Injection-production relation calculation method based on well location topological relation

Abstract

The invention relates to the technical field of oil and gas field development, in particular to an injection-production relation calculation method based on a well location topological relation, and mainly solves the problem that an existing injection-production relation calculation method has limitations in the aspects of processing complex well network layout, reflecting the actual migration process of fluid and accurately calculating the flow contribution rate. The method comprises the following steps: acquiring standardized layered well location data; constructing an inter-well triangular mesh to obtain a triangular mesh topological relation set; generating a Thiessen polygon in the control range of each well to obtain a well pattern control range set; effective grids in the injection and production well connecting lines are tracked, and an inter-well dominant injection and production connecting line set is obtained; and calculating the flow contribution rate of each connecting line between the injection and production wells to obtain a time-sharing and layered injection and production relationship. According to the injection-production relation calculation method, the migration capacity of fluid between wells is comprehensively reflected, deviation caused by static hypothesis in a traditional method is avoided, the flow contribution rate has higher reference value through the processing result, and the development contribution between injection and production wells can be accurately quantified.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a method for calculating injection-production relationship based on well location topology. Background Technology

[0002] In oilfield development, calculating the injection-production relationship is a crucial step in evaluating well production efficiency and optimizing production strategies. Traditional methods for calculating this relationship largely rely on static geological models and fixed well network layouts, using preset injection-production radii or empirical formulas to delineate the injection-production range and then estimate the flow contribution between each well. However, as oilfield development progresses, underground reservoir conditions become increasingly complex, and well network layouts become more flexible and varied, gradually revealing many shortcomings of traditional methods.

[0003] Firstly, when dealing with complex well network layouts, since dense and sparse well networks coexist in the same development area, the preset injection-production search radius cannot well adapt to different well network density conditions when calculating injection-production relationships. Even with the addition of constraints such as the effective angle of the second line and the number of wells, unreasonable combinations of injection-production relationships can still occur, resulting in problems such as excessive or missing combinations of connectivity relationships.

[0004] Secondly, during the long-term development of oilfields, the combination relationship of working wells within a single layer is constantly changing, and the physical properties between wells also change with the development process. Especially under the condition of dense well network, the interference between wells is serious. The traditional calculation method based on static geological model and preset injection-production radius is too simplified. It does not take into account the real-time working well network situation within a single layer at different development stages, nor does it take into account factors such as formation permeability, porosity, and fluid pressure. Therefore, it cannot accurately depict the dynamic relationship between injection and production wells.

[0005] Third, existing technologies also have biases in calculating the flow contribution rate. The flow contribution rate refers to the proportion of the flow rate in a single direction among each injection and production well to the total flow rate. It reflects the degree of displacement and production contribution of each well in different connecting directions. Traditional methods often rely on empirical formulas or preset weights for calculation, ignoring the influence of key factors such as pore volume, permeability, and pressure difference, resulting in significant deviations between the calculated results and the actual situation.

[0006] In summary, existing technologies have limitations in handling complex well network layouts, reflecting actual fluid transport processes, and accurately calculating flow contribution rates. Therefore, it is necessary to explore more comprehensive methods for calculating well network injection-production relationships, addressing the coexistence of dense and sparse well networks, and then calculating the flow contribution between each well. Summary of the Invention

[0007] To overcome the limitations of existing injection-production relationship calculation methods in handling complex well network layouts, reflecting actual fluid migration processes, and accurately calculating flow contribution rates, this invention provides an injection-production relationship calculation method based on well topology. This method comprehensively reflects the fluid migration capacity between wells, optimizes the calculation process by combining actual data, avoids the biases caused by static assumptions in traditional methods, and makes the flow contribution rate more valuable for reference, thus helping to accurately quantify the development contribution between injection and production wells.

[0008] The technical solution of this invention is: a method for calculating injection-production relationship based on well location topology, comprising:

[0009] S1. Obtain standardized stratified well location data;

[0010] S2. Using the triangulation method, construct an inter-well triangular mesh based on the standardized well location dataset to obtain a set of triangular mesh topological relationships;

[0011] S3. Based on the set of triangular mesh topological relationships, generate Thiessen polygons within the control range of each well, and determine the list of meshes within the polygons to obtain the set of well network control ranges.

[0012] S4. Based on the well network control range set, according to the target layer coordinate vector and flow field direction of the injection and production wells, trace the effective grids within the connection from the injection well to the production well, and mark the target grids with large attribute field values ​​to obtain the set of advantageous injection and production connection lines between wells.

[0013] S5. Calculate the flow contribution rate of each connection between the injection and production wells, and optimize the flow contribution rate to obtain the optimized time-sharing and layered injection-production relationship.

[0014] Furthermore, in step S1, geological model and single-well time-sharing and layered working system data are first obtained, and well location coordinates are standardized to obtain standardized layered well location data.

[0015] Furthermore, the triangulation method in step S2 adopts the Delaunay triangulation method.

[0016] Furthermore, in step S3, the calculation expression for the Thiessen polygon is:

[0017]

[0018]

[0019] in,( , ),( , () represents the coordinates of the endpoints of the shared edge;

[0020] ( , () is the midpoint of the perpendicular bisector;

[0021] Well location coordinates ( , () represents the plane coordinates of each well.

[0022] Furthermore, in step S4, the formula for tracking the injection-import connection is:

[0023]

[0024] In the formula: Let i be the set of the i-th injection-sampling connections;

[0025] For vector lines;

[0026] Along the well pair connection line, search for the grid representing the maximum conductivity within a 90-degree range to form the j-th vector line. :

[0027]

[0028] In the formula: Let be the attribute value representing the maximum conduction capacity on the j-th vector line.

[0029] Furthermore, in step S5, when calculating the flow contribution rate, the flow contribution rate between injection and production is calculated based on the well network injection-production relationship and simulation results, combined with the pressure difference between injection and production wells, the pore volume of the grid through which the injection-production line passes, and permeability factors.

[0030] Furthermore, in step S5, the flow contribution rate is calculated using the following formula. :

[0031]

[0032] In the formula: This represents the minimum conduction capacity value of the cell on the i-th injection-sampling line;

[0033] Let be the pressure difference between the injection well and the production well in the i-th injection-production line;

[0034] This is the distance between the injection well and the production well.

[0035] Furthermore, the minimum conduction capacity value of the cell on the i-th injection-sampling line. Calculate using the following formula:

[0036]

[0037] Where: PV is the pore volume of the mesh;

[0038] Permeability in the X direction;

[0039] Let Y be the permeability in the Y direction.

[0040] Furthermore, in step S5, the algorithm for optimizing the contribution rate is as follows:

[0041]

[0042] In the formula: To optimize the objective;

[0043] The input feature vector;

[0044] These are the parameters of the neural network;

[0045] This is a prediction model.

[0046] Furthermore, in step S5, the input feature vector includes dynamic flow field parameters and historical production data.

[0047] This invention offers the following advantages: By adopting the above-mentioned solution, this invention addresses the coexistence of sparse and dense well networks, thereby calculating the flow contribution between each well. This invention employs Delaunay triangulation and Thiessen polygon generation algorithms, and tracks the connections between wells in real time based on fluid transport direction, inter-well grid attribute characteristics, and injection-production angle. This accurately characterizes the spatial topological relationship between injection and production wells, avoiding problems such as unreasonable combinations of injection-production relationships and incompatibility with real-time working well networks in traditional methods. It improves the applicability under both sparse and dense well network conditions and avoids the problems of over-combination or omission of connectivity relationships caused by methods that only track injection-production relationships based on search radius, which are unsuitable for coexisting sparse and dense well networks.

[0048] This invention introduces parameters such as pore volume, permeability, and pressure difference to comprehensively reflect the fluid transport capacity between wells. It optimizes the calculation process by combining actual data, avoiding the bias caused by static assumptions in traditional methods. The normalized processing results make the flow contribution rate more valuable for reference and help to accurately quantify the development contribution between injection and production wells.

[0049] Through experimental testing of this invention, it was found that when the well spacing is large, the Thiessen polygon method reduces cross-judgment errors and ensures reasonable area division. The dynamic injection-production connection tracking method avoids the problem of connection interference under dense well networks and is more suitable for complex well network environments.

[0050] This invention significantly reduces the cost of manual adjustments and the time cost of relying on human experience for judgment. The calculation results of the injection-production relationship provide accurate boundary conditions for adjusting the well network structure, optimizing injection-production parameters, and modifying model attributes, thereby improving the scientific nature and feasibility of oilfield development plans. Attached Figure Description

[0051] Figure 1 This is a flowchart of the present invention;

[0052] Figure 2 This is a schematic diagram of triangular mesh partitioning, where a is the well location map and b is a schematic diagram after triangular partitioning;

[0053] Figure 3 Here are schematic diagrams of the Thiessen polygon, where a is a schematic diagram after triangulation and b is a schematic diagram of the Thiessen polygon.

[0054] Figure 4 This is a schematic diagram of the injection-production connection calculation method.

[0055] Figure 5 This is a schematic diagram of the calculation results of the injection-production relationship. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The technical solutions in the embodiments of the present invention will be clearly and completely described. 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.

[0057] Depend on Figure 1 As shown, a method for calculating injection-production relationship based on well location topology includes the following steps:

[0058] S1. Obtain geological model and single-well time-sharing and layered working system data, and standardize the well location coordinates to obtain standardized layered well location data. The purpose of this step is to load the reservoir numerical model, and through data format parsing, extract grid attributes, well types, perforation section information, production and pressure data at different time stages of the work area, providing input data for constructing working well network relationships, tracking injection and production lines, and calculating flow contribution.

[0059] S2. Using the Delaunay triangulation method, based on the standardized well location dataset, an inter-well triangular mesh is constructed to obtain a set of triangular mesh topological relationships. Using time-segmented and layer-segmented working well coordinates as input data, incremental insertion, edge flipping, and divide-and-conquer algorithms are comprehensively applied to construct the Delaunay well network triangles, generating triangular meshes with well points as vertices. This ensures the stability and uniqueness of the subdivision results, providing a foundation for Thiessen polygon construction and ensuring the rationality of the well network topological relationships. See [link to documentation]. Figure 2 , Figure 2 In the diagram, a is the well location map, and b is a schematic diagram after triangulation.

[0060] The properties of the Delaunay well pattern triangle are:

[0061] First, maximize the minimum angle; maximize the minimum interior angle of the triangulated triangle to avoid elongated triangles. The calculation expression for the Delaunay triangulation method is:

[0062] max(min(∠A,∠B,∠C)),

[0063] Among them, ∠A, ∠B, and ∠C are the interior angles of the triangle;

[0064] Second, the empty circle property, which means that there are no other point sets inside the circumcircle of a triangle.

[0065] S3. Based on the triangular mesh topology set, generate Thiessen polygons within the control range of each well, and determine the mesh list within the polygons to obtain the well network control range set. (See...) Figure 3 In the figure, a is a schematic diagram after triangulation, and b is a schematic diagram of Thiessen polygons. The control range of each well is generated by Thiessen polygons to delineate the well network area; the control boundaries of injection and production wells are clearly defined to avoid overlapping areas.

[0066] The boundary of the Thiessen polygon is formed by the perpendicular bisectors of the sides shared by adjacent triangles. The Thiessen polygon is calculated as follows:

[0067]

[0068]

[0069] in,( , ),( , () represents the coordinates of the endpoints of the shared edge;

[0070] ( , () is the midpoint of the perpendicular bisector;

[0071] Well location coordinates ( , () represents the plane coordinates of each well.

[0072] S4. Based on the well network control range set, according to the target layer coordinate vector and flow field direction of the injection and production wells, trace the effective grids within the connection from the injection well to the production well, and mark the target grids with large attribute field values ​​to obtain the set of advantageous injection and production connection lines between wells.

[0073] When calculating the injection-production connection, the coordinate vectors of the injection and production wells are considered. Starting from the injection well, the grid within a 90-degree range of the injection-production vector is used as a candidate, and the grid with the larger attribute field value is the target grid, until the production well is reached. See [link to calculation]. Figure 4 Dynamically track the effective grid within the injection-sampling connection to provide an accurate grid set for calculating the flow contribution rate.

[0074] The formula for tracking the connection between injection and collection lines is:

[0075]

[0076] In the formula: Let i be the set of the i-th injection-sampling connections;

[0077] For vector lines;

[0078] Along the well pair connection line, search for the grid representing the maximum conductivity within a 90-degree range to form the j-th vector line. :

[0079]

[0080] In the formula: Let be the attribute value representing the maximum conduction capacity on the j-th vector line.

[0081] S5. Calculate the flow contribution rate of each connection between the injection and production wells, and use an artificial intelligence algorithm to optimize the flow contribution rate to obtain the optimized time-sharing and stratified injection-production relationship, see [link to relevant documentation]. Figure 5 .

[0082] When calculating the flow contribution rate, based on the well network injection-production relationship and simulation results, and considering the pressure difference between injection and production wells, the pore volume of the grid through which the injection-production line passes, and permeability factors, the flow contribution rate between injection and production is calculated. In this step, the strength of the injection-production relationship is quantified by calculating the flow capacity between injection and production, providing data support for production adjustments.

[0083] The flow contribution rate is calculated using the following formula. :

[0084]

[0085] In the formula: This represents the minimum conduction capacity value of the cell on the i-th injection-sampling line;

[0086] Let be the pressure difference between the injection well and the production well in the i-th injection-production line;

[0087] The distance from the injection well to the production well;

[0088] The minimum conduction capacity value of the cell on the i-th injection-progression line. Calculate using the following formula:

[0089]

[0090] Where: PV is the pore volume of the mesh;

[0091] Permeability in the X direction;

[0092] Let Y be the permeability in the Y direction.

[0093] Calculate the flow contribution rate Then, an artificial intelligence algorithm is used to optimize the contribution rate. The artificial intelligence algorithm is a neural network algorithm, expressed as follows:

[0094]

[0095] In the formula: To optimize the target, the calculation results of the traffic contribution rate were revised;

[0096] The input feature vector includes dynamic flow field parameters and historical production data, such as injection-production angle, pressure difference, and flow field characteristics.

[0097] The parameters of the neural network are automatically adjusted based on the training data.

[0098] This is a predictive model used to fit actual production data with calculated results.

[0099] Optimizing the accuracy of flow contribution rate calculation can reduce errors, improve the practical applicability of injection-production relationship calculation results, and reduce the cost of manual intervention.

[0100] As can be seen from the above, the present invention uses Delaunay triangulation and Thiessen polygon generation algorithms to accurately characterize the spatial topological relationship of injection and production wells, avoiding the problem of unclear division of injection and production range in traditional methods; based on the fluid transport direction and injection-production angle, the connection between wells is tracked in real time, which significantly improves the applicability under sparse and dense well network conditions.

[0101] This invention introduces parameters such as pore volume, permeability, and pressure difference to comprehensively reflect the fluid transport capacity between wells, dynamically adjusts the formula weights, and optimizes the calculation process by combining actual data. It avoids the bias caused by static assumptions in traditional methods, and the normalization results make the flow contribution rate more valuable for reference, which helps to accurately quantify the development contribution between injection and production wells.

[0102] The method of this invention reduces cross-identification errors and ensures reasonable area division when the well spacing is large by using the Thiessen polygon method. The dynamic injection-production connection tracking method avoids the connection interference problem under dense well network and is more suitable for complex well network environment.

[0103] This invention combines artificial intelligence algorithms to achieve data correction and calculation parameter optimization, significantly reducing the cost of manual adjustment. It dynamically adjusts and calculates the injection-production relationship in real time, reducing the time cost of relying on human experience judgment. The calculation results of the injection-production relationship provide accurate boundary conditions for adjusting the well network structure, optimizing injection-production parameters, and modifying model attributes, thereby improving the scientific nature and feasibility of oilfield development plans.

[0104] Example 1: Injection-Production Relationship Analysis of Sparse Well Patterns

[0105] In a certain oilfield area, the well network is sparse, comprising 5 water injection wells and 8 production wells, with an average distance of approximately 800 meters between them. It is necessary to analyze the relationships between these wells to optimize water injection development. The specific steps are as follows:

[0106] S1. Obtain geological model data and single-well time-sharing and layer-sharing work system data within the region, and preprocess the data.

[0107] The input parameters are: coordinates of injection and production wells (plane coordinate system: UTM) and geological model data (grid size 50m×50m).

[0108] The processing result is the generation of a standardized dataset A1:

[0109] A1={(x1,y1),(x2,y2),…,(x 13 ,y 13 )}

[0110] S2. Construct an inter-well triangular mesh based on the standardized well location dataset A1.

[0111] The method is as follows: Delaunay triangulation is used to generate the mesh.

[0112] The parameters are: the maximum radius of the empty circle is 1000 meters.

[0113] Result: A set of triangular mesh topological relationships was obtained, containing 25 triangles, ensuring that each triangle conforms to the empty circle property.

[0114] S3. Based on the triangular mesh topology set in step S2, generate the Thiessen polygons for the control range of each well.

[0115] The method is as follows: calculate the center of the circumcircle of the triangle and the perpendicular bisector, and then divide the control area.

[0116] Result: Generate a set of well network control polygons C1 for injection and production wells, C1={P1,P2,…,P13}.

[0117] S4. Based on the well network control polygon set C1, perform injection and production line tracing according to the injection and production well coordinate vectors and flow field direction.

[0118] Parameter settings: Injection-production angle ≤ 60°; pressure difference ≥ 0.5 MPa; fault influence: negligible.

[0119] Results: Dynamic tracking of valid connections, such as the connection between water injection well W1 and production wells P1, P3, and P5.

[0120] S5. Calculate the flow contribution rate of each connection between injection and production wells, and optimize the flow contribution rate results.

[0121] The parameters used in the calculation were: pore volume (PV): 50-120 m³; pressure difference (ΔP): 1.0 MPa; distance (L): 500-900 meters.

[0122] Results: The inter-well flow contribution matrix E1 was calculated.

[0123]

[0124] The calculated flow contribution rate is optimized and dynamically adjusted: a neural network model is trained using historical output data to optimize the flow contribution rate value.

[0125] The above embodiments accurately identify the connectivity between injection and production wells, optimize the water injection volume allocation of injection wells based on the contribution rate calculation results, improve the average production efficiency of production wells by about 8%, and enhance the water injection development effect.

[0126] Example 2: Dynamic Injection-Production Relationship Analysis of Dense Well Networks

[0127] In a high-recovery oilfield area with a dense well network, including 10 water injection wells and 20 production wells, the average distance between injection and production wells is approximately 300 meters. Dynamic adjustments to the water injection strategy are needed to balance fluid flow between wells. The specific steps are as follows:

[0128] S1. Obtain geological model data and single-well time-sharing and layer-sharing work system data within the region, and preprocess the data.

[0129] Input parameters: coordinates of injection and production wells and geological model data, grid size 25m×25m.

[0130] Processing result: Generate standardized dataset A2:

[0131] A2={(x1,y1),(x2,y2),…,(x 30 ,y 30 )}

[0132] S2. Based on the standardized well location dataset A2, a mesh is generated using Delaunay triangulation to construct the initial topology.

[0133] Parameter: Maximum radius of the empty circle is 500 meters.

[0134] Result: A set of triangular mesh topological relationships containing 100 triangles.

[0135] S3. Based on the set of triangular mesh topological relationships, generate the Thiessen polygons for the control range of each well.

[0136] Method: Calculate the perpendicular bisector and divide the control area.

[0137] Result: The set of control polygons B2 for injection and production wells was generated.

[0138] S4. Based on the well network control polygon set B2, perform injection and production line tracing according to the injection and production well coordinate vectors and flow field direction.

[0139] Parameter settings: Injection-production angle ≤ 45°; Pressure difference ≥ 1.0 MPa; Fault influence: Included.

[0140] Results: Dynamic tracking showed effective connections, such as the connection between injection well W3 and production wells P7 and P10.

[0141] S5. Calculate the flow contribution rate of each connection between injection and production wells, and optimize the flow contribution rate results.

[0142] The parameters used in the calculation are: pore volume (PV): 20-60 m³; permeability in the X and Y directions (KX,KY): 50-450 md; pressure difference (ΔP): 0.8 MPa; distance (L): 200-400 m.

[0143] Results: Flow contribution matrix E2:

[0144]

[0145] The calculated flow contribution rate was optimized and dynamically adjusted: dynamic flow field data was introduced, and the injection and extraction parameters were adjusted quarterly. After optimization, the accuracy of connectivity was improved to 98%. After dynamically adjusting the water injection strategy, the pressure field distribution in the region became more uniform.

[0146] As can be seen from the above, through dynamic tracking and optimized calculation, the injection-progression relationship was accurately identified, the development strategy was optimized, and production efficiency and resource utilization were significantly improved.

[0147] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for calculating injection-production relationship based on well location topology, characterized in that... include: S1. Obtain standardized stratified well location data; S2. Using the triangulation method, construct an inter-well triangular mesh based on the standardized well location dataset to obtain a set of triangular mesh topological relationships; S3. Based on the set of triangular mesh topological relationships, generate Thiessen polygons within the control range of each well, and determine the list of meshes within the polygons to obtain the set of well network control ranges. S4. Based on the well network control range set, according to the target layer coordinate vector and flow field direction of the injection and production wells, trace the effective grids within the connection from the injection well to the production well, and mark the target grids with large attribute field values ​​to obtain the set of advantageous injection and production connection lines between wells. S5. Calculate the flow contribution rate of each connection between the injection and production wells, and optimize the flow contribution rate to obtain the optimized time-sharing and layered injection-production relationship.

2. The injection-production relationship calculation method based on well location topology as described in claim 1, characterized in that: In step S1, the geological model and single-well time-sharing and layered working system data are first obtained, and the well location coordinates are standardized to obtain standardized layered well location data.

3. The injection-production relationship calculation method based on well location topology as described in claim 1, characterized in that: The triangulation method used in step S2 is the Delaunay triangulation method.

4. The injection-production relationship calculation method based on well location topology as described in claim 1, characterized in that: In step S3, the calculation expression for the Thiessen polygon is: in,( , ),( , ) represents the coordinates of the endpoints of the shared edge; ( , () is the midpoint of the perpendicular bisector; Well location coordinates ( , () represents the plane coordinates of each well.

5. The injection-production relationship calculation method based on well location topology according to claim 1, characterized in that: In step S4, the formula for tracking the injection-sampling connection is: In the formula: Let i be the set of the i-th injection-sampling connections; For vector lines; Along the well pair connection line, search for the grid representing the maximum conductivity within a 90-degree range to form the j-th vector line. : In the formula: Let be the attribute value representing the maximum conduction capacity on the j-th vector line.

6. The injection-production relationship calculation method based on well location topology according to claim 1, characterized in that: In step S5, when calculating the flow contribution rate, the flow contribution rate between injection and production is calculated based on the well network injection-production relationship and simulation results, combined with the pressure difference between injection and production wells, the pore volume of the grid through which the injection-production line passes, and permeability factors.

7. The injection-production relationship calculation method based on well location topology as described in claim 6, characterized in that: In step S5, the flow contribution rate is calculated using the following formula. : In the formula: This represents the minimum conduction capacity value of the cell on the i-th injection-sampling line; Let be the pressure difference between the injection well and the production well in the i-th injection-production line; This is the distance between the injection well and the production well.

8. The injection-production relationship calculation method based on well location topology as described in claim 7, characterized in that: The minimum conduction capacity value of the cell on the i-th injection-pollution connection line Calculate using the following formula: In the formula: PV is the pore volume of the mesh; Permeability in the X direction; Let Y be the permeability in the Y direction.

9. The injection-production relationship calculation method based on well location topology as described in claim 8, characterized in that: In step S5, the algorithm for optimizing the contribution rate is as follows: In the formula: To optimize the objective; The input feature vector; These are the parameters of the neural network; This is a prediction model.

10. The injection-production relationship calculation method based on well location topology according to claim 9, characterized in that: In step S5, the input feature vector includes dynamic flow field parameters and historical production data.

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