CN115110936A - Method and device for determining cluster perforation position of compact oil horizontal well - Google Patents
Method and device for determining cluster perforation position of compact oil horizontal well Download PDFInfo
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Abstract
The application provides a method and a device for determining a clustering perforation position of a compact oil-water horizontal well. The method comprises the following steps: classifying the oil layers of the compact oil horizontal well in the preset area to obtain oil layers of different types; establishing a factor database influencing the oil layer yield; acquiring the actual measurement yield of each section of the single well; determining main factors influencing the yield of oil layers of different types at different periods according to the factor database and the actually measured yield of each section of a single well; constructing a capacity dessert model according to the main factors; constructing an intelligent clustering model at least according to the dessert productivity model; and determining the final clustering perforation position based on the intelligent clustering model. The individual analysis of the oil layer of the compact oil horizontal well is realized, so that the accuracy of the determined final clustering perforation position is higher.
Description
Technical Field
The application relates to the field of compact oil horizontal wells, in particular to a method and a device for determining clustered perforation positions of compact oil horizontal wells, a computer readable storage medium and a processor.
Background
The reserve of compact oil reservoirs in China is about 106.7-111.5 multiplied by 10 8 t, as a new field after 'shale gas' is followed by global unconventional oil and gas exploration and development, how to find an economic and effective development mode is always a hotspot and difficulty of research. In basin areas such as Erdos, Ersongarian, Songliao, Sichuan and the like in China, industrial production has been carried out successively, but because dense oil reservoirs mostly have the characteristics of poor physical properties, strong heterogeneity and the like, how to utilize a development mode of 'long horizontal well + multi-stage fracturing' is urgent.
The tracer monitoring technology is characterized in that water-soluble and oil-soluble tracers are respectively injected into a perforation position in a fracturing process, oil and water samples are respectively taken at certain time intervals, the yield of each fracturing section can be visually obtained, and the yield of the fracturing section is analyzed.
At present, horizontal wells have more oil layer classification schemes, mainly comprise geological factors such as oil saturation, porosity and the like, and engineering factors such as Young modulus and the like are added in part of oil layer division, but the division schemes are mostly considered based on 2-3 factors, however, the oil layer of a horizontal well, especially a compact oil horizontal well, is often controlled by more factors, so that a more reasonable oil layer division method is needed to search for the commonalities of different types of oil layers and summarize the corresponding production rules of different oil layers, and a reasonable development mode is favorably explored.
Under a long horizontal well development mode, the characteristics of fast reservoir physical property change, strong heterogeneity and the like are faced, at present, main oil reservoir yield control factors are mostly summarized in a past vertical well development mode, the current long horizontal well development mode is probably not applicable, factors influencing the long horizontal well development are far superior to those influencing the common vertical well development, and in actual production, a part of fracturing sections still have the phenomenon that fracturing stages and production stages show completely different production characteristics, so that the factors influencing the oil reservoir yield of a compact oil horizontal well are determined in different types and different time periods, and one of the problems which needs to be solved at present is provided.
At present, a horizontal well fracturing technology is mainly utilized in development of a compact oil reservoir, clustering perforation is used as the most direct influence factor of high-efficiency mining, development cost and subsequent mining yield are directly influenced, but the current clustering scheme still stays on the basis of more than ten logging curves, a manual visual judgment stage is carried out, although some people establish a mathematical model by using several logging curves or mechanical parameters, most of the models are based on personal experience (weights are given by experts and the like), although the models simplify original data, the model possibly has certain problems due to the fact that the influence factors of the yield of compact oil reservoirs in all areas are not changed, and therefore logging information of high-latitude and high-sample size needs to be more efficiently and reasonably integrated to form a scientific and reasonable clustering scheme.
In the conventional horizontal well sectional clustering process, the oil layer is divided by continuously taking the earlier-stage vertical well fracturing into consideration reference factors such as porosity, permeability and oil saturation, and then the perforation points are artificially judged based on a conventional logging curve. Some researchers respectively establish a geological dessert model and an engineering dessert model for fracturing optimization exploration (a method for optimizing shale gas horizontal well clustering perforation well sections by using logging information, Xiexing, 2015, the invention patent, a horizontal well segmentation fracturing perforation scheme optimization method, Jianting, 2016, the invention patent, a method for realizing high-efficiency fracturing of compact oil horizontal wells, Wushulin, 2019, the invention patent), based on information such as porosity, permeability, oil-gas content, cracks, fracture fracturing curves and compressibility indexes, but the invention patent considers that production factors are few, basically ignores that production time is different, and factors influencing yield are different; different influence factor weights of the horizontal well staged fracturing perforation scheme optimization method are mostly empirical parameters, and are influenced by human factors more; the method for realizing the efficient fracturing of the compact oil horizontal well is basically in a manual division stage, mass data and excessive influence parameters need to be analyzed, and the working efficiency is limited to a certain degree.
Disclosure of Invention
The application mainly aims to provide a method, a device, a computer readable storage medium and a processor for determining a clustering perforation position of a tight oil horizontal well, so as to solve the problem that the clustering perforation scheme adopted in the development of a tight oil reservoir in the prior art is low in accuracy.
To achieve the above object, according to one aspect of the present application, there is provided a method of determining a location of a tight oil horizontal well clustered perforation, comprising: classifying the oil layers of the compact oil horizontal well in the preset area to obtain oil layers of different types; establishing a factor database influencing the oil layer yield; acquiring the actual measurement yield of each section of the single well; determining main factors influencing the production of the oil layers of different types at different periods according to the factor database and the measured production of each section of the single well; constructing an energy-producing dessert model according to the main factors; constructing an intelligent clustering model at least according to the dessert productivity model; and determining a final clustering perforation position based on the intelligent clustering model.
Further, classifying the oil layers of the compact oil horizontal well in the preset area to obtain different types of oil layers, wherein the steps comprise: acquiring geological parameters and engineering parameters; and classifying the oil layers of the compact oil horizontal well in the preset area according to the geological parameters and the engineering parameters to obtain the oil layers of different types.
Further, constructing an energy-producing dessert model based on the primary factors, comprising: determining the influence weight of each main factor on the measured yield of each section of the single well; constructing the dessert model based on the impact weights.
Further, according to the factor database and the measured yield of each section of the single well, determining main factors influencing the yields of the oil layers of different types in different periods, including: performing correlation analysis on each factor in the factor database and the actual measurement yield of each section of the single well to obtain an analysis result; and determining the main factors according to the analysis result.
Further, constructing an intelligent clustering model based at least on the dessert model, comprising: determining a primary perforation position according to the productive dessert model, the sedimentation condition, the well cementation condition and the crack initiation condition; and constructing the intelligent clustering model at least according to the primary perforation position, the sedimentary facies, the well cementation quality and the coupling position.
Further, constructing the intelligent clustering model at least according to the primary perforation position, the sedimentary facies, the well cementation quality and the coupling position, wherein the method comprises the following steps: setting the primary perforation location as an output; setting the sedimentary facies, the cementing quality, and the collar location as input samples; and training the output sample and the input sample by using a decision tree algorithm to obtain the intelligent clustering model.
Further, obtaining the measured production of each section of the single well comprises: and acquiring the actually measured yield of each section of the single well by adopting a tracer monitoring technology.
According to another aspect of the present application, there is provided an apparatus for determining a location of a tight oil horizontal well cluster perforation, comprising: the classification unit is used for classifying the oil layers of the compact oil horizontal well in the preset area to obtain oil layers of different types; the device comprises an establishing unit, a judging unit and a judging unit, wherein the establishing unit is used for establishing a factor database influencing the yield of an oil layer; the acquisition unit is used for acquiring the actual measurement yield of each section of the single well; the first determining unit is used for determining main factors influencing the production of the oil layers of different types in different periods according to the factor database and the measured production of each section of the single well; a first construction unit for constructing an energy producing dessert model based on the principal factors; a second construction unit for constructing an intelligent clustering model at least according to the dessert model; and the second determining unit is used for determining the final clustering perforation position based on the intelligent clustering model.
According to another aspect of the application, a computer-readable storage medium is provided, which comprises a stored program, wherein the program when executed controls an apparatus in which the computer-readable storage medium is located to perform any one of the methods for determining locations of tight oil horizontal well clustered perforations.
According to another aspect of the application, a processor is provided for running a program, wherein the program is run to perform any one of the methods for determining locations of clustered perforations of tight oil horizontal wells.
By applying the technical scheme of the application, the oil layers of the compact oil horizontal well in the preset area are classified to obtain different types of oil layers, namely, the oil layers are classified, a factor database influencing the yield of the oil layers is established, the actual measured yield of each section of the single well is obtained, and then main factors influencing the yields of the oil layers of different types in different periods are determined according to the factor database and the actual measured yields of each section of the single well, namely, the individual analysis of the oil layers is realized, and further, a capacity dessert model is constructed according to the main factors; then constructing an intelligent clustering model at least according to the sweet spot model; and finally, determining the final clustering perforation position based on the intelligent clustering model. According to the scheme, the intelligent clustering model established in a time-phased and classified manner is adopted, so that the individual analysis of the oil layer of the compact oil horizontal well is realized, and the accuracy of the determined final clustering perforation position is higher.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 illustrates a flow chart of a method of determining locations of tight oil horizontal well clustered perforations in accordance with an embodiment of the present application;
FIG. 2 shows a schematic of an apparatus for determining locations of tight oil horizontal well clustered perforations in accordance with an embodiment of the present application;
FIG. 3 shows a decision tree model result graph according to an embodiment of the application;
FIG. 4 shows a comparison graph of the production effect of intelligent clustered wells.
Detailed Description
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the application herein. Moreover, 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.
It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. Also, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element or "connected" to the other element through a third element.
As introduced in the background art, in order to solve the problem of low accuracy of the clustering perforation scheme adopted in tight oil reservoir development in the prior art, embodiments of the present application provide a method, an apparatus, a computer-readable storage medium, and a processor for determining a clustering perforation position of a tight oil-horizontal well.
According to an embodiment of the present application, a method of determining a location of a tight oil horizontal well cluster perforation is provided.
FIG. 1 is a flow chart of a method of determining locations of tight oil horizontal well clustered perforations in accordance with an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
s101, classifying oil layers of a compact oil horizontal well in a preset area to obtain oil layers of different types;
step S102, establishing a factor database influencing oil layer yield;
s103, acquiring the actual measurement yield of each section of the single well;
step S104, determining main factors influencing the production of the oil layers of different types in different periods according to the factor database and the actually measured production of each section of the single well;
step S105, constructing an productivity dessert model according to the main factors;
s106, constructing an intelligent clustering model at least according to the dessert model;
and S107, determining the final clustering perforation position based on the intelligent clustering model.
Specifically, the predetermined area may be a certain oil field.
In particular, the different periods include tracer monitoring of the frac earlier and production periods.
Specifically, the scheme can be suitable for the field of oil reservoirs of compact oil length horizontal wells.
Specifically, the factor database for influencing the oil layer yield is rich in production factors, the rich production factors are combined with the actual measurement yield of each section of a single well, the determined main factors influencing the yields of oil layers of different types in different periods are more accurate, and the influence of human factors is smaller.
Specifically, because the factors influencing the yield of the long horizontal well are more, and the factors influencing the yield of different regions can also change, the invention constructs a database of the factors influencing the yield, collects 8 conventional well logging curves, and collects 8 geological factors, namely the oil layer thickness, the mud content, the porosity, the oil saturation, the physical property parameter CZ of the reservoir, the sensitive parameter CW of the reservoir, the oil porosity KH, the mechanical factors Poisson ratio, Young modulus, brittleness, volume modulus, shear modulus, ACS conventional calculation shear wave, the longitudinal and transverse wave ratio, the Lame constant, the sand production index, the fracture coefficient, the static Poisson ratio, the static Young modulus, the pore pressure of the reservoir, the bottom hole flowing pressure, the maximum horizontal stress (logging, evaluation), the minimum horizontal stress (logging, evaluation), the maximum and minimum horizontal stress difference (logging, evaluation), the compressive strength, the cohesive force, the internal friction angle and the like, the well completion parameters comprise the total single-stage sand pressing amount, the total single-stage hydraulic fluid amount, the single-stage fracturing sand-fluid ratio and the like. The database collects more than 40 influence factors such as geological factors, reservoir mechanics factors, well completion parameters and the like, and new influence factors are continuously added along with abundant data so as to find key factors influencing the yield of the horizontal well in different areas.
In the scheme, the oil layers of the compact oil horizontal well in the preset area are classified to obtain different types of oil layers, namely, the oil layers are classified, a factor database influencing the yield of the oil layers is established, the actually measured yield of each section of the single well is obtained, and then main factors influencing the yields of the oil layers of different types in different periods are determined according to the factor database and the actually measured yields of each section of the single well, namely, the personalized analysis of the oil layers is realized, and further, a capacity dessert model is constructed according to the main factors; then constructing an intelligent clustering model at least according to the sweet spot model; and finally, determining the final clustering perforation position based on the intelligent clustering model. According to the scheme, the intelligent clustering model established in a time-phased and classified manner is adopted, so that the individual analysis of the oil layer of the compact oil horizontal well is realized, and the accuracy of the determined final clustering perforation position is higher.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
In one embodiment of the present application, classify the tight oil horizontal well oil reservoir in the predetermined area, obtain the oil reservoir of different grade type, include: acquiring geological parameters and engineering parameters; and classifying the oil layers of the compact oil horizontal well in the preset area according to the geological parameters and the engineering parameters to obtain the oil layers of different types. Specifically, geological parameters and engineering related parameters are used as input parameters by using a K-means clustering method, and different types of oil layers are clustered and divided. So as to realize the classification of the oil layer.
In a specific embodiment, when oil layer division is carried out, geological parameters such as porosity, permeability, oil saturation, reservoir physical property parameter CZ, reservoir sensitive parameter CW and oil porosity KH and engineering parameters such as Poisson ratio, Young modulus and brittleness are comprehensively considered, and oil layers with similar conditions are searched based on a clustering method so as to carry out oil layer division based on whole region (predetermined region) and multi-factor control. Wherein:
KH＝φ×So oil
Wherein phi is porosity, CNL is neutron log curve, DEN is density log curve, So Oil Is the oil saturation.
In one embodiment of the present application, constructing the dessert model based on the above-mentioned key factors comprises: determining the influence weight of each main factor on the actual measurement yield of each section of the single well; and constructing the sweet spot model according to the influence weight.
Specifically, the main factors are used as input targets, the actual measurement yield of each section of the single well is used as a result, and the influence weights of the main factors influencing the yield to the actual measurement yield are obtained according to a gray system:
wherein, r (a) 0 ,a i ) Is a point correlation coefficient, r (X) 0 ,X i ) As a degree of gray correlation, W i For weighting, the resolution factor ε is taken to be 0.5.
According to the weight, a 'sweet spot production model' TD is constructed in the period of time of oil layer and meristem.
TD 1 ＝W 1 X 1 +W 2 X 2 +W 3 X 3 +…+W n X n
…
TD n ＝W 1 X 1 +W 2 X 2 +W 3 X 3 +…+W n X n
Wherein: TD1 … TDn is respectively different periods and different oil layer production sweetness, W1 … Wn is respectively weighted by a gray system, and X1 … Xn is respectively a correlation matrix to calculate main yield influencing factors.
In an embodiment of the present application, determining, according to the factor database and the measured production of each section of the single well, main factors affecting the production of the different types of the oil reservoirs at different periods, includes: performing correlation analysis on each factor in the factor database and the actual measurement yield of each section to obtain an analysis result; and determining the main factors according to the analysis result. Specifically, a factor having a significant correlation larger than a predetermined value is selected as a main factor. In order to determine the final clustering perforation position more accurately, factors with the correlation smaller than 0.5 in the main factors are removed. The difference of the factors influencing the yield of different regions, different periods and different oil layers is considered, so as to meet the requirements of meeting the practical and on-site purposes.
In one embodiment of the present application, constructing an intelligent clustering model based at least on the dessert model comprises: determining a primary perforation position according to the productive dessert model, the sedimentation condition, the well cementation condition and the crack initiation condition; and constructing the intelligent clustering model at least according to the primary perforation position, the sedimentary facies, the well cementation quality and the coupling position. According to the 'productive dessert model', lithology, sedimentary facies, well cementation quality, coupling position, crack initiation conditions and the like as consideration factors, perforation position selection is carried out, and a suitable position with high productive sweetness, appropriate lithology and sedimentary facies, good well cementation quality, avoidance of 2-3m of the coupling and appropriate mechanical property is selected as a perforation point. Because various influence factors are analyzed according to the actual yield, a plurality of main influence factors are reconstructed, and a single productivity sweet spot curve is established, the analysis amount of related factors is greatly reduced.
In an embodiment of the present application, constructing the intelligent clustering model at least according to the primary perforation position, the sedimentary facies, the cementing quality, and the coupling position includes: setting the primary perforation position as output; setting the sedimentary facies, the cementing quality, and the collar location as input samples; and training the output and the input samples by using a decision tree algorithm to obtain the intelligent clustering model. Lithology, rock mechanical homogeneity, etc. may also be included in the input sample. After the original primary perforation position is selected, manual adjustment needs to be carried out according to the positions of the couplings, as the number of the couplings is more than 50 and the number of the couplings is often required to be divided at 70 positions of a single well, the perforation scheme is often changed once, the work needs to be repeated for many times, so that a large amount of repeated work exists, and the division schemes in the past are often different due to different subjective judgments of individuals, after an intelligent model is constructed, the model automatically learns the geological expert clustering experience, saves a large amount of manual time, and realizes the efficient and accurate clustering scheme design.
In one embodiment of the present application, obtaining measured production for each section of a single well includes: and acquiring the actually measured yield of each section of the single well by adopting a tracer monitoring technology. Of course, techniques other than tracer monitoring techniques may be used to obtain the actual production of each section of a single well, and those skilled in the art may select an appropriate technique according to actual conditions.
The embodiment of the application also provides a device for determining the clustering perforation position of the compact oil-water horizontal well, and it needs to be explained that the device for determining the clustering perforation position of the compact oil-water horizontal well in the embodiment of the application can be used for executing the method for determining the clustering perforation position of the compact oil-water horizontal well in the embodiment of the application. The device for determining the clustering perforation position of the tight oil horizontal well provided by the embodiment of the application is described below.
FIG. 2 is a schematic diagram of an apparatus for determining locations of tight oil horizontal well cluster perforations in accordance with an embodiment of the present application. As shown in fig. 2, the apparatus includes:
the classification unit 10 is used for classifying the oil layers of the compact oil horizontal well in the preset area to obtain oil layers of different types;
a building unit 20 for building a factor database affecting the oil reservoir yield;
the acquiring unit 30 is used for acquiring the actual measurement yield of each section of the single well;
a first determining unit 40, configured to determine, according to the factor database and the measured production of each section of the single well, main factors that affect the production of the different types of oil reservoirs at different periods;
a first construction unit 50 for constructing a sweet spot model according to the above-mentioned major factors;
a second construction unit 60 for constructing an intelligent clustering model at least according to the dessert production model;
and a second determining unit 70, configured to determine a final clustered perforation location based on the intelligent clustering model.
In the scheme, a classification unit classifies oil layers of a compact oil horizontal well in a preset area to obtain different types of oil layers, namely classification of the oil layers is realized, a building unit builds a factor database influencing the yield of the oil layers, an acquisition unit obtains the actual measurement yield of each section of a single well, a first determining unit determines main factors influencing the yields of the oil layers of different types in different periods according to the factor database and the actual measurement yields of each section of the single well, namely personalized analysis of the oil layers is realized, and a first building unit builds a productivity dessert model according to the main factors; the second construction unit constructs an intelligent clustering model at least according to the dessert model; the second determination unit determines a final clustered perforation position based on the intelligent clustering model. According to the scheme, the intelligent clustering model established in a time-phased and classified manner is adopted, so that the individual analysis of the oil layer of the compact oil horizontal well is realized, and the accuracy of the determined final clustering perforation position is higher.
In one embodiment of the application, the classification unit comprises a first obtaining module and a classification module, wherein the first obtaining module is used for obtaining geological parameters and engineering parameters; and the classification module is used for classifying the oil layers of the compact oil horizontal well in the preset area according to the geological parameters and the engineering parameters to obtain the oil layers of different types. Specifically, geological parameters and engineering related parameters are used as input parameters by using a K-means clustering method, and different types of oil layers are clustered and divided. So as to realize the classification of the oil layer.
In an embodiment of the present application, the first construction unit includes a first determination module and a first construction module, the first determination module is configured to determine a weight of an influence of each of the main factors on the measured production of each section of the single well; the first construction module is used for constructing the productivity dessert model according to the influence weight.
In an embodiment of the present application, the first determining module includes an analyzing submodule and a determining submodule, and the analyzing submodule is configured to perform correlation analysis on each of the main factors and each of the actually measured yields of the single well, so as to obtain an analysis result; the determining submodule is used for determining the influence weight according to the analysis result.
In an embodiment of the application, the first determining unit includes an analyzing module and a third determining module, and the analyzing module is configured to perform correlation analysis on each factor in the factor database and the measured yield of each section of the single well to obtain an analysis result; and the third determining module is used for determining the main factors according to the analysis result. Specifically, a factor having a significant correlation larger than a predetermined value is selected as a main factor. In order to determine the final clustering perforation position more accurately, factors with the correlation smaller than 0.5 in the main factors are removed. The difference of factors influencing the yield of different regions, different periods and different oil layers is considered, so that the requirements of meeting the actual and on-site purposes are met.
In one embodiment of the present application, the second construction unit comprises a second determination module and a second construction module, the second determination module is configured to determine a primary perforation location according to the productive dessert model, the sedimentation condition, the cementing condition, and the initiation condition; and the second construction module is used for constructing the intelligent clustering model at least according to the primary perforation position, the sedimentary facies, the well cementation quality and the coupling position. According to the 'productive dessert model' and lithology, sedimentary facies, well cementation quality, coupling position, fracture initiation condition and the like as consideration factors, perforation position selection is carried out, and the perforation point is selected from the places with high productive sweetness, appropriate lithology and sedimentary facies, good well cementation quality, avoidance of coupling of 2-3m and appropriate mechanical property. Because various influence factors are analyzed according to the actual yield, a plurality of main influence factors are reconstructed, and a single productivity sweet spot curve is established, the analysis amount of related factors is greatly reduced.
In one embodiment of the present application, the second building module is further configured to set the primary perforation location as an output; setting the sedimentary facies, the cementing quality, and the collar location as input samples; and training the output and the input samples by using a decision tree algorithm to obtain the intelligent clustering model. Lithology, rock mechanical homogeneity, etc. may also be included in the input sample. After the initial perforation position is selected, manual adjustment needs to be carried out according to the positions of the couplings, the number of the couplings is more than 50 because a single well is often divided into 70 perforation positions, the perforation scheme is often changed once, the work needs to be repeated for many times, a large amount of repetitive work exists, the division schemes are often different due to different subjective judgments of individuals, after an intelligent model is built, the model automatically learns the geological expert clustering experience, a large amount of manual time is saved, and the efficient and accurate clustering scheme design is realized.
In an embodiment of the present application, the obtaining unit is further configured to obtain the measured production of each section of the single well by using a tracer monitoring technique.
The device for determining the cluster perforation position of the tight oil horizontal well comprises a processor and a memory, wherein the classifying unit, the establishing unit, the obtaining unit, the first determining unit, the first constructing unit, the second determining unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. One or more than one inner core can be arranged, and the precise determination of the clustering perforation position of the compact oil-water horizontal well is realized by adjusting the parameters of the inner cores.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the invention provides a computer readable storage medium, which comprises a stored program, wherein when the program runs, an apparatus where the computer readable storage medium is located is controlled to execute the method for determining the clustering perforation position of the tight oil horizontal well.
The embodiment of the invention provides a processor for running a program, wherein the program runs to execute the method for determining the clustering perforation position of the tight oil horizontal well.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein when the processor executes the program, at least the following steps are realized:
s101, classifying oil layers of a compact oil horizontal well in a preset area to obtain oil layers of different types;
step S102, establishing a factor database influencing oil layer yield;
s103, acquiring the actual measurement yield of each section of the single well;
step S104, determining main factors influencing the yields of the oil layers of different types in different periods according to the factor database and the measured yields of each section of the single well;
step S105, constructing a sweet spot model according to the main factors;
s106, constructing an intelligent clustering model at least according to the dessert model;
and S107, determining the final clustering perforation position based on the intelligent clustering model.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program initialized with at least the following method steps when executed on a data processing device:
s101, classifying oil layers of a compact oil horizontal well in a preset area to obtain oil layers of different types;
step S102, establishing a factor database influencing oil layer yield;
s103, acquiring the actual measurement yield of each section of the single well;
step S104, determining main factors influencing the yields of the oil layers of different types in different periods according to the factor database and the measured yields of each section of the single well;
step S105, constructing an productivity dessert model according to the main factors;
s106, constructing an intelligent clustering model at least according to the dessert model;
and S107, determining the final clustering perforation position based on the intelligent clustering model.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
Examples
The embodiment relates to a specific method for determining a clustering perforation position of a compact oil-water horizontal well, which specifically comprises the following steps:
an intelligent clustering perforation method for a compact oil horizontal well based on tracer monitoring, which takes an actual production work area as an example, comprises the following steps:
step 1: selecting logging data of porosity, permeability, oil saturation, reservoir physical property parameter CZ, reservoir physical property parameter CW, oil porosity KH, Poisson ratio, Young modulus and brittleness at different depths in a single well target section, carrying out K-means cluster analysis on the data, and dividing n types of oil layers according to requirements.
And 2, step: a database of factors affecting production is built, where more than 40 parameters (as shown in table 1) of geological factors, reservoir mechanics factors, and completion parameters are collected based on existing data.
TABLE 1 database of yield-affecting factors
And step 3: and (3) according to the oil layer division type in the step (1), carrying out correlation analysis on the actual measurement yield result of the tracer agent and the influence factors in the database in the step (2) in different periods, screening the factors with obvious correlation (the correlation is more than 0.5) as main influence factors, analyzing the correlation among the main influence factors, rejecting the factors with the correlation more than 0.9, and realizing the dimension reduction treatment.
And 4, step 4: setting the finally determined main factors influencing the yield as an input target, taking the actual measurement yield of the tracer as an output result, carrying out grey system analysis, determining the weight of each influencing factor, summarizing the characteristics (shown in table 2) of different types of oil layers expressed in different production periods, establishing a mathematical model, and constructing a productivity dessert curve, wherein the value of the curve is high or low and indicates the potential yield after perforation at the point because the actual yield is taken as a prediction target, and the specific model is as follows:
TABLE 2 production characteristics of different oil layers in different periods
(1) Evaluation model before fracturing ═ 0.0455 × permeability +0.0455 × KH +0.0438 × cohesion +0.0416 × young's modulus +0.041 × porosity +0.0408 × ramet +0.0405 × bulk modulus +0.0402 × sand production index +0.0394 × CNL (first-type reservoir)) + (0.089 × RT +0.0898 × RI +0.0898 × RXO +0.0887 × KH +0.0872 × brittleness (second-type reservoir));
(2) production-period evaluation model ═ 0.5115 × reservoir thickness +0.4885 × fracturing fluid volume (first-type reservoir)) + (0.106 × fracturing sand volume +0.1014 × CZ +0.1005 × porosity +0.1003 × fracture coefficient +0.1002 × poisson's ratio +0.1002 × aspect ratio +0.1001 × DEN +0.0999 × fracturing sand fluid ratio +0.099 × oil saturation +0.0984 × KH (second-type reservoir)).
And 5: the geology specialist takes the lithology, sedimentary facies, well cementation quality, coupling position and mechanical uniformity as consideration factors according to the productive dessert curve and carries out perforation position optimization;
step 6: and (3) taking the optimal perforation position of the geological expert as a result, taking the consideration factor as an input label, performing decision tree learning to obtain 683 samples for training the current example of the decision tree model, and keeping 293 samples as later-stage test data, wherein the comprehensive accuracy is as high as 94% (a decision tree model result graph is shown in figure 3), so that the intelligent model can basically meet the requirement of later-stage clustering.
And 7: after the intelligent model is established, comparison and verification are carried out on wells with similar geological conditions, under the same transformation length, the method is an intelligent clustering model, so that the dividing efficiency of a single well is improved by more than 300% compared with that of an artificial well, the production effect performance is better in the later period, and as shown in fig. 4, a comparison graph of the production effect of the intelligent clustering well is shown.
From the above description, it can be seen that the above-described embodiments of the present application achieve the following technical effects:
1) the method for determining the cluster perforation position of the compact oil horizontal well obtains oil layers of different types by classifying oil layers of the compact oil horizontal well in a preset area, namely classification of the oil layers is realized, a factor database influencing the yield of the oil layers is established, the actually measured yield of each section of a single well is obtained, main factors influencing the yields of the oil layers of different types in different periods are determined according to the factor database and the actually measured yield of each section of the single well, namely personalized analysis of the oil layers is realized, and a productivity dessert model is further established according to the main factors; then constructing an intelligent clustering model at least according to the sweet spot model; and finally, determining the final clustering perforation position based on the intelligent clustering model. According to the scheme, the intelligent clustering model established in a time-phased and classified manner is adopted, so that the individual analysis of the oil layer of the compact oil horizontal well is realized, and the accuracy of the determined final clustering perforation position is higher.
2) According to the device for determining the cluster perforation position of the compact oil horizontal well, a classification unit classifies oil layers of the compact oil horizontal well in a preset area to obtain oil layers of different types, namely classification of the oil layers is realized, a building unit builds a factor database influencing the yield of the oil layers, an acquisition unit obtains the actually measured yield of each section of a single well, a first determining unit determines main factors influencing the yield of the oil layers of different types in different periods according to the factor database and the actually measured yield of each section of the single well, namely personalized analysis of the oil layers is realized, and a first building unit builds a productivity dessert model according to the main factors; the second construction unit constructs an intelligent clustering model at least according to the dessert model; the second determination unit determines a final clustered perforation position based on the intelligent clustering model. According to the scheme, the intelligent clustering model established in a time-phased and classified manner is adopted, so that the individual analysis of the oil layer of the compact oil horizontal well is realized, and the accuracy of the determined final clustering perforation position is higher.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method for determining a cluster perforation position of a tight oil horizontal well is characterized by comprising the following steps:
classifying the oil layers of the compact oil horizontal well in the preset area to obtain oil layers of different types;
establishing a factor database influencing the oil layer yield;
acquiring the actual measurement yield of each section of the single well;
determining main factors influencing the production of the oil layers of different types at different periods according to the factor database and the measured production of each section of the single well;
constructing a sweet spot model according to the main factors;
constructing an intelligent clustering model at least according to the dessert productivity model;
and determining a final clustering perforation position based on the intelligent clustering model.
2. The method of claim 1, wherein classifying tight oil horizontal well reservoirs within a predetermined area to obtain different types of reservoirs comprises:
acquiring geological parameters and engineering parameters;
and classifying the oil layers of the compact oil horizontal well in the preset area according to the geological parameters and the engineering parameters to obtain the oil layers of different types.
3. The method of claim 1, wherein constructing the sweet spot model based on the principal factors comprises:
determining the influence weight of each main factor on the measured yield of each section of the single well;
constructing the dessert model based on the impact weights.
4. The method of claim 3, wherein determining from the factor database and the measured production for each section of the single well, the primary factors affecting production for different types of the pay zones at different times comprises:
performing correlation analysis on each factor in the factor database and the actual measurement yield of each section of the single well to obtain an analysis result;
and determining the main factors according to the analysis result.
5. The method of claim 1, wherein constructing an intelligent clustering model based at least on the dessert model comprises:
determining a primary perforation position according to the productive dessert model, the sedimentation condition, the well cementation condition and the crack initiation condition;
and constructing the intelligent clustering model at least according to the primary perforation position, the sedimentary facies, the well cementation quality and the coupling position.
6. The method of claim 5, wherein constructing the intelligent clustering model based on at least the primary perforation location, sedimentary facies, cementing quality, and collar location comprises:
setting the primary perforation location as an output;
setting the sedimentary facies, the cementing quality, and the collar location as input samples;
and training the output sample and the input sample by using a decision tree algorithm to obtain the intelligent clustering model.
7. The method of claim 1, wherein obtaining measured production from each section of a single well comprises:
and acquiring the actually measured yield of each section of the single well by adopting a tracer monitoring technology.
8. An apparatus for determining a location of a tight oil horizontal well cluster perforation, comprising:
the classification unit is used for classifying the oil layers of the compact oil horizontal well in the preset area to obtain oil layers of different types;
the device comprises an establishing unit, a judging unit and a judging unit, wherein the establishing unit is used for establishing a factor database influencing the yield of an oil layer;
the acquisition unit is used for acquiring the actual measurement yield of each section of the single well;
the first determining unit is used for determining main factors influencing the yields of the oil layers of different types in different periods according to the factor database and the measured yields of each section of the single well;
a first construction unit for constructing an energy producing dessert model based on the principal factors;
a second construction unit for constructing an intelligent clustering model at least according to the dessert model;
and the second determining unit is used for determining the final clustering perforation position based on the intelligent clustering model.
9. A computer-readable storage medium comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for determining locations of tight horizontal well clustered perforations of any one of claims 1 to 7.
10. A processor for executing a program, wherein the program is executed to perform the method for determining a clustered perforation location for tight oil horizontal wells according to any one of claims 1 to 7.
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