CN112363242B - Reservoir fluid identification method and device based on logging fusion - Google Patents
Reservoir fluid identification method and device based on logging fusion Download PDFInfo
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- 230000002159 abnormal effect Effects 0.000 claims abstract description 38
- 239000011435 rock Substances 0.000 claims description 88
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 32
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- 239000004927 clay Substances 0.000 claims description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 21
- 235000019580 granularity Nutrition 0.000 description 21
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- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
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- 238000005516 engineering process Methods 0.000 description 1
- 229910052900 illite Inorganic materials 0.000 description 1
- NLYAJNPCOHFWQQ-UHFFFAOYSA-N kaolin Chemical compound O.O.O=[Al]O[Si](=O)O[Si](=O)O[Al]=O NLYAJNPCOHFWQQ-UHFFFAOYSA-N 0.000 description 1
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- VGIBGUSAECPPNB-UHFFFAOYSA-L nonaaluminum;magnesium;tripotassium;1,3-dioxido-2,4,5-trioxa-1,3-disilabicyclo[1.1.1]pentane;iron(2+);oxygen(2-);fluoride;hydroxide Chemical compound [OH-].[O-2].[O-2].[O-2].[O-2].[O-2].[F-].[Mg+2].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[K+].[K+].[K+].[Fe+2].O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2 VGIBGUSAECPPNB-UHFFFAOYSA-L 0.000 description 1
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- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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Abstract
The invention relates to a reservoir fluid identification method and a device based on logging fusion, which belong to the technical field of reservoir fluid property identification, and the method comprises the following steps: acquiring an air-logging full hydrocarbon curve of a target interval, and acquiring a natural potential curve and a base line of the target interval; according to the difference between the natural potential curve of the target interval and the baseline, calculating to obtain a negative abnormal amplitude curve of the natural potential; and identifying the fluid property of the target interval according to the gas-measured full hydrocarbon curve and the negative abnormal amplitude curve. According to the invention, the gas-logging full hydrocarbon curve of the target interval in the logging data is obtained, the natural potential curve and the base line of the target interval in the logging data are calculated, the negative abnormal amplitude between the natural potential curve and the base line is calculated, and the fluid property of the target interval is identified by combining the gas-logging full hydrocarbon curve, so that the water content and the gas content of the target interval can be reliably identified.
Description
Technical Field
The invention belongs to the technical field of reservoir fluid property identification, and particularly relates to a reservoir fluid identification method and device based on logging fusion.
Background
At present, reservoir fluid property identification is carried out through logging data or logging data, the logging data is first hand data for reservoir fluid property identification, the type of reservoir rock and the granularity of minerals can be effectively identified through technologies such as rock debris logging, core logging and X-ray element logging, the physical properties of the reservoir can be qualitatively known through logging core nuclear magnetic resonance, the oil-gas content can be qualitatively known through gas logging and groove face observation, the main parameters of gas logging reaction oil-gas content are the oil content level and the gas logging total hydrocarbon value, and the identification parameters are single. Further, only the gas content can be determined by using the gas-measurement all hydrocarbon curve morphology, in combination with the lithology and physical properties of the corresponding sandstone section, as shown in fig. 1.
For example, byFor G well region X group Y 1 Comparing and analyzing the gas-measuring full hydrocarbon curve of the section to obtain X groups Y 1 As shown in FIG. 2, the section gas layer identification plate can be seen that the gas measurement of the total hydrocarbon value and the display thickness have certain discrimination on the productivity. Meanwhile, the water content of the reservoir cannot be reflected by the size of the gas-logging total hydrocarbon value, so that the gas content of the reservoir can only be qualitatively estimated by logging data, and the water content of the reservoir cannot be judged.
In logging data, rock logging response is comprehensive reflection of factors such as rock framework, pores, pore fluid and the like, for conventional sandstone logging interpretation, lithology and permeability of a reservoir are identified by utilizing natural gamma and natural potential curves, porosity of the reservoir is identified by utilizing acoustic time difference, neutrons and density curves, oil-gas properties of the reservoir are identified by utilizing resistivity curves, a gas layer is identified by utilizing the 'digging effect' of neutrons and density curves, and in addition, the property of the reservoir fluid is further identified by utilizing special logging projects such as nuclear magnetic resonance, dipole acoustic wave, electric imaging, thermal neutron imaging and the like. The "digging effect" is an important basis for identifying the gas layer, but in complex reservoirs, the influence of the fluid property on the logging response is covered up to a certain extent due to the rock skeleton parameter change, so that the "digging effect" is not obvious, and the gas-bearing layer judgment is influenced. Meanwhile, the electrical progenitor value of the logging curve is also an important parameter for judging the gas content of the reservoir, the deep detection resistivity value is theoretically larger than the shallow detection resistivity value, the array induction resistivity curve shows low invasion characteristics, the reservoir is represented as an oil-gas layer, the water content of the stratum can cause the measured resistivity value to be obviously reduced, the deep detection resistivity value is obviously smaller than the shallow detection depth, and the array induction resistivity curve shows high invasion characteristics, as shown in fig. 3.
However, in a complex reservoir, due to various rock mineral components and large rock structure differences, the rock skeleton parameters are greatly changed, the logging response difference of different fluid properties is covered up to a certain extent, the logging response is not obvious, and logging data is greatly influenced by borehole conditions, so that the logging data and logging data are limited to a certain extent in the reservoir fluid identification process, and in actual production application, reliable identification of the reservoir fluid properties cannot be realized by solely depending on the logging data or the logging data.
Disclosure of Invention
The invention aims to provide a reservoir fluid identification method based on logging fusion, which is used for solving the problem that reservoir fluid evaluation is inaccurate in the prior art, and simultaneously provides a reservoir fluid identification device based on logging fusion, which is also used for solving the problem that reservoir fluid evaluation is inaccurate in the prior art.
In order to solve the technical problems, the invention provides a reservoir fluid identification method based on logging fusion, which comprises the following steps:
acquiring an air-logging full hydrocarbon curve of a target interval, and acquiring a natural potential curve and a base line of the target interval; according to the difference between the natural potential curve of the target interval and the baseline, calculating to obtain a negative abnormal amplitude curve of the natural potential; and identifying the fluid property of the target interval according to the gas-measurement full hydrocarbon curve and the negative abnormal amplitude curve of the natural potential.
According to the invention, the gas-logging full hydrocarbon curve of the target interval in the logging data is obtained, the natural potential curve and the base line of the target interval in the logging data are calculated, the negative abnormal amplitude between the natural potential curve and the base line is calculated, and the fluid property of the target interval is identified by combining the gas-logging full hydrocarbon curve, so that the water content and the gas content of the target interval can be reliably identified.
After determining the fluid properties of the target interval, the lithology of the target interval is also identified by:
s1) determining the mineral content ranges of different rock types according to logging data;
s2) extracting lithology sensitive parameters according to logging data, and counting logging response ranges of the corresponding lithology sensitive parameters under different rock types;
and S3) identifying the rock type of the target interval according to the mineral content ranges of different rock types obtained in the step S1) and the logging response ranges of corresponding lithology sensitive parameters under different rock types obtained in the step S2).
According to logging data and logging data, the data ranges of different rock types, including the mineral content range and the logging response range of each lithology sensitive parameter, are determined, and the rock type of the target interval can be accurately determined.
In order to be able to identify the rock type and rock granularity of the target interval, further comprising: according to logging data, determining clay content ranges of different rock types, and combining step S2) to obtain logging response ranges of corresponding lithology sensitive parameters under different rock types, and identifying rock granularity of a target interval.
Further, to achieve fluid property identification of the target interval, the gas logging full hydrocarbon curve of the target interval is obtained through logging data acquisition.
Further, in order to calculate a negative abnormal amplitude curve of the natural potential, the natural potential curve and the baseline of the target interval are obtained through logging data.
In order to solve the technical problems, the invention also provides a reservoir fluid identification device based on logging fusion, which comprises a processor and a control unit, wherein the processor is used for executing instructions to realize the following steps:
acquiring an air-logging full hydrocarbon curve of a target interval, and acquiring a natural potential curve and a base line of the target interval; according to the difference between the natural potential curve of the target interval and the baseline, calculating to obtain the negative abnormal amplitude of the natural potential; and identifying the fluid property of the target interval according to the gas-logging full hydrocarbon curve and the negative abnormal amplitude of the natural potential.
The processor can reliably identify the water content and the gas content of the target interval by acquiring the gas-logging full hydrocarbon curve of the target interval in logging data, the natural potential curve and the base line of the target interval in logging data, and by calculating the negative abnormal amplitude between the natural potential curve and the base line and identifying the fluid property of the target interval by combining the gas-logging full hydrocarbon curve.
Further, the processor is also configured to identify lithology of the target interval by:
s1) determining the mineral content ranges of different rock types according to logging data;
s2) extracting lithology sensitive parameters according to logging data, and counting logging response ranges of the corresponding lithology sensitive parameters under different rock types;
and S3) identifying the rock type of the target interval according to the mineral content ranges of different rock types obtained in the step S1) and the logging response ranges of corresponding lithology sensitive parameters under different rock types obtained in the step S2).
The processor determines the data range of different rock types according to logging data and logging data, wherein the data range comprises a mineral content range and a logging response range of each lithology sensitive parameter, and can accurately determine the rock type of a target interval.
In order to be able to identify the rock type and rock granularity of the target interval, the processor is further configured to: according to logging data, determining clay content ranges of different rock types, and combining step S2) to obtain logging response ranges of corresponding lithology sensitive parameters under different rock types, and identifying rock granularity of a target interval.
Further, to achieve fluid property identification of the target interval, the gas logging full hydrocarbon curve of the target interval is obtained through logging data acquisition.
Further, in order to calculate a negative abnormal amplitude curve of the natural potential, the natural potential curve and the baseline of the target interval are obtained through logging data.
Drawings
FIG. 1 is a schematic representation of a prior art technique for identifying fluid properties using full hydrocarbon curve morphology;
FIG. 2 is a schematic diagram of a G-well X-group Y obtained by the prior art 1 Segment gas layer identification map;
FIG. 3 is a Q1 well Y obtained using the prior art 1 Segment combination log graphs;
FIG. 4 is a graph of negative anomaly amplitude curves and gas-measured total hydrocarbons in combination modes 1-5 for a first embodiment of the identification method of the present invention;
FIG. 5 is an explanatory view of a reservoir in combination modes 1 to 5 according to the first embodiment of the identification method of the present invention;
FIG. 6 is a cross-sectional view of a logging-lithology granularity, mineral and gas-bearing interpretation of a first embodiment of the identification method of the present invention;
FIG. 7 is a diagram of rock type identification for a target interval according to a first embodiment of the identification method of the present invention;
FIG. 8 is a graph of rock granularity identification of a target interval according to a first embodiment of the identification method of the present invention;
FIG. 9 is a graph showing the response relationship among lithology, electrical properties and pore structure corresponding to different reservoir types according to the first embodiment of the identification method of the present invention;
FIG. 10 is a diagram of comprehensive interpretation results of reservoir characteristics of a Q2 well logging fusion in accordance with an embodiment of the identification method of the present invention;
FIG. 11 is a schematic diagram of a G-well X-group Y according to a first embodiment of the identification method of the present invention 1 Section sandstone granularity and gas production relationship diagram;
FIG. 12 is a schematic diagram of a G-well X-group Y according to a first embodiment of the identification method of the present invention 1 A segment lithology, electrical property and gas content relationship diagram;
FIG. 13 is a graph showing the relationship between the peak morphology and the porosity, and the pore structure of the T2 spectrum according to the first embodiment of the identification method of the present invention;
FIG. 14 is a schematic diagram of a G-well X-group Y in accordance with an embodiment of the present invention 1 Schematic of the porosity of the segment as a function of irreducible water saturation;
FIG. 15 is a schematic diagram of a G-well X-group Y in accordance with an embodiment of the present invention 3 Schematic of the porosity of the segment as a function of irreducible water saturation;
FIG. 16 is a diagram showing a Q1 well Y in accordance with one embodiment of the present invention 1 A log combination outcome schematic of the section;
FIG. 17 is a diagram of a Q3 well Y in accordance with an embodiment of the present invention 1 A log combination outcome schematic of the section;
FIG. 18 is a flow chart of reservoir fluid identification based on logging fusion according to a first embodiment of the identification method of the present invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings.
An identification method embodiment one:
as shown in fig. 18, a gas-measured full hydrocarbon curve of the target interval is acquired, and a natural potential curve (SP curve) and a base line of the target interval are acquired; and calculating to obtain a negative abnormal amplitude curve of the natural potential according to the difference between the natural potential curve of the target interval and the baseline.
Identifying fluid properties of the target interval from the gas-measured full hydrocarbon curve and the negative abnormal amplitude curve, comprising the following five combination modes:
combination mode 1: when the negative abnormal amplitude curve of the natural potential shows that the negative abnormal amplitude is larger than the set value I 0 Judging that the natural potential (SP) is negative abnormal; the gas-measurement full hydrocarbon curve is full, namely the thickness of the gas-measurement full hydrocarbon curve is larger than or basically equal to the thickness of the reservoir layer of the SP negative abnormal section, and the gas-measurement full hydrocarbon curve is judged to be a gas layer; the negative abnormal amplitude curve and the gas-measured full hydrocarbon curve in the mode are shown in fig. 4 and 5;
combination mode 2: when the negative abnormal amplitude curve of the natural potential shows that the negative abnormal amplitude is larger than the set value I 0 Judging that the SP is abnormal negatively, and the gas-measured full hydrocarbon curve does not show thickness, or shows that the thickness of the reservoir layer with thickness smaller than that of the SP abnormal section is larger than a set value I 1 Judging the water layer; the negative abnormal amplitude curve and the gas-measured full hydrocarbon curve in the mode are shown in fig. 4 and 5;
combination mode 3: when the negative abnormal amplitude curve of the natural potential shows that the negative abnormal amplitude is larger than the set value I 0 Judging that the SP is abnormal and the shape of the gas-measurement all-hydrocarbon curve is spike-shaped; and the amplitude of reservoir thickness of the gas-measurement full hydrocarbon curve, which shows that the thickness is smaller than that of the SP negative anomaly section, is smaller than a set value I 2 ，I 2 <I 1 Judging the water layer as a gas-containing water layer; the negative abnormal amplitude curve and the gas-measured full hydrocarbon curve in the mode are shown in fig. 4 and 5;
combination mode 4: when the negative abnormal amplitude curve of the natural potential is in a zigzag shape and the form of the gas-measurement all-hydrocarbon curve is corresponding or opposite to the negative abnormal amplitude curve of the natural potential, judging that the gas-difference layer is strong in heterogeneity; the negative abnormal amplitude curve and the gas-measured full hydrocarbon curve in the mode are shown in fig. 4 and 5;
combination mode 5: when it is determined that SP is free of negative abnormality based on negative abnormality amplitude curve of natural potential, and qiThe measured full hydrocarbon curve has no display thickness or the display thickness is smaller than the set value I 3 ，I 3 ≤I 2 Judging as a dry layer; the negative abnormal amplitude curve and the gas-measured total hydrocarbon curve in this mode are shown in fig. 4 and 5.
After determining the fluid properties of the target interval, the lithology of the target interval is also identified by:
s1) determining the mineral content ranges of different rock types according to logging data, and determining the clay content ranges of different rock types.
For example, the ancient mudstone main clay minerals on the erdos basin are illite, kaolinite, montmorillonite and chlorite, and the sandstone main particle minerals are quartz, feldspar and cuttings. The clay mineral content reflects the amount of the argillite content, the particle mineral content reflects the amount of the argillite content, the argillite clay mineral element content is shown in table 1, and the sandstone particle mineral element content is shown in table 2.
TABLE 1
TABLE 2
According to the mineral and element composition of sandstone, quartz minerals provide Si and O elements; the feldspar provides K, na, ca, al, si, O elements, and the rock debris provides Si, O, al, mg, K, fe, ti elements; according to the mineral and element composition of mudstone, the mudstone provides elements such as Al, si, fe and the like. Therefore, three elements of Si, al and Fe are the most sensitive parameters of the sections of the reaction sand and the mudstone, wherein the content of the Si mainly reflects the content of the sandstone particle minerals, the content of the Al and the Fe mainly reflects the content of the mudstone clay minerals, and the correlation between the element content and the clay mineral content and the particle mineral content is established through statistics of a large amount of sand and mudstone element data of the Huidos basin as follows:
C clay mineral ＝6.0(Al％+Fe％/2.07-2.23)
C Particulate mineral ＝4.9(Si％-22.8)
Wherein C is Clay mineral Represents the clay mineral content, C Particulate mineral The content of the granular mineral is represented by Al%, the content of the Al element is represented by Fe%, the content of the Fe element is represented by Fe%, and the content of the Si element is represented by Si%.
According to the ranges of the contents of the respective elements (Al, fe, si elements) of the respective rock types, the ranges of the clay mineral contents (i.e., clay content ranges) of the respective rock types, and the ranges of the particulate mineral contents (i.e., mineral content ranges) can be obtained in combination with the above formulas.
S2) extracting lithology sensitive parameters according to logging data, and counting logging response ranges of the lithology sensitive parameters under different rock types, wherein the logging response value ranges of the lithology sensitive parameters (GR, DEN, CNL, AC, LLD) under different rock types of a G well region X group Y1 section are shown in a table 3, and GR is a natural gamma logging value, DEN is a density logging value, CNL is a neutron porosity logging value, AC is a sonic time difference logging value and LLD is resistivity.
TABLE 3 Table 3
| Lithology of rock | GR(API) | DEN(g/cm 3 ) | CNL(％) | AC(μs/m) | LLD(Ω·m) |
| Gravel-containing coarse sandstone | 37-68 | 2.38-2.52 | 8.3-21.7 | >220 | 8.1-61.1 |
| Coarse sandstone | 53-76 | 2.43-2.55 | 8.4-17.1 | 214-247 | 9.2-16.7 |
| Middle sandstone | 61-89 | 2.48-2.62 | 10.4-14.4 | 206-232 | 10.0-13.5 |
| Fine sandstone | 90-130 | >2.55 | 11.8-31.1 | <220 | 17.9-30.4 |
| Mudstone | >120 | >2.60 | >20 | <210 | —— |
Since natural gamma logging is a common means of determining sandstone granularity, natural gamma logging is used as the lithology sensitive parameter of choice. As other embodiments, any one or several of them may also be selected as the selected lithology-sensitive parameters. Then, according to the corresponding logging response values of different rock types, logging parameters sensitive to lithology are extracted, and a logging and logging combined lithology granularity, mineral and gas-containing interpretation section diagram is established and is shown in fig. 6.
S3) identifying the rock type of the target interval according to the mineral content ranges of different rock types obtained in the step S1) and the logging response ranges of each lithology sensitive parameter under different rock types obtained in the step S2).
Taking natural gamma logging values as selected lithology sensitive parameters as an example, counting natural gamma logging values and mineral content data corresponding to different rock types, as shown in fig. 7, dividing identification areas of different rock types according to the natural gamma logging values and mineral content data distribution of different rock types, namely, the mineral content ranges of different rock types and the logging response ranges of the natural gamma logging values, dividing the identification areas of four rock types through straight lines in fig. 7, and detecting the identification areas of the natural gamma logging values and the mineral content of a target interval when the rock types of the target interval are identified, so that the rock types are determined.
S4) according to the clay content ranges of different rock types obtained in the step S1), combining the step S2) to obtain the logging response ranges of each lithology sensitive parameter under different rock types, and identifying the rock granularity of the target interval.
Taking natural gamma logging values as selected lithology sensitive parameters as an example, counting natural gamma logging values and clay content data corresponding to different rock granularities, as shown in fig. 8, dividing identification areas of different rock granularities according to the natural gamma logging values and clay content data distribution of different rock granularities, namely clay content ranges of different rock granularities and logging response ranges of the natural gamma logging values, dividing the identification areas of three rock granularities through straight lines in fig. 8, and detecting the identification areas of the natural gamma logging values and clay content of a target interval when the rock granularities of the target interval are identified, so as to determine the rock granularity.
The lithology recognition is carried out on the Q2 well according to the recognition area in fig. 7, and the results are shown in fig. 9 and 10, and have good consistency with the core observation description results, so that the lithology recognition method of the target interval can provide basis for rapid recognition of the on-site lithology.
Based on the lithology recognition, the rock type and the productivity relationship are counted, the rock type is closely related to the physical property and the productivity of a reservoir, taking a G well region X group Y1 section as an example, a layer with better productivity mainly develops in coarse-grained sandstones such as coarse sandstone, gravel-containing coarse sandstone and the like, as shown in fig. 11, and lithology, electrical property and gas content interpretation based on logging fusion are obtained according to test data and logging electrical response characteristics, as shown in fig. 12, so that an interpretation basis can be provided for rapid field recognition.
After determining the fluid properties and lithology of the target interval, the porosity, irreducible water saturation and permeability of the corresponding reservoir type are also calculated by the following steps:
f1 According to the nuclear magnetic test data, acquiring T2 spectrum average cut-off values, porosities and irreducible water saturation information of sandstone of different groups, and obtaining the relation between the spectrum peak form of the T2 spectrum and the porosities and pore structures through analysis, wherein the relation is shown in figure 13;
f2 According to the information obtained in F1), establishing a functional relation G1 between the morphology and the porosity of the T2 spectrum and the pore structure; establishing a functional relationship G2 between the porosity of each group of sections and the irreducible water saturation, for example, the functional relationship G2 between two intervals (Y1 section and Y3 section) of the group X of G well areas is shown in FIG. 14 and FIG. 15;
f3 Based on the nuclear magnetic test, adopting the selected T2 spectrum cut-off value to conduct grouping segment reprocessing interpretation on the nuclear magnetic logging data, comparing the relation between the conventional logging data and the nuclear magnetic logging data, and obtaining pore structures and nuclear magnetic response characteristics of different reservoirs by comparing the spectrum peak forms of typical layers;
f3 Combining lithology, logging lithology and electrical characteristics to establish response relations among lithology, electrical characteristics and pore structures corresponding to different reservoir types, as shown in table 9;
f4 According to the gas measurement display of the target interval, the lithology characteristics of the reservoir and the response characteristics of the logging curve, the lithology and the electrical property corresponding to the corresponding reservoir type are obtained, the corresponding lithology and the electrical property are substituted into the response relation to obtain the pore structure of the reservoir type, the functional relation G1 is substituted to obtain the porosity, and the functional relation G2 is substituted to obtain the irreducible water saturation of the corresponding reservoir type;
f5 And F) calculating the permeability according to the bound saturated water and the porosity obtained in the step F4).
Taking a Q1 well as an example, the logging combined result of the Y1 section of the Q1 well is shown in FIG. 16, the 5 th layer well hole of the well is regular, a natural gamma curve is in a toothed box shape, a rock chip logging is mainly performed on coarse sandstone, the lithology is unitary, the negative anomaly amplitude of the natural potential is larger, and the lithology of the reservoir is purer and the permeability is better; the three-porosity (acoustic wave, neutron and density) curve has certain change, the acoustic wave time difference average value is 245.0 mu s/m, the density curve is obvious in left bias, and the average value is 2.41g/cm 3 The neutron average value is 8.5%, the reaction porosity is good, the nuclear magnetism shows that the spectrum peak form of the layer is mainly full single peak, the spectrum peak is obvious after being dragged, the pores are mainly medium and large pores, and the whole shows that the pore structure and the seepage channel of the layer are good, and the physical property of the reservoir is good; the resistivity curve is in a typical convex shape, the neutron density 'digging effect' is obvious, the whole hydrocarbon curve is in a middle full shape, the whole hydrocarbon value is medium, but the overlapping area of the whole hydrocarbon curve and the natural potential curve is large, the nuclear magnetism shows that the spectrum shift signal is obvious, the spectrum shift signal response is poor, the gas content of the reaction layer is good, namely, the property of the fluid (namely the reservoir type) can be judged to be a gas layer through the combination mode 1 in the embodiment, and the 'digging effect' according to the resistivity curve shape and the neutron density can be further verified to be the gas layer.
According to the lithology recognition method of the target interval and the calculation method of the porosity, the irreducible water saturation and the permeability of the reservoir type in the embodiment, the interpretation result of the interval is determined as follows: lithology is coarse sandstone, clay content is 4.6%, porosity is 14.5%, permeability is 1.24mD, and gas saturation is 58.3%. And the test result shows that the daily gas production is 48503m 3 D, daily liquid yield 0.2m 3 And/d, the method belongs to a typical industrial airflow high-yield well, and the test result is consistent with the judgment result, so that the reliability of the reservoir fluid identification method is further verified.
Taking a Q3 well as an example, the logging combined result of the Y1 section of the Q3 well is shown in figure 17, the 2 nd layer of the well has overall borehole rules except slightly expanded bottom, a natural gamma curve is in a box shape, a rock chip logging shows that lithology is in coarse, medium and fine mixed layer development, lithology is complex, natural potential negative anomaly amplitude is smaller than that of the 86 nd well, and the lithology of the layer is pure, but rock granularity is in coarse, medium and fine mixed layer development characteristics; the three-porosity curve has a certain change, the difference value is higher as a whole when the sound wave is generated at the upper part of the reservoir, the density value is lower, the neutron value is larger, the average value is 265.0 mu s/m, and the density average value is 2.37g/cm 3 The neutron mean value is 16.5%, the reaction porosities are different, the overall porosities are good, the nuclear magnetism shows that the spectrum peak forms of the layer are mixed and exist in a single mode and a double mode, the double modes are mainly communicated, the spectrum peak has a trailing phenomenon, the layer has a complex pore structure, the interval porosities show that the medium pore diameter and the large pore diameter coexist, the seepage channel is good, the reservoir physical properties are different, but the overall preference is achieved; the upper part of the resistivity curve is typically concave, the value is lower, the resistivity curve at the lower part is obviously increased, the neutron density at the middle lower part is obviously provided with an excavating effect, the shape of the full hydrocarbon curve is in a middle full shape, the overlapping area of the full hydrocarbon curve and the natural potential curve is larger, the nuclear magnetism display spectrum shift signal is not obvious, and the response of the nuclear magnetism display spectrum shift signal is smaller, namely, the fluid property (namely the reservoir type) can be judged to be a gas-containing water layer through the combination mode 3 in the embodiment.
According to the lithology recognition method of the target interval and the calculation method of the porosity, the irreducible water saturation and the permeability of the reservoir type in the embodiment, the interpretation result of the reservoir is determined as follows: the clay content was 5.7%, the porosity was 21.3%, the permeability was 7.09mD, and the gas saturation was 43.5%. Test results show that daily gas production is 14550.0m 3 D, daily liquid yield 13.1m 3 And/d, belonging to high-yield gas-water well, the test result is identical with decision result, so that it further proves that said invention reservoir layerReliability of the fluid identification method.
According to the invention, the gas-logging full hydrocarbon curve of the target interval in the logging data is obtained, the natural potential curve and the base line of the target interval in the logging data are calculated, the negative abnormal amplitude between the natural potential curve and the base line is calculated, and the fluid property of the target interval is identified by combining the gas-logging full hydrocarbon curve, so that the reliable identification of the water content and the gas content of the target interval is realized.
Identification method embodiment two:
in the method for identifying lithology of the target interval in the first embodiment of the identifying method, S1), S2), S3) and S4) represent the sequence of one of the steps, and other time sequences exist, so in this embodiment, the step S1) and the step S2) may be performed simultaneously without separating the sequence of the time sequences; step S3) and step S4) may be performed simultaneously without timing sequence.
Identification method embodiment three:
in the first and second embodiments of the above-described identification method, the identification of the rock type and the rock granularity of the target interval is performed in addition to the identification of the fluid property of the target interval, and in this embodiment, the identification of only the rock type may be performed in addition to the identification of the fluid property of the target interval, without identifying the rock granularity, as other embodiments.
Identification device embodiment:
the embodiment provides a reservoir fluid identification device based on logging fusion, which comprises a processor, wherein the processor is used for executing instructions to realize the following steps on the basis of the explanation of conventional logging interpretation software such as ForWord, logik and special logging interpretation software such as LogVision, petroSite, as shown in fig. 5:
acquiring an air-logging full hydrocarbon curve of a target interval, and acquiring a natural potential curve and a base line of the target interval; according to the difference between the natural potential curve of the target interval and the baseline, calculating to obtain a negative abnormal amplitude curve of the natural potential; and identifying the fluid property of the target interval according to the gas-measurement full hydrocarbon curve and the negative abnormal amplitude curve of the natural potential.
In addition, the processor is further configured to identify lithology of the target interval through steps S1) to S4) in the identification method embodiment. It should be noted that, the processor in this embodiment may be a computer, a microprocessor, such as an ARM, or a programmable chip, such as an FPGA, a DSP, or the like.
The reservoir fluid identification device in this embodiment is actually a computer solution, namely a software architecture, based on the reservoir fluid identification method of logging fusion according to the present invention, and the device is a processing process corresponding to the method flow. Since the description of the above method in the identification method embodiment is sufficiently clear and complete, it will not be described in detail.
Claims (6)
1. The reservoir fluid identification method based on logging fusion is characterized by comprising the following steps of:
acquiring an air-logging full hydrocarbon curve of a target interval, and acquiring a natural potential curve and a base line of the target interval;
according to the difference between the natural potential curve of the target interval and the baseline, calculating to obtain a negative abnormal amplitude curve of the natural potential;
identifying fluid properties of a target interval according to the gas-measured full hydrocarbon curve and a negative abnormal amplitude curve of natural potential;
the method further includes lithology identification of the target interval, including the steps of:
s1) determining the mineral content ranges of different rock types according to logging data;
s2) extracting lithology sensitive parameters according to logging data, and counting logging response ranges of the corresponding lithology sensitive parameters under different rock types;
s3) identifying the rock type of the target interval according to the mineral content ranges of different rock types obtained in the step S1) and the logging response ranges of corresponding lithology sensitive parameters under different rock types obtained in the step S2);
further comprises: according to logging data, determining clay content ranges of different rock types, and combining step S2) to obtain logging response ranges of corresponding lithology sensitive parameters under different rock types, and identifying rock granularity of a target interval.
2. The logging fusion-based reservoir fluid identification method of claim 1, wherein the gas logging full hydrocarbon curve of the target interval is obtained through logging data acquisition.
3. The logging fusion-based reservoir fluid identification method of claim 1, wherein the natural potential curve and baseline of the target interval are obtained from logging data.
4. Reservoir fluid identification device based on logging fuses, which is characterized by comprising a processor and a control unit, wherein the processor is used for executing instructions to realize the following steps:
acquiring an air-logging full hydrocarbon curve of a target interval, and acquiring a natural potential curve and a base line of the target interval;
according to the difference between the natural potential curve of the target interval and the baseline, calculating to obtain a negative abnormal amplitude curve of the natural potential;
identifying fluid properties of a target interval according to the gas-measured full hydrocarbon curve and a negative abnormal amplitude curve of natural potential;
the processor is further configured to identify lithology of the target interval by:
s1) determining the mineral content ranges of different rock types according to logging data;
s2) extracting lithology sensitive parameters according to logging data, and counting logging response ranges of the corresponding lithology sensitive parameters under different rock types;
s3) identifying the rock type of the target interval according to the mineral content ranges of different rock types obtained in the step S1) and the logging response ranges of corresponding lithology sensitive parameters under different rock types obtained in the step S2);
the processor is further configured to: according to logging data, determining clay content ranges of different rock types, and combining step S2) to obtain logging response ranges of corresponding lithology sensitive parameters under different rock types, and identifying rock granularity of a target interval.
5. The logging fusion-based reservoir fluid identification device of claim 4, wherein the gas logging full hydrocarbon curve of the target interval is obtained from logging data.
6. The logging fusion-based reservoir fluid identification device of claim 4, wherein the natural potential profile and baseline of the target interval are obtained from log data acquisition.
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