CN110608032B - Oil well yield prediction method based on light hydrocarbon logging and computer equipment - Google Patents
Oil well yield prediction method based on light hydrocarbon logging and computer equipment Download PDFInfo
- Publication number
- CN110608032B CN110608032B CN201910880155.1A CN201910880155A CN110608032B CN 110608032 B CN110608032 B CN 110608032B CN 201910880155 A CN201910880155 A CN 201910880155A CN 110608032 B CN110608032 B CN 110608032B
- Authority
- CN
- China
- Prior art keywords
- light hydrocarbon
- oil
- yield
- parameters
- water
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 229930195733 hydrocarbon Natural products 0.000 title claims abstract description 152
- 150000002430 hydrocarbons Chemical class 0.000 title claims abstract description 152
- 239000004215 Carbon black (E152) Substances 0.000 title claims abstract description 151
- 239000003129 oil well Substances 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000004519 manufacturing process Methods 0.000 claims abstract description 101
- 238000012417 linear regression Methods 0.000 claims abstract description 43
- 238000005259 measurement Methods 0.000 claims abstract description 33
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 117
- 238000004590 computer program Methods 0.000 claims description 7
- 239000003921 oil Substances 0.000 description 86
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 42
- YXFVVABEGXRONW-UHFFFAOYSA-N Toluene Chemical compound CC1=CC=CC=C1 YXFVVABEGXRONW-UHFFFAOYSA-N 0.000 description 42
- 238000010276 construction Methods 0.000 description 9
- 238000012360 testing method Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 8
- 230000000704 physical effect Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 4
- 239000012530 fluid Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- OFBQJSOFQDEBGM-UHFFFAOYSA-N Pentane Chemical compound CCCCC OFBQJSOFQDEBGM-UHFFFAOYSA-N 0.000 description 2
- ATUOYWHBWRKTHZ-UHFFFAOYSA-N Propane Chemical compound CCC ATUOYWHBWRKTHZ-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 239000010779 crude oil Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- NNPPMTNAJDCUHE-UHFFFAOYSA-N isobutane Chemical compound CC(C)C NNPPMTNAJDCUHE-UHFFFAOYSA-N 0.000 description 2
- QWTDNUCVQCZILF-UHFFFAOYSA-N isopentane Chemical compound CCC(C)C QWTDNUCVQCZILF-UHFFFAOYSA-N 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- UAEPNZWRGJTJPN-UHFFFAOYSA-N methylcyclohexane Chemical compound CC1CCCCC1 UAEPNZWRGJTJPN-UHFFFAOYSA-N 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- XDTMQSROBMDMFD-UHFFFAOYSA-N Cyclohexane Chemical compound C1CCCCC1 XDTMQSROBMDMFD-UHFFFAOYSA-N 0.000 description 1
- OTMSDBZUPAUEDD-UHFFFAOYSA-N Ethane Chemical compound CC OTMSDBZUPAUEDD-UHFFFAOYSA-N 0.000 description 1
- 150000004945 aromatic hydrocarbons Chemical class 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004587 chromatography analysis Methods 0.000 description 1
- AFABGHUZZDYHJO-UHFFFAOYSA-N dimethyl butane Natural products CCCC(C)C AFABGHUZZDYHJO-UHFFFAOYSA-N 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000001282 iso-butane Substances 0.000 description 1
- GYNNXHKOJHMOHS-UHFFFAOYSA-N methyl-cycloheptane Natural products CC1CCCCCC1 GYNNXHKOJHMOHS-UHFFFAOYSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000178 monomer Substances 0.000 description 1
- IJDNQMDRQITEOD-UHFFFAOYSA-N n-butane Chemical compound CCCC IJDNQMDRQITEOD-UHFFFAOYSA-N 0.000 description 1
- -1 n-paraffin Chemical class 0.000 description 1
- 239000012188 paraffin wax Substances 0.000 description 1
- 239000001294 propane Substances 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Geology (AREA)
- Mining & Mineral Resources (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geophysics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The application relates to an oil well yield prediction method based on light hydrocarbon logging and computer equipment, wherein the method comprises the following steps: acquiring actual measurement data of an oil well with known production, wherein the actual measurement data comprises: a data set of a plurality of light hydrocarbon parameters and a data set of a plurality of yields corresponding to the light hydrocarbon parameters; determining a correlation coefficient between each light hydrocarbon parameter and the yield based on the actual measurement data; for each light hydrocarbon parameter, if the correlation coefficient between the light hydrocarbon parameter and the yield meets the preset condition, taking the corresponding light hydrocarbon parameter as an independent variable parameter of the multiple linear regression model; performing linear fitting on the basis of actual measurement data corresponding to light hydrocarbon parameters serving as independent variable parameters to determine regression coefficients of the multiple linear regression model; acquiring a data set of light hydrocarbon parameters serving as independent variable parameters of an oil well with unknown yield; and determining the yield of the oil well with unknown yield by using a multivariate linear regression model based on the acquired data set of the light hydrocarbon parameters of the oil well with unknown yield.
Description
Technical Field
The application relates to the field of oil well yield prediction, in particular to a light hydrocarbon logging-based oil well yield prediction method and computer equipment.
Background
The light hydrocarbon well logging technology is based on the gas chromatographic analysis principle and the analysis of C in crude oil with light hydrocarbon component analyzer 9 A well logging method for the content of 103 monomer hydrocarbons such as n-paraffin, isoparaffin, cycloparaffin and arene is provided. The light hydrocarbon logging mainly analyzes four samples, namely drilling fluid, rock debris drilling fluid mixed sample, well wall coring and rock core.
Analysis of light hydrocarbon components to obtain CH 4 (methane), C 2 H 6 (ethane), C 3 H 8 (propane), iC 4 H 10 (Isobutane) and nC 4 H 10 (n-butane), iC 5 H 12 (isopentane), nC 5 H 12 (n-pentane), BZ (benzene), TOL (toluene), CYC 6 (cyclohexane), MCYC 6 Quantitative parameters such as (methylcyclohexane), light hydrocarbon abundance, peak output and the like form various interpretation standards and plates, and data support is provided for quantitative evaluation of oil-bearing property and water-bearing property of the reservoir.
Although many parameters are measured in light hydrocarbon loggingHowever, the light hydrocarbon abundance, the peak number, the BZ, the TOL, the BZ/CYC provided by the light hydrocarbon logging 6 、TOL/MCYC 6 、22DMC 4 /CYC 6 、22DMC 5 /CYC 6 、33DMC 5 /CYC6、(C 1 -C 5 )/∑(C 1 -C 9 ) And the like, only the fluid in the reservoir can be identified to be crude oil, and the daily oil production of the reservoir cannot be predicted.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the application provides a method and a computer device for predicting the production of an oil well based on light hydrocarbon logging.
In a first aspect, the present application provides a method for predicting oil well production based on light hydrocarbon logging, comprising: acquiring actual measurement data of a well with known production in a predetermined area, wherein the actual measurement data comprises: a data set of a plurality of light hydrocarbon parameters, and a data set of water production and/or oil production corresponding to the plurality of light hydrocarbon parameters; determining a correlation coefficient between each light hydrocarbon parameter and the water and/or oil production based on the actual measurement data; for each light hydrocarbon parameter, if the correlation coefficient between the light hydrocarbon parameter and the water yield and/or the oil yield meets the preset condition, taking the corresponding light hydrocarbon parameter as an independent variable parameter of the multiple linear regression model; performing linear fitting by using a least square method based on actual measurement data corresponding to light hydrocarbon parameters serving as independent variable parameters, and determining a regression coefficient of a multiple linear regression model; acquiring a data set of light hydrocarbon parameters of an oil well with unknown yield in a preset area as independent variable parameters; and determining the water yield and/or oil yield of the oil well with unknown yield by using a multivariate linear regression model based on the acquired data set of the light hydrocarbon parameters of the oil well with unknown yield.
In some embodiments, the correlation coefficient comprises a positive correlation and a negative correlation, wherein a negative correlation indicates a negative correlation between the light hydrocarbon parameter and the water and/or oil production, and a positive correlation indicates a positive correlation between the light hydrocarbon parameter and the water and/or oil production.
In some embodiments, if the correlation coefficient between the light hydrocarbon parameter and the water and/or oil production satisfies the predetermined condition, using the corresponding light hydrocarbon parameter as the independent variable parameter of the multiple linear regression model includes: and taking the N light hydrocarbon parameters with the maximum absolute value of the correlation coefficient as the independent variable parameters of the multiple linear regression model.
In some embodiments, the predetermined area is obtained based on the similarity division of oil-water properties, and the difference of the similarity of oil-water properties between the oil wells in the predetermined area is within a preset range.
In a second aspect, the present application provides a method for predicting oil well production based on light hydrocarbon logging, comprising: acquiring a data set of preset light hydrocarbon parameters of an oil well with unknown yield in a preset area, wherein the preset light hydrocarbon parameters correspond to independent variable parameters of a multiple linear regression model; the multivariate linear regression model is determined and obtained based on actual measurement data of an oil well with known yield in a preset area, wherein the independent variable parameters are light hydrocarbon parameters of which the correlation coefficients between the actual measurement data and the water yield and/or the oil yield meet preset conditions, and the regression coefficients of the multivariate linear regression model are obtained by performing linear fitting on the basis of the actual measurement data corresponding to the light hydrocarbon parameters serving as the independent variable parameters by using a least square method; and determining the water yield and/or oil yield of the oil well with unknown yield by using a multivariate linear regression model based on the acquired data set of the preset light hydrocarbon parameters.
In certain embodiments, the correlation coefficient comprises a positive correlation and a negative correlation, wherein a negative correlation indicates a negative correlation between the light hydrocarbon parameter and the water and/or oil production and a positive correlation indicates a positive correlation between the light hydrocarbon parameter and the water and/or oil production.
In some embodiments, the independent variable parameter of the multiple linear regression model is the N light hydrocarbon parameters with the largest absolute value of the correlation coefficient between the actual measured data and the water and/or oil production.
In some embodiments, the predetermined area is divided based on a similarity of oil and water properties, and a difference in the similarity of oil and water properties between wells within the predetermined area is within a predetermined range.
In a third aspect, the present application provides a computing device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor; the computer program is executed by a processor to realize the steps of the oil well production prediction method based on light hydrocarbon logging.
In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium stores an oil well production prediction program based on light hydrocarbon logging, and when the oil well production prediction program based on light hydrocarbon logging is executed by a processor, the steps of the oil well production prediction method based on light hydrocarbon logging of the present application are implemented.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the method provided by the embodiment of the application, the accuracy of oil-water layer evaluation is improved through the prediction of daily oil production and daily water production, the quantitative interpretation of light hydrocarbon logging is really realized, and the method is beneficial to efficient exploration and development of oil fields. The difficult problem that the characteristic parameters with good correlation between the daily produced oil and the daily produced water and the light hydrocarbon logging parameters cannot be quickly and accurately screened due to numerous parameters is solved; the method is suitable for quantitative interpretation of light hydrocarbon logging in all oil field blocks, and has wide application prospect.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flowchart of an embodiment of a method for constructing a prediction model according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of one embodiment of a yield prediction method provided in an embodiment of the present application;
FIG. 3 is a hardware block diagram of an embodiment of a computing device according to an embodiment of the present disclosure;
FIG. 4 is an example 1A 1 well 1410.0-1419.0m light hydrocarbon gas chromatogram provided in an embodiment of the present application;
FIG. 5A is an explanatory drawing of the A1 well 1410.0-1419.0m light hydrocarbon TOL/MCYC6-BZ/CYC6 of example 1, provided by an embodiment of the present application;
FIG. 5B is an explanatory drawing of an A1 well 1410.0-1419.0m light hydrocarbon nC7/MCYC6-BZ/CYC6 of example 1 as provided in an embodiment of the present application;
FIG. 6 is an A2 well 1543.0-1547.0m light hydrocarbon gas chromatogram of example 2 provided by an embodiment of the present application;
FIG. 7A is an explanatory drawing of TOL/MCYC6-BZ/CYC6 for an A2 well 1543.0-1547.0m light hydrocarbon of example 2, as provided in an embodiment herein; and
FIG. 7B is an explanatory drawing of the A2 well 1543.0-1547.0m light hydrocarbon nC7/MCYC6-BZ/CYC6 of example 2, provided by an embodiment of the present application.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The method can analyze and deduce the oil well to be put into operation in practical application, budget the daily oil yield of each reservoir and preferably select the most promising oil layer for putting into operation, thereby realizing the purpose of obtaining the maximum benefit with the least investment and effectively reducing the exploration and development cost of the oil field. The method determines independent variable parameters and regression coefficients of a multiple linear regression model for predicting the yield of the oil well based on actual measurement data of the oil well with known yield, and predicts the yield of the oil well with unknown yield by the multiple linear regression model. And according to actual measurement data, light hydrocarbon parameters with high yield correlation are used as variable parameters of the multiple linear regression model. By the method, the oil well yield prediction based on light hydrocarbon logging is realized. The method is particularly suitable for the yield prediction of oil wells with similar oil-water properties.
In an embodiment of the present application, a method for predicting oil well production includes: a prediction model construction stage and an oil well yield prediction stage. And a prediction model construction stage, wherein a prediction model is constructed based on the actual measurement data of one or more oil wells with known production in the region. And in the oil well yield prediction stage, the yield of the oil well with unknown yield is determined by using the yield prediction model based on the light hydrocarbon parameters of the oil well with unknown yield. The prediction model construction phase and the oil well production prediction phase are explained below.
Fig. 1 is a flowchart of an implementation manner of a prediction model construction method provided in an embodiment of the present application, and as shown in fig. 1, the prediction model construction method in the embodiment of the present application includes steps S102 to S108.
Step S102, acquiring actual measurement data of an oil well with known yield, wherein the actual measurement data comprises: a data set of a plurality of light hydrocarbon parameters, and a data set of water production and/or oil production corresponding to the plurality of light hydrocarbon parameters.
And step S104, determining correlation coefficients between each light hydrocarbon parameter and the water yield and/or the oil yield based on the actual measurement data.
And S106, regarding each light hydrocarbon parameter, if the correlation coefficient between the light hydrocarbon parameter and the water yield and/or the oil yield meets the preset condition, taking the corresponding light hydrocarbon parameter as the independent variable parameter of the multiple linear regression model.
And S108, performing linear fitting by using a least square method based on actual measurement data corresponding to light hydrocarbon parameters serving as independent variable parameters, and determining regression coefficients of the multiple linear regression model.
In this embodiment, the yield prediction model is a multiple linear regression model, the independent variable parameter of the multiple linear regression model is a light hydrocarbon parameter whose correlation coefficient with the water yield and/or the oil yield satisfies a preset condition, and the regression coefficient of the multiple linear regression model is obtained by performing linear fitting using a least square method based on actual measurement data corresponding to the light hydrocarbon parameter as the independent variable parameter.
In this embodiment, the production prediction model is stored for use in predicting the production of wells for which production is unknown. In this embodiment, the production prediction model is able to predict the production of an oil well having a high similarity to the oil-water properties of an oil well of known production. In some embodiments, the oil-water property similarity between wells in the same region is high, so the production prediction model can predict the production of wells with known production and unknown production in the region of the wells.
In some embodiments, in step S104, the correlation coefficient includes a positive correlation and a negative correlation, wherein a negative correlation indicates a negative correlation between the light hydrocarbon parameter and the water and/or oil production, and a positive correlation indicates a positive correlation between the light hydrocarbon parameter and the water and/or oil production.
In some embodiments, in step S106, if the correlation coefficient between the light hydrocarbon parameter and the water and/or oil production satisfies the predetermined condition, using the corresponding light hydrocarbon parameter as the independent variable parameter of the multiple linear regression model includes: and taking the N light hydrocarbon parameters with the maximum absolute value of the correlation coefficient as the independent variable parameters of the multiple linear regression model.
In certain embodiments, the light hydrocarbon parameters include: light hydrocarbon abundance, peak number, BZ, TOL, BZ/CYC 6 、TOL/MCYC 6 、22DMC 4 /CYC 6 、22DMC 5 /CYC 6 、33DMC 5 /CYC6、(C 1 -C 5 )/∑(C 1 -C 9 ) And so on.
Fig. 2 is a flowchart of an embodiment of a yield prediction method provided in an embodiment of the present application, and as shown in fig. 2, the method includes steps S202 to S204.
Step S202, acquiring a data set of light hydrocarbon parameters of an oil well with unknown yield in a preset area as independent variable parameters.
And step S204, determining the water yield and/or the oil yield of the oil well with unknown yield by using a multiple linear regression model based on the acquired data set of the light hydrocarbon parameters of the oil well with unknown yield.
In this embodiment, the multiple linear regression model is determined based on actual measurement data of an oil well with known yield in a predetermined region, wherein the independent variable parameter is a light hydrocarbon parameter whose correlation coefficient with the water yield and/or the oil yield satisfies a preset condition in the actual measurement data, and the regression coefficient of the multiple linear regression model is obtained by performing linear fitting using a least square method based on the actual measurement data corresponding to the light hydrocarbon parameter as the independent variable parameter.
In some embodiments, the correlation coefficient comprises a positive correlation and a negative correlation, wherein a negative correlation indicates a negative correlation between the light hydrocarbon parameter and the water and/or oil production, and a positive correlation indicates a positive correlation between the light hydrocarbon parameter and the water and/or oil production.
In some embodiments, if the correlation coefficient between the light hydrocarbon parameter and the water and/or oil production satisfies a predetermined condition, using the corresponding light hydrocarbon parameter as an independent variable parameter of the multiple linear regression model includes: and taking the N light hydrocarbon parameters with the maximum absolute value of the correlation coefficient as the independent variable parameters of the multiple linear regression model.
In some embodiments, the multiple linear regression model is obtained in advance by a prediction model construction method as shown in fig. 1.
In certain embodiments, the light hydrocarbon parameters include: light hydrocarbon abundance, peak number, BZ, TOL, BZ/CYC 6 、TOL/MCYC 6 、22DMC 4 /CYC 6 、22DMC 5 /CYC 6 、33DMC 5 /CYC6、(C 1 -C 5 )/∑(C 1 -C 9 ) And the like.
In some embodiments, the predetermined area is obtained based on similarity division of oil and water properties, and the difference of the similarity of oil and water properties between the oil wells in the predetermined area is within a preset range.
Fig. 3 is a schematic hardware structure diagram of an implementation of the computing device according to the embodiment of the present disclosure, and as shown in fig. 3, the computing device 1 is communicatively connected to the light hydrocarbon analysis tool 2, and acquires a data set of light hydrocarbon parameters obtained by light hydrocarbon logging from the light hydrocarbon analysis tool 2. The computing device 1 comprises a processor 11 and a memory 12, on which memory 12 computer programs and data sets etc. are stored that are executable on the processor 11.
Referring to fig. 3, the computer program stored on the memory 12 includes: a predictive model construction module 121 for constructing a production predictive model 122 based on actual measured data 124 for one or more wells for which production is known. In this embodiment, the prediction model building module 121 is configured to implement the prediction model building method shown in fig. 1, resulting in the yield prediction model 122.
Referring to fig. 3, the data stored on the memory 12 includes: actual measurement data 124 for the predictive model construction module 121 to construct the production prediction module 122. The actual measurement data 124 includes: a data set of light hydrocarbon parameters for one or more wells with known production, and a corresponding data set of known water production and data set of known oil production.
Referring to FIG. 3, a constructed production prediction model 122 is stored in memory 12 for predicting production from an unknown production well.
Referring to fig. 3, the computer program stored on the memory 12 includes: a prediction module 123 for determining the production of the unknown-producing well using the production prediction model 122 based on the data set 125 of light hydrocarbon parameters for the unknown-producing well. In the present embodiment, the prediction module 123 is configured to implement the production prediction method shown in fig. 2, and obtain the production of the oil well with unknown production.
In the present embodiment, the yield prediction model 122 is a multiple linear regression model. And the prediction model building module 121 is configured to determine the independent variable parameters and the regression coefficients of the multiple linear regression model. In some embodiments, the prediction model construction module 121 determines a correlation coefficient of the light hydrocarbon parameter and the yield based on the actual measurement data 124, and selects the light hydrocarbon parameter whose correlation coefficient satisfies a preset condition as an independent variable parameter of the multiple linear regression model.
By way of illustrative example, the light hydrocarbon analysis tool 2 providing light hydrocarbon parameters includes: light hydrocarbon abundance, peak number, BZ, TOL, BZ/CYC 6 、TOL/MCYC 6 、22DMC 4 /CYC 6 、22DMC 5 /CYC 6 、33DMC 5 /CYC6、(C 1 -C 5 )/∑(C 1 -C 9 ) And the like.
The following description will be given by taking an Excel table as an example.
1 calculating the correlation coefficient
As an example, the corel formula in an Excel table is selected to calculate the correlation. The CORREL formula in the Excel table is used for simpler and more convenient operation, and meanwhile, the correlation coefficient calculated by the CORREL formula is between-1 and 1, so that the positive and negative correlations of each parameter of daily oil production, daily water production and light hydrocarbon logging can be directly reflected.
The daily oil production data and the daily water production oil test data are respectively arranged in an A column and a B column in Excel, other light hydrocarbon parameters are arranged in a C column, a D column and the following columns, the number of rows of all data is kept consistent, the daily oil production data is replaced by 0, and all data are in a numerical value format and have no null data.
If the daily oil production is located in the line 2 to the line 65000 of the column A and the light hydrocarbon logging parameter Benzene (BZ) is located in the line 2 to the line 65000 of the column D, the correlation coefficient calculation formula of the daily oil production and the Benzene (BZ) is as follows:
ρxy＝CORREL($A$2:$A$65000,D$2:D$65000)
the larger the absolute value of the correlation coefficient is, the better the correlation between the two groups of data is, wherein the correlation coefficient is a positive correlation and a negative correlation. And performing evolution and power processing on the optimized original parameters with higher absolute values of the correlation coefficients, then performing correlation coefficient calculation, and finally optimizing 2-5 original parameters with higher absolute values of the correlation coefficients or derived parameters after evolution and power processing to participate in multi-parameter fitting calculation of daily oil production and daily water production.
2 establishing fitting formula
2.1 principle of fitting
The least squares method is usually used to fit the known data linearly, whereas the LINEST function in Excel tables is the best fit of a straight line to the known data using the least squares method and returns an array describing this straight line. Therefore, the combination of LINEST and INDEX functions in the Excel table is adopted to return the coefficients of the multiple linear regression formula.
2.2 data fitting
The daily oil and water production multiparameter fitting formula is as follows:
y＝m 1 ×x 1 +m 2 ×x 2 +m 3 ×x 3 +…+m n ×x n +b
x is to be n 、x n-1 、…、x 1 The optimized independent variable parameters are sequentially set in A, B, C and 8230, and the data of daily oil or water is set in x 1 The first column to the right of the parameter; for example, if the independent variable parameters are located in columns A-E from row 2 to row 146, and the daily oil production parameters are located in column F from row 2 to row 146, the coefficient calculation formula of the multiple regression formula is:
m n ＝INDEX(LINEST($F$2:$F$146,$A$2:$E$146,1,FALSE),1,n)
b＝INDEX(LINEST($F$2:$F$146,$A$2:$E$146,1,FALSE),1,n+1)
example of prediction of 3-day oil and water production
According to a daily oil production and daily water production fitting formula, the daily oil production and the daily water production of single-point light hydrocarbon data of the reservoir are predicted, and the reservoir oil-water property is finely explained and quantitatively explained by combining the reservoir thickness and the physical property.
The embodiments of the present application are described below by way of examples.
Example 1: a1 well
The well A1, the well section 1410.0-1419.0m, the thickness 9.0m, the lithology is brown gray oil stain fine sandstone, the average porosity of the core physical analysis is 11.56%, the average permeability is 0.26mD, and the physical property of the layer is better relative to the physical property level of the layer in the region. The layer analyzes 24 light hydrocarbon samples, typical light hydrocarbon parameters are shown in table 1, and a typical light hydrocarbon gas chromatogram is shown in fig. 4.
TABLE 1 A1 typical light hydrocarbon parameters for well 1410.0-1419.0m
According to the interpretation criteria in the related art, the number of peaks exceeding 65 and the total hydrocarbon exceeding 100000 can be interpreted as an oil layer, the layer has an average value of the number of peaks exceeding 69 and an average value of the total hydrocarbon exceeding 197031, and the layer should be interpreted as an oil layer. According to TOL/MCYC6-BZ/CYC6, nC7/MCYC6-BZ/CYC6, the layer is also to be interpreted as an oil-water layer and an oil layer (as shown in FIGS. 5A and 5B).
By applying the method to predict the daily oil and water production of the light hydrocarbon data of the layer, 1 oil-containing water layer, 23 oil-containing water layers (shown in table 2) in 24 samples are obtained, the average daily oil production is 9.95t, and the average daily water production is 11.58m 3 The layer is the same as oil and water.
TABLE 2 prediction statistics of light hydrocarbon parameters daily oil production and daily water production of A1 wells 1410.0-1419.0m
| Depth of well | Daily oil (t/d) | Daily water (m) 3 /d) | Water content (%) | Conclusion |
| 1410.02 | 13.82 | 11.89 | 46.25 | Oil-water layer |
| 1410.44 | 9.85 | 11.95 | 54.80 | Oil-water homolayer |
| 1411.00 | 5.68 | 10.66 | 65.26 | Oil-water layer |
| 1411.68 | 9.53 | 11.90 | 55.53 | Oil-water layer |
| 1412.00 | 0.49 | 10.89 | 95.71 | Oil-water layer |
| 1412.55 | 6.99 | 10.27 | 59.51 | Oil-water homolayer |
| 1412.85 | 9.37 | 12.02 | 56.17 | Oil-water layer |
| 1413.00 | 8.67 | 11.41 | 56.82 | Oil-water layer |
| 1413.19 | 5.37 | 10.00 | 65.04 | Oil-water homolayer |
| 1413.50 | 9.46 | 10.90 | 53.53 | Oil-water layer |
| 1413.93 | 13.75 | 12.78 | 48.18 | Oil-water homolayer |
| 1414.00 | 10.88 | 11.90 | 52.25 | Oil-water layer |
| 1414.25 | 5.81 | 8.98 | 60.71 | Oil-water layer |
| 1415.00 | 18.24 | 15.20 | 45.46 | Oil-water homolayer |
| 1415.33 | 7.03 | 10.44 | 59.75 | Oil-water layer |
| 1415.95 | 6.10 | 9.58 | 61.09 | Oil-water layer |
| 1416.00 | 16.91 | 14.46 | 46.10 | Oil-water homolayer |
| 1416.58 | 10.22 | 10.52 | 50.71 | Oil-water layer |
| 1416.90 | 10.89 | 11.57 | 51.52 | Oil-water layer |
| 1417.42 | 9.39 | 9.92 | 51.37 | Oil-water homolayer |
| 1418.00 | 10.19 | 11.27 | 52.52 | Oil-water layer |
| 1418.04 | 11.21 | 12.49 | 52.69 | Oil-water layer |
| 1418.56 | 16.54 | 14.04 | 45.92 | Oil-water homolayer |
| 1419.00 | 12.33 | 12.80 | 50.93 | Oil-water layer |
| Average out | 9.95 | 11.58 | 55.74 | Oil-water layer |
The oil test proves that the daily oil yield of the layer is 11.05t, and the daily water yield is 9.60m 3 After the method is applied, the error between the daily oil production and the oil test is predicted to be-9.95 percent, the error between the daily water production and the oil test is predicted to be 20.63 percent, and the explanation conclusion is consistent with the oil test conclusion.
Example 2: a2 well
A2 well, well section 1543.0-1547.0m, thickness 4.0m, lithology is brown gray oil spot fine sandstone, the average porosity of core physical property analysis is 10.28%, the average permeability is 0.39mD, and the physical property of the layer is better relative to the physical property level of the layer in the region. The layer is used for analyzing 19 light hydrocarbon samples, typical light hydrocarbon parameters are shown in table 3, and a typical light hydrocarbon gas chromatogram is shown in fig. 6.
TABLE 3 typical light hydrocarbon parameters of 1543.0-1547.0m for A2 well
According to the interpretation standard of the related art, the number of peaks out is more than 65 and the total hydrocarbon is more than 100000, which can be interpreted as an oil layer, the number of peaks out of the layer is 67 on average and the total hydrocarbon is 209452 on average, which can be interpreted as an oil layer. According to two explanation plates of TOL/MCYC6-BZ/CYC6, nC7/MCYC6-BZ/CYC6, the layer is also explained as an oil-water layer and an oil layer (as shown in figures 7A and 7B).
By applying the method to predict the daily oil production and the daily water production of the light hydrocarbon data of the layer, 15 water layers, 3 oil-containing water layers and 1 oil-water layer are obtained in 19 samples (see table 4).
TABLE 4 prediction statistics of light hydrocarbon parameters daily oil production and daily water production of the A2 well 1543.0-1547.0m
| Well depth (m) | Daily oil (t/d) | Water of daily production (m) 3 /d) | Water content (%) | Conclusion |
| 1543.24 | 3.36 | 8.98 | 72.78 | Oil-water homolayer |
| 1544.10 | 0.00 | 7.87 | 100.00 | Aqueous layer |
| 1544.57 | 0.00 | 11.87 | 100.00 | Aqueous layer |
| 1544.78 | 0.00 | 11.55 | 100.00 | Aqueous layer |
| 1545.20 | 0.00 | 11.17 | 100.00 | Aqueous layer |
| 1545.50 | 0.00 | 11.25 | 100.00 | Aqueous layer |
| 1545.80 | 0.00 | 11.76 | 100.00 | Aqueous layer |
| 1546.00 | 0.00 | 6.72 | 100.00 | Aqueous layer |
| 1546.10 | 0.00 | 10.02 | 100.00 | Aqueous layer |
| 1546.60 | 0.00 | 11.49 | 100.00 | Aqueous layer |
| 1547.00 | 0.00 | 7.76 | 100.00 | Aqueous layer |
| 1547.06 | 0.00 | 8.13 | 100.00 | Aqueous layer |
| 1547.84 | 0.00 | 10.75 | 100.00 | Aqueous layer |
| 1547.88 | 0.11 | 9.83 | 98.86 | Oil-water layer |
| 1548.30 | 0.00 | 7.74 | 100.00 | Aqueous layer |
| 1548.70 | 0.00 | 11.17 | 100.00 | Aqueous layer |
| 1548.90 | 0.00 | 11.64 | 100.00 | Aqueous layer |
| 1549.12 | 1.79 | 8.49 | 82.61 | Oil-containingAqueous layer |
| 1549.82 | 0.57 | 12.28 | 95.55 | Oil-water layer |
| Average | 0.31 | 10.02 | 97.03 | Oil-water layer |
Although the average daily oil production was predicted to be 0.31t, the average daily water production was 10.02m3, which is an oil-containing water layer; but 78.95% of the sample in this layer was a water layer with daily oil production of 0.00t, located in the upper part of the reservoir, and this layer was physically good; therefore, the daily oil yield of the layer is 0.00t and the daily water yield is 10.02m through comprehensive analysis 3 The comprehensive interpretation is the aqueous layer. The oil testing proves that the daily oil yield of the layer is 0.00t, and the daily water yield is 12.30m 3 After the method is applied, the daily oil production is consistent with the oil test, the error between the daily water production and the oil test is-18.54 percent, and the explanation conclusion is consistent with the oil test conclusion.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
While the present invention has been described with reference to the particular illustrative embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but is intended to cover various modifications, equivalent arrangements, and equivalents thereof, which may be made by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for predicting oil well production based on light hydrocarbon logging is characterized by comprising the following steps:
acquiring actual measurement data of a well with known production in a predetermined area, the actual measurement data comprising: a plurality of light hydrocarbon parameter data sets, and a plurality of light hydrocarbon parameter corresponding water yield data sets or oil yield data sets;
determining a correlation coefficient between each light hydrocarbon parameter and water or oil production based on the actual measurement data;
for each light hydrocarbon parameter, if the correlation coefficient between the light hydrocarbon parameter and the water yield or the oil yield meets a preset condition, taking the corresponding light hydrocarbon parameter as an independent variable parameter of a multiple linear regression model;
performing linear fitting by using a least square method based on actual measurement data corresponding to light hydrocarbon parameters serving as independent variable parameters, and determining a regression coefficient of a multiple linear regression model;
acquiring a data set of light hydrocarbon parameters serving as independent variable parameters of an oil well with unknown yield in a preset area;
determining the water yield or oil yield of the oil well with unknown yield by using the multiple linear regression model based on the acquired data set of the light hydrocarbon parameters of the oil well with unknown yield;
the light hydrocarbon parameters refer to quantitative parameters obtained by analyzing light hydrocarbon components during light hydrocarbon well logging, and include light hydrocarbon abundance, peak output, BZ, TOL, BZ/CYC 6 、TOL/MCYC 6 、22DMC 4 /CYC 6 、22DMC 5 /CYC 6 、33DMC 5 /CYC6、(C 1 -C 5 )/∑(C 1 -C 9 )。
2. The method of claim 1, wherein the correlation coefficient comprises a positive correlation and a negative correlation, wherein a negative correlation indicates a negative correlation between the light hydrocarbon parameter and the water or oil production, and a positive correlation indicates a positive correlation between the light hydrocarbon parameter and the water or oil production.
3. The method of claim 1 or 2, wherein if the correlation coefficient between the light hydrocarbon parameter and the water or oil yield satisfies a predetermined condition, using the corresponding light hydrocarbon parameter as the independent variable parameter of the multiple linear regression model comprises: and taking the N light hydrocarbon parameters with the maximum absolute values of the correlation coefficients as the independent variable parameters of the multiple linear regression model.
4. The method of claim 1, wherein the predetermined zones are partitioned based on similarity of oil and water properties, and the difference of similarity of oil and water properties between oil wells in the predetermined zones is within a preset range.
5. A method for predicting oil well production based on light hydrocarbon logging is characterized by comprising the following steps:
acquiring a data set of preset light hydrocarbon parameters of an oil well with unknown yield in a preset area, wherein the preset light hydrocarbon parameters correspond to independent variable parameters of a multiple linear regression model; the multivariate linear regression model is determined and obtained based on actual measurement data of an oil well with known yield in the preset area, wherein the independent variable parameters are light hydrocarbon parameters of which the correlation coefficient between the actual measurement data and the water yield or the oil yield meets preset conditions, and the regression coefficients of the multivariate linear regression model are obtained by performing linear fitting on the basis of the actual measurement data corresponding to the light hydrocarbon parameters serving as the independent variable parameters by using a least square method;
and determining the water yield and/or the oil yield of the oil well with unknown yield by using the multiple linear regression model based on the acquired data set of the preset light hydrocarbon parameters.
6. The method of claim 5, wherein the correlation coefficients comprise positive and negative correlation coefficients, wherein a negative correlation coefficient indicates a negative correlation between the light hydrocarbon parameter and the water or oil production, and a positive correlation coefficient indicates a positive correlation between the light hydrocarbon parameter and the water or oil production.
7. The method of claim 5 or 6, wherein the independent variable parameters of the multiple linear regression model are the N light hydrocarbon parameters with the largest absolute value of the correlation coefficient between the actual measurement data and the water or oil production.
8. The light hydrocarbon logging-based oil well production prediction method of claim 5, wherein the predetermined region is divided based on similarity of oil and water properties, and the difference of the similarity of oil and water properties between the oil wells in the predetermined region is within a preset range.
9. A computing device, wherein the computing device comprises:
a memory, a processor, and a computer program stored on the memory and executable on the processor;
the computer program, when executed by the processor, implementing the steps of the method of any one of claims 1 to 8.
10. A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon a light hydrocarbon logging-based well production prediction program, which when executed by a processor, implements the steps of the light hydrocarbon logging-based well production prediction method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910880155.1A CN110608032B (en) | 2019-09-18 | 2019-09-18 | Oil well yield prediction method based on light hydrocarbon logging and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910880155.1A CN110608032B (en) | 2019-09-18 | 2019-09-18 | Oil well yield prediction method based on light hydrocarbon logging and computer equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110608032A CN110608032A (en) | 2019-12-24 |
| CN110608032B true CN110608032B (en) | 2023-01-24 |
Family
ID=68892925
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910880155.1A Active CN110608032B (en) | 2019-09-18 | 2019-09-18 | Oil well yield prediction method based on light hydrocarbon logging and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110608032B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113611371B (en) * | 2021-08-03 | 2023-06-02 | 中国石油大学(北京) | Method for judging validity of light hydrocarbon parameters in crude oil associated with natural gas reservoir based on boiling point of light hydrocarbon |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101183154A (en) * | 2007-11-30 | 2008-05-21 | 辽河石油勘探局 | Geological logging explanation evaluating method |
| CN103510946A (en) * | 2012-06-19 | 2014-01-15 | 中国石油集团长城钻探工程有限公司 | Method for evaluating reservoir fluid property through gas logging data |
| CN105868508A (en) * | 2016-04-28 | 2016-08-17 | 西南石油大学 | Productivity quantitative prediction method based on gas logging information |
| CN109142682A (en) * | 2018-09-06 | 2019-01-04 | 中国石油集团渤海钻探工程有限公司 | A kind of unit volume rock void fraction and formation testing effect evaluation method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7191850B2 (en) * | 2004-10-28 | 2007-03-20 | Williams Danny T | Formation dip geo-steering method |
| US20120179379A1 (en) * | 2011-01-10 | 2012-07-12 | Saudi Arabian Oil Company | Flow Profile Modeling for Wells |
| CA2909170C (en) * | 2013-06-10 | 2020-02-18 | Exxonmobil Upstream Research Company | Determining well parameters for optimization of well performance |
-
2019
- 2019-09-18 CN CN201910880155.1A patent/CN110608032B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101183154A (en) * | 2007-11-30 | 2008-05-21 | 辽河石油勘探局 | Geological logging explanation evaluating method |
| CN103510946A (en) * | 2012-06-19 | 2014-01-15 | 中国石油集团长城钻探工程有限公司 | Method for evaluating reservoir fluid property through gas logging data |
| CN105868508A (en) * | 2016-04-28 | 2016-08-17 | 西南石油大学 | Productivity quantitative prediction method based on gas logging information |
| CN109142682A (en) * | 2018-09-06 | 2019-01-04 | 中国石油集团渤海钻探工程有限公司 | A kind of unit volume rock void fraction and formation testing effect evaluation method |
Non-Patent Citations (4)
| Title |
|---|
| 东濮凹陷北部低渗透性储集层录井产能预测方法研究;刘小红等;《录井工程》;20110925(第03期);全文 * |
| 准噶尔盆地气测多元线性回归解释方法研究;李建成等;《录井工程》;20121225(第04期);全文 * |
| 焉耆盆地地化-气测一体化解释评价研究;方锡贤等;《录井工程》;20020325(第01期);全文 * |
| 鄂尔多斯盆地镇泾油田低孔低渗油藏油气水层快速评价方法;王飞龙;《录井工程》;20080325(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110608032A (en) | 2019-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Weatherill et al. | Delineation of shallow seismic source zones using K-means cluster analysis, with application to the Aegean region | |
| Wei et al. | Predicting injection profiles using ANFIS | |
| US8392164B2 (en) | Method for evaluating an underground reservoir production scheme taking account of uncertainties | |
| CN110413952B (en) | Method and device for predicting exploration degree of oil-gas-containing basin | |
| CN105715253A (en) | Prediction method for flowing bottomhole pressure of gas well | |
| CN110608032B (en) | Oil well yield prediction method based on light hydrocarbon logging and computer equipment | |
| CN102704924B (en) | Method and device for determining effective dry layer | |
| Marfo et al. | A new flowing bottom hole pressure prediction model using M5 prime decision tree approach | |
| US9121256B2 (en) | Method of constructing a flow model associated with capillary pressure curves and/or relative permeability curves | |
| CN110988997A (en) | Hydrocarbon source rock three-dimensional space distribution quantitative prediction technology based on machine learning | |
| Ariadji et al. | Optimization of vertical well placement for oil field development based on basic reservoir rock properties using a genetic algorithm | |
| Verga et al. | Improved application of assisted history matching techniques | |
| Zargar et al. | Oil‐CO2 minimum miscible pressure (MMP) determination using a stimulated smart approach | |
| Wang et al. | Combined application of unsupervised and deep learning in absolute open flow potential prediction: a case study of the Weiyuan Shale gas reservoir | |
| Zhang | Estimating uncertainties in integrated reservoir studies | |
| CN111155986B (en) | Method, device, equipment and system for determining well spacing of multi-layer commingled production gas well | |
| CN116335648A (en) | Method for quantitatively evaluating dessert quality of conglomerate horizontal well based on logging while drilling data | |
| Caers et al. | Integration of engineering and geological uncertainty for reservoir performance prediction using a distance-based approach | |
| Ani et al. | Ranking of geostatistical models and uncertainty quantification using Signal Detection Principle (SDP) | |
| Aybeniz | Current Approaches in Prediction of PVT Properties of Reservoir Oils | |
| CN110570102A (en) | Reservoir evaluation method | |
| Costa et al. | Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs | |
| US12123863B2 (en) | Processes and systems for determining if downhole fluids are in equilibrium or non-equilibrium | |
| Scheidt et al. | Uncertainty quantification using distances and kernel methods–application to a deepwater turbidite reservoir | |
| CN113944460B (en) | Method and device for predicting condensate oil content of shale gas in different development stages |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20201021 Address after: No. 6, liupukang, Xicheng District, Beijing 100120 Applicant after: CHINA NATIONAL PETROLEUM Corp. Applicant after: CNPC GREATWALL DRILLING Co. Address before: 124010 No.77, youyou street, Xinglongtai District, Panjin City, Liaoning Province logging company Applicant before: LOGGING COMPANY, CNPC GREATWALL DRILLING Co. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |