CN114139242A - Water flooded layer well logging evaluation method based on lithofacies - Google Patents
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Abstract
The invention relates to a flooded layer evaluation method, in particular to a flooded layer well logging evaluation method based on lithofacies. The method comprises the following steps: performing single correlation analysis on the lithofacies, and preferably selecting lithofacies characterization parameters; constructing a lithofacies quantitative identification mode, establishing a lithofacies discrimination function, and carrying out quantitative identification and division on lithofacies; establishing different lithofacies differential physical property parameter models; calculating the resistivity of the mixed solution of the formation water; building residual oil saturation models of different lithofacies; establishing permeability models and water content models of different lithofacies; establishing a water flooding subdivision standard based on different lithofacies; and carrying out quantitative evaluation on the water flooded layer. The method improves the interpretation precision of the flooded layer and solves the problem that the well logging interpretation coincidence rate of the existing flooded layer is low.
Description
Technical Field
The invention relates to a flooded layer evaluation method, in particular to a flooded layer well logging evaluation method based on lithofacies.
Background
At present, all oil fields which are developed by water injection have a flooding phenomenon. Solving the problem of flooding has become a global issue. China is one of countries with high proportion of water injection and development oil fields in the world, most oil fields are in multi-well, multi-layer and multi-direction water-seeing stages, but about 40 percent of the oil fields can be produced under the condition of 80 to 90 percent of produced water. On the one hand, the high water content oil field in China still has great potential of potential excavation and recovery efficiency improvement; on the other hand, under the condition of high water yield, the water flooded layer is required to be accurately evaluated, and the underground residual oil distribution is cleared. Therefore, the research of the logging interpretation method of the water flooded layer has very important significance.
In China, underground oil-water distribution in a high water content mining stage becomes very complex, and the difficulty of accurately evaluating a water flooded layer is increasing, so that great difficulty is brought to the formulation of potential excavation measures of an oil field in the future. The logging evaluation of the water flooded layer mainly aims to clear the oil-water distribution condition of the underground reservoir, determine a favorable residual oil distribution area, provide guidance for further excavation and submergence of an oil-gas field and well position adjustment, and achieve the purposes of oil stabilization, water control and recovery ratio improvement. At present, the logging interpretation precision of a water flooded layer is not high, the coincidence rate is low, and the complicated residual oil distribution of an oil field is not accurately grasped. Therefore, the method for improving the well logging interpretation precision of the water flooded layer is suitable for the requirement of the current oil field development situation and is an important technical guarantee for realizing the stable yield of the oil field.
Disclosure of Invention
The invention mainly aims to provide a flooded layer well logging evaluation method based on a lithofacies, which improves the interpretation precision of a flooded layer and solves the problem of low interpretation coincidence rate of the existing flooded layer well logging.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a water flooded layer well logging evaluation method based on lithofacies, which comprises the following steps: performing single correlation analysis on the lithofacies, and preferably selecting lithofacies characterization parameters; constructing a lithofacies quantitative identification mode, establishing a lithofacies discrimination function, and carrying out quantitative identification and division on lithofacies; establishing different lithofacies differential physical property parameter models; calculating the resistivity of the mixed solution of the formation water; building residual oil saturation models of different lithofacies; establishing permeability models and water content models of different lithofacies; establishing a water flooding subdivision standard based on different lithofacies; and carrying out quantitative evaluation on the water flooded layer.
Preferably, the method further comprises, prior to the single correlation analysis, subdividing the lithofacies types to define the lithofacies composition of the reservoir.
Further preferably, on the basis of the originally divided sedimentary facies and sedimentary microfacies, the lithofacies types are subdivided under the control of three levels of sedimentary facies level, microfacies level and lithology level according to the lithology characteristic difference of the stratum, a lithofacies type division scheme is established, and the lithofacies types of the implementation area are determined.
Preferably, the corresponding relation between different lithofacies and logging response characteristics is analyzed by utilizing the core scale logging, the logging response characteristics of the lithofacies are summarized, the distribution range of lithofacies logging parameters is counted, single correlation analysis is carried out through a double-parameter intersection diagram, and lithofacies characterization parameters are preferably selected.
Preferably, a Bayesian discriminant analysis method is applied by using the characteristic parameters reflected by each lithofacies to establish an interpretation model of each lithofacies, and wells with unexplained lithofacies are continuously and automatically interpreted:
in the formula: y isi-an ith lithofacies type; a. thek-a coefficient; xk-curve data.
Preferably, on the basis of lithofacies identification, physical property parameter models of different lithofacies are established:
Φi＝a+b*AC
lgKi＝a+b*Φi+c*Vsh
in the formula: phii-porosity data of the ith facies; AC-sonic moveout data; ki-permeability data of the ith facies; vsh-mud content data.
Preferably, the method for calculating the resistivity of the formation water mixed liquor comprises the following steps: calculating the resistivity R of the pure water layer by adopting an Archie formulawReading the deviation amplitude delta SP of the natural unit base line, and carrying out the resistivity R of the mixed liquid of the water flooded stratum by the following formulawzThe calculation of (2):
in the formula: rwz-formation-mixture resistivity; rw-formation water resistivity of pure water layer; Δ SP — natural unit baseline offset amplitude; k-argillaceous content data.
Preferably, the corrected saturation analysis data and logging response parameters are adopted, and a multivariate regression method is utilized to establish residual oil saturation models of different lithofacies:
lgSwi＝a+b*Φi+c*lgRT+d*lgRwz
Soi＝100-Swi
in the formula: swi-the water saturation of the ith facies; soi-residual oil saturation of the i-th lithofacies.
Preferably, a permeability model and a water content model of different lithofacies are established by analyzing a plurality of facies permeability experimental data:
Kroi＝a*Swoi 2-b*Swoi+c
Krwi＝a1*Swwi 2-b1*Swwi+c1
wherein: swoi-water saturation corresponding to the i-th rock phase oil phase permeability; swwi-the water saturation corresponding to the water permeability of the ith lithofacies; a. b, c, a1, b1, c 1-coefficients; kroi-an oil phase permeability model of the ith lithofacies; krwi-a water phase permeability model of the ith lithofacies; mu.so-oil viscosity; mu.sw-water viscosity; fwiAnd (4) a water content model of the ith lithofacies.
Preferably, the flooding degree criterion is as follows:
and (4) water flooding: fw is less than or equal to 20; flooding with weak water: fw is more than 20 and less than or equal to 40; flooding with medium water: fw is more than 40 and less than or equal to 80; flooding with strong water: fw is more than 80 and less than or equal to 90; extremely strong flooding: fw > 90.
Compared with the prior art, the invention has the following advantages:
according to the method, the model is established according to different lithofacies, the interpretation precision of the relevant parameters of the target well is greatly improved, the water logging interpretation result is compared with the rock core washing degree, the coincidence rate reaches 93%, and the problem that the existing water logging interpretation coincidence rate is low is solved.
In addition, the method is simple, easy to operate and beneficial to popularization, and provides a feasible method for further excavation of the oil field.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of lithofacies types for the zone of implementation in accordance with an embodiment of the present invention;
FIG. 2 is a preferred diagram of facies characterization parameters for the region of interest in accordance with one embodiment of the present invention;
fig. 3 is a diagram of the results of facies verification and facies identification for a cored well in an implementation area in accordance with an embodiment of the present invention: a, a lithofacies verification diagram of a core well of a 29-J254 well; b, 28-K266 well lithofacies recognition result graph.
Fig. 4 is a precision comparison diagram of the non-lithofacies and lithofacies-separated building and explaining model of the implementation area in a specific embodiment of the present invention: a is an accuracy analysis chart of an interpretation model established by the non-divided lithofacies; b, establishing an accuracy analysis chart of an explanation model for the lithofacies;
FIG. 5 is a graph of facies permeability curves for different facies types in the implementation zone in accordance with one embodiment of the present invention: a is a facies permeability curve of different lithofacies types of the heart beach dam; b is a facies permeability curve of different lithofacies types of the riverway;
fig. 6 is an explanatory result diagram of comparison between the lithofacies non-dividing, lithofacies dividing and core analysis data of the implementation zone in an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the stated features, steps, operations, and/or combinations thereof, unless the context clearly indicates otherwise.
In order to make the technical solutions of the present invention more clearly understood by those skilled in the art, the technical solutions of the present invention will be described in detail below with reference to specific embodiments.
The method comprises the following steps:
in the formula: y isi-an ith lithofacies type; a. thek-a coefficient; xk-curve data.
Step 3, establishing physical property parameter models of different lithofacies on the basis of lithofacies identification:
Φi＝a+b*AC
lgKi＝a+b*Φi+c*Vsh
in the formula: phii-porosity data of the ith facies; AC-sonic moveout data; ki-permeability data of the ith facies; vsh-mud content data.
in the formula: rwz-formation-mixture resistivity; rw-formation water resistivity of pure water layer; Δ SP — natural unit baseline offset amplitude; k-argillaceous content data.
Step 5, establishing residual oil saturation models of different lithofacies: and (3) establishing residual oil saturation models of different lithofacies by adopting the corrected saturation analysis data and logging response parameters and utilizing a multivariate regression method:
lgSwi＝a+b*Φi+c*lgRT+d*lgRwz
Soi＝100-Swi
in the formula: swi-the water saturation of the ith facies; soi-residual oil saturation of the i-th lithofacies.
Step 6, establishing a water content and relative permeability explanation model: establishing permeability models and water content models of different lithofacies by analyzing a plurality of facies permeability experimental data:
Kroi＝a*Swoi 2-b*Swoi+c
Krwi＝a1*Swwi 2-b1*Swwi+c1
wherein: swoi-water saturation corresponding to the i-th rock phase oil phase permeability; swwi-the water saturation corresponding to the water permeability of the ith lithofacies; a. b, c, a1, b1, c 1-coefficients; kroi-an oil phase permeability model of the ith lithofacies; krwi-a water phase permeability model of the ith lithofacies; mu.so-oil viscosity; mu.sw-water viscosity; fwiAnd (4) a water content model of the ith lithofacies.
Step 7, establishing a water flooding subdivision standard based on different lithofacies; the flooding degree judgment standard is as follows:
and (4) water flooding: fw is less than or equal to 20; flooding with weak water: fw is more than 20 and less than or equal to 40; flooding with medium water: fw is more than 40 and less than or equal to 80; flooding with strong water: fw is more than 80 and less than or equal to 90; extremely strong flooding: fw > 90.
And 8, carrying out quantitative evaluation on the flooded layer according to the established quantitative evaluation mode of the flooded layer.
The method comprises the following steps:
and 2, logging by utilizing the core scales, analyzing the corresponding relation between different lithofacies of the implementation area and logging response characteristics, summarizing the logging response characteristics of the lithofacies, counting the distribution range of lithofacies logging parameters, performing single correlation analysis by using a double-parameter intersection diagram, and preferably selecting lithofacies characterization parameters.
Step 3, constructing a lithofacies quantitative identification mode, establishing a lithofacies discrimination function, and carrying out quantitative identification and division on lithofacies: establishing an interpretation model of each lithofacies by using the characteristic parameters reflected by each lithofacies and applying a Bayesian discriminant analysis method, and continuously and automatically interpreting wells with unexplained lithofacies:
in the formula: y isi-an ith lithofacies type; a. thek-a coefficient; xk-curve data.
Φi＝a+b*AC
lgKi＝a+b*Φi+c*Vsh
in the formula: phii-porosity data of the ith facies; AC-sonic moveout data; ki-permeability data of the ith facies; vsh-mud content data.
Step 5, determining the resistivity of the stratum water mixed liquid: calculating the resistivity R of the pure water layer by adopting an Archie formulawReading the deviation amplitude delta SP of the natural unit base line, and carrying out the resistivity R of the mixed liquid of the water flooded stratum by the following formulawzThe calculation of (2):
in the formula: rwz-formation-mixture resistivity; rw-formation water resistivity of pure water layer; Δ SP — natural unit baseline offset amplitude; k-argillaceous content data.
Step 6, establishing residual oil saturation models of different lithofacies: and (3) establishing residual oil saturation models of different lithofacies by adopting the corrected saturation analysis data and logging response parameters and utilizing a multivariate regression method:
lgSwi＝a+b*Φi+c*lgRT+d*lgRwz
Soi＝100-Swi
in the formula: swi-the water saturation of the ith facies; soi-residual oil saturation of the i-th lithofacies.
Step 7, establishing a water content and relative permeability explanation model: establishing permeability models and water content models of different lithofacies by analyzing a plurality of facies permeability experimental data:
Kroi＝a*Swoi 2-b*Swoi+c
Krwi＝a1*Swwi 2-b1*Swwi+c1
wherein: swoi-water saturation corresponding to the i-th rock phase oil phase permeability; swwi-the water saturation corresponding to the water permeability of the ith lithofacies; a. b, c, a1, b1, c 1-coefficients; kroi-an oil phase permeability model of the ith lithofacies; krwi-a water phase permeability model of the ith lithofacies; mu.so-oil viscosity; mu.sw-water viscosity; fwiAnd (4) a water content model of the ith lithofacies.
and (4) water flooding: fw is less than or equal to 20; flooding with weak water: fw is more than 20 and less than or equal to 40; flooding with medium water: fw is more than 40 and less than or equal to 80; flooding with strong water: fw is more than 80 and less than or equal to 90; extremely strong flooding: fw > 90.
And 9, carrying out quantitative evaluation on the flooded layer according to the established quantitative evaluation standard of the flooded layer.
West Ng6 of the eastern solitary oilfield seven-londong region using the method described in example 23+4Carrying out quantitative evaluation on a water flooded layer:
the method comprises the steps of subdividing lithofacies types under the control of three levels of sedimentary facies level, micro-facies level and lithology level according to lithology characteristic differences of strata on the basis of originally divided sedimentary facies and sedimentary microfacies, establishing a lithofacies type division scheme, and determining the lithofacies types of an implementation area. The resulting lithofacies types include: the fine sand lithofacies of the cardiac beach, the fine sand lithofacies of the river channel and the fine sand lithofacies of the river channel are shown in figure 1.
And 3, establishing a lithofacies quantitative interpretation mode by using the characteristic parameters reflected by each type of lithofacies and applying Bayes discriminant analysis theory:
the equation for discriminating the lithofacies of the fine sandstone of the heart beach dam is as follows:
lithofacies1＝-104.60+20.05*GR–801.76*△SP+819.03*AC+12.54*ΔML
the discrimination equation of the siltstone facies of the heart beach dam is as follows:
lithofacies2＝-106.70+34.32*GR–1706.86*△SP+1051.78*AC+19.28*ΔML
the equation for discriminating the fine sandstone facies of the riverway:
lithofacies3＝-95.38+35.81*GR–1873.63*△SP+1035.58*AC+22.15*ΔML
the equation for distinguishing the siltstone lithofacies of the riverway:
lithofacies4＝-62.18+30.10*GR–1618.51*△SP+842.24*AC+25.17*ΔML
in the formula: lithofacies-is lithofacies type; GR-Natural Gamma Curve value, API; Δ SP — natural potential amplitude difference, MV; difference in AC-sound wave, us/m; Δ ML — microelectrode amplitude difference, MV.
II, verifying a rock phase recognition result by using the coring well, wherein the rock core recognition accuracy rate reaches 92%; and (4) performing lithofacies identification and division on the non-coring wells by utilizing a lithofacies quantitative interpretation mode. The results of core well facies verification and facies identification are shown in FIG. 3.
And 4, on the basis of lithofacies identification, combining a single correlation method with a multiple regression method to establish differential physical property parameter models of different lithofacies:
core beach fine sandstone lithofacies: phi 0.5127 AC-25.261
lgK＝2.531Φ-0.179Vsh+2.713
Cardiac flat siltstone lithofacies: phi 0.7282 AC-53.693
lgK＝6.439Φ-1.605Vsh+1.036
Riverway fine sandstone lithofacies: phi 0.8444 AC-67.216
lgK＝0.947Φ+4.259Vsh+2.910
Riverway siltstone lithofacies: phi 0.6074 AC-42.541
lgK＝8.255Φ+0.317Vsh-0.005
In the formula: AC-sonic time difference, us/m; K-Permeability, 10-3μm2(ii) a Φ — porosity, f; vsh-argillaceous content, f.
Step 5, solving the resistivity R of the pure water layer by adopting an Archie formulawReading the deviation amplitude delta SP of the natural unit base line, and carrying out the resistivity R of the mixed liquid of the water flooded stratum by the following formulawzThe calculation of (2):
in the formula: rwz-formation-mixture resistivity; rw-formation water resistivity of pure water layer; Δ SP
-natural unit baseline offset amplitude; k-argillaceous content data.
⒌, establishing residual oil saturation models of different lithofacies by using a multivariate regression method and corrected saturation analysis data and logging response parameters:
core beach dam fine sandstone lithofacies: lgSw 0.10339+0.07882lg phi-0.3774 lgrinl-0.2356 lgRwz
Siltstone lithofacies of the heart beach dam: lgSw is 0.35581-0.38676lg phi-0.3482 lgrinl-0.2651 lgRwz
Riverway fine sandstone lithofacies: lgSw is 0.19984-0.10448lg phi-0.3720 lgrinl-0.2493 lgRwz
Riverway siltstone lithofacies: lgSw is 0.33209-0.34091lg phi-0.3577 lgrinl-0.2693 lgRwz
Residual oil saturation: so 1-Sw
In the formula: so-residual oil saturation; sw-water saturation;
-normalizing the data; e (Φ) -mathematical expectation; sigma-variance.
Step ⒍, analyzing a plurality of phase permeability experimental data, and establishing relative permeability models and water content models of different lithofacies:
core beach fine sandstone lithofacies: kro ═ 0.9372 × Sw2-2.8758*Sw+1.638；
Krw＝1.7309*Sw2-1.0271*Sw+0.151
Cardiac flat siltstone lithofacies:
Kro＝8.3262*Sw2-11.543*Sw+4.014
Krw＝2.9956*Sw2-2.3418*Sw+0.464
riverway fine sandstone lithofacies:
Kro＝5.5536*Sw2-7.7081*Sw+2.685
Krw＝3.4653*Sw2-2.4257*Sw+0.432
riverway siltstone lithofacies:
Kro＝7.3015*Sw2-9.2833*Sw+2.968
Krw＝3.6571*Sw2-2.5149*Sw+0.443
the water content model is as follows:
in the formula: kro-is oil phase permeability; krw-is the water phase permeability; sw-water saturation; fw-water content; mu.so-the viscosity of the oil; mu.sw-the viscosity of water. The facies permeability curves for the different lithofacies types are shown in FIG. 5.
And 7, determining the flooding degree discrimination standard according to the calculated water content Fw of the flooding layer as follows:
and (4) water flooding: fw is less than or equal to 20
Flooding with weak water: fw is more than 20 and less than or equal to 40
Flooding with medium water: fw is more than 40 and less than or equal to 80
Flooding with strong water: fw is more than 80 and less than or equal to 90
Extremely strong flooding: fw >90
By utilizing core analysis data comparison of the core well and applying a facies-based water flooded layer interpretation method, the interpretation precision of the seven-zone West Ng63+ 4 parameter is improved, as shown in Table 1 and figure 6.
TABLE 1 comparison table of well logging interpretation parameters of different lithofacies of seven regions of east solitary, west Ng63+ 4 Xin beach dam
The water logging interpretation result is compared with the core washing degree, and the coincidence rate reaches 93 percent, which is specifically shown in table 2.
TABLE 2 statistical table of core well verification and interpretation conclusions
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A water flooded layer well logging evaluation method based on lithofacies is characterized by comprising the following steps: performing single correlation analysis on the lithofacies, and preferably selecting lithofacies characterization parameters; constructing a lithofacies quantitative identification mode, establishing a lithofacies discrimination function, and carrying out quantitative identification and division on lithofacies; establishing different lithofacies differential physical property parameter models; calculating the resistivity of the mixed solution of the formation water; building residual oil saturation models of different lithofacies; establishing permeability models and water content models of different lithofacies; establishing a water flooding subdivision standard based on different lithofacies; and carrying out quantitative evaluation on the water flooded layer.
2. The method of claim 1, further comprising, prior to the single correlation analysis, subdividing the lithofacies types to define lithofacies composition of the reservoir.
3. The method as claimed in claim 2, wherein based on the originally divided sedimentary facies and sedimentary microfacies, the lithofacies types are subdivided under the control of sedimentary facies order, microfacies order and lithology order according to lithology characteristic differences of the stratum, and lithofacies type division schemes are established to determine the lithofacies types of the implementation zones.
4. The method as claimed in claim 1, wherein the corresponding relation between different lithofacies and logging response characteristics is analyzed by using core scale logging, the logging response characteristics of the lithofacies are summarized, the distribution range of lithofacies logging parameters is counted, single correlation analysis is performed through a double-parameter intersection graph, and lithofacies characterization parameters are preferably selected.
5. The method as claimed in claim 1, wherein a Bayesian discriminant analysis method is applied to the characteristic parameters reflected by each lithofacies to establish an interpretation model of each lithofacies, and the well of unexplained lithofacies is continuously and automatically interpreted:
in the formula: y isi-an ith lithofacies type; a. thek-a coefficient; xk-curve data.
6. The method of claim 1, wherein the physical parameter models of different lithofacies are established based on lithofacies identification:
Φi＝a+b*AC
lg Ki＝a+b*Φi+c*Vsh
in the formula: phii-porosity data of the ith facies; AC-sonic moveout data; ki-permeability data of the ith facies; vsh-mud content data.
7. The method of claim 1, wherein the method of calculating the formation water mixture resistivity comprises: calculating the resistivity R of the pure water layer by adopting an Archie formulawReading the deviation amplitude delta SP of the natural unit base line, and carrying out the resistivity R of the mixed liquid of the water flooded stratum by the following formulawzThe calculation of (2):
in the formula: rwz-formation-mixture resistivity; rw-formation water resistivity of pure water layer; Δ SP — natural unit baseline offset amplitude; k-argillaceous content data.
8. The method of claim 1, wherein the corrected saturation analysis data and logging response parameters are used to build a model of residual oil saturation for different lithofacies using a multivariate regression method:
lgSwi＝a+b*Φi+c*lgRT+d*lgRwz
Soi＝100-Swi
in the formula: swi-the water saturation of the ith facies; soi-residual oil saturation of the i-th lithofacies.
9. The method of claim 1, wherein the permeability model and the water content model of different lithofacies are established by analyzing a plurality of facies permeability experimental data:
Kroi＝a*Swoi 2-b*Swoi+c
Krwi＝a1*Swwi 2-b1*Swwi+c1
wherein: swoi-water saturation corresponding to the i-th rock phase oil phase permeability; swwi-the water saturation corresponding to the water permeability of the ith lithofacies; a. b, c, a1, b1, c 1-coefficients; kroi-an oil phase permeability model of the ith lithofacies; krwi-a water phase permeability model of the ith lithofacies; mu.so-oil viscosity; mu.sw-water viscosity; fwiAnd (4) a water content model of the ith lithofacies.
10. The method of claim 1, wherein the flooding criteria is:
and (4) water flooding: fw is less than or equal to 20; flooding with weak water: fw is more than 20 and less than or equal to 40; flooding with medium water: fw is more than 40 and less than or equal to 80; flooding with strong water: fw is more than 80 and less than or equal to 90; extremely strong flooding: fw > 90.
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| CN117332301A (en) * | 2023-10-17 | 2024-01-02 | 大庆油田有限责任公司 | Flooding layer interpretation method for reservoir classification evaluation |
| CN117495085A (en) * | 2023-10-31 | 2024-02-02 | 大庆油田有限责任公司 | Well site implementation risk quantitative evaluation method |
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| CN117332301A (en) * | 2023-10-17 | 2024-01-02 | 大庆油田有限责任公司 | Flooding layer interpretation method for reservoir classification evaluation |
| CN117495085A (en) * | 2023-10-31 | 2024-02-02 | 大庆油田有限责任公司 | Well site implementation risk quantitative evaluation method |
| CN117495085B (en) * | 2023-10-31 | 2024-06-04 | 大庆油田有限责任公司 | Well site implementation risk quantitative evaluation method |
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