CN108562944A - A kind of parameter of pore structure computational methods based on Electrical imaging porosity spectrum - Google Patents
A kind of parameter of pore structure computational methods based on Electrical imaging porosity spectrum Download PDFInfo
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- 239000011148 porous material Substances 0.000 title claims abstract description 61
- 238000001228 spectrum Methods 0.000 title claims abstract description 49
- 238000003384 imaging method Methods 0.000 title claims abstract description 24
- 238000000205 computational method Methods 0.000 title abstract 2
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 claims abstract description 17
- 229910052753 mercury Inorganic materials 0.000 claims abstract description 17
- 238000002474 experimental method Methods 0.000 claims abstract description 8
- 238000003825 pressing Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 13
- 230000015572 biosynthetic process Effects 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 10
- 230000003595 spectral effect Effects 0.000 claims description 8
- 239000011435 rock Substances 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 3
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 abstract description 8
- 238000005481 NMR spectroscopy Methods 0.000 abstract description 4
- 238000011161 development Methods 0.000 abstract description 4
- 239000003345 natural gas Substances 0.000 abstract description 4
- 239000003208 petroleum Substances 0.000 abstract description 3
- 238000012512 characterization method Methods 0.000 abstract 1
- 239000000463 material Substances 0.000 abstract 1
- 238000011156 evaluation Methods 0.000 description 12
- 230000000694 effects Effects 0.000 description 4
- 239000007789 gas Substances 0.000 description 3
- 239000003209 petroleum derivative Substances 0.000 description 2
- 238000011158 quantitative evaluation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
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- Geophysics And Detection Of Objects (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a kind of parameter of pore structure computational methods based on Electrical imaging porosity spectrum, belong to evaluating reservoir field in petroleum natural gas exploration.Including：The core sample representative to stratum pore structure characteristic is chosen first, carries out pressure mercury experiment；And composed by Electrical imaging material computation porosity, the definition that parameter is composed further according to porosity carries out the calculating that porosity composes parameter；By pressing mercury experimental data to calculate parameter of pore structure, the corresponding porosity of scale composes parameter, obtains corresponding parameter of pore structure accounting equation.Porosity spectrum parameter is finally utilized to realize that full well section continuous and quantitative calculates parameter of pore structure.The present invention is in the case where certain blocks lack nuclear magnetic resonance logging data and electric imaging logging abundant information, the continuous characterization of pore structure cannot be carried out by pressing mercury data again simultaneously, quantitatively calculate RESERVOIR PORE STRUCTURE parameter, accuracy rate is high, and reliable foundation is provided for current China's oil natural gas exploration and development.
Description
Technical Field
The invention relates to the field of reservoir evaluation in petroleum and natural gas exploration and development, in particular to a pore structure parameter calculation method based on an electrical imaging porosity spectrum.
Background
With the continuous deepening of the petroleum exploration in China, the logging evaluation of a complex reservoir becomes the key point of the petroleum exploration in China. The evaluation of the pore structure is the key point and the difficulty of the evaluation of the complex reservoir, and the evaluation of the pore structure can be used for evaluating the quality of the reservoir more accurately. At present, the pore structure is generally evaluated through mercury intrusion curve forms obtained through mercury intrusion experiment data and calculated pore structure parameters in China, but the mercury intrusion experiment cost is high, and continuous and quantitative evaluation cannot be carried out on the whole well due to discrete data. In order to realize continuity and quantification of pore structure evaluation, related researches are carried out in China on a method for carrying out quantitative calculation on pore structure parameters through nuclear magnetic logging data, so that continuous treatment of the whole well section can be realized, but at present, the problem of less nuclear magnetic data generally exists in domestic oil fields, and the regional overall evaluation is difficult. The calculation of the porosity spectrum by the electrical imaging data is a mature method in China, and the distribution of large and small pores in a reservoir can be effectively distinguished. In China, the porosity spectrum is further mined by solving parameters such as variance and mean value of the porosity spectrum, but the research on solving the pore structure parameter of the reservoir through the porosity spectrum is not available.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a pore structure parameter calculation method based on an electrical imaging porosity spectrum, which can calculate the pore structure parameters more accurately and rapidly through electrical imaging data, solves the problem that the quantitative calculation of the pore structure parameters of the reservoir cannot be realized due to the lack of nuclear magnetic resonance logging data, can timely serve or meet the requirements of actual production, realizes the quantitative calculation of the pore structure parameters of the oil and gas reservoir and has obvious application effect.
The invention is realized by the following technical scheme.
A pore structure parameter calculation method based on an electrical imaging porosity spectrum comprises the following steps:
selecting an oil and gas field where an electric imaging logging is located, and carrying out a mercury intrusion test on a rock core sample with a representative formation pore structure characteristic;
step (2), calculating an electric imaging porosity spectrum through electric imaging data, and constructing new spectrum parameters including maximum porosity, porosity mean, sorting coefficient, skewness, microscopic mean coefficient and kurtosis;
step (3), calculating the porosity spectrum parameters according to the definition of the porosity spectrum parameters;
step (4), calculating pore structure parameters through mercury pressing capillary pressure experiment data, and scaling corresponding porosity spectrum parameters to obtain corresponding pore structure parameter calculation equations;
and (5) calculating the continuous quantitative pore structure parameters of the whole well section through the porosity spectrum parameters.
And (3) performing mercury injection experiments according to the standard regulation of GB/T29171-2012 'determination of rock capillary pressure curve'.
The step (2) is to construct the maximum porosity P in the new spectral parametersmax: i.e., the maximum porosity value in the porosity spectrum, is calculated by the following formula:
wherein,is the ith cell gap value.
The step (2) is to construct a new porosity mean value P in the spectrum parametersM: i.e., the average reservoir porosity, is calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities at the time of the ith porosity.
The step (2) is to construct a new sorting coefficient S in the spectrum parametersp: i.e., the degree of sorting that characterizes the porosity in the formation, is calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the average porosity.
The step (2) is to construct new skewness S in the spectrum parameterskp: i.e., the asymmetry that characterizes the porosity distribution in the formation, to describe the distribution of the magnitude of the porosity, is calculated by the following equation:
wherein,is the ith cell gap value; n is a radical ofiTo cut off to the i-th porosity, ofThe number of the cells; pMIs the mean value of porosity, SpIs the sorting coefficient.
in the step (2), a new microscopic mean coefficient α in the spectrum parameters is constructed, namely the mean condition of the porosity distribution of the stratum is reflected and is calculated according to the following formula:
wherein, PMIs the mean value of porosity, PmaxThe maximum porosity is used.
The step (2) is to construct a new peak state S in the spectrum parametersg: i.e., characterizing the degree of convexity of the porosity spectrum, as calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the mean value of porosity, SpIs the sorting coefficient.
In the step (4), the pore structure parameters are calculated according to the following formula:
PS＝a·f(Pp)+b
wherein, PsIs a pore structure parameter; ppIs a porosity spectrum parameter; a and b are fitting coefficients; f (P)p) Is represented by PpIs an equation for the variable.
And (5) substituting the spectral parameters constructed in the step (3) into the function equation obtained in the step (4), so that continuous quantitative calculation of pore structure parameters can be realized.
Under the condition that some blocks lack nuclear magnetic resonance data and the electric imaging logging data are rich, the mercury intrusion data can not be continuously represented, new spectrum parameters can be constructed through the electric imaging porosity spectrum, reservoir pore structure parameters are calculated quantitatively, the accuracy is high, the relative error of the microscopic mean coefficient is not more than 19.6%, the relative error of the sorting coefficient is not more than 18.72%, and the relative error of the radius mean value is not more than 18.75% in the pore structure parameters. The method basically meets the requirements of on-site exploration and development, provides a reliable basis for the petroleum and natural gas exploration and development in China at present, and has important significance for mining the application of electrical imaging data in pore structure evaluation.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a graph of the calculated pore structure parameter results of the present invention.
Detailed Description
The invention is further described in detail below with reference to the drawings and examples, but the invention is not limited thereto.
The invention relates to a pore structure parameter calculation method based on an electric imaging porosity spectrum, which comprises the following steps:
selecting an oil-gas field where an electric imaging logging is located, and carrying out mercury pressing experiments on a core sample with a representative formation pore structure characteristic according to the standard regulation of GB/T29171-2012 'determination of rock capillary pressure curve';
calculating a porosity spectrum through the electric imaging data, and constructing new spectrum parameters including maximum porosity, a porosity mean value, a sorting coefficient, skewness, a microscopic mean value coefficient and a kurtosis;
maximum porosity Pmax: i.e., the maximum porosity value in the porosity spectrum, is calculated by the following formula:
wherein,is the ith cell gap value.
Mean value of porosity PM: i.e., the average reservoir porosity, is calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities at the time of the ith porosity.
Sorting coefficient Sp: i.e., the degree of sorting that characterizes the porosity in the formation, is calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the average porosity.
Skewness Skp: i.e., the asymmetry that characterizes the porosity distribution in the formation, to describe the distribution of the magnitude of the porosity, is calculated by the following equation:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the mean value of porosity, SpIs the sorting coefficient.
the microscopic mean coefficient α the mean condition reflecting the porosity distribution of the stratum and is calculated by the following formula:
wherein, PMIs the mean value of porosity, PmaxThe maximum porosity is used.
Peak state Sg: i.e., characterizing the degree of convexity of the porosity spectrum, as calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the mean value of porosity, SpIs the sorting coefficient.
Step three, calculating the porosity spectrum parameters according to the definition of the porosity spectrum parameters;
and step four, calculating pore structure parameters through mercury intrusion experimental data, and scaling corresponding porosity spectrum parameters to obtain corresponding pore structure parameter calculation equations.
Calculating the pore structure parameters according to the following formula:
PS＝a·f(Pp)+b
wherein, PsIs a pore structure parameter; ppIs a porosity spectrum parameter; a and b are fitting coefficients; f (P)p) Is represented by PpIs an equation for the variable.
And step five, realizing continuous quantitative calculation of pore structure parameters of the whole well section through the porosity spectrum parameters.
The invention provides a method for evaluating a pore structure through an electric imaging porosity spectrum, which can effectively and quickly calculate the pore structure parameters of a reservoir in certain blocks which lack nuclear magnetic resonance logging data and cannot carry out continuous quantitative evaluation on the pore structure of the reservoir, meets the requirement of continuous quantitative reservoir evaluation in the whole well section, has good correspondence with the pore structure parameters obtained by mercury intrusion experimental data, can accurately reflect the pore structure of the reservoir, can timely serve or meet the requirement of interpretation and evaluation of logging data, and has obvious application effect.
Fig. 1 is a result chart of the pore structure parameter calculated by the invention, and the accuracy is higher by comparing the calculated pore structure parameter with the pore structure parameter calculated by the mercury intrusion experiment, as shown in the following table, the application effect is good.
As can be seen from Table 1 above, the pore structure parameters have a relative error of the microscopic mean coefficient of not more than 19.6%, a relative error of the sorting coefficient of not more than 18.72%, and a relative error of the radial mean of not more than 18.75%. According to the invention, by constructing the relationship between the porosity spectrum parameters and the pore structure parameters, the pore structure parameters of the reservoir can be calculated quantitatively and accurately, the accuracy is high, the problems of less nuclear magnetic data and difficulty in regional overall evaluation in an oil field are solved, the requirements of well logging data interpretation and evaluation are met, and the application effect is good.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A pore structure parameter calculation method based on an electrical imaging porosity spectrum is characterized by comprising the following steps:
selecting an oil and gas field where an electric imaging logging is located, and carrying out a mercury intrusion test on a rock core sample with a representative formation pore structure characteristic;
step (2), calculating an electric imaging porosity spectrum through electric imaging data, and constructing new spectrum parameters including maximum porosity, porosity mean, sorting coefficient, skewness, microscopic mean coefficient and kurtosis;
step (3), calculating the porosity spectrum parameters according to the definition of the porosity spectrum parameters;
step (4), calculating pore structure parameters through mercury pressing capillary pressure experiment data, and scaling corresponding porosity spectrum parameters to obtain corresponding pore structure parameter calculation equations;
and (5) substituting the porosity spectrum parameters into a pore structure parameter calculation equation to realize the calculation of the continuous quantitative pore structure parameters of the whole well section.
2. The method of claim 1, wherein step (1), mercury intrusion test, is performed according to GB/T29171-2012 "determination of rock capillary pressure curve".
3. The method of claim 1, wherein in step (2), the maximum porosity P of the new spectral parameters is constructedmax: i.e., the maximum porosity value in the porosity spectrum, is calculated by the following formula:
wherein,is the ith cell gap value.
4. The method of claim 1, wherein step (2) comprises constructing a new mean value of porosity P in the spectral parametersM: i.e., the average reservoir porosity, is calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiTo stop at the ith porosity, the poresThe number of gaps.
5. The method of claim 1, wherein step (2) comprises constructing a new spectral parameter having a sorting coefficient Sp: i.e., the degree of sorting that characterizes the porosity in the formation, is calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the average porosity.
6. The method of claim 1, wherein step (2) comprises constructing new spectral parameters with a distortion of Skp: i.e., the asymmetry that characterizes the porosity distribution in the formation, to describe the distribution of the magnitude of the porosity, is calculated by the following equation:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the mean value of porosity, SpIs the sorting coefficient.
7. the method of claim 1, wherein in step (2), the microscopic mean coefficient α of the new spectral parameters is constructed, i.e., the mean value reflecting the porosity distribution of the formation, and is calculated according to the following formula:
wherein, PMIs the mean value of porosity, PmaxThe maximum porosity is used.
8. The method of claim 1, wherein step (2) comprises constructing new spectral parameters with a peak state Sg: i.e., characterizing the degree of convexity of the porosity spectrum, as calculated by the following formula:
wherein,is the ith cell gap value; n is a radical ofiThe number of porosities when the porosity reaches the ith; pMIs the mean value of porosity, SpIs the sorting coefficient.
9. The method of claim 1, wherein step (4) of calculating the pore structure parameter is performed according to the following equation:
PS＝a·f(Pp)+b
wherein, PsIs a pore structure parameter; ppIs a porosity spectrum parameter; a and b are fitting coefficients; f (P)p) Is represented by PpIs an equation for the variable.
10. The method of claim 1, wherein the pore structure parameters have a microscopic mean relative error of no greater than 19.6%, a sorting coefficient relative error of no greater than 18.72%, and a radial mean relative error of no greater than 18.75%.
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Application publication date: 20180921 |