CN116044385B - Isotope tracing flow logging water absorption profile interpretation method - Google Patents
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Abstract
The invention relates to a water absorption profile interpretation method of an isotope tracing flow log, which is mainly used for resolving tracing gamma spectra acquired by the isotope tracing flow log to obtain water flow velocity at all parts of a well so as to calculate the water absorption capacity of each water absorption layer. The tracing gamma spectrum contains one or more tracing peaks, the depth and time corresponding to the peak positions of the tracing peaks are the depth and time of the detector passing through the tracer, and the water flow speed at the position can be calculated by using the peak positions of the two tracing peaks. The invention adopts Gaussian distribution to characterize the tracing peak, firstly uses ant colony optimization to obtain rough estimation of the peak position and peak height of the tracing peak, then uses the value as an initial solution, adopts simplex algorithm to carry out depth optimization on the tracing peak parameter, and obtains accurate characteristic parameter of the tracing peak. The flow space where the tracer is located can be judged by combining the correlation of the parameters of the tracing peaks at different moments and the tubular column structure, then the water flow direction can be judged according to the time-depth relation, and finally the interpretation of the water absorption profile is realized.
Description
Technical Field
The invention relates to a water absorption profile interpretation method of an isotope tracing flow well logging, which is mainly used for resolving tracing gamma spectra acquired by the isotope tracing flow well logging and accurately solving main characteristic parameters such as peak position, peak width and the like of a tracing peak. The flow space of the tracing peak is identified by combining the correlation of the characteristic parameters of the tracing peak in different time periods and the tubular column structure, then the water flow speed is calculated according to the corresponding depth and time of the two tracing peaks, meanwhile, the water flow direction is judged by utilizing the time-depth relation of the tracing peaks, and finally, the accurate interpretation of the water absorption profile of the water injection well is realized. The invention adopts the intelligent optimization method to solve the characteristic parameters of the tracing peak, is an accurate and efficient well logging data analysis method, realizes the accurate interpretation of the water absorption profile, and lays a foundation for the formulation of the subsequent oilfield development scheme.
Background
The monitoring of the water absorption profile is of great significance to the development of the middle and later stages of the oil field. The water absorption capacity of each water absorption layer can be quantized, so that the water injection development effect can be effectively evaluated, and the interlayer contradiction can be revealed. There are a number of techniques available to achieve the measurement of water absorption profile including turbine flow logging, pulsed neutron oxygen activated logging, spectral noise logging, distributed Temperature Sensors (DTS), distributed acoustic wave sensors (DAS), etc. Isotope labeled flow logging is also one of them, and due to the characteristics of the radioisotope tracer, the logging technique has particular advantages in the measurement of water absorption profile over other logging techniques. The isotope labeled flow logging technology does not need the starting speed and is less influenced by the flow pattern, so the method is suitable for being applied to medium-low flow water injection wells or multiphase fluid injection wells. In addition, because the radioactive tracer can pass through the water distributor and enter the oil collar in the air, the logging technology can monitor the water flow in the oil pipe and the collar at the same time, and can also effectively measure the water absorption profile under the complex tubular column structure.
Two types of interpretation methods of isotope labeled flow logging data are presented at present, namely gamma intensity analysis and transit time analysis. The gamma intensity analysis has two schemes to calculate the water absorption of each layer, the first scheme measures the gamma ray intensity at the water absorption layer, and the water absorption of the perforation layer is considered to be in direct proportion to the gamma ray intensity at the perforation layer; another approach measures the decay rate of gamma ray intensity at the water-absorbing layer, considering that the water absorption of the perforated layer is proportional to the gamma ray decay rate. In order to accurately calculate the intensity of gamma rays at the water absorption layer, a gamma counting correction method and a Self geometric method are also sequentially provided. The transit time analysis is to measure the time taken by the tracer slug to pass a certain distance, and three embodiments can measure the water flow speed to calculate the water absorption of each layer. The first solution is a velocity method, in which the instrument measures the time of arrival of a tracer slug at a single detector or through the separation of the two detectors, i.e. the transit time, using a single detector or a double detector. The Ford method and the multiple velocity measurement method are two methods by which the scheme obtains the transit time. The second solution is the double pulse method, where the instrument releases two tracer slugs at the measurement site, and the water flow rate is related to the distance between these two slugs. The third scheme is a tracking method, wherein the instrument releases the tracer slug above the measuring point, then the instrument repeatedly descends and ascends to track the tracer, and the water flow speed can be calculated according to the measured depths and the measured time of the tracer slug at different moments.
The time-of-flight analysis has higher accuracy and depth resolution than the gamma intensity analysis. However, with the wide application of the separate injection and water injection schemes, explanation of the complex flow conditions downhole is required. This presents challenges for traditional interpretation methods. Complete monitoring of the flow of the tracer slugs in the measurement well section is required in the case of a stratified injection, including also the separation of the tracer slugs as they pass through the water distributor. The key of measuring the water flow velocity under the complex tubular column structure is extraction of flow information and transit time, and especially the water flow in oil pipes and annular spaces. The method is characterized in that water flow signals are separated, the flow space and the water flow direction of fluid are accurately identified, and the water flow speed of each place is calculated based on the transit time, so that the method is the basis for explaining the water absorption profile of the isotope tracing flow well logging.
Disclosure of Invention
The invention aims to provide an isotope tracing flow logging water absorption profile interpretation method which is mainly used for resolving tracing gamma spectra acquired by isotope tracing flow logging, extracting characteristic parameters of tracing peaks, realizing quantitative interpretation of water absorption profiles based on the extracted characteristic parameters and evaluating the water absorption effect of each water absorption layer.
The isotope tracing flow well logging mainly records the gamma counting rate in the measuring process, and the depth of the detector and the measuring time are recorded in the measuring process. When the detector passes through the tracer slug, a tracing peak is generated in the tracing gamma spectrum, and obviously, the tracer slug can be passed twice in the descending and lifting processes of the instrument, so that the generated two tracing peaks can reflect the transit time and the transit distance of the tracer slug, and the water flow speed can be calculated according to the two parameters. The trace peak collected by the instrument can be characterized by Gaussian distribution, namely:
Where h is the peak height of the gaussian, p is the peak position of the gaussian, and σ reflects the peak width of the gaussian.
In order to effectively identify and quantitatively calculate the water flow in the complex tubular column structure, the characteristic parameters of the tracing peak, especially the characteristic peak position which can represent the depth and the measuring time of the tracer slug, need to be accurately extracted. In order to achieve the technical aim, the water absorption profile interpretation method of the isotope labeled flow well logging is characterized by comprising the following steps of:
Step 1: and correcting the gamma background of the tracing gamma spectrum. Because the collected tracer gamma spectrum is inevitably affected by natural background, the background correction is needed to be carried out on the actually measured tracer gamma spectrum. The invention adopts linear function to estimate background baseline in tracing gamma spectrum, and N counts before and after are selected to calculate background function analytic style:
Wherein Y is the background count rate, X is the trace gamma spectrum address, Y1 is the average value of the previous N paths of gamma count rates, and X1 is the average value of the previous N paths of addresses; y2 is the average of the last N gamma count rates, and X2 is the average of the last N addresses.
And subtracting and fitting the original tracing gamma spectrum to obtain a background baseline, and obtaining a pure tracing peak for subsequent processing.
Step 2: an ant colony optimization algorithm is used to determine the range of peak positions in the tracer gamma spectrum. The determination of the characteristic peak position of the tracer gamma spectrum is actually a search for local maxima in the gamma spectrum. When the ant colony optimization algorithm is adopted to identify peak positions, firstly, the whole spectral line is divided into a plurality of subsections, if the depth or time represented by the left and right end point addresses of the gamma spectral line is a and b respectively, and the whole spectral line is divided into n subsections, the ith subsection can be expressed as:
Ii＝[a+(i-1)m,a+im] (3)
wherein m is the length of the subsection and may be expressed as (b-a)/n; the left and right end points of the ith sub-segment may then be denoted as a+ (i-1) m and a+im.
In the initial state, each sub-segment is regarded as placing an ant, the pheromones in each sub-segment are equal, the pheromone increment of each sub-segment is initialized to 0, and each ant represents the adaptability of the corresponding sub-segment. For trace peak identification, the average gamma count rate within each sub-segment can be considered as a criterion for evaluating sub-segment suitability, with obviously greater suitability closer to the peak position.
After initialization, each ant can move according to a certain rule according to the respective adaptability. In the optimization process, each ant is only allowed to move into its adjacent subsections, and Neighbor (I i) is represented as the other subsections to which ants in subsection I i can move:
The sub-segments at both ends of the trace gamma spectrum have only one direction of movement, while the other sub-segments have two directions of movement. The objective function required for the optimization may be determined based on the adaptability of the current sub-segment to the neighboring sub-segments. Ants in the current sub-section are allowed to move to the adjacent sub-section if the adaptation of the current sub-section is smaller than that of the adjacent sub-section, and not otherwise. The set of subsections that allow ants in the ith subsection to move is defined as Allowed (I i), which is a subset of Neighbor (I i). In the kth iteration, the probability of the ith ant in the sub-segment moving into the jth sub-segment can be expressed as:
where τ j is the pheromone in the j-th subsection; alpha is the information heuristic factor, beta is the desired heuristic factor, and eta is the difference in the adaptability of the two subsections.
If an ant moves from the current sub-segment to an adjacent sub-segment, then the ant will leave a pheromone in the adjacent sub-segment, and the change in all pheromones in the sub-segments can be expressed as:
where Δτ j (t) is the total pheromone variation of the jth sub-segment in the t-th iteration; Is the change in pheromone in the jth subsection caused by the movement of the p-th ant; q is the number of ants moved into the j-th subsection.
Because the pheromone in the nature volatilizes along with time, in the optimization process, the pheromone also gradually decays along with the increase of the iteration times. The pheromone in the sub-segment in the t+1st iteration can be expressed as:
τj(t+1)＝(1-ρ)τj(t)+Δτj(t) (7)
Where ρ is the pheromone volatilization factor.
When the tracer gamma spectrum is processed by the ant colony optimization algorithm, the ants in each sub-segment will gradually move towards the local maxima in the tracer gamma spectrum according to the above-mentioned rules. When all ants stop moving, the iteration stops, and all ants are distributed in sub-segments where local maxima exist. However, the peak position and other characteristic parameters of the tracing peak still cannot be accurately determined by adopting ant colony optimization. Firstly, the subsections obtained by segmentation have a certain resolution limit, the more subsections are, the more accurate the searched peak position is, but the more subsections can cause the reduction of the optimized operation efficiency; in addition, there are unavoidable statistical fluctuations in radioactivity and noise in the trace gamma spectrum, and too many sub-segments are also prone to the occurrence of false peaks. Therefore, the ant colony optimization algorithm can only provide a roughly reasonable range for peak positions.
Step 3: and (5) performing depth optimization by using a simplex algorithm, and extracting characteristic parameters of the tracing peak. The range of the tracer peak position obtained by the ant colony optimization algorithm can be used as an initial solution of the simplex algorithm, so that depth optimization is realized, and accurate characteristic parameters of the tracer peak are determined. The simplex algorithm is a typical local optimization algorithm, has a high convergence speed and does not need to derive an objective function. The algorithm has the defect that the optimized result is greatly influenced by the initial solution, so that the problem can be effectively solved by adopting the result of the ant colony optimization algorithm. When the simplex algorithm solves for the n-dimensional objective function f (x), a simplex is created that includes n+1 vertices, each representing a potential solution. In order to obtain the optimal solution, the simplex is continuously updated through reflection, expansion, contraction and compression until the solution precision meets the requirement.
For a trace gamma spectrum characterized by n gaussian distributions, only the peak height is a linear parameter, while both the peak position and the peak width are nonlinear parameters. The trace gamma spectrum can thus be expressed as:
S＝HTX (8)
Wherein S is a vector representing the trace gamma spectrum, and H is a vector composed of peak heights, i.e., h= [ H 1,h2,...,hn]T; x is a vector composed of gaussian distributed nonlinear parameters, i.e. x= [ X 1,X2,...,Xn]T, where
When the simplex algorithm is adopted for depth optimization, the linear parameters can be directly calculated through linear regression, and the simplex algorithm is mainly used for solving the nonlinear parameters, so that the efficiency of the algorithm is improved. The initial solution obtained by the ant colony optimization algorithm also improves the stability of the simplex algorithm.
Step 4: and judging the flow space of the tracing peak according to the correlation among the characteristic parameters of different tracing peaks and the combination of the tubular column structure. The main parameters of the characteristic peak at different moments in the flow process of the same tracing peak have stronger correlation, so that the tracing peak can be traced. The flow space of the tracer slug can be judged according to the positions of the water distributor and the packer in the tubular column structure, and the main flow information of the underground tracer peak can be obtained by combining the analysis.
Step 5: and calculating the water flow speed at each position according to the time-depth relation of the tracing peaks. Each trace gamma spectrum corresponds to a depth and a time, and the peak position of the trace peak represents the measured depth and time when the detector of the instrument passes through the tracer slug, and the underground water flow speed can be calculated according to the time-depth relation by continuously tracing the trace peak. The direction of water flow can be determined according to the following four criteria:
(1) If T2> T1 and D2> D1, the water flow is a downflow;
(2) If T2> T1 and D2< D1, the water flow is an upward flow;
(3) If T2< T1 and D2< D1, the water flow is a downflow;
(4) If T2< T1 and D2> D1, the water flow is the up-flow.
Wherein D1, T1, D2 and T2 are depths and times corresponding to the peak positions of the tracer peaks generated by the same tracer slug. According to the depth difference and the time difference of the tracer slug movement, the velocity of the water outlet flow can be calculated as follows:
wherein v a is apparent fluid velocity; Δh is the distance traveled by the tracer in the two measurements; Δt is the time it takes for the tracer to move this distance.
Step 6: and correcting the underground water flow speed according to the wellhead metering flow, and calculating the water absorption profile. There is a certain difference between the actual calculated apparent fluid velocity and the actual average velocity, mainly due to the non-uniformity of the fluid flow velocity. The velocity profile correction factor may be calculated from the wellhead measured flow and the measured flow as in equation (11). Thereby correcting the water flow speed of each measuring point.
Wherein v a is apparent fluid velocity; v m is the average velocity of the fluid; c a is the velocity profile correction coefficient.
The invention provides an isotope labeled flow well logging water absorption profile interpretation method, and a flow chart of the method is shown in figure 1. When the tracing gamma spectrum is extracted from the tracing peak, the approximate range of the tracing peak position is firstly determined by utilizing an ant colony optimization algorithm, an initial solution is provided for a simplex algorithm in the obtained tracing peak position range, the depth optimization is carried out, the characteristic parameters of the tracing peak are obtained, and then the flow space where the tracing peak is positioned is judged through the correlation analysis among the characteristic parameters of the tracing peak. Judging the water flow direction according to the time-depth relation of the tracer, calculating the apparent fluid speed of the water flow according to the depth difference and the time difference of two tracer peaks generated by the same tracer slug, and finally correcting the water flow of each measuring point based on the calculated speed profile correction coefficient of the wellhead injection flowmeter, thereby realizing the interpretation of the final water absorption profile.
Advantageous effects
Compared with the prior art, the invention has the remarkable advantages that: and (1) the accuracy of the calculated result is high. The characteristic parameters of the tracing peak can be accurately calculated by combining the ant colony optimization algorithm with the simplex algorithm, and a plurality of water flow signals can be effectively separated. And (2) the algorithm design is reasonable and stable. The ant colony optimization algorithm and the simplex algorithm complement each other, the range of the peak position of the tracer peak provided by the ant colony algorithm can be used as an initial solution when the simplex algorithm is optimized, the problem that the simplex algorithm is sensitive to the initial solution is effectively solved, and the algorithm stability is improved. And (3) accurate quantitative interpretation. On the basis of accurately calculating characteristic parameters of the tracing peaks, the apparent fluid speed of the measuring points can be obtained by utilizing the time-depth relation, meanwhile, the speed profile correction coefficient is calculated according to the metering flow of the wellhead, the flow of each measuring point is corrected, and the accurate quantitative interpretation of the water absorption profile is realized.
Drawings
Fig. 1 is a flow chart of an explanation method for water absorption profile of an isotope labeled flow well logging.
Fig. 2 is a trace path of two isotope labeled flow logs in an application example.
Fig. 3 is an optimization process of the ant colony optimization algorithm for processing the tracer gamma spectrum.
Fig. 4 shows the key trace peak processing results of the first measurement in the application example.
Fig. 5 shows the key trace peak processing results of the second measurement in the application example.
Fig. 6 is a water absorption profile interpretation result diagram of the real well logging in the application example.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. 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 invention.
Application example:
The embodiment is a typical water injection well for implementing a layered injection allocation scheme, and the water absorption profile of the well is measured by adopting an isotope labeled flow logging. The well is provided with two perforation layers, and the depth range of layer 1 is 2223.0 m-2228.0 m; the depth of the layer 2 ranges from 2232.0m to 2238.0m. A separator is positioned over each perforation layer at 2218.7m and 2229.3m, respectively. Two water distributors were used to control the flow into the annulus, at 2229.3m and 2239.6m respectively. The diameters of the well tubing and casing were 139.7mm and 62mm, respectively. The bottom of the oil pipe is provided with a plug, fresh water is injected into the oil pipe, and the average flow is 23.07m 3/d. According to the tubular string structure of the well, injected water flows from the tubing well water distributor into the oil casing annulus and eventually into the perforation layers, water entering the first perforation layer forms a lower water stream, and water entering the second perforation layer forms an upper water stream.
The isotope labeled flow logging instrument used in logging mainly comprises a radioactive tracer injector and a gamma detector. The instrument had an outer diameter of 38mm and was placed into the well from the tubing and two measurements were made at the measurement interval. In the first measurement, the tracer is released at 2190.6m above layer 1; in the second measurement, the tracer is released at 2222.8 m. During the well logging process, the instrument is repeatedly lowered and raised to track the tracer so that the flow of the tracer throughout the measurement process is recorded for analysis of the subsequent water absorption of each layer. The instrument trace path for the two measurements is shown in fig. 2.
And then optimizing the tracing gamma spectrum by adopting an ant colony algorithm to obtain an initial solution. Fig. 3 illustrates the process of optimizing the ant colony optimization algorithm by taking an actual measured tracer gamma spectrum as an example. FIG. 3 (a) is a trace peak after background correction; FIG. 3 (b) is the result of dividing the original trace gamma spectrum into a plurality of sub-segments, and placing one ant in each sub-segment; FIG. 3 (c) is a process of ant movement during the optimizing process, and it can be seen that the ants are continuously moving toward the local maximum; fig. 3 (d) is the final optimizing result of the ant colony optimization algorithm, and it can be seen that all ants have moved to the sub-section containing the local maximum value in the spectrum peak, and the two sub-sections with the highest counting rate can be selected as the initial solution of the next optimization by combining the pipe column structure.
In the process of two measurements, a series of key trace peaks are collected by repeatedly lifting and lowering the instrument, and fig. 4 and fig. 5 respectively show original trace spectrum data collected in the two measurements and trace spectrums obtained by fitting by adopting the method. Wherein, characteristic parameters of the obtained tracing peak are solved by adopting a simplex optimization algorithm and are shown in a table 1. The fitting error in table 1 is calculated using equation (12):
Wherein Err is the fitting error, n is the number of addresses in the trace spectrum, S i and Raw spectral data and fitted spectral data, respectively; m is the maximum count rate in the original spectrum. It can also be seen from the data in table 1 that the fitting error for all trace spectra is less than 5%, which also demonstrates the effectiveness of the present invention. In addition, the flow space where the tracing peak is located can be identified according to the correlation between the pipe column structure and the characteristic parameters of each tracing peak.
TABLE 1 Peak characteristic parameters for Trace
From fig. 4 (a) to fig. 4 (d) it can be seen that there is only one trace peak in the trace gamma spectrum, since the measurement area is above the first water distributor, at which time the tracer slug is flowing down the tubing. When the measurement time reaches 300sec, the tracer slug starts to pass through the first water distributor, so that the tracer peaks show a tendency to separate as shown in fig. 4 (e). The tracer then flows simultaneously in the tubing and annulus, and the response at both places causes overlapping peaks in the tracer gamma spectra. From fig. 4 (f) to fig. 4 (h), it can be seen that two distinct tracer peaks appear in the tracer gamma spectrum, and the distance between the two tracer peaks gradually increases over time. When the measurement time reaches 800sec, the tracer slug in the tubing reaches the second water distributor, so 3 tracer peaks can be observed in fig. 4 (i).
In a second measurement, the flow conditions of the downhole tracer slugs may be analyzed in a similar manner. The tracer is released above the second water distributor, below the first, so that the second measurement reflects mainly the water absorption of the second perforated layer. In fig. 5 (a) and 5 (b) it can be seen that the tracer is now flowing down the tubing. When the measurement time reaches 300sec, two peaks can be found in fig. 5 (c), indicating that the tracer slug passed through the second water distributor. The flow in the tubing will then no longer flow due to the presence of the plug and the trace peak created by the flow in the annulus will gradually decrease in depth over time, mainly due to the upward flow in the annulus, as shown in figures 5 (d) and 5 (f).
And defining the middle points of the depths corresponding to the two adjacent tracing peaks as measuring point depths, and determining the underground water flow condition according to the time-depth relation, the depths corresponding to the tracing peaks and the time. The water flow information calculated from the trace peak characteristic parameters in table 1 is shown in table 2. Although the calculated flow rate in table 2 is different from the measured flow rate of the wellhead, the velocity profile correction coefficient can be calculated according to the measured flow rate of the wellhead so as to correct the flow rate value of each measuring point, the water absorption of each perforation layer calculated according to the corrected measuring point flow rate is shown in table 3, and the interpretation result diagram of the drawn water absorption profile is shown in fig. 6, wherein not only the water absorption of each layer but also the water flow information in the oil pipe and the oil jacket annulus are shown.
Table 2 measurement point water flow information
TABLE 3 results of interpretation of the water absorption profile
Claims (1)
1. An isotope labeled flow logging water absorption profile interpretation method is characterized by comprising the following steps:
Step 1: the background of the tracing gamma spectrum is estimated by adopting a linear function, the background correction of the tracing gamma spectrum is realized, and the analytic formula of the background function obtained by counting and calculating N times before and after the selection of the spectrum is as follows:
wherein Y is the background count rate, X is the trace gamma spectrum address, Y1 is the average value of the previous N paths of gamma count rates, and X1 is the average value of the previous N paths of addresses; y2 is the average value of the gamma counting rate of the last N channels, X2 is the average value of the address of the last N channels, and the original tracing gamma spectrum is subtracted and fitted to obtain a background baseline so as to obtain a pure tracing peak for subsequent processing;
Step 2: firstly determining the range of peak positions in a tracer gamma spectrum by using an ant colony optimization algorithm, firstly dividing the whole spectral line into a plurality of subsections, and if the depth or time represented by the left and right end point addresses of the gamma spectral line is a and b respectively and the whole spectral line is divided into n subsections, then the ith subsection is expressed as:
Ii＝[a+(i-1)m,a+im] (2)
Where m is the length of the sub-segment, denoted as (b-a)/n, after which the left and right end points of the ith sub-segment are denoted as a+ (I-1) m and a+im, in the initial state each sub-segment is considered to hold one ant, and the pheromones in each sub-segment are equal, the pheromone increment of each sub-segment is initialized to 0, the adaptability of each ant is equal to the average count rate in the sub-segment, and during the optimization, each ant is only allowed to move into its neighboring sub-segment, and Neighbor (I i) is denoted as the other sub-segment to which the ants in sub-segment I i can move:
if the adaptation of the current sub-segment is smaller than the adjacent sub-segment, then the ants in the current sub-segment are Allowed to move to the adjacent sub-segment, otherwise, the set of sub-segments that allow the ants in the ith sub-segment to move is defined as Allowed (I i), which is a subset of Neighbor (I i), and in the kth iteration, the probability that the ith ant in the sub-segment moves into the jth sub-segment is expressed as:
where τ j is the pheromone in the j-th subsection; α is the information heuristic factor, β is the desired heuristic factor, η is the difference in the adaptability of the two subsections, if an ant moves from the current subsection to an adjacent subsection, then the ant will leave a pheromone in the adjacent subsection, and the change in all pheromones in the subsections is expressed as:
Wherein Deltaτ j (t) is the total pheromone variation of the jth sub-segment in the t-th iteration; The change of the pheromone in the jth sub-section caused by the movement of the p-th ant, q is the number of ants moving into the jth sub-section, and the pheromone in the nature volatilizes along with time, so that the pheromone also gradually decays along with the increase of the iteration number in the optimization process, and the pheromone in the t+1th iteration sub-section is expressed as:
τj(t+1)＝(1-ρ)τj(t)+Δτj(t) (6)
wherein ρ is a pheromone volatilization factor;
step 3: the range of the tracer peak position obtained by the ant colony optimization algorithm is used as an initial solution of a simplex algorithm, the simplex algorithm is used for carrying out depth optimization, characteristic parameters of the tracer peak are extracted, for the tracer gamma spectrum characterized by n Gaussian distribution, only the peak height is a linear parameter, and the peak position and the peak width are nonlinear parameters, so that the tracer gamma spectrum is expressed as:
S＝HTX (7)
Wherein S is a vector representing the trace gamma spectrum, and H is a vector composed of peak heights, i.e., h= [ H 1,h2,...,hn]T; x is a vector composed of gaussian distributed nonlinear parameters, i.e. x= [ X 1,X2,...,Xn]T, where
When the simplex algorithm is adopted for depth optimization, the linear parameters are directly calculated through linear regression, the simplex algorithm is mainly used for solving nonlinear parameters, and the stability of the simplex algorithm is improved through an initial solution obtained by the ant colony optimization algorithm;
Step 4: judging the flow space of the tracer peak according to the correlation among the characteristic parameters of different tracer peaks and the pipe column structure, tracking the tracer peak according to the stronger correlation among the main parameters of the characteristic peaks at different moments in the flow process of the same tracer peak, judging the flow space of the tracer slug according to the positions of a water distributor and a packer in the pipe column structure, and comprehensively analyzing the main flow information of the underground tracer peak;
Step 5: calculating the water flow speed at each place according to the time-depth relation of the tracing peaks, wherein each trace gamma spectrum corresponds to a depth and a time, the peak position of the tracing peak represents the measured depth and the time when a detector of an instrument passes through the tracer slug, the underground water flow speed is calculated according to the time-depth relation through continuously tracing the tracing peaks, and the water flow direction judgment is based on the following four criteria: (1) if T2> T1 and D2> D1, the water flow is a downflow; (2) if T2> T1 and D2< D1, the water flow is an upper water flow; (3) if T2< T1 and D2< D1, the water flow is a downflow; (4) If T2< T1 and D2> D1, the water flow is the upper water flow, wherein D1, T1, D2 and T2 are the depths and the times corresponding to the peak positions of the tracing peaks generated by the same tracer slug, and the water flow speed is calculated according to the depth difference and the time difference of the movement of the tracer slug:
Where v a is apparent fluid velocity, Δh is the distance traveled by the tracer in two measurements, and Δt is the time it takes for the tracer to travel this distance:
Step 6: correcting the underground water flow speed according to the wellhead metering flow, calculating the water absorption profile, wherein a certain difference exists between the actual calculated apparent fluid speed and the actual average speed, which is mainly caused by the uneven fluid flow speed, and calculating a speed profile correction coefficient according to the wellhead metering flow and the metering flow, as shown in the formula (10):
where v a is the apparent fluid velocity, v m is the average velocity of the fluid, and C a is the velocity profile correction factor.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4622463A (en) * | 1983-09-14 | 1986-11-11 | Board Of Regents, University Of Texas System | Two-pulse tracer ejection method for determining injection profiles in wells |
| CN1061459A (en) * | 1990-08-24 | 1992-05-27 | 切夫里昂研究和技术公司 | Utilize the method for unstable radioactive isotope assay steam injection section |
| CN1081491A (en) * | 1992-07-15 | 1994-02-02 | 中国核动力研究设计院 | A kind of survey of wells by radioactive isotope tracking method |
| WO2001083388A2 (en) * | 2000-04-28 | 2001-11-08 | Corning Incorporated | Glass preform and methods and apparatus for manufacture thereof |
| CN103020349A (en) * | 2012-12-08 | 2013-04-03 | 清华大学 | Modeling method of etching yield in plasma etching process |
| CN103352689A (en) * | 2013-07-03 | 2013-10-16 | 中国石油大学(华东) | Method for determining well diameter with radioactive tracing well measuring technology |
| ITGE20130009A1 (en) * | 2013-01-24 | 2014-07-25 | Istituto Italiano Di Tecnologia | METHOD FOR THE CONFIGURATION OF PLANAR TRANSDUCER LINES FOR THE DEVELOPMENT OF SIGNALS WITH BROADBAND THROUGH THREE-DIMENSIONAL BEAMFORMING AND SIGNAL PROCESSING SYSTEMS USING THIS METHOD, IN PARTICULAR ACOUSTIC CAMERA |
| CN104481516A (en) * | 2014-11-26 | 2015-04-01 | 杭州泛太石油设备科技有限公司 | Continuous tracer logging method and logger thereof |
| WO2016167751A1 (en) * | 2015-04-14 | 2016-10-20 | Halliburton Energy Services, Inc. | Methods of cleaning invert emulsion drilling fluids |
| CN106150481A (en) * | 2015-04-01 | 2016-11-23 | 中国石油天然气股份有限公司 | Profile-log of water injection measuring method based on natural gamma baseline |
| CN110469291A (en) * | 2019-07-24 | 2019-11-19 | 西南石油大学 | A kind of Profile Control in Injection Well optimization method |
| CN111472760A (en) * | 2020-06-01 | 2020-07-31 | 西南石油大学 | Novel method for extracting related flow well logging tracing peak |
| WO2020228547A1 (en) * | 2019-05-16 | 2020-11-19 | 浙江大学 | Sound velocity profile inversion method based on inverted multi-beam echometer |
| CN114251088A (en) * | 2021-12-22 | 2022-03-29 | 河南省科学院同位素研究所有限责任公司 | Efficient zero-radioactive-emission well logging tracer and preparation method thereof |
| CN114856537A (en) * | 2022-05-04 | 2022-08-05 | 西南石油大学 | Optimized calculation method for water flow velocity of pulse neutron oxygen activation logging |
-
2023
- 2023-01-09 CN CN202310026022.4A patent/CN116044385B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4622463A (en) * | 1983-09-14 | 1986-11-11 | Board Of Regents, University Of Texas System | Two-pulse tracer ejection method for determining injection profiles in wells |
| CN1061459A (en) * | 1990-08-24 | 1992-05-27 | 切夫里昂研究和技术公司 | Utilize the method for unstable radioactive isotope assay steam injection section |
| CN1081491A (en) * | 1992-07-15 | 1994-02-02 | 中国核动力研究设计院 | A kind of survey of wells by radioactive isotope tracking method |
| WO2001083388A2 (en) * | 2000-04-28 | 2001-11-08 | Corning Incorporated | Glass preform and methods and apparatus for manufacture thereof |
| CN103020349A (en) * | 2012-12-08 | 2013-04-03 | 清华大学 | Modeling method of etching yield in plasma etching process |
| ITGE20130009A1 (en) * | 2013-01-24 | 2014-07-25 | Istituto Italiano Di Tecnologia | METHOD FOR THE CONFIGURATION OF PLANAR TRANSDUCER LINES FOR THE DEVELOPMENT OF SIGNALS WITH BROADBAND THROUGH THREE-DIMENSIONAL BEAMFORMING AND SIGNAL PROCESSING SYSTEMS USING THIS METHOD, IN PARTICULAR ACOUSTIC CAMERA |
| CN103352689A (en) * | 2013-07-03 | 2013-10-16 | 中国石油大学(华东) | Method for determining well diameter with radioactive tracing well measuring technology |
| CN104481516A (en) * | 2014-11-26 | 2015-04-01 | 杭州泛太石油设备科技有限公司 | Continuous tracer logging method and logger thereof |
| CN106150481A (en) * | 2015-04-01 | 2016-11-23 | 中国石油天然气股份有限公司 | Profile-log of water injection measuring method based on natural gamma baseline |
| WO2016167751A1 (en) * | 2015-04-14 | 2016-10-20 | Halliburton Energy Services, Inc. | Methods of cleaning invert emulsion drilling fluids |
| WO2020228547A1 (en) * | 2019-05-16 | 2020-11-19 | 浙江大学 | Sound velocity profile inversion method based on inverted multi-beam echometer |
| CN110469291A (en) * | 2019-07-24 | 2019-11-19 | 西南石油大学 | A kind of Profile Control in Injection Well optimization method |
| CN111472760A (en) * | 2020-06-01 | 2020-07-31 | 西南石油大学 | Novel method for extracting related flow well logging tracing peak |
| CN114251088A (en) * | 2021-12-22 | 2022-03-29 | 河南省科学院同位素研究所有限责任公司 | Efficient zero-radioactive-emission well logging tracer and preparation method thereof |
| CN114856537A (en) * | 2022-05-04 | 2022-08-05 | 西南石油大学 | Optimized calculation method for water flow velocity of pulse neutron oxygen activation logging |
Non-Patent Citations (4)
| Title |
|---|
| Natural Recyclable High-efficiency Oil-water Separation and Interfacial Dye Adsorption Material: Duck down Fiber;Jin Fang;Journal of Natural Fibers;20221231;第19卷(第3期);6497-6508 * |
| On the use of MnO2 cartridges for the plutonium determination in seawater;Mats Eriksson;Journal of Environmental Radioactivity;20190412;66-75 * |
| 水驱砂岩油藏优势渗流通道识别与预测 ——以蓬莱19-3油田为例;张航宇;工程科技Ⅰ辑;20220615(6);18-51 * |
| 注钆中子寿命测井在找漏找窜中的应用;牛朋;笱顺超;刘东明;马焕英;陈彦竹;石油管材与仪器;20161231;2(001);68-71 * |
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