Method for analyzing oxygen activation spectra of low injection fluid in wellbore

Abstract

The application discloses a kind of wellbore low injection fluid oxygen activation spectrum analysis method, comprising at least the following steps, the characteristic data of multiple oxygen activation standard water flow time spectrum samples is obtained;The measured water flow time spectrum is fitted and calculated to determine the preliminary peak position time in water flow time spectrum;The preliminary peak position time in measured water flow time spectrum is modified and calculated to obtain the best peak position time;Fluid velocity is calculated using characteristic data and the best peak position time;According to fluid velocity and wellbore cross-sectional area, the fluid flow value per unit time is calculated, using the above scheme, the error of wellbore fluid flow is reduced, the uncertainty problem in the process of wellbore fluid flow calculation is reduced, and the accurate description of the micro change amount in the process of wellbore fluid flow is realized.

Description

Technical Field

[0001] This invention relates to the fields of petroleum development and engineering, specifically to a method for oxygen activation spectral analysis of low-injection fluids in wellbores. Background Technology

[0002] Injection profile logging is a crucial technique for understanding formation energy replenishment and evaluating reservoir development effectiveness under water-drive or polymer-drive development methods in oilfields. Currently, commonly used injection profile testing techniques include isotope tracing and oxygen activation spectroscopy (OES). Isotope tracing injection profile logging suffers from issues such as tubing contamination and the risk of surface water pollution during shallow testing. OES, on the other hand, offers advantages such as being environmentally friendly and unaffected by fouling and leakage, making it the optimal method for injection profile testing in environmentally sensitive areas. With the increasing demand for refined water injection in oilfields, the industry is characterized by more multi-injection wells and lower single-layer water absorption. Low-flow-rate testing has become a practical production challenge. When measuring water flow in the annular space of the oil-casing system, flow rates below 30 m³ / d present uncertainties such as difficulty in identifying flow spectrum peaks, strong subjective factors in peak selection, and a lack of data support for traditional empirical correction methods. Calculated results can be as high as 1.85 times higher than the actual injected water volume, failing to effectively support decisions regarding refined water injection development measures in oilfields. Summary of the Invention

[0003] The purpose of this invention is to provide an oxygen activation spectrum analysis method for low-injection fluids in wellbores, which reduces the error of fluid flow rate in wellbores, eliminates the uncertainty in the calculation process of fluid flow rate in wellbores, and realizes the accurate description of micro-changes in fluid flow in wellbores.

[0004] This application provides a method for analyzing the oxygen activation spectrum of low-injection fluid in a wellbore, which involves acquiring characteristic data from multiple oxygen-activated standard water flow time spectrum samples; fitting and calculating the measured water flow time spectrum to determine the initial peak time in the water flow time spectrum; refining and calculating the initial peak time in the measured water flow time spectrum to obtain the optimal peak time; calculating the fluid velocity using the characteristic data and the optimal peak time; and calculating the fluid flow rate per unit time based on the fluid velocity and the cross-sectional area of ​​the wellbore.

[0005] In some optional embodiments, the step of obtaining feature data from multiple oxygen-activated standard water flow time spectrum samples includes: performing data analysis on multiple oxygen-activated standard water flow time spectra to obtain various feature data in the water flow time spectrum samples and the relationship between some feature data.

[0006] In some alternative embodiments, various feature data include: labeled traffic Q s Source distance L, activation time t a Peak time t p Peak height h, peak width w, and flow velocity v.

[0007] In some optional embodiments, the relationship between the partial feature data includes peak time t. p The relationship between peak height h and peak width w and water flow velocity v satisfies a Gaussian normal distribution function.

[0008] In some optional embodiments, the step of fitting the measured flow time spectrum to determine the preliminary peak time in the flow time spectrum includes obtaining the peak time t using a Gaussian normal distribution function. p The Gaussian normal distribution function formula for peak height h and peak width w is: , where x i For time spectrum count rate, t p h represents peak time, w represents peak height, and w represents peak width.

[0009] In some optional embodiments, the activation time t is determined. a The influencing factors, the attenuation factor of the source distance L, and the flow rate v correction factor are based on the activation time t. a The influence factor, the attenuation factor of the source distance L, and the flow velocity v correction factor are used to adjust the initial peak time in the measured flow time spectrum.

[0010] In some optional embodiments, the activation time t is obtained. a Under the same source distance L but different flow velocities v, the spectral data sets were confirmed to have a source distance of L and a standard flow velocity dataset of V={ν1,ν2,ν3,……,ν n}, Measured peak time dataset T p , ={t p , 1,t p , 2,t p , 3, ..., t p , n The measured flow velocity dataset is V. , ={L / t p , 1, L / t p , 2, L / t p , 3, ..., L / t p , n}

[0011] In some optional embodiments, the standard flow velocity V and the measured flow velocity V are compared. , Perform linear regression; the linear regression equation is V = k3 * V ,Based on the standard flow velocity V and the measured flow velocity V , The linear regression equation determines the specific value of K3.

[0012] In some alternative embodiments, the calculation of fluid velocity using characteristic data and optimal peak time is described. The steps include calculating the fluid velocity. The formula is:

[0013] Where L is the source distance, t a t is the activation time. p K is the peak position time, k1 and b1 are the activation time influence factors, k2 is the source distance attenuation factor, and k3 is the velocity correction factor.

[0014] In some optional embodiments, the formula for calculating the fluid flow rate per unit time based on the fluid velocity and the wellbore cross-sectional area includes: Y = *S, where Y is the fluid flow rate per unit time. Let S be the fluid velocity and S be the cross-sectional area of ​​the wellbore.

[0015] This application has the following technical advantages over the prior art: This application provides a method for acquiring characteristic data from multiple oxygen-activated standard water flow time spectrum samples, fitting the measured water flow time spectrum to determine the initial peak time, refining the initial peak time to obtain the optimal peak time, calculating the fluid velocity using the characteristic data and the optimal peak time, and calculating the fluid flow rate per unit time based on the fluid velocity and the wellbore cross-sectional area. This method reduces the error in fluid flow rate within the wellbore, eliminates uncertainties in the calculation process, and achieves an accurate description of minute changes in fluid flow within the wellbore. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of an embodiment of the present invention for analyzing oxygen activation spectra of low-injection fluids in wellbores; Figure 2 This is a schematic diagram comparing the peak width of the time spectrum of low-injection fluid in a wellbore at different flow rates, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram comparing the peak positions of low-injection fluid in a wellbore at the same flow rate under different activation times, provided by an embodiment of the present invention. Figure 4 This is a schematic diagram of the spectral characteristics of low-injection fluid in a wellbore at different source distances, provided by an embodiment of the present invention. Figure 5 This is a schematic diagram of velocity correction for low-injection fluid in a wellbore provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This invention provides a method for analyzing oxygen activation spectra of low-injection fluids in wellbores, such as... Figure 1 As shown, the method includes at least the following steps: acquiring feature data from multiple oxygen-activated standard water flow time spectrum samples; fitting and calculating the measured water flow time spectrum to determine the initial peak time in the water flow time spectrum; adjusting and calculating the initial peak time in the measured water flow time spectrum to obtain the optimal peak time; calculating the fluid velocity using the feature data and the optimal peak time; and calculating the fluid flow rate per unit time based on the fluid velocity and the cross-sectional area of ​​the wellbore.

[0021] Specifically, by adopting the above scheme, the error in fluid flow rate within the wellbore is reduced, the uncertainty in the calculation process is eliminated, and the accurate description of minute changes in fluid flow within the wellbore is achieved. This invention reduces the lower limit of flow rate within the wellbore from 8m³ / s. 3 / d adjusted to 5m 3 / d, The lower limit of the flow rate within the annular space is reduced from 30m³ 3 / d adjusted to 8m 3 / d improves testing accuracy and resolution under low flow conditions.

[0022] Specifically, the feature data includes multiple types, namely: labeled flow Q. s Source distance L, activation time t a Peak time t p The parameters include peak height h, peak width w, and flow velocity v. Optionally, the source distance L is the detector-source distance.

[0023] In some optional embodiments, the step of obtaining feature data from multiple oxygen-activated standard water flow time spectrum samples includes: performing data analysis on multiple oxygen-activated standard water flow time spectra to obtain various feature data in the water flow time spectrum samples and the relationship between some feature data.

[0024] Specifically, the relationships between some characteristic data include peak time t. p The relationship between peak height h and peak width w and water flow velocity v satisfies a Gaussian normal distribution function.

[0025] In some alternative embodiments, such as Figure 2 As shown, the steps for fitting and calculating the measured flow time spectrum to determine the preliminary peak time in the flow time spectrum include obtaining the peak time t using the Gaussian normal distribution function. p The Gaussian normal distribution function formula for peak height h and peak width w is: , where x i For time spectrum count rate, t p h represents peak time, w represents peak height, and w represents peak width.

[0026] In some optional embodiments, the activation time t is determined. a The influencing factors, the attenuation factor of the source distance L, and the flow rate v correction factor are based on the activation time t. a The influence factor, the attenuation factor of the source distance L, and the flow velocity v correction factor are used to adjust the initial peak time in the measured flow time spectrum.

[0027] Specifically, such as Figure 3 As shown, the factors influencing the regression activation time, k1 and b1, are determined under the condition that the flow velocity v and source distance L are constant, and the activation time t is... a Analyze the spectral data set under different conditions to determine the time t it takes for the water to flow through the detector at the marked flow rate. s Define the activation time dataset as X={t} a1 ,t a2 ,t a3 ,t a4 ,……,t an The peak position time offset dataset is Y={t} s -t p1 ,t s -t p2 ,t s -t p3 ,t s -t p4 ,……,t s -t pn Linear regression determines the function Y = k1·X + b1, where k1 and b1 are the factors, and k1 and b1 are the activation time t, respectively.a Influencing factors.

[0028] Furthermore, such as Figure 4 As shown, the attenuation factor k2 of the source distance L is determined. The flow rate v and activation time t are analyzed. a For spectral data sets under conditions of constant source distance L but different source distances L, the correlation coefficient of spectral lines with different source distances L is calculated iteratively by gradually shifting the sampling interval. The shift step size is determined when the correlation coefficient is the largest. The time quantity corresponding to this step size is the measured time difference Δt between the fluid and the other detector. , Define the flow velocity ν as constant and the source distance difference Δl as follows: then the standard time difference Δt = Δl / ν. Establish the measured time difference dataset X = {Δt}. , 1,△t , 2,△t , 3,……,△t , n}, Standard Time Difference Dataset Y = {Δt1, Δt2, Δt3, ..., Δt} n The X and Y data points are defined as a straight line passing through the origin. Linear regression is used to determine the K2 factor in the function Y=k2·X, where K2 is the attenuation factor of the detector source distance L.

[0029] In some optional embodiments, spectral data sets are obtained under the conditions of the same activation time ta, the same source distance L, and different flow velocities v, confirming that the source distance is L and the standard flow velocity dataset is V={ν1,ν2,ν...} 3, ……,ν n}, Measured peak time dataset Tp,={tp , 1, tp , 2, tp , 3, ..., tp , n}, the measured flow velocity dataset is V , ={L / tp , 1, L / tp , 2, L / tp , 3, ..., L / tp , n}.

[0030] In some alternative embodiments, such as Figure 5 As shown, the standard flow velocity V and the measured flow velocity V , Perform linear regression; the linear regression equation is V = k³·V , Based on the standard flow velocity V and the measured flow velocity V , The linear regression equation determines the specific value of K3, where K3 is the velocity correction factor.

[0031] In some alternative embodiments, fluid velocity is calculated using characteristic data and optimal peak time. The steps include calculating the fluid velocity. The formula is:

[0032] Where L is the source distance, t a t is the activation time. p K is the peak position time, k1 and b1 are the activation time influence factors, k2 is the source distance attenuation factor, and k3 is the velocity correction factor.

[0033] In some alternative embodiments, the formula for calculating the fluid flow rate per unit time based on the fluid velocity and the wellbore cross-sectional area includes: P = *S, where P is the fluid flow rate per unit time. Let S be the fluid velocity and S be the cross-sectional area of ​​the wellbore.

[0034] Specifically, the daily fluid injection volume is calculated as: P = *S*24, where P is the fluid flow rate per hour.

[0035] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for analyzing oxygen activation spectra of low-injection fluids in wellbores, characterized in that, Obtain characteristic data from multiple oxygen-activated standard water flow time spectrum samples; The measured flow time spectrum was fitted and calculated to determine the initial peak time in the flow time spectrum; The preliminary peak time in the measured water flow time spectrum is adjusted and calculated to obtain the optimal peak time; Calculate fluid velocity using characteristic data and optimal peak time; Calculate the fluid flow rate per unit time based on the fluid velocity and the cross-sectional area of ​​the wellbore. Determine the activation time t a The influencing factors, the attenuation factor of the source distance L, and the flow rate v correction factor are based on the activation time t. a The influence factors, the attenuation factor of the source distance L, and the flow velocity v correction factor are used to adjust the initial peak time in the measured flow time spectrum. Obtain activation time t a Under the same source distance L but different flow velocities v, the spectral data sets were confirmed to have a source distance of L and a standard flow velocity dataset of V={ν1,ν2,ν3,……,ν n }, Measured peak time dataset T p , ={t p , 1,t p , 2,t p , 3, ..., t p , n The measured flow velocity dataset is V. , ={L / t p , 1, L / t p , 2, L / t p , 3, ..., L / t p , n }; For standard flow velocity V and measured flow velocity V , Perform linear regression; the linear regression equation is V = k3 * V , Based on the standard flow velocity V and the measured flow velocity V , The specific value of K3 is determined by the linear regression equation; The fluid velocity is calculated using characteristic data and optimal peak time. The steps include calculating the fluid velocity. The formula is: Where L is the source distance, t a t is the activation time. p K is the peak position time, k1 and b1 are the activation time influence factors, k2 is the source distance attenuation factor, and k3 is the velocity correction factor. Determine the factors k1 and b1 that influence the regression activation time, assuming a constant flow velocity v, source distance L, and activation time t. a Analyze the spectral data set under different conditions to determine the time t it takes for the water to flow through the detector at the marked flow rate. s Define the activation time dataset as X={t} a1 ,t a2 ,t a3 ,t a4 ,……,t an The peak position time offset dataset is Y={t} s -t p1 ,t s -t p2 ,t s -t p3 ,t s -t p4 ,……,t s -t pn Linear regression determines the function Y = k1·X + b1, where k1 and b1 are the factors, and k1 and b1 are the activation time t, respectively. a Influence factors; Determine the source distance L and attenuation factor k2, and analyze the flow rate v and activation time t. a For spectral data sets under conditions of constant source distance L but different source distances L, the correlation coefficient of spectral lines with different source distances L is calculated iteratively by gradually shifting the sampling interval. The shift step size is determined when the correlation coefficient is the largest. The time quantity corresponding to this step size is the measured time difference Δt between the fluid and the other detector. , Define the flow velocity ν as constant and the source distance difference Δl as follows: then the standard time difference Δt = Δl / ν. Establish the measured time difference dataset X = {Δt}. , 1,△t , 2,△t , 3,……,△t , n }, Standard Time Difference Dataset Y = {Δt1, Δt2, Δt3, ..., Δt} n The X and Y data points are defined as a straight line passing through the origin. Linear regression is used to determine the K2 factor in the function Y=k2·X, where K2 is the attenuation factor of the detector source distance L.

2. The method for analyzing oxygen activation spectra of low-injection fluids in wellbore according to claim 1, characterized in that, The steps for obtaining characteristic data from multiple oxygen-activated standard water flow time spectrum samples include: Data analysis was performed on multiple oxygen-activated standard water flow time spectra to obtain various characteristic data and the relationships between some characteristic data in the water flow time spectrum samples.

3. The method for analyzing oxygen activation spectra of low-injection fluids in wellbore according to claim 2, characterized in that, The various feature data include: labeled flow Q s Source distance L, activation time t a Peak time t p Peak height h, peak width w, and flow velocity v.

4. The method for analyzing oxygen activation spectra of low-injection fluids in wellbore according to claim 3, characterized in that, The relationships between the aforementioned feature data include peak time t. p The relationship between peak height h and peak width w and water flow velocity v satisfies a Gaussian normal distribution function.

5. The method for analyzing oxygen activation spectra of low-injection fluids in wellbore according to claim 4, characterized in that, The step of fitting the measured flow time spectrum to determine the preliminary peak time in the flow time spectrum includes obtaining the peak time t using a Gaussian normal distribution function. p The Gaussian normal distribution function formula for peak height h and peak width w is: , where x i For time spectrum count rate, t p h represents peak time, w represents peak height, and w represents peak width.

6. The method for analyzing oxygen activation spectra of low-injection fluids in wellbore according to claim 1, characterized in that, The formula for calculating the fluid flow rate per unit time based on the fluid velocity and the cross-sectional area of ​​the wellbore includes: Y = *S, where Y is the fluid flow rate per unit time. Let S be the fluid velocity and S be the cross-sectional area of ​​the wellbore.

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