CN114089437A - Quantitative identification method for lithogenic facies of rich rock debris tight reservoir - Google Patents
Quantitative identification method for lithogenic facies of rich rock debris tight reservoir Download PDFInfo
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Abstract
The invention provides a quantitative identification method for lithogenic facies of a rock-debris-rich tight reservoir, which comprises the following steps: step 1, determining a research area and selecting data points; step 2, resetting the rock core and carrying out well logging data standardization processing; step 3, dividing lithogenic phases; step 4, fitting a mathematical function relation between the three factors and a logging curve; and 5, judging the lithogenic facies of the non-coring well section. The quantitative identification method for the lithogenic facies of the rock-debris-rich tight reservoir innovatively provides a dividing scheme for the lithogenic facies of the rock-debris-rich tight reservoir from the aspects of pore cause, tight cause and tight mineral types; and identifying different lithogenic facies by using a three-factor method, so that the lithogenic facies type of the whole well section can be judged.
Description
Technical Field
The invention relates to the technical field of oil field exploration, in particular to a quantitative identification method for a lithogenic facies of a rock-debris-rich tight reservoir.
Background
Diagenesis is a necessary stage of reservoir development and is also a key factor for controlling development and distribution of a high-quality reservoir, different diagenesis can have transformation effects of different degrees on porosity, permeability and connectivity of the reservoir, and further differences exist in the process of filling oil gas into the reservoir with different diagenesis phases, so that the enrichment degree and the distribution range of the oil gas are further influenced. Therefore, the correct judgment of the diagenesis is of great significance relative to the search of a high-quality oil and gas storage space, and theoretical guidance and technical support are provided for oil and gas exploration and deployment.
The prior people have various lithofacies dividing schemes and no uniform or fixed classifying method. At present, scholars at home and abroad divide and name diagenesis mainly according to diagenesis minerals, diagenesis events, diagenesis environments and the like, and directly reflect the characteristics of diagenesis and diagenesis stages. At present, the diagenesis is comprehensively judged by predecessors mainly according to characteristics of core, under-mirror thin slice mineral identification, X-ray diffraction whole-rock analysis, deposition and the like, however, the diagenesis evaluation can be completed only in a well section with a core, so that the diagenesis evaluation is lack of continuity in the longitudinal direction, the spreading rule of the diagenesis in the plane and the longitudinal direction cannot be determined, and the difficulty and the inaccuracy for searching a high-quality reservoir are increased.
In the application No.: 201610687331.6, relates to a method for researching shale fine particle sedimentary lithofacies, which comprises the following steps: step 1, determining a shale fine particle sedimentary facies division scheme by combining three elements of rock components, structure and organic matter content; step 2, establishing a mineral component, organic matter and structural logging identification model; step 3, according to the logging identification model established in the step 2, carrying out logging model identification on the shale lithofacies types of the single well subsection in the research area; step 4, restraining and correcting the shale lithofacies type identified by the logging model; and 5, identifying the sedimentary facies of the fine particles of the shale in the whole area and researching the distribution rule. There are five disadvantages to this application, first, no core homing and log value correction is performed. This step is very important, on one hand, the value accuracy of the logging value is seriously influenced, and on the other hand, if a set of method suitable for the whole area is established, data errors caused by logging data among different wells under human factors, machine factors, operation factors and other factors are necessarily involved, and logging correction is indispensable. Second, in the case of the identification by this method, only the facies can be identified based on the phenomenon, but the nature or cause of the facies cannot be expressed from the method of identification. Thirdly, the same lithology parameter should establish a mathematical function relationship with fixed logging parameters, rather than arbitrarily selecting a logging curve only according to statistical parameter fitting rate, regardless of geological parameter meanings; fourthly, the division standards of the calculated parameters such as Vcl, Vca, Vsi, TOC and the like and specific lithofacies are not specified in the patent; fifthly, the compaction degree difference of different buried depth shales is not considered, so that the logging identification is interfered, and the logging identification result can be seriously influenced.
Therefore, a novel quantitative identification method for diagenetic facies of the rich-debris tight reservoir is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a quantitative identification method for diagenetic facies of a rich detritus compact reservoir, which can expand the continuity of the diagenetic facies in the longitudinal direction and predict the diagenetic facies of all well sections of each well by using a three-factor well logging identification method.
The object of the invention can be achieved by the following technical measures: the quantitative identification method for the lithogenic facies of the rich rock debris tight reservoir comprises the following steps: step 1, determining a research area and selecting data points; step 2, resetting the rock core and carrying out well logging data standardization processing; step 3, dividing lithogenic phases; step 4, fitting a mathematical function relation between the three factors and a logging curve; and 5, judging the lithogenic facies of the non-coring well section. The object of the invention can also be achieved by the following technical measures:
in step 1, a research area is determined, and data points of a rock core, a porosity test and an under-mirror thin slice mineral test or an X whole rock diffraction test are selected.
In step 2, correcting the depth of the rock core according to the observation result of the rock core and the response characteristic of the logging curve; when the lithofacies of the non-coring sections of the multiple wells are judged, one of the wells is selected as a standard well, the maximum value and the minimum value of each logging curve value are respectively read, and the maximum value and the minimum value of the logging curve values corresponding to other wells are both corresponding to the standard well through standardization.
In step 2, for the acoustic moveout curve AC, the normalized formula is:
wherein, ACSign boardThe normalized AC log value for the sample well at that point; ACmin markIs the minimum value of the AC well log of the standard well; ACmax markThe maximum value of the AC logging curve of the standard well; ACSample (A)An AC log value for the sample well at that point; ACmin sampleIs the AC log minimum for the sample well; ACmax sampleIs the AC log maximum for the sample well.
In step 3, determining the lithogenic facies type according to the identification result of sedimentary facies, mineral composition under the mirror and diagenesis or X whole rock diffraction analysis result and rock core porosity test analysis result, and reading three factor values of a porosity factor phi, a soft rock debris factor R and a calcite factor F of different lithogenic facies, thereby determining the division standard for determining the lithogenic facies by utilizing the three factors; for different regions, the divided lithofacies types have certain differences, and the judgment standards of the corresponding phi and R, F factors also have differences.
In step 4, respectively reading the acoustic time difference curve AC, the density curve DEN, the natural gamma curve GR, the resistivity curve RT and the deep lateral resistivity curve R of the depth of the known lithogenic well sectionlldShallow lateral resistivity curve RllsWhere Δ RT is RlldAnd RllsAnd selecting the type of the logging curve with higher sensitivity to each factor according to the numerical difference, and fitting the mathematical relationship between the logging curve and the corresponding factors of the porosity factor phi, the soft rock debris factor R and the calcite factor F.
In step 4, the established formula is specifically as follows:
φ＝a1AC+b1DEN+c1GR+d1ΔRT+e1 (2)
R＝a2AC+b2DEN+c2GR+d2RT+e2 (3)
F＝a3AC+b3DEN+c3GR+d3RT+e3 (4)
wherein, a1，a2，a3，b1，b2，b3，c1，c2，c3Fitting coefficients of all logging curve values are obtained; e.g. of the type1，e2，e3Is a constant term.
And step 5, respectively calculating three types of logging factor values of the non-coring intervals according to formulas (2), (3) and (4), judging the lithogenic phase indexes according to the three factors, and determining the lithogenic phase of different intervals of each well.
The invention discloses a quantitative identification method for a lithogenic facies of a rock-debris-rich tight reservoir, namely a three-factor well logging identification method. From the aspect of applicability, the essence of searching the favorable diagenetic phase zone is to determine the distribution of a high-porosity phase zone, so that diagenetic phases can be divided into two types of porosity phases and compact phases according to physical parameters such as porosity, permeability and the like. Further, depending on the cause of the porosity, the pore phase may be formedDividing the phase into a primary pore and a secondary pore coexisting phase and a primary pore; the dense phase can be subdivided into a highly soft rock chip compacted dense phase and a calcite cemented dense phase, taking into account the compact formation and the mineral composition. In summary, in a tight reservoir rich in cuttings, its lithogenic phases can be divided into two major categories and four minor categories. According to analysis, the key for dividing lithogenic facies of the rich-debris tight reservoir is to determine the sizes of three factors: porosity factor (. PHI.), soft rock debris factor (R), calcite factor (F). Wherein the porosity factor is the porosity of the core test, the soft rock debris factor is the sum of the contents of volcanic rock, clay mineral, metamorphic rock, sedimentary rock and cement, and the calcite factor is the content of calcite cement. And establishing a set of standards according to the three types of factor values, and determining the value ranges of the three types of factors of various lithofacies to judge the lithofacies. Because the logging information has better continuity and can reflect formation information more completely, the patent predicts the lithogenic facies by using the logging information. According to the characteristics and the differences of the three factors, the response characteristics on the logging curve also show obvious differences. The well logging curves with obvious response characteristics to the three factors comprise an acoustic time difference curve (AC), a density curve (DEN), a natural gamma curve (GR), a resistivity curve (RT) and a deep lateral resistivity curve (R)lld) Shallow lateral resistivity curve (R)lls). The AC logging curve reflects the porosity of a reservoir, and the larger the AC value is, the higher the porosity is; the size of the DEN logging curve is closely related to the porosity, the compactness, the types and the content of the constituent minerals; the GR log is the intensity of gamma rays emitted during the radioactive nuclear decay process naturally existing in a measured rock stratum in a well, the parameter is related to the rock types, mineral composition and rock particle size, generally speaking, the GR value of volcanic rocks, radioactive soft mud or rocks with high argillaceous content is large; the RT log has obvious correlation with lithology and porosity, and generally, calcite is larger than clay mineral, mudstone is larger than sandstone; to RlldAnd RllsFor well logging, the difference (Δ RT) between the two is generally used, and when there is a difference in the magnitude of the deep and shallow lateral resistivity, it indicates that the formation is permeable, i.e. the pore permeability is better. According to the response characteristics of the logging curves, a least square method is utilizedAnd respectively establishing mathematical function relations between the values of the logging curves and the three factors. And applying the functional relation of the three factors to the well sections without coring, calculating the numerical values of the three factors of each well section, and determining the lithogenic facies according to the three factor standard of lithogenic facies division.
Therefore, from the background of the invention, the innovation points of the invention are the following three points: firstly, innovatively providing a dividing scheme aiming at a lithogenic phase of a rich rock debris tight reservoir from the perspectives of a pore formation cause, a tight formation cause and a tight mineral type; secondly, identifying different lithogenic facies by using a three-factor method, thereby judging the lithogenic facies type of the whole well section, and determining the compact reservoir formation cause according to the value of the three factors; thirdly, data preprocessing is carried out, including core correction and well logging data standardization, and errors caused by human factors and machine factors are avoided.
Drawings
FIG. 1 is a flow chart of an embodiment of the quantitative identification method for lithogenic facies of a tight reservoir rich in cuttings of the present invention;
FIG. 2 is an exemplary illustration of the subtopic lithofacies division of a Zhuan 2-well core segment in accordance with one embodiment of the present invention;
FIG. 3 is a diagram illustrating the banked 2 well lithofacies division in accordance with an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in fig. 1, fig. 1 is a flow chart of the quantitative identification method of the lithogenic facies of the rock-cuttings-rich tight reservoir of the present invention.
And 102, resetting the core and carrying out standardized processing on the logging data. Correcting the depth of the rock core according to the observation result of the rock core and the response characteristic of the logging curve; when the lithofacies of the non-coring sections of the multiple wells are judged, one of the wells needs to be selected as a standard well, the maximum value and the minimum value of each logging curve value are respectively read, and the maximum value and the minimum value of the logging curve values corresponding to other wells are both corresponding to the standard well through standardization. Taking an AC log as an example, the normalized formula is:
wherein, ACSign boardNormalizing the AC logging value of the point of the sample well; ACmin markIs the minimum value of the AC well log of the standard well; ACmax markThe maximum value of the AC logging curve of the standard well; ACSample (A)An AC log value for the sample well at that point; ACmin sampleIs the AC log minimum for the sample well; ACmax sampleIs the AC log maximum for the sample well.
If there is only one well, then there is no need to perform a well log normalization process.
And 103, dividing lithogenic facies. Determining the type of the lithogenic facies according to identification results of sedimentary facies, mineral composition under a mirror and diagenesis or X full-rock diffraction analysis results and core porosity test analysis results, reading three factor values of a porosity factor (phi), a soft rock debris factor (R) and a calcite factor (F) of different lithogenic facies, and further determining the division standard for determining the lithogenic facies by utilizing the three factors. For different regions, the divided lithofacies types have certain differences, and the judgment standards of the corresponding phi and R, F factors also have differences.
And step 104, fitting the mathematical function relation between the three factors and the logging curve. Respectively reading an acoustic time difference curve (AC), a density curve (DEN), a natural gamma curve (GR), a resistivity curve (RT) and a deep lateral resistivity curve (Rll) of the depth of a known lithogenic well sectiond) Shallow lateral resistivity curve (Rll)s) Selecting the logging curve type with higher sensitivity to each factor, and fitting the mathematical relationship between the logging curve type and the corresponding phi and R, F factors. The formula established by the invention is as follows:
φ＝a1AC+b1DEN+c1ΔGR+d1ΔRT+e1 (2)
R＝a2AC+b2DEN+c2GR+d2RT+e2 (3)
F＝a3AC+b3DEN+c3GR+d3RT+e3 (4)
wherein, a1，a2，a3，b1，b2，b3，c1，c2，c3Fitting coefficients of all logging curve values are obtained; e.g. of the type1，e2，e3Is a constant term.
The invention determines the lithofacies according to the core observation phenomenon, the mineral petrology analysis, the X diffraction test result and the under-mirror observation result of the coring interval, further extracts three types of characteristic factors phi and R, F, determines the limit value of dividing the three types of factors into the lithofacies, establishes the mathematical relationship between the three types of factors and the typical logging curve (sound wave time difference, density curve, natural gamma curve, resistivity curve, deep lateral resistivity curve and shallow lateral resistivity curve) data, applies the mathematical relationship to the non-coring interval, and calculates the three types of factors phi and R, F on the basis of reading the corresponding logging curve data, thereby judging the lithofacies of the reservoir of the non-coring interval. By utilizing the method, the lithologies of reservoirs with different depths in the whole well section can be determined, a theoretical basis is provided for searching for favorable reservoirs, technical support is provided for evaluation of oil and gas reservoirs, the method can be widely applied to various fields of oil and gas resource geological exploration, favorable area optimization and the like, and has important significance for oil and gas exploration deployment.
In a specific embodiment of the application of the method, taking the banker 2 well of the Moxisle district in the Quaszel basin as an example, the lithogenic facies of the non-core section is judged by utilizing the lithogenic facies of the core well section, and the method comprises the following steps:
(1) and (4) counting depth points of data which are simultaneously subjected to porosity test and under-mirror thin slice mineral test or X-ray whole rock diffraction test and are 4348.32m-4351.99m in the core interval of the Zhuan 2 well, and sorting different types of data.
(2) And (4) resetting the core and standardizing logging data. According to the observation result of the banked 2 well core, comparing with the response characteristic of the logging curve, correcting the core depth, and moving the true depth down 0.02m compared with the original depth; in this embodiment, because the non-coring phase identification is performed only for the well, no well log normalization process is required.
(3) And dividing lithogenic phases. Comprehensively judging the lithofacies type according to the identification result of sedimentary facies, mineral composition under the mirror and diagenesis or the X whole rock diffraction analysis result and the core porosity test analysis result. According to the reservoir development characteristics of the region, the lithogenic facies can be divided into two major categories and four minor categories. First, the lithogenic phase can be divided into two types, a pore phase and a dense phase, according to physical parameters such as porosity and permeability. Further, the pore phase may be divided into a primary pore and secondary pore coexisting phase and a primary pore according to the pore cause; the dense phase can be subdivided into a highly soft rock chip compacted dense phase and a calcite cemented dense phase, taking into account the compact formation and the mineral composition. From the observation result under the mirror, the main pore types of the primary pore are compacted residual inter-granular pores and cemented residual inter-granular pores, the inter-granular interior and the particle edges have no obvious corrosion phenomenon, the pore walls are flat and straight, the shapes are mostly quadrangle and triangle, the lithogenic phase develops in medium-grain and fine-grain sandstone, the cemented matter content is low, the particles are mostly in point-line contact, and the connectivity is better (figure 2); the coexistence and intermiscibility of primary pores and secondary pores has a very good corrosion effect, feldspar, rock debris, even a cementing material such as analcime, calcite and the like are corroded to form inter-granular dissolving pores, intra-granular dissolving pores, dissolving seams and casting mold holes, the primary pores and the secondary pores of the lithogenic phase are relatively developed, most of the particles are in point-line contact, the connectivity is good, and the reservoir is a good reservoir (figure 2); in the high soft rock debris compacted dense phase, the content of rigid particles such as quartz, feldspar and the like is relatively low, the content of soft rock debris such as altered volcanic eruption rock debris, altered rock debris and the like is relatively high, concave-convex-linear contact is mainly adopted among particles under a mirror, a small amount of erosion pores are locally seen, the physical property of the lithogenic phase reservoir stratum is very poor, and the face porosity is less than 1 percent (figure 2); the distribution of the compact calcite cemented phase has strong heterogeneity and higher soft rock debris content. The subtopic calcite crystal-connected filling pores are almost completely lost, the reservoir becomes extra dense, and the lithogenic phase reservoir is poor in pore and permeability and belongs to a dense layer (figure 2). In FIG. 2, Q-quartz; cal-calcite; i-illite; p-porosity.
And determining the values of three types of factors phi and R, F corresponding to different lithogenic facies according to the test result, and further determining the judgment standard of each lithogenic facies, which is detailed in table 1.
TABLE 1 division Standard Table for different lithofacies Using three types of factors
(4) And fitting a mathematical function relation of the three factors and the logging curve. Respectively reading AC, DEN, GR, RT, R of the depth of the known lithofacies well sectionlldAnd RllsAnd (3) selecting the logging curve type with higher sensitivity to each factor according to the logging curve numerical value, fitting the mathematical relationship between the logging curve type and the corresponding factor, and counting specific data as shown in a table 2.
TABLE 2 diagenesis quantitative characterization correlation data statistics table
The formula established by the invention is as follows:
φ＝0.544AC-1.367DEN-0.128GR-0.168ΔRT-11.794 (5)
R2＝0.955
R＝-1.342AC+34.743DEN-0.208GR-0.587RT+60.017 (6)
R2＝0.938
F＝-1.554AC-43.959DEN+0.014GR-0.007RT+222.012 (7)
R2＝0.973
(5) lithogenic facies of non-cored intervals are identified. And (3) respectively calculating the numerical values of the three logging factors of the non-coring intervals according to formulas (2), (3) and (4), judging the lithogenic phase indexes according to the three factors, and determining the lithogenic phases of different intervals of each well (figure 3).
(6) And (5) verifying the method. When the fitting formula is used for calculating three factors of phi and R, F through the logging curve, the error rate of the actually measured data and the calculated result is less than 10%, and the method is effective and accurate. In this embodiment, as shown in table 3, when three types of factors and 54 sets of data are calculated in total, the error rate of only 2 data points is greater than 10%, the fitting rate is good overall, and the squares of the correlation coefficients are 0.955, 0.938, and 0.973, respectively, which verifies that the method is effective and feasible.
TABLE 3 statistical table of error of calculation results of three kinds of factors
Claims (8)
1. The quantitative identification method for the lithogenic facies of the rich-debris tight reservoir is characterized by comprising the following steps of:
step 1, determining a research area and selecting data points;
step 2, resetting the rock core and carrying out well logging data standardization processing;
step 3, dividing lithogenic phases;
step 4, fitting a mathematical function relation between the three factors and a logging curve;
and 5, judging the lithogenic facies of the non-coring well section.
2. The quantitative identification method for lithogenic facies of a tight reservoir rich in rock debris according to claim 1, characterized in that in step 1, a research area is determined, and data points of a rock core, a porosity test and an under-mirror slice mineral test or an X whole rock diffraction test are selected.
3. The quantitative identification method for the lithogenic facies of the tight reservoir rich in rock debris according to claim 1, characterized in that in step 2, the core depth is corrected according to the observation result of the core and the response characteristic of a logging curve; when the lithofacies of the non-coring sections of the multiple wells are judged, one of the wells is selected as a standard well, the maximum value and the minimum value of each logging curve value are respectively read, and the maximum value and the minimum value of the logging curve values corresponding to other wells are both corresponding to the standard well through standardization.
4. The method for quantitatively judging lithogenic facies of a tight reservoir rich in cuttings according to claim 3, wherein in step 2, for the acoustic moveout curve AC, the normalized formula is:
wherein, ACSign boardThe normalized AC log value for the sample well at that point; ACmin markIs the minimum value of the AC well log of the standard well; ACmax markThe maximum value of the AC logging curve of the standard well; ACSample (A)An AC log value for the sample well at that point; ACmin sampleIs the AC log minimum for the sample well; ACmax sampleIs the AC log maximum for the sample well.
5. The quantitative identification method for lithogenic facies of the tight reservoir rich in rock debris according to claim 1, characterized in that in step 3, the lithogenic facies type is determined according to sedimentary facies, mineral composition under a mirror, lithogenic action identification results or X whole rock diffraction analysis results, core porosity test analysis results, and the values of three factors of a pore factor phi, a soft rock debris factor R and a calcite factor F of different lithogenic facies are read, so that the division standard for determining the lithogenic facies by using the three factors is determined; for different regions, the divided lithofacies types have certain differences, and the judgment standards of the corresponding phi and R, F factors also have differences.
6. The method for quantitatively judging the lithogenic facies of the tight reservoir rich in rock debris according to claim 1, wherein in step 4, a sonic time difference curve AC, a density curve DEN, a natural gamma curve GR, a resistivity curve RT and a deep lateral resistivity curve R of the depth of a well section of a known lithogenic facies are respectively readlldShallow lateral resistivity curve RllsWhere Δ RT is RlldAnd RllsAnd selecting the type of the logging curve with higher sensitivity to each factor according to the numerical difference, and fitting the mathematical relationship between the logging curve and the corresponding factors of the porosity factor phi, the soft rock debris factor R and the calcite factor F.
7. The method for quantitatively judging the lithogenic facies of the tight reservoir rich in cuttings according to claim 6, wherein in step 4, the established formula is specifically as follows:
φ＝a1AC+b1DEN+c1GR+d1ΔRT+e1 (2)
R＝a2AC+b2DEN+c2GR+d2RT+e2 (3)
F＝a3AC+b3DEN+c3GR+d3RT+e3 (4)
wherein, a1，a2，a3，b1，b2，b3，c1，c2，c3Fitting coefficients of all logging curve values are obtained; e.g. of the type1，e2，e3Is a constant term.
8. The method for quantitatively judging the lithogenic facies of the tight reservoir rich in rock debris according to claim 7, wherein in step 5, three kinds of logging factor values of the non-coring interval are respectively calculated according to the formulas (2), (3) and (4), and the lithogenic facies of different intervals of each well are determined according to the index for judging the lithogenic facies by the three factors.
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| WO2016161914A1 (en) * | 2015-04-07 | 2016-10-13 | 四川行之智汇知识产权运营有限公司 | Method for predicting reservoir lithogenous phase using geology and logging information |
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