Porosity clustering calculation method and device based on nuclear magnetic resonance logging T2 spectrum

Abstract

The present invention relates to the field of well logging technology, and is a porosity clustering calculation method and device based on nuclear magnetic resonance logging T2 spectrum. The former includes picking up half-peak trajectory data of clay bound water spectrum peak, fitting processing using Gaussian fitting method and its characteristics, and obtaining clay bound water spectrum; curve integration processing of clay bound water spectrum to obtain clay bound water porosity; repeating the above steps to obtain movable fluid spectrum and movable fluid porosity; using original nuclear magnetic resonance logging T2 spectrum to subtract clay bound water spectrum and movable fluid spectrum to obtain capillary bound fluid spectrum, and curve integration processing is performed on it to obtain capillary bound fluid porosity. The present invention uses Gaussian function to perform sub-spectrum fitting, which can complete the missing spectrum of clay bound water and effectively avoid the superposition effect of the tail spectrum of the sub-spectrum with large signal intensity on the adjacent sub-spectrum. And it can eliminate the noise of nuclear magnetic tail spectrum, thereby improving the calculation accuracy of porosity.

Description

Technical Field

[0001] The invention relates to the technical field of well logging, and is a porosity clustering calculation method and device based on nuclear magnetic resonance logging T2 spectrum. Background Art

[0002] Since the detection depth of NMR logging is relatively shallow, for conventional reservoirs, the detection range is basically the invasion zone of drilling fluid filtrate, and the response of NMR logging is mainly the surface relaxation of water. The total porosity of NMR is the sum of clay-adsorbed water and effective porosity; effective porosity is the sum of capillary bound fluid and movable fluid porosity. The calculation cutoff value of effective porosity is generally 3ms, and it is believed that the pore volume corresponding to less than 3ms is the volume of clay-adsorbed water; for clastic reservoirs, the pore volume of movable fluid is calculated using a cutoff value of 33ms (the cutoff value varies slightly due to different skeleton properties). The T2 cutoff value is a key parameter that affects the calculation of NMR bound water saturation, effective porosity, etc., and is of great significance for the accurate calculation of reservoir parameters.

[0003] At present, there are two main methods for determining the T2 cutoff value of NMR: one is the fixed cutoff value method: (1) using the porosity accumulation method to determine the T2 cutoff value; (2) using the mercury injection capillary pressure curve method to determine the T2 cutoff value; (3) using the bound water saturation to back-calculate the T2 cutoff value. The other is the variable cutoff value method: (1) using the comprehensive physical property index to determine the T2 cutoff value of low-porosity and low-permeability reservoirs; (2) using the NMR logging T2 spectrum to calculate the T2 cutoff value. However, no matter which method is based on the mathematical statistics of a large amount of data, it does not fully consider the reservoir pore structure characteristics and fluid occurrence state, and cannot reflect the actual situation of various reservoirs. In addition, the original NMR logging T2 spectrum often has the problem of missing clay bound water spectrum, the superposition of the tail spectrum of the strong signal sub-spectrum on the adjacent sub-spectrum, and the influence of tail spectrum noise. The conventional T2 cutoff method cannot eliminate the influence of these factors, which can easily cause large errors in porosity. Summary of the invention

[0004] The present invention provides a porosity clustering calculation method and device based on nuclear magnetic resonance logging T2 spectrum, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problem that the porosity calculation method based on nuclear magnetic resonance logging T2 spectrum cannot eliminate the lack of clay bound water spectrum and tail spectrum noise, resulting in large porosity calculation errors.

[0005] One of the technical solutions of the present invention is achieved by the following measures: a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum, comprising:

[0006] Pick up half peak trajectory data of the clay bound water spectrum peak, perform fitting processing using the Gaussian fitting method and its characteristics, and obtain the clay bound water spectrum;

[0007] The clay bound water spectrum is processed by curve integration to obtain the clay bound water porosity;

[0008] Repeat the above steps to obtain the movable fluid spectrum and movable fluid porosity;

[0009] The capillary bound fluid spectrum was obtained by subtracting the clay bound water spectrum and movable fluid spectrum from the original NMR logging T2 spectrum. The capillary bound fluid spectrum was then processed by curve integration to obtain the capillary bound fluid porosity.

[0010] The following are further optimizations and / or improvements to the above technical solutions:

[0011] The above method of obtaining clay bound water spectrum includes:

[0012] Pick up half peak trajectory data of clay bound water spectrum peak and perform mirror symmetry processing on it;

[0013] The fitting relationship is generated by using the Gaussian fitting method, and the trajectory data after mirror processing is fitted to obtain the clay bound water spectrum, wherein the fitting relationship is as follows;

[0014]

[0015] Among them, a, b and c are parameters to be estimated, which are the amplitude of the Gaussian curve peak, the position of the Gaussian curve peak on the x-axis and the spread width of the curve respectively.

[0016] The above also includes determining the positions of the clay bound water spectrum peak and the movable fluid spectrum peak in the nuclear magnetic resonance logging T2 spectrum before picking up the half peak trajectory data of the clay bound water spectrum peak and the movable fluid spectrum peak, including:

[0017] Determine the clay type at the logging location, and determine the location of the clay bound water spectrum peak based on the clay type;

[0018] Determine the viscosity of the crude oil at the logging location and determine the movable fluid spectrum peak using the following formula;

[0019]

[0020] Among them, T 2bp is the T2 value corresponding to the NMR volume relaxation peak, that is, the position of the movable fluid spectrum peak; η is the viscosity of crude oil; T is the formation temperature.

[0021] The above also includes standardizing the nuclear magnetic resonance logging T2 spectrum using the following formula:

[0022]

[0023] Among them, y is the standardized NMR logging T2 spectrum, and y0 is the original NMR logging T2 spectrum; is the total porosity of the formation; M is the number of sample points after the discretization of the T2 spectrum of nuclear magnetic resonance logging.

[0024] The second technical solution of the present invention is achieved by the following measures: a porosity clustering calculation device based on nuclear magnetic resonance logging T2 spectrum, including a clay bound water porosity processing unit, a movable fluid porosity processing unit and a capillary bound fluid porosity processing unit;

[0025] The clay bound water porosity processing unit picks up the half-peak trajectory data of the clay bound water spectrum peak, performs fitting processing using the Gaussian fitting method and its characteristics, and obtains the clay bound water spectrum; performs curve integration processing on the clay bound water spectrum to obtain the clay bound water porosity;

[0026] The movable fluid porosity processing unit picks up the half-peak trajectory data of the movable fluid spectrum peak, performs fitting processing using the Gaussian fitting method and its characteristics, and obtains the movable fluid spectrum; performs curve integration processing on the movable fluid spectrum to obtain the movable fluid porosity;

[0027] The capillary bound fluid pore processing unit uses the original nuclear magnetic resonance logging T2 spectrum to subtract the clay bound water spectrum and the movable fluid spectrum to obtain the capillary bound fluid spectrum, and performs curve integration processing on it to obtain the capillary bound fluid porosity.

[0028] The following are further optimizations and / or improvements to the above technical solutions:

[0029] The above-mentioned clay bound water porosity processing unit and movable fluid porosity processing unit both include a first acquisition unit and a second acquisition unit;

[0030] A first acquisition unit picks up half peak trajectory data of clay bound water spectrum peak / movable fluid spectrum peak, performs fitting processing using a Gaussian fitting method and its characteristics, and acquires the clay bound water spectrum / movable fluid spectrum;

[0031] The second acquisition unit performs curve integration processing on the clay bound water spectrum / movable fluid spectrum to obtain the clay bound water porosity / movable fluid porosity.

[0032] The above also includes a standardization unit and a position determination unit;

[0033] Standardization unit, use the following formula to standardize the NMR logging T2 spectrum;

[0034]

[0035] Among them, y is the standardized NMR logging T2 spectrum, and y0 is the original NMR logging T2 spectrum; is the total porosity of the formation; M is the number of sample points after the discretization of the T2 spectrum of nuclear magnetic resonance logging;

[0036] The position determination unit determines the positions of the clay bound water spectrum peak and the movable fluid spectrum peak in the nuclear magnetic resonance logging T2 spectrum before picking up the half peak trajectory data of the clay bound water spectrum peak and the movable fluid spectrum peak, including:

[0037] Determine the clay type at the logging location, and determine the location of the clay bound water spectrum peak based on the clay type;

[0038] Determine the viscosity of the crude oil at the logging location and determine the movable fluid spectrum peak using the following formula;

[0039]

[0040] Among them, T 2bp is the T2 value corresponding to the NMR volume relaxation peak, that is, the position of the movable fluid spectrum peak; η is the viscosity of crude oil; T is the formation temperature.

[0041] The present invention utilizes the theory that the nuclear magnetic resonance logging T2 spectrum is the sum of multiple components that satisfy the normal Gaussian distribution, decomposes the nuclear magnetic resonance logging T2 spectrum into three sub-spectra, namely, the nuclear magnetic clay adsorption water spectrum, the capillary bound fluid spectrum and the movable fluid spectrum, realizes the effective separation of the clay adsorption water spectrum, the capillary bound fluid spectrum and the movable fluid spectrum, and respectively calculates the porosity of the three sub-spectra, completes the porosity clustering calculation of the nuclear magnetic resonance logging T2 spectrum, and uses the Gaussian function to fit the sub-spectrum, which can complete the missing spectrum of the clay bound water, effectively avoid the superposition effect of the tail spectrum of the sub-spectrum with large signal intensity on the adjacent sub-spectrum. And it can eliminate the noise of the nuclear magnetic tail spectrum, thereby improving the calculation accuracy of the porosity. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Attached Figure 1 The present invention is a flow chart of the method.

[0043] Attached Figure 2 This is a flow chart of the method for obtaining clay bound water spectrum in Example 2 of the present invention.

[0044] Attached Figure 3 This is a distribution diagram of relaxation peaks of crude oil at different temperatures in the target work area in Example 6 of the present invention.

[0045] Attached Figure 4 Schematic diagram of sub-spectrum decomposition of the target work area in Example 6 of the present invention.

[0046] Attached Figure 5 This is a comparison chart of the oil saturation accuracy calculated by different methods in the target work area in Example 6 of the present invention.

[0047] Attached Figure 6 This is a spectrum result diagram of each component in the target work area in Example 6 of the present invention.

[0048] Attached Figure 7This is a comparison chart of movable oil porosity calculated by different methods in the target work area in Example 6 of the present invention.

[0049] Attached Figure 8 This is a schematic diagram of the device structure of Example 7 of the present invention.

[0050] Attached Fig. 9 This is a schematic diagram of the device structure of Example 8 of the present invention. DETAILED DESCRIPTION

[0051] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0052] The present invention will be further described below in conjunction with embodiments and drawings:

[0053] Embodiment 1: As attached Figure 1 As shown, the embodiment of the present invention discloses a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum, comprising:

[0054] Step S101, picking up half peak trajectory data of the clay bound water spectrum peak, performing fitting processing using the Gaussian fitting method and its characteristics, and obtaining the clay bound water spectrum;

[0055] Step S102, performing curve integration processing on the clay bound water spectrum to obtain the clay bound water porosity;

[0056] Step S103, repeat the above steps to obtain the movable fluid spectrum and the movable fluid porosity;

[0057] Step S104, subtracting the clay bound water spectrum and the movable fluid spectrum from the original nuclear magnetic resonance logging T2 spectrum to obtain the capillary bound fluid spectrum, and performing curve integration processing on it to obtain the capillary bound fluid porosity.

[0058] Since the peak positions of the NMR logging T2 spectra corresponding to different clay types are different, there are large errors in the clay bound water volume calculated using a fixed cutoff value. Similarly, affected by factors such as formation composition, pore structure, wetting phase, and fluid viscosity, the cutoff value for calculating the movable fluid pore volume is also constantly changing in the vertical direction, resulting in errors in the pore volume calculated by conventional methods. In addition, the original NMR logging T2 spectrum often has problems such as the lack of clay bound water spectrum, the superposition of the tail spectrum of the strong signal sub-spectrum on the adjacent sub-spectrum, and the noise of the tail spectrum. The conventional T2 cutoff method cannot eliminate the influence of these factors, which can easily cause large errors in porosity.

[0059] Therefore, an embodiment of the present invention discloses a porosity clustering calculation method based on the nuclear magnetic resonance logging T2 spectrum. The theory that the nuclear magnetic resonance logging T2 spectrum is the sum of multiple components that satisfy the normal Gaussian distribution is used to decompose the nuclear magnetic resonance logging T2 spectrum into three sub-spectra, namely, the nuclear magnetic clay adsorption water spectrum, the capillary bound fluid spectrum and the movable fluid spectrum, thereby achieving effective separation of the clay adsorption water spectrum, the capillary bound fluid spectrum and the movable fluid spectrum, and calculating the porosities of the three sub-spectra respectively, thereby completing the porosity clustering calculation of the nuclear magnetic resonance logging T2 spectrum. Here, Gaussian fitting can be used to supplement the missing spectrum of the clay bound water spectrum and remove the tail noise, thereby effectively eliminating the influence of the missing problem of the clay bound water spectrum and the tail spectrum noise in the nuclear magnetic resonance logging T2 spectrum, and improving the calculation accuracy of the porosity of the nuclear magnetic resonance logging T2 spectrum.

[0060] The above step S103, repeating the above steps to obtain the movable fluid spectrum and the movable fluid porosity, specifically includes:

[0061] A. Pick up half peak trajectory data of the movable fluid spectrum peak, perform fitting processing using the Gaussian fitting method and its characteristics, and obtain the movable fluid spectrum;

[0062] B. Perform curve integration processing on the movable fluid spectrum to obtain the movable fluid porosity.

[0063] In the above steps S101 and S103, half peak trajectory data of the clay bound water spectrum peak / movable fluid spectrum peak are picked up, wherein the position of the clay bound water spectrum peak is obtained in the nuclear magnetic resonance logging T2 spectrum.

[0064] Embodiment 2: As attached Figure 2 As shown, the embodiment of the present invention discloses a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum, wherein obtaining clay bound water spectrum further comprises:

[0065] Step S201, picking up half peak trajectory data of the clay bound water spectrum peak and performing mirror symmetry processing on it;

[0066] Step S202, using a Gaussian fitting method to generate a fitting relationship, fitting the trajectory data after mirror processing, and obtaining a clay bound water spectrum, wherein the fitting relationship is as follows;

[0067]

[0068] Among them, a, b and c are parameters to be estimated, which are the amplitude of the Gaussian curve peak, the position of the Gaussian curve peak on the x-axis and the spread width of the curve respectively.

[0069] The process of obtaining the movable fluid spectrum is the same as the process of obtaining the clay bound water spectrum here. In the above step S201, the half peak trajectory data of the clay bound water spectrum peak / movable fluid spectrum peak can be picked up by manual interaction. And due to the mirror symmetry characteristics of the Gaussian function, the picked half peak trajectory data is mirror symmetric in this embodiment.

[0070] Example 3: The embodiment of the present invention discloses a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum, further comprising standardizing the nuclear magnetic resonance logging T2 spectrum using the following formula:

[0071]

[0072] Among them, y is the standardized NMR logging T2 spectrum, and y0 is the original NMR logging T2 spectrum; is the total porosity of the formation; M is the number of sample points after the discretization of the T2 spectrum of nuclear magnetic resonance logging.

[0073] Here, the original NMR logging T2 spectrum pair is standardized to accurately determine the positions of the clay bound water spectrum peak and the movable fluid spectrum peak in the NMR logging T2 spectrum, pick up the required half-peak trajectory data of the clay bound water spectrum peak / movable fluid spectrum peak, and further derive accurate clay bound water porosity, movable fluid porosity, and capillary bound fluid porosity.

[0074] Embodiment 4: The embodiment of the present invention discloses a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum, further comprising determining the positions of the clay bound water spectrum peak and the movable fluid spectrum peak in the nuclear magnetic resonance logging T2 spectrum before picking up the half-peak trajectory data of the clay bound water spectrum peak and the movable fluid spectrum peak, including:

[0075] A. Determine the clay type at the logging location, and determine the location of the clay bound water spectrum peak based on the clay type;

[0076] After measuring the T2 relaxation time of various types of clay, it was found that the relaxation times of these clays covered a wide range, among which the T2 distribution peak of kaolinite was in the range of 8 to 16 ms, the T2 peak of illite was in the range of 1 to 2 ms, and the T2 peak of montmorillonite was in the range of 0.3 to 1 ms. It can be seen that the position of the clay bound water spectrum peak in the nuclear magnetic resonance logging T2 spectrum is different for different types of clay. Therefore, this embodiment determines the range of the clay bound water spectrum peak according to the clay type, so as to find the position of the clay bound water spectrum peak in the nuclear magnetic resonance logging T2 spectrum.

[0077] B. Determine the viscosity of the crude oil at the logging location and determine the movable fluid spectrum peak using the following formula;

[0078]

[0079] Among them, T 2bp is the T2 value corresponding to the NMR volume relaxation peak, in ms, i.e. the position of the movable fluid spectrum peak; η is the viscosity of crude oil, in mPa.s; T is the formation temperature, in ℃.

[0080] There are many factors that affect the viscosity of formation crude oil, mainly including the chemical composition of crude oil, dissolved gas content, temperature and pressure. Therefore, the transverse volume relaxation peaks of crude oil measured in the laboratory under different temperature conditions are different and have corresponding characteristics. At the same time, it is found during actual well logging that the actual well logging nuclear magnetic resonance spectrum has obvious volume relaxation peaks of crude oil, and the relaxation time is completely consistent with the laboratory measurement results. Therefore, the formation temperature of the target area can be determined, and the T2 value corresponding to the nuclear magnetic resonance volume relaxation peak can be calculated using the formation temperature through the above formula, and then the movable fluid spectrum peak can be found in the nuclear magnetic resonance logging T2 spectrum.

[0081] In summary, the present invention further considers the influencing factors of geology and logging environment, provides a stable basis for subsequent sub-spectrum decomposition and porosity calculation, and further reduces the calculation error of porosity.

[0082] Example 6: The process of determining the target work area and calculating the porosity of the target work area based on the nuclear magnetic resonance logging T2 spectrum by the porosity clustering calculation method based on the nuclear magnetic resonance logging T2 spectrum of the present invention is as follows.

[0083] 1. Obtain the standardized NMR logging T2 spectrum of the target work area;

[0084] 2. Determine the positions of clay bound water spectrum peak and movable fluid spectrum peak by using clay type and crude oil viscosity. X-ray diffraction analysis data of the target work area shows that the main types of clay minerals are montmorillonite and illite-montmorillonite mixed layers. Therefore, determine the position of clay bound water spectrum peak according to clay type. The distribution diagram of crude oil transverse volume relaxation peak measured in the laboratory under different temperature conditions in the target work area is shown in the attached figure. Figure 3 As shown, since the temperature of the middle part of the shale oil in the target work area is about 80°C, the corresponding viscosity of the crude oil is 12.5mP.s, and the T2 value (volume relaxation time) corresponding to the nuclear magnetic resonance volume relaxation peak is about 200ms, so the movable fluid transverse volume relaxation peak (movable fluid spectrum peak) is distributed at about 200ms.

[0085] 3. Pick up the half-peak trajectory data of the clay bound water spectrum peak / movable fluid spectrum peak, and use the Gaussian fitting method and its characteristics to fit the data to obtain the clay bound water spectrum / movable fluid spectrum; perform curve integration on the clay bound water spectrum / to obtain the clay bound water porosity / movable fluid porosity. Use the original nuclear magnetic resonance logging T2 spectrum to subtract the clay bound water spectrum and movable fluid spectrum to obtain the capillary bound fluid spectrum, and perform curve integration on it to obtain the capillary bound fluid porosity.

[0086] The three sub-spectra obtained here are as follows Figure 4 As shown, spectrum A is the clay bound water spectrum with a porosity of 4.4%, spectrum C is the movable fluid spectrum with a porosity of 6.6%, and spectrum B is the capillary bound fluid spectrum with a porosity of 6.0%.

[0087] By the attached Figure 4 It can be seen that the original nuclear magnetic resonance logging T2 spectrum has the lack of clay bound water spectrum and the noise of tail spectrum. If the total porosity is directly obtained, it will be affected by the lack of clay bound water spectrum and the noise of tail spectrum, so that the total porosity has a large error; after using the method of the present invention, by picking up the trajectory data of half a peak of the clay bound water spectrum in the original nuclear magnetic resonance logging T2 spectrum, and after processing by mirror symmetry and Gaussian fitting series, the missing spectrum of the clay bound water spectrum is supplemented to obtain the clay bound water spectrum (A spectrum) that satisfies the Gaussian distribution, and then according to the clay bound water spectrum (A spectrum), the porosity obtained is more accurate. By the same method, the movable fluid spectrum (C spectrum) obtained effectively removes the tail spectrum noise signal. Therefore, the present invention can effectively eliminate the influence of the problems such as the lack of nuclear magnetic resonance logging T2 spectrum and the noise of tail spectrum on the porosity calculation, and improve the calculation accuracy of porosity.

[0088] Attached Figure 5 The figure is a comparison chart of the oil saturation accuracy calculated by the method of the present invention and the T2 cutoff method for a well in the target work area, wherein the horizontal axis is depth, and the vertical axis is the oil saturation calculated by the method of the present invention (dots), the oil saturation calculated by the T2 cutoff method (squares), and the oil saturation data of the laboratory analysis (triangles). Error analysis shows that the relative error calculated by the method of the present invention is 3.24%, and the relative error calculated by the T2 cutoff method is 13.87%. Compared with the T2 cutoff method, the calculation accuracy of the method of the present invention is significantly improved.

[0089] Attached Figure 6 The figure is a result diagram of the spectra of each component obtained by using the method of the present invention for a well in the target work area, wherein the first track is the depth, the second track is the nuclear magnetic resonance logging T2 spectrum, the third track is the clay adsorbed water spectrum, the fourth track is the bound fluid spectrum, and the fifth track is the movable fluid spectrum. By comparison, it can be seen that the present invention realizes the effective separation of the nuclear magnetic resonance logging T2 spectrum and avoids the calculation error caused by the selection of the cutoff value of the nuclear magnetic resonance logging T2 spectrum.

[0090] Attached Figure 7 The figure is a comparison result diagram of movable oil porosity calculated by the method of the present invention and the T2 cutoff method for a well in the target work area. The first track in the figure is the wellbore (CALI) and the natural gamma curve (GR), the second track is the depth, the third track is the resistivity curve, the fourth track is the porosity curve, the fifth track is the nuclear magnetic resonance logging T2 spectrum, the sixth track is the comparison of the movable fluid porosity (solid line) calculated from 35MS and the movable fluid porosity (dashed line) calculated by the technology of the present invention, the seventh track is the sweet spot thickness section identified by the T2 cutoff method, and the eighth track is the sweet spot thickness section identified by the method of the present invention. The sweet spot thickness of the 3450-4400 meter section of the well is 800m using the traditional technology T2 cutoff method, while the sweet spot thickness identified by the present invention is only 99m, and the movable fluid porosity is relatively low, which clarifies the reason why the maximum daily production of the well after pressure in section 25 is only 8.5t and the annual production is only 1493t.

[0091] Embodiment 7: As attached Figure 8 As shown, the embodiment of the present invention discloses a porosity clustering calculation device based on nuclear magnetic resonance logging T2 spectrum, including a clay bound water porosity processing unit, a movable fluid porosity processing unit and a capillary bound fluid porosity processing unit;

[0092] The clay bound water porosity processing unit picks up the half-peak trajectory data of the clay bound water spectrum peak, performs fitting processing using the Gaussian fitting method and its characteristics, and obtains the clay bound water spectrum; performs curve integration processing on the clay bound water spectrum to obtain the clay bound water porosity;

[0093] The movable fluid porosity processing unit picks up the half-peak trajectory data of the movable fluid spectrum peak, performs fitting processing using the Gaussian fitting method and its characteristics, and obtains the movable fluid spectrum; performs curve integration processing on the movable fluid spectrum to obtain the movable fluid porosity;

[0094] The capillary bound fluid pore processing unit uses the original nuclear magnetic resonance logging T2 spectrum to subtract the clay bound water spectrum and the movable fluid spectrum to obtain the capillary bound fluid spectrum, and performs curve integration processing on it to obtain the capillary bound fluid porosity.

[0095] Wherein the clay bound water porosity processing unit and the movable fluid porosity processing unit both include a first acquisition unit and a second acquisition unit;

[0096] A first acquisition unit picks up half peak trajectory data of clay bound water spectrum peak / movable fluid spectrum peak, performs fitting processing using a Gaussian fitting method and its characteristics, and acquires the clay bound water spectrum / movable fluid spectrum;

[0097] The second acquisition unit performs curve integration processing on the clay bound water spectrum / movable fluid spectrum to obtain the clay bound water porosity / movable fluid porosity.

[0098] Embodiment 8: As attached Fig. 9 As shown, the embodiment of the present invention discloses a porosity clustering calculation device based on nuclear magnetic resonance logging T2 spectrum, which further includes a standardization unit and a position determination unit;

[0099] Standardization unit, use the following formula to standardize the NMR logging T2 spectrum;

[0100]

[0101] Among them, y is the standardized NMR logging T2 spectrum, and y0 is the original NMR logging T2 spectrum; is the total porosity of the formation; M is the number of sample points after the discretization of the T2 spectrum of nuclear magnetic resonance logging;

[0102] The position determination unit determines the positions of the clay bound water spectrum peak and the movable fluid spectrum peak in the nuclear magnetic resonance logging T2 spectrum before picking up the half peak trajectory data of the clay bound water spectrum peak and the movable fluid spectrum peak, including:

[0103] Determine the clay type at the logging location, and determine the location of the clay bound water spectrum peak based on the clay type;

[0104] Determine the viscosity of the crude oil at the logging location and determine the movable fluid spectrum peak using the following formula;

[0105]

[0106] Among them, T 2bp is the T2 value corresponding to the NMR volume relaxation peak, that is, the position of the movable fluid spectrum peak; η is the viscosity of crude oil; T is the formation temperature.

[0107] Embodiment 9: The embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is configured to execute a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum when running.

[0108] The above storage medium may include, but is not limited to: a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0109] Embodiment 10: The embodiment of the present invention discloses an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement a porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum.

[0110] The electronic device mentioned above further includes a transmission device and an input / output device, wherein the transmission device and the input / output device are both connected to the processor.

[0111] The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum, characterized in that: include: The following formula is used to standardize the T2 spectrum of nuclear magnetic resonance logging; Wherein, y is the standardized NMR logging T2 spectrum, y(i) is the i-th data point in the standardized NMR logging T2 spectrum, y0 is the original NMR logging T2 spectrum, and i is the i-th data point in the original NMR logging T2 spectrum; is the total porosity of the formation; M is the number of sample points after the discretization of the T2 spectrum of the nuclear magnetic resonance logging, and j is the jth sample point after the discretization of the T2 spectrum of the nuclear magnetic resonance logging; In the NMR logging T2 spectrum, determine the positions of the clay bound water peak and the movable fluid peak, including: Determine the clay type at the logging location, and determine the location of the clay bound water spectrum peak based on the clay type; Determine the viscosity of the crude oil at the logging location and determine the movable fluid spectrum peak using the following formula; Among them, T 2bp is the T2 value corresponding to the NMR volume relaxation peak, i.e., the position of the mobile fluid spectrum peak; η is the viscosity of crude oil; T is the formation temperature; Pick up the half peak trajectory data of the clay bound water spectrum peak, use the Gaussian fitting method and its characteristics to perform fitting processing, and obtain the clay bound water spectrum, including: Pick up half peak trajectory data of clay bound water spectrum peak and perform mirror symmetry processing on it; The fitting relationship is generated by using the Gaussian fitting method, and the trajectory data after mirror processing is fitted to obtain the clay bound water spectrum, wherein the fitting relationship is as follows; Among them, a, b and c are the parameters to be estimated, which are the amplitude of the Gaussian curve peak, the position of the Gaussian curve peak on the x-axis and the spread width of the curve respectively; The clay bound water spectrum is processed by curve integration to obtain the clay bound water porosity; Repeat the above steps to obtain the movable fluid spectrum and movable fluid porosity; The capillary bound fluid spectrum was obtained by subtracting the clay bound water spectrum and movable fluid spectrum from the original NMR logging T2 spectrum. The capillary bound fluid spectrum was then processed by curve integration to obtain the capillary bound fluid porosity.

2. A porosity clustering calculation device based on nuclear magnetic resonance logging T2 spectrum using the method as claimed in claim 1, characterized in that: It includes a clay bound water porosity processing unit, a movable fluid porosity processing unit and a capillary bound fluid porosity processing unit; Standardization unit, use the following formula to standardize the NMR logging T2 spectrum; Wherein, y is the standardized NMR logging T2 spectrum, y(i) is the i-th data point in the standardized NMR logging T2 spectrum, y0 is the original NMR logging T2 spectrum, and i is the i-th data point in the original NMR logging T2 spectrum; is the total porosity of the formation; M is the number of sample points after the discretization of the T2 spectrum of the nuclear magnetic resonance logging, and j is the jth sample point after the discretization of the T2 spectrum of the nuclear magnetic resonance logging; The position determination unit determines the positions of the clay bound water spectrum peak and the movable fluid spectrum peak in the nuclear magnetic resonance logging T2 spectrum, including: Determine the clay type at the logging location, and determine the location of the clay bound water spectrum peak based on the clay type; Determine the viscosity of the crude oil at the logging location and determine the movable fluid spectrum peak using the following formula; Among them, T 2bp is the T2 value corresponding to the NMR volume relaxation peak, i.e., the position of the mobile fluid spectrum peak; η is the viscosity of crude oil; T is the formation temperature; The clay bound water porosity processing unit picks up the half-peak trajectory data of the clay bound water spectrum peak, performs fitting processing using the Gaussian fitting method and its characteristics, and obtains the clay bound water spectrum; performs curve integration processing on the clay bound water spectrum to obtain the clay bound water porosity; The movable fluid porosity processing unit picks up the half-peak trajectory data of the movable fluid spectrum peak, performs fitting processing using the Gaussian fitting method and its characteristics, and obtains the movable fluid spectrum; performs curve integration processing on the movable fluid spectrum to obtain the movable fluid porosity; The capillary bound fluid pore processing unit uses the original nuclear magnetic resonance logging T2 spectrum to subtract the clay bound water spectrum and the movable fluid spectrum to obtain the capillary bound fluid spectrum, and performs curve integration processing on it to obtain the capillary bound fluid porosity; Wherein, the clay bound water porosity processing unit and the movable fluid porosity processing unit both include a first acquisition unit and a second acquisition unit; The first acquisition unit picks up half of the peak trajectory data of the clay bound water spectrum peak / movable fluid spectrum peak, performs mirror symmetry processing on it, generates a fitting relationship using a Gaussian fitting method, fits the trajectory data after mirror processing, and obtains the clay bound water spectrum / movable fluid spectrum, wherein the fitting relationship is as follows; Among them, a, b and c are the parameters to be estimated, which are the amplitude of the Gaussian curve peak, the position of the Gaussian curve peak on the x-axis and the spread width of the curve respectively; The second acquisition unit performs curve integration processing on the clay bound water spectrum / movable fluid spectrum to obtain the clay bound water porosity / movable fluid porosity.

3. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum as claimed in claim 1 when running.

4. An electronic device, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the porosity clustering calculation method based on nuclear magnetic resonance logging T2 spectrum as claimed in claim 1.

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