the three-dimensional seismic curvature, coherence, maximum likelihood body, and ant body attribute volumes can all reflect different characteristics and information of the underground geological body. However, the recognition effects of different seismic attributes may be different. Therefore, it is necessary to select seismic attributes with good recognition effects for calculation to overcome the subjectivity in fracture interpretation and achieve automatic fracture recognition.

[0081] Step 203: Use the drilling information in the actual drilling process to calibrate the fracture seismic attributes of the study area, and select the fracture seismic attributes with a coincidence degree greater than the preset threshold as the fracture seismic facies.

[0082] In the embodiments of the present specification, in some embodiments, the drilling information includes lost circulation, well kick, and total hydrocarbon display. During the process of extracting seismic attribute volumes in step 202, it is not clear whether lost circulation, well kick, or other situations occur during the drilling process. Therefore, it is necessary to calibrate the three-dimensional curvature, coherence, maximum likelihood volume, and ant volume attributes in step 302 using the lost circulation, well kick, and total hydrocarbon display during the drilling process of the sample well, and select the seismic attributes with a matching degree greater than the preset threshold as the fracture seismic facies. The fracture seismic facies selected in this way has a high matching degree with the drilling process and accurately reflects the characteristics of the fracture development of the underground geological body.

[0083] In this specification, in addition to drilling information such as lost circulation and well kick, data such as logging and vertical seismic profiles can also be used to calibrate and calibrate the post-stack seismic data after denoising to ensure the consistency between the seismic reflection horizons and the geological horizons.

[0084] Step 204, according to the seismic interpretation horizons, fault interpretation information, and the fracture seismic facies of the post-stack seismic data, establish an initial model of the fracture seismic facies corresponding to the study area.

[0085] In this step, the corresponding seismic interpretation horizons and fault interpretation information are determined according to the post-stack seismic data. According to the seismic interpretation horizons, fault interpretation information, and grid model, the fracture seismic facies is sampled into the grid model to establish an initial model of the fracture seismic facies corresponding to the study area. For the description of establishing the initial model of the fracture seismic facies, see Figure 3 。

[0086] Figure 3 The following is a flowchart of a method for constructing an initial model of fracture seismic facies according to an embodiment of the present specification, which specifically includes the following steps:

[0087] Step 301, perform structural interpretation on the post-stack seismic data to determine the seismic interpretation horizons and fault interpretation information.

[0088] In this step, according to the wave field characteristics of the seismic profiles in the post-stack seismic data after preprocessing, different structural layers are divided. The geological attributes of each reflection horizon are identified and interpreted, including the thickness, lithology, contact relationship, etc. of the strata, so as to determine the seismic interpretation horizons. Further, faults are identified on the seismic profile and judged based on phenomena such as the dislocation, bifurcation, merger, and distortion of the reflection wave isophase axis. Using special waves (such as section waves and diffracted waves) and breakpoint control, the position and shape of the fault plane are determined, so as to determine the fault interpretation information.

[0089] Step 302, use the seismic interpretation horizons and the fault interpretation information to establish a grid model, and input the fracture seismic facies into the grid model to obtain an initial model of the fracture seismic facies.

[0090] In this specification, based on seismic interpretation horizons and fault interpretation information, and using a grid model, an initial model of fracture seismic facies corresponding to the study area is established.

[0091] Among them, vertically in the grid model, the fracture seismic facies of the study area need to be considered, and the fracture seismic facies are sampled into the grid model to optimize the vertical resolution of the grid model, thereby obtaining the initial model of fracture seismic facies.

[0092] Specifically, during the process of seismic exploration and acquisition of post-stack seismic data, the seismic CDP bin is determined.

[0093] According to the seismic CDP bin, the length of the longitudinal grid in the grid model is determined.

[0094] Among them, the seismic CDP bin is a basic unit divided for data acquisition and processing during the process of seismic data acquisition and processing. The size and shape of the CDP bin are determined according to the exploration needs and geological conditions. The CDP spacing (i.e., the size of the bin) will affect the resolution and imaging quality of seismic data. In order to ensure that the initial model of fracture seismic facies of the subsequent structure matches the seismic resolution facies, it is necessary to control the planar grid of the grid model to be consistent with the seismic CDP bin, so as to ensure that the longitudinal grid of the grid model is consistent with the seismic sampling rate.

[0095] Figure 4 The following is a flowchart of a method for constructing a fracture seismic facies model constrained by fracture density according to an embodiment of this specification, which specifically includes the following steps:

[0096] Step 401, normalize the fracture density data of the sample well to obtain the fracture density of the known sample well. In this step, using the imaging logging or multi-pole acoustic logging data of the sample well, statistical analysis of the fracture density of the sample well is carried out to obtain the fracture density data of the sample well. Among them, it can be collected that the fracture development density in the fracture-developed section of the sample well is different, or there is no fracture development in some well sections. Therefore, the normalized fracture density data of the sample well is called the fracture density of the known sample well, indicating that the points on the sample well where the fracture density data is collected are known, but the points without fracture density data or where the fracture density data cannot be collected can be understood as unknown points. For the points without fracture density data or where the fracture density data cannot be collected, they can be simulated or estimated according to the grids with fracture density data near the points. Therefore, in the subsequent steps, it is necessary to sample the fracture density data of each point into the initial model of fracture seismic facies constructed above.

[0097] Therefore, in this step, the points corresponding to the fracture density of the known sample well can be called adjacent points, and the unknown points can be called points to be estimated.

[0098] Step 402: Simulate the fracture density of the point to be estimated according to the initial fracture seismic facies model and the fracture density of the known sample wells.

[0099] In this step, the initial fracture seismic facies model corresponding to the sample well is used as the known model, and sequential Gaussian random co-kriging simulation is performed on the normalized fracture density of the known sample wells. Specifically, according to the known model and the fracture density data of adjacent points, the fracture density of the point to be estimated is simulated, so that the fracture densities of adjacent points and the point to be estimated can be determined, and further the construction of the fracture seismic facies model constrained by the fracture density can be realized. For specific descriptions, see Figure 5 .

[0100] Step 403: Sample the fracture density of the known sample wells and the fracture density of the point to be estimated into the initial fracture seismic facies model according to the vertical resolution of the sample wells and the resolution of the initial fracture seismic facies model, so as to obtain a fracture seismic facies model constrained by the fracture density. In this step, the vertical resolution of the well trajectory of the sample well can be determined according to the sampling rate of the fracture density interpretation data in the vertical direction of the wellbore. The resolution of the initial fracture seismic facies model can be adjusted according to the vertical resolution of the sample well, and then the fracture density of each point is sampled into the initial fracture seismic facies model.

[0101] Figure 5 The following is a flowchart of a method for simulating the fracture density of a point to be estimated according to an embodiment of the present specification, which specifically includes the following steps:

[0102] Step 501: Determine the weight of the fracture density to be simulated at the first adjacent point according to the covariance between the fracture density to be simulated between the first adjacent point and the second adjacent point, the covariance between the fracture density to be simulated and the fracture density of the known sample wells between the first adjacent point and the point to be estimated, and the covariance between the fracture density to be simulated between the point to be estimated and the second adjacent point.

[0103] Specifically, the weight of the fracture density to be simulated at the first adjacent point is determined according to the following formula:

[0104]

[0105] where N represents the total number of adjacent points, α represents the serial number of the adjacent point, represents the weight of the adjacent point x α , represents the covariance between the variable z2 to be simulated at the adjacent point x α and the adjacent point x β ; represents the covariance between the variable z2 to be simulated and the known variable z1 at the adjacent point x α and the point to be estimated x0, represents the variable z2 to be simulated at the adjacent point x βThe covariance between the point and the point to be estimated \(x_0\). Among them, the point to be estimated \(x_0\) represents the point where there is no sample well's fracture density data in the nearby grid, and the adjacent points represent the points where there is sample well's fracture density data in the nearby grid; specifically, \(x\) α is the first adjacent point in this step, and \(x\) β is the second adjacent point in this step.

[0106] The variable to be simulated \(z_2\) is the fracture density to be simulated, and the known variable is the fracture density of the known points in the initial model of fracture seismic facies.

[0107] Step 502: Determine the weight of the known sample well fracture density at the point to be estimated according to the covariance between the known sample well fracture density and the fracture density to be simulated between the first adjacent point and the second adjacent point, the variance of the known sample well fracture density at the point to be estimated, and the covariance between the known sample well fracture density and the fracture density to be simulated at the point to be estimated.

[0108] Specifically, determine the weight of the known sample well fracture density at the point to be estimated according to the following formula:

[0109] Among them,

[0110] represents the covariance between the variable to be simulated \(Z_2\) at the point to be estimated \(x_0\) and the second adjacent point \(x\) β ; represents the covariance between the variable to be simulated \(Z_2\) and \(z_1\) between the first adjacent point \(x\) α and the second adjacent point \(x\) β ; represents the variance of the known variable \(z_1\) at the point to be estimated \(x_0\); represents the covariance between the known variable \(z_1\) and the variable to be simulated \(z_2\) at the point to be estimated \(x_0\).

[0111] Step 503: Determine the co-Kriging mean of the fracture density to be simulated at the point to be estimated according to the weight of the fracture density to be simulated at the first adjacent point, the weight of the known sample well fracture density at the point to be estimated, the variable value of the fracture density to be simulated at the first adjacent point, and the variable value of the known sample well fracture density at the point to be estimated.

[0112] Specifically, determine the co-Kriging mean of the fracture density to be simulated at the point to be estimated according to the following formula:

[0113]

[0114] \(\sigma\) 2 (x_0)=Var[Z_2(x_0) * -Z_2(x_0)];

[0115] Among them, \(Z_2(x_0)\)* represents the co-Kriging mean of the variable Z2 to be simulated at the point x0 to be estimated, and Z2(x0) represents the variable value of the variable Z2 to be simulated at the point x0 to be estimated; Z2(x α ) represents the variable value of the variable Z2 to be simulated at the first neighboring point x α ; represents the weight coefficient of the variable Z2 to be simulated at the first neighboring point x α ; represents the weight of the known variable Z1 at the point x0 to be estimated.

[0116] Step 504: Simulate the fracture density at the points to be estimated according to the co-Kriging means of the fracture density to be simulated at all points to be estimated in the initial seismic facies model. The co-Kriging mean of the density to be simulated at the points to be estimated in Step 503 is the simulated fracture density at the points to be estimated.

[0117] Figure 6 The following is a flow chart of a method for constructing a fracture prediction model according to an embodiment of the present specification, which specifically includes the following steps:

[0118] Step 601: Extract the pressure drop data from the hydraulic fracturing pressure construction curve of the sample well and normalize it to obtain the normalized pressure drop data.

[0119] In this step, according to the hydraulic fracturing pressure construction curve of the sample well, the well section with a low pressure drop and the pressure values before and after the pressure drop are extracted. As Figure 11 shown in the pressure drop diagram of the hydraulic fracturing fracture development section of WellA well, between time 22 and time 30, the oil pressure curve of WellA well drops suddenly. From this, the data of the well section where the pressure suddenly drops and the pressure values before and after the pressure drop can be collected. After collecting the pressure drop data, the pressure drop data is normalized, and the normalized data can be called the normalized pressure drop data.

[0120] Step 602: Determine the points corresponding to the normalized pressure drop data as known points. In this step, the position of the pressure data value in the well section is determined as a known point, indicating that the pressure value at a certain position in the well section is known. For the other regions of the well section where the pressure is stable and there is no sudden pressure drop, since there is no pressure drop data, these regions are determined as unknown points.

[0121] Step 603: Use the Gaussian random co-Kriging simulation method to simulate the pressure drop data at the unknown points. In this step, in the same way as Figure 5 , the pressure drop data at the unknown points in the initial fracture seismic facies model is simulated to obtain the simulated pressure drop data at the unknown points. Specifically, it includes:

[0122] Determine the weight of the known pressure drawdown data at the point to be estimated based on the covariance between the known pressure drawdown data and the pressure drawdown data to be simulated between the first neighboring point and the second neighboring point, the variance of the known pressure drawdown data at the point to be estimated, and the covariance between the known pressure drawdown data and the pressure drawdown data to be simulated at the point to be estimated.

[0123] Based on the weight of the pressure drawdown data to be simulated at the first neighboring point, the weight of the known pressure drawdown data at the point to be estimated, the variable value of the pressure drawdown data to be simulated at the first neighboring point, and the variable value of the known pressure drawdown data at the point to be estimated, determine the co-Kriging mean of the pressure drawdown data to be simulated at the point to be estimated. The co-Kriging mean of the pressure drawdown data to be simulated at the point to be estimated is the simulated pressure drawdown data of the unknown point.

[0124] Step 604: According to the pressure drawdown data of the unknown point and the known points, sample into the fracture seismic facies model constrained by the fracture density, and construct a fracture prediction model. In this step, the pressure drawdown data of the unknown point and the known points are sampled into the fracture seismic facies model constrained by the fracture density according to the scale ratio of the initial fracture seismic facies model and the scale ratio of the fracture seismic facies model constrained by the fracture density.

[0125] In this specification, the advantages of dynamic fracture identification using hydraulic fracturing pressure drawdown are utilized, combined with the fracture density interpretation data in the wellbore vertical direction and the post-stack seismic data, to achieve the prediction and evaluation of effective fractures.

[0126] As Figure 7 shown is a schematic structural diagram of a device for constructing a post-stack fracture prediction model combining well and seismic data according to an embodiment of this specification. In this figure, the basic structure of the device for constructing a post-stack fracture prediction model combining well and seismic data is described. The functional units and modules therein can be implemented in software, or a general-purpose chip or a specific chip can be used to implement the construction of the post-stack fracture prediction model combining well and seismic data. The device specifically includes:

[0127] A model construction unit 701, configured to construct an initial fracture seismic facies model of the study area according to post-stack seismic data and drilling information;

[0128] A fracture seismic attribute determination unit 702, configured to obtain the fracture density interpretation data of the sample well and determine the fracture seismic attributes of the sample well;

[0129] A fracture seismic facies model determination unit 703, configured to sample the fracture seismic attributes into the initial seismic facies model to obtain a fracture seismic facies model constrained by the fracture density;

[0130] The fracture prediction model determination unit 704 is configured to obtain the hydraulic fracturing pressure drop data of the sample well, sample the hydraulic fracturing pressure drop data into a fracture seismic facies model constrained by fracture density, and obtain a fracture prediction model.

[0131] This solution utilizes the advantages of high precision and high reliability in the vertical wellbore fracture density interpretation of imaging logging and multi-pole acoustic logging data, and the advantages of post-stack three-dimensional seismic data in fracture prediction and dynamic fracture identification of hydraulic fracturing pressure drop. It effectively combines the advantages of logging, seismic, and hydraulic fracturing to perform well-seismic combined fracture prediction. This method can improve the accuracy and reliability of fracture prediction and achieve the prediction and evaluation of effective fractures.

[0132] Figure 8 This is a fracture prediction map of a post-stack ant body in an embodiment of this specification. The figure shows that after extracting the ant body attributes from the post-stack seismic data, fractures in three wells, namely WellA, WellB, and WellC, are predicted.

[0133] Figure 9 This is a single-well imaging logging fracture density interpretation map of WellB in an embodiment of this specification. The left half of the figure is a cumulative fracture schematic diagram of WellB, and the right half is a fracture density schematic diagram of WellB. It can be seen from the left half of the figure that fractures appear at a well depth of 3500 meters and are concentrated at an inclination angle of about 90°.

[0134] Figure 10 This is a planar map of seismic fracture prediction constrained by imaging logging fracture density in an embodiment of this specification. After sampling the fracture seismic attributes into the initial seismic facies model, the Figure 10 shown seismic fracture prediction result is obtained.

[0135] Figure 11 This is a pressure drop map of the fracture development section of WellA during hydraulic fracturing in an embodiment of this specification. In the figure, between time 22 and time 30, the oil pressure curve of WellA undergoes a pressure drop, from which the data of the well section where the pressure suddenly drops and the pressure values before and after the pressure drop can be collected.

[0136] Figure 12A This is a high-precision planar map of fracture prediction constrained by imaging logging static fracture density and hydraulic fracturing dynamic pressure drop in an embodiment of this specification; Figure 12B This is a high-precision cross-sectional map of fracture prediction constrained by imaging logging static fracture density and hydraulic fracturing dynamic pressure drop in an embodiment of this specification.

[0137] Such as Figure 13As shown, a computer device provided by an embodiment of this specification. The post-stack fracture prediction model construction method combining well and seismic data described in this application can be applied to the computer device. The computer device 1302 may include one or more processors 1304, such as one or more central processing units (CPUs), and each processing unit may implement one or more hardware threads. The computer device 1302 may also include any memory 1306 for storing any kind of information such as code, settings, data, etc. Non-limiting examples include any combination of the following: any type of RAM, any type of ROM, flash memory devices, hard disks, optical discs, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 1302. In one case, when the processor 1304 executes the associated instructions stored in any memory or combination of memories, the computer device 1302 may perform any operation of the associated instructions. The computer device 1302 also includes one or more drive mechanisms 1308 for interacting with any memory, such as a hard disk drive mechanism, an optical disc drive mechanism, etc.

[0138] The computer device 1302 may also include an input / output module 1310 (I / O) for receiving various inputs (via the input device 1312) and for providing various outputs (via the output device 1314). A specific output mechanism may include a presentation device 1316 and an associated graphical user interface (GUI) 1318. In other embodiments, the input / output module 1310 (I / O), the input device 1312, and the output device 1314 may not be included, and it may only be a computer device in a network. The computer device 1302 may also include one or more network interfaces 1320 for exchanging data with other devices via one or more communication links 1322. One or more communication buses 1324 couple the components described above together.

[0139] The communication link 1322 may be implemented in any way, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1322 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0140] Corresponding to Figures 1 to 6 the method in, an embodiment of this specification also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is run by a processor, it executes the steps of the above method.

[0141] An embodiment of this specification also provides a computer-readable instruction. When a processor executes the instruction, the program therein causes the processor to execute the method as Figures 1 to 6 shown.

[0142] It should be understood that in various embodiments of this specification, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this specification.

[0143] It should also be understood that in the embodiments of this specification, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this specification generally represents an "or" relationship between the associated objects before and after.

[0144] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this specification can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this specification.

[0145] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0146] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be electrical, mechanical, or other forms of connection.

[0147] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this specification.

[0148] In addition, each functional unit in the various embodiments of this specification may be integrated into a processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this specification, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0150] Specific embodiments are applied in this specification to elaborate on the principle and implementation manner of this specification. The description of the above embodiments is only used to help understand the method and its core idea of this specification; at the same time, for those of ordinary skill in the art, according to the idea of this specification, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this specification.

Claims

1. A method for constructing a post-stack fracture prediction model combining well and seismic data, characterized in that The method includes: Constructing an initial model of fracture seismic facies in the study area based on post-stack seismic data and well drilling information; Obtaining the fracture density interpretation data of the sample wells and determining the fracture seismic attributes of the sample wells; Sampling the fracture seismic attributes into the initial model of seismic facies to obtain a fracture seismic facies model constrained by fracture density; Obtaining the hydraulic fracturing pressure drop data of the sample wells and sampling the hydraulic fracturing pressure drop data into the fracture seismic facies model constrained by fracture density to obtain a fracture prediction model, which includes: Extracting the pressure drop data from the hydraulic fracturing pressure construction curve of the sample wells and normalizing it to obtain normalized pressure drop data; Determining the points corresponding to the normalized pressure drop data as known points; Using the Gaussian random co-kriging simulation method to simulate the pressure drop data of unknown points; Sampling the pressure drop data of unknown points and known points into the fracture seismic facies model constrained by fracture density to construct a fracture prediction model.

2. The method according to claim 1, wherein The well drilling information includes at least one of well loss, well kick or total hydrocarbon display.

3. The method according to claim 2, wherein Constructing an initial model of fracture seismic facies in the study area based on post-stack seismic data and well drilling information includes: Obtaining the post-stack seismic data after denoising; Extracting seismic three-dimensional curvature, coherence, maximum likelihood volume and ant volume attribute volumes from the post-stack seismic data respectively; Using the well drilling information in the actual drilling process to calibrate the fracture seismic attributes in the study area, and selecting the fracture seismic attributes with a matching degree greater than a preset threshold as fracture seismic facies; Establishing an initial model of fracture seismic facies corresponding to the study area according to the seismic interpretation horizons, fault interpretation information of the post-stack seismic data and the fracture seismic facies.

4. The method according to claim 3, wherein Establishing an initial model of fracture seismic facies corresponding to the study area according to the seismic interpretation horizons, fault interpretation information of the post-stack seismic data and the fracture seismic facies includes: Conducting structural interpretation on the post-stack seismic data to determine the seismic interpretation horizons and fault interpretation information; Establishing a grid model using the seismic interpretation horizons and the fault interpretation information, and inputting the fracture seismic facies into the grid model to obtain an initial model of fracture seismic facies.

5. The method according to claim 4, wherein Sampling the fracture seismic attributes into the initial model of seismic facies to obtain a fracture seismic facies model constrained by fracture density includes: Normalizing the fracture density data of the sample wells to obtain the fracture density of known sample wells; Simulating the fracture density of the points to be estimated according to the initial model of fracture seismic facies and the fracture density of known sample wells; Sampling the fracture density of known sample wells and the fracture density of the points to be estimated into the initial model of fracture seismic facies according to the vertical resolution of the sample wells and the resolution of the initial model of fracture seismic facies to obtain a fracture seismic facies model constrained by fracture density.

6. The method according to claim 5, wherein Determining the fracture density of the points to be estimated as the fracture density to be simulated, and simulating the fracture density of the points to be estimated according to the initial model of fracture seismic facies and the fracture density of known sample wells includes: Determine the weight of the to-be-simulated fracture density at the first neighboring point according to the covariance between the to-be-simulated fracture density at the first neighboring point and the second neighboring point, the covariance between the to-be-simulated fracture density and the known sample well fracture density at the first neighboring point and the to-be-estimated point, and the covariance between the to-be-simulated fracture density at the to-be-estimated point and the second neighboring point; Determine the weight of the known sample well fracture density at the to-be-estimated point according to the covariance between the known sample well fracture density and the to-be-simulated fracture density at the first neighboring point and the second neighboring point, the variance of the known sample well fracture density at the to-be-estimated point, and the covariance between the known sample well fracture density and the to-be-simulated fracture density at the to-be-estimated point; Determine the co-Kriging mean of the to-be-simulated fracture density at the to-be-estimated point according to the weight of the to-be-simulated fracture density at the first neighboring point, the weight of the known sample well fracture density at the to-be-estimated point, the variable value of the to-be-simulated variable fracture density at the first neighboring point, and the variable value of the known sample well fracture density at the to-be-estimated point; Simulate the fracture density of the to-be-estimated point according to the co-Kriging mean of the to-be-simulated fracture density at all to-be-estimated points in the initial seismic facies model.

7. An apparatus for constructing a post-stack fracture prediction model combining well and seismic data, characterized in that, The device includes: A model construction unit for constructing an initial fracture seismic facies model of the study area according to the post-stack seismic data and the drilling information; A fracture seismic attribute determination unit for obtaining the fracture density interpretation data of the sample well and determining the fracture seismic attributes of the sample well; A fracture seismic facies model determination unit for sampling the fracture seismic attributes into the initial seismic facies model to obtain a fracture seismic facies model constrained by the fracture density; A fracture prediction model determination unit for obtaining the hydraulic fracturing pressure drop data of the sample well and sampling the hydraulic fracturing pressure drop data into the fracture seismic facies model constrained by the fracture density to obtain a fracture prediction model, which includes: Extract the pressure drop data from the hydraulic fracturing pressure construction curve of the sample well and normalize it to obtain the normalized pressure drop data; Determine the points corresponding to the normalized pressure drop data as known points; Use the Gaussian random co-Kriging simulation method to simulate the pressure drop data of the unknown points; According to the pressure drop data of the unknown points and the known points, sample them into the fracture seismic facies model constrained by the fracture density to construct a fracture prediction model.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1 to 6.

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