A small-angle horizontal well fracturing scheme optimization design method

Abstract

The present application relates to the technical field of horizontal well volume fracturing development, and particularly relates to a small-angle horizontal well fracturing scheme optimization design method. The method comprises the following steps: screening reservoir post-fracturing productivity influencing factors of horizontal wells in a target research area, obtaining production data of the fractured horizontal wells, determining main control factors of the horizontal well productivity, presetting a critical angle interval of the horizontal wells, simulating post-fracturing productivity changes of the horizontal wells, determining a fracturing reconstruction process template of the horizontal wells, determining target horizontal wells, comprehensively considering fracture drainage area and support number, calculating cluster spacing values of the target horizontal wells, establishing a production prediction empirical formula, calculating and calibrating predicted productivity of the target horizontal wells, simulating post-fracturing productivity of the target horizontal wells through orthogonal test design, and determining a fracturing optimization template of the target horizontal wells in combination with output-input ratio. The method provided by the present application can effectively guide process selection and parameter optimization of the small-angle horizontal well fracturing scheme.

Description

Technical Field

[0001] This invention relates to the field of horizontal well volumetric fracturing development technology, and in particular to an optimized design method for fracturing schemes in horizontal wells with small included angles. Background Technology

[0002] In small-block sandstone reservoirs, the vertical direction shows numerous but thin oil layers, while the horizontal plane shows small and irregularly shaped sand bodies, exhibiting strong planar heterogeneity. This makes it difficult to construct a regular water-drive well network for development. "Horizontal wells + volumetric fracturing" is a key technology for improving the development effect of small-block sandstone reservoirs. Due to the influence of fault cutting, the reservoir properties and trap areas vary within different fault blocks. To ensure the distribution of horizontal well sections within the sand bodies, the wellbore trajectory of horizontal wells is generally deployed to maximize the control of sand body distribution within the fault block. This results in significant variations in the angle between the horizontal wellbore azimuth and the azimuth of the maximum horizontal principal stress, making it impossible to form an optimal fracture distribution pattern. Such horizontal wells with small angles are prone to forming oblique fractures along the wellbore direction. The fracture modification area is small, preventing the effective utilization of sand bodies in the far-wellbore zone, severely restricting the effective development of small-block sandstone reservoirs.

[0003] Patent document CN115600408A discloses a method, apparatus, and medium for optimizing fracturing clusters in horizontal wells with small included angles. The fracturing cluster optimization method includes the following steps: S1 For the target horizontal well, simulate the multi-cluster fracture propagation state under different fracture parallel spacings Sp to determine the optimal fracture parallel spacing Sp*, wherein the angle α between the wellbore trajectory of the target horizontal well and the direction of the maximum horizontal principal stress is [0°, 90°], and the fracture parallel spacing Sp is the projection distance of the perforation cluster in the direction of the minimum horizontal principal stress; S2 Based on the optimal fracture parallel spacing Sp*, determine the optimal fracture cluster spacing Sc*; S3 Optimize the fracturing cluster scheme according to the optimal fracture cluster spacing Sc*. Patent document CN114676631A discloses a method for recommending fracturing parameters for horizontal wells in shale gas reservoirs. This method includes: S1, based on the actual reservoir geological parameters and fracturing parameters of the gas well, using the grey relational analysis method to determine the main controlling factors of gas well productivity, and formulating horizontal well productivity simulation schemes with different combinations of reservoir geological parameters and fracturing parameters; S2, using ECLIPSE software to establish a numerical simulation model of horizontal wells in shale gas reservoirs, forming a database of shale gas reservoir fracturing horizontal well productivity simulations under all schemes; S3, using the database combined with an artificial neural network algorithm to train the machine learning model of shale gas reservoir fracturing horizontal well productivity, generating a machine learning model of shale gas reservoir fracturing horizontal well productivity; S4, combining the machine learning model of shale gas reservoir fracturing horizontal well productivity, completing the recommendation of the optimal combination of main controlling factors for fracturing under the actual reservoir geological parameters of the gas well.

[0004] Currently, the optimization technology for horizontal well fracturing parameters mainly employs numerical simulation methods for fracture propagation. These methods assume the wellbore trajectory is orthogonal to the direction of the maximum horizontal principal stress, and that artificial fractures are transversely shaped. After simulating inter-cluster stress interference and fracture propagation, simple conversions are performed for small angles. This approach fails to accurately evaluate the fracturing parameters of small-angle horizontal wells limited by small-fault reservoir conditions, particularly the impact of cluster spacing on the effectiveness of fracturing measures. Consequently, accurate optimization of fracturing schemes for such horizontal wells is impossible. Therefore, a method for optimizing fracturing schemes for small-angle horizontal wells is proposed. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] This invention provides an optimization design method for fracturing schemes in horizontal wells with small angles, which overcomes the problem in the prior art that uses numerical simulation of fracture propagation to optimize fracturing parameters of horizontal wells. This results in the inability to accurately evaluate the fracturing parameters of horizontal wells with small angles that are limited by small fault block reservoir conditions, especially the impact of cluster spacing on the effect of fracturing measures, and thus the inability to accurately optimize fracturing schemes for such horizontal wells.

[0007] (II) Technical Solution

[0008] To address the above problems, this invention provides an optimized design method for fracturing schemes in horizontal wells with small included angles, comprising:

[0009] Step S1: Determine the target study area and screen the factors affecting reservoir post-compression productivity of horizontal wells in the oilfield within the target study area;

[0010] Step S2: Divide the horizontal wells in the target study area determined in Step S1 into horizontal wells in fractured oilfields and horizontal wells in unfractured oilfields, obtain the production data of horizontal wells in fractured oilfields, and, in conjunction with the reservoir post-fracture production capacity influencing factors screened in Step S1, use the grey relational analysis method to determine the main controlling factors of oilfield horizontal well production capacity.

[0011] Step S3: Preset the critical angle range of the oilfield horizontal well, simulate the post-fracturing production capacity change of the oilfield horizontal well under the main control factors of the oilfield horizontal well production capacity determined in step S2, and determine the oilfield horizontal well fracturing and stimulation process template.

[0012] Step S4: Determine the target oilfield horizontal well based on the critical angle range determined in Step S3, and calculate the cluster spacing value of the target oilfield horizontal well by combining the oilfield horizontal well fracturing process template, the fracture drainage area and the number of proppant, and taking into account the fracture drainage area and the number of proppant.

[0013] Step S5: Based on the production data of the fractured horizontal wells in the oilfield determined in Step S2, establish an empirical formula for predicting the production of the target oilfield horizontal wells determined in Step S4, and calculate and calibrate the predicted production capacity of the target oilfield horizontal wells.

[0014] Step S6: Combining the cluster spacing value determined in Step S4 and the predicted production capacity calibrated in Step S5, under the different combinations of main control factors for production capacity of horizontal wells in oilfields determined in Step S2, simulate the post-pressure production capacity of horizontal wells in the target oilfield through orthogonal experimental design.

[0015] Step S7: Based on the post-fracturing production capacity determined in Step S6, and in conjunction with the output-input ratio, determine the fracturing optimization template for the target oilfield horizontal wells determined in Step S4.

[0016] Preferably, in step S1, the factors affecting reservoir post-fracturing productivity include reservoir geological parameters and fracturing operation parameters.

[0017] Preferably, the reservoir geological parameters include the length of the horizontal section, the length of the oil layer section, the effective thickness, permeability, porosity, crude oil viscosity, and formation pressure; the fracturing construction parameters include the number of fracturing clusters, the cluster spacing, the angle between the oilfield horizontal wellbore and the artificial fracture, the total fluid volume, the total sand volume, the average sand addition intensity per fracture, the average fluid intensity per fracture, and the average sand ratio.

[0018] Preferably, in step S2, the production data of the fractured horizontal well in the oilfield includes the length of the fractured horizontal well, the thickness of the oil layer, the permeability, and the production in the first year.

[0019] Preferably, in step S2, the specific steps for determining the main controlling factors of oilfield horizontal well productivity using the grey relational analysis method include:

[0020] The cumulative annual production after fracturing of horizontal wells in the fracturing oilfield is used as the parent sequence, and reservoir geological parameters and fracturing operation parameters are used as the child sequences.

[0021] The raw data is dimensionless, and the absolute difference matrix and relative correlation coefficient between each parameter and the output are calculated.

[0022] The correlation degree of each parameter was determined by mean value processing, and the main controlling factors of oilfield horizontal well productivity were screened by sorting the correlation degree.

[0023] Preferably, in step S3, the main factors controlling the productivity of the oilfield horizontal well include effective thickness, porosity, permeability, crude oil viscosity, formation pressure, number of fracturing clusters, angle between the oilfield horizontal wellbore and artificial fractures, cluster spacing, average sand addition intensity per fracture, average fluid intensity per fracture, and average sand ratio.

[0024] Preferably, in step S3, based on the average values ​​of influencing factors such as effective thickness, porosity, crude oil viscosity, formation pressure, number of fracturing clusters, cluster spacing, average single-fracture sand addition intensity, average single-fracture fluid intensity, and average sand ratio, the post-fracturing productivity changes of horizontal wells in oilfields under different permeability and oilfield horizontal wellbore-artificial fracture angle combinations are simulated, and a productivity change curve chart of the angle between artificial fractures and horizontal well sections is established. When the actual angle between artificial fractures and horizontal well sections is within the critical angle range, the temporary plugging and diversion process is determined as the target process, and the fracture control volume is increased through the target process.

[0025] Preferably, in step S4, the actual included angle between the horizontal wellbore and the artificial fracture of the target oilfield horizontal well is less than the critical angle range, where the critical angle range is:

[0026] When the reservoir permeability is ≤1mD, the critical angle is 0°-30°;

[0027] When the reservoir permeability is ≥10mD, the critical angle is 20°-40°.

[0028] Preferably, in step S5, the empirical formula for production forecasting is:

[0029] Q = alt(0.6lhK) - b

[0030] In the formula: Q is the predicted design production, t / d; l is the design horizontal well length, m; h is the oil layer thickness, m; K is the oil layer permeability, mD; α and b are regression coefficients, α = 6.62, b = 41.6.

[0031] Preferably, in step S6, the orthogonal experimental design adopts L9(3) 4 An orthogonal array was used to simulate the post-pressurization production capacity under different combinations of cluster spacing, number of clusters, and construction scale.

[0032] (III) Beneficial Effects

[0033] This invention provides an optimized design method for fracturing schemes in small-angle horizontal wells. It determines the target oilfield horizontal wells based on the critical angle range, which are then classified as small-angle horizontal wells. Utilizing historical production data from existing horizontal wells in the block, and considering factors influencing post-fracturing reservoir productivity, it accurately evaluates the impact of fracturing parameters on the effectiveness of fracturing measures in small-angle horizontal wells. This further guides the process optimization and parameter optimization of fracturing schemes for small-angle horizontal wells. Obtaining post-fracturing reservoir productivity in the oilfield is relatively easy. By simplifying the calculation methods for different parameter values ​​during the calculation process, the method becomes simpler and more reliable. The results meet the requirements of actual field applications and are more suitable for the early optimization of fracturing schemes in small-fault sandstone reservoirs. It allows for targeted optimization of subsequent well location adjustments and single-well fracturing during the actual well placement process. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the optimized design method for fracturing schemes in horizontal wells with small included angles, as described in an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram illustrating the control of the drainage area for fracturing in a horizontal well with a small included angle, according to an embodiment of the present invention.

[0036] Figure 3 This is a graph showing the change in production capacity of the combination of the angle between the horizontal wellbore and the artificial fracture in an oilfield, according to an embodiment of the present invention. Detailed Implementation

[0037] 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.

[0038] Figure 1 This is a flowchart of the optimized design method for fracturing schemes in small-angle horizontal wells according to an embodiment of the present invention, such as... Figure 1 As shown, this invention provides an optimized design method for fracturing schemes in horizontal wells with small included angles, comprising:

[0039] Step S1: Determine the target study area and screen the factors affecting reservoir post-compression productivity of horizontal wells in the oilfield within the target study area;

[0040] Step S2: Divide the horizontal wells in the target study area determined in Step S1 into horizontal wells in fractured oilfields and horizontal wells in unfractured oilfields, obtain the production data of horizontal wells in fractured oilfields, and, in conjunction with the reservoir post-fracture production capacity influencing factors screened in Step S1, use the grey relational analysis method to determine the main controlling factors of oilfield horizontal well production capacity.

[0041] Step S3: Preset the critical angle range of the oilfield horizontal well, simulate the post-fracturing production capacity change of the oilfield horizontal well under the main control factors of the oilfield horizontal well production capacity determined in step S2, and determine the oilfield horizontal well fracturing and stimulation process template.

[0042] Step S4: Determine the target oilfield horizontal well based on the critical angle range determined in Step S3, and calculate the cluster spacing value of the target oilfield horizontal well by combining the oilfield horizontal well fracturing process template, the fracture drainage area and the number of proppant, and taking into account the fracture drainage area and the number of proppant.

[0043] Step S5: Based on the production data of the fractured horizontal wells in the oilfield determined in Step S2, establish an empirical formula for predicting the production of the target oilfield horizontal wells determined in Step S4, and calculate and calibrate the predicted production capacity of the target oilfield horizontal wells.

[0044] Step S6: Combining the cluster spacing value determined in Step S4 and the predicted production capacity calibrated in Step S5, under the different combinations of main control factors for production capacity of horizontal wells in oilfields determined in Step S2, simulate the post-pressure production capacity of horizontal wells in the target oilfield through orthogonal experimental design.

[0045] Step S7: Based on the post-fracturing production capacity determined in Step S6, and in conjunction with the output-input ratio, determine the fracturing optimization template for the target oilfield horizontal wells determined in Step S4.

[0046] In this method, in step S1, the factors affecting reservoir post-fracturing productivity include reservoir geological parameters and fracturing operation parameters. The reservoir geological parameters include horizontal section length, oil layer section length, effective thickness, permeability, porosity, crude oil viscosity, and formation pressure. The fracturing operation parameters include the number of fracturing clusters, cluster spacing, angle between the oilfield horizontal wellbore and the artificial fracture, total fluid volume, total sand volume, average single-fracture sand addition intensity, average single-fracture fluid intensity, and average sand ratio.

[0047] In practical applications, in step S2, the production data of fractured horizontal wells in oilfields includes the length of the fractured horizontal well, reservoir thickness, permeability, and first-year production. It is important to note that the specific steps for determining the main controlling factors of oilfield horizontal well productivity using the grey relational analysis method include:

[0048] The cumulative annual production after fracturing of horizontal wells in the fracturing oilfield is used as the parent sequence, and reservoir geological parameters and fracturing operation parameters are used as the child sequences.

[0049] The raw data is dimensionless, and the absolute difference matrix and relative correlation coefficient between each parameter and the output are calculated.

[0050] The correlation degree of each parameter was determined by mean value processing, and the main controlling factors of oilfield horizontal well productivity were screened by sorting the correlation degree.

[0051] In this method, in step S3, the main factors controlling the productivity of horizontal wells in oilfields include effective thickness, porosity, permeability, crude oil viscosity, formation pressure, number of fracturing clusters, angle between the horizontal wellbore and artificial fractures, cluster spacing, average sand addition intensity per fracture, average fluid intensity per fracture, and average sand ratio.

[0052] In practical applications, in step S3, based on the average values ​​of influencing factors such as effective thickness, porosity, crude oil viscosity, formation pressure, number of fracturing clusters, cluster spacing, average single-fracture sand addition intensity, average single-fracture fluid intensity, and average sand ratio, the post-fracturing productivity changes of horizontal wells in oilfields under different permeability and oilfield horizontal wellbore-artificial fracture angle combinations are simulated. A productivity change curve chart of the angle between artificial fractures and horizontal well sections is established. When the actual angle between artificial fractures and horizontal well sections is within the critical angle range, the temporary plugging and diversion process is determined as the target process, and the fracture control volume is increased through the target process.

[0053] In this method, in step S4, the actual included angle between the horizontal wellbore and the artificial fracture in the target oilfield horizontal well is less than the critical angle range, which is:

[0054] When the reservoir permeability is ≤1mD, the critical angle is 0°-30°;

[0055] When the reservoir permeability is ≥10mD, the critical angle is 20°-40°.

[0056] In practical applications, the spacing between horizontal well clusters with small included angles is calculated by considering the oil drainage area of ​​the fractures and the number of proppant particles.

[0057] Among them, A and A r These are the aspect ratios of the oil drain area when θ = 90° and θ < 90°, respectively.

[0058]

[0059] In the formula: A is the aspect ratio of the fracture drainage area, dimensionless; X is the well spacing, m; Y is the fracture spacing, m; K f For fracture permeability, mD; K is reservoir permeability, mD; V p Let m be the crack volume. 3 V r For the control volume of a horizontal well reservoir, m 3 A r X represents the aspect ratio of the drainage area of ​​the oblique fracture, which is dimensionless; e Y is the length of the oblique crack, in meters. e The spacing between the oblique cracks is in meters (m); S c θ is the cluster spacing, in meters; θ is the angle between the artificial fracture and the horizontal well section, in degrees.

[0060] In practical applications, the empirical formula for yield prediction in step S5 is:

[0061] Q = alt(0.6lhK) - b

[0062] In the formula: Q is the predicted design production, t / d; l is the design horizontal well length, m; h is the oil layer thickness, m; K is the oil layer permeability, mD; α and b are regression coefficients, α = 6.62, b = 41.6.

[0063] In this method, in step S6, the orthogonal experimental design adopts L9(3 4 An orthogonal array was used to simulate the post-pressurization production capacity under different combinations of cluster spacing, number of clusters, and construction scale.

[0064] This invention provides an optimized design method for fracturing schemes in small-angle horizontal wells. Based on evaluating and predicting the impact of geological and engineering parameters on the effectiveness of fracturing measures, it optimizes the clustering parameters and construction technology of small-angle horizontal wells, guiding subsequent well location adjustments and single-well fracturing design optimization during the actual well placement process, thereby improving the effectiveness of fracturing stimulation measures in small-angle horizontal wells. The working principle of this optimized design method for fracturing schemes in small-angle horizontal wells is described in detail below:

[0065] Step 1: Determine the target study area and screen the factors affecting reservoir post-compression productivity of horizontal wells in the oilfield within the target study area;

[0066] Step 2: Divide the horizontal wells in the target study area into fractured horizontal wells and unfractured horizontal wells, obtain the production data of fractured horizontal wells, and, in conjunction with the screened reservoir post-fracture production capacity influencing factors, use the grey relational analysis method to determine the main controlling factors of oilfield horizontal well production capacity.

[0067] Step 3: Preset the critical angle range of the oilfield horizontal well, simulate the post-fracturing production capacity change of the oilfield horizontal well under the main factors controlling the production capacity of the oilfield horizontal well, and determine the oilfield horizontal well fracturing and stimulation process template;

[0068] Step 4: Determine the target oilfield horizontal well based on the critical angle range, and calculate the cluster spacing value of the target oilfield horizontal well by combining the oilfield horizontal well fracturing process template, the fracture drainage area and the number of proppant, and taking into account the fracture drainage area and the number of proppant.

[0069] Step 5: Based on the production data of the horizontal wells in the fractured oilfield, establish an empirical formula for predicting the production of the horizontal wells in the target oilfield, and calculate and calibrate the predicted production capacity of the horizontal wells in the target oilfield.

[0070] Step Six: Combining the cluster spacing value and the calibrated predicted production capacity, the post-pressure production capacity of the target oilfield horizontal well is simulated through orthogonal experimental design under different combinations of the main controlling factors of production capacity of horizontal wells in different oilfields;

[0071] Step 7: Based on the post-fracturing production capacity and the output-input ratio, determine the fracturing optimization template for horizontal wells in the target oilfield.

[0072] In this embodiment, since there are many factors affecting the fracturing effect of horizontal wells, and considering the actual situation of horizontal well development in the oilfield, common parameters such as temperature and rock mechanics parameters are excluded due to the development of fault blocks in the horizontal well area. The reservoir geological parameters selected include horizontal section length, oil layer section length, effective thickness, permeability, porosity, crude oil viscosity, and formation pressure. The fracturing construction parameters include the number of fracturing clusters, cluster spacing, angle between the horizontal wellbore and the artificial fracture, total fluid volume, total sand volume, average sand addition intensity per fracture, average fluid intensity per fracture, and average sand ratio.

[0073] In practical applications, the cumulative annual production of fractured horizontal wells in small-block sandstone oilfields is used as a reference parent sequence, and reservoir geological parameters and fracturing operation parameters are used as comparative sub-sequences to evaluate the correlation between various parameters and production, and to determine the main controlling factors of oilfield horizontal well production capacity.

[0074] Among them, the data units and orders of magnitude of the various parameters of the analysis sequence under the initial conditions are different, and the numerical differences are large. The mean method is used to perform dimensionless processing on the original data.

[0075]

[0076] i=1,2,3…,m; k=1,2,3…,n

[0077] After dimensionless data processing, the absolute difference matrix between the parameters of the corresponding sequence and the annual cumulative oil production after horizontal well pressure is obtained, and the relative correlation coefficient is calculated.

[0078] Δ oi (k)=|x i (k)-x o (k)|

[0079]

[0080] In the formula: ξ oi (k) is the relative correlation coefficient between the i-th parameter and the annual cumulative oil production after horizontal well pressure in the k-th horizontal well; ρ is the correlation resolution coefficient, which is usually taken as 0.5.

[0081] The correlation coefficients of all parameters were averaged, and the correlation degree R between each parameter and the annual cumulative oil production after horizontal well pressure was calculated. oi：

[0082]

[0083] In the formula: R oi Let R be the correlation degree of the i-th parameter. oiThe correlation coefficient is such that the closer the value is to 1, the closer the relationship between the parameter and the cumulative oil production, and the greater the impact on production capacity. This allows us to rank the factors affecting production capacity and obtain the main controlling factors of oilfield horizontal well production capacity that affect post-pressurization production capacity.

[0084] Table 1. Correlation between influencing factors and horizontal well productivity

[0085]

[0086] In this embodiment, Table 1 shows the correlation between influencing factors and horizontal well productivity. As shown in Table 1, according to the grey relational analysis results, the main geological influencing factors of oil production after horizontal well fracturing are the length of the oil-bearing section encountered, the oil-bearing thickness, and the length of the horizontal section; while the main engineering influencing factors are the cluster spacing, the angle between the horizontal wellbore and the artificial fracture, the sand injection intensity, and the fluid injection intensity. The grey relational analysis of the fracturing data shows that the post-fracturing productivity of horizontal wells is mainly affected by the volume of sand bodies controlled by the horizontal well geologically. A high oil-bearing layer encounter rate and the overall utilization of the vertical reservoir thickness are the geological guarantees for high horizontal well productivity. Furthermore, the fracturing engineering plan should focus on the impact of the angle between the horizontal wellbore and the artificial fracture on productivity, optimize the fracture cluster spacing and construction scale, and improve the effectiveness of fracturing measures.

[0087] In practical applications, the average values ​​of influencing factors such as effective thickness, porosity, crude oil viscosity, formation pressure, number of fracturing clusters, cluster spacing, average single-fracture sand addition intensity, average single-fracture fluid intensity, and average sand ratio are used to simulate the post-fracture productivity changes under different permeability and combinations of horizontal wellbore and artificial fracture angles. Based on the simulation results, a productivity change curve chart is established under different reservoir permeability conditions for the angle changes between artificial fractures and horizontal well sections.

[0088] In this embodiment, as Figure 3 As shown, for reservoir permeability ≤1mD, large-angle fractures are more effective for fracturing. Small-angle oblique fractures should be placed at angles that are orthogonal to the horizontal well section. If the actual angle is less than 30°, temporary plugging and redirection techniques are preferred to increase the controlled fracture volume. For reservoir permeability ≥10mD, small-angle oblique fractures approximate the longitudinal fracture shape and are more effective than transverse fractures. Fractures in the 20°-40° range should be avoided as much as possible. If the actual angle is within the critical angle range, temporary plugging and redirection techniques are preferred to increase the controlled volume.

[0089] In practical applications, such as Figure 2 As shown, the spacing between horizontal well clusters with small angles is calculated based on the combined fracture drainage area and proppant quantity. The permeability of the propped fracture is 1×10⁻⁶. 5 mD, reservoir permeability 1mD, fracture volume 6.0×10 2 m 3 The controlled reservoir volume of the horizontal well is 2.0 × 10⁻⁶. 6 m 3The angle between the artificial fracture and the horizontal well section is 30.0°. Calculate the aspect ratio of the fracture drainage area:

[0090]

[0091] In addition, the design fracture length is 400m, the angle between the artificial fracture and the horizontal well section is 30.0°, and the spacing of the horizontal well cluster with small angle is calculated.

[0092]

[0093] In this embodiment, an empirical formula for predicting horizontal well production is established based on the length of the constructed horizontal well, the oil layer thickness, permeability, and the first-year production. The production rate of a single well is determined according to the horizontal well design parameters. Table 2 shows the statistical table of fitting parameters for the production capacity of horizontal wells already in production in the oilfield. As shown in Table 2, the predicted production capacity of the horizontal wells in the target oilfield is: Q = 6.62ln(0.6lhK) - 41.6

[0094] In practical applications, the target horizontal well has a horizontal section length of 700m, an oil layer thickness of 10m, a reservoir permeability of 10mD, and a predicted average production of 28.9t / d in the first year.

[0095] Table 2. Statistics on Fitting Parameters for Production Capacity of Horizontal Wells Already in Oilfields

[0096]

[0097] In this embodiment, by using the average value of the main control factors of oilfield horizontal well production capacity and the calculated cluster spacing value, different discrete values ​​are selected. After fixing the included angle, the production capacity changes under different cluster spacing, different number of clusters, and different scales are adjusted and simulated. The simulation scheme conducts simulation of fracturing scheme through orthogonal experimental design to obtain the post-fracturing production capacity under different combinations of parameter values ​​of the main control factors.

[0098] Table 3L9(3) 4 Orthogonal simulation data table

[0099]

[0100]

[0101] In practical applications, Table 3 is the L9(34) orthogonal simulation data table. As shown in Table 3, the orthogonal simulation results show that the number of cluster combinations and the cluster spacing have the greatest impact on the production capacity results. Orthogonal recommendation: 2 clusters + small spacing + large scale + low liquid and high sand ratio.

[0102] Table 4. Output-input ratio data for simulations with different combinations of fracturing parameters.

[0103]

[0104] In this embodiment, considering the integration of technology and economy, the optimal fracturing scheme for a small-angle horizontal well is determined by combining the output-input ratio. The output benefit is calculated based on the predicted cumulative production capacity, fitting the decreasing trend of horizontal wells to calculate the cumulative production; the price per ton of oil is calculated based on the international oil price of the current year. The input includes drilling, logging, perforation, fracturing, and infrastructure costs. The fracturing investment cost is calculated based on the fracturing scale, and the total investment cost is predicted by combining the proportion of the fracturing construction cost of the already constructed horizontal wells to the total cost. The output-input ratio is the ratio of the output benefit of the fracturing measures to the total investment cost of the horizontal well, and is dimensionless. The higher the output-input ratio of the horizontal well fracturing, the higher the economic benefit, and the better the scheme. In practical applications, Table 4 shows the output-input ratio data simulated for different combinations of fracturing parameters. As shown in Table 4, the optimal fracturing scheme for a small-angle horizontal well is Scheme 2, recommended as: 2 clusters + optimal spacing + large scale + low fluid, high sand ratio.

[0105] This invention provides an optimized design method for fracturing schemes in small-angle horizontal wells. It determines the target oilfield horizontal wells based on the critical angle range, which are then classified as small-angle horizontal wells. Utilizing historical production data from existing horizontal wells in the block, and considering factors influencing post-fracturing reservoir productivity, it accurately evaluates the impact of fracturing parameters on the effectiveness of fracturing measures in small-angle horizontal wells. This further guides the process optimization and parameter optimization of fracturing schemes for small-angle horizontal wells. Obtaining post-fracturing reservoir productivity in the oilfield is relatively easy. By simplifying the calculation methods for different parameter values ​​during the calculation process, the method becomes simpler and more reliable. The results meet the requirements of actual field applications and are more suitable for the early optimization of fracturing schemes in small-fault sandstone reservoirs. It allows for targeted optimization of subsequent well location adjustments and single-well fracturing during the actual well placement process.

[0106] 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. An optimized design method for fracturing schemes in horizontal wells with small included angles, characterized in that, include: Step S1: Determine the target study area and screen the factors affecting reservoir post-compression productivity of horizontal wells in the oilfield within the target study area; Step S2: Divide the horizontal wells in the target study area determined in Step S1 into horizontal wells in fractured oilfields and horizontal wells in unfractured oilfields, obtain the production data of horizontal wells in fractured oilfields, and, in conjunction with the reservoir post-fracture production capacity influencing factors screened in Step S1, use the grey relational analysis method to determine the main controlling factors of oilfield horizontal well production capacity. Step S3: Preset the critical angle range of the oilfield horizontal well, simulate the post-fracturing production capacity change of the oilfield horizontal well under the main control factors of the oilfield horizontal well production capacity determined in step S2, and determine the oilfield horizontal well fracturing and stimulation process template. Step S4: Determine the target oilfield horizontal well based on the critical angle range determined in Step S3, and calculate the cluster spacing value of the target oilfield horizontal well by combining the oilfield horizontal well fracturing process template, the fracture drainage area and the number of proppant, and taking into account the fracture drainage area and the number of proppant. Step S5: Based on the production data of the fractured horizontal wells in the oilfield determined in Step S2, establish an empirical formula for predicting the production of the target oilfield horizontal wells determined in Step S4, and calculate and calibrate the predicted production capacity of the target oilfield horizontal wells. Step S6: Combining the cluster spacing value determined in Step S4 and the predicted production capacity calibrated in Step S5, under the different combinations of main control factors for production capacity of horizontal wells in oilfields determined in Step S2, simulate the post-pressure production capacity of horizontal wells in the target oilfield through orthogonal experimental design. Step S7: Based on the post-fracturing production capacity determined in Step S6, and in conjunction with the output-input ratio, determine the fracturing optimization template for the target oilfield horizontal wells determined in Step S4.

2. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 1, characterized in that, In step S1, the factors affecting reservoir post-fracturing productivity include reservoir geological parameters and fracturing operation parameters.

3. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 2, characterized in that, The reservoir geological parameters include horizontal section length, oil layer section length, effective thickness, permeability, porosity, crude oil viscosity, and formation pressure; the fracturing construction parameters include the number of fracturing clusters, cluster spacing, angle between the oilfield horizontal wellbore and the artificial fracture, total fluid volume, total sand volume, average sand addition intensity per fracture, average fluid intensity per fracture, and average sand ratio.

4. The optimized design method for fracturing schemes in horizontal wells with small included angles according to claim 1, characterized in that, In step S2, the production data of the fractured horizontal well in the oilfield includes the length of the fractured horizontal well, the thickness of the oil layer, the permeability, and the production in the first year.

5. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 1, characterized in that, In step S2, the specific steps for determining the main controlling factors of oilfield horizontal well productivity using the grey relational analysis method include: The cumulative annual production after fracturing of horizontal wells in the fracturing oilfield is used as the parent sequence, and reservoir geological parameters and fracturing operation parameters are used as the child sequences. The raw data is dimensionless, and the absolute difference matrix and relative correlation coefficient between each parameter and the output are calculated. The correlation degree of each parameter was determined by mean value processing, and the main controlling factors of oilfield horizontal well productivity were screened by sorting the correlation degree.

6. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 1, characterized in that, In step S3, the main factors controlling the productivity of the oilfield horizontal well include effective thickness, porosity, permeability, crude oil viscosity, formation pressure, number of fracturing clusters, angle between the oilfield horizontal wellbore and artificial fractures, cluster spacing, average sand addition intensity per fracture, average fluid intensity per fracture, and average sand ratio.

7. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 6, characterized in that, In step S3, based on the average values ​​of influencing factors such as effective thickness, porosity, crude oil viscosity, formation pressure, number of fracturing clusters, cluster spacing, average single-fracture sand addition intensity, average single-fracture fluid intensity, and average sand ratio, the post-fracturing productivity changes of horizontal wells in oilfields under different permeability and oilfield horizontal wellbore-artificial fracture angle combinations are simulated. A productivity change curve chart of the angle between artificial fractures and horizontal well sections is established. When the actual angle between artificial fractures and horizontal well sections is within the critical angle range, the temporary plugging and diversion process is determined as the target process, and the fracture control volume is increased through the target process.

8. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 7, characterized in that, In step S4, the actual included angle between the horizontal wellbore and the artificial fracture in the target oilfield horizontal well is less than the critical angle range, where the critical angle range is: When the reservoir permeability is ≤1mD, the critical angle is 0°-30°; When the reservoir permeability is ≥10mD, the critical angle is 20°-40°.

9. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 1, characterized in that, In step S5, the empirical formula for production forecasting is: Q = alt(0.6lhK) - b Where: Q is the predicted design production rate, t / d; l is the design horizontal well length, m; h is the oil layer thickness, m; K is the oil layer permeability, mD; α and b are regression coefficients, α = 6.62 and b = 41.

6.

10. The optimized design method for fracturing schemes in small-angle horizontal wells according to claim 1, characterized in that, In step S6, the orthogonal experimental design adopts L9(3) 4 An orthogonal array was used to simulate the post-pressurization production capacity under different combinations of cluster spacing, number of clusters, and construction scale.

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