CN112016753B - Ternary coupling-based metamorphic rock buried hill productivity prediction method - Google Patents
Ternary coupling-based metamorphic rock buried hill productivity prediction method Download PDFInfo
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- G—PHYSICS
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Abstract
The invention discloses a metamorphic rock down-the-hill productivity prediction method based on ternary coupling, which comprises the steps of firstly, establishing a lithology characterization coefficient calculation model based on morphological changes of dark mineral content differences in neutron and density curves; secondly, normalizing the amplitude difference and the midpoint response value of the deep and shallow resistivity, and establishing a crack characterization index calculation method; thirdly, establishing a correction method of the total hydrocarbon value of the gas logging, and establishing a metamorphic rock buried hill oil gas response coefficient by utilizing the amplitude and the morphological change of the corrected total hydrocarbon value of the gas logging; and finally, applying the calculation result and the method to the calculation of the coupling coefficient, and establishing a productivity prediction equation by utilizing the coupling coefficient and the divided effective thickness of the reservoir and combining the actual drilling test result of the metamorphic rock and the submarine mountain. The invention provides the metamorphic rock diving mountain productivity prediction method, which improves productivity prediction precision, realizes metamorphic rock diving mountain productivity quantitative evaluation, and has important guiding significance for logging evaluation of similar reservoirs.
Description
Technical Field
The invention belongs to the field of evaluation of metamorphic rock and the field of rock physical parameters, and particularly relates to a metamorphic rock and the field of capacity prediction of a metamorphic rock and the field of rock physical parameters based on the coupling of lithology, cracks and oil gas filling capacity.
Background
In recent years, with the breakthrough of the Bohai sea deep down-the-country mountain, the important objective layer of Bohai sea research moves downwards, and more exploratory wells reveal a fractured down-the-country reservoir. However, in the exploration evaluation, the mismatch exists between the productivity of the oil and gas well and the conditions of porosity, crack development and the like in the actual test. When the porosity is large, the actual test effect is poor; at lower porosities, the actual test throughput is relatively high, which creates a major doubt for geology and logging researchers.
The reservoir productivity prediction technology has important guiding significance for evaluating the exploration effect of the oil and gas field and developing well deployment. At present, the productivity prediction technology at home and abroad is mainly used for estimating reservoir types such as sandstone, conglomerate, tight layer, carbonate rock and the like according to logging data. The conventional productivity prediction method mainly depends on conventional logging, nuclear magnetic resonance logging, imaging logging and other methods.
The productivity prediction method based on conventional logging data is mainly used for shallow sandstone reservoirs, has a simple pore structure, and performs productivity prediction by integrating parameters such as porosity, permeability, saturation, reservoir thickness and the like. The metamorphic rock is complex in down-the-hole lithology, the reservoir is strong in heterogeneity, the reservoir space is mainly provided with slits and holes, the reservoir permeability and saturation parameter determination difficulty is high, and larger errors are brought by carrying out productivity prediction only by means of conventional logging.
The capacity prediction method based on nuclear magnetic resonance logging is mainly used for combining a capillary pressure curve form of a laboratory with oil testing capacity aiming at a reservoir with a complex pore structure, and establishing an intuitive classification standard of the natural capacity level of the reservoir based on the capillary pressure curve form. Based on visual classification, the laboratory pore structure parameters and the oil test productivity result are combined, and effective porosity, permeability, displacement pressure, pore roar average value, separation coefficient and maximum mercury inlet saturation are preferably used as reservoir classification standards to construct a reservoir classification comprehensive evaluation index curve.
Wherein:
ZZ is a reservoir classification comprehensive evaluation index, and is dimensionless;
calculating effective porosity,%; a step of
K is the nuclear magnetic resonance logging to calculate permeability and mD;
S px for calculating the sorting coefficient based on nuclear magnetic resonance logging, dimensionless;
S max calculating a maximum mercury saturation,%;
D M calculating a pore throat radius average value based on nuclear magnetic resonance logging, and having no dimension;
P d the pressure value of the displacement is calculated based on nuclear magnetic resonance logging and is MPa.
Based on the reservoir classification composite evaluation index, the production capacities of different types of reservoirs are determined as in table 1. In a metamorphic rock down-the-hole mountain, the influence of factors such as the mineralization degree of drilling fluid, the well temperature and the like is high, the nuclear magnetic resonance logging environment influence is high, the logging quality is poor, and the productivity prediction requirement is difficult to meet.
TABLE 1 reservoir Classification evaluation criteria Table
The productivity prediction method based on imaging logging data is mainly used for carrying out productivity prediction on carbonate reservoirs by quantitatively picking up cracks through imaging logging and establishing the relation between parameters such as crack density, crack porosity and the like and productivity. Because the crack picking up is carried out manually, the picking up of different interpreters is different in yield and quantity, and the crack picking up process is long in time, the popularization of the method is poor, and the yield prediction result is also highly uncertain.
Disclosure of Invention
In order to solve the problem of the prior art that productivity prediction is performed on the metamorphic rock diving hill, the invention combines the conventional logging data, the test data, the logging data, the imaging logging data and other data, provides a metamorphic rock diving hill productivity prediction method based on lithology, cracks and oil gas filling capacity coupling, greatly improves the metamorphic rock diving hill productivity prediction precision, and lays a foundation for subsequent metamorphic rock diving hill exploration evaluation and development well deployment.
The technical scheme of the invention is operated according to the following steps:
step (1) obtaining density and neutron logging values, and calculating lithology characterization coefficients;
step (2), calculating a crack characterization index according to resistivity logging;
step (3), calculating an oil gas response coefficient according to the gas logging and the specific gravity of the drilling fluid;
and (4) establishing a capacity prediction model and predicting the capacity.
The calculation method in the steps (1), (2), (3) and (4) mainly comprises the following steps:
step (1): determination of rock characteristic characterization coefficient Lith of metamorphic rock down-the-hill
For metamorphic rock, the lithology is complex and the logging response characteristics are disordered due to the differences of actions of raw rock, magma hot fluid, construction movement and the like. The metamorphic rock mineral composition is mainly composed of three light-colored minerals and one or two dark-colored minerals; the light-colored minerals mainly comprise quartz, alkaline feldspar and plagioclase, and the dark-colored minerals mainly comprise biotite, amphibole and pyroxene. When the light-colored minerals in the rock mineral components have higher content, the rock has good brittleness, and cracks and joint seams are easy to generate under the action of structural stress; when the content of dark minerals in the rock mineral components is high, the rock has strong toughness and is not easy to crack. Because the neutron and density logging response characteristic values of the light-colored minerals and the dark-colored minerals have larger differences, the neutron and density curves can be used for representing the differences of metamorphic rock and down-the-hill mineral components.
Wherein:
lith is a normalized lithology characterization coefficient and is dimensionless;
ρ is the measured density value of the well logging, g/cm 3 ；
ρ max G/cm, the maximum value of the density logging response 3 ；
ρ min G/cm, the minimum value of the density logging response 3 ；
F is a measured neutron value of the well;
f is the maximum value of neutron logging response;
is the minimum of neutron logging response, f.
Step (2): metamorphic rock buried hill crack characterization index K f Determination of
The reservoir space of the metamorphic rock submarine mountain is mainly provided with a seam and a hole, and the configuration relationship between the seam and the hole directly influences the effectiveness of the submarine mountain oil-gas layer and plays a decisive role in the contribution of productivity. According to past experience, the friction coefficient of the crack has a good exponential relation with the output capacity, but the crack pickup is influenced by factors such as interpreters, logging quality and the like, and the uncertainty is large. According to the water tank model experiment, the crack occurrence and the bilateral logging have a better correlation, so that the crack characterization index is established based on the deep and shallow resistivity amplitude difference and the midpoint response value, and the development characteristics of the metamorphic rock and the down-the-hill cracks can be effectively judged.
Wherein:
K f characterizing an index for the normalized fracture, dimensionless;
K f1 is the difference value of the deep and shallow resistivity amplitude, and has no dimension;
K f1MAX is the maximum value of the amplitude difference of the deep resistivity and the shallow resistivity, and has no dimension;
K f2 the mid-point response values of the deep and shallow resistivity are dimensionless;
K f2MAX maximum value of midpoint response values of deep and shallow resistivity, dimensionless;
R D deep resistivity, Ω·m;
R S is shallow resistivity, Ω·m;
R max is the maximum value of resistivity logging, Ω·m;
R min is the minimum value of resistivity logging, Ω·m.
Step (3): determination of metamorphic rock buried hill oil Gas response coefficient Gas
The gas logging amplitude value and the morphological change are direct manifestations of the oil gas migration response of the metamorphic rock and the down-the-hole mountain, but the gas logging value is greatly influenced by factors such as the specific gravity of drilling fluid. According to the statistics of the data such as the specific gravity of the drilling fluid of the Bohai sea drilled metamorphic rock down-the-hole mountain, gas logging and the like, the specific gravity of the drilling fluid is less than or equal to 1.11g/cm 3 When the specific gravity of the drilling fluid is reduced, the gas logging total hydrocarbon value is obviously increased, so that the gas logging value needs to be corrected.
TG＝TGAS+10 -7.6884*mw+8.2825 (6)
On the basis, the amplitude value and the morphological change of the gas logging are synthesized, and the oil gas response coefficient of the metamorphic rock and the submarine mountain is established as follows:
wherein:
gas is a normalized oil Gas response coefficient, and is dimensionless;
TG is corrected gas logging total hydrocarbon value,%;
TGAS is the measured total hydrocarbon value of the gas logging,%;
mw is the specific gravity of the drilling fluid, g/cm 3 ；
TG base For the corrected gas logging total hydrocarbon matrix value,%;
TG max for the maximum value of corrected gas logging total hydrocarbon values,%;
TG min for the minimum corrected gas logging total hydrocarbon value,%.
Step (4): metamorphic rock down-the-hill productivity prediction model determination
(1) Based on conventional logging, imaging logging, array acoustic wave and production logging, and combining data of stratum testing, drilling coring, assay analysis and the like, an effective thickness lower limit standard meeting regional rules can be finally established, namely that the total porosity is more than or equal to 2%, the longitudinal wave time difference is more than or equal to 53us/ft, and the deep resistivity is less than or equal to 510 Ω·m. According to the above criteria, the effective reservoir thickness H of the reservoir can be accurately identified.
(2) The capacity of the metamorphic rock diving hill test depends on multiple factors such as the intensity of oil gas filling, the development condition of cracks, lithology characteristics and the like. On the basis of comprehensive lithology, cracks and oil gas filling capacity, the quantitative evaluation of the liquid production capacity can be realized.
Based on the ternary coupling coefficient, determining the productivity prediction model as follows by establishing an objective function:
Q＝0.0268*(F*H) 2.6326 (R 2 ＝0.9544) (9)
wherein:
q is the predicted energy production value, 10 4 m 3 /d；
F is a ternary coupling coefficient, and is dimensionless;
h is the log interpretation reservoir thickness, m;
r is a correlation coefficient and is dimensionless.
The invention has the following effective effects: the invention provides a productivity prediction calculation method for a non-test interval of a metamorphic rock down-the-hill mountain. Based on neutron and density curve morphological characteristics, establishing lithology characterization coefficients for judging the brittle characteristics of the metamorphic rock and diving mountains, and indicating crack development and preservation; based on the separation degree and midpoint position characteristics of the dual lateral logging, a fracture characterization index is established for judging the development degree of the fracture of the reservoir and the permeability of the reservoir; correcting the gas measurement total hydrocarbon value on the basis of considering the influence of the specific gravity of the drilling fluid on the gas measurement, integrating the amplitude and the base value change, and establishing an oil gas response coefficient for representing the oil gas filling response characteristic; on the basis of the research, lithology, cracks and oil gas filling are subjected to ternary coupling, and a correlation between a coupling coefficient and productivity is established for predicting the productivity change of the metamorphic rock down-the-hill reservoir. The method disclosed by the invention has the advantages that the predicted result is well matched with the actual well test result, and the method has important guiding significance for subsequent metamorphic rock down-the-road mountain exploration evaluation, development and deployment.
Drawings
FIG. 1 is a flow chart of a metamorphic rock diving mountain productivity prediction technology provided by an embodiment of the invention;
FIG. 2a is a graph showing the relationship between the neutron and density curves of mixed granite and the content of iron and magnesium elements provided by the embodiment of the invention;
FIG. 2b is a graph showing the relationship between the neutron and density curves of gneiss and the content of iron and magnesium elements provided by the embodiment of the invention;
FIG. 2c is a graph showing the relationship between neutron and density curves of the amphibole type and the content of iron and magnesium elements provided by the embodiment of the invention;
FIG. 3a is a graph of deep/shallow resistivity versus fracture density for an embodiment of the present invention;
FIG. 3b is a graph of deep/shallow resistivity versus fracture porosity provided by an embodiment of the present invention;
FIG. 4 is a comparison of a resistivity log and a production profile of a W-well provided by an embodiment of the present invention;
FIG. 5 is a log of an X-well gas provided by an embodiment of the present invention;
FIG. 6 is a log diagram of a high-yield segment gas for a Y-well test according to an embodiment of the present invention;
FIG. 7 is a log of a Z-well test low-production gas log provided by an embodiment of the present invention;
FIG. 8 is a graph of the log calibration and the oil and gas response coefficients for an embodiment of the present invention;
FIG. 9 is a graph of W-well coupling coefficient results provided by an embodiment of the present invention;
FIG. 10 is a diagram showing a prediction of the productivity of a condensate layer according to an embodiment of the present invention;
FIG. 11 is a diagram of N-well capacity prediction results provided by an embodiment of the present invention.
Detailed Description
The following describes a metamorphic rock diving mountain productivity prediction method based on ternary coupling in detail by referring to the embodiment and the attached drawings.
As shown in the flow chart of FIG. 1, the metamorphic rock diving mountain productivity prediction method based on ternary coupling is operated according to the following steps:
step (1) obtaining density and neutron logging values, and calculating lithology characterization coefficients;
step (2), calculating a crack characterization index according to resistivity logging;
step (3), calculating an oil gas response coefficient according to the gas logging and the specific gravity of the drilling fluid;
and (4) establishing a capacity prediction model and predicting the capacity.
The calculation method in the steps (1), (2), (3) and (4) mainly comprises the following steps:
step (1): determination of rock characteristic characterization coefficient Lith of metamorphic rock down-the-hill
For metamorphic rock, the lithology is complex and the logging response characteristics are disordered due to the differences of actions of raw rock, magma hot fluid, construction movement and the like. The metamorphic rock mineral composition is mainly composed of three light-colored minerals and one or two dark-colored minerals; the light-colored minerals mainly comprise quartz, alkaline feldspar and plagioclase, and the dark-colored minerals mainly comprise biotite, amphibole and pyroxene. When the light-colored minerals in the rock mineral components have higher content, the rock has good brittleness, and cracks and joint seams are easy to generate under the action of structural stress; when the content of dark minerals in the rock mineral components is high, the rock has strong toughness and is not easy to crack. As shown in FIG. 2, the specific gravity of Fe and Mg elements is small, which indicates that the dark mineral content is low, and the neutron and density curves are characterized by positive difference; the proportion of Fe and Mg elements is increased, the content of dark minerals is increased, and a neutron and density curve is characterized by negative difference. Therefore, the neutron and density curves are used for representing the differences of mineral components of the metamorphic rock and the down-the-hill rock.
Wherein:
lith is a normalized lithology characterization coefficient and is dimensionless;
ρ is the measured density value of the well logging, g/cm 3 ；
ρ max G/cm, the maximum value of the density logging response 3 ；
ρ min G/cm, the minimum value of the density logging response 3 ；
F is a measured neutron value of the well;
f is the maximum value of neutron logging response;
is the minimum of neutron logging response, f.
Calculating according to the formula (1), when the metamorphic rock down-the-hill dark-colored minerals are low in content and the neutron and density curves are in positive difference, the calculated lithology characterization coefficient Lith is larger than 0.5; when the content of the dark minerals is higher and the neutron and density curves are in negative difference, the calculated lithology characterization coefficient Lith is smaller than 0.5.
Step (2): metamorphic rock buried hill crack characterization index K f Determination of
The reservoir space of the metamorphic rock submarine mountain is mainly provided with a seam and a hole, and the configuration relationship between the seam and the hole directly influences the effectiveness of the submarine mountain oil-gas layer and plays a decisive role in the contribution of productivity. According to past experience, the friction coefficient of the crack has a good exponential relation with the output capacity, but the crack pickup is influenced by factors such as interpreters, logging quality and the like, and the uncertainty is large. According to the water tank model experiment, the fracture occurrence and the dual lateral logging have better correlation, and meanwhile, as shown in fig. 3, the fracture density, the fracture porosity, the deep and shallow resistivity ratio of the reservoir development stage have better consistency. Therefore, the crack characterization index is established based on the deep and shallow resistivity amplitude difference and the midpoint response value, and the development characteristics of the metamorphic rock buried hill cracks can be effectively judged.
Wherein:
K f characterizing an index for the normalized fracture, dimensionless;
K f1 is the difference value of the deep and shallow resistivity amplitude, and has no dimension;
K f1MAX is the maximum value of the amplitude difference of the deep resistivity and the shallow resistivity, and has no dimension;
K f2 the mid-point response values of the deep and shallow resistivity are dimensionless;
K f2MAX maximum value of midpoint response values of deep and shallow resistivity, dimensionless;
R D deep resistivity, Ω·m;
R S is shallow resistivity, Ω·m;
R max is the maximum value of resistivity logging, Ω·m;
R min is the minimum value of resistivity logging, Ω·m.
As shown in FIG. 4, the 4 th curve K f1 The 5 th curve way K is the difference between the deep and shallow resistivity amplitude f2 The 6 th curve way K is the midpoint response value of the deep and shallow resistivity f The index was characterized for normalized cracks. Normalized crack characterization index K in curve 6 of the graph f The method has good coincidence relation with the production contribution capacity of each layer section determined by the 7 th liquid production profile well logging, high production capacity and corresponding fracture characterization index K f The value is large.
Step (3): determination of metamorphic rock buried hill oil Gas response coefficient Gas
The gas logging data is a direct basis for finding out the oil-gas layer in the oil-gas field exploration, development and evaluation process. The amplitude value and the morphological change of the gas logging are direct manifestation of the oil gas migration response of the metamorphic rock and the down-the-hole mountain, but the gas logging is greatly influenced by factors such as the specific gravity of drilling fluid, the gas logging matrix, the drilling engineering and the like. As shown in fig. 5, lane 2 mw is the drilling fluid density and TGAS is the gas logging total hydrocarbon value; lanes 5C 1, C2 and C3 are respectively methane, ethane and propane gas components. As the specific gravity of the drilling fluid decreases, the gas total hydrocarbon value TGAS and the component methane C1, ethane C2, propane C3 values increase.
According to the statistics of the data such as the specific gravity of the drilling fluid of the Bohai sea drilled metamorphic rock down-the-hole mountain, gas logging and the like, the specific gravity of the drilling fluid is less than or equal to 1.11g/cm 3 In the case of gas logging, the total hydrocarbon value is obviously increased along with the reduction of the specific gravity of the drilling fluid, so that the gas logging is neededAnd correcting the gas logging value.
TG＝TGAS+10 -7.6884*mw+8.2825 (6)
Wherein:
TG is corrected gas logging total hydrocarbon value,%;
TGAS is the measured total hydrocarbon value of the gas logging,%;
mw is the specific gravity of the drilling fluid, g/cm 3 。
Meanwhile, by combining data such as testing, logging and the like, a well with high productivity is tested, the fluctuation of gas logging is large, the total hydrocarbon value of gas logging in a reservoir section is high, and the components are complete, as shown in figure 6; wells with low production capacity were tested, gas logging was poor, or the total hydrocarbon values, composition, and heave were small in the reservoir and tight sections as shown in figure 7. On the basis, the amplitude value and the morphological change of the gas logging are synthesized, and the oil gas response coefficient of the metamorphic rock and the submarine mountain is established as follows:
wherein:
gas is a normalized oil Gas response coefficient, and is dimensionless;
TG base for the corrected gas logging total hydrocarbon matrix value,%;
TG max for the maximum value of corrected gas logging total hydrocarbon values,%;
TG min for the minimum corrected gas logging total hydrocarbon value,%.
The total hydrocarbon value of the gas logging is corrected by the formula (6), and the oil gas response coefficient is calculated by the formula (7). As shown in fig. 8, lane 3 TG is the GAS total hydrocarbon correction value and lane 4 GAS is the normalized hydrocarbon response coefficient.
Step (4): metamorphic rock down-the-hill productivity prediction model determination
(1) The metamorphic rock is affected by factors such as the buried depth, the parent rock components and the like, the logging response characteristics are complex, the reservoir heterogeneity is strong, and the effective reservoir is difficult to divide. According to the data of cable stratum testing, sampling, drilling coring and the like, the conventional logging, imaging logging, array acoustic wave and element logging are combined, and the lower limit standard of the effective thickness of the metamorphic rock down-the-hole mountain reservoir is established, namely, the total porosity is more than or equal to 2%, the longitudinal wave time difference is more than or equal to 53us/ft, and the deep resistivity is less than or equal to 510 Ω & m. According to the above criteria, the effective reservoir thickness H of the reservoir can be accurately identified.
(2) The capacity of the metamorphic rock diving hill test depends on multiple factors such as the intensity of oil gas filling, the development condition of cracks, lithology characteristics and the like. The higher the lithology characterization coefficient Lith of the metamorphic rock down-the-hill mountain is, namely the fewer lithology dark minerals are, the stronger the brittleness is, which is favorable for crack development and preservation; fracture characterization index K f The higher, i.e., the better the fracture development, the better the reservoir permeability; the higher the oil Gas response coefficient Gas is, the higher the oil Gas filling capacity of the surface reservoir is. The productivity of the metamorphic rock diving hill test is in direct proportion to the oil gas filling strength, the crack development condition and the lithology characteristics, and the quantitative evaluation of the liquid production capacity can be realized on the basis of comprehensive lithology, cracks and oil gas filling capacity.
Calculating lithology characterization coefficient Lith by using the step (1), and calculating fracture characterization index K by using the step (2) f And (3) calculating an oil Gas response coefficient Gas by using the step (3), and calculating a coupling coefficient F by using a formula (8). As shown in fig. 9, F in curve 9 is the calculated coupling coefficient and QZI in curve 10 is the throughput capacity of each section of the production profile log. The consistency of the coincidence relation between the coupling coefficient F calculated based on lithology, cracks and oil gas filling capacity and the production contribution capacity QZI of each layer section determined by the production profile logging is better, and the relation between the production capacity and the capacity characterization parameter of the W well is shown in Table 2.
TABLE 2W well throughput capability and capacity characterization parameter relationship table
Based on the ternary coupling coefficient, by establishing an objective function, determining a condensate gas layer productivity prediction model as follows:
Q＝0.0268*(F*H) 2.6326 (R 2 ＝0.9544) (9)
wherein:
q is the predicted energy production value, 10 4 m 3 /d；
F is a ternary coupling coefficient, and is dimensionless;
h is the log interpretation reservoir thickness, m;
r is a correlation coefficient and is dimensionless.
Combining the coupling coefficient with the condensate layer unobstructed flow relationship graph, as shown in FIG. 10, can make capacity predictions for new wells. FIG. 10 shows a graph 9 as the result of ternary coupling coefficient calculation, a graph 10 as the result of effective reservoir partitioning, an average N-well coupling coefficient value of 0.0265 calculated by equation (8), an effective reservoir thickness of 106.8m, and a capacity of 0.41×10 for predicting condensate gas barrier free flow by equation (9) 4 m 3 /d, actual test yield 1.1X10 4 m 3 And/d, the unobstructed flow is 0.03X10 4 m 3 And/d, the predicted result is identical with the actual test result.
Claims (5)
1. The metamorphic rock diving mountain productivity prediction method based on ternary coupling is characterized by comprising the following steps of:
step (1) obtaining density and neutron logging values, and calculating lithology characterization coefficients;
step (2), calculating a crack characterization index according to resistivity logging;
step (3), calculating an oil gas response coefficient according to the gas logging and the specific gravity of the drilling fluid;
step (4) productivity prediction model establishment;
and (5) calculating each characterization coefficient according to the steps (1), (2) and (3) for a new well of the reservoir energy of the metamorphic rock and the down-the-hole mountain to be predicted, and quantitatively predicting according to the prediction model of the step (4).
2. The metamorphic rock-submersible mountain productivity prediction method based on ternary coupling according to claim 1, wherein the metamorphic rock-submersible mountain lithology characterization coefficient Lith of step (1) is determined:
because the neutron and density logging response characteristic values of the light-colored minerals and the dark-colored minerals have larger differences, the differences of metamorphic rock and down-the-hill mineral components can be represented by utilizing neutron and density curves:
wherein:
lith is a normalized lithology characterization coefficient and is dimensionless;
ρ is the measured density value of the well logging, g/cm 3 ；
ρ max G/cm, the maximum value of the density logging response 3 ；
ρ min G/cm, the minimum value of the density logging response 3 ；
F is a measured neutron value of the well;
f is the maximum value of neutron logging response;
is the minimum of neutron logging response, f.
3. The ternary coupling-based metamorphic rock-diving mountain productivity prediction method according to claim 1, wherein the metamorphic rock-diving mountain crack characterization index K in the step (2) is f And (3) determining:
according to the water tank model experiment, the crack occurrence and the bilateral logging have a better correlation, so that the crack characterization index is established based on the deep and shallow resistivity amplitude difference and the midpoint response value, and the development characteristics of the metamorphic rock and the down-the-hill cracks can be effectively judged:
wherein:
K f characterizing an index for the normalized fracture, dimensionless;
K f1 is the difference value of the deep and shallow resistivity amplitude, and has no dimension;
K f1max is the maximum value of the amplitude difference of the deep resistivity and the shallow resistivity, and has no dimension;
K f2 the mid-point response values of the deep and shallow resistivity are dimensionless;
K f2max maximum value of midpoint response values of deep and shallow resistivity, dimensionless;
R D deep resistivity, Ω·m;
R S is shallow resistivity, Ω·m;
R max is the maximum value of resistivity logging, Ω·m;
R min is the minimum value of resistivity logging, Ω·m.
4. The ternary coupling-based metamorphic rock and down-the-hill hydrocarbon response coefficient Gas determination method is characterized in that the metamorphic rock and down-the-hill hydrocarbon response coefficient Gas determination in the step (3):
correcting the gas logging value:
TG＝TGAS+10 -7.6884*mw+8.2825 (5)
on the basis, the amplitude value and the morphological change of the gas logging are synthesized, and the oil gas response coefficient of the metamorphic rock and the submarine mountain is established as follows:
wherein:
gas is a normalized oil Gas response coefficient, and is dimensionless;
TG is corrected gas logging total hydrocarbon value,%;
TGAS is the measured total hydrocarbon value of the gas logging,%;
mw is the specific gravity of drilling fluid, g/cm 3 ；
TG base For the corrected gas logging total hydrocarbon matrix value,%;
TG max for the maximum value of corrected gas logging total hydrocarbon values,%;
TG min for the minimum corrected gas logging total hydrocarbon value,%.
5. The metamorphic rock-diving mountain productivity prediction method based on ternary coupling according to claim 1, wherein the metamorphic rock-diving mountain productivity prediction model in the step (4) is determined by:
the capacity of the metamorphic rock diving hill test depends on the oil gas filling strength, the crack development condition and lithology characteristic multifactorial factors; on the basis of comprehensive lithology, cracks and oil gas filling capacity, the quantitative evaluation of the liquid production capacity can be realized;
based on the ternary coupling coefficient, determining the productivity prediction model as follows by establishing an objective function:
Q＝0.0268*(F*H) 2.6326 (R 2 ＝0.9544) (8)
wherein:
q is the predicted energy production value, 10 4 m 3 /d；
F is a ternary coupling coefficient, and is dimensionless;
h is the log interpretation reservoir thickness, m;
r is a correlation coefficient and is dimensionless.
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| CN114035227B (en) * | 2021-11-11 | 2023-07-07 | 中国海洋石油集团有限公司 | Metamorphic rock buried hill reservoir porosity prediction method based on XRD while drilling whole-rock logging |
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