CN110927814A - Crack prediction method based on lithofacies configuration - Google Patents
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Abstract
The invention discloses a crack prediction method based on a lithofacies configuration, which comprises the following steps of: predicting relevant factors influencing the development of the crack, wherein the relevant factors comprise lithofacies configuration, organic carbon content of the source rock, curvature, distance from fault and rock texture; according to the imaging logging fracture density statistical data and the statistical values of the relevant factors, performing correlation analysis of the relevant factors and the fracture development degree, and determining geological main control factors influencing the fracture development; and establishing a multi-parameter fracture density calculation model based on the lithofacies configuration according to the obtained geological master control factors, and predicting the fracture. On the premise of fully considering that the development degree of the fractures of the tight sandstone reservoir is controlled by structural and non-structural factors, the invention obtains geological main control factors by taking the prediction of relevant factors influencing the fracture development as a main line and the relevance of each relevant factor and the fracture development as a key, establishes a multi-parameter fracture density calculation model and realizes the fracture prediction of the tight sandstone reservoir.
Description
Technical Field
The invention relates to the technical field of reservoir fractures, in particular to a fracture prediction method based on lithofacies configuration.
Background
Cracks are natural macroscopic plane-shaped discontinuous structures formed by structural deformation or physical diagenesis in rocks (Nelson, 1985), and cracks of fracture structures belonging to the same category as faults are difficult to identify and predict due to relatively small scale, so that the cracks are a worldwide research problem. Low permeability reservoirs have strong heterogeneity and anisotropy due to depositional, diagenetic and tectonic effects. The development characteristics of low permeability reservoir fractures affected by the superposition of early sedimentary diagenesis and later tectonic effects are more complex.
The method has the advantages that the developed cracks in the reservoir can serve as a reservoir space, and can also communicate dispersed and isolated pores and karst caves in the stratum, so that the effective porosity of the reservoir is increased to enable the reservoir to be a high-quality reservoir, and therefore accurate prediction of underground crack space distribution is important research content in the oil and gas exploration process. At present, the determination method for cracks mainly comprises a geophysical prediction method such as a dry method, a curvature method, an ant tracking method and the like. However, due to the influence of factors such as lithology, physical properties, fluids, fracture development degree, fracture occurrence, fracture filling degree and the like, the fracture response characteristics of conventional logging are not obvious, the geophysical prediction of fractures is difficult, and strong multi-solution and uncertainty exist. Therefore, by analyzing the main control factors of the tight sandstone reservoir fracture development, a quantitative fracture prediction model based on geological analysis is established, accurate prediction of longitudinal and transverse distribution of the fractures is completed, and the method has important significance for guiding oil and gas exploration and development.
Also, factors that affect fracture development include both tectonic and non-tectonic factors. Wherein, the structural factors include fault, structural deformation strength and the like; non-structural factors include lithologic composition, sandstone texture, physical properties, thickness, etc. (Zeng et al, 2010; gu lie et al, 2017; wangcwei et al, 2014). Fractures typically develop in more aggressive formations such as sandstone. Meanwhile, the formation of cracks is controlled by the thickness of the rock mechanical layer with consistent or similar rock mechanical properties, and the larger the thickness is, the lower the crack development degree is (scleral et al, 2017; Larsen et al, 2010). In the past, a great deal of research and analysis based on structural factors are carried out aiming at the development degree of cracks. However, the difference in the development degree of fractures in different intervals under the same local structural feature cannot be reasonably explained.
Disclosure of Invention
The invention aims to overcome the defects that in the prior art, only construction factors are considered in the research of the development degree of cracks, a quantitative crack prediction model based on geological analysis is not established, the longitudinal and transverse distribution of the cracks cannot be accurately predicted, and the oil-gas exploration and development cannot be effectively guided, and provides a crack prediction method based on a lithofacies configuration.
In order to achieve the above purpose, the invention provides the following technical scheme:
a fracture prediction method based on a lithofacies configuration comprises the following steps:
the method comprises the following steps: predicting relevant factors influencing the development of the crack, wherein the relevant factors comprise lithofacies configuration, organic carbon content of the source rock, curvature, distance from fault and rock texture;
step two: according to the imaging logging fracture density statistical data and the statistical values of the relevant factors, performing correlation analysis of the relevant factors and the fracture development degree, and determining geological main control factors influencing the fracture development;
step three: and establishing a multi-parameter fracture density calculation model based on the lithofacies configuration according to the obtained geological master control factors, predicting the fracture, and obtaining a fracture prediction result which can be used for guiding oil and gas exploration and development.
Preferably, in the first step, the lithofacies configuration refers to longitudinal combination patterns, stacking relations and plane distribution of different lithologies, and represents combinations of different deposition environments and energy units (Miall, 2014). Therefore, the lithofacies configuration can simultaneously characterize the distribution, combination pattern and stacking relationship of the dryable rock stratum and the rock mechanical layer. The invention provides quantitative characterization parameters of the lithofacies configuration, which comprise lithofacies percentage content, lithofacies layer density, lithofacies average monolayer thickness and lithofacies thickness variation coefficient, wherein,
lithofacies percentage content is lithofacies thickness/statistical unit formation thickness
Rock phase layer density is equal to rock phase layer number/statistical unit stratum thickness
Average thickness of lithofacies (total thickness/number of lithofacies)
Lithofacies thickness variation coefficient (standard deviation of lithofacies thickness/average monolayer thickness)
By means of these four parameters, the proportion of rock facies, the stacking and interbedding between different rock facies can be quantitatively described.
Before calculating the characteristic parameters of the lithofacies configuration, a statistical unit needs to be determined firstly. If the statistics is carried out according to the sand group, the combination relation of the upper surrounding rock and the lower surrounding rock cannot be accurately described due to small unit thickness. However, if counted in groups or sub-segments, due to the large cell thickness, the overall statistics may mask the better type of lithofacies profile interval in which fracture development is favored. Therefore, aiming at the sandstone and the combination characteristics of the upper and lower surrounding rocks thereof, the invention adopts a moving window method to carry out statistics on the configuration characterization parameters. The method comprises the following specific steps: taking the window size of 60m and the step length of 10m as an example, moving to the top of an interval to be counted, such as a position with the well depth of 1000m, and respectively calculating 4 rock facies configuration parameter values for different rock facies at a window of 1000-1060 m; then moving down a step length (such as 10m), namely a window of 1010-1070 m, and respectively calculating 4 rock facies configuration parameter values for different rock facies; and repeating the steps until the bottom depth of the window is the bottom depth of the layer to be counted. If the total thickness of the interval to be counted is 100m, the window size is 60m, and the step size is 10m, the process needs to be repeated for 5 times. It should be emphasized that the window size and the moving step length are not fixed values and need to be adjusted according to the actual geological conditions.
Preferably, in the first step, the organic carbon content of the source rock is obtained by collecting single-well shale cores or rock debris to perform organic carbon content experimental analysis or a logging prediction method. Wherein, the well logging prediction method can pass through delta 1gAnd establishing an organic carbon content model by at least one of methods such as R and multiple linear regression, and predicting the organic carbon content.
Preferably, in the first step, the curvature and the distance from the fault are mainly obtained by analyzing the structural position of the crack development section or the sample point.
Preferably, in the first step, the rock texture is obtained by performing experimental analysis such as single-well longitudinal up-system sampling and slice identification.
Preferably, in the second step, the specific analysis process of the correlation analysis of the related factors and the crack development degree is as follows: and respectively establishing a relational expression between each parameter and the fracture density according to the imaging logging fracture density statistical data and the statistical values of the related factors, gradually regressing and analyzing the influence degree of each related factor on the fracture line density, and identifying geological main control factors influencing the fracture density and the fracture development.
Preferably, in the third step, the interval of the known lithofacies configuration type is used as a training sample, a discriminant analysis method is adopted to establish recognition models of different lithofacies configurations, and the recognition models are adopted to discriminate and group unknown samples.
Preferably, in the third step, a Bayes discrimination method is adopted to predict the rock phase configuration type, specifically, the posterior probability of each layer section to be determined from a certain type is calculated according to a Bayes criterion, and then the posterior probabilities are compared to classify the layer sections to be determined into the type with the maximum probability. If the thickness of the stratigraphic unit (such as a subsection) to be counted exceeds the window size, a plurality of discrimination types can exist, and the configuration class which is most beneficial to fracture development is taken as the configuration of the stratigraphic unit.
Compared with the prior art, the invention has the beneficial effects that:
the method provided by the invention is characterized in that on the premise that the development degree of the compact sandstone reservoir fracture is controlled by structural and non-structural factors, the prediction of related factors influencing the fracture development is taken as a main line, the relevance of each related factor and the fracture development is taken as a key, the geological main control factor is obtained, a multi-parameter fracture density calculation model is established, the fracture prediction of the compact sandstone reservoir is finally realized, a reliable multi-parameter geological prediction method is provided for the fracture prediction of the compact sandstone reservoir, and the reliability of the fracture prediction is improved. The effective fracture prediction method is beneficial to determining the distribution range of the relatively high-quality reservoir in the tight sandstone gas area and improving the success rate of drilling the target on the oil gas.
Description of the drawings:
FIG. 1 is a schematic diagram of a fracture prediction method based on lithofacies configuration according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
In this embodiment, taking the five segments of the stropanthus divericatus as an example, as shown in fig. 1, a fracture prediction method based on a lithofacies configuration includes the following steps:
the method comprises the following steps: and (3) predicting relevant factors influencing the development of the fracture, wherein the relevant factors comprise the lithofacies configuration of five sections of all single well beards in the west of Chuanxiong, the organic carbon content of the hydrocarbon source rock, the curvature, the distance from the fault and the rock texture.
Lithofacies configuration refers to the longitudinal combination pattern, stacking relationship and planar distribution of different lithologies, representing the combination of different deposition environments and energy units (Miall, 2014). Therefore, the lithofacies configuration can simultaneously characterize the distribution, combination pattern and stacking relationship of the dryable rock stratum and the rock mechanical layer. The invention provides quantitative characterization parameters of the lithofacies configuration, which comprise lithofacies percentage content, lithofacies layer density, lithofacies average monolayer thickness and lithofacies thickness variation coefficient, wherein,
lithofacies percentage content is lithofacies thickness/statistical unit formation thickness
Rock phase layer density is equal to rock phase layer number/statistical unit stratum thickness
Average thickness of lithofacies (total thickness/number of lithofacies)
Lithofacies thickness variation coefficient (standard deviation of lithofacies thickness/average monolayer thickness)
By means of these four parameters, the proportion of rock facies, the stacking and interbedding between different rock facies can be quantitatively described.
Before calculating the characteristic parameters of the lithofacies configuration, a statistical unit needs to be determined firstly. If the statistics is carried out according to the sand group, the combination relation of the upper surrounding rock and the lower surrounding rock cannot be accurately described due to small unit thickness. However, if counted in groups or sub-segments, due to the large cell thickness, the overall statistics may mask the better type of lithofacies profile interval in which fracture development is favored. Therefore, aiming at the sandstone and the combination characteristics of the upper and lower surrounding rocks thereof, the invention adopts a moving window method to carry out statistics on the configuration characterization parameters. The method comprises the following specific steps: taking the window size of 60m and the step length of 10m as an example, moving to the top of an interval to be counted, such as a position with the well depth of 1000m, and respectively calculating 4 rock facies configuration parameter values for different rock facies at a window of 1000-1060 m; then moving down a step length (such as 10m), namely a window of 1010-1070 m, and respectively calculating 4 rock facies configuration parameter values for different rock facies; and repeating the steps until the bottom depth of the window is the bottom depth of the layer to be counted. If the total thickness of the interval to be counted is 100m, the window size is 60m, and the step size is 10m, the process needs to be repeated for 5 times. It should be emphasized that the window size and the moving step length are not fixed values and need to be adjusted according to the actual geological conditions.
The organic carbon content of the source rock is obtained by collecting single-well shale rock cores or rock debris to carry out organic carbon content experimental analysis or a logging prediction method. Wherein, the well logging prediction method can pass through delta 1gAnd establishing an organic carbon content model by at least one of methods such as R and multiple linear regression, and predicting the organic carbon content.
The curvature and the distance from the fault are mainly obtained by analyzing the structural position of the crack development section or the sample point.
The rock texture is obtained by sampling the single-well longitudinal upward system, and carrying out experimental analysis such as slice identification.
Step two: and according to the imaging logging fracture density statistical data and the statistical values of the relevant factors, performing relevance analysis of the relevant factors and the fracture development degree, and determining geological main control factors influencing the fracture development. The specific analysis process of the correlation analysis of the related factors and the crack development degree is as follows: and respectively establishing a relational expression between each parameter and the fracture density according to the imaging logging fracture density statistical data and the statistical values of the related factors, gradually regressing and analyzing the influence degree of each related factor on the fracture line density, and identifying geological main control factors influencing the fracture density and the fracture development.
The correlation analysis of the five segments of the Strophanthus divericatus is as follows:
(B1) correlation of lithofacies configuration and fracture development degree
The five-section fracture density has obvious parameter relation with the mudstone percentage, the mudstone layer density, the siltstone percentage, the average thickness of the siltstone single layer, the medium and fine sandstone percentage, the medium and fine sandstone layer density and the like. Wherein, the cracks are mainly developed in the interbedded siltstone and the fine sandstone.
According to the clustering analysis result of the rock phase configuration parameters of five sections of samples of Sichuan-xi beard, the samples can be divided into 5 types, and the 5 types correspond to a sand-rich type (configuration A), a relatively sand-rich thin mud-thin sand interbedded type (configuration B1), a relatively mud-rich thick mud-thin sand interbedded type (configuration B2), a relatively medium-rich thin sand thick mud-thick sand interbedded type (configuration B3) and a mud-rich type (C)5 rock phase configuration modes respectively.
The relation between five sections of different lithofacies configuration modes and the fracture density and the dip angle shows that the configuration A fracture does not develop, the fracture density is lower than 0.5 strip/m, and the proportion of the medium-angle and high-angle seams is higher; the structural B1 crack is relatively developed and mainly takes a middle-angle seam and a high-angle seam; the structural B2 crack is relatively developed and mainly comprises a horizontal crack and a low-angle crack; configuration B3 crack did not develop; the structural C cleft did not develop overall, with a small number of low angle clefts.
(B2) Correlation of organic carbon content of source rock and crack development degree
Five sections of gas reservoirs belong to self-generation and self-storage and source-storage integrated gas reservoirs, the reservoir layer is developed in a hydrocarbon source rock stratum, and the reservoir sandstone is in direct contact with the hydrocarbon source rock in a large area. The hydrocarbon source rock hydrocarbon-generating expansion force is the main reservoir forming power of the gas reservoir on one hand, and on the other hand, a hydrocarbon-generating pressurizing seam can be formed in the reservoir, so that the storage permeability of the reservoir is effectively improved. Research shows that the organic carbon content of the hydrocarbon source rock has different influences on the development degree of the five-section and 3-section sub-fracture. The crack density of the upper sub-section and the lower sub-section of the palpus five has no obvious correlation with the organic carbon content of the high-efficiency hydrocarbon source rock, which may be related to that the hydrocarbon source rock of the upper sub-section and the lower sub-section of the palpus five is not developed and the maturity of the hydrocarbon source rock of the upper sub-section is lower, and the hydrocarbon generating expansibility is smaller due to the lower organic carbon content and the lower maturity of the hydrocarbon source rock, so that a large amount of hydrocarbon generating pressurized cracks are not easy to form; there is a positive correlation between the density of the sub-section cracks in the five sections and the content of organic carbon in the source rock.
(B3) Correlation of curvature and distance from fault with crack development degree
There is a positive correlation between the crack density and the structural curvature of the five sections, and a large curvature generally corresponds to a higher crack density. However, there is no significant correlation between fracture density and distance from fault.
(B4) Correlation of rock texture with degree of fracture development
The five-section sand reservoir of the Strophanthus divericatus is mainly powder and fine-grain rock debris sand, the component difference of the debris is not large, the component maturity is low, and the influence of the rock structure on the fracture density of the reservoir is not large.
In conclusion, the main control factors influencing the development of the five-section fracture of the Strophanthus diversicus in Chuanxi include rock phase configuration, organic carbon content of hydrocarbon source rock and tectonic curvature. The organic carbon content and the structural curvature of the source rock can be obtained according to the distribution prediction research and the structure fine explanation of the source rock, and the distribution prediction of the lithofacies configuration can be used for identifying different configuration types by applying a discriminant analysis method on the basis of completing the calculation of the lithofacies configuration characterization parameters of each group of sections.
Step three: and establishing a multi-parameter fracture density calculation model based on the lithofacies configuration according to the obtained geological master control factors, predicting the fracture, and obtaining a fracture prediction result which can be used for guiding oil and gas exploration and development.
The method is characterized in that aiming at the prediction of the lithofacies configuration, the interval of the known lithofacies configuration type is used as a training sample, a discriminant analysis method is adopted to establish recognition models of different lithofacies configurations, and the recognition models are adopted to discriminate and group unknown samples. Predicting the lithofacies configuration type by adopting a Bayes discrimination method, specifically, calculating the posterior probability of each layer section to be discriminated from a certain type according to a Bayes criterion, then comparing the posterior probabilities, and classifying the layer section to be discriminated into the type with the maximum probability. If the thickness of the stratigraphic unit (such as a subsection) to be counted exceeds the window size, a plurality of discrimination types can exist, and the configuration class which is most beneficial to fracture development is taken as the configuration of the stratigraphic unit.
The five subsections have similar characteristics in configuration distribution, the mountain front belt is mainly of the configuration A, the configuration C is mainly distributed in eastern anterior delta-riparian deposition, and the configuration B1 favorable for high-angle development and the configuration B2 favorable for medium-low-angle seam development are discontinuously distributed along the front edge ring belt of the delta.
Through stepwise regression analysis, main factors for controlling the density of the five-section cracks of the Sichuan whiskers are further defined. Wherein, the upper sub-section of the five sections is mainly influenced by the lithofacies configuration and the curvature, and the middle and lower sub-sections are jointly controlled by the lithofacies configuration, the curvature and the TOC content of the high-quality hydrocarbon source rock, so that a multi-parameter fracture density calculation model of the 3 sub-sections of the five sections is established:
five upper subsections: density of cracks ═ e(2.55+ 9.19X curvature-1.13X lithofacies configuration)(R2＝0.68)
Five middle subsections: density of cracks ═ e(1.62-1.01X lithofacies configuration + 0.14X high quality source rock)(R2＝0.66)
Five subsections are needed: density of cracks ═ e(1.87+ 0.21X curvature-1.01X lithofacies configuration + 0.04X high-quality hydrocarbon source rock)(R2＝0.64)
In the formula: when the lithofacies configuration is A, B3 or C, the configuration parameter takes the value of 3; when the lithofacies configuration is B2, the configuration parameter takes the value of 2; when the lithofacies configuration is B1, the configuration parameter takes the value of 1, wherein R in the formula2Is the goodness of fit of the fitting formula.
Crack density calculation shows that the crack development regions of the five lower subsections of the seta diversica in Sichuan are mainly distributed in filial piety, Mali hupensis and Luodan areas; the fissure development of the middle sub-section is widely distributed, and the filial piety spring, the Xinchang, the Tongtong concave north part, the east slope and the southern Mali region are distributed; the crack development zones of the upper sub-segment are mainly distributed in the areas of Malus hupehensis and New Zealand.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; all such modifications and variations are intended to be included herein within the scope of this disclosure and the appended claims.
Claims (10)
1. A fracture prediction method based on a lithofacies configuration is characterized by comprising the following steps:
the method comprises the following steps: predicting relevant factors influencing the development of the crack, wherein the relevant factors comprise lithofacies configuration, organic carbon content of the source rock, curvature, distance from fault and rock texture;
step two: according to the imaging logging fracture density statistical data and the statistical values of the relevant factors, performing correlation analysis of the relevant factors and the fracture development degree, and determining geological main control factors influencing the fracture development;
step three: and establishing a multi-parameter fracture density calculation model based on the lithofacies configuration according to the obtained geological master control factors, predicting the fracture, and obtaining a fracture prediction result which can be used for guiding oil and gas exploration and development.
2. The method for predicting the fractures based on the lithofacies configuration in the first step is characterized in that the quantitative characterization parameters of the lithofacies configuration in the first step comprise lithofacies percentage content, lithofacies layer density, lithofacies average monolayer thickness and lithofacies thickness variation coefficient.
3. The method for predicting the crack based on the lithofacies configuration as claimed in claim 2, wherein in the first step, the quantitative characterization parameters of the lithofacies configuration are counted by a moving window method.
4. The method for predicting the crack based on the lithofacies configuration as claimed in claim 1, wherein in the first step, the organic carbon content of the hydrocarbon source rock is obtained by collecting rock cores or rock debris of single-well shale to perform organic carbon content experimental analysis or a logging prediction method.
5. The method for predicting the fractures based on the lithofacies configuration as claimed in claim 1, wherein in the first step, the curvature and the distance from the fault are obtained by analyzing the position of the fracture development segment or the structure where the sample point is located.
6. The method for predicting cracks based on lithofacies configuration as claimed in claim 1, wherein in the first step, the rock configuration is obtained by sampling the system on the longitudinal direction of a single well and performing slice identification experiment analysis.
7. The method for predicting the fractures based on the lithofacies configuration as claimed in claim 1, wherein in the second step, a relation between each parameter and the fracture density is respectively established according to imaging logging fracture density statistical data and statistical values of the related factors, stepwise regression is performed to analyze the influence degree of each related factor on the fracture line density, and geological main control factors influencing the fracture density and the fracture development are identified.
8. The method for predicting the cracks based on the lithofacies configuration as claimed in any one of claims 1 to 7, wherein in the third step, intervals of known lithofacies configuration types are used as training samples, a discriminant analysis method is adopted to establish recognition models of the lithofacies configurations of different types, and the recognition models are adopted to discriminate and group unknown samples.
9. The method according to claim 8, wherein in the third step, a Bayes criterion is used to predict the type of the lithofacies configuration, specifically, the posterior probability of each layer to be determined from a certain type is calculated according to a Bayes criterion, and then the posterior probabilities are compared, and the layer to be determined is classified into the type with the highest probability.
10. The method for predicting fractures based on lithofacies configuration as claimed in claim 9, wherein in step three, if the thickness of the stratigraphic unit to be counted exceeds the window size, there are multiple discrimination types, and the configuration class most favorable for fracture development is used as the configuration of the stratigraphic unit.
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