CN109653725B - Mixed reservoir flooding degree logging interpretation method based on sedimentary microfacies and rock facies - Google Patents
Mixed reservoir flooding degree logging interpretation method based on sedimentary microfacies and rock facies Download PDFInfo
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Abstract
The invention provides a well logging interpretation method of a mixed-volume reservoir water logging degree based on a sedimentary microfacies and a rock facies, which comprises the following steps: carrying out fine comparison and division on the stratum; carrying out classification research on deposition microphase-rock facies; establishing a quadric relation chart of each facies, establishing a logging interpretation model suitable for a research area according to the sedimentary microfacies-rock facies, processing actual data by taking a sedimentary microfacies-rock facies discrimination model as a basis, and analyzing the difference of logging interpretations of various reservoirs on the basis; the method is characterized in that a waterflooding layer quantitative evaluation method based on entropy weight is utilized to conduct research on the waterflooding condition of the commingled reservoir, a comprehensive evaluation standard of the waterflooding layer of the commingled reservoir is established, the waterflooding level of an oil layer is divided, and a waterflooding mode of a target layer section is summarized. The method improves the exploitation degree and the ultimate recovery ratio of the out-of-control reserve of the oil deposit of the mixed-volume reservoir, effectively reduces the decrement amplitude of the oil field yield, prolongs the stable production period of the oil field, obviously improves the ultimate recovery ratio of the oil deposit, and obtains obvious development effect and benefit.
Description
Technical Field
The invention relates to the technical field of oil and gas field development, in particular to a well logging interpretation method for the water logging degree of a mixed reservoir based on sedimentary microfacies and rock facies.
Background
Regarding the study of mixed rocks, Mount first proposed in 1984 the concept of "mixed sediments" (mixed sediments) to express the product of mixed deposition of land-source detritus and carbonate, and the term "mixed rocks", which was proposed in 1990 by yangming and shaqing, to characterize this particular deposition phenomenon, refers to a type of sedimentary rock in which land-source detritus is mixed with carbonate particles and stucco, and belongs to the transition type between typical land-source detritus rock and carbonate rock. In 2000, Guofusheng et al proposed that the term "mixed sedimentary system" was used to characterize the interbed or interlayer combination mixed sedimentary of the land source clastic rock and the carbonate rock, and the research on mixed sedimentary rock was further enhanced and the research on control factors of mixed sedimentary was more comprehensive.
The explanation research of the water flooded layer is one of the main contents of the excavation and potential reserves in the middle and later periods of the water flooding development oil field, and is an important basis for adjusting the development scheme of the oil field, optimizing the injection and production well pattern and improving the recovery ratio. From the seventies of the twentieth century, the flooding condition of the water-flooding oil field is actively researched and researched at home and abroad by applying different methods. Archie (Archie) in 1942 proposes a famous Archie formula, and firstly proposes two most basic parameters and an interpretation relation in well logging interpretation, so that well logging becomes an important method for reservoir evaluation, and a foundation for explaining and researching a water flooded layer is laid.
Petrophysical experiments are the basis for waterflood logging evaluation, and the study was initially started from the core. Relevant experts at home and abroad have made a great deal of research in this respect. The research of foreign experts from the most basic rock-electricity experiment to the digital simulation flooding process is performed, the research of a flooding layer in China is most representative of obtaining a 'U' -shaped curve and a 'S' -shaped curve between resistivity and water saturation, when fresh water is injected into the stratum, the relation between the resistivity of the formation water and the water saturation is no longer a straight line relation but a 'U' -shaped curve or an 'S' -shaped curve characteristic, the condition that the same Rt value can correspond to different saturations is indicated, namely the resistivity is high and is not necessarily an oil layer, the resistivity is low and is not necessarily a water layer, and reliable experimental basis is provided for finally and accurately determining the distribution of the residual oil.
A logging method for a water flooded layer is a means for people to know the water flooded layer, and parameters and problems such as porosity, permeability, saturation, shale content, median particle size, water yield and effective thickness are more and more in the process of developing a water-flooding oil field by logging. The establishment of a full water flooded layer logging series is a prerequisite for well-exploiting, logging, explaining and analyzing the water-drive oil field. Conventional logging methods include: 0.25 meter gradient, 0.45 meter gradient, 2.5 meter gradient electrode system, natural potential, natural gamma, lateral, sonic, micro-spherical focusing, offset density, caliper and well deviation logging, etc. In recent years, the C/O specific energy spectrum logging technology and the PND logging technology develop rapidly, and flooding information can be obtained more intuitively and accurately.
The well logging interpretation method of the water flooded layer mainly comprises three parts: the conventional logging data qualitatively determines the flooded layer, quantitatively calculates the saturation and water content of the residual oil, and comprehensively determines the flooded layer. The introduction of mathematical methods plays a great role in promoting the explanation of the water flooded layer regardless of qualitative or quantitative judgment, such as an artificial neural network method, a grey system theory method, a fuzzy statistical method, a grey identification method, a normal distribution method and a support vector machine method.
Most oil fields in China enter the medium and high water content exploitation stage, and the water injection development proportion is high. Many oil field development practices show that most oil fields are in a multi-well, multi-layer and multi-direction water-breakthrough stage through water injection development, the average water content of the oil fields is over 80 percent, but the crude oil recovery rate is only about 30 percent generally, and nearly 70 percent of residual oil reserves still remain underground. Because the oil reservoir flooding condition is complex, the residual oil distribution rule is not clear, and the later exploitation difficulty of the oil field is more and more increased. Particularly, the oil deposit of the limestone-sandstone mixed accumulation reservoir has lower extraction degree and more complex distribution of the residual oil, so that the explanation work of the water logging degree of the mixed accumulation reservoir needs to be solved urgently in the residual oil excavation potential of the oil deposit of the mixed accumulation reservoir, but the important significance of the explanation of the water logging degree of the mixed accumulation reservoir at present does not give enough attention.
A great deal of research work on logging evaluation of a water flooded layer has been carried out in various oil and gas fields at home and abroad, the logging explanation of the water flooded layer of the clastic rock reservoir has been greatly improved, but the evaluation on the water flooded degree of the mixed reservoir is ignored all the time, and still stays in a qualitative and semi-quantitative state, and a mature method for evaluating the water flooded degree of the mixed reservoir is not available.
The existing water flooded layer well logging evaluation method cannot fully combine well logging, geological and oil field development data, and the existing method depends on an empirical formula too much and ignores the influence of water flooding development on the original model.
The main reason for the problem is that mixed deposition is a relatively new research field in sedimentology, mainly refers to the mixed deposition effect of land source debris and carbonate in the same deposition environment, and covers three geological phenomena of mixed rock, mixed-deposit strata system and sporadic mixed deposition, so that the evaluation difficulty of the flooding degree is very high in the mixed-deposit reservoir research due to the complex geological characteristics of the mixed-deposit reservoir research.
A secondary reason for this problem is that dynamic and static combination studies in the evaluation study of the degree of flooding present great difficulties. Geological, well logging, oil field development dynamic data and the like are not fully utilized so far, and do not reach a good practical stage, and no major breakthrough and achievement is obtained. Therefore, a novel well logging interpretation method based on the water logging degree of the sedimentary microfacies and the rock facies mixed reservoir is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a mixed-volume reservoir flooding degree logging interpretation method based on sedimentary microfacies and rock facies, which aims at the mixed-volume reservoir flooding condition and realizes the quantitative evaluation of the mixed-volume reservoir flooding layer based on the entropy weight by providing a concept of 'flooding index'.
The object of the invention can be achieved by the following technical measures: the mixed-volume reservoir water logging interpretation method based on the sedimentary microfacies and the rock facies comprises the following steps: step 1, carrying out fine comparison and division of stratums; step 2, analyzing the sedimentary microfacies planar distribution and evolution process of the mixed-volume reservoir by combining the well logging facies research, and carrying out the classification research of sedimentary microfacies-rock facies on the basis; step 3, establishing a quadric relation chart of each facies zone, establishing a logging interpretation model suitable for a research area according to the sedimentary microfacies-rock facies, processing actual data by taking the sedimentary microfacies-rock facies discrimination model as a basis, and analyzing the difference of logging interpretation of various reservoirs on the basis; and 4, researching the flooding condition of the mixed reservoir by using a flooding layer quantitative evaluation method based on entropy weight, establishing a comprehensive evaluation standard of the flooding layer of the mixed reservoir, dividing the flooding level of an oil layer, and summarizing a flooding mode of a target layer section.
The object of the invention can also be achieved by the following technical measures:
in step 1, stratum contrast and subdivision are carried out by fully utilizing coring data, logging data and logging data, different sedimentary microfacies types are divided through core observation and analytical assay data analysis research on the basis of the stratum contrast and subdivision, and a corresponding logging facies mode is established by utilizing sedimentary microfacies research results of a coring well and combining logging data analysis.
The step 2 comprises the following steps:
step 2a, comparing and dividing stratums, dividing unified layer groups within an oil field range, clearing spatial change rules of layer groups of all levels, and providing accurate isochronous stratum grillage for sedimentary microfacies-rock facies research;
step 2b, analyzing the sedimentary microfacies, analyzing the sedimentary environment according to lithological signs and ancient biomarkers by analyzing coring data, further analyzing a single well according to the characteristics of the components of sedimentary sand bodies, the granularity of debris particles and sorting, and geological factors such as single-layer thickness and longitudinal combination forms of the sedimentary sand bodies, sedimentary structure, sedimentary sequence characteristics, the oil-containing property of sedimentary bodies and the contact relation between the sedimentary bodies and underburden, and comprehensively judging the sedimentary microfacies;
and 2c, performing rock facies-logging facies analysis, comprehensively utilizing the rock core, granularity analysis, logging and logging information based on key well sedimentary microfacies research, extracting a plurality of electrical parameters reflecting sedimentary microfacies, establishing a rock facies-logging facies distinguishing model of the mixed accumulation reservoir by using a principal component analysis method, and further developing sedimentary microfacies-rock facies-logging facies classification research.
In step 3, the sedimentary microfacies-rock facies controls the quadriversal properties, i.e., lithology, physical properties, oil-bearing properties and electrical properties, and the sedimentary microfacies-rock facies constraints are utilized to perform logging interpretation so as to accurately obtain various reservoir parameters.
The step 3 comprises the following steps:
step 3a, utilizing a trend surface analysis method to carry out pretreatment and standardization on logging data; the logging information is digitized, and the depth correction and the environment correction of the logging information are carried out so as to improve the quality of the logging information; meanwhile, the well logging data are standardized to eliminate instrument scale errors, manual operation errors and correction errors, so that the well logging data have uniform scales in the whole oil field range;
step 3b, reservoir bed quadriversal relation research is carried out to reveal the relation between reservoir bed parameters and logging response, and geological basis is provided for the establishment of an explanation model;
step 3c, establishing a microphase logging interpretation model, establishing a plurality of microphase-lithofacies establishing logging interpretation models by adopting a rock core scale logging method according to a conclusion obtained by the quadriversal relationship and a related research result and taking sedimentary facies belt constraint logging interpretation model establishment as a theoretical guide, and establishing reservoir parameters of shale content, a median of granularity, porosity, permeability, irreducible water saturation, residual oil saturation and oil-water relative permeability;
and 3d, performing logging interpretation and processing, performing multi-well processing and interpretation on logging information in different facies according to the established reservoir parameter interpretation model and the idea of reservoir evaluation of sedimentary microfacies-rock facies zone constraint logging, performing well-by-well processing on the logging information of the wells, and outputting the main reservoir parameter values of porosity, shale content, permeability, median particle size, oil saturation and water saturation and well logging interpretation result diagrams point by point or layer by layer.
Step 4 comprises the following steps:
and 4a, determining quantitative evaluation parameters of the flooded layer, and selecting 8 parameters of sedimentary microfacies-rock phases, water production rate, oil saturation, resistivity, residual oil saturation, irreducible water saturation, porosity and permeability which have different degrees of influence on the flooded layer as the quantitative evaluation parameters of the flooded layer.
Step 4b, carrying out normalization processing, carrying out dimensionless processing on the parameters to obtain a fuzzy evaluation matrix R, and normalizing to a [0, 1] interval;
step 4c, adopting an entropy weight method as a method for determining weight;
step 4d, determining a flooding index Ifw
Ifw＝WB (19)
Ifw＝(i1,i2,i3,…in) (20)
IfwThe flooding index is multiplied by the weight W and a matrix B (blj) of various parameters to be evaluated to obtain a set of flooding indexes of a sample to be evaluated,
wherein,
and i islE (0,1) (l ═ 1,2,3, …, n); that is, ilThe larger the value, the weaker the flooding degree, ilThe smaller the value is, the stronger the flooding degree is; wherein n is the number of parameters participating in evaluation; and m is the number of samples participating in evaluation.
In step 4d, aiming at the geological characteristics of the mixed reservoir oil deposit, the production reality of the oil deposit is integrated, and the water flooded layer is divided into six levels: i isfwNo water logging is more than or equal to 0.50, and I is more than or equal to 0.45fwLess than 0.50 is weak water logging, I is more than or equal to 0.40fwLess than 0.45 is weak water logging, I is more than or equal to 0.35fwLess than 0.40 is moderate water logging, I is more than or equal to 0.30fwLess than 0.35 is strongly watered, IfwAnd (3) strongly flooding the surface by less than 0.30, and plotting the flooding degree on the surface through quantitative evaluation of a flooding layer to obtain a plan view of the flooding condition.
The invention discloses a well logging interpretation method for the water logging degree of a mixed-volume reservoir based on a sedimentary microfacies and a rock facies, relates to the field of well logging interpretation research of water logging layers in oil and gas field stable yield research, realizes quantitative evaluation of the water logging layers of the mixed-volume reservoir based on an entropy weight by providing a concept of a water logging index, and establishes a set of accurate and feasible well logging interpretation method for evaluating the water logging degree of the mixed-volume reservoir. The method provides a method for carrying out classified evaluation on the mixed-volume reservoir by utilizing sedimentary facies research and rock facies research; a method for establishing a logging interpretation model suitable for a research area according to the sedimentary microfacies-rock facies and performing logging interpretation is provided; the quantitative interpretation of the flooding condition is mainly carried out on the mixed volume reservoir stratum. The invention can develop quantitative well logging interpretation in the mixed-volume reservoir oil deposit with complex lithology, and can improve the interpretation precision of the mixed-volume reservoir through the sedimentary microfacies-rock facies fine classification research; the quantitative evaluation of the water logging condition of the oil reservoir of the mixed-volume reservoir is realized, and the problem of difficult interpretation of the water logging layer of the oil reservoir of the mixed-volume reservoir is solved. The mixed-accumulation reservoir water logging interpretation method based on the sedimentary microfacies and the rock facies solves the problems of complex water logging condition, unclear knowledge of residual oil distribution rules, increased reserve excavation difficulty and the like in the development process, is applied to a plurality of limestone-sandstone mixed-accumulation reservoir oil reservoirs, improves the utilization degree and the final recovery ratio of out-of-control reserve of the mixed-accumulation reservoir oil reservoirs, effectively reduces the yield decrement amplitude of an oil field, prolongs the stable production period of the oil field, obviously improves the final recovery ratio of the oil reservoir, and obtains obvious development effect and benefit.
Drawings
FIG. 1 is a flow chart of an embodiment of a well logging interpretation method for water logging of a commingled reservoir based on sedimentary microfacies and rock facies according to the invention;
FIG. 2 is a histogram of carbonate beach microphase for an embodiment of the present invention;
FIG. 3 is a microphase mode histogram of a shallow beach dam according to an embodiment of the present invention;
FIG. 4 is a deposition microphase-petrofacies planar layout of an embodiment of the present invention;
FIG. 5 is a graph of the relationship between the muddy content (Vsh) of the sand dam of the shallow beach and the porosity (por) according to an embodiment of the present invention;
FIG. 6 is a schematic illustration of a clastic rock log response characteristic in an embodiment of the present invention;
FIG. 7 is a schematic representation of carbonate logging response characteristics in accordance with an embodiment of the present invention;
FIG. 8 is a graph illustrating the results of a well log interpretation in accordance with an embodiment of the present invention;
FIG. 9 is a plot of formation factor F versus reservoir porosity φ for clastic rock in an embodiment of the present invention;
FIG. 10 is a graph of a formation factor F versus reservoir porosity φ for carbonate rock in an embodiment of the present invention;
FIG. 11 is a plot of clastic rock resistivity increase factor I versus water saturation Sw for an embodiment of the present invention;
FIG. 12 is a graph of carbonate resistivity increase factor I versus water saturation Sw for an embodiment of the present invention;
FIG. 13 is a plan view of the flooded area of the commingled reservoir 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 a well logging interpretation method based on the mixed-volume reservoir flooding degree of sedimentary microfacies and rock facies. The specific process is as follows:
step 101, firstly, carrying out stratum comparison and subdivision by utilizing coring data, logging data and the like, dividing different sedimentary microfacies types through researches such as core observation, analysis and assay data analysis and the like on the basis of the stratum comparison and subdivision, and establishing a corresponding logging facies mode by utilizing sedimentary microfacies research results of a coring well and combining logging data analysis.
And 102, analyzing the sedimentary microfacies planar distribution and evolution process of the mixed-volume reservoir in combination with the well logging facies research, and realizing the classification research of sedimentary microfacies-rock facies on the basis.
And carrying out sedimentary microfacies research aiming at the mixed volume reservoir, and carrying out common classification evaluation on the sedimentary microfacies and the rock facies according to the difference between the rock facies and the logging characteristics, so that the sedimentary microfacies-the rock facies are subjected to subdivision research, and the division standard of a research area is established.
Comparing and dividing stratums: the stratum contrast and division are deeply carried out, so that the unified layer group division can be carried out within the range of the oil field, the space change rule of the reservoir layers of all layer groups is cleared, and an accurate isochronous stratum framework is provided for sedimentary microfacies-rock facies research.
Deposition microphase analysis: by analyzing coring data, the sedimentary environment is analyzed according to lithological signs, ancient biological signs and the like, and then single wells are analyzed according to the characteristics of the components, the particle sizes, the sorting and the like of sedimentary sand bodies (figure 2 and figure 3), the geological factors of the single-layer thickness, the longitudinal combination form (single layer, mutual layer, interlayer and the like), the sedimentary structure, the sedimentary sequence characteristics, the oil-containing property of sedimentary bodies, the contact relation (integration, filling and cutting) between the sedimentary bodies and the underburden stratum and the like, and sedimentary microfacies are comprehensively judged (figure 4).
Rock facies-log facies analysis: the method is based on the research of key well sedimentary microfacies, comprehensively utilizes information such as rock cores, granularity analysis, well logging and the like, extracts a plurality of electrical parameters reflecting the sedimentary microfacies, establishes a rock facies-well logging facies distinguishing model of a mixed accumulation reservoir by applying a principal component analysis method, and further develops the research of sedimentary microfacies-rock facies-well logging facies classification.
103, establishing a 'quadric' relation chart of each facies zone, establishing a logging interpretation model suitable for a research area according to the sedimentary microfacies-rock facies, and processing actual data by taking the sedimentary microfacies-rock facies discrimination model as a basis, thereby analyzing the difference of the logging interpretation of various reservoirs.
The sedimentary system of the mixed-volume reservoir is variable, the reservoir types are various, the oil-water relationship is complex, the corresponding logging response characteristics also have special change rules, and the sedimentary microfacies-rock facies are considered to control the 'quadriversal', namely the lithology, the physical property, the oil-bearing property and the electrical property, of the reservoir through analysis, so that the sedimentary microfacies-rock facies constraint is utilized to carry out logging interpretation so as to accurately obtain various reservoir parameters
Preprocessing and standardizing logging data: the operation of the step is mainly to carry out the digitalization of logging information, the depth correction of the logging information, the environmental correction and the like so as to improve the quality of the logging information; meanwhile, the well logging data are standardized to eliminate various errors such as instrument scale errors, manual operation errors, correction errors and the like, so that the well logging data have uniform scales in the whole oil field range, the comparability of the well logging data is enhanced, and the interpretation precision is improved. Trend surface analysis is mainly used.
The method uses a curved surface represented by a mathematical function to fit or approximate the distribution of a certain characteristic of a geologic body on the space, namely, the fitting of a polynomial trend surface is carried out on the logging response characteristic value of a multi-well standard layer and the geodetic coordinates of the logging response characteristic value, and the fitting surface is considered to have consistency with the original trend surface of the stratum.
Reservoir "tetrasexual" relationship study: the reservoir "quadriversal" relationship refers to the relationship between lithology, physical property, oil-bearing property and electrical property of a reservoir (fig. 5), and the purpose of the research on the "quadriversal" relationship is to reveal the relationship between reservoir parameters and logging response and provide geological basis for the establishment of an explanation model. Table 1 shows the correlation between the porosity, permeability, shale content and median particle size in different sedimentary microphases-lithofacies, and it can be seen from table 1 that the correlation between the shale content of the reservoir and the median particle size is good, the correlation coefficient is about 0.8, and the correlation between the shale content and the median particle size can be reflected from a relation graph (fig. 5) between the shale content and the median particle size. FIG. 5 is only one of many interaction graphs, and it is necessary to find the correlation relationship for the intersection of the four. Reservoir logging response characteristic analysis needs to be carried out before the quadric-sexual relation research, fig. 6 and 7 illustrate corresponding logging characteristics under different lithofacies conditions, for example, GR and SP of sandstone in fig. 6 are both reduced, GR and SP of limestone in fig. 7 are basically similar to mudstone, and the quadric-sexual relation analysis of different types of reservoirs is carried out through qualitative corresponding logging analysis.
TABLE 1 lithology, physical properties and electrical parameters correlation coefficient statistics
Establishing a microphase logging interpretation model: an accurate reservoir parameter logging interpretation model is established and is the basis for quantitatively evaluating the oil reservoir. According to the conclusion and the relevant research results obtained by the 'quadrisexual' relationship, the sedimentary facies belt constraint logging explanation model is established as a theoretical guide, a core scale logging method is adopted, and a plurality of microphase-lithofacies logging explanation models such as carbonate beaches, shore shallow lake sand dams and the like are established, wherein the microphase-lithofacies logging explanation models mainly relate to the explanation models of reservoir parameters such as shale content, median particle size, porosity, permeability, irreducible water saturation, residual oil saturation, oil-water relative permeability and the like.
The calculated porosity value is directly related to the accuracy of the calculated value of the oil saturation and the permeability. The research of the relationship of the 'tetrad' shows that the correlation between the porosity and the acoustic wave time difference is good, and the porosity is calculated by utilizing the acoustic wave time difference.
Phase F1: phi 0.2681. AC-53.78 (R0.84) (1)
Phase F2: phi 0.3134. AC-62.69 (R0.81) (2)
Phase F3: phi 0.3084. AC-61.89 (R0.81) (3)
Phase F4: phi 0.2559 · AC-46.26 (R0.83) (4)
Phase F5: phi 0.4751. AC-100.82 (R0.76) (5)
Wherein phi is porosity; AC: acoustic moveout, a logging data; r: correlation coefficient of regression formula.
Permeability is an important parameter for evaluating a reservoir. The research of the 'quadrisexual' relationship shows that the different micro-phase permeability and the median of the particle size have good correlation, and considering that the porosity is the most main factor influencing the permeability, the permeability explanation model is established by combining the median of the particle size and the porosity.
Phase F1: k is 1.025. e0.1135·φ(R＝0.80) (6)
Phase F2: lgk ═ 5.64 · Lg (Φ) +3.07 · Lg (md) -2.43(R ═ 0.87) (7)
Phase F3: lgk ═ 4.88. Lg (phi) + 2.75. Lg (md) -1.91(R ═ 0.89) (8)
Phase F4: lgk ═ 5.54 · Lg (Φ) +3.95 · Lg (md) -2.29(R ═ 0.84) (9)
Phase F5: lgk ═ 6.41 · Lg (Φ) +3.98 · Lg (md) -2.45(R ═ 0.95) (10)
Wherein, K: permeability, which is a parameter of the permeability of the reservoir; md: median particle size, is understood to mean the diameter of the average grain of sand in sandstone. The formula for the median Md particle size is not used in the F1 phase because carbonate is the dominant.
How to accurately calculate the remaining oil saturation by using the conventional logging information is a key technology. The physical experiment of the water flooded layer rock shows that: resistivity index (I) and water saturation (S) of flooded rockW) Is a straight line in a dual logarithmic coordinateIn accordance with the Archie formula I ═ b/Sw nThis model, therefore, can utilize the Archie's formula to calculate the water saturation Sw of the flooded layer. The calculation model is as follows:
in the initial stage: sw＝[a·b·Rw/(φm·Rt)]1/n (11)
Adjusting period: sw＝[a·b·Rz/(φm·Rt)]1/n (12)
So＝1-Sw (13)
a is the lithology coefficient, b is the lithology index, and ab is two numbers related to the nature of the rock. m is the cementation exponent, n is the saturation exponent, and four are constants in a particular formation and are key parameters in the Archie's equation. Rw: (ii) a Rt: (ii) a Sw: water saturation, which may be understood as the content of formation water in the rock fissures; so: oil saturation, which is understood as the oil content in the rock crevices, So + Sw is 1. Phi is porosity; the well logging interpretation method based on sedimentary microfacies and lithofacies requires the calculation of a, b, m, n parameters for different types of reservoirs, such as fig. 9-12, different types of reservoirs obtain different parameters, and are mainly used in geophysical well logging. The mineral particles that make up the rock, except for the argillaceous material, are generally electrically non-conductive. The resistivity of the argillaceous sedimentary rock is thus substantially determined by the resistivity and the porosity (Φ) of the brine contained in the pores. Resistivity (R) of such rocks0) Proportional to the saturated brine resistivity (Rw), i.e. Rw ═ F R0Where F is referred to as the formation factor. The formation factor F remains substantially constant for a given formation. The formation factor (F) is related to the porosity φ and pore structure, and the empirical relationship is: f ═ a/ψ m, where a and m are coefficients relating to pore structure. From the electrical logging data, porosity, or known porosity, can be calculated using the relationship between F and porosity, and used to determine formation factor for further determination of water saturation. I is the distribution characteristic of oil and water in pores when the rock contains water and oil, the water surrounds the surface of the rock and is distributed in the center of the pores, therefore, the resistivity Rt of the oil-containing rock is higher than that of the rock containing water, and the higher the oil saturation is generallyThe higher the resistivity of the oil bearing rock, the higher the I ═ Rt/R0，ROIs the rock resistivity.
Fourthly, well logging interpretation and processing: and carrying out multi-well processing and interpretation on logging information by different facies according to the established reservoir parameter interpretation model and the idea of reservoir evaluation of sedimentary microfacies-rock facies zone constraint logging. And then carrying out well-by-well processing on the well logging information, and outputting various main reservoir parameter values such as porosity, shale content, permeability, median particle size, oil saturation, water saturation and the like and well logging interpretation achievement diagrams by a computer point-by-point or layer-by-layer, wherein as shown in figure 8, figure 8 is an interpretation result, porosity, permeability and the like.
The method is characterized in that logging data are interpreted to obtain a series of parameters, the parameters provide basic data bodies for evaluation of a subsequent water flooded layer, an interpreted main formula is an Archie formula, different interpretation templates are established by classification of different small layers by using two classification methods of rock facies and sedimentary facies, and then the different interpretation templates are respectively calculated to obtain more accurate data
And step 104, researching the flooding condition of the commingled reservoir by using a flooding layer quantitative evaluation method based on the entropy weight, establishing a comprehensive evaluation standard of the flooding layer of the commingled reservoir, dividing the flooding level of an oil layer, and summarizing a flooding mode of a target layer section.
And injecting water to displace oil in pore spaces of the oil layer, so that the oil saturation of the oil layer is reduced, and the water saturation is increased. In addition, after the injected water is mixed with the undisturbed formation water, the conductivity of the formation is changed, and the logging response is changed in a series manner. The method adopts an evaluation method based on entropy weight to carry out quantitative evaluation on the water flooded layer, and adopts a plurality of parameter fitting water flooded degree comprehensive evaluation parameters 'water flooded indexes' to quantitatively evaluate the water flooded layer. The main contents are as follows:
firstly, determining quantitative evaluation parameters of a water flooded layer: in a specific oil reservoir research, the sedimentary microfacies-rock facies, the water production rate, the oil saturation, the resistivity, the residual oil saturation, the irreducible water saturation, the porosity and the permeability are found to have different degrees of influence on a water flooded layer, and the 8 parameters are selected as quantitative evaluation parameters of the water flooded layer.
Normalization treatment: because the parameters have different dimensions and orders of magnitude, the parameters are subjected to non-dimensionalization processing to obtain a fuzzy evaluation matrix R which is generally normalized to a [0, 1] interval. The process is actually to obtain a membership function of the parameters, and the size of the decision factor of each parameter can be obtained by using the membership function.
③ determining the weight: an "entropy weight" method is adopted as a method of determining the weight. The concept of entropy stems from thermodynamics, which is defined as follows: when the system may be in several different states, each state occurs with a probability Pi(i ═ 1,2 … n), then the entropy of the system is:
h (x): entropy weight, p (xi): the probability of each state occurring.
The entropy value h (x) is actually a measure of the uncertainty of the system. As can be seen from the above formula, the entropy of the system has extreme values, and when the probability of the system in various states is equal, P is equali1/n ( i 1,2 … n), with the largest entropy value, is
It follows that as the number of states n of the system increases, the entropy of the system also increases, but at a much lower rate than n. If the system is in only one state and its probability of occurrence P i1/n, the entropy of the system is equal to zero, indicating that the system has no uncertainty and the system can determine completely, i.e.:
when b isijWhen the content is equal to 0, the content,
bij is the matrix to be evaluated. H (P)j) Characterizing parameters
From the extreme entropy, the closer the level values of the parameters are, the larger the entropy value is. Using maximum entropy value, i.e. H (P)j)maxThe entropy value obtained by the above formula is normalized to obtain a characterizing parameter H (P) lgnj) Entropy of relative importance:
e (Pj) is the entropy value of the j index calculated.
Wj: and finally forming a weight vector.
Obtaining different weights corresponding to each parameter to obtain a weight vector W ═ W1,w2,w3,…wm)。
Fourthly, flooding index IfwDetermining
Ifw＝WB (19)
Ifw＝(i1,i2,i3,…in) (20)
IfwThe flooding index is multiplied by the weight W and a matrix B (bij) of various parameters to be evaluated to obtain a set of flooding indexes of a sample to be evaluated,
wherein,
and i islE (0,1) (l ═ 1,2,3, …, n). That is, ilThe larger the value, the weaker the flooding degree, ilThe smaller the value, the stronger the flooding degree.
In summary, a certain number of data in wells are selected to form samples, then an entropy weight calculation method is used to calculate through a certain number of samples to obtain a weight W, the weight vector is the weight of each parameter, then the parameter of the flooding index Ifw of each small layer of each well can be calculated through the weight, it can be understood that multiple factors are multiplied by different weights and then added to obtain a number, the number can comprehensively judge the flooding degree, but the degree of flooding at the end is required to be researched, and the next evaluation standard is required to be further researched. The evaluation method of the flooding layer is also a mathematical method introduced in the aspect of thermodynamics, and the method is not innovative, but the method is used for the flooding evaluation of the commingled deposit reservoir, and the previous well logging explanation is classified, so the basis of the flooding layer evaluation is a classified parameter system, and the calculated result is also classified.
Aiming at the geological characteristics of the oil deposit of the mixed-volume reservoir, the production reality of the oil deposit is integrated, and a water flooded layer is divided into six grades: not flooded with water (I)fwNot less than 0.50), and weak water logging (not less than 0.45I)fwLess than 0.50) and weak flooding (I is more than or equal to 0.40)fwLess than 0.45) and moderate water logging (I is more than or equal to 0.35fwLess than 0.40) and strong flooding (I is more than or equal to 0.30)fwLess than 0.35) and strong flooding (I)fw< 0.30). Through quantitative evaluation of a water flooded layer, the water flooded degree can be sketched out on a plane to obtain a plane diagram of the water flooded condition, and the plane diagram is obtained after the water flooded degree is sketched out on the plane in fig. 13.
The evaluation criteria are the division according to the water production of the production well. For example, the produced liquid contains more than 95% of water and is strongly watered, 80-90% of water corresponds to IfwThen a stronger water flooding is determined. The water logging degree of the corresponding intervals is different along with the water content, so that an evaluation criterion is defined.
According to the well logging interpretation method of the water logging degree of the mixed-volume reservoir based on the sedimentary microfacies and the rock facies, aiming at the geology and the development characteristics of the mixed-volume reservoir, the sedimentary facies distribution of sedimentary facies planes, the evolution process and the like are researched, the sedimentary microfacies research is carried out on the mixed-volume reservoir, the rock facies subdivision is carried out on various sedimentary microfacies, and the classification standard of the mixed-volume reservoir is established; then carrying out logging difference evaluation on the clastic rock reservoir and the carbonate reservoir, establishing a logging interpretation model by means of a 'quadric' relationship research according to sedimentary microfacies-rock phases, carrying out classified logging evaluation on the clastic rock reservoir and the carbonate reservoir, and accurately calculating the oil saturation; and carrying out quantitative evaluation on the water flooded layer on the basis, and further establishing a mixed-volume reservoir water flooded mode.
Claims (6)
1. The mixed-volume reservoir flooding degree logging interpretation method based on the sedimentary microfacies and the rock facies is characterized by comprising the following steps of:
step 1, carrying out fine comparison and division of stratums;
step 2, analyzing the sedimentary microfacies planar distribution and evolution process of the mixed-volume reservoir by combining the well logging facies research, and carrying out the classification research of sedimentary microfacies-rock facies on the basis;
step 3, establishing a quadric relation chart of each facies zone, establishing a logging interpretation model suitable for a research area according to the sedimentary microfacies-rock facies, processing actual data by taking the sedimentary microfacies-rock facies discrimination model as a basis, and analyzing the difference of logging interpretation of various reservoirs on the basis;
step 4, researching the flooding condition of the commingled reservoir by using a flooding layer quantitative evaluation method based on entropy weight, establishing a comprehensive evaluation standard of the flooding layer of the commingled reservoir, dividing the flooding level of an oil layer, and summarizing a flooding mode of a target layer section;
the step 2 further comprises:
step 2a, comparing and dividing stratums, dividing unified layer groups within an oil field range, clearing spatial change rules of layer groups of all levels, and providing accurate isochronous stratum grillage for sedimentary microfacies-rock facies research;
step 2b, analyzing the sedimentary microfacies, analyzing the sedimentary environment according to lithological signs and ancient biomarkers by analyzing coring data, further analyzing a single well according to the characteristics of the components of sedimentary sand bodies, the granularity of debris particles and sorting, and geological factors such as single-layer thickness and longitudinal combination forms of the sedimentary sand bodies, sedimentary structure, sedimentary sequence characteristics, the oil-containing property of sedimentary bodies and the contact relation between the sedimentary bodies and underburden, and comprehensively judging the sedimentary microfacies;
and 2c, performing rock facies-logging facies analysis, comprehensively utilizing the rock core, granularity analysis, logging and logging information based on key well sedimentary microfacies research, extracting a plurality of electrical parameters reflecting sedimentary microfacies, establishing a rock facies-logging facies distinguishing model of the mixed accumulation reservoir by using a principal component analysis method, and further developing sedimentary microfacies-rock facies-logging facies classification research.
2. The well logging interpretation method for the water logging degree of the mixed-volume reservoir based on the sedimentary microfacies and the rock facies as claimed in claim 1, wherein in step 1, the core data, the logging data and the logging data are fully utilized to carry out stratum comparison and subdivision, on the basis of the stratum comparison and subdivision, different sedimentary microfacies types are divided through core observation and analysis data analysis research, and the corresponding well logging facies mode is established by utilizing the sedimentary microfacies research result of the core well and combining with the logging data analysis.
3. The method as claimed in claim 1, wherein in step 3, the sedimentary microfacies-rock facies control the reservoir quadriversal properties, i.e. lithology, physical properties, oil-bearing properties and electrical properties, and the sedimentary microfacies-rock facies constraints are used to perform well logging interpretation so as to accurately obtain various reservoir parameters.
4. The well logging interpretation method for the degree of flooding of the commingled reservoir based on sedimentary microfacies and rock facies as claimed in claim 3, wherein step 3 comprises:
step 3a, utilizing a trend surface analysis method to carry out pretreatment and standardization on logging data; the logging information is digitized, and the depth correction and the environment correction of the logging information are carried out so as to improve the quality of the logging information; meanwhile, the well logging data are standardized to eliminate instrument scale errors, manual operation errors and correction errors, so that the well logging data have uniform scales in the whole oil field range;
step 3b, reservoir bed quadriversal relation research is carried out to reveal the relation between reservoir bed parameters and logging response, and geological basis is provided for the establishment of an explanation model;
step 3c, establishing a microphase logging interpretation model, establishing a plurality of microphase-lithofacies establishing logging interpretation models by adopting a rock core scale logging method according to a conclusion obtained by the quadriversal relationship and a related research result and taking sedimentary facies belt constraint logging interpretation model establishment as a theoretical guide, and establishing reservoir parameters of shale content, a median of granularity, porosity, permeability, irreducible water saturation, residual oil saturation and oil-water relative permeability;
and 3d, performing logging interpretation and processing, performing multi-well processing and interpretation on logging information in different facies according to the established reservoir parameter interpretation model and the idea of reservoir evaluation of sedimentary microfacies-rock facies zone constraint logging, performing well-by-well processing on the logging information of the wells, and outputting the main reservoir parameter values of porosity, shale content, permeability, median of granularity, oil saturation and water saturation and well logging interpretation achievement diagrams point by point or layer by layer.
5. The well logging interpretation method for the degree of flooding of the commingled reservoir based on sedimentary microfacies and rock facies as claimed in claim 1, wherein the step 4 comprises:
step 4a, determining quantitative evaluation parameters of a flooded layer, and selecting 8 parameters of sedimentary microfacies-rock phases, water production rate, oil saturation, resistivity, residual oil saturation, irreducible water saturation, porosity and permeability which have different degrees of influence on the flooded layer as the quantitative evaluation parameters of the flooded layer;
step 4b, carrying out normalization processing, carrying out dimensionless processing on the parameters to obtain a fuzzy evaluation matrix R, and normalizing to a [0, 1] interval;
step 4c, adopting an entropy weight method as a method for determining weight;
step 4d, determining a flooding index Ifw
Ifw＝WB (19)
Ifw＝(i1，i2，i3，...，in) (20)
IfwFlooding index, using weight W and matrix B, i.e. B, of various parameters to be evaluated1jMultiplying to obtain a set of flooding indexes of a sample to be evaluated,
wherein,
and i islE (0,1), l ═ 1,2,3, Λ, n; that is, ilThe larger the value, the weaker the flooding degree, ilThe smaller the value is, the stronger the flooding degree is; wherein n is the number of parameters participating in evaluation; and m is the number of samples participating in evaluation.
6. The mixed-volume reservoir flooding degree logging interpretation method based on sedimentary microfacies and rock facies as claimed in claim 5, wherein in step 4d, aiming at the geological characteristics of the mixed-volume reservoir oil deposit, the production reality of the oil deposit is integrated, and the flooding layer is divided into six grades: i isfwNo water logging is more than or equal to 0.50, and I is more than or equal to 0.45fwLess than 0.50 is weak water logging, I is more than or equal to 0.40fwLess than 0.45 is weak water logging, I is more than or equal to 0.35fwLess than 0.40 is moderate water logging, I is more than or equal to 0.30fwLess than 0.35 is strongly watered, IfwAnd (3) strongly flooding the surface by less than 0.30, and plotting the flooding degree on the surface through quantitative evaluation of a flooding layer to obtain a plan view of the flooding condition.
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