CN111624676B - Hydrocarbon-generating potential prediction method for hydrocarbon source rock - Google Patents
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Abstract
The invention provides a method for predicting hydrocarbon-generating potential of a hydrocarbon source rock. The method comprises the following steps: s11, selecting a hydrocarbon source rock sample with known hydrocarbon generation potential, and obtaining the content of standard macroelements in the hydrocarbon source rock and the conventional logging response parameters of the hydrocarbon source rock; s12, establishing a relational expression of hydrocarbon generation potential, the content of each constant element and logging parameters, and screening the constant elements and the logging parameters related to the hydrocarbon generation potential; determining a weight coefficient W of the constant elements and the logging parameters in hydrocarbon potential prediction; s13, performing experimental analysis on the source rock to be predicted to obtain the content of the macroelements determined in the S12 and logging parameters of the corresponding source rock; s14, establishing a constant element factor score coefficient matrix and a logging parameter factor score coefficient matrix which can reflect the hydrocarbon-producing potential of the hydrocarbon source rock, and calculating respective comprehensive scores F; s15, calculating a comprehensive score Q reflecting the hydrocarbon-generating potential of the rock to be predicted and the known hydrocarbon source according to the weight coefficient W obtained in the step S12; and S16, calculating the hydrocarbon generation potential of the hydrocarbon source rock to be predicted.
Description
Technical Field
The invention relates to the technical field of geological research, in particular to a hydrocarbon-source rock hydrocarbon-generating potential prediction method.
Background
The determination of hydrocarbon-generating potential of the hydrocarbon source rock is directly related to the aspects of drilling investment, drilling success rate and the like, and for the recess which does not realize the hydrocarbon-generating potential of the hydrocarbon source rock, whether the hydrocarbon-generating potential of the hydrocarbon source rock in the recess can be accurately predicted is very important for improving the oil and gas exploration benefit.
At present, a method for determining the hydrocarbon-generating potential of a hydrocarbon source rock is mainly determined by a laboratory organic geochemical analysis method, although the method can qualitatively and quantitatively determine the hydrocarbon-generating potential of the hydrocarbon source rock, the hydrocarbon-generating potential of the hydrocarbon source rock cannot be accurately reflected if a few test samples exist due to the heterogeneity of organic matter occurrence states in the hydrocarbon source rock samples; the testing costs are very expensive if the hydrocarbon-producing potential of the hydrocarbon source rock is statistically determined by a large number of test samples. Therefore, the hydrocarbon-producing potential of the hydrocarbon source rock determined by the conventional organic geochemistry method cannot meet the exploration requirement of low exploration degree depression with few hydrocarbon source rock samples.
Disclosure of Invention
One object of the present invention is to provide a method for predicting hydrocarbon-producing potential of a hydrocarbon source rock; the method aims to accurately evaluate the hydrocarbon generation potential of the low-exploration-degree depression with few hydrocarbon source rock samples and provide a basis for exploration investment and exploratory well drilling.
In order to achieve the above objects, in one aspect, the present invention provides a method for predicting hydrocarbon-producing potential of a hydrocarbon source rock, wherein the method comprises the following steps:
s11, selecting a hydrocarbon source rock sample with known hydrocarbon generation potential, and obtaining the contents of 11 standard macroelements and 11 conventional logging response parameters of the hydrocarbon source rock;
s12, establishing a relational expression of hydrocarbon generation potential, the content of each constant element and logging response parameters, and screening the constant elements and the logging response parameters related to the hydrocarbon generation potential; determining a weight coefficient W of the constant element and the logging response parameter in hydrocarbon potential prediction;
s13, performing experimental analysis on the source rock to be predicted to obtain the content of the macroelements determined in the S12 and the logging response parameters of the corresponding source rock;
s14, establishing a constant element main factor score coefficient matrix and a logging response parameter main factor score coefficient matrix which can reflect the hydrocarbon-producing potential of the hydrocarbon source rock, and calculating respective comprehensive scores F;
s15, calculating a hydrocarbon potential comprehensive score Q of the source rock to be predicted and the known hydrocarbon potential according to the weight coefficient W obtained in the step S12;
and S16, calculating the hydrocarbon generation potential of the hydrocarbon source rock to be predicted.
According to some embodiments of the invention, the 11 normal macroelements are Ca, K, Ti, P, Mn, Si, Al, Fe, Na, Mg, and H.
According to some embodiments of the present invention, the 11 conventional log response parameters of the source rock are GR (natural gamma), DT (acoustic moveout), LLD (deep lateral resistivity), MSFL (microsphere resistivity), LLS (shallow lateral resistivity), DEN (formation density), NPHI (neutron porosity), SP (natural gamma), U (radioactivity U), CAL (borehole diameter), and DIP (formation DIP).
According to some embodiments of the present invention, step S12 is to screen the macroelements and the log response parameters for characterizing the hydrocarbon potential based on the correlation analysis, and determine the weighting factor W of the macroelements and the log response parameters in the prediction of the hydrocarbon potential according to the correlation coefficient between the macroelements and the hydrocarbon potential.
According to some embodiments of the present invention, step S12 calculates the weight coefficient W of the constant element and the log response parameter in the hydrocarbon potential prediction by the following formula:
wherein i represents a constant element or log response parameter, W i A weighting factor that is a constant element or a logging response parameter; k is the number of preferred constant elements, C k A correlation coefficient for each macroelement to hydrocarbon-generating potential; j is the number of the preferred logging response parameters, C j A correlation coefficient of each log response parameter with hydrocarbon production potential.
According to some embodiments of the present invention, step S14 is based on multivariate statistical factor analysis, and the combined score F of each major factor of the macroelements and the microelements is obtained.
According to some embodiments of the present invention, in step S14, a scoring coefficient matrix of major factors of the log response parameters and the macroelements that can reflect the hydrocarbon-producing potential of the hydrocarbon source rock is established based on multivariate statistical factor analysis, and corresponding major factors are extracted to calculate a composite score F of each major factor of the macroelements and the microelements.
According to some embodiments of the present invention, in step S15, a hydrocarbon potential comprehensive score Q reflecting the source rock to be predicted and the source rock with known hydrocarbon potential is obtained by calculating the variance contribution rate C of the macroelements and the different main factors of the log response parameters.
According to some embodiments of the present invention, the variance contribution rate C can be calculated according to conventional statistical software such as SPSS.
According to some embodiments of the present invention, in step S15, a hydrocarbon potential synthesis score Q reflecting the source rock to be predicted and the known hydrocarbon potential is obtained by calculating the variance contribution rate C of the macroelements, the different main factors of the log response parameters, and the weight coefficient W according to the following formula:
wherein n is the number of major factors of the constant elements, m is the number of major factors of logging response parameters, W Constant quantity Is the weight coefficient, W, of a constant element Logging well Is the weight coefficient of the logging response parameter.
According to some embodiments of the present invention, in step S16, the hydrocarbon-producing potential of the hydrocarbon source rock to be predicted (recessed) is calculated by using a method of similarity.
According to some embodiments of the invention, step S16 is a hydrocarbon-producing potential P of the hydrocarbon source rock based on a known hydrocarbon-producing potential It is known that And step S15, calculating the hydrocarbon potential of the corresponding hydrocarbon source rock to be predicted (depressed) by a class comparison method.
According to some embodiments of the invention, step S16 is a hydrocarbon-producing potential P of the hydrocarbon source rock based on the known hydrocarbon-producing potential It is known that And the corresponding hydrocarbon source rock calculated in step S15 is hydrocarbon-producingPotential comprehensive score Q, and calculating hydrocarbon generation potential P of the hydrocarbon source rock to be predicted (depressed) by using a class-comparison method through the following formula To be predicted ：
P To be predicted ＝P It is known that /Q It is known that *Q To be predicted
Wherein Q It is known that Synthesis of a score, Q, for Hydrocarbon potential of a Hydrocarbon Source rock of known Hydrocarbon potential To be predicted And (4) integrating the hydrocarbon-generating potential of the hydrocarbon source rock to be predicted.
Wherein it is understood that Q is a generic term for a hydrocarbon-producing potential composite score of a hydrocarbon source rock, which includes Q It is known that And Q To be predicted ；Q It is known that A hydrocarbon-bearing potential composite score, Q, for a hydrocarbon source rock of known hydrocarbon-bearing potential To be predicted Is the hydrocarbon potential comprehensive score of the hydrocarbon source rock to be predicted.
According to some embodiments of the invention, the source rock is a depressed source rock.
According to some embodiments of the invention, the method comprises the steps of:
s11, selecting a hydrocarbon source rock sample with known hydrocarbon generation potential, and obtaining the content of standard macroelements in the hydrocarbon source rock and conventional logging response parameters of the hydrocarbon source rock;
s12, establishing a relational expression of hydrocarbon generation potential, the content of each constant element and logging response parameters, and screening the constant elements and the logging response parameters related to the hydrocarbon generation potential; determining a weight coefficient W of the constant element and the logging response parameter in hydrocarbon potential prediction, wherein the weight coefficient W is the weight coefficient of the constant element and the logging response parameter;
i represents a constant element or log response parameter, W i A weighting factor that is a constant element or a logging response parameter; k is the number of preferred constant elements, C k A correlation coefficient for each macroelement to hydrocarbon-generating potential; j is the number of the preferred logging response parameters, C j A correlation coefficient for each log response parameter with hydrocarbon production potential;
s13, performing experimental analysis on the source rock to be predicted to obtain the content of the macroelements determined in the S12 and the logging response parameters of the corresponding source rock;
s14, establishing a constant element main factor score coefficient matrix and a logging response parameter main factor score coefficient matrix which can reflect the hydrocarbon-producing potential of the hydrocarbon source rock, and calculating respective comprehensive scores F;
s15, according to the weight coefficient W obtained in S12, calculating a comprehensive score Q reflecting the hydrocarbon-generating potential of the rock to be predicted and the known hydrocarbon source, wherein
Wherein n is the number of major factors of the constant elements, and m is the number of major factors of the logging response parameters.
S16, Hydrocarbon-producing potential P based on known hydrocarbon source rock It is known that And step S15, calculating the hydrocarbon potential P of the hydrocarbon source rock to be predicted by a class ratio method according to the corresponding hydrocarbon potential comprehensive score Q of the hydrocarbon source rock calculated in the step S15 To be predicted ＝P It is known that /Q It is known that *Q To be predicted 。
In conclusion, the invention provides a method for predicting hydrocarbon-generating potential of a hydrocarbon source rock. The method of the invention has the following advantages: 1, calculating hydrocarbon-generating potential of a hydrocarbon source rock by a method combining inorganic constant element content and logging response parameters, which is a supplement to a method for obtaining the hydrocarbon-generating potential of the hydrocarbon source rock through conventional organic geochemistry tests; the hydrocarbon-producing potential of the hydrocarbon source rock obtained by the method has scientific rationality, the hydrocarbon-producing potential is closely related to the content of organic matters, the organic matters are formed by biological deposition, and the survival of the organisms is based on the water body environment with the appropriate content of the macroelements and response characteristics on logging response parameters; and 3, by mathematical statistics comprehensive analysis and in the process of calculating the hydrocarbon potential comprehensive score, the weight coefficients of the macroelements and the logging response parameters are introduced, the finally obtained result has objectivity, and meanwhile, unnecessary operation cost of an organic test experiment can be greatly reduced by using the method.
Drawings
Fig. 1 is a flowchart of an implementation of the hydrocarbon-producing potential prediction method of the hydrocarbon source rock of this example 1.
Detailed Description
The following detailed description is provided for the purpose of illustrating the embodiments and the advantageous effects thereof, and is not intended to limit the scope of the present disclosure.
Example 1
The flow chart of the implementation of the hydrocarbon-producing potential prediction method for the hydrocarbon source rock of the embodiment shown in fig. 1 includes:
s11, obtaining hydrocarbon source rock samples A, B, C, D with known hydrocarbon generation potential P of 19.53mg/g, 6.21mg/g, 12.64mg/g and 25.12mg/g, and obtaining the content percentage of macroelements in a laboratory by a titration method, wherein the macroelements comprise 11 standard elements such as Ca, K, Ti and the like; and obtaining conventional logging data corresponding to 11 hydrocarbon source rock samples such as GR (natural gamma), DT (acoustic time difference), LLD (deep lateral resistivity), MSFL (microsphere resistivity), LLS (shallow lateral resistivity), DEN (formation density), NPHI (neutron porosity), SP (natural gamma), U (radioactivity U), CAL (borehole diameter), DIP (DIP angle) and the like according to the logging response parameters.
S12, taking the hydrocarbon generation potential P of A, B, C, D four groups of data as a Y coordinate, taking each macroelement or logging response parameter as an X coordinate, establishing a correlation relation between the macroelements and the X coordinate, and analyzing to determine that the correlation coefficients of Ca, K, Fe, Mg and H and the hydrocarbon generation potential are greater than 0.5, and respectively: 0.52, 0.56, 0.61, 0.64, 0.66, e.g. P and Mg are given by the following relations: p-0.64 Mg-0.022; these 5 macroelements are preferred as parameters that can characterize the hydrocarbon-generating potential; in addition, the correlation coefficient of the GR, DT, LLD and U logging response parameters and the hydrocarbon generation potential is more than 0.5, which is considered as follows: 0.51, 0.53, 0.55, 0.61, therefore, these 4 logging response parameters are preferred as parameters characterizing the hydrocarbon-generating potential. And finally, calculating a formula according to the weight coefficient:
(i represents a constant element or log response parameter, W) i A weighting factor that is a constant element or a logging response parameter; k is the number of preferred constant elements, C k For each constant element the correlation with hydrocarbon potentialCounting; j is the number of the preferred logging response parameters, C j A correlation coefficient of each log response parameter with hydrocarbon production potential. ) Calculating to obtain W Constant quantity ＝0.52，W Logging well ＝0.48。
S13, selecting 4 hydrocarbon source rocks to be predicted, and numbering the rocks respectively as follows: E. h, G, L, respectively carrying out experimental analysis to obtain the contents of Ca, K, Fe, Mg and H of the preferred macroelements S12; meanwhile, acquiring logging response parameters corresponding to GR, DT, LLD and U of the source rock according to the logging curve;
s14, respectively forming 5 groups of data by 4 groups of constant element data and logging response parameters of the hydrocarbon source rock to be predicted, 1 group of constant element data and logging response parameters of the hydrocarbon source rock with known hydrocarbon potential, respectively adopting a multivariate statistical factor analysis method to standardize the element content, utilizing a dimensionality reduction thought, establishing a constant element (table 1) and logging response parameter principal factor score coefficient matrix, and grouping the constant element and logging response parameter principal factor score coefficient matrixes according to the relevance of the variables, so that the relevance of the variables in different groups is low, the relevance of the variables in the same group is high, each group of variables is represented by a basic structure and is represented as a non-observable comprehensive variable, the basic structure is called as a principal factor, and the variance contribution rates of different principal factors are calculated while determining the principal factor (table 1). Finally, 5 constant elements are classified into 3 main factors, 4 logging response parameters are classified into 2 main factors, and if the main factor 1 of the constant elements is as follows: ca. K is composed of 2 elements, wherein the main factor 2 is Fe, and the main factor 3 is Mg and H; multiplying the coefficient corresponding to the corresponding element in each main factor by the sum of the normalized element contents to obtain a comprehensive score F of each main factor; and the calculation method of the logging response parameter main factor comprehensive score is the same. The calculated overall scores of the constant elements and the main factors of the logging response parameters are shown in table 2.
S15, because the content of the macroelements and the logging response parameters can represent the hydrocarbon-generating potential of the hydrocarbon source rock, the macroelements and the logging response parameter main factors are integrated according to the weight coefficients, and the hydrocarbon-generating potential of the hydrocarbon source rock can be reflected. According to the variance contribution rate of different main factors of the constant element and the logging response parameter, the constant element and the variance contribution rate are comparedMultiplying the corresponding F values, respectively calculating to obtain reflection values of the constant elements and the logging response parameters on hydrocarbon generation potential responses, then respectively multiplying by weight coefficients, and adding the two to obtain an integral response value Q of the constant elements and the logging response parameters on hydrocarbon generation potential of the hydrocarbon source rock, wherein the corresponding mathematical expression is as follows:
(n is the number of major factors of the constant elements, and m is the number of major factors of the logging response parameters). The calculation results in table 2, and as can be seen from the table, the total ranking of the hydrocarbon generation potential of 5 hydrocarbon source rock samples is A, E, H, G, L; representing the variability of the hydrocarbon-producing potential of the hydrocarbon source rock. A high score indicates a high hydrocarbon-producing potential of the hydrocarbon source rock.
S16, the hydrocarbon-generating potential of the hydrocarbon source rock of the sample A is known to be 19.53mg/g, and the corresponding hydrocarbon-generating potential response value of the hydrocarbon source rock is 2.14; E. h, G, L the response value is: 1.45, 1.03, 0.79, 0.60 according to the formula P To be predicted ＝P It is known that /Q It is known that ×Q To be predicted The hydrocarbon potential of E, H, G, L was calculated to be: 13.07mg/g, 7.56mg/g, 6.32mg/g and 4.40mg/g, and compared with the actual hydrocarbon generation potential, the error ranges of the four are within 20% (as shown in table 3), which indicates that the prediction result has good effect and can be applied to the hydrocarbon generation potential prediction and evaluation of the hydrocarbon source rock.
TABLE 1 constant elements
Note: the different colours marked on the table represent the elements contained in the different factors
TABLE 2
TABLE 3
| Sample number | E | H | G | L |
| Predicted value (mg/g) | 13.23 | 9.42 | 7.25 | 5.43 |
| Measured value (mg/g) | 15.67 | 8.58 | 6.65 | 4.92 |
| Error value | 15.57％ | -9.79％ | 9.02％ | -10.37％ |
Claims (6)
1. A method for predicting the hydrocarbon-producing potential of a hydrocarbon source rock, wherein the method comprises the following steps:
s11, selecting a hydrocarbon source rock sample with known hydrocarbon generation potential, and obtaining the contents of 11 standard macroelements and 11 logging response parameters in the hydrocarbon source rock;
s12, establishing a relational expression of hydrocarbon generation potential, the content of each constant element and logging response parameters, and screening the constant elements and the logging response parameters related to the hydrocarbon generation potential; determining a weight coefficient W of the constant element and the logging response parameter in hydrocarbon potential prediction;
s13, performing experimental analysis on the source rock to be predicted, and acquiring the content of the constant elements determined in the step S12 and the logging response parameters of the corresponding source rock;
s14, establishing a constant element main factor score coefficient matrix and a logging response parameter main factor score coefficient matrix which can reflect the hydrocarbon-producing potential of the hydrocarbon source rock, and calculating respective comprehensive scores F;
s15, calculating and obtaining a hydrocarbon potential comprehensive score Q reflecting the source rock to be predicted and the known hydrocarbon potential according to the constant elements, the variance contribution rate C of different main factors of logging response parameters and the weight coefficient W obtained in the step S12 by the following formula:
wherein C is n Is the nth main factor variance contribution rate of the constant element, F n The integral score is the nth major factor of the constant elements; c m For the mth main factor variance contribution rate, F, of the logging response parameter m Comprehensively scoring the mth logging response parameter principal factor; n is the number of major factors of the constant elements, m is the number of major factors of logging response parameters, W Constant quantity Is a weight coefficient, W, of a constant element Logging well Weighting coefficients for logging response parameters
S16 hydrocarbon-producing potential P of hydrocarbon source rock based on known hydrocarbon-producing potential It is known that And step S15, calculating the hydrocarbon potential P of the hydrocarbon source rock to be predicted by a class ratio method according to the corresponding hydrocarbon potential comprehensive score Q of the hydrocarbon source rock calculated in the step S15 To be predicted ：
P To be predicted ＝P It is known that /Q It is known that *Q To be predicted
Wherein Q It is known that Synthesis of a score, Q, for Hydrocarbon potential of a Hydrocarbon Source rock of known Hydrocarbon potential To be predicted And (4) integrating the hydrocarbon-generating potential of the hydrocarbon source rock to be predicted.
2. The prediction method of claim 1, wherein the 11 normal macroelements of step S11 are Ca, K, Ti, P, Mn, Si, Al, Fe, Na, Mg and H; the 11 logging response parameters are GR, DT, LLD, MSFL, LLS, DEN, NPHI, SP, U, CAL, and DIP.
3. The prediction method according to claim 1, wherein step S12 is based on correlation analysis, the macroelements and the log response parameters that can characterize the hydrocarbon potential are screened, and the weighting factor W of the macroelements and the log response parameters in the hydrocarbon potential prediction is determined according to the magnitude of the correlation coefficient between the macroelements, the log response parameters and the hydrocarbon potential.
4. The prediction method according to claim 3, wherein W is calculated by the formula:
wherein W Constant quantity Is a constant element weight coefficient, W Logging well A logging response parameter weight coefficient; k is the number of preferred constant elements, C k A correlation coefficient for each macroelement to hydrocarbon-generating potential; j is the number of the preferred logging response parameters, C j A correlation coefficient of each log response parameter with hydrocarbon production potential.
5. The prediction method according to claim 1, wherein step S14 is based on multivariate statistical factor analysis, and comprises establishing a constant element and log response parameter principal factor score coefficient matrix capable of reflecting hydrocarbon-producing potential of the hydrocarbon source rock, extracting corresponding principal factors, and calculating a composite score F of each principal factor of the constant element and the log response parameter.
6. A prediction method according to any one of claims 1 to 5, wherein the source rock is a depressed source rock.
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