Quantitative characterization method, device, equipment and medium for sectional connectivity of horizontal well

Abstract

The invention discloses a quantitative characterization method, device and equipment for horizontal well section connectivity and a medium. The method comprises the steps that the target water injection rate of a target horizontal water injection well and the target liquid production rate of a target horizontal production well in an oil reservoir are collected; determining a target parameter; calculating a predicted liquid production rate of the target horizontal production well in the next time step after the preset period based on the target water injection rate and the target parameters; adjusting the target parameter based on the predicted liquid production rate and the target liquid production rate, and calculating to obtain a new predicted liquid production rate based on the target water injection rate and the adjusted target parameter until the predicted liquid production rate meeting the condition is obtained; and quantitatively representing the segmented connectivity between the target horizontal water injection well and the target horizontal production well by adopting a target connectivity coefficient in the target parameters corresponding to the predicted liquid production rate meeting the condition. According to the scheme, quantitative characterization of the sectional connectivity between the horizontal water injection well and the horizontal production well is achieved.

Description

Technical Field

[0001] This invention relates to the field of oilfield development technology, and in particular to a method, apparatus, equipment and medium for quantitative characterization of segmental connectivity in horizontal wells. Background Technology

[0002] Years of reservoir development have led to more complex distributions of remaining oil and flow fields. Accurately identifying the dominant flow field factors and determining the injection-production response relationship are fundamental to achieving efficient flow field control and improving oil recovery. Furthermore, dynamic connectivity between wells can reflect dominant seepage channels and provide effective support for describing remaining oil distribution and for profile control and water shut-off.

[0003] Currently, dynamic connectivity between wells is typically determined using well test analysis, tracers, and numerical simulation methods. However, well test analysis and tracers can disrupt normal production and are costly, while numerical simulation methods are difficult to establish accurate models due to the numerous influencing factors and complex seepage patterns in actual reservoirs. Consequently, existing methods for determining dynamic connectivity between wells mainly focus on vertical wells, directional wells, and horizontal wells as a whole, making it difficult to effectively and quantitatively characterize the segmental connectivity between horizontal wells. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for quantitative characterizing the segmental connectivity of horizontal wells, so as to achieve quantitative characterization of the segmental connectivity between horizontal wells.

[0005] According to one aspect of the present invention, a method for quantitatively characterizing the segmental connectivity of a horizontal well is provided, the method comprising:

[0006] The target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir are collected. The target water injection rate includes the water injection rate at each time step within the preset period, and the target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections, and the target horizontal production well includes multiple production sections.

[0007] The target parameters are determined, including: the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; the target connectivity coefficient includes the connectivity coefficient between each water injection section and each production section.

[0008] Calculate the predicted fluid production rate of the target horizontal production well at the next time step after a preset cycle based on the target injection rate and target parameters;

[0009] The target parameters are adjusted based on the predicted liquid production rate and the target liquid production rate, and a new predicted liquid production rate is calculated based on the target water injection rate and the adjusted target parameters until a predicted liquid production rate that meets the conditions is obtained. The deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets the preset threshold.

[0010] The segmental connectivity between the target horizontal injection well and the target horizontal production well is quantitatively characterized by using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions.

[0011] According to another aspect of the present invention, a quantitative characterization device for the segmental connectivity of a horizontal well is provided, the device comprising:

[0012] The first acquisition module is used to acquire the target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir. The target water injection rate includes the water injection rate at each time step within a preset period, and the target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections, and the target horizontal production well includes multiple production sections.

[0013] The first determining module is used to determine the target parameters, which include: the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; the target connectivity coefficient includes the connectivity coefficient between each water injection section and each production section respectively;

[0014] The first calculation module is used to calculate the predicted production rate of the target horizontal production well at the next time step after a preset cycle, based on the target water injection rate and target parameters.

[0015] The first adjustment module is used to adjust the target parameters based on the predicted liquid production rate and the target liquid production rate, and to calculate a new predicted liquid production rate based on the target water injection rate and the adjusted target parameters, until a predicted liquid production rate that meets the conditions is obtained, and the deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets the preset threshold.

[0016] The first characterization module is used to quantitatively characterize the segmental connectivity between the target horizontal injection well and the target horizontal production well by using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for quantitative characterization of horizontal well segment connectivity according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided storing computer instructions for causing a processor to execute a method for quantitative characterization of horizontal well segment connectivity according to any embodiment of the present invention.

[0020] The technical solution of this invention involves collecting the target water injection rate of a target horizontal water injection well and the target fluid production rate of a target horizontal production well in an oil reservoir; determining target parameters, including the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; calculating the predicted fluid production rate of the target horizontal production well at the next time step after a preset period based on the target water injection rate and the target parameters; adjusting the target parameters based on the predicted fluid production rate and the target fluid production rate, and calculating a new predicted fluid production rate based on the target water injection rate and the adjusted target parameters, until a predicted fluid production rate that meets the conditions is obtained. This achieves the goal of targeting the target fluid production rate based on the collected target fluid production rate and the repeatedly calculated predicted fluid production rate. The target parameters are continuously adjusted until a predicted production rate whose deviation from the target production rate meets a preset threshold is obtained. This predicted production rate is then used as the qualified predicted production rate, so as to obtain the target parameters corresponding to the qualified predicted production rate. The target connectivity coefficient in the target parameters corresponding to the qualified predicted production rate is used to quantitatively characterize the segmented connectivity between the target horizontal injection well and the target horizontal production well. This achieves quantitative characterization of the connectivity between each injection segment of the target horizontal injection well and each production segment of the target horizontal production well by using the connectivity coefficients of each injection segment included in the target connectivity coefficients corresponding to the qualified predicted production rate.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a method for quantitative characterization of segmental connectivity in horizontal wells, provided in an embodiment of the present invention;

[0024] Figure 2This is a schematic diagram of a target graph neural network provided in an embodiment of the present invention;

[0025] Figure 3 A flowchart illustrating another method for quantitative characterization of segmental connectivity in horizontal wells provided in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of a quantitative characterization device for segmental connectivity of a horizontal well, provided in an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram of an electronic device for implementing a quantitative characterization method for segmental connectivity of horizontal wells, as provided in an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Figure 1 This is a flowchart illustrating a method for quantitatively characterizing the segmental connectivity of horizontal wells according to an embodiment of the present invention. This embodiment is applicable to situations where the segmental connectivity between horizontal injection wells and horizontal production wells in an oil reservoir is quantitatively standardized. The method can be executed by a device for quantitatively characterizing the segmental connectivity of horizontal wells. This device can be implemented in hardware and / or software and can be configured in an electronic device that implements the quantitative characterization method for segmental connectivity of horizontal wells. Figure 1 As shown, the quantitative characterization method for the segmental connectivity of this horizontal well includes:

[0031] S101. Collect the target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir. The target water injection rate includes the water injection rate at each time step within the preset period. The target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections, and the target horizontal production well includes multiple production sections.

[0032] In this context, a horizontal water injection well can refer to an injection well whose wellbore extends into the reservoir at a target angle to inject water into the reservoir. The target angle is less than a preset angle with respect to the horizontal plane. For example, the preset angle could be 15°. The water injection rate can refer to the volume or mass of water injected into the reservoir per unit time. The water injection section can refer to the section of the wellbore in which water is injected into the reservoir. The preset period can include multiple consecutive time steps. The time step can be one of days, months, or years. For example, when the time step is days, the target water injection rate can include the water injection rate per day over 30 days.

[0033] A horizontal production well refers to a production well that extends into the reservoir at a target angle to extract oil and gas. Production rate refers to the total amount of oil and gas extracted from the reservoir per unit time. A production section refers to the section of the production wellbore from which oil and gas are extracted from the reservoir.

[0034] Specifically, the target water injection rate of the target horizontal water injection well can be obtained by collecting dynamic water injection data from the reservoir. Similarly, the target production rate of the target horizontal production well under the influence of the target water injection rate can be obtained by collecting dynamic production data from the reservoir.

[0035] Optionally, the multiple injection sections of the target horizontal water injection well are determined based on the completion information of the target horizontal water injection well; the multiple production sections of the target horizontal production well are determined based on the completion information of the target horizontal production well.

[0036] S102. Determine the target parameters, which include: the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; the target connectivity coefficient includes the connectivity coefficient between each water injection section and each production section.

[0037] The water injection segment's splitting coefficient indicates the proportion of the water injection rate in the water injection segment relative to the total water injection rate of the target horizontal well. The production segment's splitting coefficient indicates the proportion of the production rate in the production rate of the target production well. The connectivity coefficient between the water injection and production segments quantifies the ease with which injected water flows from the water injection segment to the production segment.

[0038] S103. Calculate the predicted production rate of the target horizontal production well at the next time step after a preset period based on the target water injection rate and target parameters.

[0039] Specifically, the effective water injection rate of the target horizontal injection well at one time step can be determined based on the target water injection rate. Based on the effective water injection rate and the segmentation coefficient of each injection segment, the water injection rate of each injection segment can be calculated. Based on the water injection rate of each injection segment and the connectivity coefficient between each injection segment and a production segment, the production rate of that production segment can be calculated. Furthermore, based on the production rate of each production segment and the segmentation coefficient of each production segment, the predicted production rate of the target horizontal production well at the next time step after a preset period can be calculated.

[0040] S104. Adjust the target parameters based on the predicted liquid production rate and the target liquid production rate, and calculate a new predicted liquid production rate based on the target water injection rate and the adjusted target parameters, until a predicted liquid production rate that meets the conditions is obtained, and the deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets the preset threshold.

[0041] Specifically, if the deviation between the predicted production rate calculated based on the target parameters and the target production rate does not meet a preset threshold, the target parameters are adjusted based on this deviation, and a new predicted production rate for the target horizontal production well is obtained based on the target injection rate and the adjusted target parameters. If the deviation between the new predicted production rate and the target production rate does not meet the preset threshold, the adjusted target parameters are adjusted again based on the deviation, and the predicted production rate is recalculated again. This process continues until the deviation between the latest calculated predicted production rate and the target production rate meets the preset threshold, at which point the latest calculated predicted production rate is taken as the qualified predicted production rate.

[0042] S105. Using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions, quantitatively characterize the segmental connectivity between the target horizontal injection well and the target horizontal production well.

[0043] Segmental connectivity refers to the connectivity between each injection segment of the target horizontal injection well and each production segment of the target horizontal production well. Specifically, after obtaining the predicted production rate that meets the conditions, the target parameters corresponding to the calculated predicted production rate can be obtained. Furthermore, the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions can be used as a reference connectivity coefficient. The segmental connectivity between the target horizontal injection well and the target horizontal production well can be quantitatively characterized using the connectivity coefficients between each injection segment and each production segment included in the reference connectivity coefficient.

[0044] As an optional embodiment of the present invention, the quantitative characterization method for segmental connectivity of horizontal wells further includes the following steps A1-A3:

[0045] Step A1: Establish a target graph neural network based on target parameters and target adjacency matrix; the element values ​​in the target adjacency matrix are used to quantify the connection relationship between two water injection sections, or the connection relationship between two production sections, or the connection relationship between the water injection section and the production section; the target graph neural network can calculate the predicted production rate of the target horizontal production well at the next time step after a preset period based on the target water injection rate.

[0046] Step A2: Train the target graph neural network based on the target water injection rate and the target liquid production rate until the target graph neural network calculates a predicted liquid production rate that meets the conditions based on the target water injection rate.

[0047] Step A3: Determine the target parameters corresponding to the predicted liquid production rate that meet the conditions based on the trained target graph neural network.

[0048] In this target adjacency matrix, each element can be set to either a first preset value or a second preset value. The first preset value indicates a connection between two water injection sections, two production sections, or a connection between a water injection section and a production section. The second preset value indicates no connection between two water injection sections, two production sections, or a connection between a water injection section and a production section. The first preset value can be 1, and the second preset value can be 0.

[0049] For example, the target adjacency matrix It can be represented as:

[0050] ;

[0051] in, Indicates the number of water injection sections. Indicates the number of production sections.

[0052] For details, please refer to Figure 2 A target directed graph can be constructed based on target parameters and a target adjacency matrix. This target directed graph includes multiple target nodes determined by the target adjacency matrix and the edges between these nodes; the coefficients of the edge associations between the target nodes are determined based on the target parameters. The multiple target nodes include target horizontal injection well nodes, each injection segment node, target horizontal production well nodes, and each production segment node. The edge associations between target horizontal injection well nodes and injection segment nodes are determined by the splitting coefficient of the injection segment. The edge association between the target horizontal production well node and the production section node is related to the splitting coefficient of the production section. The edge association between nodes in the water injection section and nodes in the production section has a connectivity coefficient between the water injection section and the production section. Furthermore, a target graph neural network can be established based on the target directed graph.

[0053] The target graph neural network is trained under supervision based on the target water injection rate and the target production rate. Training is considered complete when the deviation between the predicted production rate obtained from the target graph neural network based on the target water injection rate and the target production rate meets the budget threshold. Furthermore, based on the trained target graph neural network, the target parameters corresponding to the qualified predicted production rates can be determined, thereby improving the accuracy of quantitatively representing the segmental connectivity between the target horizontal water injection well and the target horizontal production well.

[0054] As an optional implementation of this invention, the process of determining the target adjacency matrix includes: determining the location information of each water injection section and each production section based on the geological data of the reservoir; and determining the target adjacency matrix based on a preset radius, the location information of each water injection section, and the location information of each production section.

[0055] The location information of the water injection section can refer to its spatial coordinates within the reservoir. Similarly, the location information of the production section can also refer to its spatial coordinates within the reservoir. Specifically, based on the location information of each water injection section, the distance between two water injection sections can be determined. Furthermore, if the distance between two water injection sections is not greater than a preset radius, it indicates a connection between the two sections, and the element values ​​corresponding to the two water injection sections in the target adjacency matrix can be set to a first preset value. If the distance between two water injection sections is greater than the preset radius, it indicates no connection between the two sections, and the element values ​​corresponding to the two water injection sections in the target adjacency matrix can be set to a second preset value. Likewise, by setting the element values ​​corresponding to the two production sections in the target adjacency matrix, and setting the element values ​​corresponding to the water injection section and the production section in the target adjacency matrix, the target adjacency matrix can be obtained.

[0056] As an optional embodiment of the present invention, the training process of the target graph neural network includes: determining a target error value, wherein the target error value includes at least one of a first error value, a second error value, and a third error value; the first error value is the absolute error between the predicted production rate calculated by the target graph neural network and the target production rate; the second error value is the relative error between the predicted production rate calculated by the target graph neural network and the target production rate; the third error value is the absolute error between the first production rate and the second production rate, wherein the first production rate is determined based on the target water injection rate, the splitting coefficient of the water injection section, and the connectivity coefficient between the water injection section and the production section, and the second production rate is determined based on the target production rate and the splitting coefficient of the production section; and training the target graph neural network based on the target error value.

[0057] Specifically, based on the target error value, the splitting coefficients of each water injection segment, each production segment, and the target connectivity coefficient in the target graph neural network can be trained and adjusted to achieve supervised training of the target graph neural network.

[0058] For example, an oil reservoir may have multiple target-level water injection wells and multiple target-level production wells. Furthermore, the first error value... The following methods can be used for calculation:

[0059] ;

[0060] in, The number of production wells at the target level. For the target liquid production rate, The predicted production rate is determined by the target graph neural network.

[0061] Second error value The following methods can be used for calculation:

[0062] .

[0063] Third error value The following methods can be used for calculation:

[0064] ;

[0065] in, For the number of production sections, The number of water injection sections, The effective injection rate is determined based on the target injection rate. Indicates the first The splitting coefficient of each water injection section, Indicates the first The first water injection section and the first Connectivity coefficient between production segments Indicates the first The splitting coefficient of each production segment Indicates the first production rate. This indicates the second production rate.

[0066] The technical solution of this invention involves collecting the target water injection rate of a target horizontal water injection well and the target fluid production rate of a target horizontal production well in an oil reservoir; determining target parameters, including the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; calculating the predicted fluid production rate of the target horizontal production well at the next time step after a preset period based on the target water injection rate and the target parameters; adjusting the target parameters based on the predicted fluid production rate and the target fluid production rate, and calculating a new predicted fluid production rate based on the target water injection rate and the adjusted target parameters, until a predicted fluid production rate that meets the conditions is obtained. This achieves the goal of targeting the target fluid production rate based on the collected target fluid production rate and the repeatedly calculated predicted fluid production rate. The target parameters are continuously adjusted until a predicted production rate whose deviation from the target production rate meets a preset threshold is obtained. This predicted production rate is then used as the qualified predicted production rate, so as to obtain the target parameters corresponding to the qualified predicted production rate. The target connectivity coefficient in the target parameters corresponding to the qualified predicted production rate is used to quantitatively characterize the segmented connectivity between the target horizontal injection well and the target horizontal production well. This achieves quantitative characterization of the connectivity between each injection segment of the target horizontal injection well and each production segment of the target horizontal production well by using the connectivity coefficients of each injection segment included in the target connectivity coefficients corresponding to the qualified predicted production rate.

[0067] Figure 3 This is a flowchart illustrating another method for quantitatively characterizing the segmental connectivity of horizontal wells, provided as an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of determining target parameters in the aforementioned embodiments based on the technical solutions described above. Solutions not described in detail in this embodiment are found in the aforementioned embodiments. This embodiment can be combined with various optional solutions from one or more of the aforementioned embodiments. Figure 3 As shown, the quantitative characterization method for the segmental connectivity of this horizontal well includes:

[0068] S201. Collect the target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir. The target water injection rate includes the water injection rate at each time step within the preset period. The target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections, and the target horizontal production well includes multiple production sections.

[0069] S202. Determine the splitting coefficient of each water injection section and the splitting coefficient of each production section based on the geological data of the reservoir.

[0070] In this context, reservoir geological data refers to data used to describe and characterize reservoir geological features, fluid properties, and reservoir spatial distribution patterns. Specifically, based on the reservoir geological data, the splitting coefficients for each water injection section and each production section are determined to improve the efficiency of adjusting these coefficients.

[0071] As an optional embodiment of the present invention, determining the splitting coefficient of each water injection section and the splitting coefficient of each production section based on the geological data of the reservoir includes the following steps B1-B4:

[0072] Step B1: Determine the first information based on geological data. The first information includes the injection length of each injection section and the reference information of each injection section, including permeability, reservoir thickness and porosity.

[0073] Step B2: Determine the splitting coefficient of each water injection section based on the first information.

[0074] Step B3: Determine the second information based on geological data. The second information includes the production length of each production section and the reference information of each production section.

[0075] Step B4: Determine the splitting coefficient for each production segment based on the second information.

[0076] The water injection length of the injection section refers to the length of the wellbore corresponding to the water injection section that allows water to be injected into the reservoir. The production length of the production section refers to the length of the wellbore corresponding to the production section that allows oil and gas to be extracted from the reservoir. Permeability can be used to indicate the ease with which fluids flow through the pores of rock. Reservoir thickness refers to the vertical thickness of the rock strata capable of storing and producing oil and gas. Porosity refers to the percentage of the volume of all pore spaces in a rock relative to the total volume of the rock.

[0077] Specifically, the water injection section's segmentation coefficient can be determined based on the water injection length and reference information, and the segmentation coefficient of each water injection section is determined based on the first information. Similarly, the production section's segmentation coefficient can be determined based on the production length and reference information, and the segmentation coefficient of each production section is determined based on the second information.

[0078] For example, in a target horizontal injection well, the injection rate allocated to an injection segment is related not only to its own injection length and reference information, but also to the injection length and reference information of its adjacent injection segments. Therefore, the segmentation coefficient of an injection segment is set based on its own injection length and reference information, as well as the injection length and reference information of its adjacent injection segments. Similarly, the segmentation coefficient of a production segment is set based on its own production length and reference information, as well as the production length and reference information of its adjacent production segments.

[0079] S203. Determine the target connectivity coefficient based on the target water injection rate, the splitting coefficient of each water injection section, the target liquid production rate, and the splitting coefficient of each production section.

[0080] Specifically, the target connectivity coefficient can be determined based on the water injection rate at each time step in the target water injection rate, the splitting coefficient of each water injection segment, the target liquid production rate, and the splitting coefficient of each production segment, so as to improve the efficiency of adjusting the target connectivity coefficient.

[0081] Optionally, when determining the target connectivity coefficient, the connectivity coefficients between each water injection section and each production section included in the target connectivity coefficient are normalized.

[0082] As an optional implementation of this invention, determining the target connectivity coefficient based on the target water injection rate, the splitting coefficient of each water injection segment, the target fluid production rate, and the splitting coefficient of each production segment includes: determining the effective water injection rate of the target horizontal water injection well in one time step based on the target water injection rate; and determining the target connectivity coefficient based on the effective water injection rate, the splitting coefficient of each water injection segment, the target fluid production rate, and the splitting coefficient of each production segment.

[0083] The effective water injection rate can be the average of the water injection rates at each time step within the target water injection rate, or a weighted sum of the water injection rates at each time step within the target water injection rate. Optionally, based on the target water injection rate, the effective water injection rate is determined by a first multilayer sensor, and the effective water injection rate is adjusted by adjusting the parameters in the first multilayer sensor.

[0084] Specifically, the water injection rate of each injection segment is calculated based on the effective water injection rate and the splitting coefficient of each injection segment; the liquid production rate of each production segment is calculated based on the target liquid production rate and the splitting coefficient of each production segment. Furthermore, the target connectivity coefficient is determined based on the water injection rate of each injection segment and the liquid production rate of each production segment.

[0085] S204. Calculate the predicted production rate of the target horizontal production well at the next time step after a preset cycle based on the target water injection rate and target parameters.

[0086] S205. Adjust the target parameters based on the predicted liquid production rate and the target liquid production rate, and calculate a new predicted liquid production rate based on the target water injection rate and the adjusted target parameters, until a predicted liquid production rate that meets the conditions is obtained, and the deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets the preset threshold.

[0087] Optionally, based on the water injection length of each water injection segment and the reference information of each water injection segment, the splitting coefficient of each water injection segment is determined by a second multilayer sensor; based on the production length of each production segment and the reference information of each production segment, the splitting coefficient of each production segment is determined by a third multilayer sensor; based on the water injection rate of each water injection segment and the liquid production rate of each production segment, the target connectivity coefficient is determined by a fourth multilayer sensor; and the parameters in the second, third, and fourth multilayer sensors are adjusted based on the deviation to achieve adjustment of the target parameters.

[0088] S206. Using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions, quantitatively characterize the segmental connectivity between the target horizontal injection well and the target horizontal production well.

[0089] The technical solution of this invention collects the target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir; determines the splitting coefficient of each water injection section and the splitting coefficient of each production section based on the reservoir's geological data; determines the target connectivity coefficient based on the target water injection rate, the splitting coefficient of each water injection section, the target fluid production rate, and the splitting coefficient of each production section, thereby realizing the setting of target parameters through reservoir geological data, target water injection rate, and target fluid production rate, which reduces the difficulty of adjusting target parameters and improves calculation efficiency; calculates the predicted fluid production rate of the target horizontal production well at the next time step after a preset period based on the target water injection rate and target parameters; and calculates the target connectivity coefficient based on the predicted fluid production rate and the target fluid production rate. The target parameters for the production rate are adjusted, and a new predicted production rate is calculated based on the target injection rate and the adjusted target parameters until a predicted production rate that meets the conditions is obtained, so as to obtain the target parameters corresponding to the predicted production rate that meets the conditions. The target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions is used to quantitatively characterize the segmented connectivity between the target horizontal injection well and the target horizontal production well. This realizes that the connectivity between each injection segment of the target horizontal injection well and each production segment of the target horizontal production well is quantitatively characterized by the connectivity coefficient between each injection segment and each production segment included in the target connectivity coefficient corresponding to the predicted production rate that meets the conditions.

[0090] Figure 4 This is a schematic diagram of a device for quantitatively characterizing the segmental connectivity of horizontal wells, provided in an embodiment of the present invention. This embodiment is applicable to situations where the segmental connectivity between horizontal injection wells and horizontal production wells in an oil reservoir is quantitatively standardized. The device can be implemented in hardware and / or software. Figure 4 As shown, the quantitative characterization device for the segmental connectivity of this horizontal well includes:

[0091] The first acquisition module 301 is used to acquire the target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir. The target water injection rate includes the water injection rate at each time step within a preset period, and the target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections, and the target horizontal production well includes multiple production sections.

[0092] The first determining module 302 is used to determine the target parameters, which include: the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; the target connectivity coefficient includes the connectivity coefficient between each water injection section and each production section respectively.

[0093] The first calculation module 303 is used to calculate the predicted fluid production rate of the target horizontal production well at the next time step after a preset period based on the target water injection rate and target parameters.

[0094] The first adjustment module 304 is used to adjust the target parameters based on the predicted liquid production rate and the target liquid production rate, and to calculate a new predicted liquid production rate based on the target water injection rate and the adjusted target parameters, until a predicted liquid production rate that meets the conditions is obtained, and the deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets the preset threshold.

[0095] The first characterization module 305 is used to quantitatively characterize the segmental connectivity between the target horizontal injection well and the target horizontal production well by using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions.

[0096] Based on any of the above optional technical solutions, optionally, the first determining module 302 includes: a second determining unit and a third determining unit. The second determining unit is used to determine the splitting coefficient of each water injection section and the splitting coefficient of each production section based on the reservoir's geological data; the third determining unit is used to determine the target connectivity coefficient based on the target water injection rate, the splitting coefficient of each water injection section, the target production rate, and the splitting coefficient of each production section.

[0097] Based on any of the above-mentioned optional technical solutions, optionally, the second determining unit includes: a third determining subunit, a fourth determining subunit, a fifth determining subunit, and a sixth determining subunit. The third determining subunit is used to determine first information based on geological data, the first information including the injection length of each injection section and reference information for each injection section, the reference information including: permeability, reservoir thickness, and porosity; the fourth determining subunit is used to determine the splitting coefficient of each injection section based on the first information; the fifth determining subunit is used to determine second information based on geological data, the second information including the production length of each production section and reference information for each production section; the sixth determining subunit is used to determine the splitting coefficient of each production section based on the second information.

[0098] Based on any of the above-mentioned optional technical solutions, the third determining unit optionally includes: a seventh determining subunit and an eighth determining subunit. The seventh determining subunit is used to determine the effective water injection rate of the target horizontal water injection well in one time step based on the target water injection rate; the eighth determining subunit is used to determine the target connectivity coefficient based on the effective water injection rate, the splitting coefficient of each water injection segment, the target production rate, and the splitting coefficient of each production segment.

[0099] Based on any of the above-mentioned optional technical solutions, the quantitative characterization device for the segmented connectivity of the horizontal well may optionally include: a first establishment module, a first training module, and a ninth determination module. The first establishment module is used to establish a target graph neural network based on target parameters and a target adjacency matrix; the element values ​​in the target adjacency matrix are used to quantify the connection relationship between two injection segments, or between two production segments, or between an injection segment and a production segment; the target graph neural network can calculate the predicted production rate of the target horizontal production well at the next time step after a preset period based on the target injection rate; the first training module is used to train the target graph neural network based on the target injection rate and the target production rate until the target graph neural network calculates a predicted production rate that meets the conditions based on the target injection rate; the ninth determination module is used to determine the target parameters corresponding to the predicted production rate that meets the conditions based on the trained target graph neural network.

[0100] Based on any of the above optional technical solutions, the process of determining the target adjacency matrix may include: determining the location information of each water injection section and each production section based on the geological data of the reservoir; and determining the target adjacency matrix based on the preset radius, the location information of each water injection section, and the location information of each production section.

[0101] Based on any of the above optional technical solutions, optionally, the training process of the target graph neural network includes: determining a target error value, which includes at least one of a first error value, a second error value, and a third error value; the first error value is the absolute error between the predicted liquid production rate calculated by the target graph neural network and the target liquid production rate; the second error value is the relative error between the predicted liquid production rate calculated by the target graph neural network and the target liquid production rate; the third error value is the absolute error between the first liquid production rate and the second liquid production rate, wherein the first liquid production rate is determined based on the target water injection rate, the splitting coefficient of the water injection section, and the connectivity coefficient between the water injection section and the production section, and the second liquid production rate is determined based on the target liquid production rate and the splitting coefficient of the production section; and training the target graph neural network based on the target error value.

[0102] The technical solution of this invention involves: acquiring the target water injection rate of a target horizontal water injection well and the target fluid production rate of a target horizontal production well in an oil reservoir through a first acquisition module 301; determining target parameters through a first determination module 302, including the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; calculating the predicted fluid production rate of the target horizontal production well at the next time step after a preset period through a first calculation module 303 based on the target water injection rate and the target parameters; and adjusting the target parameters through a first adjustment module 304, and calculating a new predicted fluid production rate based on the target water injection rate and the adjusted target parameters, until a predicted fluid production rate that meets the conditions is obtained, thus realizing the acquisition of the target fluid production rate. The target parameters are continuously adjusted based on the predicted production rate obtained from repeated calculations until a predicted production rate whose deviation from the target production rate meets a preset threshold is obtained. This predicted production rate is then used as the qualified predicted production rate, so as to obtain the target parameters corresponding to the qualified predicted production rate. The first characterization module 305 uses the target connectivity coefficient in the target parameters corresponding to the qualified predicted production rate to quantitatively characterize the segmented connectivity between the target horizontal injection well and the target horizontal production well. This achieves quantitative characterization of the connectivity between each injection segment of the target horizontal injection well and each production segment of the target horizontal production well by using the connectivity coefficients between each injection segment and each production segment included in the target connectivity coefficients corresponding to the qualified predicted production rate.

[0103] The quantitative characterization device for horizontal well segment connectivity provided in this embodiment of the invention can execute the quantitative characterization method for horizontal well segment connectivity provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0104] Figure 5 This is a schematic diagram of an electronic device for implementing a quantitative characterization method for segmented connectivity of horizontal wells, provided as an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0105] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0106] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0107] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the quantitative characterization method of horizontal well segment connectivity.

[0108] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0109] In some embodiments, the method for quantitatively characterizing horizontal well segment connectivity can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for quantitatively characterizing horizontal well segment connectivity described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for quantitatively characterizing horizontal well segment connectivity by any other suitable means (e.g., by means of firmware).

[0110] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0114] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0115] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0116] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0117] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for quantitatively characterizing the segmental connectivity of a horizontal well, characterized in that, The method includes: The target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir are collected. The target water injection rate includes the water injection rate at each time step within a preset period. The target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections and the target horizontal production well includes multiple production sections. The target parameters are determined, including: the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; the target connectivity coefficient includes the connectivity coefficient between each water injection section and each production section respectively. Based on the target water injection rate and the target parameters, calculate the predicted fluid production rate of the target horizontal production well at the next time step after the preset cycle; The target parameters are adjusted based on the predicted liquid production rate and the target liquid production rate, and a new predicted liquid production rate is calculated based on the target water injection rate and the adjusted target parameters until a predicted liquid production rate that meets the conditions is obtained. The deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets a preset threshold. The segmental connectivity between the target horizontal injection well and the target horizontal production well is quantitatively characterized by using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions.

2. The method according to claim 1, characterized in that, Determine the target parameters, including: Based on the geological data of the reservoir, the splitting coefficients of each water injection section and each production section are determined. The target connectivity coefficient is determined based on the target water injection rate, the splitting coefficient of each water injection section, the target liquid production rate, and the splitting coefficient of each production section.

3. The method according to claim 2, characterized in that, Based on the geological data of the reservoir, the splitting coefficients of each water injection section and each production section are determined, including: Based on the geological data, first information is determined, which includes the injection length of each injection section and reference information for each injection section, including: permeability, reservoir thickness, and porosity. The splitting coefficient of each water injection section is determined based on the first information; Based on the geological data, a second piece of information is determined, which includes the production length of each production section and reference information for each production section. The splitting coefficient for each production section is determined based on the second information.

4. The method according to claim 2, characterized in that, The target connectivity coefficient is determined based on the target water injection rate, the splitting coefficient of each water injection section, the target liquid production rate, and the splitting coefficient of each production section, including: The effective water injection rate of the target horizontal water injection well in one time step is determined based on the target water injection rate. The target connectivity coefficient is determined based on the effective water injection rate, the splitting coefficient of each water injection section, the target liquid production rate, and the splitting coefficient of each production section.

5. The method according to claim 1, characterized in that, The method further includes: A target graph neural network is established based on target parameters and a target adjacency matrix; the element values ​​in the target adjacency matrix are used to quantify the connection relationship between two water injection sections, or to quantify the connection relationship between two production sections, or to quantify the connection relationship between a water injection section and a production section; the target graph neural network can calculate the predicted production rate of the target horizontal production well at the next time step after the preset cycle based on the target water injection rate; The target graph neural network is trained based on the target water injection rate and the target liquid production rate until the target graph neural network calculates a predicted liquid production rate that meets the conditions based on the target water injection rate. The target parameters corresponding to the predicted production rate that meet the conditions are determined based on the trained target graph neural network.

6. The method according to claim 5, characterized in that, The process of determining the target adjacency matrix includes: Based on the geological data of the reservoir, the location information of each water injection section and the location information of each production section are determined; The target adjacency matrix is ​​determined based on the preset radius, the location information of each water injection section, and the location information of each production section.

7. The method according to claim 6, characterized in that, The training process of the target graph neural network includes: A target error value is determined, which includes at least one of a first error value, a second error value, and a third error value; the first error value is the absolute error between the predicted liquid production rate calculated by the target graph neural network and the target liquid production rate; the second error value is the relative error between the predicted liquid production rate calculated by the target graph neural network and the target liquid production rate; the third error value is the absolute error between the first liquid production rate and the second liquid production rate, wherein the first liquid production rate is determined based on the target water injection rate, the splitting coefficient of the water injection section, and the connectivity coefficient between the water injection section and the production section, and the second liquid production rate is determined based on the target liquid production rate and the splitting coefficient of the production section; The target graph neural network is trained based on the target error value.

8. A quantitative characterization device for the segmental connectivity of a horizontal well, characterized in that, The device includes: The first acquisition module is used to acquire the target water injection rate of the target horizontal water injection well and the target fluid production rate of the target horizontal production well in the reservoir. The target water injection rate includes the water injection rate at each time step within a preset period, and the target fluid production rate is the fluid production rate at the next time step after the preset period. The target horizontal water injection well includes multiple water injection sections, and the target horizontal production well includes multiple production sections. The first determining module is used to determine target parameters, which include: the splitting coefficient of each water injection section, the splitting coefficient of each production section, and the target connectivity coefficient; the target connectivity coefficient includes the connectivity coefficient between each water injection section and each production section respectively; The first calculation module is used to calculate the predicted fluid production rate of the target horizontal production well at the next time step after the preset cycle based on the target water injection rate and the target parameters. The first adjustment module is used to adjust the target parameters based on the predicted liquid production rate and the target liquid production rate, and to calculate a new predicted liquid production rate based on the target water injection rate and the adjusted target parameters, until a predicted liquid production rate that meets the conditions is obtained, and the deviation between the predicted liquid production rate that meets the conditions and the target liquid production rate meets a preset threshold. The first characterization module is used to quantitatively characterize the segmental connectivity between the target horizontal injection well and the target horizontal production well by using the target connectivity coefficient in the target parameters corresponding to the predicted production rate that meets the conditions.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the quantitative characterization method for segmental connectivity of horizontal wells according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the quantitative characterization method for segmental connectivity of horizontal wells as described in any one of claims 1-7.

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