Shale oil well EUR prediction method considering natural fracture influence

Abstract

The invention provides a shale oil well EUR prediction method considering natural fracture influence, which comprises the following steps: numbering rock cores of different fracturing sections after reservoir fracturing through exploration geological data, then preparing rock core slices for CT scanning, and extracting rock core images; after image preprocessing, edge detection, crack position and shape recognition and morphological processing technology optimization, crack information is extracted, parameters needed by a rock core crack tree fractal network are calculated, and finally the rock core crack tree fractal porous medium model is used for calculating the reservoir crack permeability. And the EUR of the shale oil well under the consideration of the influence of the natural fractures is obtained by combining the material balance equation and the shale oil multi-section fractured horizontal well productivity equation. According to the method, the shale oil reservoir can be better simulated by using the fractal porous medium model embedded with the tree fractal network, the precision of a calculation result is improved, and the calculation method is easy to popularize.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas field development, and in particular, relates to a shale oil well EUR prediction method taking into account the influence of natural fractures. Background Art

[0002] Faced with the current situation of declining reserves and mining potential of traditional oil resources, shale oil, as a resource-rich emerging unconventional energy, has attracted much attention internationally despite the huge difficulty in development. Shale oil reservoirs are characterized by low porosity, low permeability, difficult reserves, and crisscrossed fracture networks. Evaluating the EUR (ultimate recoverable reserves) of shale oil wells is the basis for determining reserves, adjusting development plans, and rationally allocating production. Due to the complex natural fracture network in the reservoir, the natural fracture network must be considered comprehensively when calculating the EUR of shale oil wells to improve the accuracy of the calculation. The current general dual-medium model has advantages in computational efficiency, but its assumed uniformity does not match the complexity of the actual fracture system, and the fractal fracture structure embedded in the matrix network includes straight lines and intersecting lines, which cannot fully simulate the radial flow at the bottom of the well. Summary of the invention

[0003] The embodiment of the present application provides a shale oil well EUR prediction method that takes into account the influence of natural fractures. When calculating the EUR (ultimate recovery) of a shale oil well, the natural fracture network is comprehensively considered to improve the accuracy of the calculation result, thereby better evaluating the EUR of the shale oil well, which is of great help in determining reserves, adjusting development plans, and rationally allocating production.

[0004] The embodiment of the present application provides a shale oil well EUR prediction method considering the influence of natural fractures, including:

[0005] S1, numbering the cores corresponding to different fracturing stages after fracturing of the reservoir where the target shale oil well is located, making the cores into core slices and performing CT scanning to extract core images;

[0006] S2, extracting fracture information from the core image and optimizing it;

[0007] S3, obtaining relevant parameters required for calculating the core fracture tree fractal network from the extracted and optimized fracture information;

[0008] S4, using the obtained core fracture tree fractal network related parameters, using the core fracture tree fractal porous media model to calculate the reservoir fracture permeability under the influence of natural fractures;

[0009] S5, calculate the boundary supply pressure using the material balance equation;

[0010] S6, using the permeability and boundary supply pressure under the influence of natural fractures, combined with the productivity equation of shale oil wells, the productivity of shale oil wells under the influence of natural fractures is obtained;

[0011] S7, select a capacity decline model that meets the conditions, obtain the parameters of the capacity decline model by fitting the production data, obtain the capacity decline curve by combining the calculated capacity, and finally obtain the EUR of the shale oil well.

[0012] Among them, in step S1, the cores corresponding to different fracturing sections after the reservoir fracturing of the target shale oil well are numbered; the cores are made into core slices through surface treatment, cutting, bonding, grinding, dyeing and packaging steps, and each obtained core slice is subjected to CT scanning imaging to extract the core image.

[0013] Among them, in step S2, the extracted core image is pre-processed by denoising, contrast enhancement and grayscale operations, and then the position and shape of the cracks are identified by edge detection technology, and then the crack information is extracted by image segmentation method. Finally, the cracks are morphologically operated by morphological processing technology to optimize the crack extraction results.

[0014] Among them, in step S3, the relevant parameters required for calculating the core fracture tree fractal network are obtained from the extracted and optimized fracture information: the fractal dimension D of the core fracture tree fractal network is obtained by the box counting method or the log-log plot method. f ; By comparing the fractal dimension and fracture diameter of the fracture network at different scales, the fractal length ratio α and the fractal diameter ratio β of the core fracture tree fractal network are obtained; the total number of branches n of the core fracture tree fractal network is obtained by image analysis algorithm or manual counting; the total number of network branch levels m of the core fracture tree fractal network is obtained by counting the number of branches in all horizontal directions in the fracture network; the maximum initial diameter d in the core fracture tree fractal network is measured by image processing software 0max , minimum initial diameter d 0min The angle of each branch is used to obtain the branching angle θ of the core fracture tree fractal network.

[0015] Among them, in step S4, the reservoir fracture permeability K considering the influence of natural fractures is obtained by using the obtained core fracture tree fractal network related parameters and the fractal porous medium model calculation formula of the core fracture tree fractal network. f :

[0016]

[0017] Where K f represents the reservoir fracture permeability, D frepresents the fractal dimension of the core fracture tree fractal network, α represents the fractal length ratio of the core fracture tree fractal network, β represents the fractal diameter ratio of the core fracture tree fractal network, n represents the total number of branches of the core fracture tree fractal network, m represents the total number of network branch levels of the core fracture tree fractal network, d 0max represents the maximum initial diameter in the core fracture tree fractal network, d 0min represents the minimum initial diameter in the core fracture tree fractal network, l0 represents the initial length in the core fracture tree fractal network, and θ represents the branching angle of the core fracture tree fractal network.

[0018] In step S5, the boundary supply pressure P is calculated using the material balance equation. e , the calculation formula is as follows:

[0019]

[0020] Where P e represents the supply boundary pressure, P wf Indicates the bottom hole flowing pressure, N p represents the volume of shale oil produced, B o represents the crude oil volume coefficient, N represents the geological reserves of shale oil, B oi Represents the volume coefficient of crude oil at the original formation pressure, C p The compressibility factor of the rock pore volume, C w Indicates the compressibility of formation water, S wc represents bound water saturation, R s Indicates the dissolved gas-oil ratio, B g Represents the gas volume coefficient, W p B w Indicates the volume of produced water.

[0021] In step S6, the permeability under the influence of natural fractures calculated by the fractal porous media model of the core fracture tree fractal network and the boundary supply pressure P calculated by the material balance equation are e , combined with the productivity equation of shale oil wells, the productivity of shale oil wells under the influence of natural fractures is obtained:

[0022]

[0023] Where Q h represents the productivity of shale oil horizontal wells, K m represents the reservoir matrix permeability, K f represents the reservoir fracture permeability, h represents the reservoir width, a represents the reservoir length, b represents the reservoir thickness, L represents the length of the horizontal well, P e represents the supply boundary pressure, P wfrepresents the bottom hole flowing pressure, μ represents the viscosity of the fluid, B represents the volume coefficient, C1 and C2 represent the weighted coefficients related to the hydraulic fracture system in the reservoir and the wellbore properties, respectively, and r w represents the radius of the horizontal wellbore, r e represents the effective oil leakage radius, A' represents the conversion factor 0.738, S CA represents the fracture shape factor, S represents the vertical hydraulic fracturing fracture skin coefficient, C represents the average opening width of the fracture, L f represents the average length of the crack, ω represents the crack azimuth, represents the Euler constant 1.781, C A2 represents the horizontal well shape factor, and M represents the number of fractures in the horizontal well.

[0024] In step S7, a capacity decline model that meets the conditions is selected, and the parameters of the capacity decline model are obtained by fitting the production data. h Get the capacity decline curve and finally get the EUR of shale oil wells:

[0025] Hyperbolic Model:

[0026] Aprs Model:

[0027] Where Q h represents the production capacity of shale oil horizontal wells, t represents the production time, v represents the decline parameter, and e represents the adjustment parameter.

[0028] The EUR prediction method for shale oil wells considering the influence of natural fractures in the embodiment of the present application has the following beneficial effects:

[0029] The present application uses a fractal porous media model embedded in a tree-shaped fractal network to better simulate shale oil reservoirs, improve the accuracy of calculation results, and the calculation method is easy to promote. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a shale oil well EUR prediction method considering the influence of natural fractures in an embodiment of the present application;

[0031] Figure 2 Schematic diagram of porous media with a tree-shaped fractal network. DETAILED DESCRIPTION

[0032] The present application is further described below in conjunction with the accompanying drawings and embodiments.

[0033] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present invention, and different embodiments can be replaced or combined, so this application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more of all other possible combinations of features A, B, C, and D, even though the embodiment may not be clearly recorded in the following text.

[0034] Embodiment 1

[0035] like Figure 1 As shown, the EUR prediction method of shale oil wells considering the influence of natural fractures in the present application includes: S1, numbering the cores corresponding to different fracturing sections after the reservoir fracturing of the target shale oil well, making the cores into core slices and performing CT scanning to extract the core image; S2, extracting the fracture information from the core image and optimizing it; S3, obtaining the relevant parameters required for calculating the core fracture tree fractal network from the extracted and optimized fracture information; S4, using the obtained core fracture tree fractal network related parameters, using the core fracture tree fractal porous medium model to calculate the reservoir fracture permeability considering the influence of natural fractures; S5, using the material balance equation to calculate the boundary supply pressure; S6, using the permeability and boundary supply pressure considering the influence of natural fractures, combined with the production capacity equation of the shale oil well to obtain the production capacity of the shale oil well under the influence of natural fractures; S7, selecting a capacity decline model that meets the conditions, obtaining the parameters of the capacity decline model by fitting the production data, and obtaining the capacity decline curve in combination with the calculated capacity, and finally obtaining the EUR of the shale oil well.

[0036] There is a complex natural fracture network in shale oil reservoirs. When calculating EUR (ultimate recovery) of shale oil wells, the natural fracture network factor is comprehensively considered to improve the accuracy of the calculation results, thereby better evaluating the EUR (ultimate recovery) of shale oil wells, which is of great help in determining reserves, adjusting development plans and rationally allocating production.

[0037] Embodiment 2

[0038] The present application provides a EUR prediction method for shale oil wells considering the influence of natural fractures. The cores of different fracturing stages after reservoir fracturing are numbered by exploration geological data, and then core slices are prepared for CT scanning to extract core images. After image preprocessing, edge detection, fracture position and shape recognition, and morphological processing technology optimization, the fracture information is extracted and the parameters required for the core fracture tree fractal network are calculated. Finally, the core fracture tree fractal porous medium model is used to calculate the reservoir fracture permeability, and the material balance equation is combined with the shale oil multi-stage fracturing horizontal well production equation to obtain the EUR of the shale oil well under the influence of natural fractures.

[0039] The specific implementation steps include:

[0040] Step S1, numbering the cores corresponding to different fracturing sections after the reservoir fracturing of the target shale oil well; making the cores into core slices through surface treatment, cutting, bonding, grinding, dyeing and packaging, and performing CT scanning imaging on each obtained core slice to extract the core image.

[0041] Step S2, pre-processing the extracted core image by denoising, contrast enhancement and grayscale, then identify the position and shape of the cracks by edge detection technology, and then extract the crack information by threshold segmentation, region growing and edge tracking methods, and finally perform morphological operations on the cracks by morphological processing techniques such as corrosion, expansion, opening and closing operations, optimize the extraction results of the cracks, and improve the accuracy of subsequent parameter calculations.

[0042] Step S3, obtaining the relevant parameters required for calculating the core fracture tree fractal network from the extracted and optimized fracture information: obtaining the fractal dimension D of the core fracture tree fractal network by the box counting method or the log-log plot method f ; By comparing the fractal dimension and fracture diameter of the fracture network at different scales, the fractal length ratio α and the fractal diameter ratio β of the core fracture tree fractal network can be obtained; the total number of branches n of the core fracture tree fractal network can be obtained by image analysis algorithm or manual counting; the total number of network branch levels m of the core fracture tree fractal network can be obtained by counting the number of branches in all horizontal directions in the fracture network; the maximum initial diameter d in the core fracture tree fractal network can be measured by image processing software 0max , minimum initial diameter d 0min The angle of each branch is used to obtain the branching angle θ of the core fracture tree fractal network.

[0043] Step S4, using the obtained core fracture tree fractal network related parameters, and using the fractal porous media model calculation formula of the core fracture tree fractal network to obtain the reservoir fracture permeability K under the influence of natural fractures f ;

[0044]

[0045] Where K f represents the reservoir fracture permeability, D f represents the fractal dimension of the core fracture tree fractal network, α represents the fractal length ratio of the core fracture tree fractal network, β represents the fractal diameter ratio of the core fracture tree fractal network, n represents the total number of branches of the core fracture tree fractal network, m represents the total number of network branch levels of the core fracture tree fractal network, d 0max represents the maximum initial diameter in the core fracture tree fractal network, d 0min represents the minimum initial diameter in the core fracture tree fractal network, l0 represents the initial length in the core fracture tree fractal network, and θ represents the branching angle of the core fracture tree fractal network.

[0046] Step S5, using the material balance equation to calculate the boundary supply pressure P e , the calculation formula is as follows:

[0047]

[0048] Where P e represents the supply boundary pressure, P wf Indicates the bottom hole flowing pressure, N p represents the volume of shale oil produced, B o represents the crude oil volume coefficient, N represents the geological reserves of shale oil, B oi Represents the volume coefficient of crude oil at the original formation pressure, C p The compressibility factor of the rock pore volume, C w Indicates the compressibility of formation water, S wc represents bound water saturation, R s Indicates the dissolved gas-oil ratio, B g Represents the gas volume coefficient, W p B w Indicates the volume of produced water.

[0049] Step S6, using the fractal porous media model of the core fracture tree fractal network to calculate the permeability under the influence of natural fractures and the boundary supply pressure P calculated by the material balance equation e , combined with the productivity equation of shale oil wells, the productivity of shale oil wells under the influence of natural fractures is obtained:

[0050]

[0051] Where Q h represents the productivity of shale oil horizontal wells, K m represents the reservoir matrix permeability, K frepresents the reservoir fracture permeability, h represents the reservoir width, a represents the reservoir length, b represents the reservoir thickness, L represents the length of the horizontal well, P e represents the supply boundary pressure, P wf represents the bottom hole flowing pressure, μ represents the viscosity of the fluid, B represents the volume coefficient, C1 and C2 represent the weighted coefficients related to the hydraulic fracture system in the reservoir and the wellbore properties, respectively, and r w represents the radius of the horizontal wellbore, r e represents the effective oil leakage radius, A' represents the conversion factor 0.738, S CA represents the fracture shape factor, S represents the vertical hydraulic fracturing fracture skin coefficient, C represents the average opening width of the fracture, L f represents the average length of the crack, ω represents the crack azimuth, represents the Euler constant 1.781, C A2 represents the horizontal well shape factor, and M represents the number of fractures in the horizontal well.

[0052] Step S7, select a suitable capacity decline model, obtain the parameters of the capacity decline model by fitting the production data, and combine the calculated capacity Q h Get the capacity decline curve and finally get the EUR of shale oil wells:

[0053] Hyperbolic Model:

[0054] Aprs Model:

[0055] Where Q h represents the production capacity of shale oil horizontal wells, t represents the production time, v represents the decline parameter, and e represents the adjustment parameter.

[0056] The present application uses a fractal porous medium model embedded in a tree-shaped fractal network to better simulate shale oil reservoirs and improve the accuracy of calculation results; the calculation method is easy to promote.

[0057] Embodiment 3

[0058] A shale oil reservoir is developed by horizontal well fracturing. Through hydraulic fracturing, 23 vertical hydraulic fractures pass through the oil layer. The effective oil layer thickness h is 22.4m, the matrix permeability is 0.01~0.25mD, and the average matrix permeability K m is 0.06mD, the viscosity of shale oil is 1.4mPa·s, the volume coefficient is 1.2, and the original gasoline ratio is 61.7m 3 / m 3 .

[0059] In the first step, the cores corresponding to different fracturing stages of the target shale oil well reservoir are taken and made into thin slices, which are then scanned by CT to extract the core images.

[0060] In the second step, the core image is preprocessed by denoising, contrast enhancement and grayscale, and the fracture position and shape are identified through edge detection technology. The fracture information is extracted by using threshold segmentation, region growing and edge tracking methods.

[0061] The third step is to optimize the fracture extraction results through morphological processing technology to obtain the relevant parameters for calculating the core fracture tree fractal network, the fractal dimension D f =1.9, fractal length ratio α=0.34, fractal diameter ratio β=0.72, total number of branches n=5, total number of network branch levels m=3, maximum initial diameter d 0max =5×10-2m, minimum initial diameter d 0min =5×10-4m, branching angle θ=1.09.

[0062] The fourth step is to obtain the reservoir fracture permeability K under the influence of natural fractures by using the relevant parameters of the core fracture tree fractal network and the calculation formula of the fractal porous medium model of the core fracture tree fractal network. f =201.7mD.

[0063] The fifth step is to calculate the boundary supply pressure P using the material balance equation in combination with the production data of the shale oil well. e =55.4Mpa.

[0064] The sixth step is to calculate the production capacity of shale oil wells under the influence of natural fractures. By consulting the data of the shale oil reservoir, we can know that:

[0065] Shale reservoir data:

[0066]

[0067] Horizontal well data:

[0068]

[0069] Fracturing data:

[0070]

[0071] The viscosity of the fluid is μ = 1.4 mPa·s, and the volume coefficient is B o =1.2, crude oil density 0.78g / m 3 Conversion factor A' = 0.738, S CA Represents the fracture shape factor, hydraulic fracturing fracture skin coefficient S = -3.36, Euler constant M represents the number of fractures in the horizontal well. Combined with the productivity equation of shale oil wells, Q of shale oil wells under the influence of natural fractures is obtained. h =57.68t / d.

[0072] Step 7: Select the hyperbolic model as the capacity decline model. By fitting the production data, we get the parameter v = 0.00132 of the hyperbolic model. Combined with the calculated capacity Q h The final EUR of the shale oil well is 51051.7t.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A shale oil well EUR prediction method considering the influence of natural fractures, characterized in that: include: S1, numbering the cores corresponding to different fracturing stages after fracturing of the reservoir where the target shale oil well is located, making the cores into core slices and performing CT scanning to extract core images; S2, extracting fracture information from the core image and optimizing it; S3, obtaining relevant parameters required for calculating the core fracture tree fractal network from the extracted and optimized fracture information; S4, using the obtained core fracture tree fractal network related parameters, using the core fracture tree fractal porous media model to calculate the reservoir fracture permeability under the influence of natural fractures; S5, calculate the boundary supply pressure using the material balance equation; S6, using the permeability and boundary supply pressure under the influence of natural fractures, combined with the productivity equation of shale oil wells, the productivity of shale oil wells under the influence of natural fractures is obtained; S7, select a capacity decline model that meets the conditions, obtain the parameters of the capacity decline model by fitting the production data, obtain the capacity decline curve by combining the calculated capacity, and finally obtain the EUR of the shale oil well.

2. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1, characterized in that: In step S1, the cores corresponding to different fracturing stages after the reservoir fracturing of the target shale oil well are numbered; the cores are made into core slices through surface treatment, cutting, bonding, grinding, dyeing and packaging steps, and each obtained core slice is subjected to CT scanning imaging to extract the core image.

3. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1 or 2, characterized in that: In step S2, the extracted core image is preprocessed by denoising, contrast enhancement and grayscale operations, and then the position and shape of the cracks are identified by edge detection technology, and then the crack information is extracted by image segmentation method. Finally, the cracks are morphologically operated by morphological processing technology to optimize the crack extraction results.

4. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1 or 2, characterized in that: In step S3, the relevant parameters required for calculating the core fracture tree fractal network are obtained from the extracted and optimized fracture information: the fractal dimension D of the core fracture tree fractal network is obtained by the box counting method or the log-log plot method. f ; By comparing the fractal dimension and fracture diameter of the fracture network at different scales, the fractal length ratio α and the fractal diameter ratio β of the core fracture tree fractal network are obtained; the total number of branches n of the core fracture tree fractal network is obtained by image analysis algorithm or manual counting; the total number of network branch levels m of the core fracture tree fractal network is obtained by counting the number of branches in all horizontal directions in the fracture network; the maximum initial diameter d in the core fracture tree fractal network is measured by image processing software 0max , minimum initial diameter d 0min The angle of each branch is used to obtain the branching angle θ of the core fracture tree fractal network.

5. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1 or 2, characterized in that: In step S4, the reservoir fracture permeability K considering the influence of natural fractures is obtained by using the obtained core fracture tree fractal network related parameters and the fractal porous medium model calculation formula of the core fracture tree fractal network. f : Where K f represents the reservoir fracture permeability, D f represents the fractal dimension of the core fracture tree fractal network, α represents the fractal length ratio of the core fracture tree fractal network, β represents the fractal diameter ratio of the core fracture tree fractal network, n represents the total number of branches of the core fracture tree fractal network, m represents the total number of network branch levels of the core fracture tree fractal network, d 0max represents the maximum initial diameter in the core fracture tree fractal network, d 0min represents the minimum initial diameter in the core fracture tree fractal network, l0 represents the initial length in the core fracture tree fractal network, and θ represents the branching angle of the core fracture tree fractal network.

6. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1 or 2, characterized in that: In step S5, the boundary supply pressure P is calculated using the material balance equation. e , the calculation formula is as follows: Where P e represents the supply boundary pressure, P wf Indicates the bottom hole flowing pressure, N p represents the volume of shale oil produced, B o represents the crude oil volume coefficient, N represents the geological reserves of shale oil, B oi Represents the volume coefficient of crude oil at the original formation pressure, C p The compressibility factor of the rock pore volume, C w Indicates the compressibility of formation water, S wc represents bound water saturation, R s Indicates the dissolved gas-oil ratio, B g Represents the gas volume coefficient, W p B w Indicates the volume of produced water.

7. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1 or 2, characterized in that: In step S6, the permeability under the influence of natural fractures calculated by the fractal porous media model of the core fracture tree fractal network and the boundary supply pressure P calculated by the material balance equation are e , combined with the productivity equation of shale oil wells, the productivity of shale oil wells under the influence of natural fractures is obtained: Where Q h represents the productivity of shale oil horizontal wells, K m represents the reservoir matrix permeability, K f represents the reservoir fracture permeability, h represents the reservoir width, a represents the reservoir length, b represents the reservoir thickness, L represents the length of the horizontal well, P e represents the supply boundary pressure, P wf represents the bottom hole flowing pressure, μ represents the viscosity of the fluid, B represents the volume coefficient, C1 and C2 represent the weighted coefficients related to the hydraulic fracture system in the reservoir and the wellbore properties, respectively, and r w represents the radius of the horizontal wellbore, r e represents the effective oil leakage radius, A' represents the conversion factor 0.738, S CA represents the fracture shape factor, S represents the vertical hydraulic fracturing fracture skin factor, C represents the average opening width of the fracture, and L f represents the average length of the crack, ω represents the crack azimuth, γ represents the Euler constant 1.781, C A2 represents the horizontal well shape factor, and M represents the number of fractures in the horizontal well.

8. The EUR prediction method for shale oil wells considering the influence of natural fractures according to claim 1 or 2, characterized in that: In step S7, a capacity decline model that meets the conditions is selected, and the parameters of the capacity decline model are obtained by fitting the production data, and the calculated capacity Q is combined with the calculated capacity Q h Get the capacity decline curve and finally get the EUR of shale oil wells: Hyperbolic Model: Aprs Model: Where Q h represents the production capacity of shale oil horizontal wells, t represents the production time, v represents the decline parameter, and e represents the adjustment parameter.

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