Optimization methods, devices, equipment, and media for sand addition scale

Abstract

This application provides a method, apparatus, equipment, and medium for optimizing proppant addition scale. The method includes: performing sensitivity analysis on the reservoir to be treated to determine the main controlling factors and their weights affecting the horizontal well production of the reservoir; obtaining a three-dimensional geological model of the reservoir; performing sweet spot evaluation processing on the three-dimensional geological model according to the main controlling factors and their weights to obtain a three-dimensional geological model divided into three-dimensional sweet spots; establishing a relationship chart based on the three-dimensional geological model divided into three-dimensional sweet spots; wherein the relationship chart is used to characterize the relationship between the three-dimensional sweet spot index, proppant addition scale, and cumulative production; and determining the target proppant addition scale based on the relationship chart. This allows for the selection of the optimal proppant addition scale, improving proppant utilization and oilfield economic development benefits.

Description

Technical Field

[0001] This application relates to the field of oil production engineering technology, and in particular to an optimization method, apparatus, equipment and medium for sand addition scale. Background Technology

[0002] Unconventional oil and gas resources are a key area of ​​global energy development. These resources typically exhibit low porosity and low permeability in their reservoirs, posing significant challenges to their development. Currently, horizontal well volumetric fracturing technology is widely used in unconventional oil and gas development. Volumetric fracturing increases the reservoir's drainage area and conductivity by creating a complex network of fractures within the reservoir. The scale of proppant injection is a crucial factor determining the fracture conductivity and its duration of effectiveness, directly impacting fracturing efficiency and oil and gas production.

[0003] Existing fracturing designs typically determine the proppant addition scale based on the sweet spot quality of the reservoir. For example, reservoirs with higher sweet spot quality use more proppant, while reservoirs with lower sweet spot quality use less proppant. However, traditional sweet spot evaluation methods are usually based on a one-dimensional perspective, which can only characterize the sweet spot quality within a limited range around the wellbore. They lack the ability to continuously model the three-dimensional space of the entire well section and cannot characterize the distribution pattern of reservoir quality in three-dimensional space. This makes it difficult for fracturing designs to accurately match the changes in reservoir compressibility in three-dimensional space.

[0004] Therefore, existing technologies suffer from problems such as difficulty in determining the scale of sand addition and low proppant utilization. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for optimizing the scale of sand addition, so as to determine the optimal scale of sand addition and improve the utilization rate of proppant.

[0006] In a first aspect, embodiments of this application provide a method for optimizing the scale of sand addition, comprising:

[0007] Sensitivity analysis was performed on the reservoir to be treated to determine the main controlling factors and their weights that affect the production of horizontal wells in the reservoir.

[0008] Obtain a three-dimensional geological model of the reservoir to be processed;

[0009] Based on the main control factors and their weights, the three-dimensional geological model is subjected to a dessert evaluation process to obtain a three-dimensional geological model that divides the three-dimensional dessert body.

[0010] Based on the three-dimensional geological model that divides the three-dimensional dessert body, a relationship map is established; the relationship map is used to characterize the relationship between the three-dimensional dessert index, sand addition scale and cumulative yield.

[0011] Determine the target sand addition scale based on the relationship diagram.

[0012] In one possible implementation, sensitivity analysis is performed on the reservoir to be treated to determine the main controlling factors affecting the production of horizontal wells in the reservoir and the weights of these main controlling factors, including:

[0013] Obtain preset geological engineering parameters;

[0014] Based on the preset geological engineering parameters, obtain the orthogonal experimental results of the reservoir to be treated;

[0015] Based on the results of the orthogonal experiment, a sensitivity analysis was performed on the reservoir to be treated.

[0016] Based on the results of sensitivity analysis, the main controlling factors and their weights affecting the production of horizontal wells in the reservoir to be treated were determined.

[0017] In one possible implementation, obtaining a three-dimensional geological model of the reservoir to be processed includes:

[0018] Acquire well logging curves and seismic data of the reservoir to be processed;

[0019] Based on well logging curve information and seismic data, a three-dimensional geological model of the reservoir to be treated was established using sequential Gaussian simulation.

[0020] In one possible implementation, a sweet spot evaluation process is performed on the three-dimensional geological model based on the controlling factors and their weights to obtain a three-dimensional geological model divided into three-dimensional sweet spot volumes, including:

[0021] Based on the main control factors and their weights, normalization is performed to obtain normalized data for the main control factors.

[0022] Based on the normalized data of the main controlling factors, finite element calculations are performed to obtain the reservoir quality function;

[0023] Based on the reservoir quality function and the three-dimensional geological model, the three-dimensional geological model of the sweet spot is determined.

[0024] In one possible implementation, the three-dimensional geological model of the three-dimensional sweet spot is determined based on the reservoir quality function and the three-dimensional geological model, including:

[0025] The three-dimensional sweet spot evaluation index is determined based on the reservoir quality function and the three-dimensional geological model;

[0026] Based on the three-dimensional dessert evaluation index, a three-dimensional geological model of the dessert body is determined.

[0027] In one possible implementation, a relational map is established based on a three-dimensional geological model that divides the three-dimensional dessert body, including:

[0028] Obtain the preset 3D dessert type and preset sand addition scale parameters;

[0029] Based on the preset three-dimensional dessert type and preset sand addition scale parameters, the three-dimensional geological model dividing the three-dimensional dessert body is subjected to production capacity simulation processing to obtain production capacity simulation results.

[0030] Based on the capacity simulation results, a relationship diagram was created.

[0031] In one possible implementation, determining the target sand addition scale based on the relational diagram includes:

[0032] Obtain the target three-dimensional dessert index;

[0033] Based on the target three-dimensional dessert index and relationship diagram, determine the target sand addition scale to meet the cumulative production requirements.

[0034] Secondly, embodiments of this application provide an optimization processing device for sand addition scale, including: a first processing module, used to perform sensitivity analysis processing on the reservoir to be treated, so as to determine the main controlling factors and the weights of the main controlling factors affecting the production of horizontal wells in the reservoir to be treated;

[0035] The acquisition module is used to acquire the three-dimensional geological model of the reservoir to be processed;

[0036] The second processing module is used to perform sweet spot evaluation processing on the three-dimensional geological model according to the main control factors and their weights, so as to obtain a three-dimensional geological model divided into three-dimensional sweet spot bodies.

[0037] The third processing module is used to establish a relationship map based on the three-dimensional geological model that divides the three-dimensional dessert body; the relationship map is used to characterize the relationship between the three-dimensional dessert index, sand addition scale and cumulative yield.

[0038] The determination module is used to determine the target sand addition scale based on the relationship diagram.

[0039] Thirdly, embodiments of this application provide an optimized processing device for sand addition, comprising: a memory and a processor;

[0040] The memory stores the instructions that the computer executes;

[0041] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0042] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0043] The method, apparatus, equipment, and medium for optimizing sand addition scale provided in this application embodiment perform sensitivity analysis on the reservoir to be treated to determine the main controlling factors and their weights affecting the production of horizontal wells in the reservoir. Based on these main controlling factors and their weights, a sweet spot evaluation is performed on the obtained three-dimensional geological model to obtain a three-dimensional geological model divided into three-dimensional sweet spot bodies. This reasonably characterizes the heterogeneity of the reservoir and accurately represents the distribution of three-dimensional sweet spots. A relationship chart is established based on the three-dimensional geological model divided into three-dimensional sweet spot bodies. This relationship chart is used to represent the relationship between the three-dimensional sweet spot index, sand addition scale, and cumulative production. Based on the relationship chart, the target sand addition scale is determined, thereby selecting the optimal sand addition scale and improving the accuracy of reservoir description. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0045] Figure 1 A schematic diagram of an optimized processing system architecture for sand addition scale provided in an embodiment of this application;

[0046] Figure 2 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 1 ;

[0047] Figure 3 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 2 ;

[0048] Figure 4 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 3 ;

[0049] Figure 5 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 4 ;

[0050] Figure 6 The illustration provided for the embodiments of this application is a comparison diagram of the accuracy of linear and nonlinear methods;

[0051] Figure 7 A schematic diagram of a three-dimensional geological model of a three-dimensional dessert body provided in the embodiments of this application;

[0052] Figure 8 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 5 ;

[0053] Figure 9A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 6 ;

[0054] Figure 10 Cumulative yields of different three-dimensional dessert types and different sand addition scales provided in the embodiments of this application Figure 1 ;

[0055] Figure 11 Cumulative yields of different three-dimensional dessert types and different sand addition scales provided in the embodiments of this application Figure 2 ;

[0056] Figure 12 The cumulative yield and pressure variation cloud maps provided for different three-dimensional dessert types and different sand addition scales are provided for the embodiments of this application;

[0057] Figure 13 A comparative schematic diagram of oil layer classification based on well logging curves and oil layer classification based on three-dimensional sweet spot evaluation index provided for embodiments of this application;

[0058] Figure 14 A schematic diagram illustrating the changes in oil yield under different sand addition scales provided in the embodiments of this application;

[0059] Figure 15 A comparative schematic diagram showing the sand-adding method in the prior art provided for the embodiments of this application and the sand-adding method provided in this application;

[0060] Figure 16 A schematic diagram of a sand-addition scale optimization processing device provided in an embodiment of this application;

[0061] Figure 17 A schematic diagram of the structure of the sand-addition scale optimization processing equipment provided in the embodiments of this application.

[0062] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0063] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0064] It should be noted that all data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0065] The following is an explanation of the terms used in this application:

[0066] Sweet spot quality: Sweet spot refers to the area or section in an oil and gas reservoir that has the highest development value and the best economic benefits. Sweet spot quality refers to the evaluation of the overall quality of the sweet spot, including geological conditions, fluid characteristics and engineering recoverability.

[0067] Unconventional oil and gas are stored in unconventional reservoirs. Due to the geological characteristics of unconventional reservoirs, which are usually characterized by low porosity and low permeability, the development of unconventional oil and gas is more difficult.

[0068] In existing technologies, unconventional oil and gas can be developed using horizontal well volumetric fracturing. Volumetric fracturing can form a complex fracture network in the reservoir, increasing the reservoir's drainage area and conductivity. The scale of proppant injection directly determines the conductivity and lifespan of the fractures. Therefore, the scale of proppant injection is a core parameter affecting the effectiveness of volumetric fracturing and oil and gas production.

[0069] For example, if the proppant is sand, in the existing fracturing design process, the amount of sand added depends on the sweet spot quality. More sand is added for Class I reservoirs, less sand is added for Class II reservoirs, and less sand or no sand is added for Class III reservoirs.

[0070] However, traditional methods for evaluating sweet spot quality, which characterize the static sweet spot zone of the reservoir through well logging interpretation, only describe the sweet spot quality of the reservoir from a one-dimensional perspective. They can only characterize the sweet spot quality within a 20cm radius around the wellbore and lack three-dimensional continuous modeling of the entire well section. This makes it difficult to accurately characterize the distribution pattern of the sweet spot quality in three-dimensional space. Consequently, the fracturing design process cannot accurately match the spatial changes in reservoir compressibility, thus preventing proppant from fully entering the favorable reservoir area, reducing proppant utilization, and restricting the economic development benefits of the oilfield.

[0071] To address the aforementioned issues, this application provides an optimization method for proppant addition scale. The core concept involves: conducting sensitivity analysis on the reservoir to be treated to determine the main controlling factors and their weights affecting horizontal well production; then, based on these main controlling factors and their weights, performing sweet spot evaluation on the obtained three-dimensional geological model to construct a refined three-dimensional geological model; thereby reasonably characterizing the reservoir's heterogeneity and accurately representing the three-dimensional sweet spot distribution; and establishing a relationship chart between the three-dimensional sweet spot index, proppant addition scale, and cumulative production based on the three-dimensional geological model that delineates the three-dimensional sweet spot volume; finally, determining the optimal proppant addition scale to improve proppant utilization efficiency and enhance the economic development benefits of the oilfield.

[0072] Optionally, Figure 1 This is a schematic diagram of an optimized processing system architecture for sand addition scale provided in an embodiment of this application. Figure 1 As shown, the optimized processing system architecture for sand addition scale includes at least one of data acquisition device 101, processing device 102, and display device 103.

[0073] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the above architecture. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0074] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface, and the data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface.

[0075] The processing device 102 can perform sensitivity analysis on the reservoir to be treated to determine the main controlling factors and their weights that affect the production of horizontal wells in the reservoir; based on the main controlling factors and their weights, it can perform sweet spot evaluation processing on the obtained three-dimensional geological model to obtain a three-dimensional geological model that divides the three-dimensional sweet spot body; based on the three-dimensional geological model that divides the three-dimensional sweet spot body, it can establish a relationship chart; the relationship chart is used to characterize the relationship between the three-dimensional sweet spot index, the scale of sand addition, and the cumulative production; based on the relationship chart, it can determine the target scale of sand addition.

[0076] The display device 103 can also be a touch screen or the screen of a terminal device, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0077] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0078] Figure 2 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 1 ,like Figure 2 As shown, the method includes:

[0079] S201. Perform sensitivity analysis on the reservoir to be treated to determine the main controlling factors and their weights that affect the production of horizontal wells in the reservoir to be treated.

[0080] In this embodiment, sensitivity analysis is performed on the reservoir to be treated to determine the main controlling factors and their weights that affect the production of horizontal wells in the reservoir to be treated.

[0081] The main controlling factors include porosity, permeability, Poisson's ratio, Young's modulus, and oil saturation. By determining the main controlling factors and their weights that affect the production of horizontal wells in the reservoir to be treated, the main controlling factors affecting reservoir production were accurately identified and quantified, ensuring the accuracy of reservoir description.

[0082] S202. Obtain the three-dimensional geological model of the reservoir to be processed.

[0083] In this embodiment, by processing and integrating the well logging curve information and seismic data information of the reservoir to be treated, a three-dimensional geological model of the reservoir to be treated is established by sequential Gaussian simulation, which ensures the accuracy and reliability of the three-dimensional geological model of the reservoir to be treated.

[0084] S203. Based on the main control factors and their weights, the three-dimensional geological model is subjected to dessert evaluation processing to obtain a three-dimensional geological model that divides the three-dimensional dessert body.

[0085] In this embodiment, a three-dimensional sweet spot evaluation index is constructed based on the main control factors and their weights. This index is then introduced into a three-dimensional geological model to quantitatively characterize formation quality and assess the distribution of reservoir quality in three-dimensional space. This process determines the three-dimensional geological model of the sweet spot, providing detailed geological information for the reservoir and improving the accuracy of reservoir description.

[0086] S204. Based on the three-dimensional geological model that divides the three-dimensional dessert body, establish a relationship map; the relationship map is used to characterize the relationship between the three-dimensional dessert index, sand addition scale and cumulative yield.

[0087] In this embodiment, a numerical model is established. Based on the three-dimensional geological model that divides the three-dimensional sweet spot, porosity, permeability, Poisson's ratio, Young's modulus, and oil saturation are simulated respectively to predict the corresponding cumulative production, so as to ensure that the initial conditions of the model are consistent with the boundary conditions and the actual reservoir conditions.

[0088] S205. Determine the target sand addition scale based on the relationship diagram.

[0089] In this embodiment, the optimal proppant addition scale for the target area is determined based on the relationship map characterizing the relationship between the three-dimensional sweet spot index, proppant addition scale, and cumulative production, thereby improving the utilization rate of proppant and thus enhancing the economic development benefits of the oilfield.

[0090] The method for optimizing proppant addition scale provided in this application involves performing sensitivity analysis on the reservoir to be treated to determine the main controlling factors and their weights affecting the production of horizontal wells in the reservoir; then, based on the main controlling factors and their weights, performing sweet spot evaluation processing on the obtained three-dimensional geological model to construct a refined three-dimensional geological model; thereby accurately describing the three-dimensional sweet spot distribution; and establishing a relationship chart between the three-dimensional sweet spot index, proppant addition scale, and cumulative production based on the three-dimensional geological model that divides the three-dimensional sweet spot bodies; thus determining the optimal proppant addition scale to improve proppant utilization and enhance the economic development benefits of the oilfield.

[0091] Figure 3 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the sensitivity analysis of the reservoir to be treated in step S201 above is performed to determine the main controlling factors affecting the production of horizontal wells in the reservoir to be treated and the weights of the main controlling factors. The method includes:

[0092] S301. Obtain preset geological engineering parameters.

[0093] In this embodiment, the preset geological engineering parameters include porosity, permeability, Poisson's ratio, Young's modulus, oil saturation, brittleness index, horizontal stress difference, total organic carbon content, thermal maturity, closure pressure, pressure coefficient, fracture density, and fracture filling properties.

[0094] S302. Based on the preset geological engineering parameters, obtain the orthogonal experimental results of the reservoir to be treated.

[0095] In this embodiment, different values ​​are set according to preset geological engineering parameters, and an orthogonal table is generated for orthogonal experiments. By screening different preset combinations of geological engineering parameters, the orthogonal experimental results of the reservoir to be treated are obtained. The variance decomposition of the orthogonal experimental results is performed to determine the order of influence of each preset geological engineering parameter on the orthogonal experimental results, thereby improving the efficiency and reliability of the selection of geological engineering parameters.

[0096] S303. Based on the orthogonal experiment results, sensitivity analysis was performed on the reservoir to be treated to obtain the sensitivity analysis results.

[0097] In this embodiment, for example, the top five preset geological engineering parameters in terms of their influence on the orthogonal experimental results are selected, input into the preset geological engineering model, and reservoir behavior and production are simulated. The parameters are then perturbed, and the sensitivity analysis results are determined.

[0098] S304. Based on the results of sensitivity analysis, determine the main controlling factors and their weights that affect the production of horizontal wells in the reservoir to be treated.

[0099] In this embodiment, for example, based on the results of sensitivity analysis, an analytic hierarchy process (AHP) is used to establish a hierarchical judgment matrix. The hierarchical judgment matrix is ​​shown in Table 1. The main controlling factors are, in order, porosity, permeability, rare saturation, Young's modulus, and Poisson's ratio. A consistency ratio (CR) is calculated to perform consistency checks.

[0100] Table 1 Hierarchical Judgment Matrix

[0101]

[0102] If CR is less than 0.1, the characterization hierarchy judgment matrix passes the consistency test; the eigenvalue method is used to calculate the weight of each controlling factor, and the weight of each controlling factor is shown in Table 2.

[0103] Table 2 Weights of each controlling factor

[0104]

[0105] The optimization method for sand addition scale provided in this application embodiment obtains orthogonal experimental results based on preset geological engineering parameters, performs sensitivity analysis, thereby determining the main control factors and weights, accurately identifying and quantifying the key geological engineering parameters affecting reservoir production, and ensuring the accuracy of reservoir description.

[0106] Figure 4 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 3 ,like Figure 4 As shown, in this embodiment... Figure 2 Based on the embodiments, the method for obtaining the three-dimensional geological model of the reservoir to be processed in step S202 above will be described in detail. The method includes:

[0107] S401. Obtain well logging curve information and seismic data information of the reservoir to be processed.

[0108] In this embodiment, the well logging curve information of the reservoir to be processed includes reservoir lithology, porosity, and fluid saturation, while seismic data information is used to identify geological structures, sequence stratigraphy, and reservoir continuity. The well logging curve information of the reservoir to be processed is denoised and outlier removed, while the seismic data information of the reservoir to be processed is denoised, gain adjusted, and time-depth converted. The well logging curve information and seismic data information are matched and integrated to ensure the accuracy and consistency of the data.

[0109] S402. Based on well logging curve information and seismic data, a three-dimensional geological model of the reservoir to be treated is established using sequential Gaussian simulation.

[0110] In this embodiment, based on well logging interpretation and constrained by regional geological understanding and variograms, an initial three-dimensional geological model is constructed and divided into 620,000 grids, with the vertical stratification of the reservoir at 0.5m. Each grid cell represents a small volume within the reservoir. Sequential Gaussian simulation is used to model the geological variables of the reservoir to be processed. The accuracy of the initial three-dimensional geological model is verified by comparing the obtained well logging curves and seismic data of the reservoir to be processed. The initial three-dimensional geological model is then adjusted based on the verification results to obtain an accurate three-dimensional geological model of the reservoir to be processed.

[0111] The optimization method for sand addition scale provided in this application embodiment processes and integrates well logging curve information and seismic data information of the reservoir to be treated, establishes a three-dimensional geological model of the reservoir to be treated using sequential Gaussian simulation, and verifies and optimizes the three-dimensional geological model to ensure the accuracy and reliability of the three-dimensional geological model of the reservoir to be treated.

[0112] Figure 5 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 4 ,like Figure 5 As shown, in this embodiment... Figures 2 to 4 Based on the illustrated embodiment, the method of performing a dessert evaluation on the three-dimensional geological model according to the main control factors and their weights in step S203 above to obtain a three-dimensional geological model divided into three-dimensional dessert bodies is described in detail. The method includes:

[0113] S501. Based on the main control factors and their weights, normalization processing is performed to obtain normalized data of the main control factors.

[0114] In this embodiment, range normalization is performed based on the controlling factors and their weights to eliminate the influence of dimensions, resulting in normalized data for the controlling factors. The range normalization process is shown in the following formula:

[0115]

[0116] In the formula, i is the identifier of the controlling factor, and P i P is the i-th controlling factor; N(Pi) is the normalization function of the i-th controlling factor; i，min It is the minimum value of the i-th master control factor; P i，max The maximum value of the i-th master control factor.

[0117] S502. Based on the normalized data of the main control factors, perform finite element calculations to obtain the reservoir quality function.

[0118] In this embodiment, by comparing the accuracy of linear and nonlinear methods, the determination coefficient R of the linear model corresponding to the linear method can be obtained. 2 The coefficient of determination (COP) reached 91.6%, indicating that the linear model can explain 91.6% of the variations in the reservoir under treatment. This is superior to the COP of the corresponding nonlinear model, which ranges from 85.9% to 90.1%. Therefore, the optimal basic model is determined to be the linear weighted model. The COP refers to the degree of fit between the model and the data. Figure 6 This diagram illustrates the comparison of the accuracy of linear and nonlinear methods. Nonlinear methods include product models, exponential models, and piecewise models. Besides the coefficient of determination, other metrics can be used to compare the accuracy of linear and nonlinear methods, such as mean squared error (MSE). Figure 6 MSE shown ×10 -1 ), mean absolute error (e.g.) Figure 6 The MAE shown), root mean square error (e.g.) Figure 6 (RMSE shown).

[0119] Using the optimal basic model and the finite element method, and based on the normalized data of the main controlling factors, finite element calculations are performed to calculate the reservoir quality function for each grid cell, thus constructing the reservoir quality function. The reservoir quality function for each grid cell is shown in the following formula:

[0120]

[0121] In the formula, f(x, y, z) is the reservoir quality function; x, y, z are the coordinates of the grid cells, respectively; n is the number of controlling factors; and i is the identifier of the controlling factor. Let be the weight of the i-th controlling factor; Let be the normalization function for the i-th master control factor.

[0122] Based on the reservoir quality function and the three-dimensional geological model, the three-dimensional geological model of the sweet spot is determined.

[0123] Optionally, based on the reservoir quality function and the three-dimensional geological model, a three-dimensional geological model of the sweet spot is determined, including:

[0124] S503. Based on the reservoir quality function and the three-dimensional geological model, determine the three-dimensional sweet spot evaluation index.

[0125] In this embodiment, based on the reservoir quality function, the reservoir quality of each grid cell in the three-dimensional geological model is triple-integrated to obtain the three-dimensional sweet spot evaluation index.

[0126] The formula for calculating the three-dimensional dessert evaluation index is as follows:

[0127]

[0128] In the formula, SQ is the three-dimensional sweet spot evaluation index, which is dimensionless; Ω is the evaluation range of the three-dimensional volume (400m horizontally and 40m vertically); f(x, y, z) is the reservoir quality function.

[0129] S504. Based on the three-dimensional dessert evaluation index, determine the three-dimensional geological model of the three-dimensional dessert body.

[0130] In this embodiment, for example, based on the three-dimensional sweet spot evaluation index, three-dimensional sweet spot bodies are sequentially constructed in a reservoir to be treated with a length of 400m, a width of 60m, and a height of 40m. Thus, within a single well, a total of 40 sweet spot bodies are generated, thereby determining the three-dimensional geological model of the sweet spot bodies; wherein, the three-dimensional geological model of the sweet spot bodies is as follows: Figure 7 As shown, Figure 7 KHW8303, KHW8304, KHW8305, and KHW8306 are well numbers, and their corresponding general stratigraphic sequences are shown (e.g., ...). Figure 7 (SQGeneral shown).

[0131] The optimization method for sand addition scale provided in this application eliminates the dimensional influence between different main control factors by normalizing the main control factors and their weights. It adopts a linear weighted average as the optimal basic model and uses the finite element method to determine the reservoir quality function. Based on the reservoir quality function and the three-dimensional geological model, a three-dimensional sweet spot evaluation index is determined, providing a quantitative indicator to evaluate the distribution of reservoir quality in three-dimensional space. This determines the three-dimensional geological model of the three-dimensional sweet spot, providing detailed geological information for the reservoir and improving the accuracy of reservoir description.

[0132] Figure 8A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 5 ,like Figure 8 As shown, in this embodiment... Figure 5 Based on the embodiments, the method of establishing a relational map based on the three-dimensional geological model of the three-dimensional dessert body in step S204 above will be described in detail. The method includes:

[0133] S801. Obtain the preset 3D dessert type and preset sand addition scale parameters.

[0134] In this embodiment, the preset three-dimensional dessert types are set to 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9, respectively; the preset sand addition scale parameter is 20m. 3 25m 3 30m 3 35m 3 40m 3 and 45m 3 .

[0135] S802. Based on the preset three-dimensional dessert type and preset sand addition scale parameters, perform production capacity simulation processing on the three-dimensional geological model that divides the three-dimensional dessert body to obtain the production capacity simulation results.

[0136] In this embodiment, a three-dimensional geological model of the three-dimensional dessert body is imported into numerical simulation software to establish a numerical model. The input parameters are changed according to the preset three-dimensional dessert type and the preset sand addition scale parameter to obtain the cumulative output under different three-dimensional dessert types and different sand addition scales, thereby obtaining the production capacity simulation results.

[0137] S803. Based on the capacity simulation results, establish a relationship diagram.

[0138] In this embodiment, based on the production capacity simulation results, cumulative production and pressure change cloud maps are drawn for different three-dimensional dessert types and different sand addition scales, thereby constructing a relationship map of three-dimensional desserts, sand addition scales, and cumulative production.

[0139] The sand addition scale optimization method provided in this application embodiment obtains preset three-dimensional dessert type and preset sand addition scale parameters, performs production capacity simulation processing on the three-dimensional geological model divided into three-dimensional dessert bodies to obtain production capacity simulation results, and establishes a relationship map. It effectively evaluates the impact of different three-dimensional dessert types and sand addition scales on production capacity, intuitively shows the production capacity change trend under complex geological conditions, and is conducive to optimizing the design of geological models and the effective utilization of resources.

[0140] Figure 9 A flowchart illustrating the optimization method for sand addition scale provided in the embodiments of this application. Figure 6 ,like Figure 9 As shown, in this embodiment... Figure 5 Based on the embodiments, the determination of the target sand addition scale according to the relational diagram in step S205 above will be described in detail. The method includes:

[0141] S901, Obtain the target three-dimensional dessert index.

[0142] In this embodiment, a target region is determined, and the target three-dimensional dessert index of the target region is obtained.

[0143] S902. Based on the target three-dimensional sweetness index and relationship diagram, determine the target sand addition scale to meet the cumulative production requirements.

[0144] In this embodiment, for example, Figure 10 , Figure 11 The cumulative production figures are for different three-dimensional dessert types and different amounts of added sand. Figure 10 , Figure 11 The horizontal axis represents the year of production, and the vertical axis represents the cumulative output. Figure 10 , Figure 11 It can be seen that when the three-dimensional dessert evaluation index is greater than 0.6, the production increase begins to slow down. Therefore, the oil layer is classified as follows: when the three-dimensional dessert evaluation index is greater than 0.6, it is a Class I oil layer; when the three-dimensional dessert evaluation index is greater than or equal to 0.3 and less than or equal to 0.6, it is a Class II oil layer; and when the three-dimensional dessert evaluation index is less than 0.3, it is a Class III oil layer.

[0145] Figure 12 Cloud maps showing cumulative yield versus pressure variations for different three-dimensional dessert types and varying sand addition levels. Figure 12 The horizontal axis represents the three-dimensional dessert index, and the vertical axis represents cumulative production. Figure 12 It can be seen that when the three-dimensional dessert type is a three-layer oil layer, the sand addition scale starts from 20m. 3 Increased to 25m 3 The cumulative production showed no significant change; when the three-dimensional sweet spot type was a type II oil layer, the sand addition scale increased to 35m. 3 At that time, the cumulative production growth rate slowed down, reaching an inflection point; when the three-dimensional dessert type is a type of oil layer, the sand addition scale is 40m. 3 And the sand addition scale is 45m 3 Their cumulative outputs are relatively close, with smaller increases.

[0146] Optionally, Figure 13 This is a schematic diagram comparing reservoir classification based on well logging curves and reservoir classification based on a three-dimensional sweet spot index. Figure 13 The column identifiers are segment numbers, and the row identifiers are oil layer classification based on well logging curves and oil layer classification based on three-dimensional sweet spot evaluation index, respectively, such as... Figure 13As shown, the oil reservoir classification results based on the three-dimensional sweet spot evaluation index show significant differences, which more accurately describes the reservoir conditions.

[0147] Figure 14 This diagram illustrates the changes in oil yield under different sand addition scales. Figure 14 The horizontal axis represents the scale of sand addition, and the vertical axis includes increased oil production and revenue. Figure 14 It can be seen that when the three-dimensional dessert type is a type of oil layer, the sand addition scale starts from 30m. 3 Increased to 35m 3 At that time, the oil yield is the highest and the profit is the greatest; when the three-dimensional sweet spot type is a type II oil layer, the sand addition scale starts from 20m. 3 Increased to 25m 3 At that time, the oil yield is the highest and the profit is the greatest; when the three-dimensional sweet spot type is the third type of oil layer, the sand addition scale starts from 20m. 3 Increased to 25m 3 At that time, the increase in oil production was the highest, but the returns were all negative.

[0148] Figure 15 A comparative schematic diagram of the sand-adding method in the prior art and the sand-adding method provided in this application is shown below. Figure 15 As shown, the total sand volume for the old sand addition method is 2845m³. 3 The total amount of sand added using the new sand-addition method is 2260m³. 3 The cost of the old sand-adding method is 4.064 million yuan, while the cost of the new sand-adding method is 3.229 million yuan. By comparing the total sand volume and economic benefits of the existing sand-adding methods and the sand-adding method provided in this application, it can be seen that the sand-adding method provided in this application (such as...) Figure 15 The new sand-addition method shown is compared to existing sand-addition methods (such as...). Figure 15 The old sand-addition method shown saves 585m³ of sand per well. 3 The cost is approximately 835,800 yuan.

[0149] Therefore, the optimal sand addition scale for the target area is: when the three-dimensional sweet spot type is a Class I oil layer, the sand addition scale is 35m. 3 When the three-dimensional dessert type is a second-class oil layer, the sand addition scale is 25m. 3 When the three-dimensional dessert type is a third-class oil layer, the sand addition scale is 20m. 3 .

[0150] The method for optimizing the sand addition scale provided in this application determines the target sand addition scale that meets the cumulative production requirements by obtaining the target three-dimensional sweetness index and relationship diagram, thereby achieving the selection of the optimal sand addition scale, improving the utilization rate of proppant, and effectively improving the block benefits.

[0151] Optionally, after establishing a relational map based on the three-dimensional geological model that divides the three-dimensional dessert body, the method further includes:

[0152] Automated analysis software is used to design experiments, select statistical methods, and screen for uncertainty factors based on model understanding.

[0153] The range of values ​​for uncertainty factors is set based on prior knowledge or probability distribution, and discrete value enumeration is used for discontinuous parameters.

[0154] Define the objective function, select optimization metrics, and run tasks in batches.

[0155] Figure 16 This is a schematic diagram of the structure of an optimized processing device for sand addition scale provided in an embodiment of this application, as shown below. Figure 16 As shown, the sand addition scale optimization device provided in this embodiment includes:

[0156] The first processing module 1601 is used to perform sensitivity analysis on the reservoir to be treated in order to determine the main controlling factors and their weights that affect the production of horizontal wells in the reservoir to be treated.

[0157] The acquisition module 1602 is used to acquire the three-dimensional geological model of the reservoir to be processed.

[0158] The second processing module 1603 is used to perform sweet spot evaluation processing on the three-dimensional geological model according to the main control factors and their weights, so as to obtain a three-dimensional geological model divided into three-dimensional sweet spot bodies.

[0159] The third processing module 1604 is used to establish a relationship map based on the three-dimensional geological model that divides the three-dimensional dessert body; the relationship map is used to characterize the relationship between the three-dimensional dessert index, sand addition scale and cumulative yield.

[0160] Module 1605 is used to determine the target sand addition scale based on the relationship diagram.

[0161] In one possible implementation, the first processing module 1601 can also be used for:

[0162] Obtain preset geological engineering parameters;

[0163] Based on the preset geological engineering parameters, obtain the orthogonal experimental results of the reservoir to be treated;

[0164] Based on the results of the orthogonal experiment, a sensitivity analysis was performed on the reservoir to be treated.

[0165] Based on the results of sensitivity analysis, the main controlling factors and their weights affecting the production of horizontal wells in the reservoir to be treated were determined.

[0166] In one possible implementation, module 1602 can also be used for:

[0167] Acquire well logging curves and seismic data of the reservoir to be processed;

[0168] Based on well logging curve information and seismic data, a three-dimensional geological model of the reservoir to be treated was established using sequential Gaussian simulation.

[0169] In one possible implementation, the second processing module 1603 can also be used for:

[0170] Based on the main control factors and their weights, normalization is performed to obtain normalized data for the main control factors.

[0171] Based on the normalized data of the main controlling factors, finite element calculations are performed to obtain the reservoir quality function;

[0172] Based on the reservoir quality function and the three-dimensional geological model, the three-dimensional geological model of the sweet spot is determined.

[0173] In one possible implementation, the second processing module 1603 can also be used for:

[0174] The three-dimensional sweet spot evaluation index is determined based on the reservoir quality function and the three-dimensional geological model;

[0175] Based on the three-dimensional dessert evaluation index, a three-dimensional geological model of the dessert body is determined.

[0176] In one possible implementation, the third processing module 1604 can also be used for:

[0177] Obtain the preset 3D dessert type and preset sand addition scale parameters;

[0178] Based on the preset three-dimensional dessert type and preset sand addition scale parameters, the three-dimensional geological model dividing the three-dimensional dessert body is subjected to production capacity simulation processing to obtain production capacity simulation results.

[0179] Based on the capacity simulation results, a relationship diagram was created.

[0180] In one possible implementation, module 1605 can also be used for:

[0181] Obtain the target three-dimensional dessert index;

[0182] Based on the target three-dimensional dessert index and relationship diagram, determine the target sand addition scale to meet the cumulative production requirements.

[0183] The sand addition scale optimization device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0184] Figure 17 This is a schematic diagram of the structure of the optimized sand-addition scale processing equipment provided in an embodiment of this application. (See attached diagram.) Figure 17 As shown, the sand-addition scale optimization processing device provided in this embodiment includes at least one processor 1701 and a memory 1702. Optionally, the sand-addition scale optimization processing device further includes a communication component 1703. The processor 1701, memory 1702, and communication component 1703 are connected via a bus 1704.

[0185] In a specific implementation, at least one processor 1701 executes computer execution instructions stored in memory 1702, causing at least one processor 1701 to perform the above-described method.

[0186] The specific implementation process of processor 1701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0187] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0188] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0189] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0190] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0191] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0192] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0193] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0194] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0195] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0196] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0197] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0198] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0199] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for optimizing the scale of sand addition, characterized in that, Applied to a computer device, the method includes: Sensitivity analysis was performed on the reservoir to be treated to determine the main controlling factors affecting the production of horizontal wells in the reservoir and the weight of the main controlling factors. Based on the well logging curves and seismic data of the reservoir to be treated, a three-dimensional geological model of the reservoir to be treated is established using sequential Gaussian simulation. Based on the main control factors and their weights, normalization processing is performed to obtain normalized data of the main control factors. Based on the normalized data of the main controlling factors, finite element analysis is performed to obtain the reservoir quality function. Based on the reservoir quality function, the reservoir quality of each grid cell in the three-dimensional geological model is triple-integrated to obtain the three-dimensional sweet spot evaluation index. Based on the three-dimensional dessert evaluation index, a three-dimensional geological model for dividing the three-dimensional dessert body is determined; based on the three-dimensional geological model for dividing the three-dimensional dessert body, a relationship map is established; wherein, the relationship map is used to characterize the relationship between the three-dimensional dessert evaluation index, sand addition scale, and cumulative yield; Based on the aforementioned relationship diagram, determine the target sand addition scale.

2. The method according to claim 1, characterized in that, The sensitivity analysis of the reservoir to be treated is performed to determine the main controlling factors affecting the production of horizontal wells in the reservoir and the weights of the main controlling factors, including: Obtain preset geological engineering parameters; Based on the preset geological engineering parameters, obtain the orthogonal experimental results of the reservoir to be treated; Based on the orthogonal experimental results, sensitivity analysis was performed on the reservoir to be treated. Based on the results of sensitivity analysis, the main controlling factors affecting the production of horizontal wells in the reservoir to be treated and the weights of the main controlling factors are determined.

3. The method according to claim 1, characterized in that, The process of establishing a relational map based on the three-dimensional geological model of the three-dimensional dessert body includes: Obtain the preset 3D dessert type and preset sand addition scale parameters; Based on the preset three-dimensional dessert type and the preset sand addition scale parameters, the three-dimensional geological model that divides the three-dimensional dessert body is subjected to production capacity simulation processing to obtain production capacity simulation results; Based on the production capacity simulation results, a relationship diagram is established.

4. The method according to claim 3, characterized in that, Determining the target sand addition scale based on the relationship diagram includes: Obtain the target three-dimensional dessert index; Based on the target three-dimensional dessert index and the relationship diagram, the target sand addition scale to meet the cumulative production requirements is determined.

5. An optimized processing device for sand addition scale, characterized in that, include: The first processing module is used to perform sensitivity analysis on the reservoir to be treated in order to determine the main controlling factors affecting the production of horizontal wells in the reservoir to be treated and the weight of the main controlling factors. The acquisition module is used to establish a three-dimensional geological model of the reservoir to be processed using sequential Gaussian simulation based on the well logging curve information and seismic data information of the reservoir to be processed. The second processing module is used to perform normalization processing based on the main control factor and the weight of the main control factor to obtain normalized data of the main control factor. Based on the normalized data of the main controlling factors, finite element analysis is performed to obtain the reservoir quality function. Based on the reservoir quality function, the reservoir quality of each grid cell in the three-dimensional geological model is triple-integrated to obtain the three-dimensional sweet spot evaluation index. Based on the three-dimensional dessert evaluation index, a three-dimensional geological model for dividing the three-dimensional dessert body is determined; The third processing module is used to establish a relationship map based on the three-dimensional geological model of the three-dimensional dessert body; wherein, the relationship map is used to characterize the relationship between the three-dimensional dessert evaluation index, sand addition scale and cumulative yield; The determination module is used to determine the target sand addition scale based on the relationship diagram.

6. An optimized processing device for sand addition scale, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-4.

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