CN112526622A - Pseudo-nuclear magnetic echo data calculation method based on imaging logging porosity spectrum - Google Patents

Pseudo-nuclear magnetic echo data calculation method based on imaging logging porosity spectrum Download PDF

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CN112526622A
CN112526622A CN202011544038.7A CN202011544038A CN112526622A CN 112526622 A CN112526622 A CN 112526622A CN 202011544038 A CN202011544038 A CN 202011544038A CN 112526622 A CN112526622 A CN 112526622A
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nuclear magnetic
porosity
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李晓辉
刘堂晏
李强
傅强
夏雪
许淑梅
赵文君
张亚金
赵世杰
张翠华
张振伟
李庆峰
陈洪涛
王英武
吴燕辉
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China National Petroleum Corp
China Petroleum Logging Co Ltd
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China Petroleum Logging Co Ltd
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Abstract

The invention relates to the field of petroleum and natural gas exploration and development, in particular to a pseudo-nuclear magnetic echo data extraction method based on imaging logging porosity spectrum. The method comprises the steps of collecting nuclear magnetic echo logging data and electrical imaging logging data, and acquiring the geometric mean value T of the transverse relaxation time of nuclear magnetic2LMPOR data extraction step and porosity POR data extraction step, fitting nuclear magnetic transverse relaxation time T2And the relation step between the porosity POR and the calculation of the pseudo-nuclear magnetic transverse relaxation time NT2iThe method comprises the steps of calculating a pseudo-nuclear magnetic echo and three equations. Aiming at an oil-gas-containing reservoir with a complex pore structure, a correlation equation is established by applying the transverse relaxation time of nuclear magnetic logging information and the porosity of the reservoir, data provided by a porosity spectrum of imaging logging information is applied, the transverse relaxation time of quasi-nuclear magnetism is calculated, and further quasi-nuclear magnetism echo is calculated, so that basic data are provided for finally obtaining pore structure parameters. For predicting hydrocarbon containing reservoirThe yield has extremely important application value.

Description

Pseudo-nuclear magnetic echo data calculation method based on imaging logging porosity spectrum
Technical Field
The invention relates to the field of petroleum and natural gas exploration and development, in particular to a pseudo-nuclear magnetic echo data extraction method based on imaging logging porosity spectrum.
Background
The nuclear magnetic resonance analysis technology has good application value in different fields of medical treatment, agriculture, biology, water resource and oil gas resource exploration and the like. For example, in the field of petroleum exploration and development, core nuclear magnetic experiment analysis and nuclear magnetic logging data analysis have good application effects in the research fields of reservoir saturation calculation, permeability calculation, pore structure evaluation and the like. In current oil exploration and development, volcanic rock, carbonate rock, metamorphic rock and the like are gradually main targets, and the reservoirs are characterized by complex reservoir spaces and large size and form difference of the reservoir spaces (pore spaces) of different reservoirs. The nuclear magnetic core experiment analysis and the nuclear magnetic logging data analysis well meet the technical requirements of the petroleum industry on the evaluation of complex reservoirs.
The existing logging method for evaluating the pore space of the reservoir mainly comprises the following three methods, namely, three porosity (neutrons, density and sound waves) based on conventional logging, wherein the evaluation mode of the pore space is to provide a porosity parameter, namely the percentage of the pore space in the rock volume is a relatively macroscopic expression form; second, the electrical imaging well logging, through the image processing, realize the visual visualization of the pore space (the visible part of naked eyes), further process with the software at the same time, can obtain the porosity spectrum, show all pores in the way of counting the frequency distribution, on the basis of keeping the concept of macroscopic porosity, reflect the characteristic of the pore space distribution indirectly; and thirdly, nuclear magnetic resonance logging, on the basis of providing a macro porosity parameter, pore structure characteristic parameters can be further provided, the micro distribution condition of a reservoir pore space, the relative size and the relative proportion of pore radii are reflected, and the information is the fine evaluation of the reservoir pore space and has extremely important application value for predicting the oil and gas yield of the oil and gas reservoir.
The three modes reflect the characteristics of the pore space of the reservoir in different aspects, meet different geological requirements, and particularly can provide more detailed and microscopic geological information to a certain extent. However, the acquisition of nuclear magnetic data is a high-cost wellbore construction operation, and the application of the nuclear magnetic resonance technology is limited because the research and development cost of instruments and the data inversion interpretation cost are still high and are greatly influenced by construction environments such as temperature and wellbore conditions. Therefore, it is very urgent to find a method for obtaining pore structure characteristic parameters under the condition that the nuclear magnetic logging cannot be used for obtaining logging data, especially for the areas/strata with complex geological conditions and complex reservoir spaces, and to use the available data to calculate and obtain the pore structure information.
Disclosure of Invention
The purpose of the invention is as follows: aiming at an oil-gas-containing reservoir with a complex pore structure, a correlation equation is established by applying the transverse relaxation time of nuclear magnetic logging information and the porosity of the reservoir, data provided by a porosity spectrum of imaging logging information is applied, the transverse relaxation time of quasi-nuclear magnetism is calculated, quasi-nuclear magnetic echo is further calculated and obtained, and basic data are provided for finally obtaining pore structure parameters.
The specific invention content is as follows:
a pseudo-nuclear magnetic echo data calculation method based on an imaging logging porosity spectrum comprises the following steps:
the method comprises the steps of firstly, respectively acquiring nuclear magnetic echo logging data and electrical imaging logging data of the same section of reservoir of the same well;
and secondly, processing the nuclear magnetic echo and electric imaging logging data obtained in the first step:
inversion of nuclear magnetic echo logging data to obtain nuclear magnetic T2A spectrum;
processing the electrical imaging logging data to obtain porosity spectrum data; the porosity spectrum data comprises frequency data P1，P2，…，P100；
Third step, nuclear magnetism T obtained in the second step2Further calculating the spectrum to obtain the geometric mean value T of the transverse nuclear magnetic relaxation time of 35 to 300 depth points2LMAnd porosity POR data corresponding thereto;
fourthly, applying the transverse relaxation time geometric mean value T obtained in the third step2LMFitting nuclear magnetic transverse relaxation time T with corresponding porosity POR data2Obtaining a fitting equation according to the relation between the porosity POR and the pore;
fifthly, calculating the pores of the segmentation points point by point according to the number of segmentation intervals of the electrical imaging porosity spectrum by applying a scale conversion equation (2)Value PORi；
Figure BDA0002855425740000021
PORmax、PORminMaximum and minimum values of porosity scale are respectively small numbers;
max and min are respectively the maximum value and the minimum value number of the scale points of the porosity spectrum of the electric imaging, and the default values are respectively 100 and 1;
sixthly, applying the fitting equation to obtain a porosity value POR according to the division points of the porosity spectrumiCalculating the corresponding pseudo-nuclear magnetic transverse relaxation time NT2iObtaining NT2The stationing value of the spectrum;
seventhly, calculating the pseudo-nuclear magnetic transverse relaxation time NT according to the sixth step2iAnd the frequency data P of the electro-imaging porosity spectrum obtained in the second step1、P2、…、P100Calculating the quasi-nuclear magnetic echo by applying the following echo extraction equation (3):
Figure BDA0002855425740000031
in the formula:
M(tj) Is tjThe sampling amplitude of the echo at the moment is dimensionless after being standardized;
Piis the frequency, decimal, of the ith porosity split point;
n is the collection point number of the echo data, and is default to 1800;
N1is a total of 100 porosity cut points;
epsilon is a random noise signal simulating random noise generated in the acquisition of echo data;
tjobtained from the intermediate equation (4):
tj＝T0+TE×j
j＝1,2,...N (4)
N＝1800，TE＝0.2ms，T0＝0.2ms；
tjis the current sampling time in ms;
n is the collection point number of the echo data;
TE、T0respectively the echo interval and the initial sampling time in ms.
Preferably, the method for calculating pseudo-nuclear magnetic echo data based on imaging logging porosity spectrum is characterized in that the fitting equation is as follows:
Figure BDA0002855425740000032
T2icurrent T2Spectral distribution values in ms.
Preferably, the method for calculating the pseudo-nuclear magnetic echo data based on the imaging logging porosity spectrum is characterized in that the coefficients a, b and c in the fitting equation (1) are obtained from the geometric mean value T of the transverse relaxation time obtained in the third step2LMAnd porosity POR value data, and determining by adopting a least square algorithm.
Preferably, the method for calculating the pseudo-nuclear magnetic echo data based on the imaging logging porosity spectrum is characterized in that coefficients a, b and c in the fitting equation (1) are 4.0, 0.293 and 20.53 respectively.
The invention has the beneficial effects that:
aiming at a reservoir with a complex pore structure, under the condition of lacking nuclear magnetic logging information for evaluating the pore structure, the invention can obtain simulated nuclear magnetic logging data, namely pseudo-nuclear magnetic echo data, by utilizing the data of the electrical imaging logging information. Calculation and experimental data prove that the fitting equation provided by the invention has higher prediction precision and meets the requirement of practical application. The data of the pseudo-nuclear magnetic echo can be further utilized as basic data for evaluating the pore structure.
Drawings
FIG. 1 is an electrographic porosity spectra and nuclear magnetic T of the present invention2Spectral graph example contrast plots;
FIG. 2 is a drawing of the present inventionThe first embodiment is TT1 well with porosity of 4994-5023 m well section and relaxation time T2Fitting graphs with the conversion data and the conversion equation;
FIG. 3 is a first embodiment of the present invention, TT1 well, applying fitting equation to predict the pseudo-nuclear magnetic transverse relaxation time T2T of measured nuclear magnetism2Analyzing the correlation of time;
FIG. 4 shows a pseudo-nuclear magnetic field T obtained by inversion of a classical inversion algorithm according to a first embodiment of the present invention2A spectrum;
FIG. 5 is a comparison graph of practical application results of a CT3 well according to a second embodiment of the present invention.
Detailed Description
The main content of the invention is to establish an electric imaging porosity spectrum and nuclear magnetic T2The conversion relationship between spectra (transverse relaxation times). The device comprises three parts: one is T according to nuclear magnetic logging2Spectral data, establishing transverse relaxation time geometric mean T2LMAnd porosity. Second, the porosity scale is converted into transverse relaxation time T2Calibration; and thirdly, applying a forward equation of the nuclear magnetic echo signal to generate a pseudo-nuclear magnetic echo signal.
The operation of the present invention will be described in two embodiments with reference to the accompanying drawings.
In the first embodiment, a Tong 1 well of Tong Nan block of Sichuan basin, also known as TT1 well, is selected, a pseudo-nuclear magnetic echo data calculation method based on imaging logging porosity spectrum is applied, and nuclear magnetic logging T is used2Spectral data, establishing transverse relaxation time geometric mean T2LMAnd porosity and acquiring quasi-nuclear magnetic echo.
Firstly, calling a nuclear magnetic logging instrument and an electrical imaging logging instrument, and respectively acquiring nuclear magnetic echo data and electrical imaging logging data in a same reservoir section 4994-5023 meters of a TT1 well as a basic data for implementing the method. In practical application, if the nuclear magnetic and electric imaging logging information of the same section of reservoir of the same well can be found in the existing logging information, the nuclear magnetic and electric imaging logging information can be directly called without re-logging in the field by using a nuclear magnetic logging instrument and an electric imaging logging instrument.
And secondly, processing the nuclear magnetic and electric imaging raw data obtained in the first step by using a Techog/CIFLog/Petrosite logging software platform:
obtaining nuclear magnetism T by inverting the actually measured nuclear magnetism echo data in the process of processing the nuclear magnetism logging data2A spectrum;
meanwhile, processing the electrical imaging logging data of the TT1 well to obtain an electrical imaging porosity spectrum of the well, wherein the obtained electrical imaging porosity spectrum comprises porosity frequency data P corresponding to 100 interval values of porosity1，P2，…，P100；
Electric imaging porosity spectrum, actually measured nuclear magnetic echo and nuclear magnetic T2An example graph of the spectrum is shown in fig. 1.
Thirdly, applying a Techog/CIFLog/Petrosite logging software platform to the nuclear magnetism T obtained in the second step2Further spectrum calculation is carried out, and the nuclear magnetic transverse relaxation time geometric mean value T of 153 depth points of the well section of 4994-5023 m is obtained2LMAnd porosity POR data corresponding thereto. Part of the data is shown in the following table. Regarding the sampling points, from the statistical point of view, 35 points have statistical regularity, while more than 300 points have little practical significance and are easy to introduce artifacts. Therefore, in practical applications, the number of sampling points is preferably 35 to 300. Thus obtaining the geometric mean value T of the transverse relaxation time of the nuclear magnetism of 35 to 300 depth points2LMSufficiently close results can be obtained with porosity POR data corresponding thereto.
Depth (Rice) T2Geometric mean (ms) Nuclear magnetic porosity (m)3/m3)
4994 72.50134 0.0779304
4994.1905 59.85919 0.0791746
4994.381 67.16801 0.0597758
4994.5715 57.209602 0.0443564
4994.762 40.523518 0.0429048
4994.9525 42.35621 0.0518413
4995.143 60.49459 0.0539363
4995.3335 179.55403 0.0519872
4995.524 78.33042 0.0576813
4995.7145 60.063137 0.0553124
4995.905 137.45749 0.0455183
4996.0955 508.92886 0.0415324
4996.286 354.7577 0.0421121
4996.4765 121.17574 0.0456404
4996.667 65.92776 0.049665
4996.8575 75.364685 0.0489758
4997.048 177.29846 0.0525728
4997.2385 405.3168 0.0496406
4997.429 530.10956 0.0476249
4997.6195 260.70706 0.0475793
4997.81 91.76747 0.058062
4998.0005 46.521328 0.067595
4998.191 38.727814 0.0599473
4998.3815 42.235508 0.0446526
… … …
… … …
… … …
Fourth step, geometric mean value T of time of transverse relaxation due to nuclear magnetism2LMThe relation between the porosity POR and the nuclear magnetic transverse relaxation time T is equal to2The relationship with the porosity POR; the geometric mean value T of the transverse relaxation times obtained in the third step is therefore used2LMFitting nuclear magnetic transverse relaxation time T with corresponding porosity POR data2And obtaining a fitting equation according to the relation between the porosity POR and the fitting equation. The fitting equation (1) obtained in this embodiment is;
Figure BDA0002855425740000061
T2icurrent T2A point value of spectral distribution in ms;
the coefficients a, b and c in the fitting equation (1) are the transverse relaxation time geometric mean value T obtained in the third step2LMAnd porosity POR data, determined by adopting a least square algorithm; in this embodiment, the coefficients a, b, and c in the equation are determined to be 4.0, 0.293, and 20.53, respectively, using a least squares algorithm and the form of the fitting equation (1).
FIG. 2 shows the porosity and the relaxation time T2The conversion data and the conversion equation fitting graph between the two are key graphs for implementing the invention. Practical calculation results show that when the porosity is between 0.00001 and 0.20, the relaxation time calculated by using the fitting equation is between 1.6 and 2140 ms.
Pseudo-nuclear magnetic transverse relaxation time T calculated by applying predictive fitting equation in TT1 well of the embodiment2iThe method has good consistency with the measurement result of actual observation data. Actually measuring the nuclear magnetic transverse relaxation time T in the data analysis of 153 sample points in a well section of 4994-5023 meters2And the predicted pseudo-nuclear magnetic transverse relaxation time T according to the fitting equation2iThe correlation coefficient of (a) reaches 0.803. As shown in fig. 3. The fitting equation has higher prediction precision and meets the requirement of practical application.
Fifthly, because the electrical imaging porosity spectrum calculated and output in the step two is a porosity interval value of frequency statistics,it is not a specific porosity value, so it is necessary to convert the porosity interval value scale of the electro-imaging porosity spectrum into the porosity point value scale; according to the porosity distribution range of the porosity spectrum obtained in the second step, the following scale conversion equation (2) is applied to linearly divide the porosity change range; calculating the porosity POR of the division points point by point according to the number of the division intervalsiAs a sixth step, the quasi-nuclear magnetic T is calculated2The echo point distribution is fitted to the distribution value of the porosity value POR of equation (1).
Figure BDA0002855425740000071
PORmax、PORminMaximum and minimum values of porosity scale are respectively small numbers;
max and min are respectively the maximum value and the minimum value number of the scale points of the porosity spectrum of the electric imaging, and the default values are respectively 100 and 1;
sixthly, applying a fitting equation (1) and according to the porosity value POR of the division point of the porosity spectrumiCalculating the corresponding pseudo-nuclear magnetic transverse relaxation time NT2iObtaining the quasi-nuclear magnetic transverse relaxation time NT2The stationing value of the spectrum;
seventhly, calculating the pseudo-nuclear magnetic transverse relaxation time NT according to the sixth step2iAnd the frequency data P of the electro-imaging porosity spectrum obtained in the second step1、P2、…、P100Calculating quasi-nuclear magnetic echo by using an echo extraction equation; the echo extraction equation (3) adopted in this embodiment is:
Figure BDA0002855425740000072
Figure BDA0002855425740000081
in the formula:
M(tj) Is tjAmplitude of echo samples at time, standardNo dimension exists after the transformation;
Piis the frequency, decimal, of the ith porosity split point;
n is the collection point number of the echo data, and is default to 1800;
N1is a total of 100 porosity cut points;
epsilon is a random noise signal simulating random noise generated in the acquisition of echo data; the amplitude value of epsilon is 0.5-0.8 of the average noise of nuclear magnetic echoes of the sampling well;
tjobtained from the intermediate equation (4):
tj＝T0+TE×j
j＝1,2,...N (4)
N＝1800，TE＝0.2ms，T0＝0.2ms。
tjis the current sampling time in ms;
n is the number of acquisition points of the echo data.
TE、T0Respectively the echo interval and the initial sampling time in ms.
Aiming at a reservoir with a complex pore structure, the invention obtains simulated nuclear magnetic echo data, namely pseudo-nuclear magnetic echo data, by utilizing a data processing result of electrical imaging logging data, and further utilizes the pseudo-nuclear magnetic echo data to evaluate the pore structure by utilizing the existing analysis tool. By adopting the method, the quasi-nuclear magnetic T can be obtained by calculating the quasi-nuclear magnetic echo data by using a fitting equation, a scale conversion equation and an echo extraction equation and inverting the quasi-nuclear magnetic echo data by a classical inversion algorithm (singular value decomposition (SVD))2Spectra. As shown in fig. 4. According to quasi-nuclear magnetic T2The spectrum can further provide characteristic parameters of the pore structure, reflect the microscopic distribution condition of the pore space of the reservoir, the relative size and the relative proportion of the pore radius, and the information is the fine evaluation of the pore space of the reservoir and has extremely important application value for predicting the oil and gas yield of the oil-gas reservoir.
The nuclear magnetic logging data can obtain the pore structure parameters of the reservoir through the optimized inversion of the spherical seam type, and the accurate prediction of the oil gas yield of the oil gas reservoir is realized. The spherical pores and fractures referred to herein correspond to geologically-defined pores and fractures.
In general, for a new area, 1-2 wells which have acquired nuclear magnetic logging data can be applied, and the fourth step is revised to establish a new equation. The more wells, the stronger the applicability of the equation.
For the work area in which the fitting equation (1) is established, under the condition that no nuclear magnetic logging information exists, only the porosity spectrum data of the electric imaging logging information needs to be obtained for each new well, the fifth step, the sixth step and the seventh step are executed, and the porosity spectrum of the electric imaging logging information is converted into quasi-nuclear magnetic echo to serve as the basis for further calculation of the pore structure parameters.
The following is a second embodiment of the invention, applying the acquired fitting equation, to calculate the nuclear magnetic echo.
Taking the CT3 well as an example, the well is a well in the deep desert and is inconvenient for transportation. The well depth was 7575 meters, the bottom hole temperature was 218 degrees, and the borehole size was 6.5 inches. Based on the geographic position of the well, the limitation of equipment transportation conditions and the influence of the temperature and pressure of the shaft on the measurement precision of a logging instrument, the well only carries out micro-resistivity scanning imaging logging data acquisition but does not carry out nuclear magnetic resonance logging data acquisition. As the well mainly has the characteristic of a complex pore structure, in order to better depict and evaluate the gas-containing reservoir, the method of the invention is applied, firstly, the porosity spectrum data of the electrical imaging logging of the well is obtained, the obtained fitting equation is applied, and the imaging data of the well is processed by the pseudo-nuclear magnetic echo through the fifth, sixth and seventh steps of the invention, so as to successfully obtain the pseudo-nuclear magnetic echo data. And further processing the pseudo-nuclear magnetic echo data to obtain the pore structure parameters (the ratio cd value of the fracture width to the spherical radius, namely the ratio of the fracture width to the pore radius in the geological sense) of the target reservoir. The results are shown in FIG. 5. Obtaining a pseudo-nuclear magnetic T2Spectrum and quasi-nuclear magnetic echo, and further performing multiple nuclear magnetic echo data inversion of classical meaning under different pore structure parameters to respectively obtain spherical pore spectrum and crack spectrum, and finally obtainingTo the pore structure parameter cd path.
In the implementation process of different regions (with different geological deposition environments and different pore space types and characteristics), the actual data of the region is applied to properly revise the implementation step four aiming at the difference of pore structures caused by the difference of the pore space types of the reservoir layer, so as to more accurately evaluate the pore structures of the reservoir layer.
Software copyright description:
techlog, owned by Schlumberger,
CIFLog, owned by the Petroleum exploration and development institute of China Petroleum group;
petrosine, owned by Halliburton.

Claims (4)

1. A pseudo-nuclear magnetic echo data calculation method based on an imaging logging porosity spectrum comprises the following steps:
the method comprises the steps of firstly, respectively acquiring nuclear magnetic echo logging data and electrical imaging logging data of the same section of reservoir of the same well;
and secondly, processing the nuclear magnetic echo and electric imaging logging data obtained in the first step:
inversion of nuclear magnetic echo logging data to obtain nuclear magnetic T2A spectrum;
processing the electrical imaging logging data to obtain porosity spectrum data; the porosity spectrum data comprises frequency data P1，P2，…，P100；
Third step, nuclear magnetism T obtained in the second step2Further calculating the spectrum, and acquiring the geometric mean value T of the transverse relaxation time of the nuclear magnetism at 35 to 300 depth points2LMAnd porosity POR data corresponding thereto;
fourthly, applying the transverse relaxation time geometric mean value T obtained in the third step2LMFitting nuclear magnetic transverse relaxation time T with corresponding porosity POR data2Obtaining a fitting equation according to the relation between the porosity POR and the pore;
fifthly, calculating segmentation points point by point according to the number of segmentation intervals of the electric imaging porosity spectrum by applying a scale conversion equation (2)POR value ofi；
Figure FDA0002855425730000011
PORmax、PORminMaximum and minimum values of porosity scale are respectively small numbers;
max and min are respectively the maximum value and the minimum value number of the scale points of the porosity spectrum of the electric imaging, and the default values are respectively 100 and 1;
sixthly, applying the fitting equation to obtain a porosity value POR according to the division points of the porosity spectrumiCalculating the corresponding pseudo-nuclear magnetic transverse relaxation time NT2iObtaining NT2The stationing value of the spectrum;
seventhly, calculating the pseudo-nuclear magnetic transverse relaxation time NT according to the sixth step2iAnd the frequency data P of the electro-imaging porosity spectrum obtained in the second step1、P2、…、P100Calculating the quasi-nuclear magnetic echo by applying the following echo extraction equation (3):
Figure FDA0002855425730000012
Pi＝P1,P2,…,P100
i＝1,2,…,N1
j＝1,2,…,N
in the formula:
M(tj) Is tjThe sampling amplitude of the echo at the moment is dimensionless after being standardized;
Piis the frequency, decimal, of the ith porosity split point;
n is the collection point number of the echo data, and is default to 1800;
N1is a total of 100 porosity cut points;
epsilon is a random noise signal simulating random noise generated in the acquisition of echo data;
tjfrom the intermediate formula (4)) Obtaining:
tj＝T0+TE×j
j＝1,2,...N (4)
N＝1800，TE＝0.2ms，T0＝0.2ms；
tjis the current sampling time in ms;
n is the collection point number of the echo data;
TE、T0respectively the echo interval and the initial sampling time in ms.
2. The method of calculating pseudo-nuclear magnetic echo data based on an imaged porosity spectrum according to claim 1, wherein the fitting equation is:
Figure FDA0002855425730000021
Figure FDA0002855425730000022
T2icurrent T2Spectral distribution values in ms.
3. The method for calculating pseudo-nuclear magnetic echo data based on imaging log porosity spectrum according to claim 2, wherein the coefficients a, b and c in the fitting equation (1) are obtained from the geometric mean value T of transverse relaxation time obtained in the third step2LMAnd porosity POR value data, and determining by adopting a least square algorithm.
4. The method of claim 2, wherein the coefficients a, b, c in the fitting equation (1) are 4.0, 0.293, and 20.53, respectively.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114109352A (en) * 2021-06-17 2022-03-01 中国海洋石油集团有限公司 Method for predicting porosity based on curve similarity

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040032257A1 (en) * 2002-08-09 2004-02-19 Robert Freedman Combining NMR, density, and dielectric measurements for determining downhole reservoir fluid volumes
CN102141637A (en) * 2010-01-28 2011-08-03 中国石油天然气股份有限公司 Method for continuously quantitative evaluation of pore structures of reservoir strata by utilizing nuclear magnetic resonance well logging data
CN103487837A (en) * 2013-09-13 2014-01-01 同济大学 Decomposing and synthetic method of quasi-saturated water nuclear magnetic resonance spin echo signals
CN104932027A (en) * 2015-05-06 2015-09-23 中国石油大学(北京) Reservoir classification method based on nuclear magnetic resonance logging
CN104990854A (en) * 2015-07-06 2015-10-21 中国石油天然气股份有限公司 Method and device for determining irreducible water saturation
CN106050225A (en) * 2016-06-06 2016-10-26 中国石油天然气集团公司 Method for determining 100% pure water spectrum through nuclear magnetic resonance logging spectrum T2
CN106066494A (en) * 2016-05-24 2016-11-02 中国地质大学（北京） A kind of igneous rock NMR porosity bearing calibration and T2 distribution correction method
CN109856688A (en) * 2019-01-31 2019-06-07 中国石油天然气集团有限公司 Flow net model method based on the double TW Polarimetric enhancement methods of nuclear magnetic resonance log
CN111827968A (en) * 2020-07-15 2020-10-27 长江大学 Reservoir heterogeneity evaluation method and device based on nuclear magnetic resonance logging

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040032257A1 (en) * 2002-08-09 2004-02-19 Robert Freedman Combining NMR, density, and dielectric measurements for determining downhole reservoir fluid volumes
CN102141637A (en) * 2010-01-28 2011-08-03 中国石油天然气股份有限公司 Method for continuously quantitative evaluation of pore structures of reservoir strata by utilizing nuclear magnetic resonance well logging data
CN103487837A (en) * 2013-09-13 2014-01-01 同济大学 Decomposing and synthetic method of quasi-saturated water nuclear magnetic resonance spin echo signals
CN104932027A (en) * 2015-05-06 2015-09-23 中国石油大学(北京) Reservoir classification method based on nuclear magnetic resonance logging
CN104990854A (en) * 2015-07-06 2015-10-21 中国石油天然气股份有限公司 Method and device for determining irreducible water saturation
CN106066494A (en) * 2016-05-24 2016-11-02 中国地质大学（北京） A kind of igneous rock NMR porosity bearing calibration and T2 distribution correction method
CN106050225A (en) * 2016-06-06 2016-10-26 中国石油天然气集团公司 Method for determining 100% pure water spectrum through nuclear magnetic resonance logging spectrum T2
CN109856688A (en) * 2019-01-31 2019-06-07 中国石油天然气集团有限公司 Flow net model method based on the double TW Polarimetric enhancement methods of nuclear magnetic resonance log
CN111827968A (en) * 2020-07-15 2020-10-27 长江大学 Reservoir heterogeneity evaluation method and device based on nuclear magnetic resonance logging

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
R. FREEDMAN等: "Fluid Characterization using Nuclear Magnetic Resonance Logging" *
张筠;吴见萌;朱国璋;: "致密气核磁共振测井观测模式及气水弛豫分析――以四川盆地为例" *
胡婷婷等: "核磁共振测井法估算砂砾岩储层束缚水饱和度模型构建" *
薛苗苗等: "定量评价储层孔隙结构的新方法" *
赵文君等: "有效压力对渗透率和地层因素的影响分析" *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114109352A (en) * 2021-06-17 2022-03-01 中国海洋石油集团有限公司 Method for predicting porosity based on curve similarity
CN114109352B (en) * 2021-06-17 2023-11-10 中国海洋石油集团有限公司 Method for predicting porosity based on curve similarity

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