Oil well densification and well distribution optimization method and related equipment

Abstract

The invention discloses an oil well densification and well distribution optimization method and related equipment. The method comprises the steps that main parameters influencing the densification effect are obtained; the encryption wells are selected according to the obtained main parameters influencing the encryption effect; and carrying out numerical simulation on the selected parameters of the dense well. According to the optimization method, a clustering analysis method is adopted to obtain parameters influencing the encryption effect, the parameters are utilized to establish a multiple regression equation, related calculation formulas are determined, a standardized and quantitative optimization standard of the encryption well is established, selection of the encryption well is optimized, and the method is high in operability and high in practicability. And a calculation result is matched with a numerical simulation result, the field cognition is high, and support can be provided for encryption adjustment work.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development, and in particular relates to an oil well density optimization method and related equipment. Background Art

[0002] After low permeability reservoirs enter the medium-high water-cut period, the contradiction between the plane and the profile is prominent. The residual oil recovery degree in the relatively high permeability zone and the water injection effective zone is relatively high, while the residual oil is enriched in the low permeability zone with poor physical properties or the zone not affected by water injection, and the distribution of the residual oil is complex. Under the existing primary well network conditions, conventional injection and production control measures have been difficult to further increase the water drive swept volume and tap the potential of residual oil. Economical and effective well network densification adjustment has become a necessary means to ensure the stable production of oil fields and improve the recovery rate. At present, the domestic densification well type is mainly based on conventional directional wells, high-angle and short horizontal wells to achieve the purpose of using the residual oil. However, the development results show that the effect of conventional directional well densification is slightly poor, and the high-angle and short horizontal wells have the problem of large drilling footage investment, which still restricts the stable production of oil fields. Therefore, it is urgent to invent a new method to achieve the effective use of residual oil, further increase the single well production and significantly reduce the cost. Summary of the invention

[0003] The purpose of the present invention is to solve the problems in the prior art and to provide an oil well density optimization method and related equipment.

[0004] According to a first aspect of the present invention, a method for optimizing the infill layout of oil wells is provided, comprising:

[0005] Obtain the main parameters that affect encryption effect;

[0006] Selecting infill wells according to the main parameters affecting infill effect;

[0007] The parameters of the selected infill wells are numerically simulated.

[0008] Furthermore, the main parameters affecting the encryption effect are obtained, including:

[0009] Firstly, target reservoir data are collected, and according to the collected target reservoir data, a cluster analysis method is used to obtain the main parameters affecting the densification effect;

[0010] The main parameters include remaining oil saturation, principal stress, distance from the waterline, regional comprehensive water content, oil layer thickness, inclination angle of infill wells, azimuth angle of infill wells and daily oil production of infill wells.

[0011] Further, selecting the infill wells according to the main parameters that affect the infill effect obtained includes:

[0012] The location of the infill wells is selected based on the remaining oil saturation, distance from the waterline, regional comprehensive water content and daily oil production of the infill wells;

[0013] The azimuth and inclination of the infill wells are selected according to the oil layer thickness, the infill well inclination, the infill well azimuth, the infill well daily oil production and the principal stress parameters.

[0014] Furthermore, when selecting the infill wells, regression processing is also performed on the main parameters that affect the infill effect, specifically:

[0015] When selecting the location of the infill wells, multiple regression processing is performed on the parameters of remaining oil saturation, distance from the waterline, regional comprehensive water content and daily oil production of the infill wells to obtain the first regression formula;

[0016] When selecting the infill well azimuth and infill well inclination, multiple regression processing is also performed on the parameters of oil layer thickness, infill well inclination, infill well azimuth and infill well daily oil production to obtain the second regression formula.

[0017] Furthermore, after the regression processing of the main parameters, it also includes:

[0018] An orthogonal scheme is designed based on the first regression formula and the second regression formula to predict the production of infill wells and obtain a production prediction formula.

[0019] Furthermore, the prediction formula was written into Matlab and calculated, and the set of parameters with the highest predicted yield value was selected as the best solution.

[0020] Furthermore, the parameters of the selected infill wells are added to the numerical simulation model for numerical simulation verification, and when the compliance rate is greater than or equal to a preset percentage, infill deployment is performed.

[0021] According to a second aspect of the present invention, there is provided an oil well density optimization system, comprising:

[0022] Data acquisition module: used to obtain the main parameters that affect the encryption effect;

[0023] Selection module: used for selecting the infill wells according to the main parameters affecting the infill effect;

[0024] Numerical simulation module: numerically simulate the parameters of the selected infill wells.

[0025] According to a third aspect of the present invention, there is provided an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for optimizing the density distribution of oil wells when executing the computer program.

[0026] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for optimizing the density distribution of oil wells.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The oil well densification optimization method provided by the present invention obtains the parameters affecting the densification effect by cluster analysis, establishes a multivariate regression equation using the parameters, determines the relevant calculation formulas, establishes standardized and quantitative optimization criteria for densification wells, and optimizes the selection of densification wells. The method is highly operational, and the calculation results are consistent with the numerical simulation results and have a high degree of on-site cognition, which can provide support for densification adjustment work. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A flow chart of the oil well density optimization method provided by the present invention;

[0031] Figure 2 A schematic diagram of the production line curve of well A in the oil well density optimization method provided by the present invention;

[0032] Figure 3 A schematic diagram of a production line curve of well B in the oil well density optimization method provided by the present invention;

[0033] Figure 4 This is a block diagram of the oil well density optimization system provided by the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0037] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0038] like Figure 1 According to a first aspect of the present invention, a method for optimizing the density distribution of oil wells is provided, comprising:

[0039] S100, obtaining the main parameters that affect the infilling effect; first collect the target reservoir data, and use the cluster analysis method to obtain the main parameters that affect the infilling effect based on the collected target reservoir data; the reservoir data include the remaining oil saturation distribution map, the principal stress direction, the oil layer thickness, the comprehensive water content surface distribution map, the well inclination, azimuth, and daily oil production of the implemented infilling wells, and the numerical simulation model; the main parameters include the remaining oil saturation, principal stress, distance from the waterline, regional comprehensive water content, oil layer thickness, well inclination of the infilling well, azimuth of the infilling well, and daily oil production of the infilling well;

[0040] S101, selecting the infill wells according to the main parameters that affect the infill effect; the first step is to preliminarily select the infill well locations according to the remaining oil saturation distribution map and the regional comprehensive water content map; then, when specifically selecting the infill well locations, multiple regression processing is performed according to the remaining oil saturation, distance from the waterline, regional comprehensive water content and daily oil production parameters of the infill wells to obtain the first regression formula, wherein the daily oil production Q of the infill wells is used as the dependent variable, and the remaining oil saturation So, distance from the waterline Ls and regional comprehensive water content fw are used as independent variables;

[0041] In the second step, the azimuth and inclination of the infilled wells are first selected according to the oil layer thickness, the infilled well inclination, the infilled well azimuth, the daily oil production of the infilled wells and the principal stress parameters; then, when selecting the infilled well azimuth and inclination, the oil layer thickness, the infilled well inclination, the infilled well azimuth and the daily oil production of the infilled wells are also subjected to multivariate regression processing to obtain the second regression formula, in which the daily production of the infilled well Q is taken as the dependent variable, and the oil layer thickness Ho, the infilled well inclination AZ and the infilled well azimuth DE are taken as the independent variables; finally, after the regression processing of the main parameters, an orthogonal scheme is designed according to the obtained first regression formula and the second regression formula to predict the production of the infilled wells, and the production prediction formula is obtained:

[0042] Qprediction=-2.27+0.08*So+0.009*Ls-0.009*fw+0.065*Ho+0.008*AZ-0.045*DE

[0043] The above prediction formula is written into Matlab program 1, and the ranges of the distance from the waterline Ls, the well inclination AZ of the infilled well and the azimuth DE of the infilled well are set, and the Q prediction is calculated, and the set of parameters with the highest predicted production value is selected as the best solution.

[0044] S102. Numerical simulation is performed on the parameters of the selected encrypted wells. The optimal solution parameters obtained in the above steps are added to the numerical simulation model for simulation verification. When the compliance rate is greater than or equal to the preset percentage, where the preset percentage is 90%, it indicates that the method is accurate in calculation and can be deployed for encryption.

[0045] Embodiment 1:

[0046] For example, Figure 2 As shown, a water injection development reservoir A area is selected, and the well density optimization method provided by the present invention is used to determine the well density location, including the following steps:

[0047] Step 1: Collect the remaining oil distribution map of the reservoir where the infilled wells are located, and obtain the remaining oil saturation of 60%, the oil layer thickness of 37.6m and the regional comprehensive water content of 75.2%;

[0048] Step 2: Bring the data from step 1 into matlab program 1 and calculate the distance from the waterline Ls = 140m, the infill well inclination AZ = 37.1° and the infill well azimuth DE = 293.8°

[0049] Embodiment 2:

[0050] For example, Figure 3 As shown, a water injection development reservoir B area is selected, and the well density optimization method provided by the present invention is used to determine the well density location, including the following steps:

[0051] Step 1: Collect the remaining oil distribution map of the reservoir where the infilled wells are located, and obtain the remaining oil saturation of 52%, the oil layer thickness of 50.6m, and the regional comprehensive water content of 64.9%;

[0052] Step 2: Bring the data in step 1 into the matlab program 1, and calculate the distance from the waterline Ls = 110m, the infill well inclination AZ = 70.9° and the infill well azimuth DE = 294.5°.

[0053] According to a second aspect of the present invention, there is provided an oil well density optimization system, comprising:

[0054] Data acquisition module: used to obtain the main parameters that affect the encryption effect;

[0055] Selection module: used for selecting the infill wells according to the main parameters affecting the infill effect;

[0056] Numerical simulation module: numerically simulate the parameters of the selected infill wells.

[0057] According to a third aspect of the present invention, there is provided an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the oil well density optimization method as described above when executing the computer program.

[0058] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the oil well density optimization method as described above.

[0059] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical storage, etc. Volatile memory may include random access memory (RAM) or external cache memory.

[0060] By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0061] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the density distribution of oil wells, characterized in that: include: Obtain the main parameters that affect encryption effect; Selecting infill wells according to the main parameters affecting infill effect; The parameters of the selected infill wells are numerically simulated.

2. The oil well density optimization method according to claim 1 is characterized in that: Get the main parameters that affect the encryption effect, including: Firstly, target reservoir data are collected, and according to the collected target reservoir data, a cluster analysis method is used to obtain the main parameters affecting the densification effect; The main parameters include remaining oil saturation, principal stress, distance from the waterline, regional comprehensive water content, oil layer thickness, inclination angle of infill wells, azimuth angle of infill wells and daily oil production of infill wells.

3. The oil well density optimization method according to claim 2 is characterized in that: The infill wells are selected according to the main parameters that affect the infill effect, including: The location of the infill wells is selected based on the remaining oil saturation, distance from the waterline, regional comprehensive water content and daily oil production of the infill wells; The azimuth and inclination of the infill wells are selected according to the oil layer thickness, the infill well inclination, the infill well azimuth, the infill well daily oil production and the principal stress parameters.

4. The oil well density optimization method according to claim 3 is characterized in that: When selecting the infill wells, regression processing of the main parameters affecting the infill effect is also included, specifically: When selecting the location of the infill wells, multiple regression processing is performed on the parameters of remaining oil saturation, distance from the waterline, regional comprehensive water content and daily oil production of the infill wells to obtain the first regression formula; When selecting the infill well azimuth and infill well inclination, multiple regression processing is also performed on the parameters of oil layer thickness, infill well inclination, infill well azimuth and infill well daily oil production to obtain the second regression formula.

5. The oil well density optimization method according to claim 4 is characterized in that: After the regression processing of the main parameters, it also includes: An orthogonal scheme is designed based on the first regression formula and the second regression formula to predict the production of infill wells and obtain a production prediction formula.

6. The oil well density optimization method according to claim 5 is characterized in that: The prediction formula is written into Matlab and calculated, and the set of parameters with the highest predicted yield value is selected as the best solution.

7. The oil well density optimization method according to claim 1, characterized in that: The parameters of the selected infill wells are added to the numerical simulation model for numerical simulation verification. When the compliance rate is greater than or equal to the preset percentage, infill deployment is performed.

8. An oil well density optimization system, characterized in that: include: Data acquisition module: used to obtain the main parameters that affect the encryption effect; Selection module: used for selecting the infill wells according to the main parameters affecting the infill effect; Numerical simulation module: numerically simulate the parameters of the selected infill wells.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the oil well density optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the oil well density optimization method according to any one of claims 1 to 7 are implemented.

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