CN115110942B - Horizontal well optimization processing method and device - Google Patents
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/30—Specific pattern of wells, e.g. optimizing the spacing of wells
- E21B43/305—Specific pattern of wells, e.g. optimizing the spacing of wells comprising at least one inclined or horizontal well
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/25—Methods for stimulating production
- E21B43/26—Methods for stimulating production by forming crevices or fractures
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
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Abstract
The application discloses a horizontal well optimization processing method and device. Wherein the method comprises the following steps: determining an oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well; determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades; generating a comprehensive interpretation result diagram of the horizontal well according to the geological parameters of the oil layer, the engineering parameters of the oil layer, the comprehensive index of the oil layer and the classification standard of the oil layer; determining the optimal cluster spacing of each oil layer level of the horizontal well; and (3) carrying out optimization treatment on the horizontal well according to the comprehensive interpretation result diagram and the optimal cluster distance. The method solves the technical problem that the fracturing effect of the tight oil reservoir horizontal well and the fracturing cost of the single well in the related technology cannot meet the expected requirement.
Description
Technical Field
The application relates to the field of oilfield development geology, in particular to a horizontal well optimization processing method and device.
Background
The dense oil reservoir has the characteristics of deep burial, complex lithology, poor physical properties and low natural productivity. Large-scale production is usually carried out by a horizontal well volume fracturing mode, and reservoir transformation is carried out by adopting a well cementation bridge plug perforation combined staged fracturing technology. Under the conditions of low-level operation of crude oil price and reduction of single well fracturing investment, the sectional clustering optimization design of the horizontal well is an important way for improving the transformation effect of the horizontal well and ensuring the single well to achieve production.
At present, the sectional clustering optimization research of the horizontal well at home and abroad is concentrated on the aspects of hydraulic fracture parameter optimization or fuzzy statistics identification and the like, and oil layer classification indexes and standards comprehensively considering geological factors and engineering factors are not established, so that the research on dense oil layer classification guiding perforation optimization is rarely involved, and the fracturing effect of the dense oil reservoir horizontal well and the single well fracturing cost cannot meet the expected requirements.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a horizontal well optimizing treatment method and device, which at least solve the technical problems that the fracturing effect of a tight oil reservoir horizontal well and the fracturing cost of a single well cannot meet the expected requirement in the related technology.
According to an aspect of the embodiment of the present application, there is provided a horizontal well optimization processing method, including: determining an oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well; determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades; generating a comprehensive interpretation result diagram of the horizontal well according to the reservoir geological parameters, the reservoir engineering parameters, the reservoir comprehensive index and the reservoir classification standard; determining the optimal cluster spacing of each oil layer level of the horizontal well; and carrying out optimization treatment on the horizontal well according to the comprehensive interpretation result diagram and the optimal cluster distance.
Optionally, determining the reservoir comprehensive index of the horizontal well according to the reservoir geological parameter and the reservoir engineering parameter of the horizontal well comprises: acquiring the reservoir geological parameters and reservoir engineering parameters of the horizontal well; screening oil saturation and natural gamma from the oil layer geological parameters and Young modulus from the oil layer engineering parameters; and generating an oil layer comprehensive index of the horizontal well according to the oil saturation, the natural gamma and the Young modulus.
Optionally, acquiring the reservoir geological parameter and the reservoir engineering parameter of the horizontal well includes: obtaining the geological parameters of the oil layer according to the conventional comprehensive logging data of the horizontal well, wherein the geological parameters comprise at least one of the following: porosity, oil saturation, natural gamma; obtaining the oil layer engineering parameters according to the dipole acoustic special logging data of the horizontal well, wherein the engineering parameters comprise at least one of the following: poisson's ratio, young's modulus, brittleness index.
Optionally, determining the reservoir classification criteria for the horizontal well includes: establishing an association relation between oil extraction intensity and oil layer comprehensive index; dividing oil layer grades according to the oil extraction intensity of the horizontal well to obtain each oil layer grade; determining an oil layer comprehensive index corresponding to each oil layer level according to the association relation and each oil layer level; and generating the oil layer classification standard according to the oil extraction intensity of the horizontal well, the oil layer levels and the oil layer comprehensive indexes corresponding to the oil layer levels.
Optionally, determining the optimal cluster spacing for each reservoir level of the horizontal well comprises: obtaining critical crack number and horizontal section length of the horizontal well during fracturing; obtaining the average cluster distance of the horizontal well according to the critical fracture number and the horizontal segment length; and determining the optimal cluster spacing of each oil layer level of the horizontal well according to the average cluster spacing of the horizontal well.
Optionally, the optimizing the horizontal well includes: and adjusting the perforation center position and/or the bridge plug position of the horizontal well.
Optionally, adjusting the perforation center position and/or bridge plug position of the horizontal well further comprises: and the perforation can not be performed within the preset range of the casing section hoop of the horizontal well, and the perforation and the bridge plug can not be arranged on the same casing.
According to another aspect of the embodiment of the present application, there is also provided a horizontal well optimizing processing apparatus, including: the first determining module is used for determining the oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well; the second determining module is used for determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises the following components: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades; the generation module is used for generating a comprehensive interpretation result diagram of the horizontal well according to the reservoir geological parameters, the reservoir engineering parameters, the reservoir comprehensive index and the reservoir classification standard; the third determining module is used for determining the optimal cluster spacing of each oil layer level of the horizontal well; and the optimization module is used for optimizing the horizontal well according to the comprehensive interpretation result diagram and the optimal cluster distance.
According to another aspect of the embodiment of the present application, there is further provided a computer readable storage medium, where the computer readable storage medium includes a stored program, and when the program runs, the apparatus where the computer readable storage medium is controlled to execute the horizontal well optimization processing method according to any one of the foregoing methods.
According to another aspect of the embodiment of the present application, there is also provided a processor, where the processor is configured to run a program, where the program runs to perform the horizontal well optimization processing method according to any one of the above.
In the embodiment of the application, the oil layer comprehensive index of the horizontal well is determined according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well; determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades; generating a comprehensive interpretation result diagram of the horizontal well according to the geological parameters of the oil layer, the engineering parameters of the oil layer, the comprehensive index of the oil layer and the classification standard of the oil layer; determining the optimal cluster spacing of each oil layer level of the horizontal well; according to the comprehensive interpretation result map and the optimal cluster spacing, the horizontal well is optimized, the comprehensive interpretation result map of the horizontal well is generated through the oil layer geological parameters, the oil layer engineering parameters, the oil layer comprehensive indexes and the oil layer classification standard, and the optimal cluster spacing of each oil layer level of the horizontal well is combined to optimize the horizontal well, so that the technical effects of improving the fracturing transformation effect of the horizontal well, saving fracturing investment and guaranteeing single well productivity are achieved, and the technical problems that the fracturing effect of a tight oil reservoir horizontal well and the fracturing cost of a single well cannot meet expected requirements in the related art are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a horizontal well optimization process method according to an embodiment of the present application;
FIG. 2 is a graph of horizontal well perforation optimization results in accordance with an alternative embodiment of the present application;
FIG. 3 is a graph of oil recovery intensity versus natural gamma in accordance with an alternative embodiment of the present application;
FIG. 4 is a graph of oil recovery strength versus oil saturation in accordance with an alternative embodiment of the present application;
FIG. 5 is a graph of hydraulic fracture length versus Young's modulus for a horizontal well according to an alternative embodiment of the present application;
FIG. 6 is a graph of oil recovery strength versus reservoir integrated index in accordance with an alternative embodiment of the present application;
FIG. 7 is a schematic representation of post-fracture production simulation results for different fracture spacing pressures in accordance with an alternative embodiment of the present application;
FIG. 8 is a schematic diagram of a horizontal well optimized treatment apparatus according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present application, there is provided an embodiment of a horizontal well optimization processing method, it being noted that the steps shown in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than what is shown or described herein.
FIG. 1 is a flow chart of a horizontal well optimization process according to an embodiment of the present application, as shown in FIG. 1, comprising the steps of:
step S102, determining an oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well;
step S104, determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades;
step S106, generating a comprehensive interpretation result diagram of the horizontal well according to the reservoir geological parameters, the reservoir engineering parameters, the reservoir comprehensive index and the reservoir classification standard;
step S108, determining the optimal cluster spacing of each oil layer level of the horizontal well;
and step S110, carrying out optimization treatment on the horizontal well according to the comprehensive interpretation result diagram and the optimal cluster distance.
It should be noted that the optimization of the horizontal well includes, but is not limited to, adjusting the perforation center position and/or the bridge plug position of the horizontal well.
In an alternative embodiment, the reservoir classification standard can be determined by constructing a reservoir comprehensive index considering reservoir geological parameters and reservoir engineering parameters based on actual oil testing results and production effects of produced wells and logging data. On the basis, the average cluster spacing of the horizontal well is optimized by combining on-site development test or fracturing software, the optimal cluster spacing of various oil layers is determined, and the central position of each shower hole and the bridge plug position are optimized by combining with the comprehensive well logging interpretation result diagram of the horizontal well.
The above-mentioned oil layer comprehensive index is an important index for evaluating the quality of a very tight oil reservoir by reflecting the combination parameters of the geological features and engineering features of the oil layer at the same time, wherein the larger the oil layer comprehensive index is, the better the oil layer quality is, and the better the development effect is. The integrated logging interpretation result map is a plan map generated based on integrated interpretation of related data such as drilling, logging, and logging.
Through the steps, the comprehensive interpretation result diagram of the horizontal well can be generated through the oil layer geological parameters, the oil layer engineering parameters, the oil layer comprehensive indexes and the oil layer classification standards, and the optimal cluster spacing of each oil layer level of the horizontal well is combined to optimize the horizontal well, so that the technical effects of improving the fracturing transformation effect of the horizontal well, saving fracturing investment and guaranteeing single well productivity are achieved, and the technical problems that the fracturing effect of the horizontal well of the tight oil reservoir and the fracturing cost of the single well cannot reach the expected requirement in the related art are solved.
Optionally, determining the reservoir comprehensive index of the horizontal well according to the reservoir geological parameter and the reservoir engineering parameter of the horizontal well comprises: acquiring an oil layer geological parameter and an oil layer engineering parameter of a horizontal well; screening oil saturation and natural gamma from oil layer geological parameters and Young modulus from oil layer engineering parameters; and generating an oil layer comprehensive index of the horizontal well according to the oil saturation, natural gamma and Young modulus.
In an alternative embodiment, the horizontal well is produced using volumetric fracturing, and the production is related to reservoir engineering parameters in addition to reservoir geological parameters. Alternatively, the oil test, the production test and the comprehensive logging data can be utilized, and the two parameters with the best correlation with the oil production intensity among the geological parameters are preferably oil saturation and natural gamma, wherein the natural gamma reflects the shale content of a reservoir, and the higher the shale content, the worse the reservoir property and the worse the rock compressibility. In addition, according to the microseism monitoring result, the Young modulus is preferably selected as the parameter which has the best correlation with the length of the fracturing fracture in the oil layer engineering parameters. And further calculating the oil layer comprehensive index based on the three parameters of the selected oil saturation, natural gamma and Young modulus.
In an alternative embodiment, the reservoir integrated index may be calculated using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
in the above formula, CI is the oil layerComprehensive index, dimensionless; GR is a natural gamma curve value, API; GR (glass fibre reinforced plastics) max Is a natural gamma maximum, API; GR (glass fibre reinforced plastics) min Is the natural gamma minimum, API; s is(s) o Is oil saturation, decimal.
In addition, the calculation formula of the oil layer comprehensive index can obtain that the better the oil layer quality is, the larger the oil layer comprehensive index is.
Optionally, acquiring the reservoir geological parameters and the reservoir engineering parameters of the horizontal well includes: obtaining geological parameters of an oil layer according to conventional comprehensive logging data of the horizontal well, wherein the geological parameters comprise at least one of the following: porosity, oil saturation, natural gamma; obtaining oil layer engineering parameters according to dipole acoustic special logging data of the horizontal well, wherein the engineering parameters comprise at least one of the following: poisson's ratio, young's modulus, brittleness index.
In an alternative embodiment, the reservoir geologic parameters of porosity, oil saturation, natural gamma, etc., may be calculated using conventional synthetic well log data including, but not limited to: natural gamma, borehole diameter, undisturbed formation resistivity, transition zone resistivity, flushing zone resistivity, density, acoustic time difference, compensating neutrons, and the like; reservoir engineering parameters such as young's modulus of elasticity, poisson's ratio, brittleness index, etc. may be calculated using dipole acoustic special logging data including, but not limited to: time differences, amplitudes, etc. of longitudinal waves, transverse waves, ston standing waves.
Alternatively, the following formula may be used to calculate the oil reservoir engineering parameters such as young's modulus of elasticity, poisson's ratio, brittleness index, etc:
wherein, the liquid crystal display device comprises a liquid crystal display device,
in the formula, E is Young's elastic modulus and GPa; DEN is rock density in g/cm 3 ；V p 、V s Longitudinal wave speed and transverse wave speed are respectively given in ft/us; beta is the unit conversion factor 9.290304 multiplied by 10 7 The method comprises the steps of carrying out a first treatment on the surface of the V is poisson ratio, dimensionless; BI is the brittleness index, dimensionless; k is bulk modulus, GPa; e (E) max Is the maximum value of Young's elastic modulus and GPa; e (E) min Is the minimum value of Young's elastic modulus and GPa; upsilon (v) max Is poisson ratio maximum value and is dimensionless; upsilon (v) min Is poisson ratio minimum value, dimensionless; k (K) max Is the maximum value of the bulk modulus and GPa; k (K) min Is the minimum value of the bulk modulus and GPa.
Optionally, determining the reservoir classification criteria for the horizontal well comprises: establishing an association relation between oil extraction intensity and oil layer comprehensive index; dividing oil layer grades according to the oil extraction intensity of the horizontal well to obtain each oil layer grade; determining an oil layer comprehensive index corresponding to each oil layer level according to the association relation and each oil layer level; and generating an oil layer classification standard according to the oil extraction intensity of the horizontal well, the oil layer level and the oil layer comprehensive index corresponding to the oil layer level.
In an alternative embodiment, according to the interpretation result of the oil test and the production effect of the produced well, the relation between the oil production intensity and the comprehensive index of the oil layer is established, the oil layer is divided into 1-class oil layer, 2-class oil layer, 3-class oil layer and 4-class oil layer according to the oil production intensity, and the limit value of the comprehensive index corresponding to each type of oil layer is statistically determined.
The oil layer classification criteria include, but are not limited to, oil recovery strength, oil layer grade, oil layer comprehensive index, and the like.
Optionally, determining the optimal cluster spacing for each reservoir level of the horizontal well comprises: obtaining critical crack number and horizontal section length of a horizontal well during fracturing; obtaining the average cluster spacing of the horizontal well according to the critical fracture number and the horizontal segment length; and determining the optimal cluster spacing of each oil layer level of the horizontal well according to the average cluster spacing of the horizontal well.
In an alternative embodiment, the fracturing software is used for carrying out yield simulation to find that the accumulated oil yield increase value of the horizontal well gradually slows down as the number of the artificial fractures for horizontal well fracturing transformation increases and reaches a critical value. According to the number of critical cracks and the length of the horizontal section, the average cluster distance of the horizontal well can be calculated. In addition, according to the principle that the better the comprehensive quality of the oil layer is, the smaller the cluster spacing is, the optimal cluster spacing of each oil layer level is further determined.
In addition, the average cluster spacing of the horizontal wells can be obtained according to a development pilot test of the tight reservoir besides the simulation by means of fracturing software.
Optionally, the optimizing the horizontal well includes: and adjusting the perforation center position and/or the bridge plug position of the horizontal well.
In an alternative embodiment, the center point of perforations in each cluster of horizontal wells is selected as much as possible at the location where the reservoir composite index is greatest. In addition, the cluster number of each stage of fracturing section can be obtained according to the fracturing technology level of the oil field and the fracturing equipment condition, clusters with similar Young modulus are divided into the same section, and a bridge plug is placed in the middle of the previous casing pipe at the perforation position of the first cluster of the next fracturing section.
Optionally, adjusting the perforation center position and/or the bridge plug position of the horizontal well further comprises: perforation is not possible within a preset range of the casing section hoop of the horizontal well, and perforation and bridge plugs are not provided on the same casing.
In an alternative embodiment, in order to ensure construction safety and effect, perforation cannot be performed within 2m near the casing section hoop of the horizontal well, and perforation and bridge plug cannot be arranged on the same casing.
An alternative embodiment of the present application will be described in detail below.
The embodiment provides a dense oil reservoir horizontal well perforation optimization technology based on oil layer classification, which comprises the following specific steps:
(1) The porosity curve (POR) and the oil saturation curve (so) are calculated by using the conventional comprehensive logging curve of the HW31 well, and the oil layer section is calculated according to the oil layer standard of the research area, as shown in figure 2.
(2) Utilizing dipole acoustic special logging data of M001 and M008 wells to obtain a prediction formula of a transverse wave speed curve:
V p ＝(23.9414-674.711/AC-0.315268DEN 2 -59.3969/GR) -2
wherein, AC is longitudinal wave time difference, ft/us.
(3) The rock mechanics parameters such as poisson's ratio, young's modulus, brittleness index, etc. were calculated as shown in fig. 2. Wherein E is max ＝80GPa；E min ＝10GPa；υ max ＝0.7；υ min ＝0.15；K max ＝60GPa；K min ＝4GPa。
(4) The reservoir complex index curve is calculated as shown in figure 2. Wherein GR is max ＝130API；GR min ＝0API。
(5) According to the production results of the trial production and the production effects of the produced well, the two geological parameters with the best correlation with the oil production intensity are preferably natural gamma and oil saturation, as shown in figures 3 and 4. According to microseismic monitoring results, the engineering parameter with the best correlation with the fracture length is preferably Young's modulus, and three parameters are constructed together to form an oil layer comprehensive index as shown in figure 5. And carrying out correlation analysis on the oil extraction intensity and the oil layer comprehensive index, establishing a functional relation of the oil extraction intensity and the oil layer comprehensive index, equally dividing into four grades according to the oil extraction intensity, corresponding to four types of oil layers, and counting the range of the oil layer comprehensive index of each type, as shown in figure 6. Accordingly, the oil layer was classified into 1-type oil layer, 2-type oil layer, 3-type oil layer, and 4-type oil layer by using the oil layer comprehensive index curve, as shown in table 1.
TABLE 1 Classification criteria for tight conglomerate reservoir reservoirs
(6) Determining reasonable cluster spacing of various oil layers: the fracturing software is used for simulating the accumulated oil yield of HW31 in the 1300m horizontal segment, as shown in FIG. 7, when the number of artificial cracks reaches more than 50, the accumulated oil yield increase value gradually becomes gentle, and the reasonable number of cracks is determined to be about 50-55, and the cluster spacing is about 20-25 m. Under the current low oil price background, the single well fracturing investment is reduced, 4 types of oil layers are prevented from being fractured, the average cluster spacing 11m of 1 type of oil layers, the average cluster spacing 22m of 2 types of oil layers and the average cluster spacing 28m of 3 types of oil layers are ensured to ensure the single well productivity.
(7) Optimizing the center position and bridge plug position per shower of holes as shown in fig. 2: and determining 3 clusters of each section according to the fracturing process level of the oil field and the fracturing equipment conditions, avoiding the 4 types of oil layers, separating the sections without reconstruction by bridge plugs, dividing the clusters with similar Young modulus into the same section, placing the bridge plugs in the middle of a sleeve in the perforation position of the first cluster of the next fracturing section, and selecting the perforation center point of each cluster to be at the position with the largest comprehensive index of the oil layer as far as possible.
The dense oil reservoir horizontal well subsection clustering optimization technology based on oil reservoir classification established by the embodiment guides 6 horizontal well bridge plug perforation combination fracturing of the dense oil reservoir, totally avoids 4 types of oil reservoirs 886m, saves fracturing cost 760 for tens of thousands yuan, obtains good production effect, and the average daily oil yield of a single well exceeds the design productivity of 8.2t.
The following technical effects can be achieved by the above embodiments: the first constructed oil layer comprehensive index considers geological parameters and engineering parameters at the same time, and can guide oil layer classification, segmentation clustering optimization and later production dynamic analysis. Under the condition of low oil price, the method can guide to reasonably avoid the 4 kinds of poor oil layers under the condition of ensuring the single well productivity, and save the fracturing investment. After the oil price is increased, a basis can be provided for the selection of repeated fracturing sections. In addition, the basis can be provided for the design of the later stage fracturing construction parameters through the calculated rock mechanical parameters.
Example 2
According to another aspect of the embodiment of the present application, there is further provided a horizontal well optimizing processing apparatus, and fig. 8 is a schematic diagram of the horizontal well optimizing processing apparatus according to the embodiment of the present application, and as shown in fig. 8, the horizontal well optimizing processing apparatus includes: a first determination module 802, a second determination module 804, a generation module 806, a third determination module 808, and an optimization module 810. The horizontal well optimizing treatment device will be described in detail.
A first determining module 802, configured to determine an oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well; a second determining module 804, coupled to the first determining module 802, is configured to determine an reservoir classification criterion for the horizontal well, where the reservoir classification criterion includes: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades; the generating module 806 is connected to the second determining module 804, and is configured to generate a comprehensive interpretation result map of the horizontal well according to the reservoir geological parameter, the reservoir engineering parameter, the reservoir comprehensive index and the reservoir classification standard; a third determining module 808, coupled to the generating module 806, for determining an optimal cluster pitch for each reservoir level of the horizontal well; the optimizing module 810 is connected to the third determining module 808, and is configured to perform optimization processing on the horizontal well according to the comprehensive interpretation result map and the optimal cluster distance.
In the embodiment, the horizontal well optimizing processing device can generate the comprehensive interpretation result diagram of the horizontal well through the oil layer geological parameters, the oil layer engineering parameters, the oil layer comprehensive indexes and the oil layer classification standards, and combines the optimal cluster spacing of each oil layer level of the horizontal well to optimize the horizontal well, so that the technical effects of improving the fracturing transformation effect of the horizontal well, saving fracturing investment and guaranteeing single well productivity are realized, and the technical problems that the fracturing effect of the horizontal well of a compact oil reservoir and the fracturing cost of the single well cannot meet the expected requirement in the related technology are solved.
It should be noted that each of the above modules may be implemented by software or hardware, for example, in the latter case, it may be implemented by: the above modules may be located in the same processor; and/or the above modules are located in different processors in any combination.
Here, the first determining module 802, the second determining module 804, the generating module 806, the third determining module 808, and the optimizing module 810 correspond to steps S102 to S110 in embodiment 1, and the modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of an apparatus in a computer system, such as a set of computer-executable instructions.
Example 3
According to another aspect of the embodiment of the present application, there is also provided a computer readable storage medium, where the computer readable storage medium includes a stored program, and when the program runs, the apparatus in which the computer readable storage medium is controlled to execute the horizontal well optimization processing method of any one of the above.
Alternatively, in this embodiment, the above-mentioned computer-readable storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network and/or in any one of the mobile terminals in the mobile terminal group, and the above-mentioned computer-readable storage medium includes a stored program.
Example 4
According to another aspect of the embodiment of the present application, there is also provided a processor for running a program, where the program runs to perform the horizontal well optimization processing method of any one of the above.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.
Claims (8)
1. A method of horizontal well optimization treatment, comprising:
determining an oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well;
determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades;
generating a comprehensive interpretation result diagram of the horizontal well according to the reservoir geological parameters, the reservoir engineering parameters, the reservoir comprehensive index and the reservoir classification standard;
determining the optimal cluster spacing of each oil layer level of the horizontal well;
optimizing the horizontal well according to the comprehensive interpretation result diagram and the optimal cluster distance;
the obtaining of the reservoir geological parameters and the reservoir engineering parameters of the horizontal well comprises the following steps: obtaining the reservoir geological parameters according to the conventional comprehensive logging data of the horizontal well, wherein the reservoir geological parameters comprise at least one of the following: porosity, oil saturation, natural gamma; obtaining the oil layer engineering parameters according to the dipole acoustic special logging data of the horizontal well, wherein the oil layer engineering parameters comprise at least one of the following: poisson's ratio, young's modulus, brittleness index, the special logging data of dipole sound wave at least includes: time differences and amplitudes of longitudinal waves, transverse waves and Stoney standing waves;
according to the reservoir geological parameters and reservoir engineering parameters of the horizontal well, determining the reservoir comprehensive index of the horizontal well comprises the following steps: acquiring the reservoir geological parameters and reservoir engineering parameters of the horizontal well; screening oil saturation and natural gamma from the oil layer geological parameters and Young modulus from the oil layer engineering parameters; and generating an oil layer comprehensive index of the horizontal well according to the oil saturation, the natural gamma and the Young modulus.
2. The method of claim 1, wherein determining reservoir classification criteria for the horizontal well comprises:
establishing an association relation between oil extraction intensity and oil layer comprehensive index;
dividing oil layer grades according to the oil extraction intensity of the horizontal well to obtain each oil layer grade;
determining an oil layer comprehensive index corresponding to each oil layer level according to the association relation and each oil layer level;
and generating the oil layer classification standard according to the oil extraction intensity of the horizontal well, the oil layer levels and the oil layer comprehensive indexes corresponding to the oil layer levels.
3. The method of claim 1, wherein determining an optimal cluster spacing for each reservoir level of the horizontal well comprises:
obtaining critical crack number and horizontal section length of the horizontal well during fracturing;
obtaining the average cluster distance of the horizontal well according to the critical fracture number and the horizontal segment length;
and determining the optimal cluster spacing of each oil layer level of the horizontal well according to the average cluster spacing of the horizontal well.
4. A method according to any one of claims 1 to 3, wherein optimizing the horizontal well comprises:
and adjusting the perforation center position and/or the bridge plug position of the horizontal well.
5. The method of claim 4, wherein adjusting the perforation center position and/or bridge plug position of the horizontal well further comprises:
and the perforation can not be performed within the preset range of the casing section hoop of the horizontal well, and the perforation and the bridge plug can not be arranged on the same casing.
6. A horizontal well optimized treatment device, comprising:
the first determining module is used for determining the oil layer comprehensive index of the horizontal well according to the oil layer geological parameter and the oil layer engineering parameter of the horizontal well;
the second determining module is used for determining an oil layer classification standard of the horizontal well, wherein the oil layer classification standard comprises the following components: oil layer grades corresponding to different oil extraction intensities and oil layer comprehensive indexes corresponding to the oil layer grades;
the generation module is used for generating a comprehensive interpretation result diagram of the horizontal well according to the reservoir geological parameters, the reservoir engineering parameters, the reservoir comprehensive index and the reservoir classification standard;
the third determining module is used for determining the optimal cluster spacing of each oil layer level of the horizontal well;
the optimization module is used for optimizing the horizontal well according to the comprehensive interpretation result diagram and the optimal cluster distance;
the device is also for: obtaining the reservoir geological parameters according to the conventional comprehensive logging data of the horizontal well, wherein the reservoir geological parameters comprise at least one of the following: porosity, oil saturation, natural gamma; obtaining the oil layer engineering parameters according to the dipole acoustic special logging data of the horizontal well, wherein the oil layer engineering parameters comprise at least one of the following: poisson's ratio, young's modulus, brittleness index, the special logging data of dipole sound wave at least includes: time differences and amplitudes of longitudinal waves, transverse waves and Stoney standing waves;
the device is also for: according to the reservoir geological parameters and reservoir engineering parameters of the horizontal well, determining the reservoir comprehensive index of the horizontal well comprises the following steps: acquiring the reservoir geological parameters and reservoir engineering parameters of the horizontal well; screening oil saturation and natural gamma from the oil layer geological parameters and Young modulus from the oil layer engineering parameters; and generating an oil layer comprehensive index of the horizontal well according to the oil saturation, the natural gamma and the Young modulus.
7. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program, when run, controls a device in which the computer-readable storage medium is located to perform the horizontal well optimization treatment method according to any one of claims 1 to 5.
8. A processor for running a program, wherein the program when run performs the horizontal well optimization treatment method according to any one of claims 1 to 5.
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