Improved TNIS thermal neutron imaging method

Abstract

The invention relates to the technical field of formation testing, and discloses an improved TNIS thermal neutron imaging method, which comprises the following steps: S1, measurement environment preparation; S2, well logging construction; S3, downhole instrument lowering; S4, uplink testing; S5, improved analysis of downhole data; S6, imaging and oil-water-rock analysis based on thermal neutron attenuation correction counting rate; the invention performs normal distribution analysis on multiple data samples collected and received, removes samples that do not conform to the normal distribution, and only uses samples that conform to the normal distribution, thereby improving the validity of the data; performs cluster analysis on the standard deviation of samples of multiple uplink measurements to remove data with excessive fluctuations, thereby eliminating errors caused by operation or measurement accuracy during a single uplink, improving the reliability of data used for imaging, and ensuring reliable and accurate forming.

Description

Technical Field

[0001] The present invention relates to the technical field of formation testing (E21B49 / 00), and in particular to an improved TNIS thermal neutron imaging method. Background Art

[0002] "Geological exploration" is the investigation and research activities of exploring and detecting geology through various means and methods, determining the appropriate bearing layer, determining the foundation type according to the bearing capacity of the bearing layer, and calculating the foundation parameters. It is to investigate and study the geological conditions of rocks, strata, structures, minerals, hydrology, landforms, etc. in a certain area in order to find out the quality and quantity of minerals and the technical conditions for mining and utilization, and provide the mineral reserves and geological data required for mine construction design.

[0003] Through-Casing Imaging Logging (TNIS) is used for saturation monitoring of new and old wells after casing. It can evaluate the physical properties, lithology and oil content of the reservoir through the oil casing, and is the beginning of nuclear spectrum imaging in production logging. This technology detects the thermal neutron count rate of fast neutrons emitted into the formation that are thermalized into thermal neutrons but not captured by the formation. This can analyze the capture capacity of different lithologies and fluids in the formation for thermal neutrons, and can evaluate the formation lithology, identify fluid properties, and evaluate geological characteristics such as reservoir water saturation.

[0004] Specifically, the TNIS imaging technology uses the TNIS instrument neutron generator to emit 14MeV fast neutrons into the formation, and uses two high-precision detectors (i.e., long and short source distance detectors) to record the thermal neutron count rate that is not captured by the formation from 15μs to 2700μs after the fast neutron beam is emitted. Each detector divides its time spectrum record into 180 channels, each channel is 15μs, and a thermal neutron time decay spectrum is generated based on the thermal neutron counts recorded in each channel, as well as the ratio of the neutron count rates obtained on the two neutron detectors to generate a neutron porosity indicator spectrum, and then obtain two database matrix files (imaging files).

[0005] Regarding the specific application of thermal neutrons in geological exploration, relevant technical solutions are disclosed in the prior art:

[0006] A Chinese patent application with publication number CN112814667A discloses a method for evaluating the water content of a tight gas layer based on the thermal neutron logging count rate ratio, specifically disclosing: using the near and far detector count rate values ​​of thermal neutron imaging logging to calculate its ratio Rlog; calculating the mud content Vsh and porosity, selecting an adjacent thick layer of pure mudstone section to read the near and far detector count rate ratio Rsh of the thermal neutron imaging logging; calculating the near and far detector count rate ratio Rf of the thermal neutron imaging logging reflecting the fluid in the pores, and qualitatively determining the water content of the tight sandstone gas layer; calculating water saturation based on the near and far detector count rate ratio of thermal neutron imaging, and quantitatively calculating the water content of the tight gas layer.

[0007] A Chinese patent with authorization announcement number CN115098823B discloses a method for extracting macroscopic capture cross sections of formations in neutron lifetime logging, specifically disclosing: smoothing and filtering the capture time spectrum to improve the signal-to-noise ratio; ...; selecting initial formation attenuation data points according to the background area range and performing linear fitting in a semi-logarithmic coordinate system to obtain a formation attenuation curve; subtracting the formation attenuation curve from the original spectrum to obtain wellbore attenuation data and fitting it to obtain the wellbore attenuation curve; subtracting the wellbore attenuation curve from the original spectrum, obtaining the formation attenuation data again and fitting it.

[0008] The Chinese patent application with publication number CN118265927A discloses a neural network for obtaining true borehole sigma and true formation sigma, and specifically discloses: using a pulsed neutron source and at least three dual-function radiation detectors, and predicting and comparing borehole and formation sigma through a pre-programmed neural network and a computer program of a trained neural network. These dual-function radiation detectors can detect both neutrons and gamma rays, and are pre-programmed to use pulse shape discrimination technology to distinguish neutrons from gamma rays.

[0009] However, the above prior art still has the following problems:

[0010] In the prior art, the same point is only shot once, or only shot once in one upstroke. Since there is a probability of thermal neutrons being captured by the formation, the method of directly using measured data in the prior art has a large volatility, which can easily cause imaging errors due to the probabilistic deviation of the data. Summary of the invention

[0011] In view of the above problems, the present invention proposes an improved TNIS thermal neutron imaging method, comprising the following steps:

[0012] S1. Preparation of measurement environment: determine the location points for logging, ensure that the geological conditions are stable and there is no risk of collapse, and ensure that the surrounding environment meets the requirements for logging construction;

[0013] S2. Well logging construction: Drill wells and arrange imaging equipment according to the construction plan;

[0014] S3. Downhole instrument lowering: lower the downhole instrument to the specified depth in the well according to the operating specifications;

[0015] S4. Upward test: lift the downhole instrument at a predetermined upward speed and conduct TNIS test, repeat the target shooting at the same point for a predetermined number of times, and record the thermal neutron decay count rate until the top of the designed well section is tested;

[0016] S5. Improved analysis of downhole data: Based on the improved inspection algorithm, the downhole measured data is analyzed;

[0017] The improved inspection algorithm comprises:

[0018] S51. The thermal neutron decay count rate measured at the same point in the same measurement is recorded as ,in, This data is The depth of the well was The first Thermal neutron decay count rate of the secondary thermal neutron pulse;

[0019] S52, will fixed Value and Value, with The value is a variable parameter, which constitutes a sample set of thermal neutron decay count rates ;

[0020] S53, Thermal Neutron Decay Count Rate Sample Set Perform a normal distribution test to determine whether it conforms to the normal distribution;

[0021] S531, if not, mark and remove the sample set;

[0022] S532: If yes, execute step S54;

[0023] S54. For the thermal neutron decay count rate sample set that has been tested to be in accordance with the normal distribution , calculate its sample mean and the sample standard deviation ,satisfy:

[0024] ,

[0025] ,

[0026] in, Represents a sample set of thermal neutron decay count rates The number of valid elements of ;

[0027] S55. The data obtained from multiple trips down the well are the sample standard deviation of the set of values Perform cluster analysis, remove outliers, and obtain The sample standard deviation correction set of values ;

[0028] S56. Extract sample standard deviation correction set The sample mean correction set , calculate the average and get the sample corrected mean ,satisfy:

[0029] ,

[0030] in, Represents the sample mean correction set The number of valid elements of ;

[0031] S57, correct the sample mean As a well depth The thermal neutron decay correction count rate at the point at ;

[0032] S6. Imaging and oil-water-rock analysis based on thermal neutron decay correction count rate.

[0033] Furthermore, step S2 specifically includes:

[0034] S21. The operation team arranges a crane, sets up the crane footrest, and unfolds the crane foot and boom;

[0035] S22. Place the logging vehicle upwind according to the wind direction, arrange the logging vehicle 30 meters away from the wellhead, and set up a warning belt;

[0036] S23. Install the top and bottom pulleys, tension meter, and connect downhole instruments.

[0037] Furthermore, step S3 specifically includes:

[0038] S31. When lowering the downhole instrument to the wellhead, the winch and the operation panel are zeroed, and then the downhole instrument is lowered;

[0039] S32. During the lowering process, the winch operator controls the tension change within a fixed range and adjusts the lowering speed.

[0040] Furthermore, in step S32, the lowering speed is controlled at 3000 m / h, and the positive and negative errors do not exceed 2%.

[0041] Furthermore, in step S4, the predetermined upward speed is 2.0 m / min.

[0042] Further, step S3 also includes: S33, after the downhole instrument is lowered to 200m, power is supplied to check whether the thermal neutron instrument is working normally, and whether the current and voltage during the target shooting process are normal. After confirming that they are normal, the thermal neutron instrument is further lowered to the bottom of the well.

[0043] Furthermore, it is characterized in that the downhole instrument is connected to the surface equipment via a single-core cable, the single-core cable connection works in half-duplex, the upload speed is 50 kbit / s, and the download speed is 5 Kbit / s.

[0044] The present invention also provides the application of the improved TNIS thermal neutron imaging method in gas wells, oil wells (heavy oil wells), and wells with special lithology (tight sandstone, carbonate, igneous rock).

[0045] The beneficial effects of the present invention are:

[0046] 1. In the process of an upward movement, the present invention emits thermal neutron pulses multiple times at the same point for target shooting, and performs normal distribution analysis on multiple data samples collected and arrived. For samples that do not conform to the normal distribution, it can be considered that there are measurement or equipment errors, and then they are eliminated. For samples that conform to the normal distribution, it is considered that they conform to the probability characteristics of formation capture of neutrons, and then they can be adopted, thereby improving the validity of the data;

[0047] 2. The present invention performs k-means cluster analysis on the standard deviation of samples measured multiple times. Data with excessive fluctuations can be eliminated, and then the average is calculated for the mean of the clustered samples, which can eliminate the error caused by operation or measurement accuracy during a single uplink, improve the reliability of the data used for imaging, and ensure reliable and accurate molding. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0049] Figure 2 Schematic diagram of the difference in count rate distribution of TNIS thermal neutrons in different types of formations of the present invention;

[0050] Figure 3 This is a schematic diagram of the difference in normal distribution of TNIS thermal neutrons in different types of formations according to the present invention;

[0051] Figure 4 It is a schematic diagram of intuitive display characteristics of the time spectrum of TNIS thermal neutron imaging of the present invention;

[0052] Figure 5 It is a schematic diagram comparing the differences between the TNIS thermal neutron capture value of the present invention and the traditional capture value and the theoretical capture value in different types of formations;

[0053] Figure 6 An oscilloscope for use with the method of the present invention;

[0054] Figure 7 A logging vehicle for use with the method of the present invention. DETAILED DESCRIPTION

[0055] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0056] Embodiment 1

[0057] according to Figure 1 As shown, this embodiment provides an improved TNIS thermal neutron imaging method. Figure 2-Figure 4 The changes in the count rate and capture value of TNIS thermal neutrons in different formations are shown. In different formations, the count rate of TNIS thermal neutrons presents a quadratic distribution with different means and standard deviations.

[0058] The advanced TNIS thermal neutron imaging method of the present invention comprises the following steps:

[0059] S1. Preparation of measurement environment: determine the location points for logging, ensure that the geological conditions are stable and there is no risk of collapse, and ensure that the surrounding environment meets the requirements for logging construction;

[0060] S2. Well logging construction: Drill wells and arrange imaging equipment according to the construction plan;

[0061] S3. Downhole instrument lowering: lower the downhole instrument to the specified depth in the well according to the operating specifications;

[0062] S4. Upward test: lift the downhole instrument at a predetermined upward speed and conduct TNIS test, repeat the target shooting at the same point for a predetermined number of times, and record the thermal neutron decay count rate until the top of the designed well section is tested;

[0063] S5. Improved analysis of downhole data: Based on the improved inspection algorithm, the downhole measured data is analyzed;

[0064] The improved inspection algorithm comprises:

[0065] S51. The thermal neutron decay count rate measured at the same point in the same measurement is recorded as ,in, This data is The depth of the well was The first Thermal neutron decay count rate of the secondary thermal neutron pulse;

[0066] S52, will fixed Value and Value, with The value is a variable parameter, which constitutes a sample set of thermal neutron decay count rates ;

[0067] S53, Thermal Neutron Decay Count Rate Sample Set Perform a normal distribution SW test to determine whether it conforms to the normal distribution;

[0068] Python code that automatically performs the Shapiro-Wilk test (SW test) on the measured sample to determine whether the sample conforms to the normal distribution:

[0069] import tkinter as tk

[0070] from tkinter import messagebox

[0071] import scipy.stats as stats

[0072] def shapiro_wilk_test(data):

[0073] stat, p = stats.shapiro(data)

[0074] result = f'Shapiro-Wilk statistic = {stat:.3f}, p value = {p:.3f}\n'

[0075] alpha = 0.05

[0076] if p > alpha:

[0077] result += 'The sample conforms to the normal distribution (do not reject H0)'

[0078] else:

[0079] result += 'The sample does not conform to the normal distribution (reject H0)'

[0080] return result

[0081] def on_submit():

[0082] data_input = entry.get()

[0083] try:

[0084] data = [float(x) for x in data_input.split(',')]

[0085] result = shapiro_wilk_test(data)

[0086] messagebox.showinfo("result", result)

[0087] except ValueError:

[0088] messagebox.showerror("Input error", "Please enter a valid list of numbers separated by commas")

[0089] # Create the main window

[0090] root = tk.Tk()

[0091] root.title("Shapiro-Wilk test")

[0092] # Create labels and input boxes

[0093] label = tk.Label(root, text="Please enter sample data, separated by commas:")

[0094] label.pack(padx=10, pady=10)

[0095] entry = tk.Entry(root, width=50)

[0096] entry.pack(padx=10, pady=10)

[0097] # Create a submit button

[0098] submit_button = tk.Button(root, text="submit", command=on_submit)

[0099] submit_button.pack(pady=20)

[0100] # Run the main loop

[0101] root.mainloop()

[0102] S531, if not, mark and remove the sample set;

[0103] S532: If yes, execute step S54;

[0104] S54. For the thermal neutron decay count rate sample set that has been tested to be in accordance with the normal distribution , calculate its sample mean and the sample standard deviation ,satisfy:

[0105] ,

[0106] ,

[0107] in, Represents a sample set of thermal neutron decay count rates The number of valid elements of ;

[0108] For samples that are determined to be normally distributed through the SW test, it is necessary to obtain the parameters (mean and standard deviation) of the normal distribution to which they belong. The following is the Python program code for obtaining the mean 𝜇 and standard deviation 𝜎 of samples that are determined to be normally distributed through the SW test:

[0109] import tkinter as tk

[0110] from tkinter import messagebox

[0111] import numpy as np

[0112] def calculate_normal_params(data):

[0113] mu = np.mean(data)

[0114] sigma = np.std(data, ddof=1) # Use sample standard deviation

[0115] return mu, sigma

[0116] def on_submit():

[0117] data_input = entry.get()

[0118] try:

[0119] data = [float(x) for x in data_input.split(',')]

[0120] mu, sigma = calculate_normal_params(data)

[0121] result = f'Sample mean (mu) = {mu:.3f}\nSample standard deviation (sigma) = {sigma:.3f}'

[0122] messagebox.showinfo("result", result)

[0123] except ValueError:

[0124] messagebox.showerror("Input error", "Please enter a valid list of numbers separated by commas")

[0125] # Create the main window

[0126] root = tk.Tk()

[0127] root.title("Normal distribution parameter calculation")

[0128] # Create labels and input boxes

[0129] label = tk.Label(root, text="Please enter sample data, separated by commas:")

[0130] label.pack(padx=10, pady=10)

[0131] entry = tk.Entry(root, width=50)

[0132] entry.pack(padx=10, pady=10)

[0133] # Create a submit button

[0134] submit_button = tk.Button(root, text="submit", command=on_submit)

[0135] submit_button.pack(pady=20)

[0136] # Run the main loop

[0137] root.mainloop()

[0138] S55. The data obtained from multiple trips down the well are the sample standard deviation of the set of values Perform k-means cluster analysis, remove outliers, and get The sample standard deviation correction set of values ;

[0139] S56. Extract sample standard deviation correction set The sample mean correction set , calculate the average and get the sample corrected mean ,satisfy:

[0140] ,

[0141] in, Represents the sample mean correction set The number of valid elements of ;

[0142] S57, correct the sample mean As a well depth The thermal neutron decay correction count rate at the point at ;

[0143] S6. Imaging and oil-water-rock analysis based on thermal neutron decay correction count rate.

[0144] like Figure 5 As shown, the capture rate of the method of the present invention is more stable than that of the traditional method.

[0145] Furthermore, step S2 specifically includes:

[0146] S21. The operation team arranges a crane, sets up the crane footrest, and unfolds the crane foot and boom;

[0147] S22. Place the logging vehicle upwind according to the wind direction, arrange the logging vehicle 30 meters away from the wellhead, and set up a warning belt;

[0148] S23. Install the top and bottom pulleys, tension meter, and connect downhole instruments.

[0149] Furthermore, step S3 specifically includes: S31, when lowering the downhole instrument to the wellhead, the winch and the operation panel are zeroed, and then the downhole instrument is lowered; S32, the winch driver controls the tension change and adjusts the lowering speed during the lowering process.

[0150] The oscilloscope and logging vehicle used in the present invention are as follows: Figure 6-Figure 7 shown.

[0151] Furthermore, in step S32, the lowering speed is controlled at 3000 m / h, with a positive and negative error of no more than 2%. In step S4, the predetermined upward speed is 2.0 m / min.

[0152] Further, step S3 also includes: S33, after the downhole instrument is lowered to 200m, power is supplied to check whether the thermal neutron instrument is working normally, and whether the current and voltage during the target shooting process are normal. After confirming that they are normal, the thermal neutron instrument is further lowered to the bottom of the well.

[0153] Furthermore, it is characterized in that the downhole instrument is connected to the surface equipment via a single-core cable, the single-core cable connection works in half-duplex, the upload speed is 50 kbit / s, and the download speed is 5 Kbit / s.

[0154] The present invention also provides the application of the improved TNIS thermal neutron imaging method in gas wells, oil wells, and wells of special lithology (tight sandstone, carbonate, igneous rock).

[0155] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Improved TNIS thermal neutron imaging method, characterized in that: The following steps are involved: S1. Preparation of measurement environment: determine the location points for logging, ensure that the geological conditions are stable and there is no risk of collapse, and ensure that the surrounding environment meets the requirements for logging construction; S2. Well logging construction: Drill wells and arrange imaging equipment according to the construction plan; S3. Downhole instrument lowering: lower the downhole instrument to the specified depth in the well according to the operating specifications; S4. Upward test: lift the downhole instrument at a predetermined upward speed and conduct TNIS test, repeat the target shooting at the same point for a predetermined number of times, and record the thermal neutron decay count rate until the top of the designed well section is tested; S5. Improved analysis of downhole data: Based on the improved inspection algorithm, the downhole measured data is analyzed; The improved inspection algorithm comprises: S51. The thermal neutron decay count rate measured at the same point in the same measurement is recorded as ,in, This data is The depth of the well was The first Thermal neutron decay count rate of the secondary thermal neutron pulse; S52, will fixed Value and Value, with The value is a variable parameter, which constitutes a sample set of thermal neutron decay count rates ; S53, Thermal Neutron Decay Count Rate Sample Set Perform a normal distribution test to determine whether it conforms to the normal distribution; S531, if not, mark and remove the sample set; S532: If yes, execute step S54; S54. For the thermal neutron decay count rate sample set that has been tested to be in accordance with the normal distribution , calculate its sample mean and the sample standard deviation ,satisfy: ， ， in, Represents a sample set of thermal neutron decay count rates The number of valid elements of ; S55. The data obtained from multiple trips down the well are the sample standard deviation of the set of values Perform k-means cluster analysis, remove outliers, and get The sample standard deviation correction set of values ; S56. Extract sample standard deviation correction set The sample mean correction set , calculate the average and get the sample corrected mean ,satisfy: ， in, Represents the sample mean correction set The number of valid elements of ; S57, correct the sample mean As a well depth The thermal neutron decay correction count rate at the point at ; S6. Imaging and oil-water-rock analysis based on thermal neutron decay correction count rate.

2. The improved TNIS thermal neutron imaging method according to claim 1, characterized in that: Step S2 includes: S21. The operation team arranges a crane, sets up the crane footrest, and unfolds the crane foot and boom; S22. Place the logging vehicle upwind according to the wind direction, arrange the logging vehicle 30 meters away from the wellhead, and set up a warning belt; S23. Install the top and bottom pulleys, tension meter, and connect downhole instruments.

3. The improved TNIS thermal neutron imaging method according to claim 1, characterized in that: Step S3 includes: S31. When lowering the downhole instrument to the wellhead, the winch and the operation panel are zeroed, and then the downhole instrument is lowered; S32. During the lowering process, the winch operator controls the tension change within a fixed range and adjusts the lowering speed.

4. The improved TNIS thermal neutron imaging method according to claim 3, characterized in that: In step S32, the lowering speed is controlled at 3000 m / h, and the positive and negative errors do not exceed 2%.

5. The improved TNIS thermal neutron imaging method according to claim 1, characterized in that: In step S4, the predetermined upward speed is 2.0 m / min.

6. The improved TNIS thermal neutron imaging method according to claim 3, characterized in that: Step S3 also includes: S33, after the downhole instrument is lowered to 200m, power is supplied to check whether the thermal neutron instrument is working normally, and whether the current and voltage during the target shooting process are normal. After confirming that they are normal, the thermal neutron instrument is further lowered to the bottom of the well.

7. The improved TNIS thermal neutron imaging method according to claim 1, characterized in that: The downhole instrument is connected to the surface equipment via a single-core cable, which works in half-duplex mode with an upload speed of 50 kbit / s and a download speed of 5 kbit / s.

8. Application of the improved TNIS thermal neutron imaging method according to any one of claims 1 to 7 in gas wells.

9. Application of the improved TNIS thermal neutron imaging method according to any one of claims 1 to 7 in oil wells.

10. Application of the improved TNIS thermal neutron imaging method according to any one of claims 1 to 7 in horizontal wells.

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