CN113216945B - Quantitative evaluation method for permeability of tight sandstone reservoir - Google Patents
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Abstract
The invention discloses a quantitative evaluation method for permeability of a tight sandstone reservoir, which comprises the following steps: 1: selecting a standard well, continuously sampling a core section of the standard well, and then analyzing the porosity and the permeability of the collected sample to obtain core actual measurement permeability data of each sampling point; 2: according to logging data, logging data of sampling points of a standard well are obtained, multiple regression is carried out on the logging data and the measured permeability data of the core, and a multiple relation between the logging data and the measured permeability data of the core is established; 3: and solving a correlation coefficient in the multi-element relation by multi-element regression, substituting the correlation coefficient into the multi-element relation to obtain a permeability calculation formula, and quantitatively evaluating the reservoir permeability of the logging by using the permeability calculation formula. The method can realize quantitative evaluation of the permeability of the reservoir by using the conventional logging data, and has the advantages of easy acquisition of the data, high evaluation accuracy, low cost and the like.
Description
Technical Field
The invention belongs to the technical field of oil and gas exploration and development, and particularly relates to a quantitative evaluation method for permeability of a tight sandstone reservoir.
Background
At present, the permeability evaluation of the compact sandstone reservoir at home and abroad generally adopts a conventional oil and gas reservoir evaluation method, and takes the porosity as a main evaluation object, but the porosity of the compact sandstone is extremely low and is generally less than 10%, and the porosity structure is complex, so that the porosity as the main evaluation object is difficult to truly reflect the characteristics of the reservoir.
The permeability of the tight sandstone reservoir is related to not only the porosity, but also the pore structure, the crack development degree and the like of the reservoir, is the comprehensive reflection of various characteristics in the reservoir, and can truly reflect the quality of the tight sandstone reservoir. The permeability of the tight sandstone reservoir is generally evaluated by the permeability, and the permeability evaluation can be measured through a rock core, so that the accuracy is high, but the tight sandstone reservoir is limited to local well sections of a few wells, and most wells need to be calculated by logging. The conventional well logging calculation of the permeability of the existing reservoir mainly utilizes a pore-permeability relation, firstly, core pore-permeability regression is utilized to establish an empirical relation between the permeability and the porosity, and then the permeability is calculated according to the porosity interpreted by a sonic time difference or density well logging curve and the regressed pore-permeability relation. The method has good calculation effect on the conventional pore type reservoir, but has poor correlation of the pore-permeation relation of the core actual measurement for the compact sandstone reservoir due to complex pore structure, and the calculated permeability is inaccurate. In addition, in special logging, the accuracy of stoneley wave, nuclear magnetic resonance and imaging logging to calculate permeability is relatively high, but due to high data acquisition cost, the logging on the logging is less, and the reservoir permeability evaluation of a comprehensive system is difficult to accurately develop.
In addition, other prior art techniques for permeability evaluation are also disclosed in the prior art in order to be able to improve the accuracy of the evaluation, as follows:
a tight sandstone reservoir multiparameter permeability prediction method as disclosed in publication No. CN107917865a, 4/17, 2018, comprising: (1) Determining geological master factors of permeability in the tight sandstone reservoir, wherein the geological master factors comprise porosity, granularity and crack development degree; (2) Establishing a porosity and granularity logging prediction model and a seismic prediction model; (3) determining a geological master factor of the crack development degree; (4) Establishing a crack development index model according to the geological master control factor obtained in the step (3); (5) And establishing a multi-parameter permeability comprehensive prediction model of earthquake-geological constraint. However, the method mainly predicts the permeability of the tight sandstone reservoir from the earthquake angle, and the quality of the earthquake data can influence the accuracy of reservoir permeability prediction because the earthquake resolution is lower than that of well logging, so that the permeability prediction effect is lower than the well logging calculation accuracy.
Another example is publication No. CN106841001B, which discloses a method for predicting porosity and permeability of tight sandstone based on analysis of reservoir quality master factor at 13/6/2017, which comprises the following steps: 1) Quantitative evaluation of diagenesis; 2) Selecting diagenetic factors for embodiment; 3) Selecting multiple linear stepwise regression as a data analysis method, and realizing porosity and permeability prediction through reservoir quality development main control factor analysis; 4) And carrying out regression analysis on the porosity and the permeability according to a regression analysis method. The method mainly utilizes reservoir quality main control factors to analyze permeability, takes porosity and permeability (k) as dependent variables Y, takes quartz content, feldspar content, rigid rock debris content, plastic rock debris content, hetero-base content, carbonate cement content, kaolinite content, chlorite content, illite content, siliceous cement content and apparent corrosion rate as independent variable sets (X1, X2, X3 … … X12), and establishes multi-element linear stepwise regression analysis of the independent variable sets about Y.
In addition, as disclosed in publication No. CN111561313B, on the 8 th month and 21 th year 2020, a compact sandstone reservoir parameter prediction method based on a physical model and machine learning is disclosed, and the method adopts different expert networks to construct a committee machine CM, so that even if the prediction error of a single expert is large, a plurality of expert systems can integrate the advantages of all the experts to make compensation, and meanwhile, the method integrates the physical model and the committee machine CM, so that the common driving of the physical model and sensitive logging data is realized, and the reservoir parameter prediction effect is improved. According to the method, through inputting logging data, three expert networks including a propagation neural network BPNN, an extreme learning machine ELM and a wavelet neural network WNN are utilized for combined analysis, so that reservoir parameters are calculated. However, the method is not based on the analysis of the measured permeability of the core, so that a large error still exists in the evaluation result.
The conventional logging data at present mainly comprise natural gamma, natural potential, borehole diameter, acoustic time difference, neutron, density, depth double lateral resistivity and the like, and the logging cost is low, so that the logging data are easy to obtain and are common, and basically, each well has the logging data. The density or acoustic time difference in the conventional logging curve can evaluate the porosity of the reservoir, meanwhile, the acoustic time difference can judge the seamed reservoir, natural gamma can reflect rock components and granularity to a certain extent, the pore structure is indirectly reflected, and the difference of the deep and shallow double lateral resistivity can reflect the permeability of the reservoir to a certain extent. Each log can reflect the characteristics of reservoir permeability to some extent while having some log-linear relationship, but none of the logs can effectively and truly reflect reservoir permeability. Therefore, how to more effectively utilize conventional logging data to realize the real evaluation of the permeability of the reservoir becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to overcome the technical problems in the prior art and provides a quantitative evaluation method for permeability of a tight sandstone reservoir, which can realize quantitative evaluation of the permeability of the reservoir by using the conventional logging data and has the advantages of easy acquisition of the data, high evaluation accuracy, low cost and the like.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a quantitative evaluation method for permeability of a tight sandstone reservoir comprises the following steps:
step 1: selecting an oil-gas field well with complete coring as a standard well, continuously sampling a coring section of the standard well, and then analyzing the porosity and the permeability of the acquired sample to obtain core actual measurement permeability data of each sampling point;
step 2: according to logging data, logging data of sampling points of a standard well are obtained, multiple regression is carried out on the logging data and the measured permeability data of the core, and a multiple relation between the logging data and the measured permeability data of the core is established;
step 3: and solving a correlation coefficient in the multi-element relation by multi-element regression, substituting the correlation coefficient into the multi-element relation to obtain a permeability calculation formula, and quantitatively evaluating the reservoir permeability of the logging by using the permeability calculation formula.
In step 1, when the coring section of the standard well is continuously sampled, the sampling depth is consistent with the logging data depth.
In step 1, the sampling interval for continuously sampling the coring section of the standard well is 0.2-0.3m.
In step 1, the number of samples for continuously sampling the coring section of the standard well is 80-140.
In step 1, the core section of the standard well is continuously sampled to a sampling depth of 1928-1959m.
In step 2, after logging data of each sampling point of the standard well is obtained, firstly, the logging data is subjected to standardization processing, and then multiple regression is performed.
In step 2, the acquired logging data includes natural gamma, acoustic moveout, density, deep lateral resistivity and shallow lateral resistivity.
In step 2, the method for carrying out standardized processing on the logging data comprises the following steps:
wherein, GR: natural gamma log, API;
GRmin: natural gamma minimum, API;
GR max : natural gamma maximum, API;
GR 0-1 : natural gamma standard value, noDimension is shown;
AC min : minimum acoustic time difference, μs/ft;
AC max : maximum acoustic time difference, μs/ft;
AC 0-1 : normalized value of acoustic time difference, dimensionless;
DEN: density log, g/cm;
DEN min : density logging minimum, g/cm 3 ；
DEN max : density logging maximum, g/cm 3 ；
DEN 0-1 : a density logging standardized value, dimensionless;
rt: deep lateral resistivity log, Ω m;
rxo: shallow lateral resistivity log, Ω m;
r: the ratio of the deep-shallow dual-logging resistivity difference to the deep-lateral resistivity is dimensionless;
R min : minimum ratio value of deep-shallow dual logging resistivity difference to deep-lateral resistivity, Ω;
R max : the ratio of the deep-shallow dual-logging resistivity difference to the deep-lateral resistivity is maximum, Ω < m >;
R 0-1 : the normalized value of the ratio of the deep-shallow dual-direction resistivity difference to the deep-lateral resistivity is dimensionless.
In the step 2, the established multivariate relation is:
LN(K core label 1 )＝a1*GR 0-1 label 1 +a2*AC 0-1 label 1 +a3*R 0-1 label 1 +a4*DEN 0-1 label 1 +b；
LN(K Core label 2 )＝a1*GR 0-1 label 2 +a2*AC 0-1 label 2 +a3*R 0-1 +a4*DEN 0-1 label 2 +b；
……
LN(K Core label n )＝a1*GR 0-1 n +a2*AC 0-1 n +a3*R 0-1 n +a4*DEN 0-1 n +b；
Wherein K is Core label 1 ，K Core label 2 ，……，K Core label n : each production of standard wellActually measuring permeability, mD, of a rock core of a sample point;
GR 0-1 label 1 ，GR 0-1 label 2 ，……，GR 0-1 n : natural gamma standardized values of all sampling points of a standard well are dimensionless;
AC 0-1 label 1 ，AC 0-1 label 2 ，……AC 0-1 n : standard well acoustic time difference standardized value, dimensionless;
R 0-1 label 1 ，R 0-1 label 2 ，……，R 0-1 n : the standard value of the ratio of the deep-shallow dual-direction-finding resistivity difference to the deep-lateral resistivity of each sampling point of the standard well is dimensionless;
DEN 0-1 label 1 ，DEN 0-1 label 2 ，……，DEN 0-1 n : the standard value of the density logging of each sampling point of the standard well is dimensionless;
a1 A2, a3, a4, b: the correlation coefficient is to be solved;
* : multiplying the number;
LN: natural logarithm.
In the step 3, the obtained permeability calculation formula is:
LN(K)＝a1*GR 0-1 +a2*AC 0-1 +a3*R 0-1 +a4*DEN 0-1 +b；
wherein, K: logging the calculated reservoir permeability, mD.
The invention has the advantages that:
according to the invention, the natural gamma, acoustic time difference, density and depth dual-logging resistivity difference and permeability of each sampling point of the standard well core section are utilized to carry out multiple regression on the normalized data of the ratio of the natural gamma, acoustic time difference, density and depth dual-logging resistivity difference to the deep lateral resistivity and the measured permeability of the core, and a multiple relation between the parameters and the measured permeability of the core is established, so that the method has the advantage of higher prediction accuracy in actual use.
Furthermore, the invention can realize the quantitative evaluation of the reservoir permeability by utilizing the conventional logging data, and has the characteristics of simplicity, practicability, high calculated permeability fitness, easy acquisition of data and low cost.
In addition, although log data (natural gamma logging, resistivity logging, sonic jet lag logging, neutron density logging, offset density logging) is disclosed in the publication CN111561313B, the publication merely uses neural networks or other methods to predict permeability from a logging perspective. While the documents CN107917865a and CN106841001B employ schemes for permeability prediction from the perspective of seismic and rock mineral components, respectively. Thus, the well log data disclosed in the publication CN111561313B cannot be applied to the publication CN107917865A or the publication CN 106841001B.
Drawings
FIG. 1 is a graph of multiple regression permeability versus core measured permeability.
FIG. 2 is a graph of pore-penetration regression permeability versus core measured permeability.
FIG. 3 is a graph of permeability calculation for a standard well.
FIG. 4 is a drawing of a permeability calculation for a verification well.
Detailed Description
The invention discloses a quantitative evaluation method for permeability of a tight sandstone reservoir, which is mainly based on conventional logging data for evaluation and comprises the following steps:
step 1: and selecting a certain oil-gas field well with complete coring as a standard well, and continuously sampling the coring section of the standard well. Wherein, coring is more complete, which means continuous coring of a longer well section. The sampling depth at which successive samples are taken is 1928-1959m, preferably consistent with the depth of the log data; the sampling interval for continuous sampling is 0.2-0.3m, and the preferred sampling interval is 0.25m; the number of samples for which consecutive samples are taken is 80-140, preferably 123. And after the sampling is finished, sending the collected sample to a laboratory for porosity and permeability analysis, and obtaining the core actual measurement permeability data of each sampling point.
Step 2: according to logging data, logging data of all sampling points of a standard well are obtained, and the obtained logging data mainly comprise natural gamma, acoustic time difference, density, deep lateral resistivity and shallow lateral resistivity. After logging data of all sampling points of a standard well are obtained, firstly, carrying out standardized processing on the logging data, namely the ratio of natural gamma, acoustic time difference, density and depth dual logging resistivity difference to deep lateral resistivity, so that the logging data have uniform scales, then carrying out multiple regression on the logging data after standardized processing and core actual measurement permeability data, and establishing a multiple relation between the logging data and core actual measurement permeability data.
In the step, the method for carrying out standardized processing on the logging data comprises the following steps:
wherein, GR: natural gamma log, API;
GRmin: natural gamma minimum, API;
GR max : natural gamma maximum, API;
GR 0-1 : natural gamma standardized value, dimensionless;
AC min : minimum acoustic time difference, μs/ft;
AC max : maximum acoustic time difference, μs/ft;
AC 0-1 : acoustic time difference standardChemical value, dimensionless;
DEN: density log, g/cm;
DEN min : density logging minimum, g/cm 3 ；
DEN max : density logging maximum, g/cm 3 ；
DEN 0-1 : a density logging standardized value, dimensionless;
rt: deep lateral resistivity log, Ω m;
rxo: shallow lateral resistivity log, Ω m;
r: the ratio of the deep-shallow dual-logging resistivity difference to the deep-lateral resistivity is dimensionless;
R min : minimum ratio value of deep-shallow dual logging resistivity difference to deep-lateral resistivity, Ω;
R max : the ratio of the deep-shallow dual-logging resistivity difference to the deep-lateral resistivity is maximum, Ω < m >;
R 0-1 : the normalized value of the ratio of the deep-shallow dual-direction resistivity difference to the deep-lateral resistivity is dimensionless.
In the step 2, the established multivariate relation is:
LN(K core label 1 )＝a1*GR 0-1 label 1 +a2*AC 0-1 label 1 +a3*R 0-1 label 1 +a4*DEN 0-1 label 1 +b；
LN(K Core label 2 )＝a1*GR 0-1 label 2 +a2*AC 0-1 label 2 +a3*R 0-1 +a4*DEN 0-1 label 2 +b；
……
LN(K Core label n )＝a1*GR 0-1 n +a2*AC 0-1 n +a3*R 0-1 n +a4*DEN 0-1 n +b；
Wherein K is Core label 1 ，K Core label 2 ，……，K Core label n : actually measuring permeability, mD, of a rock core of each sampling point of a standard well;
GR 0-1 label 1 ，GR 0-1 label 2 ，……，GR 0-1 n : natural gamma standardized values of all sampling points of a standard well are dimensionless;
AC 0-1 label 1 ，AC 0-1 label 2 ，……AC 0-1 n : standard well acoustic time difference standardized value, dimensionless;
R 0-1 label 1 ，R 0-1 label 2 ，……，R 0-1 n : the standard value of the ratio of the deep-shallow dual-direction-finding resistivity difference to the deep-lateral resistivity of each sampling point of the standard well is dimensionless;
DEN 0-1 label 1 ，DEN 0-1 label 2 ，……，DEN 0-1 n : the standard value of the density logging of each sampling point of the standard well is dimensionless;
a1 A2, a3, a4, b: the correlation coefficient is to be solved;
* : multiplying the number;
LN: natural logarithm.
Step 3: and solving a correlation coefficient in the multi-element relation by multi-element regression, substituting the correlation coefficient into the multi-element relation to obtain a permeability calculation formula, and quantitatively evaluating the reservoir permeability of the logging by using the permeability calculation formula.
The permeability calculation formula obtained in the step is as follows:
LN(K)＝a1*GR 0-1 +a2*AC 0-1 +a3*R 0-1 +a4*DEN 0-1 +b；
wherein, K: logging the calculated reservoir permeability, mD.
In order to verify that the method has higher accuracy, the Guangan 108 well is selected as a standard well, and the Guangan 109 well is selected for verification. The conditions and procedure for verification were as follows:
1. the coring section of the 108 wells was sampled continuously at a sampling interval of 0.2-0.3m, a sampling number of 123, and a sampling depth of 1928-1959m. Based on the verification conditions and standardized logging data values (natural Gamma (GR), acoustic time difference (AC), density (DEN), and deep-shallow dual logging resistivity difference to deep-lateral resistivity ratio (R)) of well segments 1928-1959m, a multivariate relation is established, and then the correlation coefficients in the multivariate relation are solved by multiple regression to be: a1 -2.00815, a2= 1.51343, a3= -1.15266, a4= -3.8832, b= -0.73223. And finally substituting the correlation coefficient into a multi-element relation to obtain a permeability calculation formula, wherein the permeability calculation formula is as follows:
LN(K)＝-2.00815GR 0-1 +1.51343AC 0-1 -1.15266R 0-1 -3.8832DEN 0-1 -0.73223。
2. the permeability was actually calculated for the 108 wells using the above-described permeability calculation formula, and the correlation coefficient calculated (multiple regression permeability) was 0.7847 as shown in fig. 1. And the permeability is calculated by adopting the existing pore-permeation relation, as shown in fig. 2, the logarithmic correlation coefficient of the pore-permeation regression permeability and the core measured permeability is 0.6925, and compared with the accuracy of the permeability calculated by adopting the pore-permeation relation, the method disclosed by the invention is improved by nearly 10%.
3. According to the permeability calculation formula, the permeability of the 108 wells is calculated, and as shown in fig. 3, the permeability calculated by adopting multiple regression is high in agreement with the measured permeability of the core, and is obviously superior to the pore permeation regression permeability.
4. When the above permeability calculation formula is applied to 109 wells, as shown in fig. 4, the calculated permeability is as high as the measured permeability of the core, and it is known that the quantitative evaluation method has high evaluation accuracy, simple steps and easy implementation in the practical application process.
From the above, the invention can realize quantitative evaluation of reservoir permeability by utilizing the conventional logging data, and has the characteristics of simplicity, practicality and high calculated permeability fitness.
While the invention has been described with reference to certain embodiments, it is understood that any feature disclosed in this specification may be replaced by alternative features serving the equivalent or similar purpose, unless expressly stated otherwise; all of the features disclosed, or all of the steps in a method or process, except for mutually exclusive features and/or steps, may be combined in any manner.
Claims (5)
1. The quantitative evaluation method for permeability of the tight sandstone reservoir is characterized by comprising the following steps of:
step 1: selecting an oil-gas field well with complete coring as a standard well, continuously sampling a coring section of the standard well, and then analyzing the porosity and the permeability of the acquired sample to obtain core actual measurement permeability data of each sampling point;
step 2: according to logging data, logging data of sampling points of a standard well are obtained, multiple regression is carried out on the logging data and the measured permeability data of the core, and a multiple relation between the logging data and the measured permeability data of the core is established;
step 3: solving a correlation coefficient in a multi-element relation by multi-element regression, substituting the correlation coefficient into the multi-element relation to obtain a permeability calculation formula, and quantitatively evaluating the reservoir permeability of the logging by using the permeability calculation formula;
in the step 2, after logging data of all sampling points of a standard well are obtained, firstly, carrying out standardization processing on the logging data, and then carrying out multiple regression;
in step 2, the acquired logging data includes natural gamma, acoustic time difference, density, deep lateral resistivity and shallow lateral resistivity;
in step 2, the method for carrying out standardized processing on the logging data comprises the following steps:
wherein, GR: natural gamma log, API;
GRmin: natural gamma minimum, API;
GR max : natural gamma maximum, API;
GR 0-1 : natural gamma standardized value, dimensionless;
AC min : minimum acoustic time difference, μs/ft;
AC max : maximum acoustic time difference, μs/ft;
AC 0-1 : normalized value of acoustic time difference, dimensionless;
DEN: density log, g/cm;
DEN min : density logging minimum, g/cm 3 ；
DEN max : density logging maximum, g/cm 3 ；
DEN 0-1 : a density logging standardized value, dimensionless;
rt: deep lateral resistivity log, Ω m;
rxo: shallow lateral resistivity log, Ω m;
r: the ratio of the deep-shallow dual-logging resistivity difference to the deep-lateral resistivity is dimensionless;
R min : minimum ratio value of deep-shallow dual logging resistivity difference to deep-lateral resistivity, Ω;
R max : the ratio of the deep-shallow dual-logging resistivity difference to the deep-lateral resistivity is maximum, Ω < m >;
R 0-1 : the standardized value of the ratio of the deep-shallow dual-direction resistivity difference to the deep-lateral resistivity is dimensionless;
in the step 2, the established multivariate relation is:
LN(K core label 1 )＝a1*GR 0-1 label 1 +a2*AC 0-1 label 1 +a3*R 0-1 label 1 +a4*DEN 0-1 label 1 +b；
LN(K Core label 2 )＝a1*GR 0-1 label 2 +a2*AC 0-1 label 2 +a3*R 0-1 +a4*DEN 0-1 label 2 +b；
……
LN(K Core label n )＝a1*GR 0-1 n +a2*AC 0-1 n +a3*R 0-1 n +a4*DEN 0-1 n +b；
Wherein K is Core label 1 ，K Core label 2 ，……，K Core label n : actually measuring permeability, mD, of a rock core of each sampling point of a standard well;
GR 0-1 label 1 ，GR 0-1 label 2 ，……，GR 0-1 n : natural gamma standardized values of all sampling points of a standard well are dimensionless;
AC 0-1 label 1 ，AC 0-1 label 2 ，……AC 0-1 n : standard well acoustic time difference standardized value, dimensionless;
R 0-1 label 1 ，R 0-1 label 2 ，……，R 0-1 n : the standard value of the ratio of the deep-shallow dual-direction-finding resistivity difference to the deep-lateral resistivity of each sampling point of the standard well is dimensionless;
DEN 0-1 label 1 ，DEN 0-1 label 2 ，……，DEN 0-1 n : the standard value of the density logging of each sampling point of the standard well is dimensionless;
a1 A2, a3, a4, b: the correlation coefficient is to be solved;
* : multiplying the number;
LN: natural logarithm;
in the step 3, the obtained permeability calculation formula is:
LN(K)＝a1*GR 0-1 +a2*AC 0-1 +a3*R 0-1 +a4*DEN 0-1 +b；
wherein, K: logging the calculated reservoir permeability, mD.
2. The quantitative evaluation method for permeability of a tight sandstone reservoir according to claim 1, wherein: in step 1, when the coring section of the standard well is continuously sampled, the sampling depth is consistent with the logging data depth.
3. The quantitative evaluation method for permeability of a tight sandstone reservoir according to claim 1, wherein: in step 1, the sampling interval for continuously sampling the coring section of the standard well is 0.2-0.3m.
4. The quantitative evaluation method for permeability of a tight sandstone reservoir according to claim 1, wherein: in step 1, the number of samples for continuously sampling the coring section of the standard well is 80-140.
5. The quantitative evaluation method for permeability of a tight sandstone reservoir according to claim 1, wherein: in step 1, the core section of the standard well is continuously sampled to a sampling depth of 1928-1959m.
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