A fracture attribute fusion method based on fractured low-to ultra-low permeability reservoirs

Abstract

The present application relates to the technical field of oil reservoir fracture attribute description, and particularly relates to a fracture attribute fusion method based on fractured low-permeability oil reservoirs. The method comprises the following steps: establishing a fracture intensity model by combining well data with seismic data, establishing a fracture network model according to the fracture intensity model, obtaining a natural fracture attribute volume, fitting to obtain shear wave time difference data, first establishing a density model, a longitudinal wave model and a shear wave model, then establishing a rock mechanics parameter model, establishing an overburden pressure and pore pressure model according to the overburden pressure and the pore pressure of the overlying strata of a single well, establishing a ground stress model and simulating and calculating artificial fracture attribute data according to the rock mechanics parameter model and the overburden pressure and pore pressure model; fusing the artificial fracture attribute data to the natural fracture attribute volume, obtaining a fused fracture attribute volume and establishing a target geological model, and simulating and calculating fracturing artificial fracture parameters. The method provided by the present application fuses the fracture attributes of natural fractures and artificial fractures, and improves the description accuracy of fracture attributes.

Description

Technical Field

[0001] This invention relates to the field of reservoir fracture attribute description technology, and in particular to a fracture attribute fusion method based on low- to ultra-low-permeability reservoirs with fractures. Background Technology

[0002] Currently, fracture description primarily focuses on the quantitative description of natural fractures. Based on the qualitative analysis of the development and occurrence of natural fractures in a block, the approach first comprehensively utilizes well logging and 3D seismic data. Then, single-well fracture interpretation is conducted using the "model method." In conjunction with this interpretation, a fracture strength model is established through a combination of well and seismic data. Based on this model, a natural fracture network model is built. Dynamic data is then used to dynamically correct the natural fracture network model, ultimately generating a fracture attribute volume. This attribute volume is used to quantitatively characterize fracture properties such as permeability.

[0003] Currently, the establishment of fracture attribute bodies faces two main problems: firstly, the modeling process only simulates the distribution characteristics of natural fractures, neglecting the impact of artificial fractures on reservoir stimulation, resulting in low accuracy in describing fracture attributes; secondly, because the structural fracture network model only includes natural fracture attributes and not artificial fracture attributes, the established fracture model affects the accuracy of numerical simulation of remaining oil, leading to insignificant potential tapping effects. Therefore, to address these shortcomings, a fracture attribute fusion method based on low- to ultra-low-permeability fractured reservoirs is proposed. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] This invention provides a fracture attribute fusion method based on fractured low- to ultra-low permeability reservoirs to overcome the problems of existing technologies that only simulate the distribution characteristics of natural fractures during fracture modeling, and whose structural fracture network models only contain natural fracture attributes without considering the reservoir stimulation and attributes of artificial fractures. This results in low accuracy of fracture attribute description, and the established fracture model affects the accuracy of numerical simulation of remaining oil, leading to insignificant potential tapping effects.

[0006] (II) Technical Solution

[0007] To address the above problems, this invention provides a fracture attribute fusion method for fractured low- to ultra-low permeability reservoirs, comprising:

[0008] Step S1: Determine the target study area, obtain conventional logging data and seismic trend body within the target study area, interpret and describe single-well fractures within the target study area, eliminate false fractures, establish a single-well fracture strength model based on the description results, and establish a fracture strength model within the target study area based on the single-well fracture strength model and the seismic trend body.

[0009] Step S2: Establish a natural crack network model based on the crack strength model in Step S1, and dynamically correct the natural crack network model in conjunction with the basic characteristics of the target study area to obtain the natural crack attribute body of the target study area.

[0010] Step S3: Select a well in the target study area of ​​Step S1 as the target well, obtain the X-Mac logging data of the target well, and fit the shear wave time difference data of the target study area based on the X-Mac logging data and the conventional logging data determined in Step S1.

[0011] Step S4: Based on the shear wave time difference data from step S3 and the conventional logging data from step S1, establish the density model, P-wave model, and shear wave model respectively, and establish the rock mechanics parameter model based on the three models.

[0012] Step S5: Based on the conventional logging data from Step S1, calculate the overlying strata pressure and formation pore pressure of a single well, and establish an overlying strata pressure and pore pressure model based on the overlying strata pressure and formation pore pressure of a single well.

[0013] Step S6: Based on the rock mechanics parameter model of Step S4 and the overlying strata pressure and pore pressure model of Step S5, establish the geostress model of the target study area in Step S1. By inputting different fracturing construction parameters into the geostress model, simulate and calculate the artificial fracture attribute data.

[0014] Step S7: Integrate the artificial fracture attribute data obtained in Step S6 into the natural fracture attribute body of Step S2 to obtain the fused fracture attribute body of the target study area. Establish a target geological model based on the fused fracture attribute body and simulate the artificial fracture parameters in the target study area of ​​Step S1 through the target geological model.

[0015] Preferably, in step S1, the conventional logging data includes gamma caliper curves, density logging curves, microsphere focused resistivity curves, sonic transit time curves, and lithology curves.

[0016] Preferably, the interpretation process of the single-well fracture includes determining the target sensitive curve and determining the single-well fracture identification mode. The target sensitive curve includes the microsphere focusing resistivity curve and the acoustic transit time curve. The identification method of the single-well fracture identification mode is the point-by-point mode method.

[0017] Preferably, the method for eliminating false cracks is to control the gamma caliper curve by up and down gradient probability, wherein the gradient range of the acoustic transit time does not exceed 0.003 and the gradient range of the microsphere focused resistivity does not exceed 0.01.

[0018] Preferably, the single-well fracture description result is a single-well fracture strength curve. The fracture strength calculation formula is applied to establish a single-well fracture strength model by discretizing the single-well fracture strength curve. Based on the single-well fracture strength model, a fracture strength model is established by constraining fault distance and seismic trend bodies such as ant bodies.

[0019] Preferably, in step S2, a natural fracture network model is established based on the fracture strength model and the natural fracture development characteristics of the target study area. The basic characteristics of the target study area include water content, development status, and tracers within the target study area. By combining the natural fracture network model with the water content, development status, and tracers within the target study area, the distribution trend of effective fractures between wells in the target study area is dynamically corrected. The natural fracture attribute body is obtained through the Oda analytical equivalent method, and the natural fracture attribute body includes the fracture equivalent permeability field.

[0020] Preferably, in step S3, the X-Mac logging data includes shear wave logging curves; a regression equation for the relationship between shear wave logging curves and longitudinal wave logging curves is derived using a linear statistical regression method; a shear wave calculation model for the target study area is established based on the regression equation; a shear wave time difference curve is calculated based on the shear wave calculation model; and shear wave time difference data is obtained based on the shear wave time difference curve.

[0021] Preferably, in step S4, density data is obtained from the density logging curve, P-wave time difference data is obtained from the P-wave logging curve, and density model, P-wave model and P-wave model are established between wells using seismic inversion trend constraints based on density data, P-wave time difference data and S-wave time difference data.

[0022] Preferably, a rock mechanical parameter model is established based on the density model, longitudinal wave model, and transverse wave model, in conjunction with the rock physical parameter equation. The rock mechanical parameter model includes Young's modulus model and Poisson's ratio model.

[0023] Preferably, in step S5, a continuous density curve for the entire formation is generated using density logging curves, and the overlying strata pressure of a single well in the target study area is calculated by density integration based on the determined continuous density curve for the entire formation; a formation compaction trend line is generated using sonic logging curves, and the formation pore pressure of a single well in the target study area is calculated using the Eaton method based on the determined formation compaction trend line.

[0024] Preferably, based on the determined overlying strata pressure and formation pore pressure of a single well, and with the structural depth of the target study area as the trend constraint, an overlying strata pressure and pore pressure model is established.

[0025] Preferably, the geostress model is calculated using rock mechanics and physics calculation formulas based on Young's modulus model, Poisson's ratio model, overlying strata pressure model, and pore pressure model within the target study area. The geostress model includes a minimum horizontal stress model and a maximum horizontal stress model.

[0026] Preferably, in step S6, the fracturing construction parameters include fracturing fluid dosage, pump pressure, proppant dosage, and discharge rate; the artificial fracture attribute data includes artificial fracture permeability.

[0027] Preferably, in step S7, the type of artificial crack parameters is adapted to the type of hydraulic fracturing construction parameters, and the artificial crack parameters include artificial crack length, width, and height.

[0028] Preferably, in step S7, the fused fracture attributes within the target study area are obtained based on the fused fracture attribute body. The fused fracture attributes are then used to establish a target geological model within the target study area through local mesh refinement. The target geological model includes a fused permeability model of natural and artificial fractures. The artificial fracture parameters within the target study area are calculated based on the fused permeability model of natural and artificial fractures. The calculation results of the artificial fracture parameters guide the fracturing design of fracture parameters such as the length of the main fracture and branch fractures and the conductivity within the target study area.

[0029] (III) Beneficial Effects

[0030] The fracture attribute fusion method based on fractured low- to ultra-low permeability reservoirs provided by this invention, through natural fracture modeling, geostress field modeling and fracture attribute fusion technology, accurately describes the distribution characteristics and laws of natural and artificial fractures, realizes the fusion of fracture attributes of natural and artificial fractures, thereby improving the accuracy of fracture attribute description, improving the speed of history fitting and the accuracy of numerical simulation, and guiding the block adjustment and potential tapping and fracturing parameter optimization design. Attached Figure Description

[0031] Figure 1 This is a flowchart of a fracture attribute fusion method based on fractured low- to ultra-low permeability reservoirs according to an embodiment of the present invention;

[0032] Figure 2 This is a diagram illustrating a single-well fracture identification pattern according to an embodiment of the present invention.

[0033] Figure 3 This is a diagram showing the interpretation results of fractures in a single well according to an embodiment of the present invention;

[0034] Figure 4 This is a diagram of a natural crack network model according to an embodiment of the present invention;

[0035] Figure 5 This is a linear statistical regression diagram of shear wave-longitudinal wave according to an embodiment of the present invention;

[0036] Figure 6 This is a diagram illustrating the results of interpreting the shear wave parameters in an embodiment of the present invention.

[0037] Figure 7 This is a diagram illustrating the results of rock mechanics parameter interpretation in an embodiment of the present invention.

[0038] Figure 8 This is a model diagram illustrating the fusion properties of natural and artificial cracks in an embodiment of the present invention.

[0039] Figure 9 This is a schematic diagram of a horizontal well fracturing design according to an embodiment of the present invention;

[0040] Figure 10 This is a graph showing the predicted and actual production curves of horizontal wells according to an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Figure 1 This is a flowchart of a fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to an embodiment of the present invention, as shown below. Figure 1 As shown, this invention provides a fracture attribute fusion method for fractured low- to ultra-low permeability reservoirs, comprising:

[0043] Step S1: Determine the target study area, obtain conventional logging data and seismic trend body within the target study area, interpret and describe single-well fractures within the target study area, eliminate false fractures, establish a single-well fracture strength model based on the description results, and establish a fracture strength model within the target study area based on the single-well fracture strength model and the seismic trend body.

[0044] Step S2: Establish a natural crack network model based on the crack strength model in Step S1, and dynamically correct the natural crack network model in conjunction with the basic characteristics of the target study area to obtain the natural crack attribute body of the target study area.

[0045] Step S3: Select a well in the target study area of ​​Step S1 as the target well, obtain the X-Mac logging data of the target well, and fit the shear wave time difference data of the target study area based on the X-Mac logging data and the conventional logging data determined in Step S1.

[0046] Step S4: Based on the shear wave time difference data from step S3 and the conventional logging data from step S1, establish the density model, P-wave model, and shear wave model respectively, and establish the rock mechanics parameter model based on the three models.

[0047] Step S5: Based on the conventional logging data from Step S1, calculate the overlying strata pressure and formation pore pressure of a single well, and establish an overlying strata pressure and pore pressure model based on the overlying strata pressure and formation pore pressure of a single well.

[0048] Step S6: Based on the rock mechanics parameter model of Step S4 and the overlying strata pressure and pore pressure model of Step S5, establish the geostress model of the target study area in Step S1. By inputting different fracturing construction parameters into the geostress model, simulate and calculate the artificial fracture attribute data.

[0049] Step S7: Integrate the artificial fracture attribute data obtained in Step S6 into the natural fracture attribute body of Step S2 to obtain the fused fracture attribute body of the target study area. Establish a target geological model based on the fused fracture attribute body and simulate the artificial fracture parameters in the target study area of ​​Step S1 through the target geological model.

[0050] In this fusion method, in step S1, conventional logging data include gamma caliper curves, density logging curves, microsphere focused resistivity curves, sonic transit time curves, and lithology curves.

[0051] In practical applications, the interpretation process of single-well fractures includes determining the target sensitive curve and determining the single-well fracture identification mode. The target sensitive curve includes the microsphere focusing resistivity curve and the sonic transit time curve. The identification method for single-well fracture identification mode is the point-by-point mode method.

[0052] In this fusion method, the method to eliminate false fractures is to control the gamma caliper curve by up and down gradient probability, with the gradient range of the acoustic transit time not exceeding 0.003 and the gradient range of the microsphere focusing resistivity not exceeding 0.01.

[0053] It is important to note that in practical applications, the formula for calculating the upgradient is:

[0054] TIDU RXO _ UP =Abs((RXO) (n-1) -RXO) / RXO) (1);

[0055] TIDU AC_UP =Abs((AC (n-1) -AC) / AC) (2);

[0056] Furthermore, the formula for calculating the downhill gradient is:

[0057] TIDU RXO_DOWN =Abs((RXO-RXO) (n+1) ) / RXO) (3);

[0058] TIDU AC_DOWN =Abs((AC-AC) (n+1) ) / AC) (4).

[0059] In this fusion method, the single-well fracture description result is the single-well fracture strength curve. The fracture strength calculation formula is applied to establish a single-well fracture strength model by discretizing the single-well fracture strength curve. Based on the single-well fracture strength model, a fracture strength model is established by constraining the fault distance and seismic trend bodies such as ant bodies.

[0060] In practical applications, in step S2, a natural fracture network model is established based on the fracture strength model and the natural fracture development characteristics of the target study area. The basic characteristics of the target study area include water content, development status, and tracers. By combining the natural fracture network model with the water content, development status, and tracers in the target study area, the distribution trend of effective fractures between wells in the target study area is dynamically corrected. The natural fracture attribute body is obtained through the Oda analytical equivalent method, and the natural fracture attribute body includes a fracture equivalent permeability model.

[0061] In this fusion method, in step S3, the X-Mac logging data includes shear wave logging curves; the regression equation between the shear wave logging curves and the longitudinal wave logging curves is derived by linear statistical regression method; a shear wave calculation model for the target study area is established based on the regression equation; the shear wave time difference curve is calculated based on the shear wave calculation model; and shear wave time difference data is obtained based on the shear wave time difference curve.

[0062] In practical applications, in step S4, density data is obtained from the density logging curve, and P-wave transit time data is obtained from the P-wave logging curve. Based on the density data, P-wave transit time data, and S-wave transit time data, density models, P-wave models, and S-wave models are established between wells using seismic inversion trend constraints. It is important to note that based on the density model, P-wave model, and S-wave model, and in conjunction with the rock physics parameter equations, a rock mechanics parameter model is established. This rock mechanics parameter model includes Young's modulus model and Poisson's ratio model.

[0063] The formula for calculating the Poisson's ratio of a rock is as follows:

[0064]

[0065] Furthermore, the formula for calculating Young's modulus is:

[0066]

[0067] In this fusion method, in step S5, a continuous density curve of the entire formation is generated using density logging curves. Based on the determined continuous density curve of the entire formation, the overlying strata pressure of a single well in the target study area is calculated by density integration. A formation compaction trend line is generated using sonic logging curves. Based on the determined formation compaction trend line, the formation pore pressure of a single well in the target study area is calculated using the Eaton method.

[0068] The formula for calculating formation pore pressure in a single well using the Eaton method is as follows:

[0069] Pf = P OV -(P OV -P W (Δtn / Δt) 3 (9).

[0070] In practical applications, based on the determined overlying strata pressure and formation pore pressure of a single well, and with the structural depth of the target study area as the trend constraint, a model of overlying strata pressure and pore pressure is established.

[0071] In this fusion method, the geostress model is calculated using rock mechanics and physics calculation formulas based on Young's modulus model, Poisson's ratio model, overlying strata pressure model and pore pressure model in the target study area. The geostress model includes a minimum horizontal stress model and a maximum horizontal stress model.

[0072] The calculation formula for the minimum horizontal stress model is as follows:

[0073]

[0074] Furthermore, the calculation formula for the maximum horizontal stress model is as follows:

[0075]

[0076] In practical applications, in step S6, the fracturing construction parameters include fracturing fluid dosage, pump pressure, proppant dosage, and discharge rate; the artificial fracture attribute data includes artificial fracture permeability.

[0077] In this fusion method, in step S7, the type of artificial fracture parameters is adapted to the type of fracturing construction parameters, and the artificial fracture parameters include the length, width, and height of the artificial fracture.

[0078] In practical applications, in step S7, the fused fracture attributes within the target study area are obtained based on the fused fracture attribute volume. The fused fracture attributes are then used to establish a target geological model within the target study area through local mesh refinement. The target geological model includes a fused permeability model of natural and artificial fractures. Based on the fused permeability model of natural and artificial fractures, the artificial fracture parameters within the target study area are calculated. The calculation results of the artificial fracture parameters guide the fracturing design of fracture parameters such as the length of the main fracture and branch fractures and the conductivity within the target study area.

[0079] In this fusion method, the fracture results of a single well are described by coring and imaging, and the fracture identification mode is determined. The fracture interpretation of a single well is completed by combining production dynamic data. The effective fracture distribution area is corrected by comprehensively applying dynamic data, and the fracture strength volume is constrained by multiple attributes. A discrete fracture network model is established to output the equivalent attribute field.

[0080] In practical applications, logging data is obtained through X-Mac logging and conventional logging. A regression equation between shear wave time difference and conventional logging curve is established using multivariate statistical regression to obtain shear wave time difference data for each well. A three-dimensional model of density-P-wave-S-wave is established using seismic inversion body as trend constraint. Poisson's ratio, Young's modulus, and minimum horizontal stress model are then calculated using formulas.

[0081] In this fusion method, based on the research results on the description and modeling of natural and artificial fractures in the block, the properties of artificial and natural fractures are fused, and the fused fracture model is output in a coarse manner to guide the history fitting of reservoir numerical simulation and fracturing design, thereby improving the fitting accuracy and efficiency.

[0082] This invention provides a fracture attribute fusion method for fractured low- to ultra-low permeability reservoirs, effectively simulating the propagation patterns of artificial fractures and the spatial distribution characteristics of fracture fusion attributes between natural and artificial fractures. This improves the accuracy of fracture attribute description, lays the foundation for reservoir numerical simulation and fracturing parameter optimization design, and enhances block development effectiveness. Figures 2-10 As shown, the working principle of this fracture attribute fusion method based on fractured low- to ultra-low permeability reservoirs is described in detail below:

[0083] Step 1: Determine the target study area, obtain conventional logging data and seismic trend body within the target study area, interpret and describe single-well fractures within the target study area, eliminate false fractures, establish a single-well fracture strength model based on the interpretation results, and establish a fracture strength model within the target study area based on the single-well fracture strength model and the seismic trend body.

[0084] Step 2: Establish a natural crack network model using the crack strength model, and dynamically correct the natural crack network model in conjunction with the basic characteristics of the target study area to obtain the natural crack attribute volume of the target study area;

[0085] Step 3: Select a well within the target study area as the target well, obtain the X-Mac logging data of the target well, and fit the X-Mac logging data and conventional logging data to obtain the shear wave time difference data of the target study area;

[0086] Step 4: Based on the shear wave time difference data and conventional logging data, establish density model, P-wave model and shear wave model respectively, and establish rock mechanics parameter model based on the three models;

[0087] Step 5: Based on conventional logging data, calculate the overlying strata pressure and formation pore pressure of a single well, and establish overlying strata pressure and pore pressure models based on the overlying strata pressure and formation pore pressure of a single well.

[0088] Step 6: Based on the rock mechanics parameter model and the overlying strata pressure and pore pressure model, establish the geostress model of the target study area. By inputting different fracturing construction parameters into the geostress model, simulate and calculate the artificial fracture property data.

[0089] Step 7: Integrate the artificial fracture attribute data into the natural fracture attribute volume to obtain the integrated fracture attribute volume of the target study area. Based on the integrated fracture attribute volume, establish a target geological model and simulate the artificial fracture parameters in the target study area through the target geological model.

[0090] In this embodiment, the microsphere-focused resistivity curve and the acoustic transit time curve are selected as the target sensitive curves. The microsphere-focused resistivity reflects the resistivity of the flushed zone and is less affected by the surrounding rock and oil and gas. The acoustic transit time curve is less affected by the wellbore and reflects the reservoir's physical properties. In practical applications, the presence of fractures in the rock causes a decrease in resistivity and an increase in acoustic transit time. A point-by-point model method is used to identify fractures in a single well, such as... Figure 2 As shown, seven patterns of fracture existence were identified, which were further verified through imaging and core sampling.

[0091] Furthermore, false fractures were eliminated and single-well fracture identification was completed by utilizing uplink and downlink gradient probability control and gamma caliper curves. Specifically, the gradient for identifying fractures with the minimum sonic transit time was generally 0.003, and the gradient for identifying fractures with the minimum resistivity of the flushed zone was generally 0.01. These results were verified through core and imaging logging, with a consistency rate exceeding 65%.

[0092] In this embodiment, the fracture description results of a single well are used as the basis, such as Figure 3 As shown, the fracture strength curve of a single well is calculated using the fracture strength calculation formula. The fracture strength curve of a single well is discretized to establish a single-well fracture strength model. Under the constraints of seismic trend bodies such as fault distance and ant bodies, a fracture strength model is established.

[0093] In practical applications, such as Figure 4As shown, based on the fracture strength model, a fracture network distribution model is established according to the natural fracture development characteristics in the target study area. On this basis, by combining data such as block water content, development status and tracer, the distribution trend of effective fractures between wells is corrected. The Oda analytical equivalent method is used to obtain the fracture equivalent permeability field.

[0094] In this embodiment, X-MAC logging data is used to obtain the shear wave curves of all wells in the target study area. The rock physical parameters of each well are then calculated using rock physical parameter calculation formulas, laying the foundation for establishing a rock mechanics model. In practical applications, such as... Figure 5 As shown, using X-MAC logging data, the regression equation for the relationship between shear waves and protruding waves was derived using linear statistical regression. Shear wave calculation models for other wells in the study area were established, and the shear wave time difference curves for each well were obtained, as shown below. Figure 6 As shown, this lays the foundation for interpreting rock physical parameters such as Poisson's ratio and Young's modulus of single wells.

[0095] In practical applications, based on the interpretation of rock physical parameters in a single well, density models, P-wave models, and S-wave models are established between wells using seismic data constraints. Rock mechanics models are calculated based on rock mechanics physical equations to accurately describe the distribution trend of geostress and improve the accuracy of geostress prediction.

[0096] In this embodiment, as Figure 7 As shown, the rock physical parameters of a single well are obtained using the rock mechanics and physics equations, including Young's modulus and Poisson's ratio, which are related to rock elasticity; a continuous density curve for the entire formation is generated using density logging curves, and the overlying strata pressure of a single well is calculated using density integration; a formation compaction trend line is generated using sonic logging curves, and the formation pore pressure of a single well is calculated using the Eaton method.

[0097] In this embodiment, the common feature of the overlying strata pressure and pore pressure models is that the planar changes are gentle, and there is a tendency for them to increase from top to bottom in the vertical direction. Therefore, based on the rock mechanics single-well logging interpretation results, the overlying strata pressure and pore pressure models are established with the structural depth as the trend constraint.

[0098] In practical applications, based on the rock mechanics parameter model and the overlying strata pressure and pore pressure model, the geostress model is calculated using rock mechanics physical calculation formulas on the basis of the rock physics model. The geostress model includes the minimum horizontal stress model and the maximum horizontal stress model.

[0099] In this embodiment, a profile model of mechanical parameters and principal stress of a single well is cut out along the direction of the maximum horizontal principal stress. Different fracturing construction parameters are input to simulate and describe the distribution characteristics of the three-dimensional spatial morphology and attribute parameters of artificial fractures, and obtain the attribute data of artificial fractures in the target study area.

[0100] In this embodiment, as Figure 8 As shown, artificial fracture attribute data is imported into the fracture mesh model to achieve fracture attribute fusion. Based on the fracture attribute fusion, a target geological model is established by local mesh refinement, which improves the simulation accuracy and efficiency. The target geological model includes a permeability model that integrates natural and artificial fractures. Based on the permeability model that integrates natural and artificial fractures, numerical simulation of artificial fractures is carried out to simulate and calculate the fracture height, fracture length, etc. of artificial fractures, optimize the fracturing design, and ultimately achieve the goal of complex reservoir stimulation.

[0101] In practical applications, the target study area is block B, such as... Figure 9 As shown, fracturing simulation determined that the design half-fracture length for horizontal wells is 100-140m; the sand injection intensity for horizontal wells to reach the required fracturing scale is 2.2-2.6m. 3 / m.

[0102] Table 1. Optimization of Horizontal Well Fracturing Design in Block B

[0103]

[0104] In this embodiment, Table 1 shows the optimization of horizontal well fracturing design in Block B. As shown in Table 1, Figure 10 As shown, based on the fusion fracture properties of natural and artificial fractures, a high-precision geological model was established by local mesh refinement. The numerical simulation single-well fitting accuracy was improved by 3-5 percentage points, reaching 86.8%, and the fitting efficiency was improved by 20-40%. The initial average daily oil production of the four horizontal wells was predicted to be 8.5t. In the initial stage after fracturing and production, i.e., the first three months, the actual average daily oil production of the single well was 16.9t, which was higher than the predicted target of 8.5t, and good results were achieved.

[0105] In practical applications, by using the combined fracture properties of natural and artificial fractures for fracturing simulation, fracture parameters such as the length of the main fracture and branch fractures and the conductivity were optimized. The spatial geometry, conductivity, and proppant distribution of the fracture network were described in detail, and a reasonable fracturing scale was determined to maximize the fracture-controlled reserves.

[0106] The fracture attribute fusion method based on fractured low- to ultra-low permeability reservoirs provided by this invention, through natural fracture modeling, geostress field modeling and fracture attribute fusion technology, accurately describes the distribution characteristics and laws of natural and artificial fractures, realizes the fusion of fracture attributes of natural and artificial fractures, thereby improving the accuracy of fracture attribute description, improving the speed of history fitting and the accuracy of numerical simulation, and guiding the block adjustment and potential tapping and fracturing parameter optimization design.

[0107] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention, and the patent protection scope of the present invention should be defined by the claims.

Claims

1. A method for fracturing properties in fractured low- to ultra-low permeability reservoirs, characterized in that, include: Step S1: Determine the target study area, obtain conventional logging data and seismic trend body within the target study area, interpret and describe single-well fractures within the target study area, eliminate false fractures, establish a single-well fracture strength model based on the description results, and establish a fracture strength model within the target study area based on the single-well fracture strength model and the seismic trend body. Step S2: Establish a natural crack network model based on the crack strength model in Step S1, and dynamically correct the natural crack network model in conjunction with the basic characteristics of the target study area to obtain the natural crack attribute body of the target study area. Step S3: Select a well in the target study area of ​​Step S1 as the target well, obtain the X-Mac logging data of the target well, and fit the shear wave time difference data of the target study area based on the X-Mac logging data and the conventional logging data determined in Step S1. Step S4: Based on the shear wave time difference data from step S3 and the conventional logging data from step S1, establish the density model, P-wave model, and shear wave model respectively, and establish the rock mechanics parameter model based on the three models. Step S5: Based on the conventional logging data from Step S1, calculate the overlying strata pressure and formation pore pressure of a single well, and establish an overlying strata pressure and pore pressure model based on the overlying strata pressure and formation pore pressure of a single well. Step S6: Based on the rock mechanics parameter model of Step S4 and the overlying strata pressure and pore pressure model of Step S5, establish the geostress model of the target study area in Step S1. By inputting different fracturing construction parameters into the geostress model, simulate and calculate the artificial fracture attribute data. Step S7: Integrate the artificial fracture attribute data obtained in Step S6 into the natural fracture attribute body of Step S2 to obtain the fused fracture attribute body of the target study area. Establish a target geological model based on the fused fracture attribute body and simulate the artificial fracture parameters in the target study area of ​​Step S1 through the target geological model.

2. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 1, characterized in that, In step S1, the conventional logging data includes gamma caliper curves, density logging curves, microsphere focused resistivity curves, sonic transit time curves, and lithology curves.

3. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 2, characterized in that, The interpretation process of the single-well fracture includes determining the target sensitive curve and determining the single-well fracture identification mode. The target sensitive curve includes the microsphere focused resistivity curve and the sonic transit time curve. The identification method of the single-well fracture identification mode is the point-by-point mode method.

4. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 2, characterized in that, The method for eliminating false fractures is to control the gamma caliper curve by up and down gradient probability, wherein the gradient range of the acoustic transit time does not exceed 0.003 and the gradient range of the microsphere focusing resistivity does not exceed 0.

01.

5. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 1, characterized in that, The single-well fracture description results are single-well fracture strength curves. Using fracture strength calculation formulas, a single-well fracture strength model is established by discretizing the single-well fracture strength curves. Based on the single-well fracture strength model, a fracture strength model is established by constraining fault distance and seismic trend bodies such as ant bodies.

6. In step S2, based on the fracture strength model, a natural fracture network model is established according to the natural fracture development characteristics of the target study area. The basic characteristics of the target study area include water content, development status, and tracers within the target study area. By combining the natural fracture network model with the water content, development status, and tracers within the target study area, the distribution trend of effective fractures between wells in the target study area is dynamically corrected. The natural fracture attribute volume is obtained through the Oda analytical equivalent method, and the natural fracture attribute volume includes the fracture equivalent permeability field.

7. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 2, characterized in that, In step S3, the X-Mac logging data includes shear wave logging curves; a regression equation for the relationship between shear wave logging curves and longitudinal wave logging curves is derived using a linear statistical regression method; a shear wave calculation model for the target study area is established based on the regression equation; a shear wave time difference curve is calculated based on the shear wave calculation model; and shear wave time difference data is obtained based on the shear wave time difference curve.

8. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 2, characterized in that, In step S4, density data is obtained from the density logging curve, P-wave time difference data is obtained from the P-wave logging curve, and density model, P-wave model and S-wave model are established between wells using seismic inversion trend constraints based on density data, P-wave time difference data and S-wave time difference data.

9. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 8, characterized in that, Based on the density model, longitudinal wave model, and transverse wave model, and in conjunction with the rock physics parameter equations, a rock mechanics parameter model is established, which includes the Young's modulus model and the Poisson's ratio model.

10. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 2, characterized in that, In step S5, a continuous density curve for the entire formation is generated using density logging curves. Based on the determined continuous density curve for the entire formation, the overlying strata pressure of a single well in the target study area is calculated by density integration. A formation compaction trend line is generated using sonic logging curves. Based on the determined formation compaction trend line, the formation pore pressure of a single well in the target study area is calculated using the Eaton method.

11. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 10, characterized in that, Based on the determined overlying strata pressure and formation pore pressure of a single well, and with the structural depth of the target study area as the trend constraint, an overlying strata pressure model and a pore pressure model are established.

12. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 9 or 11, characterized in that, Based on Young's modulus model, Poisson's ratio model, overlying strata pressure model, and pore pressure model within the target study area, the geostress model is calculated using rock mechanics and physics calculation formulas. The geostress model includes a minimum horizontal stress model and a maximum horizontal stress model.

13. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 1, characterized in that, In step S6, the fracturing operation parameters include fracturing fluid dosage, pump pressure, proppant dosage, and discharge rate; the artificial fracture attribute data includes artificial fracture permeability.

14. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 13, characterized in that, In step S7, the type of artificial fracture parameters is adapted to the type of hydraulic fracturing construction parameters, and the artificial fracture parameters include the length, width, and height of the artificial fracture.

15. The fracture attribute fusion method based on fractured low-to-ultra-low permeability reservoirs according to claim 1, characterized in that, In step S7, the fused fracture attributes within the target study area are obtained based on the fused fracture attribute volume. The fused fracture attributes are then used to establish a target geological model within the target study area through local mesh refinement. The target geological model includes a fused permeability model of natural and artificial fractures. The artificial fracture parameters within the target study area are calculated based on the fused permeability model of natural and artificial fractures. The calculation results of the artificial fracture parameters guide the fracturing design of fracture parameters such as the length of the main fracture and branch fractures and the conductivity within the target study area.

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