Method and device for determining well pattern deployment mode

Abstract

The invention discloses a method and a device for determining a well pattern deployment mode. The method comprises the following steps: acquiring a to-be-predicted block, and determining a plurality of sampling sand bodies corresponding to the to-be-predicted block; aiming at each candidate well pattern deployment mode, determining a water drive control degree corresponding to the candidate well pattern deployment mode based on the volume of the sand body subjected to water drive in the plurality of sampling sand bodies; and determining a target well pattern deployment mode in the candidate well pattern deployment modes based on the water drive control degree corresponding to each candidate well pattern deployment mode. According to the technical scheme, the problem that the well pattern deployment accuracy is low is solved, and the beneficial effect of improving the well pattern deployment mode accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and in particular to a method and device for determining a well network deployment mode. Background Art

[0002] Complex fault-block reservoirs have low natural energy and decrease rapidly. Selecting a reasonable injection-production well pattern to improve the oil recovery efficiency of the reservoir is the key to the efficient development of this type of reservoir. However, complex fault-block reservoirs have a developed fault system, complex structure, small oil-bearing area, and poor connectivity, making it difficult to establish an effective injection-production system in the sand body.

[0003] Complex fault-block reservoirs generally adopt the conventional process of rolling exploration and development. In the early stage of oil field development, most of them adopt the method of "occupying high points" and "hitting the ridge" to release the fault blocks in batches. Engineers' understanding of the oil field is gradually deepening and close to reality. For many years, the process of geological modeling and numerical simulation has been used to develop and deploy well networks. The field has been committed to improving the accuracy of reservoir description and iterative optimization and adjustment of well networks in complex fault-block reservoirs. Therefore, the development cycle of complex fault-block reservoirs is long, the early development is difficult, and it is difficult to have an accurate understanding of the distribution of oil-bearing sand bodies in multiple sets of oil-bearing strata in the fault block.

[0004] Before the development and drilling of complex fault-block reservoirs, there are only a small amount of exploration wells, seismic data, and limited geological knowledge. In traditional reservoir development, geological engineers characterize sand bodies based on experience, establish geological models, and use numerical simulation methods to make predictions on this basis to optimize the well network deployment method. This method characterizes deterministic sand bodies and completely relies on the experience of engineers. There are large differences in the sand bodies drawn by different engineers; in addition, numerical simulation lacks or only has a small amount of known production data for historical matching, and the prediction results are less accurate. Summary of the invention

[0005] The present invention provides a method and device for determining a well network deployment mode, so as to solve the problem of low accuracy of the existing well network deployment mode.

[0006] According to one aspect of the present invention, a method for determining a well pattern deployment mode is provided, the well pattern deployment mode comprising:

[0007] Acquire a block to be predicted, and determine a plurality of sampled sand bodies corresponding to the block to be predicted;

[0008] For each candidate well pattern deployment mode, determining the water drive control degree corresponding to the candidate well pattern deployment mode based on the volume of sand bodies that are water driven in the plurality of sampled sand bodies;

[0009] A target well pattern deployment mode among the candidate well pattern deployment modes is determined based on the water drive control degree corresponding to each of the candidate well pattern deployment modes.

[0010] According to another aspect of the present invention, a device for determining a well pattern deployment mode is provided, and the device for determining a well pattern deployment mode comprises:

[0011] A sampling sand body determination module is used to obtain a block to be predicted and determine a plurality of sampling sand bodies corresponding to the block to be predicted;

[0012] A water drive control degree determination module is used to determine the water drive control degree corresponding to each candidate well pattern deployment mode based on the volume of sand bodies that are water driven in a plurality of sampled sand bodies for each candidate well pattern deployment mode;

[0013] The well pattern deployment mode determination module is used to determine a target well pattern deployment mode among the candidate well pattern deployment modes based on the water drive control degree corresponding to each of the candidate well pattern deployment modes.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the well network deployment mode described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the well network deployment mode described in any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention determines multiple sampled sand bodies corresponding to the block to be predicted by acquiring the block to be predicted; and establishes a corresponding relationship between the block to be predicted and the multiple sampled sand bodies. Then, for each candidate well network deployment mode, the water drive control degree corresponding to the candidate well network deployment mode is determined based on the volume of sand bodies that are water-driven in the multiple sampled sand bodies; by respectively calculating the water drive control degrees corresponding to the candidate well network deployment modes, the well network deployment mode with the highest water drive control degree among the candidate well network deployment modes can be determined. Finally, based on the water drive control degrees corresponding to each of the candidate well network deployment modes, the target well network deployment mode among the candidate well network deployment modes is determined. Selecting the candidate well network deployment mode with the highest water drive control degree as the target well network deployment mode solves the problem of low accuracy of the well network deployment and has the beneficial effect of improving the accuracy of the well network deployment mode.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for determining a well pattern deployment mode provided according to Embodiment 1 of the present invention;

[0023] Figure 2a is a flow chart of a method for determining a well pattern deployment mode provided according to Embodiment 2 of the present invention;

[0024] Figure 2b It is a flow chart of calculating the degree of water flooding control of a sand body according to an optional example of a method for determining a well pattern deployment mode provided in Embodiment 2 of the present invention;

[0025] Figure 2c It is a sample schematic diagram of a rectangular sand body inverted seven-spot well pattern according to an optional example of a method for determining a well pattern deployment mode provided in Embodiment 2 of the present invention;

[0026] Figure 2d It is a sample schematic diagram of simulation results of water drive control degree under three well pattern forms and various well moment conditions according to an optional example of a method for determining a well pattern deployment mode provided in Embodiment 2 of the present invention;

[0027] Figure 3 is a structural schematic diagram of a device for determining a well pattern deployment mode provided according to a third embodiment of the present invention;

[0028] Figure 4 It is a structural schematic diagram of an electronic device for implementing the method for determining the well pattern deployment mode according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 A flowchart of a method for determining a well network deployment mode is provided for the first embodiment of the present invention. This embodiment is applicable to the well network deployment situation during oil and gas field development. The method can be executed by a device for determining a well network deployment mode. The device for determining a well network deployment mode can be implemented in the form of hardware and / or software. The device for determining a well network deployment mode can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S110, obtaining a block to be predicted, and determining a plurality of sampled sand bodies corresponding to the block to be predicted.

[0034] The block to be predicted can be understood as a complex fault block to be predicted. The sand body can be understood as a sandstone body. The sand body can include an oil sand body. The sampled sand body can be a sample sand body extracted in advance.

[0035] Specifically, a block to be predicted is obtained, and a plurality of pre-sampled sand bodies corresponding to the block to be predicted are determined.

[0036] Optionally, the determining of the plurality of sampled sand bodies corresponding to the block to be predicted includes:

[0037] Based on the exploration well data and seismic interpretation data of the block to be predicted, determine the probability distribution curve of various sand body morphological parameters corresponding to the block to be predicted;

[0038] A plurality of groups of the sand body morphology parameters are randomly extracted from the probability distribution curve of the sand body morphology parameters, and a sampling sand body is determined based on each group of the extracted sand body morphology parameters to obtain a plurality of the sampling sand bodies.

[0039] The sand body morphology parameters include at least sand body thickness, sand body length and sand body width; or include sand body area and sand body thickness. The probability distribution curve can be a distribution curve drawn according to the value range of each sand body morphology parameter.

[0040] Specifically, in the early stage of reservoir development, based on geological laws, the distribution characteristics and trends of sand bodies are predicted through exploration well data and seismic interpretation data to form probability distribution curves of various parameters of sand bodies. Exemplarily, by determining the value ranges of various sand body morphological parameters such as sand body thickness, sand body length and sand body width, or sand body area and sand body thickness corresponding to the block to be predicted, the probability distribution curves of various sand body morphological parameters are obtained. It can be understood that each sand body morphological parameter has its own corresponding value range, and the value ranges corresponding to each sand body morphological parameter may be different or the same.

[0041] After determining the probability distribution curves of various sand body morphological parameters corresponding to the block to be predicted, the parameter values ​​of the sand body morphological parameters are randomly extracted by sampling for calculation, and the sand body is determined based on the extracted sand body morphological parameters. For example: the length value, width value and height value of the sand body are randomly extracted as a set of data, and the sand body volume corresponding to the length value, width value and height value of the sand body is calculated based on the set of data. The obtained sand body volume is determined as a sampled sand body. Repeat the steps of extracting and calculating a group of data, and multiple sampled sand bodies can be calculated based on multiple sets of sand body morphological parameters.

[0042] Optionally, also include:

[0043] The probability distribution curve of the sand body morphology parameters is adjusted based on preset geological recognition parameters to update the probability distribution curve of the sand body morphology parameters.

[0044] Among them, geological knowledge parameters can be understood as empirical value parameters determined according to the degree of geological knowledge.

[0045] Specifically, based on the empirical value parameters determined by the degree of geological knowledge, the probability distribution curve of the sand body morphology parameters is manually adjusted, so that the adjusted probability distribution curve has higher accuracy. In the embodiment of the present invention, the probability distribution curve of the updated sand body morphology parameters is more accurate by manually intervening in the probability distribution curve.

[0046] Optionally, the geological knowledge parameter can be a preset fixed value, or can be obtained by randomly selecting a value within a preset value range (for example, the distribution range of the geological knowledge degree). In an optional embodiment of the present invention, the geological knowledge degree can be used as the weight of the sand body morphology parameter to update the sand body morphology parameter. Specifically, it can be expressed by the following formula:

[0047] w′=f×w

[0048] Wherein, w′ is the updated sand body morphology parameter. f is a preset constant or a geological knowledge parameter randomly extracted from the distribution range of geological knowledge degree. w is the sand body morphology parameter of the original probability distribution curve, and f can be a value greater than 0 and less than 1.

[0049] S120. For each candidate well pattern deployment mode, determine the water drive control degree corresponding to the candidate well pattern deployment mode based on the volume of sand bodies that are driven by water in the plurality of sampled sand bodies.

[0050] Among them, the well network deployment method can be understood as the deployment method of oil wells and water wells. Specifically, the well network deployment method can include well point arrangement information. Among them, the well point arrangement information at least includes the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points and at least one of the well point types. On this basis, the well network deployment method can also include well point quantity information, for example, the limit value of the total number of well points to be deployed in the block to be predicted. Specifically, it can be the limit value of the total number of well points of a certain type. For example, it can be the limit value of the total number of oil wells and / or the limit value of the total number of water wells.

[0051] Optionally, the same sand body is drilled by at least one oil well and one water well. Exemplarily, whether the sand body is water-driven can be determined by establishing a two-dimensional coordinate system. Taking the matching of a rectangular sand body and an inverted seven-point well pattern as an example, a basic point (a, b) in a sand body is randomly selected. In the well row direction, there are 2 oil wells separated by 1 water well. Therefore, there are three situations for point (a, b), namely: 1 / 3 probability of being oil well No. 1, 1 / 3 probability of being oil well No. 2, and 1 / 3 probability of being water well No. 3; calculate the coordinates of the remaining well points of different signs in the three possible situations respectively, and determine whether there are well points of different signs from point (a, b) falling in the sand body. If so, it means that the sand body is water-driven.

[0052] Specifically, it is possible to determine whether the sand body is water-driven based on the positional relationship between the water well and the well point to be determined. If the sand body is water-driven, the water-driven control degree corresponding to the candidate well pattern deployment method is determined based on the volume of the sand body driven by water. It is understandable that if the sand body is not water-driven, the volume of the sand body driven by water is zero.

[0053] S130: Determine a target well pattern deployment mode among the candidate well pattern deployment modes based on the water drive control degree corresponding to each of the candidate well pattern deployment modes.

[0054] Specifically, the water drive control degree corresponding to each candidate well network deployment mode is determined, and the candidate well network deployment modes can be sorted according to the water drive control degree, and the candidate well network deployment mode with the largest water drive control degree is selected as the target well network deployment mode.

[0055] The technical solution of the embodiment of the present invention determines multiple sampled sand bodies corresponding to the block to be predicted by acquiring the block to be predicted; and establishes a corresponding relationship between the block to be predicted and the multiple sampled sand bodies. Then, for each candidate well network deployment mode, the water drive control degree corresponding to the candidate well network deployment mode is determined based on the volume of sand bodies that are water-driven in the multiple sampled sand bodies; by respectively calculating the water drive control degrees corresponding to the candidate well network deployment modes, the well network deployment mode with the highest water drive control degree among the candidate well network deployment modes can be determined. Finally, based on the water drive control degrees corresponding to each of the candidate well network deployment modes, the target well network deployment mode among the candidate well network deployment modes is determined. Selecting the candidate well network deployment mode with the highest water drive control degree as the target well network deployment mode solves the problem of low accuracy of the well network deployment and has the beneficial effect of improving the accuracy of the well network deployment mode.

[0056] Embodiment 2

[0057] Figure 2a A flowchart of a method for determining a well network deployment mode provided in the second embodiment of the present invention, this embodiment is a further refinement of how to determine the water drive control degree corresponding to the candidate well network deployment mode based on the volume of sand bodies that are driven by water in multiple sampled sand bodies in the above embodiment. Optionally, the determination of the water drive control degree corresponding to the candidate well network deployment mode based on the volume of sand bodies that are driven by water in multiple sampled sand bodies includes: determining the sampled sand volume of each sampled sand body respectively, and determining the water drive sand volume of the sampled sand body that is driven by water under the well network deployment mode; based on the sampled sand volume and the water drive sand volume of all the sampled sand bodies, determining the water drive control degree corresponding to the candidate well network deployment mode. For specific implementation methods, please refer to the description of this embodiment. Among them, the technical features that are the same or similar to the above embodiments are not repeated here.

[0058] like Figure 2a As shown, the method includes:

[0059] S210, obtaining a block to be predicted, and determining a plurality of sampled sand bodies corresponding to the block to be predicted.

[0060] S220, for each candidate well pattern deployment mode, respectively determine the sampled sand body volume of each sampled sand body, and determine the water-driven sand body volume of the sampled sand body under the well pattern deployment mode.

[0061] Specifically, the sand body area A and sand body thickness h are randomly selected according to the parameter probability curve. Or the sand body volume is calculated according to the sand body area and sand body thickness. The specific calculation formula of the sand body volume is as follows:

[0062] V=A×h

[0063] Where V is the volume of the sand body, A is the area of ​​the randomly selected sand body, and h is the thickness of the randomly selected sand body.

[0064] Optionally, determining the volume of water-driven sand bodies of the sampled sand bodies under the well pattern deployment mode includes:

[0065] Determine a plurality of well points corresponding to the well pattern deployment mode and well point types corresponding to the well points, wherein the well point types include water wells and oil wells;

[0066] Acquire a reference well point located in the sampling sand body, and determine at least one optional setting mode corresponding to the reference well point based on the position of the reference well point and the well point type corresponding to the reference well point;

[0067] Determine the water drive state corresponding to the sampled sand body under each of the optional settings, wherein the water drive state includes being water driven and not being water driven;

[0068] The volume of the water-driven sand body of the sampled sand body that is driven by water in the well pattern deployment mode is determined based on the optional setting mode and the water-driven state.

[0069] The reference well point can be understood as a well point selected as a reference in the sampled sand body. The reference well point can be selected by random selection or by direct selection, which is not limited in this embodiment. The optional setting method can be understood as a deployment method in which the well point can be set. The water drive state can be understood as state information indicating whether the sampled sand body is subject to water drive.

[0070] Specifically, all optional settings can be determined according to the well point type and / or arrangement position. By determining a plurality of well points corresponding to the well network deployment method, and the well points of oil well type and water well type in each well point. A reference well point in the sampling sand body is determined by random selection, and the position of the reference well point and the well point type of the reference well point are determined. One or more optional settings are determined according to the position of the reference well point and the well point type corresponding to the reference well point. Exemplarily, in the case where the well network deployment method is a rectangular sand body inverted seven-point well network (such as Figure 2c In the case of the arrangement of two oil wells (shown in the figure), there are two oil wells between every two water wells, that is, in the case of two oil wells (represented by No. 1 and No. 2 respectively) and one water well (represented by No. 3), the reference well point may be oil well No. 1, oil well No. 2 or water well No. 3, etc.

[0071] In the embodiment of the present invention, the number of optional setting modes is related to the well network deployment mode. The number of optional setting modes corresponding to different well network deployment modes may be the same or different. According to the well point arrangement position and / or well point type included in the optional setting mode, and whether each optional setting mode is subject to water drive. The water drive sand body volume of the sampled sand body subject to water drive in each well network deployment mode is comprehensively determined.

[0072] Exemplarily, continue to arrange two oil wells (represented by No. 1 and No. 2 respectively) and one water well (represented by No. 3) as an example, randomly select a reference well point (a, b), determine the well point type corresponding to the well point, if the extraction well is oil well No. 1, then based on the position of the reference well point and the well point type corresponding to the reference well point, calculate the coordinates of all other water wells. Determine the relationship between the water well coordinates and the sand body boundary (width 0~w′, length 0~L) to determine whether the water well falls within the sand body. If a water well falls within the sand body, there is a 1 / 3 probability that the sand body will be water-driven. The other two cases (the extraction well is oil well No. 2 and the extraction well is water well No. 3) are similar.

[0073] Optionally, the water drive state corresponding to the sampled sand body under each of the optional settings is determined respectively, including: for each optional setting, determining whether there is at least one water well and at least one oil well in the sampled sand body; if so, determining that the sampled sand body is subject to water drive under the optional setting; if not, determining that the sampled sand body is not subject to water drive under the optional setting.

[0074] Specifically, for each optional setting mode, whether the sand body is subject to water drive is determined based on whether there is at least one water well and at least one oil well in the sampled sand body. If there is both a water well and an oil well in the sampled sand body, it can be determined that the sampled sand body is subject to water drive under this optional setting mode. It should be noted that there is no specific limitation on the number of water wells and oil wells that exist simultaneously in the sampled sand body. If there are multiple water wells and multiple oil wells in the sampled sand body, it is also determined that the sampled sand body is subject to water drive under the optional setting mode. On the contrary, if there is only one or more water wells or only one or more oil wells in the sampled sand body, it is determined that the sampled sand body is not subject to water drive under this optional setting mode.

[0075] Optionally, determining the volume of water-driven sand bodies of the sampled sand bodies that are driven by water under the well network deployment mode based on the optional setting mode and the water drive state includes: determining the total number of the optional setting modes, the total number of the optional setting modes that are driven by water, and the volume of the sampled sand bodies, and determining the volume of water-driven sand bodies of the sampled sand bodies that are driven by water under the well network deployment mode.

[0076] Specifically, according to the total number of determined optional configurations, the number of water-driven sand bodies in the optional configurations, and the volume of sampled sand bodies, the water-driven sand body volume of the sampled sand bodies under the well pattern deployment mode is calculated.

[0077] Exemplarily, the water-flooded sand volume calculation formula is as follows:

[0078]

[0079] Among them, V e is the volume of the water-driven sand body. A is the randomly selected sand body area of ​​the sampled sand body. h is the sand body thickness of the sampled sand body. It is worth noting that the number of sand bodies affected by water drive is related to the number, arrangement and well spacing of water wells and oil wells.

[0080] Optionally, under normal circumstances, the water-driven sand body volume is not greater than the sand body volume. If the calculated water-driven sand body volume is greater than the sand body volume, it is determined that the water-driven sand body volume is equal to the sand body volume.

[0081] Optionally, the determining a plurality of well points corresponding to the well pattern deployment method includes:

[0082] Determine the well point arrangement information corresponding to the well network deployment method, and determine multiple well points corresponding to the well network deployment method based on the arrangement information, wherein the well point arrangement information at least includes at least one of the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points, and the well point type.

[0083] Specifically, the arrangement information of oil wells and water wells corresponding to the well pattern deployment mode is determined, and multiple well points corresponding to the well pattern deployment mode are obtained according to the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points, and at least one well point arrangement information in the well point type. It can be understood that each well pattern deployment mode can correspond to one well point arrangement information.

[0084] S230: Determine the water drive control degree corresponding to the candidate well pattern deployment mode based on the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies.

[0085] Optionally, determining the water drive control degree corresponding to the candidate well pattern deployment mode based on the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies includes:

[0086] Calculating a first volume sum of the water-driven sand body volumes of all the sampled sand bodies, and a second volume sum of the sampled sand body volumes of all the sampled sand bodies;

[0087] The ratio of the first volume sum to the second volume sum is determined as the water drive control degree corresponding to the candidate well pattern deployment mode.

[0088] Specifically, the first volume sum of the water-driven sand body volume of all the sampled sand bodies and the second volume sum of the sampled sand body volume of all the sampled sand bodies are calculated respectively. The calculation is performed according to the calculation formula of the water-driven control degree, and the calculation result is determined as the water-driven control degree corresponding to the candidate well pattern deployment mode of the water-driven control degree. The calculation formula of the water-driven control degree is as follows:

[0089]

[0090] Where p is the degree of water flooding control. e is the volume of water-flooded sand body. V is the volume of sampled sand body.

[0091] S240: Determine a target well pattern deployment mode among the candidate well pattern deployment modes based on the water drive control degree corresponding to each of the candidate well pattern deployment modes.

[0092] Optionally, also include:

[0093] The well point arrangement information is adjusted to update the well network deployment method.

[0094] Specifically, adjusting the well point arrangement information may include adjusting at least one parameter of the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points, and the well point type. For example, while keeping the angle between the sand body extension direction and the well point arrangement direction unchanged, increase or decrease the distance between every two well points, and / or change the well point type corresponding to the well point, etc. For another example, while keeping the distance between every two well points and the well point type unchanged, adjust the angle between the sand body extension direction and the well point arrangement direction, etc.

[0095] In the embodiment of the present invention, different well point deployment modes can be formed by adjusting the above parameters, such as at least one of the deployment modes including a diamond inverted nine-point well pattern, a rectangular five-point well pattern and an inverted seven-point well pattern.

[0096] The technical solution of the embodiment of the present invention determines multiple sampled sand bodies corresponding to the block to be predicted by obtaining the block to be predicted; establishes a corresponding relationship between the block to be predicted and the multiple sampled sand bodies. Then, for each candidate well pattern deployment mode, the sampled sand body volume of each sampled sand body is determined respectively, and the water-driven sand body volume of the sampled sand body under the well pattern deployment mode is determined.

[0097] The water drive control degree corresponding to the candidate well network deployment method is determined based on the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies. First, the sampled sand body volume of each sampled sand body and the water drive sand body volume affected by water drive are determined, and then the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies are determined. Finally, based on the water drive control degree corresponding to each of the candidate well network deployment methods, the target well network deployment method in the candidate well network deployment method is determined. The candidate well network deployment method with the largest water drive control degree is selected as the target well network deployment method, which solves the problem of low accuracy of well network deployment and has a beneficial effect of improving the accuracy of the well network deployment method.

[0098] As an optional example of an embodiment of the present invention, the method for determining the well pattern deployment mode of this embodiment specifically includes the following steps:

[0099] In the early stage of reservoir development, based on geological laws, geological engineers use exploration well data and seismic interpretation data to predict the distribution characteristics and trends of sand bodies and determine the probability distribution curves of various sand body morphological parameters.

[0100] This embodiment defines the condition that the sand body is subjected to water drive as follows: the same sand body is drilled by at least one oil well and one water well. By establishing a two-dimensional coordinate system, a programming method is used to determine whether the sand body is subjected to water drive.

[0101] Take the deployment method of "rectangular sand body reverse seven-point well network" as an example. Randomly extract the reference well point (a, b), determine the well point type corresponding to the well point, if the extracted well is oil well No. 1, then based on the position of the reference well point and the well point type corresponding to the reference well point, calculate the coordinates of all other water wells. Determine the relationship between the water well coordinates and the sand body boundary (width 0~w′, length 0~L) to determine whether the water well falls in the sand body. If a water well and an oil well fall in the sand body together, there is a 1 / 3 probability that the sand body will be water-driven, and the same applies to the other two cases.

[0102] Figure 2b It is a flow chart of calculating the degree of water flooding control of a sand body according to an optional example of a method for determining a well pattern deployment mode provided in Embodiment 2 of the present invention.

[0103] like Figure 2b As shown, the steps to calculate the degree of waterflood control include:

[0104] (1) For the block to be predicted, the probability distribution curve of each sand body morphological parameter is constructed to limit the value range of each sand body morphological parameter.

[0105] (2) The sand body width w can be adjusted according to the geological parameter f, which can be expressed by the following formula:

[0106] w′=f×w

[0107] Among them, w′ is the updated sand body width, f is the geological recognition parameter, that is, the distribution range of the empirical value, and w is the sand body width before and after.

[0108] (3) Based on the angle z between the sand body extension direction and the well pattern, the well spacing s, and the well point type of each well point, the well pattern deployment mode is determined. Under this well pattern deployment mode, the sand body area A and sand body thickness h are randomly selected according to the updated probability distribution curve. The sand body volume is calculated as:

[0109] V=A×h

[0110] Where V is the volume of the sand body, A is the area of ​​the randomly selected sand body, and h is the thickness of the randomly selected sand body.

[0111] (4) Figure 2c It is a sample schematic diagram of a rectangular sand body inverted seven-point well pattern, which is an optional example of a method for determining a well pattern deployment mode provided in accordance with the second embodiment of the present invention.

[0112] like Figure 2c As shown in FIG. 1 , in the two-dimensional coordinate system of the rectangular sand body inverse seven-point well pattern, the steps for calculating the volume of the water-driven sand body include:

[0113] Take the deployment method of "rectangular sand body reverse seven-point well pattern" as an example. At this time, the water-driven sand body volume calculation formula is as follows:

[0114]

[0115] Among them, V e is the volume of the water-driven sand body. A is the area of ​​the randomly selected sand body. h is the thickness of the randomly selected sand body. 3 is the total number of optional settings corresponding to the reference well points in the rectangular sand body inverse seven-point well pattern. As mentioned above, the three optional settings are: the extraction well is oil well No. 1, the extraction well is oil well No. 2, and the extraction well is water well No. 3. The number of water-driven wells in the three cases can be the number of sand bodies that are water-driven and fall in the sand body if the well points of different numbers fall in the sand body in the three cases.

[0116] It is worth noting that the number of sand bodies affected by water drive is related to the number of water wells and oil wells, the arrangement method and the well spacing. In the present embodiment, which includes 2 oil wells and 1 water well, under the three optional settings, if the sand body is affected by water drive, the number of sand bodies affected by water drive in this case is 1. On the contrary, if the sand body is not affected by water drive, the number of sand bodies affected by water drive in this case is assigned to 0. Among the above three optional settings, if there are two optional settings in which the sand body is affected by water drive. Then the number of sand bodies affected by water drive under the three optional settings is 2. That is, under this well network deployment method, the value range of the number of sand bodies affected by water drive can be an integer between 0 and 3. When the number of sand bodies affected by water drive under the three optional settings is 0, the value of the water-driven sand body volume is also 0.

[0117] (5) Cyclic extraction calculation, where the number of extractions can be set according to actual needs, for example, 3000 times, 4000 times or 5000 times. When the extraction is completed, the water drive control degree corresponding to the well pattern deployment mode is calculated based on the following formula:

[0118]

[0119] Where p is the degree of water flooding control. e is the volume of water-flooded sand body. V is the volume of sampled sand body.

[0120] (6) adjusting at least one parameter of the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points, and the well point type to update the well point deployment mode, and using the method of the above steps (3) to (5) to calculate the water drive control degree corresponding to each well point deployment mode.

[0121] (7) Compare the water drive control levels corresponding to the various well point deployment methods, and determine the well point deployment method with the highest water drive control level as the target well point deployment method.

[0122] In one embodiment, the block to be predicted is a complex fault block reservoir block Z as an example, assuming that the oil-bearing area of ​​block Z is 8km 2 , vertically, there are two oil-bearing layers and eight small layers. The morphological parameters of each sand body are expressed in a statistical table as follows (Table 1):

[0123] Table 1

[0124]

[0125]

[0126] The sand body morphology parameters were extracted to simulate the water drive control degree under three well pattern forms and various well moment conditions. The simulation results are as follows: Figure 2d As shown. Figure 2dIt can be seen that in this block, the water drive control degree of the rectangular five-point well pattern is significantly higher than that of the other two well patterns, among which the rectangular five-point well pattern with an aspect ratio of 24 / 55 has the highest water drive control degree. Under the condition of equal well spacing, the water drive control degree results of the reverse seven-point well pattern and the rhombus reverse nine-point well pattern with an acute angle of 40 degrees (the angle between the extension direction of the sand body and the well point arrangement direction) are very similar, but the well pattern density of the reverse seven-point well pattern is less than that of the rhombus reverse nine-point well pattern with an acute angle of 40 degrees. Therefore, the rectangular five-point well pattern is the first choice, and the reverse seven-point well pattern is second.

[0127] The technical solution of the embodiment of the present invention determines the sampled sand volume of each sampled sand body for each candidate well network deployment mode, and determines the water-driven sand volume of the sampled sand body under the well network deployment mode. Based on the sampled sand volume and the water-driven sand volume of all the sampled sand bodies, the water-driven control degree corresponding to the candidate well network deployment mode is determined. First, the sampled sand volume of each sampled sand body and the water-driven sand volume that is driven by water are determined, and then the sampled sand volume and the water-driven sand volume of all the sampled sand bodies are determined. Finally, the water-driven control degree is determined according to the ratio of the sampled sand volume of all the sampled sand bodies to the water-driven sand volume, which solves the problem of low accuracy of the water-driven control degree and has the beneficial effect of improving the accuracy of the water-driven control degree and thus the accuracy of the well network deployment mode.

[0128] Embodiment 3

[0129] Figure 3 This is a schematic diagram of a structure of a device for determining a well pattern deployment mode provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a sampling sand body determination module 310, a water drive control degree determination module 320 and a well pattern deployment mode determination module 330.

[0130] Among them, the sampling sand body determination module 310 is used to obtain the block to be predicted and determine the multiple sampled sand bodies corresponding to the block to be predicted; the water drive control degree determination module 320 is used to determine the water drive control degree corresponding to the candidate well network deployment mode for each candidate well network deployment mode based on the volume of sand bodies driven by water in the multiple sampled sand bodies; the well network deployment mode determination module 330 is used to determine the target well network deployment mode among the candidate well network deployment modes based on the water drive control degree corresponding to each candidate well network deployment mode.

[0131] The technical solution of the embodiment of the present invention is to obtain the block to be predicted through the sampling sand body determination module, determine the multiple sampled sand bodies corresponding to the block to be predicted; and establish the corresponding relationship between the block to be predicted and the multiple sampled sand bodies. Then, through the water drive control degree determination module, for each candidate well network deployment mode, the water drive control degree corresponding to the candidate well network deployment mode is determined based on the volume of sand bodies that are water-driven in the multiple sampled sand bodies; by respectively calculating the water drive control degree corresponding to the candidate well network deployment mode, the well network deployment mode with the highest water drive control degree of the candidate well network deployment mode can be determined. Finally, through the well network deployment mode determination module, the target well network deployment mode among the candidate well network deployment modes is determined based on the water drive control degree corresponding to each of the candidate well network deployment modes. The candidate well network deployment mode with the highest water drive control degree is selected as the target well network deployment mode, which solves the problem of low accuracy of well network deployment and has the beneficial effect of improving the accuracy of well network deployment mode.

[0132] Optionally, the sampling sand body determination module includes:

[0133] A probability distribution curve determination unit is used to determine the probability distribution curve of various sand body morphological parameters corresponding to the block to be predicted based on the exploration well data and seismic interpretation data of the block to be predicted;

[0134] The sampling sand body acquisition unit is used to randomly extract multiple groups of sand body morphological parameters from the probability distribution curve of the sand body morphological parameters, determine a sampling sand body based on each group of the extracted sand body morphological parameters, so as to obtain multiple sampling sand bodies.

[0135] Optionally, the device for determining the well pattern deployment mode further includes:

[0136] The probability distribution curve updating module is used to adjust the probability distribution curve of the sand body morphology parameters based on preset geological recognition parameters to update the probability distribution curve of the sand body morphology parameters.

[0137] Optionally, the water flooding control degree determination module includes:

[0138] A sand body volume determination unit, used to respectively determine the sampled sand body volume of each of the sampled sand bodies, and to determine the water-driven sand body volume of the sampled sand bodies under the well pattern deployment mode;

[0139] The water drive control degree determination unit is used to determine the water drive control degree corresponding to the candidate well pattern deployment mode based on the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies.

[0140] Optionally, the sand body volume determination unit includes:

[0141] A well point type determination subunit, used to determine a plurality of well points corresponding to the well network deployment mode and well point types corresponding to the well points, wherein the well point types include water wells and oil wells;

[0142] An optional setting mode determination subunit is used to obtain a reference well point located in the sampling sand body, and determine at least one optional setting mode corresponding to the reference well point based on the position of the reference well point and the well point type corresponding to the reference well point;

[0143] A water drive state determination subunit, used to respectively determine the water drive state corresponding to the sampled sand body under each of the optional settings, wherein the water drive state includes being water driven and not being water driven;

[0144] The water-driven sand body volume determination subunit is used to determine the water-driven sand body volume of the sampled sand body driven by water in the well pattern deployment mode based on the optional setting mode and the water-driven state.

[0145] Optionally, the water drive state determination subunit is used for:

[0146] For each of the optional configurations, determining whether there is at least one water well and at least one oil well in the sampled sand body;

[0147] If so, it is determined that the sampled sand body is subject to water drive in the optional setting mode;

[0148] If not present, it is determined that the sampled sand body is not flooded with water under the optional setting.

[0149] Optionally, the water drive sand body volume determination subunit is used for:

[0150] Determine the total number of the optional settings, the total number of the optional settings that are water-driven, and the sampled sand body volume of the sampled sand body, and determine the water-driven sand body volume of the sampled sand body that is water-driven in the well pattern deployment mode.

[0151] Optionally, the water flooding control degree determination unit includes:

[0152] A volume sum calculation subunit, used for calculating a first volume sum of the water-driven sand body volumes of all the sampled sand bodies, and a second volume sum of the sampled sand body volumes of all the sampled sand bodies;

[0153] The water drive control degree determination subunit is used to determine the ratio of the first volume sum to the second volume sum as the water drive control degree corresponding to the candidate well pattern deployment mode.

[0154] Optionally, the well point type determination subunit is specifically used for:

[0155] Determine the well point arrangement information corresponding to the well network deployment method, and determine multiple well points corresponding to the well network deployment method based on the arrangement information, wherein the well point arrangement information at least includes at least one of the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points, and the well point type.

[0156] The apparatus for determining a well network deployment mode provided in an embodiment of the present invention can execute the method for determining a well network deployment mode provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0157] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0158] Embodiment 4

[0159] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0160] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0161] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0162] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as determining the method well network deployment mode.

[0163] In some embodiments, the determination of the method well network deployment mode can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the determination of the method well network deployment mode described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the determination of the method well network deployment mode in any other appropriate manner (for example, by means of firmware).

[0164] Various implementations 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0166] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0169] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0171] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a well pattern deployment mode, characterized in that: include: Acquire a block to be predicted, and determine a plurality of sampled sand bodies corresponding to the block to be predicted; For each candidate well pattern deployment mode, determining the water drive control degree corresponding to the candidate well pattern deployment mode based on the volume of sand bodies that are water driven in the plurality of sampled sand bodies; A target well pattern deployment mode among the candidate well pattern deployment modes is determined based on the water drive control degree corresponding to each of the candidate well pattern deployment modes.

2. The method according to claim 1, characterized in that The step of determining a plurality of sampled sand bodies corresponding to the block to be predicted includes: Based on the exploration well data and seismic interpretation data of the block to be predicted, determine the probability distribution curve of various sand body morphological parameters corresponding to the block to be predicted; A plurality of groups of the sand body morphology parameters are randomly extracted from the probability distribution curve of the sand body morphology parameters, and a sampling sand body is determined based on each group of the extracted sand body morphology parameters to obtain a plurality of the sampling sand bodies.

3. The method according to claim 2, characterized in that Also includes: The probability distribution curve of the sand body morphology parameters is adjusted based on preset geological recognition parameters to update the probability distribution curve of the sand body morphology parameters.

4. The method according to claim 1, characterized in that: The determining the water drive control degree corresponding to the candidate well pattern deployment mode based on the volume of the sand bodies driven by water in the plurality of sampled sand bodies comprises: Determine the sampled sand volume of each sampled sand body respectively, and determine the water-driven sand volume of the sampled sand body under the well pattern deployment mode; The water drive control degree corresponding to the candidate well pattern deployment mode is determined based on the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies.

5. The method according to claim 4, characterized in that The determining of the water-driven sand volume of the sampled sand body under the well pattern deployment mode includes: Determine a plurality of well points corresponding to the well pattern deployment mode and well point types corresponding to the well points, wherein the well point types include water wells and oil wells; Acquire a reference well point located in the sampling sand body, and determine at least one optional setting mode corresponding to the reference well point based on the position of the reference well point and the well point type corresponding to the reference well point; Determine the water drive state corresponding to the sampled sand body under each of the optional settings, wherein the water drive state includes being water driven and not being water driven; The volume of the water-driven sand body of the sampled sand body that is driven by water in the well pattern deployment mode is determined based on the optional setting mode and the water-driven state.

6. The method according to claim 5, characterized in that The respectively determining the water drive state corresponding to the sampled sand body under each of the optional setting modes includes: For each of the optional configurations, determining whether there is at least one water well and at least one oil well in the sampled sand body; If so, it is determined that the sampled sand body is subject to water drive in the optional setting mode; If not present, it is determined that the sampled sand body is not flooded with water under the optional setting.

7. The method according to claim 5, characterized in that The determining, based on the optional setting mode and the water drive state, the volume of the water drive sand body of the sampled sand body that is water drived in the well pattern deployment mode comprises: Determine the total number of the optional settings, the total number of the optional settings that are water-driven, and the sampled sand body volume of the sampled sand body, and determine the water-driven sand body volume of the sampled sand body that is water-driven in the well pattern deployment mode.

8. The method according to claim 4, characterized in that The determining the water drive control degree corresponding to the candidate well pattern deployment mode based on the sampled sand body volume and the water drive sand body volume of all the sampled sand bodies comprises: Calculating a first volume sum of the water-driven sand body volumes of all the sampled sand bodies, and a second volume sum of the sampled sand body volumes of all the sampled sand bodies; The ratio of the first volume sum to the second volume sum is determined as the water drive control degree corresponding to the candidate well pattern deployment mode.

9. The method according to claim 5, characterized in that The determining of a plurality of well points corresponding to the well pattern deployment method comprises: Determine the well point arrangement information corresponding to the well network deployment method, and determine multiple well points corresponding to the well network deployment method based on the arrangement information, wherein the well point arrangement information at least includes at least one of the angle between the sand body extension direction and the well point arrangement direction, the distance between every two well points, and the well point type.

10. A device for determining a well pattern deployment mode, characterized in that: include: A sampling sand body determination module is used to obtain a block to be predicted and determine a plurality of sampling sand bodies corresponding to the block to be predicted; A water drive control degree determination module is used to determine the water drive control degree corresponding to each candidate well pattern deployment mode based on the volume of sand bodies that are water driven in a plurality of sampled sand bodies for each candidate well pattern deployment mode; The well pattern deployment mode determination module is used to determine a target well pattern deployment mode among the candidate well pattern deployment modes based on the water drive control degree corresponding to each of the candidate well pattern deployment modes.

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