CN113032724B - Tracer yield curve noise reduction processing method, storage medium and electronic equipment - Google Patents
Tracer yield curve noise reduction processing method, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN113032724B CN113032724B CN201911247041.XA CN201911247041A CN113032724B CN 113032724 B CN113032724 B CN 113032724B CN 201911247041 A CN201911247041 A CN 201911247041A CN 113032724 B CN113032724 B CN 113032724B
- Authority
- CN
- China
- Prior art keywords
- tracer
- concentration
- maximum value
- value
- maximum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a noise reduction processing method for a tracer yield curve, a storage medium and electronic equipment, wherein the method comprises the following steps of S1, filtering the tracer yield curve; s2, primarily selecting a maximum value of the tracer yield concentration; s3, determining the maximum value of the output concentration of the tracer; s4, fusing the concentration maximum value of the original tracer and the filtered tracer output curve; and (3) fusing the original tracer concentration maximum value of the final maximum value obtained in the step (S3) in a corresponding time period with the noise-reduced smooth tracer yield curve obtained in the step (S1) to obtain a final smooth tracer yield curve without eliminating key features. The method has the advantages that the important characteristic of the maximum value of the output concentration of the original tracer is fused with noise reduction, the key characteristic of the maximum value of the output concentration of the original tracer can be reserved while the noise reduction treatment of the output curve of the tracer is carried out, and a good data foundation is laid for the interpretation of the output curve of the tracer.
Description
Technical Field
The application belongs to the technical field of oil and gas field development, and particularly relates to a tracer yield curve noise reduction processing method capable of retaining key characteristics, a storage medium and electronic equipment.
Background
In the development process of oil and gas fields, stratum energy is often needed to be supplemented through water injection and gas injection so as to obtain a better development effect on a reservoir. Because the reservoir is provided with the dominant liquid flow channels such as natural cracks, large holes and the like, the problems of water channeling, low water injection and gas injection efficiency and the like are often caused in the water injection and gas injection process. The quantitative standard and accurate recognition of connectivity between the water injection well and the production well are important preconditions and bases for making water injection and gas injection schemes and comprehensive treatment. The interwell tracer monitoring technology is an important means for quantitatively analyzing the communication characteristics among wells, and is widely applied to the development of oil and gas fields at present.
The inter-well tracer monitoring technology is used for collecting reservoir production samples in a production well by adding tracer into an injection medium, detecting the concentration of the injected tracer, and obtaining the inter-well connectivity characteristics by analyzing the change curves of the tracer production concentration at different stages. However, when the output concentration of the tracer is measured, the phenomenon of 'burrs' exists in the output curve of the tracer, namely, certain noise exists in the output curve of the tracer due to the fact that the environment of sample collection and storage is difficult to keep consistent, artificial errors in a sample detection experiment are difficult to avoid, measurement errors existing in experimental equipment are difficult to eliminate and other realistic conditions exist. In addition, the analysis process of the tracer output curve is a typical nonlinear optimization problem, and the tracer output curve with more noise can greatly reduce the fitting efficiency and the fitting precision. Thus, prior to analysis of the tracer yield curve, it is desirable to perform a noise reduction treatment to obtain a more "smooth" tracer yield curve without eliminating key features. The conventional data noise reduction method is specially designed in the fields of signal processing and the like, and the key information of the original tracer concentration maximum value is difficult to be kept when the data noise reduction method is applied to the processing of the tracer yield curve. Therefore, effective noise reduction treatment on the tracer yield curve cannot be realized, and the mine field application efficiency and effect of the tracer monitoring technology are limited.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the application provides a tracer yield curve noise reduction processing method, a storage medium and electronic equipment, which are characterized in that the original tracer yield concentration maximum value is reserved while the tracer yield curve noise reduction processing is carried out, and a good data foundation is laid for the interpretation of the tracer yield curve.
In order to achieve the above object, an embodiment of the present application provides a method for noise reduction treatment of a tracer yield curve, the method comprising the steps of,
step S1, filtering treatment of a tracer yield curve is carried out:
filtering the original tracer yield curve to obtain a smooth curve;
step S2, initially selecting a maximum value of the tracer yield concentration:
obtaining all tracer concentration maximum values in the whole tracer detection period according to the tracer output data subjected to filtering treatment in the step S1, screening effective maximum values according to the magnitude of the change amplitude of the tracer concentration, and further screening the effective maximum values according to the absolute value of the tracer concentration to obtain the tracer output concentration maximum value obtained as a primary choice;
step S3, determining the maximum value of the output concentration of the tracer:
obtaining and storing a final maximum value input in a man-machine interaction mode, wherein the final maximum value is obtained by comparing and analyzing a tracer output concentration maximum value obtained by primary selection with an original tracer concentration curve;
step S4, fusing the maximum concentration value of the original tracer and the filtered tracer yield curve:
and (3) fusing the original tracer concentration maximum value of the final maximum value obtained in the step (S3) in a corresponding time period with the noise-reduced smooth tracer yield curve obtained in the step (S1) to obtain a final smooth tracer yield curve without eliminating key features.
Optionally, the method of filtering the original tracer yield curve in step S1 is a local weighted non-parametric regression method of LOESS.
Optionally, in the step S2, the method for obtaining the maximum concentration value of all tracers in the whole tracer detection period according to the tracer output data subjected to the filtering treatment in the step S1 includes:
regarding all the tracer output concentration values in the whole tracer detection period after the treatment of the step S1, if the concentration value at a certain time point is larger than the concentration value at the previous time point, and the concentration value at the time point is larger than the concentration value at the later time point, the tracer output concentration value is considered as the maximum tracer concentration value; if the concentration value at a certain point in time is smaller than the concentration value at the previous point in time and the concentration value at the time point is smaller than the concentration value at the subsequent point in time, the concentration value at the time point is regarded as the tracer concentration minimum value.
Optionally, in the step S2, the method for screening the effective maximum value by the magnitude of the variation amplitude of the concentration of the tracer is as follows:
comparing the maximum value obtained by preliminary screening with the minimum value of the tracer adjacent to the maximum value before and after the maximum value, and when the difference between the maximum value and the minimum value adjacent to the maximum value before the maximum value is less than 20 percent or the difference between the maximum value and the minimum value adjacent to the minimum value after the maximum value is less than 20 percent, determining the maximum value as an invalid maximum value, determining the minimum value with smaller difference with the maximum value in the minimum values adjacent to the maximum value before and after the maximum value as an invalid minimum value, and deleting the invalid maximum value and the invalid minimum value;
the calculation method of the difference between the maximum value and the minimum value comprises the following steps:
dlt= (vmax-vmax)/vmax.
Optionally, in the step S2, the method for further screening the effective maximum value by using the absolute value of the concentration of the tracer is as follows:
and if the maximum value of the original tracer concentration value in the period from 5 days before the time point to 5 days after the time point is less than 0.1 x the maximum value of the original tracer concentration, the maximum value is determined to be an invalid maximum value, and the invalid maximum value is deleted.
Optionally, in the step S3, the final maximum value is obtained by comparing the tracer yield concentration maximum value obtained by the preliminary selection with the original tracer concentration curve, and includes:
the maximum value of the tracer yield concentration which does not obviously accord with the maximum value definition is deleted, namely, the smaller maximum value is deleted from the two maximum values with too short interval date.
Optionally, in the step S4, the method for fusing all the original tracer concentration maxima in the time period corresponding to the final maxima obtained in the step S3 with the smoothed tracer yield curve after noise reduction obtained in the step S1 includes:
and (3) according to the time point corresponding to the final maximum value obtained in the step (S3), obtaining the maximum concentration value of the original tracer concentration value within 5 days before the time point and 5 days after the time point, taking the maximum concentration value as the original tracer concentration maximum value, replacing the corresponding tracer output concentration maximum value in the smooth curve obtained in the step (S1) with the original tracer concentration maximum value, and storing.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a processor and a memory, wherein the memory stores computer executable instructions which are executed by the processor to realize the tracer yield curve noise reduction processing method.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program realizes the tracer yield curve noise reduction processing method when being executed.
According to the method for processing the tracer output curve in the early stage, the tracer output curve is subjected to noise reduction treatment, curve noise is eliminated, the subsequent inter-well connectivity interpretation of the tracer output curve is facilitated, in addition, the important characteristic of the maximum value of the original tracer output concentration is fused with noise reduction, the key characteristic of the maximum value of the original tracer output concentration can be reserved while the tracer output curve is subjected to noise reduction treatment, and a good data basis is laid for the interpretation of the tracer output curve.
Other aspects will become apparent upon reading and understanding the accompanying drawings and detailed description.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application. In the drawings:
fig. 1 is a flowchart of a tracer yield curve noise reduction processing method according to an embodiment of the application.
Fig. 2 is a graph of raw tracer yield.
FIG. 3 is a smoothed graph of the original tracer yield curve processed using the LOESS locally weighted nonparametric regression method.
FIG. 4 is a graph of a fusion of an original tracer concentration maxima and a filtered tracer yield curve according to an embodiment of the application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, the tracer yield curve noise reduction processing method in the embodiment of the application is suitable for interwell tracing monitoring, and comprises the following steps:
step S1, filtering treatment of a tracer yield curve is carried out:
processing the original tracer yield curve by using a LOESS local weighted nonparametric regression method to obtain a smooth curve;
step S2, initially selecting a maximum value of the tracer yield concentration:
analyzing the tracer yield data processed in the step S1, firstly obtaining all tracer concentration maximum values in the whole tracer detection period, then screening effective maximum values according to the magnitude of the change amplitude of the tracer concentration, and finally further screening the effective maximum values according to the absolute value of the tracer concentration;
step S3, determining the maximum value of the output concentration of the tracer:
further analyzing the maximum value of the tracer output concentration and the original tracer concentration curve which are initially selected in the step S2 by a tracer interpreter in a man-machine interaction mode, determining a final maximum value, and storing the final maximum value;
step S4, fusing the maximum concentration value of the original tracer and the filtered tracer yield curve:
and (3) fusing the original tracer concentration maximum value of the time period corresponding to the maximum value obtained in the step (S3) with the tracer output curve which is obtained in the step (S1) and is smooth after noise reduction, so as to obtain a final 'smooth' tracer output curve without eliminating key characteristics, and storing the final 'smooth' tracer output curve.
In the step S2, the method for obtaining the maximum value of the concentration of all the tracers in the whole tracer detection period is as follows:
regarding all the tracer output concentration values in the whole tracer detection period after the treatment of the step S1, if the concentration value at a certain time point is larger than the concentration value at the previous time point, and the concentration value at the time point is larger than the concentration value at the later time point, the tracer output concentration value is considered as the maximum tracer concentration value; if the concentration value at a certain time point is smaller than the concentration value at the previous time point and the concentration value at the time point is smaller than the concentration value at the later time point, the concentration value at the time point is considered as the trace agent concentration minimum value;
in the step S2, the method for screening the effective maximum value by the magnitude of the change amplitude of the concentration of the tracer is as follows:
comparing the maximum value obtained by preliminary screening with the minimum value of the tracer adjacent to the maximum value before and after the maximum value, if the difference between the maximum value and the minimum value adjacent to the maximum value before the maximum value is less than 20 percent or the difference between the maximum value and the minimum value adjacent to the minimum value after the maximum value is less than 20 percent, determining the maximum value as an invalid maximum value, determining the minimum value with smaller difference with the maximum value in the minimum values adjacent to the maximum value before and after the maximum value as an invalid minimum value, and deleting the invalid maximum value and the invalid minimum value;
the calculation method of the difference between the maximum value and the minimum value comprises the following steps:
dlt= (vmax-vmax)/vmax
In the step S2, the method for further screening the effective maximum value by the absolute value of the tracer concentration is as follows:
and if the maximum value of the original tracer concentration value in the period from 5 days before the time point to 5 days after the time point is less than 0.1 x the maximum value of the original tracer concentration, the maximum value is determined to be an invalid maximum value, and the invalid maximum value is deleted.
In the step S4, the method for fusing the original tracer concentration maximum value of the time period corresponding to the maximum value obtained in the step S3 with the smoothed tracer yield curve after noise reduction obtained in the step S1 includes:
and (3) according to the time point corresponding to the maximum value obtained in the step (S3), obtaining the maximum concentration value of the original tracer concentration value within 5 days before the time point and 5 days after the time point, taking the maximum concentration value as the original tracer concentration maximum value, replacing the corresponding tracer output concentration maximum value in the smooth curve obtained in the step (S1) with the original tracer concentration maximum value, and storing.
Examples
The tracer yield curve noise reduction processing method retaining key characteristics of the embodiment comprises the following steps:
s1, filtering treatment of a tracer yield curve:
processing the original tracer yield curve graph 2 by using a LOESS local weighted nonparametric regression method to obtain a smooth curve graph 3; the ordinate in fig. 2 represents the tracer concentration value. The ordinate in fig. 3 represents the tracer concentration values after the LOESS treatment.
S2, outputting a maximum value of concentration of the primary selection tracer:
and analyzing the tracer yield data after the treatment of S1.
Firstly, obtaining the maximum values of the concentration of all tracers in the whole tracer detection period as points (16,0.001645), (31,0.002760), (46,0.002422), (58,0.001099), (68,0.000892), (84,0.000530), (91,0.000403), (99,0.0012855), (110,0.000679), (121,0.0006281), (141,0.000647), (150,0.000262), (159,0.000190) and (182,0.000142), wherein the first row of brackets is the date, and the second row of brackets is the concentration value of the tracer;
obtaining the minimum value of the concentration of all the tracers in the whole tracer detection period as a point;
(3，0.00032)、(21，0.000911)、(40，0.001802)、(57，0.001093)、(65，0.000819)、(81，0.000526)、(89，0.000398)、(93，0.000353)、(106，0.000647)、(116，0.000560)、(136，0.000323)、(147，0.000224)、(156，0.000172)、(183，0.000123)、(195，0.000115)。
the difference between the obtained tracer concentration maximum value and the adjacent minimum value is as follows:
TABLE 1 statistical table of maximum results useful in screening magnitude of concentration variation of tracers
After deleting the ineffective maximum value and the ineffective minimum value in table 1, obtaining a maximum value which is screened according to the magnitude of the variation amplitude of the concentration of the tracer, obtaining a maximum concentration value of the original concentration value of the tracer within 5 days before a time point and 5 days after the time point corresponding to the maximum value, counting to obtain a maximum value of the concentration of the tracer in the whole tracer collection period as 0.002872, and according to the principle that the maximum value of the concentration of the original tracer corresponding to the screened maximum value is less than 0.1 x the maximum value of the concentration of the original tracer, identifying the maximum value as the ineffective maximum value, further obtaining the maximum value of the concentration of the tracer as shown in table 2:
TABLE 2 raw concentration maxima statistics for tracer concentration maxima
As can be seen from table 2, the original tracer concentration maximum was greater than the tracer concentration maximum by 60.58% as exemplified by the 141 sample with the date number.
S3, determining the maximum value of the output concentration of the tracer:
further analysis of the tracer yield concentration maxima initially selected in S2 (as shown in table 2) and the original tracer concentration curve (as shown in fig. 2) by a tracer interpreter determines the maxima with date numbers 16, 31, 46, 99, 141 as final maxima as shown in table 3:
TABLE 3 final tracer concentration maxima and corresponding raw concentration maxima
S4, fusing the concentration maximum value of the original tracer and the filtered tracer yield curve:
according to table 3, the original tracer concentration maximum within 5 days before and after the tracer concentration maximum was substituted for the tracer concentration maximum, and a curve was drawn as shown in fig. 4. The ordinate in fig. 4 represents the tracer concentration value.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a processor and a memory, wherein the memory stores computer executable instructions which are executed by the processor to realize the tracer yield curve noise reduction processing method.
In addition, the embodiment of the application also provides a computer readable storage medium which stores computer executable instructions, wherein the computer executable instructions realize the tracer yield curve noise reduction processing method when being executed.
It should be noted that, in this document, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (9)
1. A method for noise reduction treatment of a tracer yield curve is characterized by comprising the following steps,
step S1, filtering treatment of a tracer yield curve is carried out:
filtering the original tracer yield curve to obtain a smooth curve;
step S2, initially selecting a maximum value of the tracer yield concentration:
obtaining all tracer concentration maximum values in the whole tracer detection period according to the tracer output data subjected to filtering treatment in the step S1, screening effective maximum values according to the magnitude of the change amplitude of the tracer concentration, and further screening the effective maximum values according to the absolute value of the tracer concentration to obtain the tracer output concentration maximum value obtained as a primary choice;
step S3, determining the maximum value of the output concentration of the tracer:
obtaining and storing a final maximum value input in a man-machine interaction mode, wherein the final maximum value is obtained by comparing and analyzing a tracer output concentration maximum value obtained by primary selection with an original tracer concentration curve;
step S4, fusing the maximum concentration value of the original tracer and the filtered tracer yield curve:
and (3) fusing the original tracer concentration maximum value of the final maximum value obtained in the step (S3) in a corresponding time period with the noise-reduced smooth tracer yield curve obtained in the step (S1) to obtain a final smooth tracer yield curve without eliminating key features.
2. The method of claim 1, wherein the filtering of the raw tracer yield curve in step S1 is a local weighted non-parametric regression of the LOESS.
3. The method according to claim 1, wherein in the step S2, the method for obtaining the maximum value of the concentration of all tracers in the whole tracer detection period according to the tracer yield data subjected to the filtering treatment in the step S1 is as follows:
regarding all the tracer output concentration values in the whole tracer detection period after the treatment of the step S1, if the concentration value at a certain time point is larger than the concentration value at the previous time point, and the concentration value at the time point is larger than the concentration value at the later time point, the tracer output concentration value is considered as the maximum tracer concentration value; if the concentration value at a certain point in time is smaller than the concentration value at the previous point in time and the concentration value at the time point is smaller than the concentration value at the subsequent point in time, the concentration value at the time point is regarded as the tracer concentration minimum value.
4. The method according to claim 1, wherein in the step S2, the effective maximum value is screened by the magnitude of the variation of the concentration of the tracer according to the following method:
comparing the maximum value obtained by preliminary screening with the minimum value of the tracer adjacent to the maximum value before and after the maximum value, and when the difference between the maximum value and the minimum value adjacent to the maximum value before the maximum value is less than 20 percent or the difference between the maximum value and the minimum value adjacent to the minimum value after the maximum value is less than 20 percent, determining the maximum value as an invalid maximum value, determining the minimum value with smaller difference with the maximum value in the minimum values adjacent to the maximum value before and after the maximum value as an invalid minimum value, and deleting the invalid maximum value and the invalid minimum value;
the calculation method of the difference between the maximum value and the minimum value comprises the following steps:
dlt= (vmax-vmax)/vmax.
5. The method according to claim 1, wherein in the step S2, the effective maximum value is further screened by the absolute value of the tracer concentration by:
and if the maximum value of the original tracer concentration value in the period from 5 days before the time point to 5 days after the time point is less than 0.1 x the maximum value of the original tracer concentration, the maximum value is determined to be an invalid maximum value, and the invalid maximum value is deleted.
6. The method according to claim 1, wherein in step S3, the final maximum is obtained by comparing the tracer yield concentration maximum obtained by the preliminary selection with the original tracer concentration curve, comprising:
the maximum value of the tracer yield concentration which does not obviously accord with the maximum value definition is deleted, namely, the smaller maximum value is deleted from the two maximum values with too short interval date.
7. The method according to claim 1, wherein in the step S4, the method for fusing all the original tracer concentration maxima in the time period corresponding to the final maxima obtained in the step S3 with the smoothed tracer yield curve after noise reduction obtained in the step S1 is as follows:
and (3) according to the time point corresponding to the final maximum value obtained in the step (S3), obtaining the maximum concentration value of the original tracer concentration value within 5 days before the time point and 5 days after the time point, taking the maximum concentration value as the original tracer concentration maximum value, replacing the corresponding tracer output concentration maximum value in the smooth curve obtained in the step (S1) with the original tracer concentration maximum value, and storing.
8. An electronic device, comprising: a processor and a memory, wherein the memory stores computer executable instructions that when executed by the processor implement the tracer yield curve noise reduction method of any one of claims 1 to 7.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed, implements the tracer yield curve noise reduction processing method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911247041.XA CN113032724B (en) | 2019-12-09 | 2019-12-09 | Tracer yield curve noise reduction processing method, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911247041.XA CN113032724B (en) | 2019-12-09 | 2019-12-09 | Tracer yield curve noise reduction processing method, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113032724A CN113032724A (en) | 2021-06-25 |
| CN113032724B true CN113032724B (en) | 2023-10-27 |
Family
ID=76450804
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911247041.XA Active CN113032724B (en) | 2019-12-09 | 2019-12-09 | Tracer yield curve noise reduction processing method, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113032724B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108992054A (en) * | 2018-06-27 | 2018-12-14 | 深圳还是威健康科技有限公司 | A kind of pulse signal peak point detection method and device |
| CN110541704A (en) * | 2019-09-10 | 2019-12-06 | 大庆亿莱检验检测技术服务有限公司 | method for evaluating staged water yield of compact oil multi-stage fracturing well by using tracer |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MX346226B (en) * | 2012-03-30 | 2017-03-07 | Inst Mexicano Del Petróleo | Integral analysis method of inter-well tracer tests. |
-
2019
- 2019-12-09 CN CN201911247041.XA patent/CN113032724B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108992054A (en) * | 2018-06-27 | 2018-12-14 | 深圳还是威健康科技有限公司 | A kind of pulse signal peak point detection method and device |
| CN110541704A (en) * | 2019-09-10 | 2019-12-06 | 大庆亿莱检验检测技术服务有限公司 | method for evaluating staged water yield of compact oil multi-stage fracturing well by using tracer |
Non-Patent Citations (1)
| Title |
|---|
| 基于流管模型的裂缝性低渗透油藏井间示踪剂解释模型;陈冠中;林春阳;姜瑞忠;张伟;***;李广;;测井技术(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113032724A (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109389128B (en) | Automatic extraction method and device for electric imaging logging image characteristics | |
| CN111553303A (en) | Remote sensing ortho image dense building extraction method based on convolutional neural network | |
| CN112884747A (en) | Automatic bridge crack detection system integrating cyclic residual convolution and context extractor network | |
| CN107861162B (en) | Microelectrode logging data-based natural crack identification method and system | |
| CN116644284A (en) | Stratum classification characteristic factor determining method, system, electronic equipment and medium | |
| CN112084761A (en) | Hydraulic engineering information management method and device | |
| CN113032724B (en) | Tracer yield curve noise reduction processing method, storage medium and electronic equipment | |
| CN110111311B (en) | Image quality evaluation method and device | |
| CN116956754B (en) | Crack type leakage pressure calculation method combined with deep learning | |
| CN111626377B (en) | Lithology recognition method, device, equipment and storage medium | |
| CN112761631B (en) | Density determination method, sampling method and pollution rate determination method for pure formation water | |
| CN106055641A (en) | Human-computer interaction method and device oriented to intelligent robot | |
| CN110486009B (en) | Automatic parameter reverse solving method and system for infinite stratum | |
| CN110486008B (en) | Parameter interpretation method and system for radial composite oil reservoir | |
| CN112884348A (en) | Method for diagnosing production deviation source of aerospace initiator based on dynamic Bayesian network | |
| CN115081485B (en) | AI-based magnetic flux leakage internal detection data automatic analysis method | |
| Holub et al. | Evaluation of a pumping test with skin effect and wellbore storage on a confined aquifer in the Bela Crkva, Serbia | |
| CN115670397B (en) | PPG artifact identification method and device, storage medium and electronic equipment | |
| CN116362782A (en) | User interest point identification method and system based on big data analysis | |
| CN109376788A (en) | A kind of image analysis method based on the high discrimination of deep learning | |
| CN112377175B (en) | Method and system for optimizing drilling mud and rapidly identifying low-resistance oil-gas layer | |
| CN112412390B (en) | Method and device for evaluating second interface of well cementation based on deep learning model | |
| CN114596262A (en) | Dam monitoring and analyzing method and system based on image recognition technology | |
| CN108153817B (en) | Intelligent web page data acquisition method | |
| Liyanapathirana | Numerical simulation of deep penetration of a piezocone in a strain-softening clay |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |